Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- 1
- network = Network()
- 2
- network.cuda()
- 3
-
- 4
- criterion = nn.MSELoss()
- 5
- optimizer = optim.Adam(network.parameters(), lr=0.0001)
- 6
-
- 7
- loss_min = np.inf
- 8
- num_epochs = 10
- 9
-
- 10
- start_time = time.time()
- 11
- for epoch in range(1,num_epochs+1):
- 12
- 13
- loss_train = 0
- 14
- loss_test = 0
- 15
- running_loss = 0
- 16
- 17
- 18
- network.train()
- 19
- print('size of train loader is: ', len(train_loader))
- 20
-
- 21
- for step in range(1, len(train_loader)+1):
- 22
-
- 23
- 24
- batch = next(iter(train_loader))
- 25
- images, landmarks = batch['image'], batch['landmarks']
- 26
- print(images.shape)
- 27
- 28
- images = images.unsqueeze_(1)
- 29
-
- 30
- images = torch.cat((images,images,images),1)
- 31
- images = images.cuda()
- 32
- 33
- landmarks = landmarks.view(landmarks.size(0),-1).cuda()
- 34
- norm_image = transforms.Normalize(0.3812, 0.1123)
- 35
- for image in images:
- 36
- image = image.float()
- 37
- ##image = to_tensor(image) #TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
- 38
- image = norm_image(image)
- 39
- 40
- ###removing landmarks normalize because of the following error
- 41
- ###ValueError: Expected tensor to be a tensor image of size (C, H, W). Got tensor.size() = torch.Size([8, 8])
- 42
- 43
- 44
- for i in range(8):
- 45
- if(i%2==0):
- 46
- landmarks[:,i] = landmarks[:,i]/800
- 47
- else:
- 48
- landmarks[:,i] = landmarks[:,i]/600
- 49
- 50
- print(landmarks.shape)
- 51
- print(landmarks)
- 52
- 53
- 54
-
- 55
- 56
- ##norm_landmarks = transforms.Normalize(0.4949, 0.2165)
- 57
- landmarks [landmarks != landmarks] = 0
- 58
- landmarks = landmarks.unsqueeze_(0)
- 59
- landmarks = norm_landmarks(landmarks)
- 60
- 61
- predictions = network(images)
- 62
- 63
- # clear all the gradients before calculating them
- 64
- optimizer.zero_grad()
- 65
- 66
- print('predictions are: ', predictions.float())
- 67
- print('landmarks are: ', landmarks.float())
- 68
- # find the loss for the current step
- 69
- loss_train_step = criterion(predictions.float(), landmarks.float())
- 70
- 71
- 72
- loss_train_step = loss_train_step.to(torch.float32)
- 73
- print("loss_train_step before backward: ", loss_train_step)
- 74
- 75
- # calculate the gradients
- 76
- loss_train_step.backward()
- 77
- 78
- # update the parameters
- 79
- optimizer.step()
- 80
- 81
- print("loss_train_step after backward: ", loss_train_step)
- 82
-
- 83
- 84
- loss_train += loss_train_step.item()
- 85
- 86
- print("loss_train: ", loss_train)
- 87
- running_loss = loss_train/step
- 88
- print('step: ', step)
- 89
- print('running loss: ', running_loss)
- 90
- 91
- print_overwrite(step, len(train_loader), running_loss, 'train')
- 92
- 93
- network.eval()
- 94
- with torch.no_grad():
- 95
- 96
- for step in range(1,len(test_loader)+1):
- 97
- 98
- batch = next(iter(train_loader))
- 99
- images, landmarks = batch['image'], batch['landmarks']
- 100
- images = images.cuda()
- 101
- landmarks = landmarks.view(landmarks.size(0),-1).cuda()
- 102
- ##[8, 600, 800] --> [8,3,600,800]
- 103
- images = images.unsqueeze(1)
- 104
- images = torch.cat((images, images, images), 1)
- 105
- predictions = network(images)
- 106
-
- 107
- # find the loss for the current step
- 108
- loss_test_step = criterion(predictions, landmarks)
- 109
-
- 110
- loss_test += loss_test_step.item()
- 111
- running_loss = loss_test/step
- 112
-
- 113
- print_overwrite(step, len(test_loader), running_loss, 'Validation')
- 114
- 115
- loss_train /= len(train_loader)
- 116
- loss_test /= len(test_loader)
- 117
- 118
- print('\n--------------------------------------------------')
- 119
- print('Epoch: {} Train Loss: {:.4f} Valid Loss: {:.4f}'.format(epoch, loss_train, loss_test))
- 120
- print('--------------------------------------------------')
- 121
- 122
- if loss_test < loss_min:
- 123
- loss_min = loss_test
- 124
- torch.save(network.state_dict(), '../moth_landmarks.pth')
- 125
- print("\nMinimum Valid Loss of {:.4f} at epoch {}/{}".format(loss_min, epoch, num_epochs))
- 126
- print('Model Saved\n')
- 127
- 128
- print('Training Complete')
- 129
- print("Total Elapsed Time : {} s".format(time.time()-start_time))
- size of train loader is: 90
- torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6168, 0.4029, 0.8523, 0.3417, 0.3588, 0.5000, 0.6125, 0.5400],
- [0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [0.6286, 0.4078, 0.8063, 0.2267, 0.4788, 0.1533, 0.5953, 0.4913],
- [ nan, nan, 0.6512, 0.1717, 0.4100, 0.1983, 0.5253, 0.5240],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6361, 0.4102, 0.8650, 0.2983, 0.4888, 0.2000, 0.6702, 0.5459],
- [0.6202, 0.4066, 0.8746, 0.3376, 0.3717, 0.3090, 0.5842, 0.5165]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.1423, -0.1056, -0.3138, 0.4684, -0.1054, -0.5563, -0.0186, 0.1072],
- [-0.1047, -0.1028, -0.2962, 0.4469, -0.1573, -0.5332, -0.0197, 0.1038],
- [-0.1244, -0.0833, -0.2863, 0.4311, -0.1496, -0.4844, -0.0656, 0.0925],
- [-0.1570, -0.1024, -0.2959, 0.4236, -0.1198, -0.4870, -0.0458, 0.1049],
- [-0.1260, -0.1189, -0.3429, 0.4834, -0.1040, -0.5703, 0.0156, 0.0999],
- [-0.1380, -0.0681, -0.3151, 0.4013, -0.1561, -0.5097, -0.0721, 0.0928],
- [-0.1592, -0.1133, -0.2992, 0.4642, -0.1194, -0.5710, -0.0054, 0.0882],
- [-0.1140, -0.0634, -0.3053, 0.4357, -0.1321, -0.5395, -0.0382, 0.0839]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5631, -0.4249, 1.6509, -0.7078, -0.6289, 0.0236, 0.5432,
- 0.2083],
- [ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [ 0.6177, -0.4022, 1.4381, -1.2390, -0.0746, -1.5777, 0.4639,
- -0.0168],
- [-2.2859, -2.2859, 0.7222, -1.4930, -0.3921, -1.3698, 0.1404,
- 0.1343],
- [ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698],
- [ 0.6520, -0.3912, 1.7095, -0.9079, -0.0284, -1.3621, 0.8096,
- 0.2356],
- [ 0.5787, -0.4077, 1.7537, -0.7263, -0.5692, -0.8586, 0.4126,
- 0.1000]]], device='cuda:0')
- loss_train_step before backward: tensor(1.0104, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(1.0104, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0104049444198608
- step: 1
- running loss: 1.0104049444198608
- Train Steps: 1/90 Loss: 1.0104 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6336, 0.4191, 0.8938, 0.5167, 0.3937, 0.3517, 0.7343, 0.5748],
- [0.6197, 0.3930, 0.8793, 0.4736, 0.4152, 0.5464, 0.6308, 0.5235],
- [0.6265, 0.4251, 0.7113, 0.3550, 0.4375, 0.2117, 0.5587, 0.6118],
- [0.6271, 0.4040, 0.9000, 0.3250, 0.4938, 0.2300, 0.7192, 0.5413],
- [0.6125, 0.4035, 0.7825, 0.3100, 0.3463, 0.4900, 0.5832, 0.5637],
- [0.6114, 0.4018, 0.7213, 0.1967, 0.3763, 0.2700, 0.5875, 0.5533],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.0659, -0.2580, 0.0962, 0.1213, -0.2025, -0.6994, 0.0772, 0.1072],
- [-0.0033, -0.2372, 0.0826, 0.1461, -0.1880, -0.6930, 0.0741, 0.1244],
- [-0.0202, -0.2130, 0.0678, 0.1583, -0.1568, -0.6912, 0.0771, 0.1296],
- [ 0.0287, -0.2443, 0.0870, 0.1132, -0.1829, -0.6959, 0.0773, 0.1000],
- [-0.0197, -0.2014, 0.0873, 0.1103, -0.1730, -0.6639, 0.0322, 0.1340],
- [-0.0283, -0.2539, 0.0533, 0.1278, -0.1841, -0.6857, 0.0922, 0.1333],
- [ 0.0008, -0.2950, 0.0865, 0.1903, -0.2152, -0.7125, 0.1525, 0.1757],
- [ 0.0252, -0.2935, 0.0619, 0.1467, -0.1725, -0.6754, 0.0774, 0.1400]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6405, -0.3503, 1.8423, 0.1005, -0.4672, -0.6616, 1.1057,
- 0.3692],
- [ 0.5762, -0.4706, 1.7754, -0.0984, -0.3680, 0.2380, 0.6277,
- 0.1322],
- [ 0.6077, -0.3226, 0.9993, -0.6462, -0.2651, -1.3082, 0.2946,
- 0.5401],
- [ 0.6108, -0.4201, 1.8711, -0.7848, -0.0053, -1.2236, 1.0362,
- 0.2142],
- [ 0.5432, -0.4224, 1.3284, -0.8540, -0.6866, -0.0226, 0.4077,
- 0.3177],
- [ 0.5380, -0.4302, 1.0455, -1.3775, -0.5480, -1.0388, 0.4277,
- 0.2699],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.5292, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.5292, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.539566159248352
- step: 2
- running loss: 0.769783079624176
- Train Steps: 2/90 Loss: 0.7698 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6202, 0.4079, 0.8025, 0.2500, 0.3763, 0.3217, 0.6125, 0.5533],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1696, -0.3065, 0.4627, -0.0944, -0.2657, -0.7406, 0.1930, 0.1774],
- [ 0.1604, -0.3258, 0.4616, -0.0499, -0.2615, -0.7130, 0.2208, 0.1799],
- [ 0.1484, -0.3291, 0.4707, -0.0997, -0.2455, -0.7404, 0.1867, 0.1688],
- [ 0.1728, -0.3067, 0.4303, -0.0530, -0.2103, -0.7886, 0.2801, 0.1768],
- [ 0.1909, -0.2937, 0.4370, -0.1293, -0.2625, -0.7441, 0.1705, 0.1318],
- [ 0.1561, -0.3214, 0.4482, -0.0593, -0.2596, -0.7309, 0.2042, 0.2013],
- [ 0.1806, -0.3209, 0.4534, -0.0854, -0.2666, -0.7370, 0.2352, 0.1802],
- [ 0.1912, -0.2630, 0.4746, -0.0326, -0.2686, -0.7027, 0.2144, 0.1895]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391],
- [ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.5788, -0.4020, 1.4208, -1.1312, -0.5480, -0.8002, 0.5432,
- 0.2699],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634],
- [ 0.6042, -0.4225, 1.5420, -1.2082, -0.4730, -1.0311, 0.6380,
- -0.0220],
- [ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936]]], device='cuda:0')
- loss_train_step before backward: tensor(0.3366, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.3366, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8762048482894897
- step: 3
- running loss: 0.6254016160964966
- Train Steps: 3/90 Loss: 0.6254 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [0.6109, 0.4041, 0.6975, 0.3167, 0.3513, 0.3383, 0.5153, 0.5319],
- [0.6198, 0.4114, 0.8263, 0.3283, 0.3550, 0.3583, 0.5813, 0.5667],
- [0.6286, 0.3977, 0.9038, 0.4733, 0.3900, 0.4150, 0.7074, 0.5320],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6261, 0.4131, 0.8938, 0.3550, 0.4000, 0.2683, 0.6188, 0.5283],
- [0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3182, -0.3804, 0.7228, -0.2070, -0.2713, -0.7219, 0.3276, 0.2051],
- [ 0.3052, -0.3400, 0.7528, -0.3017, -0.3315, -0.7304, 0.2778, 0.2039],
- [ 0.3572, -0.3411, 0.7817, -0.2362, -0.2603, -0.7317, 0.3130, 0.1719],
- [ 0.3135, -0.3787, 0.8097, -0.2249, -0.3170, -0.6919, 0.3421, 0.2800],
- [ 0.3222, -0.3184, 0.7258, -0.2893, -0.2752, -0.7587, 0.2927, 0.1911],
- [ 0.3452, -0.3446, 0.7560, -0.2866, -0.3250, -0.7399, 0.3119, 0.1912],
- [ 0.3920, -0.3923, 0.8089, -0.1643, -0.3038, -0.6605, 0.3647, 0.2397],
- [ 0.2950, -0.3404, 0.7301, -0.2619, -0.2894, -0.7364, 0.2727, 0.1653]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [ 0.5359, -0.4193, 0.9358, -0.8232, -0.6635, -0.7232, 0.0943,
- 0.1710],
- [ 0.5768, -0.3857, 1.5305, -0.7694, -0.6462, -0.6308, 0.3988,
- 0.3315],
- [ 0.6174, -0.4490, 1.8885, -0.0996, -0.4845, -0.3691, 0.9814,
- 0.1715],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.6062, -0.3778, 1.8423, -0.6462, -0.4383, -1.0465, 0.5721,
- 0.1544],
- [ 0.5525, -0.4463, 1.7326, 0.0313, -0.2536, -0.0688, 0.1968,
- 0.3700],
- [ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1705, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1705, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.046713799238205
- step: 4
- running loss: 0.5116784498095512
- Train Steps: 4/90 Loss: 0.5117 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6325, 0.4066, 0.8438, 0.5350, 0.3925, 0.5267, 0.7113, 0.5795],
- [ nan, nan, 0.8938, 0.2850, 0.4662, 0.3117, 0.7406, 0.5528],
- [0.6274, 0.4087, 0.8375, 0.5700, 0.4025, 0.4800, 0.5700, 0.6117],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [0.6110, 0.3984, 0.8750, 0.4933, 0.4625, 0.4950, 0.5578, 0.5676],
- [0.6275, 0.4071, 0.8075, 0.2100, 0.4700, 0.1533, 0.6148, 0.4834],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378],
- [ nan, nan, 0.8900, 0.3217, 0.5038, 0.2233, 0.6694, 0.5609]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4885, -0.4018, 1.0979, -0.3366, -0.3702, -0.6909, 0.3982, 0.2501],
- [ 0.4422, -0.3510, 1.0551, -0.4015, -0.3460, -0.7835, 0.3912, 0.2216],
- [ 0.4735, -0.3952, 1.0856, -0.3061, -0.3689, -0.6766, 0.3673, 0.2366],
- [ 0.4811, -0.3667, 1.0538, -0.3488, -0.3313, -0.6762, 0.3805, 0.2586],
- [ 0.4752, -0.3900, 1.0767, -0.3471, -0.3499, -0.7226, 0.3713, 0.2491],
- [ 0.4760, -0.4058, 1.0386, -0.4084, -0.3484, -0.8394, 0.4057, 0.2369],
- [ 0.4448, -0.3698, 1.0587, -0.4265, -0.3316, -0.8082, 0.3901, 0.2314],
- [ 0.4433, -0.3462, 1.0494, -0.4267, -0.3098, -0.7908, 0.3494, 0.1983]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.3554e-01, -4.0805e-01, 1.6113e+00, 1.8522e-01, -4.7298e-01,
- 1.4673e-01, 9.9965e-01, 3.9055e-01],
- [-2.2859e+00, -2.2859e+00, 1.8423e+00, -9.6952e-01, -1.3233e-01,
- -8.4634e-01, 1.1349e+00, 2.6764e-01],
- [ 6.1184e-01, -3.9831e-01, 1.5824e+00, 3.4688e-01, -4.2679e-01,
- -6.8822e-02, 3.4688e-01, 5.3934e-01],
- [ 5.7079e-01, -4.0747e-01, 1.7961e+00, -2.3048e-01, -4.2102e-01,
- -9.9615e-02, 1.2187e-01, 8.9251e-02],
- [ 5.3637e-01, -4.4573e-01, 1.7557e+00, -7.2363e-03, -1.4965e-01,
- 4.6189e-04, 2.9049e-01, 3.3573e-01],
- [ 6.1264e-01, -4.0570e-01, 1.4439e+00, -1.3159e+00, -1.1501e-01,
- -1.5777e+00, 5.5366e-01, -5.2974e-02],
- [ 6.2072e-01, -3.9360e-01, 1.7788e+00, -1.1235e+00, 1.4480e-01,
- -1.0850e+00, 1.1459e+00, 1.9818e-01],
- [-2.2859e+00, -2.2859e+00, 1.8249e+00, -8.0015e-01, 4.0878e-02,
- -1.2543e+00, 8.0590e-01, 3.0505e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.5289, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.5289, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5755676329135895
- step: 5
- running loss: 0.5151135265827179
- Train Steps: 5/90 Loss: 0.5151 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6361, 0.4102, 0.8650, 0.2983, 0.4888, 0.2000, 0.6702, 0.5459],
- [0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [0.6104, 0.4029, 0.8738, 0.4900, 0.4088, 0.4533, 0.5070, 0.5510],
- [ nan, nan, 0.7240, 0.2722, 0.3900, 0.2567, 0.5168, 0.5933],
- [0.6193, 0.4108, 0.7425, 0.2350, 0.3887, 0.2750, 0.5900, 0.5717]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5403, -0.4755, 1.3845, -0.3674, -0.3488, -0.6346, 0.4944, 0.2667],
- [ 0.4828, -0.5086, 1.3642, -0.3836, -0.3648, -0.6673, 0.4748, 0.2593],
- [ 0.4669, -0.4383, 1.3275, -0.4923, -0.3288, -0.8227, 0.5028, 0.2269],
- [ 0.4408, -0.4837, 1.3020, -0.5519, -0.3340, -0.8562, 0.4644, 0.2288],
- [ 0.4844, -0.4597, 1.3352, -0.3919, -0.3426, -0.6659, 0.4823, 0.2951],
- [ 0.5130, -0.4774, 1.3368, -0.4231, -0.3680, -0.7182, 0.4799, 0.2842],
- [ 0.4028, -0.4552, 1.2646, -0.5310, -0.3368, -0.8135, 0.4102, 0.2107],
- [ 0.4311, -0.4643, 1.2980, -0.5692, -0.2955, -0.8370, 0.4452, 0.2128]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5525, -0.4463, 1.7326, 0.0313, -0.2536, -0.0688, 0.1968,
- 0.3700],
- [ 0.5778, -0.4389, 1.7107, 0.1192, -0.3921, 0.0815, 0.4741,
- 0.0711],
- [ 0.6520, -0.3912, 1.7095, -0.9079, -0.0284, -1.3621, 0.8096,
- 0.2356],
- [ 0.6158, -0.3960, 1.4092, -1.2774, -0.2074, -1.1851, 0.8491,
- 0.1917],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [ 0.5334, -0.4252, 1.7499, -0.0226, -0.3979, -0.1920, 0.0558,
- 0.2589],
- [-2.2859, -2.2859, 1.0580, -1.0288, -0.4845, -1.1004, 0.1011,
- 0.4543],
- [ 0.5746, -0.3882, 1.1436, -1.2005, -0.4903, -1.0157, 0.4393,
- 0.3546]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2597, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2597, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.8352708220481873
- step: 6
- running loss: 0.4725451370080312
- Train Steps: 6/90 Loss: 0.4725 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6289, 0.4032, 0.8419, 0.5446, 0.4075, 0.5017, 0.6312, 0.5117],
- [0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973],
- [0.6236, 0.3967, 0.8675, 0.5400, 0.3862, 0.4517, 0.5825, 0.5200],
- [0.6201, 0.4055, 0.7717, 0.1919, 0.4250, 0.2079, 0.5871, 0.5044],
- [0.6229, 0.4066, 0.8513, 0.5717, 0.4500, 0.5050, 0.6025, 0.5350],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136],
- [0.6212, 0.4033, 0.8938, 0.4167, 0.3813, 0.4267, 0.5613, 0.5583],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4695, -0.5488, 1.5943, -0.4788, -0.3550, -0.6922, 0.5460, 0.2842],
- [ 0.4509, -0.5320, 1.5898, -0.3879, -0.3391, -0.6629, 0.5457, 0.2746],
- [ 0.4613, -0.5351, 1.5649, -0.4374, -0.3564, -0.6521, 0.5522, 0.2870],
- [ 0.3650, -0.5891, 1.4587, -0.6514, -0.3122, -0.8934, 0.5336, 0.2440],
- [ 0.4334, -0.5463, 1.5269, -0.4893, -0.3581, -0.7008, 0.5142, 0.2635],
- [ 0.3762, -0.5329, 1.4488, -0.6472, -0.3316, -0.8446, 0.5024, 0.2438],
- [ 0.4500, -0.5802, 1.5537, -0.4733, -0.2985, -0.6812, 0.5522, 0.2756],
- [ 0.3715, -0.5650, 1.4511, -0.6158, -0.3295, -0.7749, 0.4584, 0.2101]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6189, -0.4238, 1.6026, 0.2295, -0.4037, 0.0313, 0.6298,
- 0.0774],
- [ 0.6240, -0.4321, 1.8423, 0.1852, -0.5885, -0.1612, 0.6962,
- 0.0111],
- [ 0.5944, -0.4538, 1.7210, 0.2083, -0.5018, -0.1997, 0.4046,
- 0.1159],
- [ 0.5785, -0.4129, 1.2785, -1.3996, -0.3227, -1.3259, 0.4258,
- 0.0438],
- [ 0.5911, -0.4080, 1.6460, 0.3546, -0.2074, 0.0467, 0.4970,
- 0.1852],
- [ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862],
- [ 0.5832, -0.4231, 1.8423, -0.3614, -0.5249, -0.3152, 0.3065,
- 0.2930],
- [ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1155, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1155, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.950811892747879
- step: 7
- running loss: 0.4215445561068399
- Train Steps: 7/90 Loss: 0.4215 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [ nan, nan, 0.8888, 0.3100, 0.5262, 0.2817, 0.7145, 0.6003],
- [0.6286, 0.4086, 0.8408, 0.2801, 0.4163, 0.2800, 0.6725, 0.5393],
- [0.6209, 0.3920, 0.8650, 0.5367, 0.4400, 0.5067, 0.6025, 0.4950],
- [0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6164, 0.4076, 0.8838, 0.4117, 0.3713, 0.5550, 0.6238, 0.5350],
- [0.6357, 0.4097, 0.9038, 0.3883, 0.4213, 0.2950, 0.6686, 0.5390]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3134, -0.6794, 1.6194, -0.7281, -0.3209, -0.8706, 0.5486, 0.2226],
- [ 0.3383, -0.6451, 1.6516, -0.6063, -0.3142, -0.8186, 0.5737, 0.2116],
- [ 0.2937, -0.6742, 1.6026, -0.7307, -0.3141, -0.8592, 0.5482, 0.2223],
- [ 0.4637, -0.6103, 1.7207, -0.4602, -0.3870, -0.5815, 0.5783, 0.2602],
- [ 0.4362, -0.6387, 1.6842, -0.5264, -0.3400, -0.6412, 0.5542, 0.2823],
- [ 0.5325, -0.5639, 1.7223, -0.4289, -0.3985, -0.5341, 0.5724, 0.2672],
- [ 0.4656, -0.5311, 1.6839, -0.4444, -0.3957, -0.5205, 0.5554, 0.2717],
- [ 0.4447, -0.5605, 1.6902, -0.5166, -0.4024, -0.6472, 0.5584, 0.2629]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7997e-01, -4.3118e-01, 1.5709e+00, -1.0311e+00, -4.4411e-01,
- -1.1081e+00, 3.8730e-01, 8.5142e-02],
- [-2.2859e+00, -2.2859e+00, 1.8192e+00, -8.5404e-01, 1.4480e-01,
- -9.8491e-01, 1.0143e+00, 4.8673e-01],
- [ 6.1742e-01, -3.9842e-01, 1.5975e+00, -9.9214e-01, -3.6328e-01,
- -9.9261e-01, 8.2047e-01, 2.0505e-01],
- [ 5.8199e-01, -4.7544e-01, 1.7095e+00, 1.9292e-01, -2.5358e-01,
- 5.4350e-02, 4.9700e-01, 4.6189e-04],
- [ 6.1083e-01, -4.0082e-01, 1.9088e+00, -2.5294e-02, -5.7691e-01,
- -3.0747e-01, 7.9054e-01, 1.4989e-01],
- [ 6.5201e-01, -4.0564e-01, 1.9173e+00, -7.6520e-02, -5.5958e-01,
- -4.5373e-01, 7.9493e-01, 1.7680e-01],
- [ 5.6143e-01, -4.0323e-01, 1.7961e+00, -3.8445e-01, -5.7113e-01,
- 2.7760e-01, 5.9515e-01, 1.8522e-01],
- [ 6.5036e-01, -3.9360e-01, 1.8885e+00, -4.9222e-01, -3.4018e-01,
- -9.2333e-01, 8.0224e-01, 2.0352e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2142, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2142, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.1650566458702087
- step: 8
- running loss: 0.3956320807337761
- Train Steps: 8/90 Loss: 0.3956 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4008, 0.8047, 0.2484, 0.4037, 0.2777, 0.6506, 0.5212],
- [0.6361, 0.4076, 0.8862, 0.5350, 0.3713, 0.4650, 0.6654, 0.5297],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6240, 0.4217, 0.8150, 0.3133, 0.4425, 0.2650, 0.5650, 0.5817],
- [0.6122, 0.3993, 0.8738, 0.4667, 0.4517, 0.4879, 0.5155, 0.4927],
- [0.6248, 0.4032, 0.7738, 0.1900, 0.4813, 0.1400, 0.5941, 0.4904],
- [0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6180, 0.4064, 0.7200, 0.2467, 0.4275, 0.2367, 0.5963, 0.6217]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3986, -0.6094, 1.7731, -0.5675, -0.4028, -0.6541, 0.6191, 0.1876],
- [ 0.5058, -0.5929, 1.8639, -0.3546, -0.4482, -0.4050, 0.6035, 0.2369],
- [ 0.3617, -0.6422, 1.7939, -0.5044, -0.3761, -0.5660, 0.5857, 0.2077],
- [ 0.2151, -0.7175, 1.6810, -0.7442, -0.3176, -0.7890, 0.5508, 0.1978],
- [ 0.4400, -0.6495, 1.8597, -0.4622, -0.3749, -0.5228, 0.6240, 0.2258],
- [ 0.2712, -0.7017, 1.7019, -0.7678, -0.3379, -0.8403, 0.5856, 0.1985],
- [ 0.3907, -0.6001, 1.8238, -0.4108, -0.4309, -0.5027, 0.5874, 0.2351],
- [ 0.3679, -0.6428, 1.7431, -0.5741, -0.3706, -0.6884, 0.6126, 0.1843]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4345, 1.4308, -1.1384, -0.4213, -1.0031, 0.7190,
- 0.1214],
- [ 0.6520, -0.4032, 1.8076, 0.1852, -0.5711, -0.1381, 0.7876,
- 0.1608],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853],
- [ 0.5964, -0.3380, 1.4785, -0.8386, -0.2420, -1.0619, 0.3238,
- 0.4008],
- [ 0.5417, -0.4417, 1.7499, -0.1304, -0.1994, -0.0324, 0.0951,
- -0.0099],
- [ 0.5999, -0.4236, 1.2880, -1.4083, -0.0630, -1.6393, 0.4584,
- -0.0208],
- [ 0.5604, -0.4620, 1.7961, -0.1997, -0.3229, 0.1082, 0.4104,
- 0.2545],
- [ 0.5687, -0.4088, 1.0397, -1.1466, -0.3113, -1.1928, 0.4681,
- 0.5855]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1039, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1039, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.268991008400917
- step: 9
- running loss: 0.36322122315565747
- Train Steps: 9/90 Loss: 0.3632 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6164, 0.4102, 0.8850, 0.4867, 0.4213, 0.5983, 0.5609, 0.5038],
- [0.6268, 0.4102, 0.8938, 0.3667, 0.4025, 0.2833, 0.6275, 0.5183],
- [0.6284, 0.4127, 0.8538, 0.5867, 0.4363, 0.5083, 0.6038, 0.5433],
- [0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901],
- [0.6200, 0.4039, 0.8880, 0.4799, 0.3625, 0.4285, 0.5866, 0.5148],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6214, 0.4040, 0.8838, 0.3500, 0.3600, 0.5183, 0.6362, 0.5200],
- [0.6113, 0.4104, 0.8650, 0.4250, 0.3925, 0.5967, 0.5787, 0.5116]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4023, -0.5970, 1.8370, -0.4402, -0.3702, -0.4494, 0.5963, 0.2152],
- [ 0.2735, -0.7746, 1.8249, -0.7597, -0.3093, -0.8099, 0.6603, 0.1850],
- [ 0.3854, -0.6540, 1.8492, -0.5377, -0.3915, -0.5254, 0.5822, 0.1906],
- [ 0.3886, -0.5986, 1.8082, -0.5924, -0.3838, -0.5982, 0.6233, 0.1837],
- [ 0.3802, -0.6962, 1.8287, -0.5726, -0.3557, -0.6003, 0.5991, 0.2111],
- [ 0.4066, -0.6400, 1.8792, -0.4734, -0.3624, -0.4860, 0.6284, 0.1999],
- [ 0.4266, -0.5321, 1.8191, -0.5020, -0.4016, -0.4485, 0.5741, 0.2038],
- [ 0.3916, -0.6078, 1.8294, -0.5132, -0.4115, -0.4810, 0.5802, 0.2019]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5614, -0.3912, 1.8018, -0.0380, -0.3402, 0.4778, 0.3049,
- 0.0412],
- [ 0.6092, -0.3913, 1.8423, -0.5923, -0.4268, -0.9772, 0.6125,
- 0.1082],
- [ 0.6166, -0.3795, 1.6575, 0.4239, -0.2709, 0.0620, 0.5028,
- 0.2237],
- [ 0.6031, -0.4307, 1.7268, -0.8002, -0.6058, -0.6462, 0.6442,
- -0.0220],
- [ 0.5781, -0.4205, 1.8159, -0.0691, -0.6116, -0.3069, 0.4236,
- 0.0919],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.5841, -0.4199, 1.7961, -0.6693, -0.6231, 0.1082, 0.6529,
- 0.1159],
- [ 0.5376, -0.3903, 1.7095, -0.3229, -0.4730, 0.4701, 0.3871,
- 0.0772]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0941, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0941, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.3630512952804565
- step: 10
- running loss: 0.33630512952804564
- Train Steps: 10/90 Loss: 0.3363 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6156, 0.4125, 0.8850, 0.4833, 0.4550, 0.5817, 0.5765, 0.5084],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4281, -0.6018, 1.9033, -0.4220, -0.3854, -0.3424, 0.6084, 0.1981],
- [ 0.4875, -0.5669, 1.9027, -0.3804, -0.4182, -0.3505, 0.6296, 0.1911],
- [ 0.3455, -0.6332, 1.7253, -0.7356, -0.3643, -0.6066, 0.5329, 0.1503],
- [ 0.3817, -0.6205, 1.8508, -0.5359, -0.3424, -0.4687, 0.5927, 0.1690],
- [ 0.4571, -0.5370, 1.8616, -0.5107, -0.3985, -0.4145, 0.6011, 0.1742],
- [ 0.5165, -0.5026, 1.9352, -0.3225, -0.4410, -0.2379, 0.6228, 0.1941],
- [ 0.2141, -0.7852, 1.7427, -0.8960, -0.2852, -0.8919, 0.6142, 0.1452],
- [ 0.5665, -0.5681, 1.9401, -0.3287, -0.4210, -0.2708, 0.6404, 0.1673]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5573, -0.3808, 1.8018, -0.0534, -0.1843, 0.4008, 0.3769,
- 0.0622],
- [ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853],
- [ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0903, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0903, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.453396238386631
- step: 11
- running loss: 0.3139451125806028
- Train Steps: 11/90 Loss: 0.3139 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4064, 0.7879, 0.2179, 0.4567, 0.1725, 0.5955, 0.5478],
- [0.6132, 0.4037, 0.6963, 0.2217, 0.4100, 0.1950, 0.5395, 0.5175],
- [0.6271, 0.4005, 0.8450, 0.6067, 0.3850, 0.4983, 0.6069, 0.4649],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [0.6127, 0.4066, 0.8550, 0.5567, 0.4662, 0.5141, 0.5070, 0.5412],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917],
- [0.6214, 0.4175, 0.7300, 0.2883, 0.4338, 0.2167, 0.5698, 0.5773],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3583, -0.6343, 1.7170, -0.7579, -0.3092, -0.6544, 0.5731, 0.1568],
- [ 0.3602, -0.6007, 1.7199, -0.7404, -0.3476, -0.6163, 0.5342, 0.1324],
- [ 0.5714, -0.4951, 1.9335, -0.2031, -0.4453, -0.1089, 0.6063, 0.1638],
- [ 0.6233, -0.4319, 1.9334, -0.2226, -0.4486, -0.0354, 0.5995, 0.1807],
- [ 0.4814, -0.5525, 1.8654, -0.3785, -0.4045, -0.2285, 0.5742, 0.1675],
- [ 0.4247, -0.6203, 1.7907, -0.5895, -0.3542, -0.4821, 0.5728, 0.1632],
- [ 0.4121, -0.5504, 1.7687, -0.6334, -0.3926, -0.4873, 0.5501, 0.1550],
- [ 0.5594, -0.5201, 1.9404, -0.2369, -0.3908, -0.1411, 0.6089, 0.1694]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5787, -0.4086, 1.3535, -1.2794, -0.1764, -1.4891, 0.4645,
- 0.2442],
- [ 0.5465, -0.4214, 0.9300, -1.2620, -0.3921, -1.3852, 0.2062,
- 0.1043],
- [ 0.6105, -0.4360, 1.6171, 0.5162, -0.5076, 0.0159, 0.5171,
- -0.1385],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [ 0.5443, -0.4079, 1.6633, 0.2853, -0.1325, 0.0888, 0.0558,
- 0.2138],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470],
- [ 0.5841, -0.3574, 1.0859, -0.9541, -0.2824, -1.2851, 0.3460,
- 0.3808],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1143, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1143, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.5676762238144875
- step: 12
- running loss: 0.29730635198454064
- Train Steps: 12/90 Loss: 0.2973 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4075, 0.8650, 0.5617, 0.4150, 0.4367, 0.5450, 0.5650],
- [0.6233, 0.4091, 0.8100, 0.2950, 0.3563, 0.3883, 0.6013, 0.5200],
- [0.6098, 0.3991, 0.8638, 0.4717, 0.4263, 0.4967, 0.5212, 0.5650],
- [0.6284, 0.4029, 0.8838, 0.3783, 0.3975, 0.2850, 0.6335, 0.5090],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402],
- [0.6189, 0.4029, 0.8375, 0.5767, 0.4745, 0.4829, 0.5551, 0.5598],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6069, 0.3975, 0.8625, 0.5083, 0.4388, 0.5483, 0.5650, 0.4967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5343, -0.4726, 1.7997, -0.3158, -0.3930, -0.2037, 0.5199, 0.1621],
- [ 0.5070, -0.4471, 1.7056, -0.5312, -0.4011, -0.3798, 0.4930, 0.1416],
- [ 0.5230, -0.4723, 1.8404, -0.3098, -0.4120, -0.1843, 0.5487, 0.1675],
- [ 0.5441, -0.4712, 1.8092, -0.4498, -0.4106, -0.3381, 0.5668, 0.1592],
- [ 0.5783, -0.4525, 1.8416, -0.2854, -0.3991, -0.1710, 0.5524, 0.1664],
- [ 0.4761, -0.5077, 1.7601, -0.4455, -0.3867, -0.3223, 0.5207, 0.1484],
- [ 0.4512, -0.5921, 1.7266, -0.6192, -0.3187, -0.5162, 0.4994, 0.1540],
- [ 0.5362, -0.4790, 1.8213, -0.3137, -0.4172, -0.1955, 0.5498, 0.1414]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5770, -0.4036, 1.7095, 0.3084, -0.3691, -0.2690, 0.2314,
- 0.3238],
- [ 0.5932, -0.3962, 1.4554, -0.9233, -0.6404, -0.4922, 0.4912,
- 0.1159],
- [ 0.5308, -0.4425, 1.7037, -0.1073, -0.3171, 0.0082, 0.1217,
- 0.3238],
- [ 0.6165, -0.4249, 1.7961, -0.5384, -0.4499, -0.9695, 0.6401,
- 0.0652],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091],
- [ 0.5726, -0.4249, 1.5824, 0.3777, -0.0942, -0.0556, 0.2782,
- 0.2997],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.5174, -0.4497, 1.6979, 0.0620, -0.2594, 0.2468, 0.3238,
- 0.0082]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0588, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0588, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.626456379890442
- step: 13
- running loss: 0.2789581830684955
- Train Steps: 13/90 Loss: 0.2790 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4086, 0.8414, 0.2974, 0.4117, 0.2274, 0.5869, 0.5391],
- [0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6125, 0.3983, 0.8750, 0.4867, 0.4275, 0.4783, 0.5413, 0.5717],
- [0.6183, 0.4076, 0.8838, 0.4517, 0.3813, 0.4483, 0.5775, 0.5633],
- [0.6204, 0.4049, 0.7975, 0.2700, 0.3937, 0.2567, 0.5700, 0.5183],
- [0.6124, 0.4069, 0.8314, 0.5001, 0.3738, 0.4650, 0.5167, 0.5402]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3386, -0.5945, 1.4882, -0.8442, -0.2679, -0.8243, 0.4488, 0.1746],
- [ 0.6488, -0.4722, 1.7840, -0.1984, -0.4139, -0.1456, 0.5088, 0.2118],
- [ 0.6712, -0.3812, 1.7589, -0.1449, -0.4616, -0.0147, 0.4819, 0.1966],
- [ 0.5852, -0.3525, 1.7167, -0.2342, -0.4497, -0.0917, 0.4490, 0.1965],
- [ 0.6722, -0.3628, 1.7972, -0.1442, -0.4551, -0.0454, 0.5214, 0.2173],
- [ 0.6903, -0.3728, 1.7680, -0.1855, -0.4479, -0.0402, 0.4770, 0.2045],
- [ 0.4033, -0.5384, 1.5374, -0.7993, -0.3055, -0.7459, 0.4317, 0.1558],
- [ 0.5944, -0.3282, 1.7185, -0.2286, -0.4429, -0.0724, 0.4695, 0.1935]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5776, -0.3987, 1.6005, -0.9121, -0.3844, -1.2358, 0.4247,
- 0.2043],
- [ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390],
- [ 0.6134, -0.3910, 1.4497, 0.3546, -0.3517, -0.0919, 0.3296,
- 0.5239],
- [ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478],
- [ 0.5432, -0.4462, 1.7557, -0.0380, -0.3113, -0.0765, 0.2141,
- 0.3546],
- [ 0.5700, -0.4032, 1.7961, -0.1997, -0.5249, -0.2151, 0.3815,
- 0.3161],
- [ 0.5798, -0.4156, 1.3977, -1.0388, -0.4672, -1.1004, 0.3469,
- 0.1082],
- [ 0.5425, -0.4067, 1.5543, 0.0241, -0.5596, -0.1381, 0.1005,
- 0.2093]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0389, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0389, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.6653256751596928
- step: 14
- running loss: 0.2618089767971209
- Train Steps: 14/90 Loss: 0.2618 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6222, 0.4072, 0.7164, 0.2166, 0.3738, 0.3167, 0.6100, 0.5533],
- [0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167],
- [0.6279, 0.4008, 0.8600, 0.4883, 0.4325, 0.5283, 0.7010, 0.5378],
- [0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389],
- [0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6552, -0.3837, 1.7376, -0.1305, -0.4385, -0.1260, 0.4828, 0.2422],
- [ 0.5077, -0.4758, 1.5026, -0.5479, -0.3470, -0.5731, 0.4024, 0.2062],
- [ 0.6341, -0.3702, 1.6776, -0.1107, -0.4211, -0.0452, 0.4231, 0.2498],
- [ 0.6070, -0.3405, 1.5338, -0.4015, -0.4672, -0.2579, 0.4081, 0.2168],
- [ 0.8312, -0.2328, 1.8114, 0.1777, -0.5426, 0.2986, 0.4698, 0.2533],
- [ 0.7075, -0.2959, 1.7456, 0.0659, -0.5056, 0.1518, 0.4310, 0.2443],
- [ 0.4628, -0.4883, 1.4341, -0.7040, -0.3237, -0.7027, 0.4005, 0.2153],
- [ 0.4701, -0.4907, 1.4618, -0.6527, -0.3600, -0.6216, 0.3729, 0.2062]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6454, -0.3719, 1.9115, -0.1304, -0.5076, -0.2844, 1.0033,
- 0.4386],
- [ 0.5711, -0.4015, 1.6979, -0.6770, -0.5365, -1.0619, 0.1712,
- 0.1494],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853],
- [ 0.5878, -0.4052, 1.0229, -1.2855, -0.5596, -0.8232, 0.5316,
- 0.2699],
- [ 0.5886, -0.4253, 1.8654, -0.3460, -0.5480, 0.3623, 0.6587,
- 0.1005],
- [ 0.6141, -0.4345, 1.6864, -0.0303, -0.2882, 0.1544, 0.9521,
- 0.1982],
- [ 0.5697, -0.4466, 1.1973, -1.1871, -0.4571, -0.9965, 0.5219,
- 0.2032],
- [ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0639, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0639, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.729193616658449
- step: 15
- running loss: 0.24861290777722994
- Train Steps: 15/90 Loss: 0.2486 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6102, 0.4020, 0.8638, 0.3717, 0.3625, 0.5017, 0.6038, 0.5500],
- [0.6371, 0.4092, 0.8337, 0.5850, 0.3950, 0.5117, 0.6559, 0.5262],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6277, 0.4083, 0.8350, 0.2717, 0.4562, 0.1800, 0.5918, 0.4878],
- [0.6198, 0.4114, 0.8263, 0.3283, 0.3550, 0.3583, 0.5813, 0.5667],
- [0.6203, 0.4073, 0.8189, 0.2398, 0.4400, 0.2054, 0.5929, 0.5501],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8263, -0.1902, 1.7374, 0.2545, -0.5898, 0.3922, 0.4587, 0.2920],
- [ 0.8305, -0.2324, 1.7869, 0.3114, -0.5759, 0.3759, 0.4640, 0.2764],
- [ 0.5266, -0.4159, 1.3851, -0.5850, -0.3763, -0.5650, 0.3748, 0.2421],
- [ 0.4363, -0.4686, 1.3255, -0.7476, -0.3138, -0.7884, 0.3587, 0.2176],
- [ 0.7096, -0.3306, 1.6011, -0.1989, -0.4514, -0.1596, 0.4091, 0.2694],
- [ 0.5078, -0.4304, 1.3690, -0.6331, -0.3530, -0.6531, 0.3775, 0.2460],
- [ 0.5176, -0.3927, 1.3475, -0.6248, -0.3892, -0.6089, 0.3561, 0.2448],
- [ 0.6879, -0.3213, 1.6219, -0.0797, -0.4829, -0.0799, 0.4250, 0.2820]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5324, -0.4293, 1.7037, -0.5692, -0.6115, 0.0313, 0.5028,
- 0.2545],
- [ 0.6569, -0.3960, 1.5651, 0.4162, -0.4614, 0.0774, 0.7438,
- 0.1447],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.6132, -0.4002, 1.5709, -1.0311, -0.1785, -1.4545, 0.4474,
- -0.0328],
- [ 0.5768, -0.3857, 1.5305, -0.7694, -0.6462, -0.6308, 0.3988,
- 0.3315],
- [ 0.5792, -0.4048, 1.4965, -1.1781, -0.2534, -1.3371, 0.4528,
- 0.2549],
- [ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.5235, -0.4273, 1.7499, -0.4306, -0.5827, -0.4614, 0.1651,
- 0.0862]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2667, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2667, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.9958606250584126
- step: 16
- running loss: 0.24974128906615078
- Train Steps: 16/90 Loss: 0.2497 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578],
- [0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [0.6161, 0.4099, 0.8738, 0.4383, 0.3788, 0.5483, 0.5605, 0.5019],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6187, 0.4104, 0.7126, 0.2069, 0.3950, 0.2517, 0.5913, 0.5683]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5092, -0.4276, 1.2993, -0.6912, -0.3900, -0.7287, 0.3428, 0.2390],
- [ 0.4638, -0.5024, 1.3391, -0.7674, -0.3217, -0.9413, 0.3813, 0.2378],
- [ 0.7581, -0.2839, 1.6582, 0.2064, -0.5467, 0.2310, 0.4485, 0.2857],
- [ 0.6093, -0.3809, 1.5054, -0.2810, -0.4603, -0.3393, 0.3837, 0.2761],
- [ 0.7945, -0.2427, 1.6784, 0.1989, -0.5599, 0.2828, 0.4505, 0.2914],
- [ 0.6689, -0.2769, 1.5637, 0.0263, -0.5515, 0.1618, 0.3862, 0.2881],
- [ 0.5313, -0.4060, 1.3328, -0.6785, -0.4032, -0.7108, 0.3818, 0.2442],
- [ 0.5118, -0.4049, 1.3256, -0.6318, -0.4154, -0.6396, 0.3457, 0.2628]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5232, -0.4189, 0.9358, -1.4006, -0.3921, -1.3698, 0.2555,
- 0.2906],
- [ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [ 0.6038, -0.3464, 1.7037, 0.3931, -0.4441, -0.2613, 0.3007,
- 0.4624],
- [ 0.5235, -0.4273, 1.7499, -0.4306, -0.5827, -0.4614, 0.1651,
- 0.0862],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [ 0.5597, -0.3928, 1.7499, -0.2613, -0.5365, 0.2468, 0.3028,
- 0.0321],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.5718, -0.3905, 1.0053, -1.3305, -0.4614, -1.1235, 0.4450,
- 0.3392]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0752, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0752, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.071053955703974
- step: 17
- running loss: 0.23947376210023374
- Train Steps: 17/90 Loss: 0.2395 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6085, 0.4008, 0.8588, 0.5200, 0.4959, 0.4977, 0.5175, 0.5283],
- [0.6275, 0.4071, 0.8075, 0.2100, 0.4700, 0.1533, 0.6148, 0.4834],
- [0.6135, 0.3994, 0.7913, 0.3050, 0.3625, 0.3050, 0.5837, 0.5050],
- [0.6127, 0.4084, 0.8700, 0.4467, 0.3987, 0.4317, 0.5013, 0.5471],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167],
- [0.6034, 0.4011, 0.7350, 0.2533, 0.3438, 0.3367, 0.5516, 0.5084],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6578, -0.3683, 1.5521, -0.1059, -0.4751, -0.2149, 0.4410, 0.2723],
- [ 0.5114, -0.4456, 1.2885, -0.7996, -0.3856, -0.9587, 0.4281, 0.2349],
- [ 0.5884, -0.3397, 1.3325, -0.5015, -0.4970, -0.4707, 0.4000, 0.2406],
- [ 0.6309, -0.3858, 1.5195, -0.1167, -0.5022, -0.1658, 0.3629, 0.2780],
- [ 0.5000, -0.4365, 1.3342, -0.7227, -0.3862, -0.8498, 0.3772, 0.2283],
- [ 0.5706, -0.3431, 1.3098, -0.5633, -0.5075, -0.5184, 0.3733, 0.2563],
- [ 0.5632, -0.3163, 1.3676, -0.3814, -0.5250, -0.3097, 0.3615, 0.2432],
- [ 0.5914, -0.3618, 1.5231, -0.0976, -0.4550, -0.1584, 0.4225, 0.2744]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5245, -0.4347, 1.6806, 0.1159, 0.0046, 0.0129, 0.1044,
- 0.1544],
- [ 0.6126, -0.4057, 1.4439, -1.3159, -0.1150, -1.5777, 0.5537,
- -0.0530],
- [ 0.5477, -0.4413, 1.3688, -0.8771, -0.6115, -0.8771, 0.4104,
- 0.0467],
- [ 0.5441, -0.3997, 1.7326, -0.2228, -0.4441, -0.2921, 0.0296,
- 0.2409],
- [ 0.5483, -0.4249, 1.5305, -0.7386, -0.6115, -0.8694, 0.3353,
- 0.1005],
- [ 0.5009, -0.4333, 1.1090, -1.1158, -0.6982, -0.7309, 0.2617,
- 0.0622],
- [ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0491, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0491, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.120140295475721
- step: 18
- running loss: 0.22889668308198452
- Train Steps: 18/90 Loss: 0.2289 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [0.6250, 0.4116, 0.8700, 0.4850, 0.4650, 0.5567, 0.6088, 0.5183],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6199, 0.4112, 0.8475, 0.3717, 0.3550, 0.4350, 0.6063, 0.6083],
- [0.6154, 0.4112, 0.7037, 0.2317, 0.4238, 0.1833, 0.5350, 0.5600],
- [0.6147, 0.4112, 0.7988, 0.3200, 0.3775, 0.2767, 0.5150, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6256, -0.3496, 1.5313, -0.1727, -0.4812, -0.2339, 0.4121, 0.2335],
- [ 0.5802, -0.3599, 1.4961, -0.1003, -0.4505, -0.1714, 0.3991, 0.2416],
- [ 0.6846, -0.3257, 1.6299, 0.1926, -0.5661, 0.1919, 0.4176, 0.2676],
- [ 0.4458, -0.4585, 1.2494, -0.8908, -0.3609, -1.0730, 0.3473, 0.1982],
- [ 0.4966, -0.3877, 1.1893, -0.8811, -0.4293, -0.9376, 0.3631, 0.2087],
- [ 0.6634, -0.3550, 1.5272, -0.1704, -0.4972, -0.2177, 0.3968, 0.2328],
- [ 0.4826, -0.3862, 1.2012, -0.8707, -0.4220, -0.9600, 0.3600, 0.1954],
- [ 0.4057, -0.4449, 1.1502, -0.9486, -0.3773, -1.0787, 0.3214, 0.1925]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [ 0.6012, -0.3846, 1.7326, -0.0457, -0.1381, 0.2853, 0.5259,
- 0.1082],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.5485, -0.4209, 1.6691, -0.4152, -0.5249, -1.1081, 0.0725,
- 0.0021],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.5774, -0.3868, 1.6286, -0.5692, -0.6462, -0.2767, 0.5143,
- 0.5239],
- [ 0.5565, -0.3864, 0.9647, -1.2159, -0.3286, -1.4391, 0.1852,
- 0.3007],
- [ 0.5532, -0.3864, 1.4035, -0.8079, -0.5423, -1.0080, 0.0928,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0405, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0405, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.160642504692078
- step: 19
- running loss: 0.21898118445747777
- Train Steps: 19/90 Loss: 0.2190 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483],
- [0.6248, 0.4185, 0.8500, 0.5767, 0.4463, 0.4550, 0.5613, 0.5917],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6205, 0.4004, 0.8938, 0.4883, 0.3663, 0.5000, 0.6357, 0.4947],
- [0.6228, 0.4004, 0.8750, 0.5250, 0.3825, 0.5233, 0.6362, 0.5000],
- [0.6115, 0.4005, 0.8838, 0.3867, 0.3763, 0.4700, 0.5800, 0.5550],
- [0.6175, 0.4013, 0.8900, 0.4500, 0.4375, 0.5850, 0.6175, 0.5383]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4995, -0.4501, 1.2798, -0.9045, -0.3884, -1.1201, 0.3507, 0.1686],
- [ 0.5374, -0.3718, 1.3481, -0.4737, -0.4760, -0.4540, 0.3643, 0.2129],
- [ 0.5177, -0.4185, 1.3734, -0.5420, -0.4201, -0.6426, 0.3885, 0.1762],
- [ 0.5296, -0.4432, 1.3546, -0.6588, -0.4503, -0.7486, 0.3094, 0.1954],
- [ 0.5990, -0.3650, 1.4727, -0.2500, -0.4999, -0.2574, 0.3996, 0.2076],
- [ 0.5845, -0.3787, 1.4616, -0.2690, -0.4853, -0.3027, 0.3903, 0.1992],
- [ 0.5524, -0.3988, 1.3570, -0.6329, -0.4578, -0.6935, 0.3651, 0.1924],
- [ 0.4944, -0.4201, 1.3606, -0.5418, -0.4362, -0.6470, 0.3477, 0.1965]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.2355e-01, -4.2731e-01, 1.7499e+00, -4.3064e-01, -5.8268e-01,
- -4.6143e-01, 1.6505e-01, 8.6245e-02],
- [ 5.5456e-01, -4.6205e-01, 1.7788e+00, -4.2294e-01, -5.1917e-01,
- -2.2633e-02, 4.2771e-01, 2.4681e-01],
- [ 5.9988e-01, -3.5304e-01, 1.6402e+00, 3.7768e-01, -2.2471e-01,
- -1.8430e-01, 3.0647e-01, 4.4696e-01],
- [ 5.5525e-01, -3.9923e-01, 1.7557e+00, -2.6898e-01, -4.9030e-01,
- -2.6898e-01, 5.4227e-02, 4.1446e-01],
- [ 5.8031e-01, -4.3672e-01, 1.8423e+00, -3.0331e-02, -5.9423e-01,
- 2.3557e-02, 6.5034e-01, -9.2270e-04],
- [ 5.9082e-01, -4.3664e-01, 1.7557e+00, 1.3903e-01, -5.1917e-01,
- 1.3133e-01, 6.5289e-01, 2.3557e-02],
- [ 5.3839e-01, -4.3610e-01, 1.7961e+00, -4.9992e-01, -5.4804e-01,
- -1.1501e-01, 3.9307e-01, 2.7760e-01],
- [ 5.6637e-01, -4.3212e-01, 1.8249e+00, -2.0739e-01, -2.6513e-01,
- 4.1617e-01, 5.6628e-01, 2.0062e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0984, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0984, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.259060598909855
- step: 20
- running loss: 0.21295302994549276
- Train Steps: 20/90 Loss: 0.2130 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6109, 0.4041, 0.6975, 0.3167, 0.3513, 0.3383, 0.5153, 0.5319],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6258, 0.4143, 0.8525, 0.2617, 0.4950, 0.1667, 0.6219, 0.4967],
- [0.6339, 0.4102, 0.8588, 0.3133, 0.4425, 0.2117, 0.6417, 0.5089],
- [0.6299, 0.4008, 0.8450, 0.5350, 0.4213, 0.5000, 0.6350, 0.5100],
- [0.6289, 0.4024, 0.9088, 0.4567, 0.3937, 0.5633, 0.7058, 0.5609],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4723, -0.4247, 1.1850, -0.9063, -0.4518, -0.8865, 0.2939, 0.1639],
- [ 0.5025, -0.3635, 1.3192, -0.5956, -0.5070, -0.4938, 0.3355, 0.1808],
- [ 0.4405, -0.4470, 1.2356, -1.0312, -0.3698, -1.2157, 0.3035, 0.1463],
- [ 0.3921, -0.5284, 1.1823, -1.2778, -0.2926, -1.5964, 0.2982, 0.1206],
- [ 0.5190, -0.3989, 1.3849, -0.7171, -0.4370, -0.7962, 0.4063, 0.1556],
- [ 0.6043, -0.3868, 1.5303, -0.0823, -0.4990, -0.1121, 0.3608, 0.1888],
- [ 0.6078, -0.3671, 1.6489, 0.1029, -0.5688, 0.1931, 0.3999, 0.2117],
- [ 0.5852, -0.4089, 1.5890, -0.1718, -0.4224, -0.2408, 0.3769, 0.1869]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3591e-01, -4.1932e-01, 9.3580e-01, -8.2325e-01, -6.6351e-01,
- -7.2317e-01, 9.4325e-02, 1.7099e-01],
- [ 5.7633e-01, -4.1470e-01, 1.3226e+00, -1.0619e+00, -6.6351e-01,
- -4.1524e-01, 5.3741e-01, 2.5450e-01],
- [ 5.9151e-01, -3.6821e-01, 1.2187e+00, -1.2313e+00, -4.3256e-01,
- -9.5412e-01, 5.7783e-01, 3.7768e-01],
- [ 6.0479e-01, -3.7229e-01, 1.6517e+00, -1.0773e+00, 4.6189e-04,
- -1.5161e+00, 5.8660e-01, 8.0947e-03],
- [ 6.4212e-01, -3.9120e-01, 1.6806e+00, -8.3865e-01, -2.4203e-01,
- -1.3082e+00, 6.7795e-01, 6.4585e-02],
- [ 6.2361e-01, -4.3441e-01, 1.6171e+00, 1.8522e-01, -3.4018e-01,
- 2.3557e-02, 6.4711e-01, 6.9746e-02],
- [ 6.1907e-01, -4.2731e-01, 1.9115e+00, -1.7660e-01, -4.6721e-01,
- 3.1609e-01, 9.7406e-01, 3.0505e-01],
- [ 5.3585e-01, -4.3703e-01, 1.7095e+00, -3.0331e-02, -8.0370e-02,
- -3.8029e-02, 1.0439e-01, 3.3918e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0438, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0438, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.30288702994585
- step: 21
- running loss: 0.20489938237837382
- Train Steps: 21/90 Loss: 0.2049 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6188, 0.4099, 0.7400, 0.2433, 0.3962, 0.2750, 0.6162, 0.5467],
- [0.6264, 0.4055, 0.8425, 0.2767, 0.4425, 0.2767, 0.7050, 0.5586],
- [0.6304, 0.4024, 0.8925, 0.4800, 0.3937, 0.4817, 0.7485, 0.5297],
- [0.6124, 0.4030, 0.8650, 0.4867, 0.4999, 0.5106, 0.5137, 0.5773],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767],
- [0.6140, 0.4034, 0.8850, 0.4317, 0.4288, 0.5067, 0.5825, 0.5533],
- [0.6185, 0.4042, 0.7700, 0.2250, 0.4062, 0.2117, 0.5763, 0.5150],
- [0.6275, 0.4081, 0.8063, 0.2017, 0.4825, 0.1583, 0.6156, 0.4869]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4667, -0.4029, 1.2225, -0.9475, -0.4710, -0.9117, 0.3017, 0.1536],
- [ 0.4264, -0.4826, 1.3187, -1.0577, -0.3452, -1.2749, 0.3562, 0.1209],
- [ 0.5903, -0.3429, 1.5855, -0.0366, -0.5336, 0.1025, 0.4147, 0.1930],
- [ 0.5301, -0.4158, 1.5460, -0.2706, -0.4314, -0.2919, 0.3681, 0.1735],
- [ 0.5612, -0.3537, 1.6040, 0.0042, -0.5038, 0.1165, 0.3997, 0.1812],
- [ 0.5596, -0.3945, 1.5997, -0.1237, -0.4685, -0.0593, 0.3873, 0.1867],
- [ 0.4114, -0.4599, 1.1883, -1.2378, -0.3738, -1.3679, 0.2803, 0.1201],
- [ 0.4364, -0.4916, 1.2448, -1.2068, -0.3356, -1.4575, 0.3233, 0.1142]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5725, -0.3926, 1.1321, -1.1620, -0.4557, -1.0157, 0.5605,
- 0.2391],
- [ 0.6075, -0.4129, 1.6055, -1.0080, -0.2420, -1.0080, 0.9704,
- 0.2944],
- [ 0.6257, -0.4273, 1.8365, -0.0688, -0.4672, -0.0611, 1.1715,
- 0.1608],
- [ 0.5428, -0.4244, 1.7095, -0.0380, 0.0232, 0.0725, 0.0866,
- 0.3806],
- [ 0.5539, -0.4031, 1.7168, -0.0594, -0.3748, 0.0543, 0.1390,
- 0.3777],
- [ 0.5499, -0.4225, 1.8018, -0.2921, -0.3055, 0.0543, 0.4046,
- 0.2699],
- [ 0.5707, -0.4189, 1.2707, -1.2467, -0.4095, -1.3082, 0.3758,
- 0.0928],
- [ 0.6124, -0.4010, 1.4381, -1.3544, -0.0573, -1.5546, 0.5573,
- -0.0369]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0400, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0400, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.342839952558279
- step: 22
- running loss: 0.19740181602537632
- Train Steps: 22/90 Loss: 0.1974 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [0.6128, 0.4118, 0.8638, 0.5333, 0.4625, 0.5267, 0.5193, 0.5475],
- [0.6267, 0.4094, 0.8712, 0.3083, 0.4400, 0.2267, 0.6250, 0.5200],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6233, 0.4091, 0.8100, 0.2950, 0.3563, 0.3883, 0.6013, 0.5200]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5874, -0.3813, 1.6545, -0.0507, -0.4417, 0.0812, 0.4304, 0.1834],
- [ 0.3994, -0.4998, 1.2229, -1.3992, -0.2548, -1.6338, 0.2909, 0.1109],
- [ 0.4773, -0.3984, 1.3342, -0.7806, -0.4434, -0.6878, 0.3460, 0.1358],
- [ 0.5034, -0.4020, 1.5718, -0.4009, -0.3795, -0.3917, 0.4109, 0.1579],
- [ 0.4831, -0.4854, 1.3986, -1.1440, -0.2725, -1.4507, 0.3654, 0.1005],
- [ 0.6052, -0.4153, 1.6555, -0.1839, -0.4558, -0.1716, 0.4230, 0.1649],
- [ 0.6080, -0.3871, 1.6687, -0.1327, -0.4663, -0.0808, 0.4349, 0.1612],
- [ 0.5008, -0.3815, 1.3538, -0.8037, -0.4405, -0.6999, 0.3763, 0.1449]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853],
- [ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386],
- [ 0.5447, -0.3838, 1.7037, 0.1775, -0.1497, 0.1467, 0.1128,
- 0.2431],
- [ 0.6089, -0.3948, 1.7383, -0.8617, -0.2536, -1.2390, 0.6009,
- 0.1159],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007],
- [ 0.5932, -0.3962, 1.4554, -0.9233, -0.6404, -0.4922, 0.4912,
- 0.1159]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0464, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0464, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.389246199280024
- step: 23
- running loss: 0.1908367912730445
- Train Steps: 23/90 Loss: 0.1908 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6261, 0.3987, 0.8688, 0.4917, 0.4300, 0.5333, 0.7010, 0.5309],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6202, 0.4064, 0.7879, 0.2179, 0.4567, 0.1725, 0.5955, 0.5478],
- [0.6307, 0.4029, 0.8650, 0.5200, 0.3763, 0.4017, 0.7311, 0.5366],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6127, 0.4115, 0.7163, 0.2883, 0.3625, 0.2950, 0.5327, 0.5495],
- [0.6286, 0.4040, 0.8696, 0.3047, 0.3924, 0.2887, 0.6300, 0.5367],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6133, -0.3910, 1.8013, 0.1958, -0.3964, 0.3140, 0.4751, 0.2048],
- [ 0.4495, -0.4382, 1.2529, -1.0922, -0.3345, -1.1163, 0.2963, 0.1465],
- [ 0.4924, -0.4259, 1.3304, -1.0964, -0.2980, -1.2180, 0.3399, 0.1222],
- [ 0.5933, -0.4085, 1.6925, -0.3447, -0.3869, -0.4754, 0.4793, 0.1284],
- [ 0.5779, -0.3745, 1.5604, -0.4256, -0.4401, -0.2684, 0.4439, 0.1614],
- [ 0.4712, -0.4171, 1.3140, -0.8936, -0.4123, -0.7876, 0.3292, 0.1524],
- [ 0.4787, -0.4535, 1.4525, -0.9943, -0.2762, -1.1924, 0.3752, 0.1188],
- [ 0.5423, -0.4336, 1.6983, -0.3355, -0.3292, -0.3300, 0.4789, 0.1588]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6059, -0.4442, 1.7268, -0.0149, -0.2998, 0.1775, 0.9521,
- 0.1661],
- [ 0.5862, -0.3530, 1.1032, -1.0619, -0.1497, -1.3852, 0.3411,
- 0.3931],
- [ 0.5787, -0.4086, 1.3535, -1.2794, -0.1764, -1.4891, 0.4645,
- 0.2442],
- [ 0.6273, -0.4249, 1.7095, 0.1159, -0.5480, -0.4306, 1.0910,
- 0.1928],
- [ 0.5206, -0.4603, 1.4670, -0.4768, -0.6577, -0.3998, 0.1836,
- 0.0021],
- [ 0.5442, -0.3855, 1.0224, -0.9541, -0.6115, -0.9233, 0.1745,
- 0.2522],
- [ 0.6174, -0.4201, 1.7309, -0.8784, -0.4735, -0.9524, 0.6242,
- 0.1931],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0366, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0366, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.425804238766432
- step: 24
- running loss: 0.18440850994860133
- Train Steps: 24/90 Loss: 0.1844 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6172, 0.4055, 0.8175, 0.2650, 0.3550, 0.3683, 0.5787, 0.5550],
- [0.6113, 0.4104, 0.8650, 0.4250, 0.3925, 0.5967, 0.5787, 0.5116],
- [0.6117, 0.4018, 0.6562, 0.1967, 0.3738, 0.2550, 0.5280, 0.5103],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901],
- [0.6154, 0.4112, 0.7037, 0.2317, 0.4238, 0.1833, 0.5350, 0.5600],
- [0.6197, 0.4118, 0.8688, 0.5517, 0.4037, 0.5233, 0.5875, 0.5600],
- [0.6026, 0.3979, 0.8550, 0.4233, 0.3613, 0.5233, 0.5582, 0.4967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5954, -0.4218, 1.5997, -0.6919, -0.3504, -0.7712, 0.4362, 0.1723],
- [ 0.5064, -0.3984, 1.6899, -0.0856, -0.4166, 0.1454, 0.4647, 0.2048],
- [ 0.5156, -0.4074, 1.3041, -1.1373, -0.3325, -1.1563, 0.3512, 0.1370],
- [ 0.5488, -0.4500, 1.6568, -0.8152, -0.2328, -1.0531, 0.4730, 0.1296],
- [ 0.5115, -0.4477, 1.4305, -1.0701, -0.2929, -1.2223, 0.3507, 0.1469],
- [ 0.5264, -0.4210, 1.3500, -1.0950, -0.3389, -1.1687, 0.3748, 0.1390],
- [ 0.5776, -0.4009, 1.8139, 0.1687, -0.4191, 0.2928, 0.5090, 0.2054],
- [ 0.5844, -0.3730, 1.7113, -0.0934, -0.4380, 0.1155, 0.4841, 0.1837]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5647, -0.4129, 1.4901, -1.0619, -0.6462, -0.5846, 0.3873,
- 0.2776],
- [ 0.5376, -0.3903, 1.7095, -0.3229, -0.4730, 0.4701, 0.3871,
- 0.0772],
- [ 0.5393, -0.4299, 0.7453, -1.3775, -0.5596, -1.1081, 0.1527,
- 0.0712],
- [ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220],
- [ 0.5565, -0.3864, 0.9647, -1.2159, -0.3286, -1.4391, 0.1852,
- 0.3007],
- [ 0.5762, -0.3840, 1.7268, 0.2622, -0.4210, 0.1313, 0.4277,
- 0.3007],
- [ 0.4974, -0.4482, 1.6633, -0.3306, -0.6173, 0.1313, 0.2925,
- 0.0081]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0310, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0310, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.456804607063532
- step: 25
- running loss: 0.17827218428254127
- Train Steps: 25/90 Loss: 0.1783 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422],
- [0.6236, 0.4084, 0.7738, 0.2133, 0.3663, 0.3233, 0.5813, 0.5567],
- [0.6115, 0.4081, 0.6725, 0.2433, 0.4088, 0.1933, 0.5167, 0.5544],
- [0.6168, 0.4029, 0.8523, 0.3417, 0.3588, 0.5000, 0.6125, 0.5400],
- [0.6273, 0.4105, 0.8988, 0.4517, 0.3912, 0.2550, 0.5894, 0.4811],
- [0.6201, 0.4027, 0.8029, 0.2728, 0.4042, 0.2310, 0.5980, 0.5391],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5044, -0.4351, 1.3826, -1.0189, -0.3350, -1.0562, 0.3842, 0.1582],
- [ 0.5649, -0.4414, 1.5426, -0.8654, -0.3159, -0.9570, 0.4214, 0.1911],
- [ 0.5411, -0.4083, 1.3608, -1.0084, -0.3764, -1.0115, 0.3643, 0.1792],
- [ 0.5614, -0.4106, 1.7066, -0.1109, -0.4423, 0.2117, 0.5177, 0.2240],
- [ 0.6142, -0.4105, 1.7577, -0.4887, -0.3124, -0.6148, 0.5242, 0.1637],
- [ 0.5997, -0.4014, 1.5340, -0.7335, -0.3835, -0.6683, 0.4661, 0.1797],
- [ 0.4949, -0.4439, 1.4315, -1.0887, -0.2691, -1.1977, 0.3793, 0.1786],
- [ 0.6371, -0.4062, 1.9366, 0.3314, -0.4247, 0.4813, 0.5960, 0.2351]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.2469, -1.0288, -0.4557, -1.2774, 0.0511,
- 0.2183],
- [ 0.5946, -0.3995, 1.2880, -1.3005, -0.5942, -0.7925, 0.3988,
- 0.2853],
- [ 0.5385, -0.4010, 0.8203, -1.1620, -0.3979, -1.3929, 0.1005,
- 0.2747],
- [ 0.5631, -0.4249, 1.6509, -0.7078, -0.6289, 0.0236, 0.5432,
- 0.2083],
- [ 0.6116, -0.3898, 1.8654, -0.1997, -0.4788, -1.1081, 0.4367,
- -0.0637],
- [ 0.5785, -0.4259, 1.4228, -1.0261, -0.4190, -1.2189, 0.4763,
- 0.2043],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853],
- [ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.679033596068621
- step: 26
- running loss: 0.17996283061802387
- Train Steps: 26/90 Loss: 0.1800 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4003, 0.8638, 0.5967, 0.3688, 0.4900, 0.6108, 0.4661],
- [0.6068, 0.3963, 0.8650, 0.4317, 0.4037, 0.5083, 0.5253, 0.4999],
- [0.6199, 0.4065, 0.7598, 0.2385, 0.4317, 0.1981, 0.5933, 0.5221],
- [0.6042, 0.3990, 0.6831, 0.2875, 0.3500, 0.3133, 0.5143, 0.5510],
- [0.6182, 0.3972, 0.8552, 0.5914, 0.3683, 0.4181, 0.5688, 0.5378],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6260, 0.4133, 0.8800, 0.5117, 0.3713, 0.3283, 0.6223, 0.4967],
- [0.6236, 0.3977, 0.8985, 0.4806, 0.3835, 0.5216, 0.6613, 0.5166]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5752, -0.4557, 1.7538, -0.0873, -0.4162, 0.0275, 0.5230, 0.2209],
- [ 0.5054, -0.4485, 1.6481, -0.3998, -0.3812, -0.2287, 0.4958, 0.1973],
- [ 0.5210, -0.4305, 1.3226, -1.2333, -0.3546, -1.2685, 0.3925, 0.1713],
- [ 0.4906, -0.4524, 1.2683, -1.1478, -0.3965, -1.0973, 0.3616, 0.2053],
- [ 0.5731, -0.4653, 1.7510, -0.3070, -0.4253, -0.3510, 0.5229, 0.1982],
- [ 0.5210, -0.4544, 1.4968, -0.9434, -0.3615, -1.0090, 0.4108, 0.1978],
- [ 0.5749, -0.4515, 1.7346, -0.6102, -0.3249, -0.7786, 0.5225, 0.1889],
- [ 0.5185, -0.4411, 1.7667, -0.0767, -0.4038, 0.1146, 0.5136, 0.2516]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6120, -0.4371, 1.7037, 0.4701, -0.5827, -0.0226, 0.5354,
- -0.1331],
- [ 0.5168, -0.4556, 1.7095, -0.2921, -0.4210, 0.0620, 0.1404,
- 0.0231],
- [ 0.5774, -0.4082, 1.2235, -1.1844, -0.2919, -1.3709, 0.4544,
- 0.1256],
- [ 0.5049, -0.4428, 0.8692, -0.9581, -0.6693, -0.8386, 0.0897,
- 0.2589],
- [ 0.5697, -0.4514, 1.6642, 0.4459, -0.5850, -0.3546, 0.3414,
- 0.1982],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.6054, -0.3767, 1.7788, 0.0774, -0.5711, -0.7694, 0.5887,
- 0.0081],
- [ 0.5944, -0.4490, 1.8643, -0.0659, -0.5147, 0.1235, 0.7684,
- 0.1004]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0442, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0442, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.7232260555028915
- step: 27
- running loss: 0.17493429835195895
- Train Steps: 27/90 Loss: 0.1749 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6277, 0.4036, 0.8688, 0.3617, 0.3925, 0.2600, 0.6132, 0.4742],
- [0.6193, 0.4108, 0.7438, 0.2700, 0.3650, 0.3683, 0.6238, 0.5717],
- [0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933],
- [0.6198, 0.4164, 0.8700, 0.5067, 0.4625, 0.5650, 0.5464, 0.5197],
- [0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309],
- [0.6163, 0.4006, 0.8788, 0.4683, 0.3663, 0.4883, 0.5887, 0.5017],
- [0.6142, 0.3982, 0.8650, 0.4883, 0.3912, 0.4317, 0.5315, 0.5350],
- [0.6198, 0.4130, 0.8762, 0.4117, 0.3650, 0.4900, 0.5707, 0.5103]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5405, -0.4394, 1.4507, -0.9549, -0.4286, -0.9721, 0.4815, 0.1903],
- [ 0.4607, -0.4560, 1.2716, -0.9976, -0.4577, -0.8585, 0.3750, 0.2287],
- [ 0.4619, -0.4713, 1.5905, -0.4083, -0.3709, -0.4039, 0.4716, 0.2178],
- [ 0.4783, -0.4746, 1.6230, -0.4580, -0.3821, -0.4283, 0.4770, 0.2247],
- [ 0.5517, -0.4558, 1.6601, -0.6311, -0.3759, -0.8260, 0.4948, 0.2009],
- [ 0.4894, -0.4661, 1.5988, -0.3804, -0.4628, -0.1826, 0.4667, 0.2203],
- [ 0.5504, -0.4706, 1.7239, -0.2985, -0.4422, -0.2883, 0.5128, 0.1965],
- [ 0.5535, -0.4622, 1.7266, -0.4120, -0.4370, -0.4260, 0.4954, 0.2181]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6134, -0.4218, 1.7268, -0.6154, -0.4730, -1.0850, 0.5463,
- -0.0957],
- [ 0.5747, -0.3886, 1.1494, -1.0388, -0.6000, -0.5846, 0.5952,
- 0.3546],
- [ 0.5959, -0.3579, 1.6055, 0.3623, -0.0573, -0.2074, 0.3122,
- 0.4547],
- [ 0.5770, -0.3624, 1.7326, 0.0543, -0.1497, 0.3238, 0.2378,
- 0.1146],
- [ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661],
- [ 0.5609, -0.4354, 1.7730, -0.1227, -0.5942, -0.0303, 0.4335,
- 0.0313],
- [ 0.5513, -0.4467, 1.7095, -0.0303, -0.4788, -0.2921, 0.1692,
- 0.1854],
- [ 0.5771, -0.3784, 1.7614, -0.3844, -0.6000, -0.0226, 0.3501,
- 0.0712]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0562, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0562, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.779460072517395
- step: 28
- running loss: 0.17069500258990697
- Train Steps: 28/90 Loss: 0.1707 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6311, 0.3998, 0.7975, 0.5767, 0.3838, 0.4850, 0.7327, 0.5343],
- [0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519],
- [0.6200, 0.3978, 0.8900, 0.4550, 0.3775, 0.5200, 0.6150, 0.5367],
- [0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6101, 0.3977, 0.8550, 0.5667, 0.3862, 0.4417, 0.5128, 0.4869],
- [ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4621, -0.4910, 1.5000, -0.4019, -0.4888, -0.2934, 0.4030, 0.2112],
- [ 0.4917, -0.4781, 1.5036, -0.9912, -0.3765, -1.2404, 0.4376, 0.1793],
- [ 0.4635, -0.4929, 1.6376, -0.2115, -0.4377, -0.1783, 0.4493, 0.2203],
- [ 0.4968, -0.4848, 1.6916, -0.1059, -0.5143, 0.0720, 0.5134, 0.2184],
- [ 0.5540, -0.4735, 1.7266, -0.2006, -0.5030, -0.1378, 0.4941, 0.2128],
- [ 0.5009, -0.4610, 1.3333, -1.0000, -0.4870, -1.0072, 0.4258, 0.1925],
- [ 0.5170, -0.4835, 1.7170, -0.1887, -0.4760, -0.2195, 0.4769, 0.1899],
- [ 0.4410, -0.5048, 1.4669, -1.0029, -0.3608, -1.2285, 0.4652, 0.1677]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6289, -0.4393, 1.3977, 0.3777, -0.5134, -0.0457, 1.0984,
- 0.1821],
- [ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634],
- [ 0.5776, -0.4484, 1.8249, -0.1843, -0.5423, 0.1159, 0.5547,
- 0.1929],
- [ 0.5865, -0.3973, 1.8423, -0.0688, -0.5192, -0.2305, 0.4162,
- 0.1159],
- [ 0.6042, -0.4225, 1.5420, -1.2082, -0.4730, -1.0311, 0.6380,
- -0.0220],
- [ 0.5320, -0.4488, 1.6633, 0.3315, -0.5018, -0.2459, 0.0828,
- -0.0370],
- [-2.2859, -2.2859, 1.6517, -1.2620, 0.2141, -1.1928, 1.1166,
- 0.2463]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2187, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2187, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.998158469796181
- step: 29
- running loss: 0.17235029206193728
- Train Steps: 29/90 Loss: 0.1724 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6216, 0.4167, 0.8588, 0.5583, 0.3975, 0.5167, 0.5775, 0.5667],
- [0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927],
- [0.6060, 0.3924, 0.8450, 0.5717, 0.4200, 0.5217, 0.5253, 0.4752],
- [0.6222, 0.3957, 0.8838, 0.5017, 0.3937, 0.4600, 0.5900, 0.5017],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6357, 0.4118, 0.8400, 0.2500, 0.5413, 0.1633, 0.6725, 0.5586],
- [0.6261, 0.4066, 0.8325, 0.2150, 0.4763, 0.2667, 0.7002, 0.5633],
- [0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4439, -0.4882, 1.6552, -0.1041, -0.5164, -0.0036, 0.4752, 0.1893],
- [ 0.4620, -0.4752, 1.6635, -0.2930, -0.4908, -0.3561, 0.4884, 0.1639],
- [ 0.4005, -0.5161, 1.5989, -0.2423, -0.4633, -0.2455, 0.4451, 0.1616],
- [ 0.4913, -0.5129, 1.6991, -0.2184, -0.4814, -0.2698, 0.4712, 0.1792],
- [ 0.4668, -0.4844, 1.6592, -0.1376, -0.4977, -0.1037, 0.4597, 0.1926],
- [ 0.3816, -0.5161, 1.4410, -0.9878, -0.3631, -1.2132, 0.4439, 0.1703],
- [ 0.4210, -0.4985, 1.4144, -1.0300, -0.4295, -1.1849, 0.4380, 0.1659],
- [ 0.4693, -0.5386, 1.6060, -0.4653, -0.4800, -0.5654, 0.4283, 0.1936]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5853, -0.3614, 1.6806, 0.2930, -0.4499, 0.1005, 0.3815,
- 0.3315],
- [ 0.6335, -0.4162, 1.7499, 0.3084, -0.4961, -0.2459, 0.6524,
- -0.0102],
- [ 0.5132, -0.4736, 1.6171, 0.3546, -0.3460, 0.1236, 0.1404,
- -0.0911],
- [ 0.5879, -0.4583, 1.7961, 0.0313, -0.4672, -0.1612, 0.4393,
- 0.0313],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007],
- [ 0.6504, -0.3840, 1.5940, -1.1312, 0.2141, -1.5315, 0.8205,
- 0.2944],
- [ 0.6059, -0.4080, 1.5594, -1.2928, -0.0861, -1.0542, 0.9485,
- 0.3157],
- [ 0.5697, -0.4113, 1.7499, -0.2767, -0.6404, -0.7155, 0.1576,
- 0.4032]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0521, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0521, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.050214387476444
- step: 30
- running loss: 0.16834047958254814
- Train Steps: 30/90 Loss: 0.1683 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334],
- [0.6263, 0.4057, 0.8800, 0.3833, 0.3650, 0.3717, 0.6375, 0.4804],
- [0.6083, 0.3957, 0.8638, 0.4950, 0.4363, 0.5083, 0.5346, 0.4980],
- [0.6175, 0.4013, 0.8900, 0.4500, 0.4375, 0.5850, 0.6175, 0.5383],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4256, -0.5307, 1.4230, -0.6611, -0.4992, -0.7421, 0.4294, 0.1491],
- [ 0.4814, -0.4975, 1.7566, -0.1036, -0.4995, -0.1922, 0.5431, 0.1585],
- [ 0.4664, -0.5085, 1.9594, 0.3104, -0.4150, 0.1499, 0.5579, 0.1315],
- [ 0.4296, -0.5129, 1.9311, 0.3247, -0.4423, 0.3089, 0.5455, 0.1614],
- [ 0.3443, -0.5417, 1.3398, -0.8960, -0.4093, -1.0721, 0.3534, 0.1745],
- [ 0.4356, -0.5197, 1.4351, -0.7563, -0.4948, -0.8742, 0.4220, 0.1475],
- [ 0.3822, -0.5374, 1.4107, -0.7609, -0.4680, -0.9120, 0.3871, 0.1718],
- [ 0.4443, -0.4906, 1.4567, -0.7145, -0.4900, -0.8297, 0.4329, 0.1366]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7296e-01, -4.5619e-01, 1.2195e+00, -1.2440e+00, -5.4966e-01,
- -7.7109e-01, 5.7045e-01, 1.7788e-01],
- [ 6.0710e-01, -4.1186e-01, 1.7788e+00, -5.1532e-01, -6.0000e-01,
- -5.6921e-01, 6.5857e-01, -6.7050e-02],
- [ 5.2379e-01, -4.5797e-01, 1.7037e+00, 4.6189e-04, -2.7090e-01,
- 6.2048e-02, 1.8356e-01, 1.4106e-02],
- [ 5.6637e-01, -4.3212e-01, 1.8249e+00, -2.0739e-01, -2.6513e-01,
- 4.1617e-01, 5.6628e-01, 2.0062e-01],
- [ 5.8620e-01, -3.5296e-01, 1.1032e+00, -1.0619e+00, -1.4965e-01,
- -1.3852e+00, 3.4111e-01, 3.9307e-01],
- [ 5.2194e-01, -4.5504e-01, 1.1415e+00, -9.1962e-01, -6.4042e-01,
- -9.3872e-01, 1.8562e-01, 1.4106e-02],
- [ 5.6028e-01, -3.7637e-01, 8.0878e-01, -1.1466e+00, -4.5566e-01,
- -1.1158e+00, 3.6420e-01, 2.3911e-01],
- [ 4.9971e-01, -4.4465e-01, 1.1610e+00, -9.7721e-01, -6.0577e-01,
- -1.0311e+00, 1.4038e-01, -1.0312e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0491, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0491, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.099356964230537
- step: 31
- running loss: 0.16449538594292057
- Train Steps: 31/90 Loss: 0.1645 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6357, 0.4139, 0.8450, 0.5883, 0.3775, 0.4950, 0.6488, 0.5297],
- [0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6198, 0.4114, 0.8263, 0.3283, 0.3550, 0.3583, 0.5813, 0.5667],
- [0.6128, 0.4115, 0.7934, 0.3778, 0.3450, 0.4033, 0.5337, 0.5456],
- [0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [0.6120, 0.4014, 0.6863, 0.2817, 0.3700, 0.2783, 0.5513, 0.5667],
- [0.6125, 0.4035, 0.7825, 0.3100, 0.3463, 0.4900, 0.5832, 0.5637],
- [ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4981, -0.5002, 1.8641, 0.2520, -0.4876, 0.1037, 0.5152, 0.1010],
- [ 0.4992, -0.4909, 1.9148, 0.4428, -0.4518, 0.3332, 0.5102, 0.1114],
- [ 0.5246, -0.4915, 1.7953, -0.2241, -0.4603, -0.4731, 0.5106, 0.1482],
- [ 0.3892, -0.4989, 1.4743, -0.3986, -0.5017, -0.3789, 0.4380, 0.1431],
- [ 0.3725, -0.5422, 1.3167, -0.9795, -0.4368, -1.2552, 0.3796, 0.1484],
- [ 0.3573, -0.5144, 1.2350, -0.8814, -0.4860, -0.9428, 0.3407, 0.1568],
- [ 0.3831, -0.4949, 1.4455, -0.4288, -0.5095, -0.3071, 0.3988, 0.1604],
- [ 0.2896, -0.5915, 1.6280, -0.6673, -0.2973, -1.0396, 0.5594, 0.1253]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.5036e-01, -3.7434e-01, 1.6171e+00, 4.3156e-01, -5.4226e-01,
- 4.6189e-04, 7.1085e-01, 1.6077e-01],
- [ 5.7829e-01, -4.3634e-01, 1.7724e+00, 4.3211e-02, -5.1531e-01,
- 8.7136e-02, 4.8401e-01, 6.6263e-02],
- [ 5.7685e-01, -3.8568e-01, 1.5305e+00, -7.6936e-01, -6.4619e-01,
- -6.3079e-01, 3.9885e-01, 3.3149e-01],
- [ 5.4440e-01, -3.8522e-01, 1.3786e+00, -5.4087e-01, -6.9238e-01,
- -4.2294e-01, 1.7915e-01, 2.3412e-01],
- [ 5.1085e-01, -4.3164e-01, 1.1436e+00, -1.3467e+00, -3.8637e-01,
- -1.4160e+00, 1.2393e-01, -5.8033e-02],
- [ 5.4099e-01, -4.3210e-01, 8.8383e-01, -9.8491e-01, -5.7691e-01,
- -1.0003e+00, 2.6028e-01, 3.3149e-01],
- [ 5.4319e-01, -4.2240e-01, 1.3284e+00, -8.5404e-01, -6.8661e-01,
- -2.2633e-02, 4.0770e-01, 3.1769e-01],
- [-2.2859e+00, -2.2859e+00, 1.6517e+00, -1.2620e+00, 2.1409e-01,
- -1.1928e+00, 1.1166e+00, 2.4627e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1953, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1953, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.294639840722084
- step: 32
- running loss: 0.16545749502256513
- Train Steps: 32/90 Loss: 0.1655 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6221, 0.4107, 0.7788, 0.3033, 0.3950, 0.2817, 0.6075, 0.5517],
- [0.6226, 0.4001, 0.8438, 0.5733, 0.3862, 0.4250, 0.5750, 0.5283],
- [0.6151, 0.4125, 0.8738, 0.4417, 0.3575, 0.3783, 0.5138, 0.5483],
- [0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783],
- [0.6263, 0.4030, 0.9000, 0.4767, 0.3800, 0.5167, 0.6415, 0.4771],
- [ nan, nan, 0.7192, 0.2346, 0.4037, 0.2050, 0.5138, 0.5650],
- [0.6099, 0.4030, 0.8638, 0.5117, 0.4983, 0.4965, 0.5086, 0.5388],
- [0.6098, 0.3991, 0.8638, 0.4717, 0.4263, 0.4967, 0.5212, 0.5650]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3085, -0.5768, 1.4610, -0.7784, -0.3601, -1.1018, 0.4599, 0.1369],
- [ 0.4371, -0.5274, 1.7047, -0.0446, -0.4892, -0.1888, 0.5027, 0.0903],
- [ 0.4908, -0.4720, 1.7375, -0.0467, -0.5059, -0.2004, 0.4971, 0.1306],
- [ 0.3127, -0.5506, 1.1132, -1.1260, -0.4676, -1.1954, 0.3508, 0.1386],
- [ 0.4899, -0.4851, 1.8631, 0.2737, -0.4897, 0.2595, 0.5035, 0.1027],
- [ 0.2675, -0.5867, 1.1643, -1.0927, -0.4417, -1.2381, 0.3678, 0.1496],
- [ 0.3908, -0.5745, 1.7423, -0.0200, -0.3634, -0.2191, 0.4888, 0.1441],
- [ 0.3999, -0.5149, 1.7153, 0.0344, -0.4431, 0.0088, 0.4822, 0.1426]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5875, -0.3888, 1.3111, -0.8848, -0.4614, -0.9849, 0.5201,
- 0.2622],
- [ 0.5900, -0.4377, 1.6113, 0.3623, -0.5018, -0.3229, 0.3700,
- 0.1544],
- [ 0.5551, -0.3807, 1.7499, -0.2459, -0.6346, -0.5384, 0.0871,
- 0.2468],
- [ 0.5483, -0.3991, 0.8030, -1.2159, -0.5018, -1.1928, 0.2624,
- 0.3852],
- [ 0.6072, -0.4247, 1.8711, -0.0842, -0.5307, 0.1005, 0.6771,
- -0.0821],
- [-2.2859, -2.2859, 1.0361, -1.2021, -0.4210, -1.3390, 0.0871,
- 0.3238],
- [ 0.5309, -0.4246, 1.7037, 0.0774, 0.0158, 0.0075, 0.0635,
- 0.2026],
- [ 0.5308, -0.4425, 1.7037, -0.1073, -0.3171, 0.0082, 0.1217,
- 0.3238]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1797, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1797, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.4743141531944275
- step: 33
- running loss: 0.1658883076725584
- Train Steps: 33/90 Loss: 0.1659 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6277, 0.4013, 0.8888, 0.4767, 0.3600, 0.3567, 0.6148, 0.4672],
- [0.6120, 0.4014, 0.6863, 0.2817, 0.3700, 0.2783, 0.5513, 0.5667],
- [0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6196, 0.4090, 0.8450, 0.3900, 0.3575, 0.3933, 0.5975, 0.5583],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6122, 0.4048, 0.7506, 0.3014, 0.3475, 0.3333, 0.5487, 0.5749],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000],
- [0.6300, 0.4102, 0.9088, 0.4433, 0.4088, 0.3067, 0.6820, 0.5540]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4537, -0.5209, 1.6994, -0.1240, -0.4797, -0.3147, 0.4962, 0.1137],
- [ 0.2515, -0.5921, 1.1194, -1.0232, -0.4625, -1.1073, 0.3233, 0.1711],
- [ 0.4298, -0.5692, 1.8640, 0.1525, -0.3792, -0.1307, 0.4597, 0.1279],
- [ 0.3892, -0.5137, 1.5070, -0.3672, -0.4984, -0.3483, 0.4242, 0.1417],
- [ 0.3929, -0.5408, 1.5922, -0.0295, -0.4774, -0.1083, 0.4134, 0.1505],
- [ 0.3016, -0.5772, 1.1988, -0.8329, -0.4858, -0.8551, 0.3505, 0.1630],
- [ 0.4089, -0.5140, 1.5588, -0.3554, -0.5156, -0.3883, 0.4443, 0.1207],
- [ 0.2977, -0.6028, 1.8114, -0.2007, -0.3145, -0.6246, 0.5291, 0.1498]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6132, -0.4324, 1.8192, -0.0842, -0.6231, -0.6385, 0.5537,
- -0.1278],
- [ 0.5410, -0.4321, 0.8838, -0.9849, -0.5769, -1.0003, 0.2603,
- 0.3315],
- [ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220],
- [ 0.5761, -0.3966, 1.6171, -0.4845, -0.6346, -0.4691, 0.4739,
- 0.2930],
- [ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478],
- [ 0.5419, -0.4160, 1.1810, -0.8939, -0.6808, -0.7463, 0.2485,
- 0.3694],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236],
- [ 0.6240, -0.3912, 1.9115, -0.2382, -0.3979, -0.8694, 0.8644,
- 0.2730]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0368, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0368, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.511073064059019
- step: 34
- running loss: 0.16209038423702998
- Train Steps: 34/90 Loss: 0.1621 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6249, 0.4142, 0.8350, 0.3283, 0.3613, 0.3700, 0.6188, 0.5400],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567],
- [0.6228, 0.4119, 0.7938, 0.2233, 0.4674, 0.1773, 0.6188, 0.5433],
- [0.6214, 0.4112, 0.7838, 0.2117, 0.3650, 0.3133, 0.5675, 0.5083],
- [ nan, nan, 0.7525, 0.2291, 0.3838, 0.3017, 0.6050, 0.5667],
- [0.6250, 0.4116, 0.8700, 0.4850, 0.4650, 0.5567, 0.6088, 0.5183],
- [0.6189, 0.4033, 0.8650, 0.5267, 0.4487, 0.5150, 0.5925, 0.5050]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3724, -0.5650, 1.5811, -0.3080, -0.4808, -0.4925, 0.4385, 0.1313],
- [ 0.2653, -0.6318, 1.3712, -0.6937, -0.3957, -0.8542, 0.4976, 0.1337],
- [ 0.1592, -0.6391, 1.2529, -0.8141, -0.3916, -0.9404, 0.3738, 0.2095],
- [ 0.1828, -0.6339, 1.3143, -0.7811, -0.3769, -1.0112, 0.4253, 0.1762],
- [ 0.4055, -0.5633, 1.4417, -0.5345, -0.5245, -0.6789, 0.4411, 0.1603],
- [ 0.2255, -0.6256, 1.2832, -0.7301, -0.4409, -0.8754, 0.3790, 0.1613],
- [ 0.5063, -0.4833, 1.8031, 0.4133, -0.4401, 0.4369, 0.4695, 0.1416],
- [ 0.5404, -0.4651, 1.7917, 0.4030, -0.4918, 0.3713, 0.4484, 0.1168]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6006, -0.3728, 1.5709, -0.7694, -0.6173, -0.5769, 0.5721,
- 0.2083],
- [ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853],
- [ 0.5908, -0.3832, 1.3804, -1.2543, -0.1270, -1.4671, 0.5721,
- 0.2237],
- [ 0.5845, -0.3864, 1.3342, -1.3082, -0.6000, -0.8386, 0.3353,
- 0.0620],
- [-2.2859, -2.2859, 1.1898, -1.2278, -0.5134, -0.8925, 0.5085,
- 0.3315],
- [ 0.6012, -0.3846, 1.7326, -0.0457, -0.1381, 0.2853, 0.5259,
- 0.1082],
- [ 0.5726, -0.4230, 1.7095, 0.1467, -0.2132, 0.0928, 0.4508,
- 0.0467]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2046, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2046, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.71563608571887
- step: 35
- running loss: 0.16330388816339628
- Train Steps: 35/90 Loss: 0.1633 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.3967, 0.7337, 0.1992, 0.4120, 0.2508, 0.6105, 0.5395],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6222, 0.4072, 0.7164, 0.2166, 0.3738, 0.3167, 0.6100, 0.5533],
- [0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167],
- [0.6236, 0.4084, 0.7738, 0.2133, 0.3663, 0.3233, 0.5813, 0.5567],
- [0.6314, 0.4107, 0.8750, 0.5100, 0.3788, 0.4900, 0.7121, 0.5864],
- [0.6124, 0.4083, 0.6954, 0.3069, 0.3650, 0.2750, 0.5163, 0.5485],
- [0.6289, 0.4032, 0.8419, 0.5446, 0.4075, 0.5017, 0.6312, 0.5117]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1656, -0.6813, 1.2116, -0.9808, -0.3851, -1.2191, 0.4084, 0.2121],
- [ 0.5132, -0.4670, 1.8552, 0.3621, -0.4981, 0.4013, 0.4923, 0.1279],
- [ 0.2068, -0.6265, 1.0946, -0.9656, -0.4810, -1.0109, 0.3559, 0.1965],
- [ 0.4097, -0.5306, 1.4900, -0.4212, -0.4880, -0.4898, 0.4622, 0.1524],
- [ 0.1992, -0.6792, 1.3864, -0.7662, -0.3886, -1.0255, 0.4300, 0.2195],
- [ 0.5568, -0.4758, 1.8857, 0.3602, -0.4827, 0.2015, 0.5142, 0.1116],
- [ 0.2005, -0.6281, 1.0860, -1.0023, -0.4493, -1.0749, 0.3525, 0.2077],
- [ 0.5050, -0.4917, 1.7101, 0.1303, -0.4779, 0.0353, 0.4208, 0.1442]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.4538, 1.1028, -1.3659, -0.3831, -1.1273, 0.5340,
- 0.2058],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.5878, -0.4052, 1.0229, -1.2855, -0.5596, -0.8232, 0.5316,
- 0.2699],
- [ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005],
- [ 0.5946, -0.3995, 1.2880, -1.3005, -0.5942, -0.7925, 0.3988,
- 0.2853],
- [ 0.6306, -0.3888, 1.7557, 0.0697, -0.5365, -0.0226, 1.0033,
- 0.4226],
- [ 0.5425, -0.3998, 0.9263, -0.8683, -0.6000, -1.0157, 0.0990,
- 0.2476],
- [ 0.6189, -0.4238, 1.6026, 0.2295, -0.4037, 0.0313, 0.6298,
- 0.0774]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0442, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0442, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.759868372231722
- step: 36
- running loss: 0.1599963436731034
- Train Steps: 36/90 Loss: 0.1600 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6203, 0.4073, 0.8189, 0.2398, 0.4400, 0.2054, 0.5929, 0.5501],
- [0.6314, 0.4050, 0.8227, 0.5431, 0.4150, 0.5517, 0.7121, 0.5690],
- [0.6179, 0.3993, 0.8925, 0.4789, 0.3879, 0.4900, 0.6041, 0.5279],
- [0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6203, 0.4078, 0.8800, 0.5083, 0.3900, 0.5000, 0.6100, 0.5583],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [0.6257, 0.4060, 0.8300, 0.2333, 0.4688, 0.2583, 0.7050, 0.5633],
- [0.6266, 0.4070, 0.8712, 0.5600, 0.3713, 0.4783, 0.5775, 0.6100]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0306, -0.7482, 1.1684, -1.1746, -0.3463, -1.4168, 0.4507, 0.2861],
- [ 0.4355, -0.4647, 1.4831, -0.1241, -0.5088, -0.0437, 0.4310, 0.1761],
- [ 0.5143, -0.4910, 1.6547, -0.1234, -0.5125, -0.2014, 0.4634, 0.1301],
- [ 0.2105, -0.5922, 1.0609, -0.9738, -0.4900, -0.8628, 0.3837, 0.2377],
- [ 0.5715, -0.4519, 1.6900, 0.0187, -0.5369, -0.0197, 0.4772, 0.1420],
- [ 0.4221, -0.5485, 1.5904, -0.2299, -0.4761, -0.3609, 0.4290, 0.1838],
- [ 0.0825, -0.7490, 1.3565, -1.0026, -0.3237, -1.2647, 0.5705, 0.2463],
- [ 0.5655, -0.4344, 1.5807, 0.0510, -0.5576, 0.0759, 0.3991, 0.1501]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5792, -0.4048, 1.4965, -1.1781, -0.2534, -1.3371, 0.4528,
- 0.2549],
- [ 0.6306, -0.4153, 1.5141, 0.2224, -0.3691, 0.2622, 1.0033,
- 0.3425],
- [ 0.5680, -0.4417, 1.8365, -0.0740, -0.4941, -0.0227, 0.5045,
- 0.1525],
- [ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469],
- [ 0.5794, -0.4023, 1.7788, 0.0620, -0.4845, 0.0236, 0.5316,
- 0.2930],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893],
- [ 0.6042, -0.4105, 1.5478, -1.2082, -0.1208, -1.0927, 0.9704,
- 0.3157],
- [ 0.6081, -0.4059, 1.7383, 0.3007, -0.5711, -0.0765, 0.3815,
- 0.5316]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0460, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0460, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.805826131254435
- step: 37
- running loss: 0.15691421976363337
- Train Steps: 37/90 Loss: 0.1569 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6188, 0.4099, 0.7400, 0.2433, 0.3962, 0.2750, 0.6162, 0.5467],
- [0.6125, 0.4035, 0.7825, 0.3100, 0.3463, 0.4900, 0.5832, 0.5637],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6131, 0.4037, 0.6907, 0.2819, 0.3688, 0.2700, 0.5217, 0.5680],
- [0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413],
- [0.6262, 0.4085, 0.8438, 0.3150, 0.4025, 0.2633, 0.6339, 0.4810],
- [0.6167, 0.4048, 0.6831, 0.3639, 0.3763, 0.3017, 0.5700, 0.5883],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3484, -0.5611, 1.1903, -0.8528, -0.5253, -0.8242, 0.4086, 0.2321],
- [ 0.5839, -0.4033, 1.4489, -0.3269, -0.5890, -0.0762, 0.4293, 0.1951],
- [ 0.6881, -0.3780, 1.8892, 0.4334, -0.4735, 0.3656, 0.5026, 0.1625],
- [ 0.3486, -0.5398, 1.1285, -0.8707, -0.5217, -0.8116, 0.3905, 0.2235],
- [ 0.1830, -0.6934, 1.5746, -0.6413, -0.2942, -0.8391, 0.6271, 0.2453],
- [ 0.4457, -0.5310, 1.4380, -0.6530, -0.4888, -0.6945, 0.5348, 0.1828],
- [ 0.3547, -0.5630, 1.3450, -0.6403, -0.4638, -0.7016, 0.4201, 0.2484],
- [ 0.1226, -0.6902, 1.2788, -0.9352, -0.3668, -1.0977, 0.4852, 0.2776]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5725, -0.3926, 1.1321, -1.1620, -0.4557, -1.0157, 0.5605,
- 0.2391],
- [ 0.5432, -0.4224, 1.3284, -0.8540, -0.6866, -0.0226, 0.4077,
- 0.3177],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.5459, -0.4215, 0.9043, -0.9838, -0.5827, -1.0388, 0.1236,
- 0.3378],
- [ 0.6240, -0.3768, 1.6575, -1.2851, 0.2949, -1.2467, 1.1276,
- 0.2142],
- [ 0.6064, -0.3990, 1.6113, -0.8309, -0.4268, -1.0696, 0.6421,
- -0.0640],
- [ 0.5626, -0.4162, 0.8692, -0.6051, -0.5480, -0.8925, 0.3469,
- 0.4316],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0582, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0582, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.863999526947737
- step: 38
- running loss: 0.15431577702494045
- Train Steps: 38/90 Loss: 0.1543 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6113, 0.4006, 0.8700, 0.5350, 0.3638, 0.3767, 0.5097, 0.4882],
- [0.6182, 0.3972, 0.8720, 0.5527, 0.3638, 0.3582, 0.5696, 0.5395],
- [0.6286, 0.4060, 0.9188, 0.4333, 0.3675, 0.4167, 0.7034, 0.5528],
- [0.6236, 0.4084, 0.7738, 0.2133, 0.3663, 0.3233, 0.5813, 0.5567],
- [0.6186, 0.3967, 0.7337, 0.1992, 0.4120, 0.2508, 0.6105, 0.5395],
- [0.6239, 0.4123, 0.8313, 0.2550, 0.4500, 0.2050, 0.6175, 0.5400],
- [0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6287, -0.4028, 1.6203, -0.1966, -0.4968, -0.2871, 0.5465, 0.1927],
- [ 0.5755, -0.4245, 1.5422, -0.3178, -0.4948, -0.3611, 0.5096, 0.1966],
- [ 0.5557, -0.4868, 1.7148, -0.1531, -0.4118, -0.2448, 0.5633, 0.2358],
- [ 0.2434, -0.6120, 1.1403, -1.0868, -0.4222, -1.0653, 0.4561, 0.3103],
- [ 0.2002, -0.6206, 0.9828, -1.2643, -0.4166, -1.2470, 0.4367, 0.3031],
- [ 0.0895, -0.6730, 1.1629, -1.1852, -0.3008, -1.2543, 0.5230, 0.3339],
- [ 0.6912, -0.3737, 1.6513, -0.1489, -0.5177, -0.0689, 0.4943, 0.1897],
- [ 0.5583, -0.4039, 1.4031, -0.2909, -0.5239, -0.1276, 0.4636, 0.2189]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5379, -0.4358, 1.7326, 0.1852, -0.6058, -0.5461, 0.0684,
- -0.0310],
- [ 0.5697, -0.4514, 1.7420, 0.2672, -0.6055, -0.6312, 0.3449,
- 0.2058],
- [ 0.6174, -0.4105, 1.9577, -0.2844, -0.5885, -0.3614, 0.9631,
- 0.2676],
- [ 0.5946, -0.3995, 1.2880, -1.3005, -0.5942, -0.7925, 0.3988,
- 0.2853],
- [ 0.5713, -0.4538, 1.1028, -1.3659, -0.3831, -1.1273, 0.5340,
- 0.2058],
- [ 0.5958, -0.3818, 1.5536, -1.1081, -0.2074, -1.3390, 0.5663,
- 0.2083],
- [ 0.5865, -0.3973, 1.8423, -0.0688, -0.5192, -0.2305, 0.4162,
- 0.1159],
- [ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0508, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0508, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.914830353111029
- step: 39
- running loss: 0.15166231674643663
- Train Steps: 39/90 Loss: 0.1517 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [0.6222, 0.3957, 0.8838, 0.5017, 0.3937, 0.4600, 0.5900, 0.5017],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822],
- [0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6357, 0.4118, 0.8400, 0.2500, 0.5413, 0.1633, 0.6725, 0.5586],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6486, -0.3410, 1.5774, -0.2222, -0.4965, -0.0596, 0.5604, 0.2172],
- [ 0.6847, -0.3742, 1.5988, -0.2892, -0.5014, -0.3354, 0.5350, 0.2231],
- [ 0.6656, -0.3219, 1.4963, -0.2681, -0.5249, -0.1370, 0.4741, 0.2231],
- [ 0.5834, -0.4588, 1.4839, -0.5551, -0.5066, -0.6224, 0.4980, 0.2645],
- [ 0.6183, -0.3602, 1.5010, -0.4473, -0.4864, -0.3845, 0.5184, 0.2457],
- [ 0.1681, -0.6630, 1.1836, -1.3643, -0.2738, -1.4343, 0.6444, 0.3350],
- [ 0.5656, -0.3962, 1.4756, -0.3737, -0.4396, -0.3481, 0.4812, 0.2817],
- [ 0.1295, -0.6485, 1.0784, -1.3811, -0.3357, -1.4434, 0.5232, 0.3749]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554],
- [ 0.5879, -0.4583, 1.7961, 0.0313, -0.4672, -0.1612, 0.4393,
- 0.0313],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5697, -0.4113, 1.7499, -0.2767, -0.6404, -0.7155, 0.1576,
- 0.4032],
- [ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.6504, -0.3840, 1.5940, -1.1312, 0.2141, -1.5315, 0.8205,
- 0.2944],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0610, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0610, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.975822579115629
- step: 40
- running loss: 0.14939556447789074
- Train Steps: 40/90 Loss: 0.1494 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6205, 0.4081, 0.8950, 0.4017, 0.3788, 0.4700, 0.5963, 0.5667],
- [0.6200, 0.3913, 0.8788, 0.5217, 0.4075, 0.5100, 0.6060, 0.4913],
- [0.6114, 0.4018, 0.7213, 0.1967, 0.3763, 0.2700, 0.5875, 0.5533],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019],
- [0.6198, 0.4164, 0.8700, 0.5067, 0.4625, 0.5650, 0.5464, 0.5197],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6122, 0.4048, 0.7506, 0.3014, 0.3475, 0.3333, 0.5487, 0.5749]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7940, -0.2830, 1.7891, -0.1529, -0.4726, -0.1025, 0.5921, 0.2275],
- [ 0.7956, -0.3095, 1.8062, 0.0619, -0.4303, 0.0046, 0.5869, 0.1973],
- [ 0.4080, -0.4914, 1.0629, -1.2522, -0.4499, -1.1842, 0.4631, 0.3219],
- [ 0.8304, -0.2782, 1.7126, -0.1263, -0.4942, -0.1525, 0.5709, 0.2095],
- [ 0.6740, -0.3268, 1.7111, -0.1091, -0.3630, -0.1135, 0.5836, 0.2540],
- [ 0.2240, -0.6083, 1.1813, -1.3228, -0.2818, -1.4002, 0.5638, 0.3649],
- [ 0.2937, -0.5621, 1.0164, -1.4729, -0.3887, -1.4923, 0.4779, 0.3411],
- [ 0.5184, -0.4149, 1.1081, -1.0105, -0.5213, -0.8176, 0.4427, 0.3183]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5799, -0.4012, 1.8480, -0.4306, -0.5365, -0.1150, 0.4681,
- 0.3315],
- [ 0.5776, -0.4784, 1.7730, 0.1236, -0.4037, 0.0697, 0.5132,
- -0.0168],
- [ 0.5380, -0.4302, 1.0455, -1.3775, -0.5480, -1.0388, 0.4277,
- 0.2699],
- [ 0.6372, -0.4129, 1.8942, -0.0765, -0.6173, -0.4768, 0.6999,
- 0.0325],
- [ 0.5770, -0.3624, 1.7326, 0.0543, -0.1497, 0.3238, 0.2378,
- 0.1146],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.5419, -0.4160, 1.1810, -0.8939, -0.6808, -0.7463, 0.2485,
- 0.3694]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0260, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0260, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.001800514757633
- step: 41
- running loss: 0.14638537840872276
- Train Steps: 41/90 Loss: 0.1464 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6239, 0.4107, 0.8162, 0.2763, 0.3625, 0.3600, 0.5988, 0.5700],
- [0.6226, 0.4185, 0.8850, 0.5500, 0.3800, 0.4250, 0.5625, 0.5617],
- [0.6212, 0.4033, 0.8938, 0.4167, 0.3813, 0.4267, 0.5613, 0.5583],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507],
- [0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4199, -0.4943, 1.0790, -1.4109, -0.3797, -1.4113, 0.5004, 0.3263],
- [ 0.7100, -0.3301, 1.7054, -0.2859, -0.3876, -0.3165, 0.5298, 0.2141],
- [ 0.6441, -0.3112, 1.6181, -0.4765, -0.3953, -0.5129, 0.5334, 0.2616],
- [ 0.4550, -0.4527, 1.3070, -1.0951, -0.3657, -1.0771, 0.5272, 0.3358],
- [ 0.7904, -0.2419, 1.7333, -0.0269, -0.4500, -0.0779, 0.5352, 0.2389],
- [ 0.7278, -0.3011, 1.6891, -0.2632, -0.4183, -0.2551, 0.5382, 0.2533],
- [ 0.3967, -0.4765, 0.9523, -1.4774, -0.4310, -1.3825, 0.4442, 0.3253],
- [ 0.7859, -0.2656, 1.7115, -0.2033, -0.4576, -0.1586, 0.5810, 0.1942]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5598, -0.4201, 1.1898, -1.3005, -0.3806, -1.3313, 0.3873,
- 0.0774],
- [ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [ 0.5960, -0.3888, 1.4840, -1.0095, -0.6115, -0.6231, 0.4797,
- 0.3469],
- [ 0.5897, -0.3527, 1.8018, 0.2545, -0.5307, -0.3229, 0.3122,
- 0.3084],
- [ 0.5832, -0.4231, 1.8423, -0.3614, -0.5249, -0.3152, 0.3065,
- 0.2930],
- [ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576],
- [ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0263, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0263, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.028124667704105
- step: 42
- running loss: 0.1435267778024787
- Train Steps: 42/90 Loss: 0.1435 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064],
- [0.6269, 0.4073, 0.8900, 0.4933, 0.3625, 0.4183, 0.6288, 0.5150],
- [ nan, nan, 0.8900, 0.3217, 0.5038, 0.2233, 0.6694, 0.5609],
- [0.6218, 0.4098, 0.7238, 0.1850, 0.4238, 0.2517, 0.6288, 0.5350],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383],
- [0.6209, 0.3920, 0.8650, 0.5367, 0.4400, 0.5067, 0.6025, 0.4950]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6704, -0.3256, 1.6446, -0.3800, -0.3220, -0.4287, 0.5034, 0.2098],
- [ 0.7600, -0.2821, 1.6958, -0.3735, -0.4332, -0.5067, 0.5104, 0.2388],
- [ 0.4023, -0.5221, 1.4291, -1.2232, -0.2208, -1.2984, 0.6148, 0.2990],
- [ 0.4486, -0.4718, 1.0584, -1.5126, -0.3953, -1.4553, 0.4707, 0.3244],
- [ 0.7212, -0.2695, 1.4876, -0.3751, -0.4994, -0.2967, 0.4663, 0.2486],
- [ 0.7195, -0.2523, 1.4674, -0.6101, -0.5033, -0.4567, 0.4606, 0.2342],
- [ 0.7293, -0.2780, 1.6936, -0.2997, -0.3912, -0.3453, 0.5069, 0.1911],
- [ 0.7337, -0.2971, 1.6755, -0.2719, -0.3963, -0.3425, 0.4923, 0.2010]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3279e-01, -4.3610e-01, 1.7268e+00, 6.9746e-02, -6.3048e-02,
- 2.0831e-01, 2.1029e-01, 5.3181e-02],
- [ 6.0964e-01, -4.0462e-01, 1.8249e+00, -7.2363e-03, -6.1155e-01,
- -3.5366e-01, 6.1824e-01, 9.2841e-02],
- [-2.2859e+00, -2.2859e+00, 1.8249e+00, -8.0015e-01, 4.0878e-02,
- -1.2543e+00, 8.0590e-01, 3.0505e-01],
- [ 5.8614e-01, -3.9292e-01, 1.0570e+00, -1.4314e+00, -3.2864e-01,
- -1.1235e+00, 6.1824e-01, 1.8522e-01],
- [ 6.4542e-01, -3.9842e-01, 1.3804e+00, 2.5450e-01, -4.5566e-01,
- -3.8029e-02, 1.1057e+00, 3.4780e-01],
- [ 5.2748e-01, -4.3957e-01, 1.5543e+00, -2.8408e-01, -5.3649e-01,
- -1.8430e-01, 1.2208e-01, 3.2654e-01],
- [ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01],
- [ 5.8199e-01, -4.7544e-01, 1.7095e+00, 1.9292e-01, -2.5358e-01,
- 5.4350e-02, 4.9700e-01, 4.6189e-04]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2217, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2217, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.249815843999386
- step: 43
- running loss: 0.14534455451161363
- Train Steps: 43/90 Loss: 0.1453 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6200, 0.4112, 0.8862, 0.4100, 0.3638, 0.4917, 0.6088, 0.6050],
- [0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [ nan, nan, 0.6992, 0.2791, 0.3950, 0.2383, 0.5483, 0.5819],
- [0.6189, 0.4029, 0.8375, 0.5767, 0.4745, 0.4829, 0.5551, 0.5598],
- [ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7581, -0.3525, 1.8243, -0.3983, -0.3701, -0.6395, 0.5296, 0.1889],
- [ 0.8480, -0.2333, 1.9051, -0.0124, -0.4633, -0.0062, 0.5412, 0.1463],
- [ 0.8636, -0.2277, 1.8561, -0.0840, -0.4672, -0.1841, 0.4694, 0.1467],
- [ 0.4291, -0.4614, 1.0137, -1.4131, -0.4086, -1.3380, 0.3718, 0.2872],
- [ 0.6720, -0.2897, 1.6456, -0.1900, -0.3317, -0.2876, 0.4857, 0.1960],
- [ 0.3500, -0.5126, 0.9928, -1.5114, -0.3518, -1.4417, 0.4003, 0.3029],
- [ 0.3663, -0.4884, 1.2399, -1.2750, -0.2920, -1.2833, 0.4599, 0.3037],
- [ 0.7835, -0.2698, 1.8654, 0.0564, -0.3705, 0.0656, 0.5092, 0.1624]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.5777, -0.3868, 1.8076, -0.3921, -0.6058, -0.0149, 0.5259,
- 0.5085],
- [ 0.5865, -0.3973, 1.8423, -0.0688, -0.5192, -0.2305, 0.4162,
- 0.1159],
- [-2.2859, -2.2859, 0.9438, -0.9967, -0.4614, -1.1851, 0.2468,
- 0.4019],
- [ 0.5726, -0.4249, 1.5824, 0.3777, -0.0942, -0.0556, 0.2782,
- 0.2997],
- [-2.2859, -2.2859, 0.8516, -1.3112, -0.4326, -1.2851, 0.0755,
- 0.2930],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.5772, -0.3913, 1.8480, -0.2459, -0.4326, 0.1929, 0.5374,
- 0.4701]]], device='cuda:0')
- loss_train_step before backward: tensor(0.5159, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.5159, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.765694998204708
- step: 44
- running loss: 0.15376579541374336
- Train Steps: 44/90 Loss: 0.1538 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6339, 0.4159, 0.8400, 0.5617, 0.3825, 0.4150, 0.7343, 0.5748],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6225, 0.4191, 0.8500, 0.4167, 0.3688, 0.3233, 0.5650, 0.5650],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389],
- [0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [0.6111, 0.4033, 0.8300, 0.3267, 0.3588, 0.3333, 0.5444, 0.5637]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5681, -0.3922, 1.6770, -0.3190, -0.3526, -0.5051, 0.4730, 0.1606],
- [ 0.6769, -0.3203, 1.6853, -0.1504, -0.4270, -0.2334, 0.4160, 0.1653],
- [ 0.4975, -0.4291, 1.5476, -0.6745, -0.3673, -0.7478, 0.4353, 0.2326],
- [ 0.7256, -0.3172, 1.7803, -0.0423, -0.4017, -0.0852, 0.4411, 0.1329],
- [ 0.7370, -0.3005, 1.8452, -0.0926, -0.4353, -0.1475, 0.4734, 0.1246],
- [ 0.3213, -0.5524, 1.2729, -1.2532, -0.2965, -1.3091, 0.4605, 0.2714],
- [ 0.2930, -0.5338, 1.3079, -1.0069, -0.2362, -0.9552, 0.4142, 0.2860],
- [ 0.5536, -0.4193, 1.4912, -0.7955, -0.4314, -0.8450, 0.4179, 0.2130]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6421, -0.3647, 1.5940, 0.3084, -0.5192, -0.3691, 1.1057,
- 0.3692],
- [ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391],
- [ 0.5894, -0.3503, 1.6402, -0.3614, -0.5827, -0.7925, 0.3238,
- 0.3238],
- [ 0.5778, -0.4389, 1.7107, 0.1192, -0.3921, 0.0815, 0.4741,
- 0.0711],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.5697, -0.4466, 1.1973, -1.1871, -0.4571, -0.9965, 0.5219,
- 0.2032],
- [ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882],
- [ 0.5366, -0.4232, 1.5478, -0.7771, -0.6289, -0.7463, 0.2288,
- 0.3177]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0398, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0398, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.80553362518549
- step: 45
- running loss: 0.15123408055967755
- Train Steps: 45/90 Loss: 0.1512 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [0.6125, 0.3983, 0.8750, 0.4867, 0.4275, 0.4783, 0.5413, 0.5717],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6142, 0.3982, 0.8650, 0.4883, 0.3912, 0.4317, 0.5315, 0.5350],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6145, 0.4008, 0.8750, 0.5383, 0.3975, 0.4650, 0.5563, 0.5533],
- [0.6136, 0.4060, 0.8025, 0.3217, 0.3650, 0.3000, 0.5060, 0.5646]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3271, -0.5318, 1.5833, -0.6055, -0.2818, -0.7147, 0.4776, 0.2189],
- [ 0.6006, -0.3905, 1.7411, -0.1581, -0.3956, -0.1973, 0.4302, 0.1447],
- [ 0.3604, -0.5165, 1.4272, -0.8646, -0.3821, -0.9327, 0.3752, 0.2403],
- [ 0.1835, -0.6378, 1.4985, -1.1170, -0.1787, -1.2397, 0.5342, 0.2482],
- [ 0.6568, -0.3744, 1.8330, -0.0872, -0.4559, -0.2435, 0.4343, 0.1183],
- [ 0.5622, -0.3841, 1.6823, -0.1538, -0.3768, -0.2230, 0.3884, 0.1781],
- [ 0.6354, -0.3549, 1.7240, -0.0143, -0.4266, -0.0859, 0.4077, 0.1498],
- [ 0.3661, -0.5067, 1.3478, -0.9482, -0.4265, -0.9791, 0.3621, 0.2470]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822],
- [ 0.5432, -0.4462, 1.7557, -0.0380, -0.3113, -0.0765, 0.2141,
- 0.3546],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.5513, -0.4467, 1.7095, -0.0303, -0.4788, -0.2921, 0.1692,
- 0.1854],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.5526, -0.4347, 1.7557, 0.2006, -0.4499, -0.1381, 0.2834,
- 0.2699],
- [ 0.5483, -0.4105, 1.4208, -0.8002, -0.6000, -0.9002, 0.0511,
- 0.3220]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0359, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0359, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.841406472027302
- step: 46
- running loss: 0.14872622765276744
- Train Steps: 46/90 Loss: 0.1487 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6336, 0.4191, 0.8938, 0.5167, 0.3937, 0.3517, 0.7343, 0.5748],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6111, 0.4033, 0.8300, 0.3267, 0.3588, 0.3333, 0.5444, 0.5637],
- [ nan, nan, 0.7612, 0.3250, 0.4037, 0.2533, 0.5438, 0.5767],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6097, 0.4000, 0.7325, 0.2667, 0.3450, 0.3517, 0.5284, 0.5045],
- [0.6250, 0.4013, 0.8525, 0.5417, 0.4037, 0.5117, 0.6325, 0.5017],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3840, -0.5315, 1.8182, -0.2895, -0.2842, -0.6059, 0.4778, 0.1746],
- [ 0.5873, -0.3852, 1.7610, 0.0130, -0.4095, -0.0445, 0.3412, 0.1994],
- [ 0.4079, -0.5127, 1.5575, -0.6584, -0.4286, -0.7907, 0.3493, 0.2210],
- [ 0.1248, -0.6520, 1.2992, -1.0770, -0.2927, -1.1115, 0.3463, 0.2811],
- [ 0.5570, -0.4181, 1.8028, -0.0158, -0.3737, -0.1759, 0.3713, 0.1611],
- [ 0.3128, -0.5459, 1.2268, -0.9812, -0.4740, -0.9341, 0.2947, 0.2475],
- [ 0.6236, -0.3824, 1.7676, 0.0590, -0.4433, -0.0679, 0.3503, 0.1704],
- [ 0.3081, -0.5636, 1.7776, -0.4745, -0.2314, -0.7536, 0.5243, 0.2039]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6405, -0.3503, 1.8423, 0.1005, -0.4672, -0.6616, 1.1057,
- 0.3692],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.5366, -0.4232, 1.5478, -0.7771, -0.6289, -0.7463, 0.2288,
- 0.3177],
- [-2.2859, -2.2859, 1.2303, -0.7848, -0.4210, -1.1158, 0.2256,
- 0.3777],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854],
- [ 0.5303, -0.4384, 1.0975, -1.0542, -0.6924, -0.6616, 0.1548,
- 0.0442],
- [ 0.6010, -0.4321, 1.6517, 0.2160, -0.4210, 0.0774, 0.6356,
- 0.0313],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1665, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1665, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 7.0079174265265465
- step: 47
- running loss: 0.1491046260963095
- Train Steps: 47/90 Loss: 0.1491 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683],
- [0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [0.6201, 0.4027, 0.8029, 0.2728, 0.4042, 0.2310, 0.5980, 0.5391],
- [ nan, nan, 0.6935, 0.1930, 0.4150, 0.2250, 0.5450, 0.5650],
- [0.6273, 0.4105, 0.8988, 0.4517, 0.3912, 0.2550, 0.5894, 0.4811],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320],
- [0.6180, 0.4064, 0.7200, 0.2467, 0.4275, 0.2367, 0.5963, 0.6217]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0535, -0.7069, 1.2344, -0.9993, -0.3315, -1.0784, 0.2902, 0.2823],
- [ 0.2657, -0.5997, 1.6602, -0.5471, -0.2962, -0.7206, 0.4205, 0.2333],
- [ 0.3856, -0.5336, 1.8462, -0.1078, -0.3412, -0.3655, 0.4130, 0.2259],
- [ 0.3312, -0.5656, 1.4524, -0.7236, -0.4373, -0.8358, 0.3779, 0.2153],
- [-0.0348, -0.7601, 1.2570, -1.0044, -0.2545, -1.0930, 0.3087, 0.3088],
- [ 0.5419, -0.4685, 2.0130, 0.1167, -0.3741, -0.3086, 0.4475, 0.1471],
- [ 0.8008, -0.2977, 1.9959, 0.6074, -0.5156, 0.4412, 0.3769, 0.1513],
- [ 0.2861, -0.5947, 1.3415, -0.6672, -0.4133, -0.7388, 0.2971, 0.2424]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084],
- [ 0.5778, -0.3930, 1.5189, -0.9387, -0.4326, -0.9156, 0.4855,
- 0.3392],
- [ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928],
- [ 0.5785, -0.4259, 1.4228, -1.0261, -0.4190, -1.2189, 0.4763,
- 0.2043],
- [-2.2859, -2.2859, 0.9175, -1.3947, -0.3691, -1.2467, 0.2314,
- 0.3238],
- [ 0.6116, -0.3898, 1.8654, -0.1997, -0.4788, -1.1081, 0.4367,
- -0.0637],
- [ 0.6273, -0.4393, 1.6402, 0.1313, -0.5076, 0.0467, 1.1532,
- 0.1715],
- [ 0.5687, -0.4088, 1.0397, -1.1466, -0.3113, -1.1928, 0.4681,
- 0.5855]]], device='cuda:0')
- loss_train_step before backward: tensor(0.3112, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.3112, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 7.319082103669643
- step: 48
- running loss: 0.15248087715978423
- Train Steps: 48/90 Loss: 0.1525 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6075, 0.4007, 0.8275, 0.4917, 0.4050, 0.5100, 0.5167, 0.5280],
- [0.6236, 0.3977, 0.8985, 0.4806, 0.3835, 0.5216, 0.6613, 0.5166],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6371, 0.4092, 0.8337, 0.5850, 0.3950, 0.5117, 0.6559, 0.5262],
- [0.6109, 0.4015, 0.7668, 0.3639, 0.3513, 0.3667, 0.5200, 0.5641],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6140, 0.4034, 0.8850, 0.4317, 0.4288, 0.5067, 0.5825, 0.5533],
- [0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2065, -0.6275, 1.5711, -0.3425, -0.3605, -0.5036, 0.3276, 0.2431],
- [ 0.3137, -0.5515, 1.6447, -0.2573, -0.4272, -0.3420, 0.3673, 0.2457],
- [ 0.3199, -0.6279, 1.8709, -0.2268, -0.3714, -0.7510, 0.4436, 0.2008],
- [ 0.3236, -0.5680, 1.6676, -0.1668, -0.4192, -0.4400, 0.3268, 0.2124],
- [ 0.1441, -0.6837, 1.2961, -0.8674, -0.4645, -0.9738, 0.2507, 0.2833],
- [ 0.4580, -0.5463, 1.8016, -0.0027, -0.4537, -0.3965, 0.3903, 0.1912],
- [ 0.2008, -0.6386, 1.6768, -0.3726, -0.3226, -0.5094, 0.3750, 0.2283],
- [ 0.3267, -0.5803, 1.8235, -0.1224, -0.3388, -0.3754, 0.4049, 0.2350]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5200, -0.4353, 1.5363, -0.0149, -0.4152, 0.0697, 0.1005,
- 0.1530],
- [ 0.5944, -0.4490, 1.8643, -0.0659, -0.5147, 0.1235, 0.7684,
- 0.1004],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.6569, -0.3960, 1.5651, 0.4162, -0.4614, 0.0774, 0.7438,
- 0.1447],
- [ 0.5357, -0.4315, 1.2557, -0.6051, -0.6635, -0.5923, 0.1159,
- 0.3198],
- [ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [ 0.5499, -0.4225, 1.8018, -0.2921, -0.3055, 0.0543, 0.4046,
- 0.2699],
- [ 0.5991, -0.3803, 1.8018, -0.0534, -0.3460, 0.1852, 0.5374,
- 0.1390]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0701, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0701, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 7.389189012348652
- step: 49
- running loss: 0.1507997757622174
- Train Steps: 49/90 Loss: 0.1508 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6193, 0.4034, 0.7757, 0.2347, 0.3733, 0.2919, 0.5930, 0.4926],
- [0.6026, 0.3979, 0.8550, 0.4233, 0.3613, 0.5233, 0.5582, 0.4967],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6201, 0.4036, 0.8596, 0.3850, 0.3492, 0.3785, 0.5978, 0.5131],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6267, 0.4094, 0.8712, 0.3083, 0.4400, 0.2267, 0.6250, 0.5200]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5294, -0.4750, 1.7715, 0.1190, -0.5351, -0.1170, 0.4085, 0.2083],
- [-0.0845, -0.8747, 1.6868, -0.7018, -0.2010, -1.1213, 0.4768, 0.2605],
- [ 0.0189, -0.7988, 1.2685, -0.9018, -0.4537, -1.1003, 0.2813, 0.2620],
- [ 0.4374, -0.4821, 1.6311, -0.0043, -0.5430, -0.1109, 0.3076, 0.2101],
- [ 0.3483, -0.5449, 1.8120, 0.2408, -0.3022, 0.0264, 0.3682, 0.2554],
- [ 0.4092, -0.5825, 1.6334, -0.1759, -0.5376, -0.3951, 0.3223, 0.2299],
- [ 0.3080, -0.5890, 1.4904, -0.3696, -0.5447, -0.5251, 0.2846, 0.2454],
- [-0.0895, -0.8739, 1.7312, -0.6509, -0.1901, -1.0867, 0.4873, 0.2669]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [ 0.5746, -0.4225, 1.2968, -1.2019, -0.5615, -0.9374, 0.4533,
- -0.0108],
- [ 0.4974, -0.4482, 1.6633, -0.3306, -0.6173, 0.1313, 0.2925,
- 0.0081],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.5783, -0.4216, 1.6847, -0.5078, -0.6732, -0.5377, 0.4752,
- 0.0839],
- [ 0.5206, -0.4603, 1.4670, -0.4768, -0.6577, -0.3998, 0.1836,
- 0.0021],
- [ 0.6089, -0.3948, 1.7383, -0.8617, -0.2536, -1.2390, 0.6009,
- 0.1159]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0571, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0571, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 7.446262441575527
- step: 50
- running loss: 0.14892524883151054
- Train Steps: 50/90 Loss: 0.1489 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6113, 0.4088, 0.6859, 0.2208, 0.4363, 0.1700, 0.5188, 0.5533],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6268, 0.4102, 0.8938, 0.3667, 0.4025, 0.2833, 0.6275, 0.5183],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6361, 0.4076, 0.8862, 0.5350, 0.3713, 0.4650, 0.6654, 0.5297],
- [0.6304, 0.4024, 0.8925, 0.4800, 0.3937, 0.4817, 0.7485, 0.5297],
- [0.6132, 0.4118, 0.8200, 0.3633, 0.3563, 0.5400, 0.5787, 0.5136]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.1917, -0.8912, 1.1326, -1.0317, -0.4090, -1.2113, 0.2342, 0.2647],
- [ 0.1447, -0.7247, 1.6322, -0.4428, -0.4324, -0.7945, 0.3599, 0.2245],
- [ 0.3594, -0.6005, 1.9348, -0.0460, -0.4138, -0.4227, 0.5189, 0.1961],
- [ 0.1087, -0.7685, 1.8399, -0.3096, -0.2694, -0.7375, 0.5243, 0.2236],
- [-0.2596, -0.9368, 1.3766, -0.8925, -0.2801, -1.1774, 0.3548, 0.2806],
- [ 0.7107, -0.3776, 1.8623, 0.5153, -0.5687, 0.2528, 0.3826, 0.1827],
- [ 0.5197, -0.4508, 1.6900, 0.1712, -0.5216, 0.0608, 0.3766, 0.1881],
- [ 0.3376, -0.5243, 1.4465, -0.2484, -0.5582, -0.1278, 0.3285, 0.2552]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5377, -0.3978, 0.8824, -1.2663, -0.2709, -1.5007, 0.1102,
- 0.2699],
- [ 0.5483, -0.4249, 1.5305, -0.7386, -0.6115, -0.8694, 0.3353,
- 0.1005],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.6092, -0.3913, 1.8423, -0.5923, -0.4268, -0.9772, 0.6125,
- 0.1082],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698],
- [ 0.6520, -0.4032, 1.8076, 0.1852, -0.5711, -0.1381, 0.7876,
- 0.1608],
- [ 0.6257, -0.4273, 1.8365, -0.0688, -0.4672, -0.0611, 1.1715,
- 0.1608],
- [ 0.5466, -0.3840, 1.5016, -0.6077, -0.6404, 0.2083, 0.3871,
- 0.0862]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0788, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0788, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 7.525062687695026
- step: 51
- running loss: 0.14755024877833386
- Train Steps: 51/90 Loss: 0.1476 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6176, 0.4017, 0.8788, 0.5100, 0.4075, 0.5250, 0.5913, 0.5033],
- [0.6225, 0.4191, 0.8500, 0.4167, 0.3688, 0.3233, 0.5650, 0.5650],
- [0.6266, 0.4070, 0.8712, 0.5600, 0.3713, 0.4783, 0.5775, 0.6100],
- [0.6111, 0.4033, 0.8300, 0.3267, 0.3588, 0.3333, 0.5444, 0.5637],
- [0.6109, 0.4009, 0.8725, 0.4700, 0.3550, 0.4167, 0.5650, 0.5117],
- [0.6203, 0.4073, 0.8189, 0.2398, 0.4400, 0.2054, 0.5929, 0.5501],
- [0.6196, 0.4088, 0.8888, 0.4583, 0.4500, 0.5683, 0.6138, 0.5883],
- [0.6176, 0.3911, 0.8738, 0.4217, 0.3488, 0.4033, 0.6025, 0.4817]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3924, -0.5213, 1.6944, 0.0514, -0.4727, -0.0790, 0.4196, 0.1729],
- [ 0.0863, -0.7315, 1.6527, -0.4401, -0.4479, -0.7708, 0.4044, 0.2342],
- [ 0.5413, -0.4434, 1.6979, 0.2409, -0.5767, 0.0799, 0.3926, 0.2002],
- [ 0.1163, -0.7415, 1.5643, -0.5669, -0.5108, -0.8526, 0.3755, 0.2119],
- [ 0.3633, -0.6097, 1.8357, -0.0659, -0.4941, -0.4124, 0.4238, 0.1571],
- [-0.2876, -0.9784, 1.4261, -0.9724, -0.2718, -1.3018, 0.4072, 0.2629],
- [ 0.2977, -0.5920, 1.6902, -0.0351, -0.3639, -0.1044, 0.4685, 0.2399],
- [ 0.3709, -0.5620, 1.5785, -0.3152, -0.6058, -0.4485, 0.4069, 0.1557]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5668, -0.4306, 1.7730, 0.0697, -0.4037, 0.1390, 0.4450,
- 0.0390],
- [ 0.5894, -0.3503, 1.6402, -0.3614, -0.5827, -0.7925, 0.3238,
- 0.3238],
- [ 0.6081, -0.4059, 1.7383, 0.3007, -0.5711, -0.0765, 0.3815,
- 0.5316],
- [ 0.5366, -0.4232, 1.5478, -0.7771, -0.6289, -0.7463, 0.2288,
- 0.3177],
- [ 0.5356, -0.4343, 1.7441, -0.1150, -0.6462, -0.3614, 0.3238,
- 0.0774],
- [ 0.5792, -0.4048, 1.4965, -1.1781, -0.2534, -1.3371, 0.4528,
- 0.2549],
- [ 0.5759, -0.3978, 1.8192, -0.1689, -0.2074, 0.3392, 0.5490,
- 0.4316],
- [ 0.5669, -0.4794, 1.7499, -0.3383, -0.6751, -0.4229, 0.4970,
- -0.0611]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0449, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0449, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 7.569976683706045
- step: 52
- running loss: 0.14557647468665472
- Train Steps: 52/90 Loss: 0.1456 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6364, 0.4144, 0.8625, 0.3083, 0.4913, 0.2000, 0.6448, 0.5274],
- [0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117],
- [ nan, nan, 0.6935, 0.1930, 0.4150, 0.2250, 0.5450, 0.5650],
- [0.6136, 0.4060, 0.8025, 0.3217, 0.3650, 0.3000, 0.5060, 0.5646],
- [0.6157, 0.4102, 0.8513, 0.3817, 0.3613, 0.3667, 0.5096, 0.5890],
- [0.6268, 0.4052, 0.8175, 0.2250, 0.4688, 0.1917, 0.6375, 0.5267],
- [0.6193, 0.4034, 0.7757, 0.2347, 0.3733, 0.2919, 0.5930, 0.4926],
- [0.6114, 0.4018, 0.7213, 0.1967, 0.3763, 0.2700, 0.5875, 0.5533]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0673, -0.7542, 1.7669, -0.3813, -0.2630, -0.6866, 0.5843, 0.1976],
- [ 0.8705, -0.3015, 2.0107, 0.5344, -0.6006, 0.4336, 0.6015, 0.1237],
- [-0.2974, -0.9372, 1.2530, -0.8868, -0.3178, -1.0227, 0.3237, 0.2703],
- [ 0.3326, -0.5938, 1.5830, -0.2542, -0.5651, -0.4246, 0.3844, 0.1999],
- [ 0.6440, -0.4098, 1.8351, 0.1432, -0.6327, -0.0068, 0.4699, 0.1745],
- [ 0.0210, -0.7968, 1.5529, -0.6347, -0.3618, -0.9225, 0.4992, 0.1978],
- [ 0.2055, -0.6765, 1.3400, -0.6754, -0.5636, -0.7407, 0.3770, 0.1858],
- [ 0.1917, -0.6800, 1.3017, -0.6446, -0.5512, -0.7384, 0.3369, 0.1998]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6537, -0.3719, 1.6979, -0.8617, -0.0169, -1.3621, 0.6926,
- 0.1501],
- [ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774],
- [-2.2859, -2.2859, 0.9175, -1.3947, -0.3691, -1.2467, 0.2314,
- 0.3238],
- [ 0.5483, -0.4105, 1.4208, -0.8002, -0.6000, -0.9002, 0.0511,
- 0.3220],
- [ 0.5581, -0.3912, 1.6460, -0.5230, -0.6173, -0.5923, 0.0681,
- 0.4348],
- [ 0.6092, -0.4143, 1.4901, -1.2467, -0.1208, -1.4006, 0.6587,
- 0.1467],
- [ 0.5746, -0.4225, 1.2968, -1.2019, -0.5615, -0.9374, 0.4533,
- -0.0108],
- [ 0.5380, -0.4302, 1.0455, -1.3775, -0.5480, -1.0388, 0.4277,
- 0.2699]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1982, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1982, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 7.7682068310678005
- step: 53
- running loss: 0.14656994020882644
- Train Steps: 53/90 Loss: 0.1466 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6239, 0.4123, 0.8313, 0.2550, 0.4500, 0.2050, 0.6175, 0.5400],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679],
- [0.6266, 0.4070, 0.8712, 0.5600, 0.3713, 0.4783, 0.5775, 0.6100],
- [0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064],
- [0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.2143, -0.9404, 1.4668, -1.0647, -0.3230, -1.3438, 0.4597, 0.2239],
- [-0.2777, -0.9847, 1.5412, -1.0364, -0.2318, -1.3185, 0.5605, 0.2012],
- [ 0.6630, -0.3745, 1.6541, 0.1284, -0.6696, 0.0945, 0.4320, 0.1933],
- [ 0.3220, -0.5820, 1.6807, -0.1678, -0.4297, -0.2776, 0.4604, 0.1703],
- [ 0.7033, -0.3861, 1.6634, 0.0126, -0.7016, -0.1221, 0.5019, 0.1436],
- [ 0.5057, -0.4786, 1.6570, -0.0877, -0.5653, -0.0970, 0.4212, 0.2048],
- [ 0.2947, -0.5987, 1.6497, -0.2465, -0.4861, -0.4751, 0.4378, 0.1929],
- [ 0.5106, -0.4723, 1.6720, -0.1766, -0.6225, -0.1774, 0.5021, 0.1598]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5958, -0.3818, 1.5536, -1.1081, -0.2074, -1.3390, 0.5663,
- 0.2083],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371],
- [ 0.6081, -0.4059, 1.7383, 0.3007, -0.5711, -0.0765, 0.3815,
- 0.5316],
- [ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532],
- [ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762],
- [ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1455, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1455, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 7.913748513907194
- step: 54
- running loss: 0.14655089840568877
- Train Steps: 54/90 Loss: 0.1466 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6261, 0.3987, 0.8688, 0.4917, 0.4300, 0.5333, 0.7010, 0.5309],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550],
- [0.6260, 0.4253, 0.8938, 0.4400, 0.3675, 0.3650, 0.5675, 0.5767],
- [0.6260, 0.4133, 0.8800, 0.5117, 0.3713, 0.3283, 0.6223, 0.4967],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5348, -0.4461, 1.6849, -0.0344, -0.5152, 0.0100, 0.5367, 0.1942],
- [-0.2356, -0.9501, 1.0968, -1.2913, -0.4973, -1.4036, 0.2616, 0.2211],
- [ 0.3467, -0.5774, 1.6509, -0.2637, -0.4446, -0.3173, 0.4580, 0.2147],
- [ 0.2504, -0.6009, 1.6104, -0.3901, -0.4216, -0.3745, 0.5047, 0.1924],
- [ 0.2240, -0.6485, 1.7115, -0.4295, -0.3777, -0.5357, 0.5990, 0.1783],
- [ 0.5265, -0.4631, 1.7272, -0.2212, -0.6034, -0.4052, 0.5230, 0.1763],
- [ 0.4698, -0.5562, 1.8330, -0.1877, -0.5408, -0.6042, 0.5758, 0.1269],
- [ 0.5352, -0.5074, 1.7070, -0.1354, -0.5545, -0.2338, 0.4854, 0.1544]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6059, -0.4442, 1.7268, -0.0149, -0.2998, 0.1775, 0.9521,
- 0.1661],
- [-2.2859, -2.2859, 0.6760, -1.4083, -0.3286, -1.4160, 0.2487,
- 0.3469],
- [ 0.5505, -0.4207, 1.7095, -0.0534, -0.0509, 0.1050, 0.3873,
- 0.3007],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776],
- [ 0.6058, -0.3216, 1.8423, -0.2536, -0.5885, -0.6000, 0.3353,
- 0.3777],
- [ 0.6054, -0.3767, 1.7788, 0.0774, -0.5711, -0.7694, 0.5887,
- 0.0081],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1391, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1391, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 8.052847858518362
- step: 55
- running loss: 0.14641541560942475
- Train Steps: 55/90 Loss: 0.1464 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6087, 0.3951, 0.8387, 0.5833, 0.4188, 0.4933, 0.5146, 0.4830],
- [0.6182, 0.3930, 0.8841, 0.3892, 0.3556, 0.4967, 0.6222, 0.5279],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6264, 0.4055, 0.8425, 0.2767, 0.4425, 0.2767, 0.7050, 0.5586],
- [0.6260, 0.4120, 0.8013, 0.2350, 0.4888, 0.1533, 0.6281, 0.4895],
- [0.6248, 0.4185, 0.8500, 0.5767, 0.4463, 0.4550, 0.5613, 0.5917]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5108, -0.4988, 1.8392, -0.1524, -0.4782, -0.4393, 0.6054, 0.1707],
- [-0.1989, -0.8882, 1.1078, -1.1526, -0.4256, -1.1660, 0.2669, 0.2613],
- [ 0.6571, -0.3982, 1.7409, 0.0728, -0.5547, -0.0445, 0.5122, 0.1667],
- [ 0.5398, -0.4333, 1.5151, -0.3352, -0.6108, -0.1219, 0.4888, 0.1776],
- [ 0.8204, -0.2803, 1.7220, 0.1719, -0.6168, 0.2006, 0.5326, 0.1883],
- [-0.0578, -0.8517, 1.5819, -0.9280, -0.2828, -1.1182, 0.6220, 0.1895],
- [-0.1826, -0.9124, 1.3633, -1.1262, -0.3392, -1.3109, 0.4546, 0.1922],
- [ 0.6652, -0.3629, 1.7669, 0.1831, -0.4866, 0.0789, 0.5693, 0.1919]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661],
- [ 0.5862, -0.3530, 1.1032, -1.0619, -0.1497, -1.3852, 0.3411,
- 0.3931],
- [ 0.5258, -0.4610, 1.5882, 0.4085, -0.3517, -0.0072, 0.0910,
- -0.0550],
- [ 0.5697, -0.4706, 1.7976, -0.4884, -0.6433, 0.0081, 0.5878,
- 0.1525],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.6075, -0.4129, 1.6055, -1.0080, -0.2420, -1.0080, 0.9704,
- 0.2944],
- [ 0.6053, -0.3830, 1.4150, -1.2005, -0.0284, -1.5777, 0.6154,
- -0.0250],
- [ 0.5999, -0.3530, 1.6402, 0.3777, -0.2247, -0.1843, 0.3065,
- 0.4470]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0662, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0662, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 8.11908669397235
- step: 56
- running loss: 0.14498369096379196
- Train Steps: 56/90 Loss: 0.1450 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433],
- [0.6329, 0.4175, 0.8550, 0.2333, 0.5425, 0.2250, 0.7398, 0.5609],
- [0.6128, 0.4118, 0.8638, 0.5333, 0.4625, 0.5267, 0.5193, 0.5475],
- [0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578],
- [0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6179, 0.3993, 0.8925, 0.4789, 0.3879, 0.4900, 0.6041, 0.5279],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4652, -0.4927, 1.4310, -0.5926, -0.5860, -0.4583, 0.4855, 0.1917],
- [ 0.0168, -0.8016, 1.7405, -0.7322, -0.1639, -0.9592, 0.7691, 0.1852],
- [ 0.9244, -0.2048, 1.9678, 0.4568, -0.4284, 0.4085, 0.6313, 0.1853],
- [ 0.0860, -0.7330, 1.3060, -0.8993, -0.4453, -0.9782, 0.3544, 0.1966],
- [ 0.0321, -0.7831, 1.3198, -0.9540, -0.4452, -1.0702, 0.3742, 0.1955],
- [-0.0107, -0.7694, 1.3218, -0.8850, -0.4071, -0.9168, 0.3962, 0.2506],
- [ 1.1011, -0.1720, 2.0850, 0.4680, -0.5672, 0.4022, 0.6618, 0.1479],
- [ 0.2294, -0.6558, 1.3344, -0.7895, -0.5082, -0.8425, 0.4000, 0.1940]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237],
- [ 0.6372, -0.3575, 1.6633, -1.2082, 0.2199, -1.2467, 1.1313,
- 0.3050],
- [ 0.5447, -0.3838, 1.7037, 0.1775, -0.1497, 0.1467, 0.1128,
- 0.2431],
- [ 0.5232, -0.4189, 0.9358, -1.4006, -0.3921, -1.3698, 0.2555,
- 0.2906],
- [ 0.5109, -0.4316, 1.1436, -1.3467, -0.3864, -1.4160, 0.1239,
- -0.0580],
- [ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.5680, -0.4417, 1.8365, -0.0740, -0.4941, -0.0227, 0.5045,
- 0.1525],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0934, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0934, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 8.212458346039057
- step: 57
- running loss: 0.14407821659717643
- Train Steps: 57/90 Loss: 0.1441 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6277, 0.4057, 0.8300, 0.2650, 0.4363, 0.1850, 0.6140, 0.4823],
- [0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6200, 0.4049, 0.8638, 0.5617, 0.4125, 0.5100, 0.6013, 0.5317],
- [0.6271, 0.4024, 0.9000, 0.3517, 0.3700, 0.4517, 0.6931, 0.5285],
- [0.6218, 0.4098, 0.7238, 0.1850, 0.4238, 0.2517, 0.6288, 0.5350],
- [0.6187, 0.4104, 0.7126, 0.2069, 0.3950, 0.2517, 0.5913, 0.5683],
- [0.6200, 0.4024, 0.8390, 0.3139, 0.3525, 0.3833, 0.6162, 0.5383],
- [ nan, nan, 0.8850, 0.2817, 0.5112, 0.2183, 0.7184, 0.5436]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1649, -0.7242, 1.5017, -0.9635, -0.3496, -1.1003, 0.5266, 0.1752],
- [ 0.5651, -0.4185, 1.4566, -0.3872, -0.5721, -0.2852, 0.4112, 0.2345],
- [ 0.9952, -0.1645, 1.9069, 0.4831, -0.4560, 0.3968, 0.5863, 0.1960],
- [ 0.6640, -0.3472, 1.5984, -0.2779, -0.5041, -0.0886, 0.5670, 0.2048],
- [ 0.1283, -0.7124, 1.3394, -0.9232, -0.3711, -1.0119, 0.4405, 0.2160],
- [ 0.1406, -0.7053, 1.3067, -0.9091, -0.4227, -0.9925, 0.3371, 0.2296],
- [ 0.6080, -0.4196, 1.5368, -0.4087, -0.5550, -0.3284, 0.4938, 0.1815],
- [ 0.0596, -0.7789, 1.7898, -0.7913, -0.1113, -1.0448, 0.7324, 0.1776]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6132, -0.4122, 1.5478, -1.0619, -0.2709, -1.4314, 0.5500,
- -0.0583],
- [ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.5776, -0.4159, 1.7037, 0.3084, -0.3806, 0.0697, 0.4912,
- 0.1698],
- [ 0.6108, -0.4273, 1.8711, -0.6616, -0.5769, -0.1997, 0.9156,
- 0.1554],
- [ 0.5861, -0.3929, 1.0570, -1.4314, -0.3286, -1.1235, 0.6182,
- 0.1852],
- [ 0.5718, -0.3905, 1.0053, -1.3305, -0.4614, -1.1235, 0.4450,
- 0.3392],
- [ 0.5779, -0.4275, 1.5894, -0.8362, -0.6577, -0.5153, 0.5605,
- 0.2006],
- [-2.2859, -2.2859, 1.8018, -0.9849, 0.0755, -1.2774, 1.0326,
- 0.2249]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1737, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1737, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 8.38615109398961
- step: 58
- running loss: 0.14458881196533813
- Train Steps: 58/90 Loss: 0.1446 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [0.6225, 0.4191, 0.8500, 0.4167, 0.3688, 0.3233, 0.5650, 0.5650],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6336, 0.4086, 0.8900, 0.3950, 0.3900, 0.2950, 0.6504, 0.5066],
- [0.6203, 0.4072, 0.8892, 0.3523, 0.3783, 0.3017, 0.5898, 0.5478],
- [0.6198, 0.4164, 0.8700, 0.5067, 0.4625, 0.5650, 0.5464, 0.5197],
- [0.6277, 0.4036, 0.8688, 0.3617, 0.3925, 0.2600, 0.6132, 0.4742]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7843, -0.2825, 1.5981, -0.0211, -0.4891, -0.0875, 0.5092, 0.2230],
- [ 0.2781, -0.6108, 1.5591, -0.6938, -0.3728, -0.8706, 0.4629, 0.2388],
- [ 0.5331, -0.3995, 1.5068, -0.4493, -0.4661, -0.2366, 0.5199, 0.2029],
- [ 0.6395, -0.3974, 1.7493, -0.2473, -0.3899, -0.2536, 0.5395, 0.1864],
- [ 0.3610, -0.5704, 1.4294, -0.7834, -0.4042, -0.8035, 0.5313, 0.1834],
- [ 0.3174, -0.6453, 1.6677, -0.7387, -0.2951, -0.9430, 0.5775, 0.1927],
- [ 0.4922, -0.4571, 1.6685, -0.2111, -0.2608, -0.2328, 0.5452, 0.2422],
- [ 0.2434, -0.6341, 1.4036, -0.9700, -0.3803, -1.0104, 0.5442, 0.1745]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778],
- [ 0.5894, -0.3503, 1.6402, -0.3614, -0.5827, -0.7925, 0.3238,
- 0.3238],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.6405, -0.3984, 1.8249, -0.4614, -0.4845, -0.9233, 0.7182,
- 0.0539],
- [ 0.5792, -0.4052, 1.8214, -0.6587, -0.5384, -0.8924, 0.4381,
- 0.2442],
- [ 0.5770, -0.3624, 1.7326, 0.0543, -0.1497, 0.3238, 0.2378,
- 0.1146],
- [ 0.6134, -0.4218, 1.7268, -0.6154, -0.4730, -1.0850, 0.5463,
- -0.0957]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0448, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0448, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 8.43092280998826
- step: 59
- running loss: 0.14289699677946205
- Train Steps: 59/90 Loss: 0.1429 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6120, 0.4014, 0.6863, 0.2817, 0.3700, 0.2783, 0.5513, 0.5667],
- [0.6229, 0.4066, 0.7612, 0.2967, 0.4250, 0.2167, 0.5925, 0.5350],
- [0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272],
- [0.6200, 0.4049, 0.8638, 0.5617, 0.4125, 0.5100, 0.6013, 0.5317],
- [ nan, nan, 0.7515, 0.2708, 0.3987, 0.2267, 0.5162, 0.5567],
- [0.6249, 0.4142, 0.8350, 0.3283, 0.3613, 0.3700, 0.6188, 0.5400],
- [0.6240, 0.4217, 0.8150, 0.3133, 0.4425, 0.2650, 0.5650, 0.5817],
- [0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2818, -0.5519, 1.1398, -0.9494, -0.4569, -0.8034, 0.3754, 0.2358],
- [ 0.2219, -0.6087, 1.2525, -1.0545, -0.3426, -0.9762, 0.4758, 0.1897],
- [ 0.3792, -0.5581, 1.6016, -0.6791, -0.3140, -0.8254, 0.5364, 0.1975],
- [ 0.9969, -0.1383, 1.8140, 0.2493, -0.3933, 0.2813, 0.6364, 0.1949],
- [ 0.0079, -0.7529, 1.2589, -1.0732, -0.2950, -1.1134, 0.4069, 0.2175],
- [ 0.4674, -0.4826, 1.6665, -0.6039, -0.3580, -0.7430, 0.5824, 0.1887],
- [-0.0475, -0.7370, 1.3782, -0.9749, -0.1228, -1.0500, 0.4923, 0.2548],
- [ 1.1495, -0.1309, 1.9559, 0.3334, -0.4791, 0.2528, 0.6781, 0.1574]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5410, -0.4321, 0.8838, -0.9849, -0.5769, -1.0003, 0.2603,
- 0.3315],
- [ 0.5911, -0.4080, 1.2303, -0.9156, -0.3229, -1.2851, 0.4508,
- 0.1852],
- [ 0.5711, -0.4015, 1.6979, -0.6770, -0.5365, -1.0619, 0.1712,
- 0.1494],
- [ 0.5776, -0.4159, 1.7037, 0.3084, -0.3806, 0.0697, 0.4912,
- 0.1698],
- [-2.2859, -2.2859, 1.1854, -1.0352, -0.4441, -1.2390, 0.0986,
- 0.2853],
- [ 0.6006, -0.3728, 1.5709, -0.7694, -0.6173, -0.5769, 0.5721,
- 0.2083],
- [ 0.5964, -0.3380, 1.4785, -0.8386, -0.2420, -1.0619, 0.3238,
- 0.4008],
- [ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1616, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1616, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 8.592534977942705
- step: 60
- running loss: 0.14320891629904509
- Train Steps: 60/90 Loss: 0.1432 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4048, 0.8488, 0.2883, 0.4463, 0.2033, 0.6321, 0.5155],
- [ nan, nan, 0.8938, 0.2850, 0.4662, 0.3117, 0.7406, 0.5528],
- [0.6275, 0.4013, 0.8850, 0.2833, 0.4975, 0.2233, 0.7058, 0.5343],
- [0.6270, 0.4267, 0.7150, 0.3317, 0.4900, 0.1817, 0.5560, 0.6183],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719],
- [0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250],
- [0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2086, -0.6300, 1.3176, -1.0979, -0.3326, -1.1253, 0.5210, 0.1824],
- [ 0.1186, -0.6808, 1.6586, -0.8910, -0.1042, -1.1124, 0.6969, 0.1947],
- [ 0.0176, -0.7576, 1.3594, -1.1991, -0.1722, -1.2693, 0.6071, 0.1854],
- [-0.0947, -0.7664, 1.0023, -1.2901, -0.3211, -1.2261, 0.3050, 0.2503],
- [ 0.9525, -0.1494, 1.7418, 0.1433, -0.4784, 0.0591, 0.4824, 0.2212],
- [ 0.8451, -0.1980, 1.6479, 0.0220, -0.4704, 0.0241, 0.4932, 0.2086],
- [ 0.8089, -0.3003, 1.8164, -0.1682, -0.4304, -0.2600, 0.5217, 0.2167],
- [ 0.7858, -0.2762, 1.6638, -0.1935, -0.4581, -0.1493, 0.5384, 0.1817]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6126, -0.4161, 1.6344, -0.9541, -0.2247, -1.3467, 0.6339,
- 0.0953],
- [-2.2859, -2.2859, 1.8423, -0.9695, -0.1323, -0.8463, 1.1349,
- 0.2676],
- [ 0.6125, -0.4321, 1.8018, -0.9772, 0.0120, -1.2543, 0.9741,
- 0.1821],
- [ 0.6101, -0.3152, 1.0166, -0.7540, -0.0226, -1.4468, 0.2823,
- 0.5702],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007],
- [ 0.5079, -0.4714, 1.6575, 0.2237, -0.4961, 0.0774, 0.1465,
- -0.1061],
- [ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390],
- [ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1915, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1915, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 8.784059438854456
- step: 61
- running loss: 0.1440009744074501
- Train Steps: 61/90 Loss: 0.1440 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [0.6233, 0.4091, 0.8100, 0.2950, 0.3563, 0.3883, 0.6013, 0.5200],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6308, 0.3990, 0.8688, 0.5183, 0.3950, 0.4983, 0.6388, 0.5133],
- [0.6239, 0.4206, 0.8750, 0.5400, 0.3688, 0.4850, 0.5737, 0.5700],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6193, 0.3930, 0.8949, 0.4437, 0.3852, 0.5435, 0.6263, 0.5263],
- [0.6339, 0.4102, 0.9088, 0.4767, 0.3925, 0.5283, 0.7509, 0.5390]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4254, -0.5102, 1.6345, -0.7446, -0.3703, -0.9065, 0.4836, 0.1739],
- [ 0.3287, -0.5379, 1.2386, -0.9834, -0.4621, -0.9095, 0.4226, 0.2145],
- [-0.0653, -0.8610, 1.5051, -1.2168, -0.0975, -1.5968, 0.5487, 0.2045],
- [ 0.6743, -0.3409, 1.6672, -0.1588, -0.3592, -0.2565, 0.5132, 0.1833],
- [ 0.8930, -0.2093, 1.7921, 0.0180, -0.4376, -0.1262, 0.5343, 0.2037],
- [ 0.2680, -0.5955, 1.4384, -0.9249, -0.3006, -1.0350, 0.5602, 0.1513],
- [ 0.6325, -0.3450, 1.5545, -0.3512, -0.4066, -0.2343, 0.5168, 0.2220],
- [ 0.6448, -0.3246, 1.5906, -0.2330, -0.3459, -0.0971, 0.5405, 0.2321]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [ 0.5932, -0.3962, 1.4554, -0.9233, -0.6404, -0.4922, 0.4912,
- 0.1159],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.6275, -0.4430, 1.7268, 0.1082, -0.4614, 0.0159, 0.6644,
- 0.0851],
- [ 0.5960, -0.3430, 1.7557, 0.2083, -0.5827, -0.0457, 0.3642,
- 0.3469],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5746, -0.4706, 1.8476, -0.2365, -0.5068, 0.2245, 0.6069,
- 0.1449],
- [ 0.6421, -0.3912, 1.9115, -0.0842, -0.4730, 0.1544, 1.1824,
- 0.2035]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0645, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0645, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 8.848596323281527
- step: 62
- running loss: 0.14271929553679882
- Train Steps: 62/90 Loss: 0.1427 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6263, 0.4233, 0.7924, 0.4626, 0.3788, 0.2883, 0.5573, 0.6047],
- [0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167],
- [0.6219, 0.3934, 0.8688, 0.5267, 0.4313, 0.4967, 0.5988, 0.4983],
- [0.6159, 0.4085, 0.6900, 0.2283, 0.4088, 0.1950, 0.5123, 0.5397],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.0193e-01, -3.5355e-01, 1.4660e+00, -3.8460e-01, -4.2701e-01,
- -3.7003e-01, 4.9220e-01, 2.1553e-01],
- [ 6.1210e-01, -3.9221e-01, 1.6649e+00, -4.7105e-01, -4.3883e-01,
- -6.3568e-01, 4.8713e-01, 1.8266e-01],
- [-1.6378e-01, -8.3230e-01, 1.1472e+00, -1.3570e+00, -2.2156e-01,
- -1.4473e+00, 4.2822e-01, 2.1547e-01],
- [ 7.4028e-01, -3.1413e-01, 1.8390e+00, -2.3177e-01, -4.3150e-01,
- -3.5873e-01, 6.1900e-01, 1.3988e-01],
- [ 3.8143e-01, -5.2839e-01, 1.5582e+00, -8.0401e-01, -3.4977e-01,
- -9.7758e-01, 5.2392e-01, 1.7169e-01],
- [ 7.3339e-01, -3.0600e-01, 1.7870e+00, -5.2512e-02, -3.5248e-01,
- -1.5096e-01, 5.9570e-01, 1.4974e-01],
- [ 1.3114e-03, -7.5873e-01, 1.0977e+00, -1.2836e+00, -3.4830e-01,
- -1.3368e+00, 3.7091e-01, 2.0337e-01],
- [ 4.9602e-01, -4.2798e-01, 1.7384e+00, -2.2984e-01, -1.6082e-01,
- -3.1048e-01, 6.1138e-01, 1.8955e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6069, -0.3309, 1.3742, -0.1493, -0.5365, -0.9541, 0.2884,
- 0.5071],
- [ 0.5470, -0.3952, 1.6377, -0.4253, -0.6289, -0.8079, 0.0249,
- 0.2116],
- [ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [ 0.5483, -0.4249, 1.5305, -0.7386, -0.6115, -0.8694, 0.3353,
- 0.1005],
- [ 0.5864, -0.4690, 1.7268, 0.1467, -0.2940, 0.0082, 0.4797,
- 0.0159],
- [ 0.5591, -0.3990, 0.9012, -1.2313, -0.3979, -1.3852, 0.0804,
- 0.2071],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0488, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0488, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 8.897438570857048
- step: 63
- running loss: 0.1412291836643976
- Train Steps: 63/90 Loss: 0.1412 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6276, 0.4120, 0.8738, 0.3133, 0.4225, 0.2217, 0.6203, 0.4892],
- [0.6135, 0.3994, 0.7913, 0.3050, 0.3625, 0.3050, 0.5837, 0.5050],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6887, -0.2958, 1.7633, -0.0927, -0.3535, -0.0904, 0.5729, 0.1684],
- [-0.0433, -0.7717, 1.3271, -1.0488, -0.1998, -1.1762, 0.4160, 0.2186],
- [ 0.9128, -0.2367, 1.9098, 0.0604, -0.4814, -0.3571, 0.5593, 0.1122],
- [ 0.2893, -0.6126, 1.7348, -0.7418, -0.1768, -1.0713, 0.6419, 0.1588],
- [ 0.4247, -0.5085, 1.3427, -0.8894, -0.4398, -0.8603, 0.4474, 0.1686],
- [ 0.3004, -0.5806, 1.2293, -0.9643, -0.3975, -1.0585, 0.3702, 0.1898],
- [ 0.3562, -0.5280, 1.3323, -0.9654, -0.3721, -1.0282, 0.4855, 0.1664],
- [ 0.7345, -0.2874, 1.6705, -0.2287, -0.4608, -0.1024, 0.5201, 0.1640]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5604, -0.4620, 1.7961, -0.1997, -0.3229, 0.1082, 0.4104,
- 0.2545],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.6128, -0.3828, 1.7499, -0.8386, -0.3344, -1.2620, 0.5792,
- -0.0263],
- [ 0.5477, -0.4413, 1.3688, -0.8771, -0.6115, -0.8771, 0.4104,
- 0.0467],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929],
- [ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1364, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1364, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 9.033822163939476
- step: 64
- running loss: 0.1411534713115543
- Train Steps: 64/90 Loss: 0.1412 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6160, 0.4086, 0.8775, 0.3983, 0.3750, 0.3783, 0.5128, 0.4882],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6168, 0.4029, 0.8523, 0.3417, 0.3588, 0.5000, 0.6125, 0.5400],
- [0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986],
- [0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422],
- [0.6250, 0.4106, 0.8700, 0.3717, 0.3588, 0.4967, 0.6038, 0.5167],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6446, -0.3956, 1.6840, -0.4547, -0.5014, -0.7255, 0.4978, 0.1312],
- [ 0.0775, -0.7255, 1.5045, -1.0049, -0.1928, -1.3421, 0.4969, 0.1804],
- [ 0.5250, -0.3771, 1.4217, -0.5524, -0.4841, -0.3258, 0.4748, 0.1695],
- [ 0.2667, -0.6293, 1.7410, -0.6791, -0.1763, -1.1061, 0.6038, 0.1466],
- [ 0.8183, -0.2901, 1.7640, -0.0631, -0.5073, -0.2813, 0.4108, 0.1293],
- [-0.0212, -0.7648, 1.1398, -1.1932, -0.3444, -1.3294, 0.3225, 0.1775],
- [ 0.6146, -0.3061, 1.4930, -0.4160, -0.4981, -0.2606, 0.4851, 0.1693],
- [ 0.5536, -0.3818, 1.6883, -0.1470, -0.2797, -0.1758, 0.5672, 0.1822]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5595, -0.3988, 1.7672, -0.4460, -0.5538, -0.5384, 0.0828,
- -0.0310],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.5631, -0.4249, 1.6509, -0.7078, -0.6289, 0.0236, 0.5432,
- 0.2083],
- [ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171],
- [ 0.5865, -0.3973, 1.8423, -0.0688, -0.5192, -0.2305, 0.4162,
- 0.1159],
- [-2.2859, -2.2859, 1.2469, -1.0288, -0.4557, -1.2774, 0.0511,
- 0.2183],
- [ 0.6010, -0.3896, 1.7326, -0.5692, -0.6289, 0.0082, 0.5028,
- 0.1005],
- [ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1453, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1453, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 9.179143756628036
- step: 65
- running loss: 0.14121759625581595
- Train Steps: 65/90 Loss: 0.1412 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852],
- [0.6364, 0.4154, 0.8938, 0.3717, 0.4500, 0.2583, 0.6448, 0.5285],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6274, 0.4003, 0.8638, 0.5967, 0.3688, 0.4900, 0.6108, 0.4661],
- [0.6226, 0.4098, 0.8912, 0.4100, 0.4025, 0.2383, 0.5763, 0.5367],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533],
- [0.6208, 0.4082, 0.8538, 0.3067, 0.3588, 0.3717, 0.6112, 0.5517]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6267, -0.3419, 1.6333, -0.1439, -0.4652, -0.1813, 0.4128, 0.1475],
- [ 0.4693, -0.4492, 1.6629, -0.2391, -0.3128, -0.3149, 0.4396, 0.1641],
- [ 0.0783, -0.7178, 1.6822, -0.9112, -0.1434, -1.2153, 0.5995, 0.1530],
- [ 0.6831, -0.3662, 1.6256, -0.3334, -0.5366, -0.4167, 0.4806, 0.1187],
- [ 0.7415, -0.2970, 1.6891, -0.0164, -0.5241, -0.1477, 0.4616, 0.1052],
- [ 0.2370, -0.6096, 1.6264, -0.7659, -0.3008, -1.0667, 0.4651, 0.1323],
- [ 0.0431, -0.7028, 1.2359, -1.1322, -0.4204, -1.2392, 0.3013, 0.1724],
- [ 0.4519, -0.4859, 1.5367, -0.7710, -0.4870, -0.8818, 0.4508, 0.1412]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171],
- [ 0.6537, -0.3671, 1.8423, -0.5692, -0.2074, -1.0927, 0.6926,
- 0.1554],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [ 0.6120, -0.4371, 1.7037, 0.4701, -0.5827, -0.0226, 0.5354,
- -0.1331],
- [ 0.5900, -0.3932, 1.8307, -0.3921, -0.4268, -1.1851, 0.3758,
- 0.1929],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699],
- [ 0.5814, -0.4003, 1.6575, -0.8694, -0.6289, -0.5692, 0.5374,
- 0.2622]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1668, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1668, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 9.345949053764343
- step: 66
- running loss: 0.14160528869339914
- Train Steps: 66/90 Loss: 0.1416 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6204, 0.4091, 0.8950, 0.4783, 0.3613, 0.4617, 0.6063, 0.5617],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507],
- [0.6198, 0.3997, 0.8582, 0.5361, 0.4117, 0.5016, 0.5942, 0.5134],
- [ nan, nan, 0.8850, 0.3000, 0.5363, 0.2250, 0.7343, 0.5771],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896],
- [0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167],
- [0.6282, 0.4034, 0.7830, 0.2080, 0.4532, 0.2080, 0.6404, 0.5323]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7712, -0.2923, 1.8211, -0.0302, -0.6109, -0.2295, 0.3809, 0.1126],
- [ 0.7003, -0.3199, 1.8258, -0.0810, -0.5108, -0.0843, 0.5129, 0.1062],
- [ 0.1465, -0.6928, 1.1311, -1.1692, -0.4661, -1.2716, 0.2790, 0.1480],
- [ 0.6411, -0.3872, 1.7449, -0.0299, -0.4848, -0.1530, 0.3797, 0.1211],
- [-0.1579, -0.8550, 1.6510, -1.0446, -0.0177, -1.3773, 0.6750, 0.1100],
- [ 0.3622, -0.5523, 1.4921, -0.6718, -0.5310, -0.9092, 0.2859, 0.1646],
- [ 0.6469, -0.3301, 1.7399, -0.1981, -0.4915, -0.0087, 0.5131, 0.1263],
- [ 0.0991, -0.7330, 1.4066, -1.1085, -0.3121, -1.4014, 0.4390, 0.1324]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5797, -0.3965, 1.8480, -0.0765, -0.6173, -0.1535, 0.5143,
- 0.3084],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237],
- [ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576],
- [ 0.5767, -0.4396, 1.6782, 0.1905, -0.3844, 0.0308, 0.4588,
- 0.0855],
- [-2.2859, -2.2859, 1.8018, -0.9002, 0.1910, -1.2467, 1.1057,
- 0.3799],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374],
- [ 0.5886, -0.4253, 1.8654, -0.3460, -0.5480, 0.3623, 0.6587,
- 0.1005],
- [ 0.6158, -0.4225, 1.3307, -1.3253, -0.1924, -1.3252, 0.6721,
- 0.1727]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1310, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1310, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 9.476928368210793
- step: 67
- running loss: 0.14144669206284766
- Train Steps: 67/90 Loss: 0.1414 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6216, 0.4167, 0.8588, 0.5583, 0.3975, 0.5167, 0.5775, 0.5667],
- [0.6280, 0.4055, 0.8600, 0.5317, 0.3800, 0.4700, 0.6275, 0.5133],
- [0.6250, 0.4146, 0.8838, 0.3933, 0.3588, 0.4283, 0.6162, 0.5367],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6156, 0.4125, 0.8850, 0.4833, 0.4550, 0.5817, 0.5765, 0.5084],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [0.6126, 0.4039, 0.8237, 0.3967, 0.3625, 0.3600, 0.5894, 0.6138],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7303, -0.2955, 1.7954, 0.1157, -0.5662, 0.0109, 0.4479, 0.1301],
- [ 0.6625, -0.3700, 1.7461, -0.0592, -0.5969, -0.2779, 0.3915, 0.0902],
- [ 0.3183, -0.5735, 1.6481, -0.6196, -0.5000, -0.7327, 0.3463, 0.1323],
- [-0.0706, -0.8395, 1.4050, -1.1966, -0.2504, -1.4328, 0.4267, 0.1387],
- [ 0.3940, -0.4962, 1.7431, -0.1674, -0.3207, -0.1481, 0.4893, 0.1618],
- [ 0.3869, -0.5238, 1.6733, -0.3139, -0.4044, -0.6556, 0.4237, 0.1367],
- [ 0.5155, -0.4132, 1.5255, -0.5827, -0.6067, -0.5232, 0.4523, 0.1157],
- [-0.1190, -0.8626, 1.4404, -1.1726, -0.2574, -1.4463, 0.4442, 0.1302]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5853, -0.3614, 1.6806, 0.2930, -0.4499, 0.1005, 0.3815,
- 0.3315],
- [ 0.6148, -0.4130, 1.6864, 0.1698, -0.5307, -0.1150, 0.6125,
- 0.0851],
- [ 0.6009, -0.3710, 1.7961, -0.4691, -0.6289, -0.3075, 0.5605,
- 0.1929],
- [ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850],
- [ 0.5573, -0.3808, 1.8018, -0.0534, -0.1843, 0.4008, 0.3769,
- 0.0622],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762],
- [ 0.5438, -0.4206, 1.5189, -0.4537, -0.6115, -0.6231, 0.4365,
- 0.5491],
- [ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0537, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0537, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 9.530608255416155
- step: 68
- running loss: 0.14015600375611992
- Train Steps: 68/90 Loss: 0.1402 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4130, 0.8762, 0.4117, 0.3650, 0.4900, 0.5707, 0.5103],
- [0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973],
- [0.6261, 0.4029, 0.8720, 0.3364, 0.3665, 0.3753, 0.6531, 0.5183],
- [0.6266, 0.4101, 0.8350, 0.2333, 0.3950, 0.2950, 0.6264, 0.4921],
- [0.6307, 0.4029, 0.8988, 0.4817, 0.3937, 0.3500, 0.7311, 0.5378],
- [0.6200, 0.4071, 0.7338, 0.1917, 0.4200, 0.2450, 0.6150, 0.5517],
- [0.6268, 0.4029, 0.8500, 0.2683, 0.3937, 0.3500, 0.6860, 0.5297],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4384, -0.5000, 1.7996, -0.2838, -0.5043, -0.3255, 0.3941, 0.1576],
- [ 0.7036, -0.3673, 1.8411, 0.1240, -0.5864, -0.0798, 0.4007, 0.1136],
- [ 0.4413, -0.5175, 1.5479, -0.5979, -0.5562, -0.5699, 0.4458, 0.1313],
- [-0.0993, -0.8470, 1.5847, -1.0225, -0.2267, -1.3204, 0.4917, 0.1482],
- [ 0.3940, -0.5685, 1.9010, -0.2202, -0.3972, -0.7088, 0.4700, 0.1093],
- [ 0.0720, -0.7794, 1.3627, -0.9925, -0.3594, -1.2348, 0.3514, 0.1815],
- [ 0.4026, -0.5244, 1.4602, -0.7272, -0.5536, -0.7108, 0.4587, 0.1436],
- [ 0.4534, -0.4552, 1.6432, -0.2746, -0.5154, -0.1603, 0.4155, 0.1531]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5771, -0.3784, 1.7614, -0.3844, -0.6000, -0.0226, 0.3501,
- 0.0712],
- [ 0.6240, -0.4321, 1.8423, 0.1852, -0.5885, -0.1612, 0.6962,
- 0.0111],
- [ 0.6059, -0.4249, 1.7420, -0.7321, -0.5931, -0.5523, 0.7307,
- 0.1080],
- [ 0.6081, -0.3918, 1.5709, -1.2082, -0.4614, -0.9233, 0.6072,
- -0.0129],
- [ 0.6273, -0.4249, 1.8654, -0.0611, -0.4672, -0.6693, 1.0910,
- 0.1982],
- [ 0.5779, -0.4054, 1.1032, -1.4006, -0.3460, -1.1543, 0.5547,
- 0.2622],
- [ 0.6092, -0.4249, 1.6402, -1.0465, -0.4672, -0.6693, 0.8827,
- 0.1608],
- [ 0.5546, -0.4620, 1.7788, -0.4229, -0.5192, -0.0226, 0.4277,
- 0.2468]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0482, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0482, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 9.57880049943924
- step: 69
- running loss: 0.13882319564404694
- Train Steps: 69/90 Loss: 0.1388 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5717, -0.4289, 1.9086, 0.0682, -0.4359, 0.2100, 0.5311, 0.1928],
- [ 0.1212, -0.7306, 1.5996, -0.9908, -0.2890, -1.1521, 0.6839, 0.1284],
- [-0.2105, -0.8776, 1.3527, -1.0402, -0.3279, -1.1361, 0.3717, 0.2074],
- [ 0.6163, -0.3671, 1.7900, -0.0668, -0.6327, -0.2917, 0.4187, 0.1631],
- [-0.0168, -0.8197, 1.1640, -1.2255, -0.4815, -1.3067, 0.3112, 0.1773],
- [ 0.3989, -0.5297, 1.7450, -0.3858, -0.5510, -0.6769, 0.3761, 0.1441],
- [ 0.6817, -0.3759, 1.8699, -0.0261, -0.6450, -0.3888, 0.4625, 0.0988],
- [ 0.3942, -0.5177, 1.7776, -0.0976, -0.3729, -0.1446, 0.4075, 0.1746]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5772, -0.3913, 1.8480, -0.2459, -0.4326, 0.1929, 0.5374,
- 0.4701],
- [ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.5902, -0.3493, 1.7961, -0.0072, -0.5942, -0.5615, 0.3180,
- 0.3161],
- [ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013],
- [ 0.6016, -0.3633, 1.7152, -0.2228, -0.6115, -0.6385, 0.5028,
- 0.2699],
- [ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.5515, -0.4225, 1.7788, -0.0534, -0.2016, 0.1929, 0.1568,
- 0.0682]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1287, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1287, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 9.707480400800705
- step: 70
- running loss: 0.13867829144001007
- Train Steps: 70/90 Loss: 0.1387 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.4108, 0.7425, 0.2350, 0.3887, 0.2750, 0.5900, 0.5717],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167],
- [ nan, nan, 0.7625, 0.2433, 0.3713, 0.2867, 0.5235, 0.5220],
- [0.6175, 0.4013, 0.7734, 0.2783, 0.4475, 0.1786, 0.5790, 0.5351],
- [0.6245, 0.4115, 0.8700, 0.4883, 0.4625, 0.5517, 0.6100, 0.5217],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1662, -0.6768, 1.4210, -0.7968, -0.5248, -0.9291, 0.3372, 0.1923],
- [ 0.4368, -0.4664, 1.9125, 0.0626, -0.3291, 0.1259, 0.5564, 0.1895],
- [ 0.7224, -0.3700, 1.8125, -0.1950, -0.6755, -0.3210, 0.5398, 0.1174],
- [-0.0464, -0.7945, 1.4098, -0.9435, -0.4752, -1.0650, 0.3346, 0.1966],
- [ 0.1214, -0.7137, 1.4418, -0.9443, -0.4571, -1.1249, 0.4019, 0.1869],
- [ 0.6116, -0.4209, 1.9821, 0.2965, -0.4020, 0.2547, 0.5225, 0.2192],
- [ 0.4644, -0.5158, 1.5609, -0.7510, -0.5658, -0.8439, 0.5432, 0.1503],
- [ 0.0048, -0.7716, 1.5697, -0.8414, -0.3493, -1.0282, 0.4463, 0.1949]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5746, -0.3882, 1.1436, -1.2005, -0.4903, -1.0157, 0.4393,
- 0.3546],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853],
- [ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005],
- [-2.2859, -2.2859, 1.2360, -1.1620, -0.5711, -0.9618, 0.1322,
- 0.1253],
- [ 0.5664, -0.4321, 1.2862, -1.0003, -0.2189, -1.4608, 0.3883,
- 0.1855],
- [ 0.5987, -0.3852, 1.7326, -0.0303, -0.1497, 0.2622, 0.5316,
- 0.1236],
- [ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929],
- [ 0.5977, -0.3792, 1.2995, -1.0311, -0.5192, -0.8386, 0.5836,
- 0.2160]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1591, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1591, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 9.866620302200317
- step: 71
- running loss: 0.13896648312958193
- Train Steps: 71/90 Loss: 0.1390 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6271, 0.4024, 0.9000, 0.3517, 0.3700, 0.4517, 0.6931, 0.5285],
- [0.6272, 0.4045, 0.8538, 0.5900, 0.3750, 0.4417, 0.5989, 0.4649],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383],
- [0.6214, 0.4175, 0.7300, 0.2883, 0.4338, 0.2167, 0.5698, 0.5773],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4457, -0.4978, 1.7293, -0.5260, -0.5322, -0.3158, 0.6324, 0.1981],
- [ 0.6052, -0.4613, 1.9613, 0.0222, -0.5985, -0.3985, 0.4678, 0.1336],
- [ 0.5346, -0.4371, 1.9981, 0.1041, -0.4223, 0.2140, 0.6126, 0.2160],
- [-0.1207, -0.8934, 1.2949, -1.2738, -0.4610, -1.4685, 0.3517, 0.1985],
- [ 0.1044, -0.7318, 1.3309, -1.0767, -0.4852, -1.1226, 0.3573, 0.2500],
- [ 0.3721, -0.5522, 1.9450, 0.0344, -0.3227, -0.1119, 0.4806, 0.2193],
- [ 0.1638, -0.7299, 1.3604, -1.0877, -0.5575, -1.2349, 0.3445, 0.1811],
- [ 0.5890, -0.4262, 1.8733, 0.0180, -0.5640, -0.0120, 0.4816, 0.2140]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6108, -0.4273, 1.8711, -0.6616, -0.5769, -0.1997, 0.9156,
- 0.1554],
- [ 0.6109, -0.4177, 1.6575, 0.4393, -0.5538, -0.2459, 0.4805,
- -0.1385],
- [ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554],
- [-2.2859, -2.2859, 1.0712, -1.2085, -0.3806, -1.3929, 0.0755,
- 0.2006],
- [ 0.5841, -0.3574, 1.0859, -0.9541, -0.2824, -1.2851, 0.3460,
- 0.3808],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1390, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1390, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 10.005621805787086
- step: 72
- running loss: 0.13896696952482065
- Train Steps: 72/90 Loss: 0.1390 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4064, 0.7879, 0.2179, 0.4567, 0.1725, 0.5955, 0.5478],
- [0.6202, 0.4054, 0.8892, 0.4428, 0.3642, 0.3249, 0.5840, 0.5426],
- [0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356],
- [0.6229, 0.4066, 0.7612, 0.2967, 0.4250, 0.2167, 0.5925, 0.5350],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667],
- [0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6081, 0.3950, 0.8538, 0.4667, 0.3850, 0.4917, 0.5342, 0.4954]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0624, -0.7368, 1.4473, -0.9337, -0.3668, -1.0164, 0.4630, 0.2204],
- [ 0.5218, -0.4831, 1.9114, -0.0674, -0.5392, -0.3152, 0.4470, 0.1608],
- [-0.0386, -0.7876, 1.6309, -0.7558, -0.2925, -0.8943, 0.5597, 0.2360],
- [ 0.2419, -0.6400, 1.4512, -0.9229, -0.4733, -0.8848, 0.5008, 0.2013],
- [ 0.2882, -0.6012, 1.3015, -0.7879, -0.6360, -0.6736, 0.3322, 0.2447],
- [ 0.7135, -0.3395, 1.9640, 0.4515, -0.5205, 0.2684, 0.5442, 0.2294],
- [-0.0281, -0.8188, 1.2931, -1.1416, -0.4590, -1.2413, 0.3957, 0.2089],
- [ 0.6038, -0.4154, 1.8860, 0.0039, -0.5550, 0.0999, 0.5708, 0.1776]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5787, -0.4086, 1.3535, -1.2794, -0.1764, -1.4891, 0.4645,
- 0.2442],
- [ 0.5788, -0.4135, 1.8214, -0.2408, -0.6039, -0.7854, 0.4115,
- 0.2203],
- [ 0.6174, -0.4225, 1.4975, -1.1709, -0.3174, -1.1806, 0.6539,
- 0.1879],
- [ 0.5911, -0.4080, 1.2303, -0.9156, -0.3229, -1.2851, 0.4508,
- 0.1852],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315],
- [ 0.6038, -0.3464, 1.7037, 0.3931, -0.4441, -0.2613, 0.3007,
- 0.4624],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.5227, -0.4615, 1.6575, -0.1304, -0.5076, -0.0149, 0.1815,
- 0.0021]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0649, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0649, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 10.070557050406933
- step: 73
- running loss: 0.1379528363069443
- Train Steps: 73/90 Loss: 0.1380 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6108, 0.4011, 0.8037, 0.3400, 0.3700, 0.2933, 0.5658, 0.5617],
- [0.6182, 0.3972, 0.8552, 0.5914, 0.3683, 0.4181, 0.5688, 0.5378],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [0.6286, 0.3977, 0.9038, 0.4733, 0.3900, 0.4150, 0.7074, 0.5320],
- [ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [0.6193, 0.4108, 0.7438, 0.2700, 0.3650, 0.3683, 0.6238, 0.5717],
- [0.6090, 0.4010, 0.7838, 0.3483, 0.3538, 0.3783, 0.5462, 0.5077]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3564, -0.5746, 1.4583, -0.8204, -0.5787, -0.8137, 0.4769, 0.2382],
- [ 0.7135, -0.3818, 1.8126, 0.1473, -0.6671, -0.2749, 0.4425, 0.1993],
- [ 0.2154, -0.6045, 1.7846, -0.2033, -0.2362, -0.1025, 0.5369, 0.2716],
- [ 0.5455, -0.4859, 1.8217, -0.0442, -0.5644, -0.1549, 0.3641, 0.2224],
- [ 0.5563, -0.4529, 1.7759, -0.2288, -0.5570, -0.2708, 0.5477, 0.1839],
- [-0.3794, -1.0313, 1.6012, -1.1200, -0.0692, -1.3016, 0.7081, 0.2265],
- [ 0.2850, -0.6013, 1.3278, -0.8779, -0.5838, -0.7285, 0.4025, 0.2657],
- [ 0.3451, -0.5976, 1.4313, -0.8132, -0.6137, -0.7096, 0.3903, 0.2457]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5355, -0.4335, 1.4266, -0.7155, -0.5769, -0.9310, 0.3275,
- 0.3087],
- [ 0.5697, -0.4514, 1.6642, 0.4459, -0.5850, -0.3546, 0.3414,
- 0.1982],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853],
- [ 0.5865, -0.3973, 1.8423, -0.0688, -0.5192, -0.2305, 0.4162,
- 0.1159],
- [ 0.6174, -0.4490, 1.8885, -0.0996, -0.4845, -0.3691, 0.9814,
- 0.1715],
- [-2.2859, -2.2859, 1.7210, -0.9772, 0.1852, -1.3698, 0.7986,
- 0.3104],
- [ 0.5747, -0.3886, 1.1494, -1.0388, -0.6000, -0.5846, 0.5952,
- 0.3546],
- [ 0.5270, -0.4336, 1.3342, -0.6770, -0.6520, -0.5384, 0.2370,
- 0.0592]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1023, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1023, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 10.172822825610638
- step: 74
- running loss: 0.13747057872446808
- Train Steps: 74/90 Loss: 0.1375 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6164, 0.4102, 0.8850, 0.4867, 0.4213, 0.5983, 0.5609, 0.5038],
- [0.6193, 0.4034, 0.7757, 0.2347, 0.3733, 0.2919, 0.5930, 0.4926],
- [0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [0.6250, 0.4008, 0.8950, 0.4183, 0.3550, 0.4383, 0.6361, 0.4927],
- [ nan, nan, 0.7225, 0.2167, 0.3987, 0.2283, 0.5427, 0.5181],
- [0.6240, 0.4217, 0.8150, 0.3133, 0.4425, 0.2650, 0.5650, 0.5817],
- [0.6293, 0.4024, 0.8750, 0.5000, 0.4012, 0.5733, 0.7121, 0.5633]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4214, -0.5060, 1.3804, -0.6720, -0.6546, -0.6298, 0.3875, 0.2875],
- [ 0.4671, -0.4624, 1.8166, 0.0909, -0.3127, 0.1770, 0.5518, 0.2869],
- [ 0.2761, -0.6391, 1.3203, -1.0693, -0.5863, -1.1693, 0.4733, 0.1975],
- [ 0.5017, -0.4808, 1.8436, -0.1449, -0.5294, -0.1855, 0.4829, 0.2564],
- [ 0.7856, -0.3420, 1.7747, -0.2400, -0.6585, -0.3377, 0.6026, 0.1817],
- [-0.2893, -0.9720, 1.1546, -1.2933, -0.3962, -1.4782, 0.3583, 0.2215],
- [-0.2827, -0.9134, 1.4449, -0.9290, -0.2064, -1.0815, 0.4354, 0.2955],
- [ 0.6116, -0.4134, 1.9124, 0.2665, -0.3739, 0.3431, 0.5887, 0.2829]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.5614, -0.3912, 1.8018, -0.0380, -0.3402, 0.4778, 0.3049,
- 0.0412],
- [ 0.5746, -0.4225, 1.2968, -1.2019, -0.5615, -0.9374, 0.4533,
- -0.0108],
- [ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [ 0.6010, -0.4345, 1.8480, -0.3537, -0.6462, -0.2613, 0.6524,
- -0.0099],
- [-2.2859, -2.2859, 1.0513, -1.2851, -0.4441, -1.2313, 0.2206,
- 0.1073],
- [ 0.5964, -0.3380, 1.4785, -0.8386, -0.2420, -1.0619, 0.3238,
- 0.4008],
- [ 0.6207, -0.4273, 1.7557, 0.0236, -0.4326, 0.3623, 1.0033,
- 0.3157]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1267, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1267, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 10.29955194145441
- step: 75
- running loss: 0.13732735921939215
- Train Steps: 75/90 Loss: 0.1373 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6132, 0.4118, 0.8200, 0.3633, 0.3563, 0.5400, 0.5787, 0.5136],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783],
- [0.6200, 0.4070, 0.8938, 0.4183, 0.3538, 0.4567, 0.6175, 0.5400],
- [0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760],
- [ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [0.6286, 0.3977, 0.9038, 0.4733, 0.3900, 0.4150, 0.7074, 0.5320]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3176, -0.5450, 1.4536, -0.6316, -0.4627, -0.1740, 0.5203, 0.3189],
- [ 0.2611, -0.6328, 1.6570, -0.4461, -0.4967, -0.8020, 0.4033, 0.2421],
- [-0.3042, -0.9527, 1.2764, -1.1099, -0.2248, -1.2061, 0.4493, 0.3028],
- [ 0.6198, -0.4280, 1.7268, -0.3493, -0.5287, -0.2621, 0.6554, 0.2258],
- [ 0.6195, -0.3894, 1.6452, 0.0312, -0.5016, -0.0553, 0.5120, 0.2784],
- [-0.1235, -0.8596, 1.2788, -0.9454, -0.4956, -1.0478, 0.2828, 0.2828],
- [ 0.5220, -0.4900, 1.7215, -0.1318, -0.5259, -0.2601, 0.3993, 0.2380],
- [ 0.5877, -0.4383, 1.7150, -0.3114, -0.5164, -0.3794, 0.6195, 0.1953]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5466, -0.3840, 1.5016, -0.6077, -0.6404, 0.2083, 0.3871,
- 0.0862],
- [ 0.6016, -0.3633, 1.7152, -0.2228, -0.6115, -0.6385, 0.5028,
- 0.2699],
- [ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854],
- [ 0.5778, -0.4061, 1.8423, -0.3537, -0.6520, -0.1766, 0.5663,
- 0.2083],
- [ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745],
- [-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837],
- [ 0.5865, -0.3973, 1.8423, -0.0688, -0.5192, -0.2305, 0.4162,
- 0.1159],
- [ 0.6174, -0.4490, 1.8885, -0.0996, -0.4845, -0.3691, 0.9814,
- 0.1715]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1469, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1469, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 10.446426160633564
- step: 76
- running loss: 0.13745297579781005
- Train Steps: 76/90 Loss: 0.1375 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6099, 0.4030, 0.8638, 0.5117, 0.4983, 0.4965, 0.5086, 0.5388],
- [0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6185, 0.4129, 0.8900, 0.4567, 0.3937, 0.5417, 0.5734, 0.5110],
- [0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760],
- [0.6150, 0.4097, 0.7468, 0.3194, 0.3825, 0.2633, 0.5463, 0.5800],
- [0.6122, 0.4006, 0.8850, 0.4217, 0.4088, 0.5517, 0.6063, 0.5517],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1982, -0.6836, 1.6525, -0.2713, -0.2641, -0.4663, 0.4898, 0.2545],
- [-0.2407, -0.9340, 1.4422, -1.1110, -0.3239, -1.3324, 0.5128, 0.2232],
- [ 0.3009, -0.5716, 1.6889, -0.2674, -0.4470, -0.1971, 0.4458, 0.2760],
- [ 0.6223, -0.3788, 1.5415, -0.1216, -0.5765, -0.2211, 0.5047, 0.2707],
- [-0.1364, -0.8355, 1.0600, -1.1444, -0.5423, -1.2415, 0.2674, 0.2796],
- [ 0.4898, -0.4481, 1.5487, -0.4247, -0.4844, -0.1815, 0.5657, 0.2896],
- [ 0.5429, -0.4586, 1.7326, -0.2233, -0.4238, 0.0051, 0.6718, 0.2817],
- [ 0.5129, -0.4504, 1.5689, -0.1454, -0.5120, -0.1783, 0.4519, 0.2615]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5309, -0.4246, 1.7037, 0.0774, 0.0158, 0.0075, 0.0635,
- 0.2026],
- [ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.5711, -0.3788, 1.8249, -0.1766, -0.4672, 0.2160, 0.3625,
- 0.0742],
- [ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745],
- [ 0.5548, -0.3936, 1.1634, -0.8105, -0.5192, -1.0696, 0.2372,
- 0.3931],
- [ 0.5417, -0.4355, 1.8018, -0.3383, -0.3979, 0.2622, 0.5143,
- 0.2622],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0694, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0694, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 10.51586477458477
- step: 77
- running loss: 0.1365696723972048
- Train Steps: 77/90 Loss: 0.1366 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6261, 0.4029, 0.8720, 0.3364, 0.3665, 0.3753, 0.6531, 0.5183],
- [0.6198, 0.3997, 0.8582, 0.5361, 0.4117, 0.5016, 0.5942, 0.5134],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6200, 0.3978, 0.8900, 0.4550, 0.3775, 0.5200, 0.6150, 0.5367],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6201, 0.4064, 0.8688, 0.5050, 0.4225, 0.5100, 0.6138, 0.5500],
- [0.6143, 0.4040, 0.8237, 0.3333, 0.4075, 0.2117, 0.5137, 0.4973],
- [0.6364, 0.4165, 0.9088, 0.4367, 0.4075, 0.3150, 0.6448, 0.5297]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4612, -0.5073, 1.4651, -0.8233, -0.5214, -0.6658, 0.6923, 0.2148],
- [ 0.5246, -0.4641, 1.5652, -0.0813, -0.4644, -0.0231, 0.4570, 0.2592],
- [ 0.3270, -0.5956, 1.6400, -0.3893, -0.4486, -0.6250, 0.4818, 0.2420],
- [ 0.6205, -0.3809, 1.6541, -0.1866, -0.4975, -0.0112, 0.6581, 0.2468],
- [ 0.2243, -0.6179, 1.4108, -0.6065, -0.5477, -0.5506, 0.3383, 0.3082],
- [ 0.5293, -0.4360, 1.5617, -0.0832, -0.4276, 0.0412, 0.4615, 0.2706],
- [-0.2347, -0.8771, 1.2084, -1.0134, -0.3904, -1.1015, 0.3591, 0.2437],
- [-0.0426, -0.7577, 1.6079, -0.4859, -0.2439, -0.7029, 0.5177, 0.2462]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6059, -0.4249, 1.7420, -0.7321, -0.5931, -0.5523, 0.7307,
- 0.1080],
- [ 0.5767, -0.4396, 1.6782, 0.1905, -0.3844, 0.0308, 0.4588,
- 0.0855],
- [ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.5776, -0.4484, 1.8249, -0.1843, -0.5423, 0.1159, 0.5547,
- 0.1929],
- [ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.5784, -0.4088, 1.7268, 0.0467, -0.3344, 0.0697, 0.5490,
- 0.2545],
- [ 0.5515, -0.4201, 1.5189, -0.7463, -0.4037, -1.3082, 0.0869,
- 0.0111],
- [ 0.6537, -0.3623, 1.9115, -0.2690, -0.4037, -0.8309, 0.6926,
- 0.1608]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0475, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0475, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 10.563358839601278
- step: 78
- running loss: 0.13542767743078563
- Train Steps: 78/90 Loss: 0.1354 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6131, 0.4064, 0.8638, 0.5200, 0.4788, 0.4783, 0.5258, 0.5867],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [0.6127, 0.4119, 0.8650, 0.5300, 0.4512, 0.5250, 0.5260, 0.5461],
- [0.6346, 0.4165, 0.9138, 0.3983, 0.3875, 0.4317, 0.7469, 0.5471],
- [ nan, nan, 0.7412, 0.2200, 0.4450, 0.1517, 0.5312, 0.4983],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6535, -0.3603, 1.6467, 0.0109, -0.3417, 0.1054, 0.5565, 0.2526],
- [-0.1297, -0.7888, 1.1310, -0.9446, -0.5002, -0.8548, 0.2892, 0.2735],
- [ 0.7948, -0.2646, 1.6848, 0.0678, -0.6741, -0.2577, 0.5078, 0.1569],
- [ 0.2547, -0.5520, 1.5519, -0.3196, -0.3354, -0.4262, 0.5298, 0.2917],
- [ 0.6743, -0.3393, 1.6766, 0.0688, -0.3600, 0.1798, 0.5958, 0.2401],
- [ 0.3992, -0.5258, 1.8062, -0.2436, -0.3569, -0.1706, 0.6703, 0.2025],
- [-0.3108, -0.9624, 1.0015, -1.3031, -0.3757, -1.3097, 0.3483, 0.2303],
- [ 0.0091, -0.7416, 1.2802, -0.9353, -0.3341, -0.8951, 0.4326, 0.2514]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5462, -0.4090, 1.7037, 0.1159, -0.0746, -0.0765, 0.1427,
- 0.4239],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822],
- [ 0.5443, -0.3831, 1.7095, 0.1621, -0.2016, 0.1390, 0.1437,
- 0.2364],
- [ 0.6454, -0.3623, 1.9346, -0.4460, -0.4961, -0.2921, 1.1642,
- 0.2409],
- [-2.2859, -2.2859, 1.1379, -1.2697, -0.2305, -1.5854, 0.1679,
- 0.0159],
- [ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2474, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2474, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 10.810774769634008
- step: 79
- running loss: 0.13684525024853175
- Train Steps: 79/90 Loss: 0.1368 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6204, 0.4049, 0.7975, 0.2700, 0.3937, 0.2567, 0.5700, 0.5183],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [ nan, nan, 0.7725, 0.2611, 0.3675, 0.2733, 0.5413, 0.5167],
- [0.6083, 0.3957, 0.8638, 0.4950, 0.4363, 0.5083, 0.5346, 0.4980],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6201, 0.4027, 0.8029, 0.2728, 0.4042, 0.2310, 0.5980, 0.5391]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5802, -0.3822, 1.6703, -0.0592, -0.3859, 0.1253, 0.4767, 0.2593],
- [-0.0557, -0.7866, 1.2931, -0.9026, -0.4576, -1.0742, 0.3170, 0.2135],
- [ 0.4856, -0.4461, 1.7900, -0.0398, -0.3855, -0.2339, 0.5998, 0.1871],
- [ 0.5420, -0.3848, 1.5297, -0.1514, -0.4714, -0.0996, 0.5090, 0.2390],
- [-0.4367, -1.0202, 1.1679, -1.1038, -0.3655, -1.1330, 0.2830, 0.2454],
- [ 0.5202, -0.4176, 1.7353, -0.0741, -0.3475, 0.0032, 0.5747, 0.1865],
- [ 0.5335, -0.4008, 1.6311, 0.0209, -0.3461, 0.0446, 0.4941, 0.2434],
- [ 0.3662, -0.5787, 1.3590, -1.0290, -0.4626, -1.0988, 0.6388, 0.1773]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6374e-01, -4.1432e-01, 1.7519e+00, -7.8656e-02, -3.0554e-01,
- -1.4935e-02, 3.7575e-01, 3.0839e-01],
- [ 5.7985e-01, -4.1555e-01, 1.3977e+00, -1.0388e+00, -4.6721e-01,
- -1.1004e+00, 3.4688e-01, 1.0824e-01],
- [ 6.4542e-01, -3.7194e-01, 1.9115e+00, -1.3041e-01, -5.0762e-01,
- -2.8437e-01, 1.0033e+00, 4.3864e-01],
- [ 6.2895e-01, -4.3453e-01, 1.3794e+00, 3.6792e-01, -4.8453e-01,
- 3.8953e-02, 9.2654e-01, 1.9283e-01],
- [-2.2859e+00, -2.2859e+00, 1.2820e+00, -1.0801e+00, -5.8845e-01,
- -1.0234e+00, 2.1409e-01, 1.0054e-01],
- [ 5.2379e-01, -4.5797e-01, 1.7037e+00, 4.6189e-04, -2.7090e-01,
- 6.2048e-02, 1.8356e-01, 1.4106e-02],
- [ 5.7864e-01, -4.1409e-01, 1.7037e+00, 1.5443e-01, -1.8624e-01,
- 7.3556e-02, 4.3926e-01, 8.5142e-02],
- [ 5.7846e-01, -4.2587e-01, 1.4228e+00, -1.0261e+00, -4.1903e-01,
- -1.2189e+00, 4.7633e-01, 2.0428e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1085, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1085, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 10.91929730400443
- step: 80
- running loss: 0.1364912163000554
- Train Steps: 80/90 Loss: 0.1365 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3978, 0.8900, 0.4550, 0.3775, 0.5200, 0.6150, 0.5367],
- [0.6186, 0.4013, 0.8191, 0.3188, 0.4279, 0.2060, 0.5767, 0.5411],
- [0.6085, 0.4008, 0.8588, 0.5200, 0.4959, 0.4977, 0.5175, 0.5283],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320],
- [0.6133, 0.4066, 0.6787, 0.2617, 0.3800, 0.2433, 0.5147, 0.5358],
- [0.6127, 0.4066, 0.8550, 0.5567, 0.4662, 0.5141, 0.5070, 0.5412],
- [0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6595, -0.3411, 1.7133, -0.1565, -0.4309, 0.0339, 0.6960, 0.1918],
- [-0.1699, -0.8419, 1.3545, -0.8984, -0.3419, -1.0833, 0.3931, 0.2011],
- [ 0.3309, -0.5462, 1.7022, -0.0703, -0.1565, -0.1186, 0.5422, 0.2256],
- [ 0.6394, -0.3299, 1.6054, -0.1054, -0.4234, 0.0326, 0.5868, 0.2038],
- [-0.1806, -0.9008, 1.0618, -1.1263, -0.4600, -1.2427, 0.2815, 0.1938],
- [ 0.5022, -0.4241, 1.6264, 0.0332, -0.2760, -0.0482, 0.5249, 0.2056],
- [ 0.4720, -0.4786, 1.7559, -0.1731, -0.4580, -0.1977, 0.5077, 0.1997],
- [ 0.3408, -0.5362, 1.4742, -0.6118, -0.5730, -0.7129, 0.3912, 0.1708]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5776, -0.4484, 1.8249, -0.1843, -0.5423, 0.1159, 0.5547,
- 0.1929],
- [ 0.5713, -0.4321, 1.4975, -0.8134, -0.3094, -1.3345, 0.3779,
- 0.2134],
- [ 0.5245, -0.4347, 1.6806, 0.1159, 0.0046, 0.0129, 0.1044,
- 0.1544],
- [ 0.6273, -0.4393, 1.6402, 0.1313, -0.5076, 0.0467, 1.1532,
- 0.1715],
- [ 0.5470, -0.4081, 0.8492, -1.0773, -0.5307, -1.1620, 0.0912,
- 0.1890],
- [ 0.5443, -0.4079, 1.6633, 0.2853, -0.1325, 0.0888, 0.0558,
- 0.2138],
- [ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0473, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0473, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 10.966615419834852
- step: 81
- running loss: 0.13539031382512162
- Train Steps: 81/90 Loss: 0.1354 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6150, 0.3935, 0.8696, 0.5158, 0.4647, 0.5329, 0.6041, 0.5153],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114],
- [0.6099, 0.4030, 0.8638, 0.5117, 0.4983, 0.4965, 0.5086, 0.5388],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683],
- [0.6203, 0.4072, 0.8892, 0.3523, 0.3783, 0.3017, 0.5898, 0.5478],
- [ nan, nan, 0.7553, 0.2722, 0.3875, 0.2550, 0.5125, 0.5633],
- [ nan, nan, 0.7515, 0.2708, 0.3987, 0.2267, 0.5162, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6577, -0.3297, 1.6868, 0.0782, -0.3042, 0.1605, 0.5671, 0.2163],
- [ 0.6820, -0.3201, 1.8332, 0.0495, -0.4130, -0.1604, 0.6454, 0.1406],
- [ 0.6271, -0.3454, 1.7065, 0.1052, -0.2852, 0.2045, 0.5946, 0.2010],
- [ 0.4953, -0.4632, 1.7364, 0.0277, -0.2168, -0.0399, 0.5300, 0.1936],
- [ 0.1193, -0.6399, 1.4553, -0.7747, -0.3613, -0.8269, 0.5000, 0.1925],
- [ 0.4106, -0.5026, 1.6519, -0.4772, -0.4520, -0.6562, 0.5282, 0.1420],
- [-0.2983, -0.9217, 1.1456, -0.9940, -0.4474, -1.0575, 0.2386, 0.2039],
- [-0.3803, -0.9827, 1.1318, -1.0392, -0.4033, -1.1177, 0.2494, 0.1996]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5548, -0.4682, 1.7309, 0.0966, -0.1394, 0.1757, 0.5045,
- 0.0942],
- [ 0.6454, -0.3719, 1.9115, -0.1304, -0.5076, -0.2844, 1.0033,
- 0.4386],
- [ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764],
- [ 0.5309, -0.4246, 1.7037, 0.0774, 0.0158, 0.0075, 0.0635,
- 0.2026],
- [ 0.5778, -0.3930, 1.5189, -0.9387, -0.4326, -0.9156, 0.4855,
- 0.3392],
- [ 0.5792, -0.4052, 1.8214, -0.6587, -0.5384, -0.8924, 0.4381,
- 0.2442],
- [-2.2859, -2.2859, 1.2030, -1.0288, -0.4961, -1.1081, 0.0813,
- 0.3161],
- [-2.2859, -2.2859, 1.1854, -1.0352, -0.4441, -1.2390, 0.0986,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1947, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1947, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 11.161314766854048
- step: 82
- running loss: 0.1361135947177323
- Train Steps: 82/90 Loss: 0.1361 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6371, 0.4092, 0.8337, 0.5850, 0.3950, 0.5117, 0.6559, 0.5262],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413],
- [0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [0.6193, 0.4034, 0.7757, 0.2347, 0.3733, 0.2919, 0.5930, 0.4926],
- [0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726],
- [ nan, nan, 0.7725, 0.2611, 0.3675, 0.2733, 0.5413, 0.5167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0737, -0.0740, 1.8738, 0.5913, -0.4413, 0.5408, 0.5651, 0.1580],
- [-0.5130, -1.0604, 1.2191, -0.9690, -0.2829, -1.0806, 0.2641, 0.2092],
- [-0.1248, -0.8598, 1.8897, -0.6127, 0.0917, -0.7495, 0.8417, 0.1448],
- [ 0.8726, -0.2283, 1.9341, 0.3120, -0.4677, 0.1949, 0.5191, 0.1281],
- [ 0.4474, -0.4906, 1.3738, -0.7276, -0.4931, -0.7259, 0.4807, 0.1319],
- [ 0.5036, -0.4894, 1.3357, -0.5319, -0.4911, -0.5718, 0.4147, 0.2147],
- [ 0.4899, -0.4758, 1.3907, -0.6307, -0.4970, -0.6554, 0.4247, 0.1343],
- [-0.4548, -1.0278, 1.3212, -0.8165, -0.3190, -0.8877, 0.2281, 0.1962]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6569, -0.3960, 1.5651, 0.4162, -0.4614, 0.0774, 0.7438,
- 0.1447],
- [-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373],
- [ 0.6240, -0.3768, 1.6575, -1.2851, 0.2949, -1.2467, 1.1276,
- 0.2142],
- [ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [ 0.5746, -0.4225, 1.2968, -1.2019, -0.5615, -0.9374, 0.4533,
- -0.0108],
- [ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700],
- [ 0.4997, -0.4446, 1.1610, -0.9772, -0.6058, -1.0311, 0.1404,
- -0.1031],
- [-2.2859, -2.2859, 1.2820, -1.0801, -0.5885, -1.0234, 0.2141,
- 0.1005]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2300, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2300, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 11.391309630125761
- step: 83
- running loss: 0.1372446943388646
- Train Steps: 83/90 Loss: 0.1372 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.4050, 0.7313, 0.2433, 0.4075, 0.2117, 0.5649, 0.5656],
- [0.6346, 0.4092, 0.7712, 0.5917, 0.4037, 0.4767, 0.7343, 0.5725],
- [0.6206, 0.4001, 0.8900, 0.3933, 0.3588, 0.3567, 0.5837, 0.5083],
- [0.6147, 0.4026, 0.6600, 0.2467, 0.4088, 0.2150, 0.5489, 0.5773],
- [0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6203, 0.4096, 0.8862, 0.4267, 0.3538, 0.4117, 0.6025, 0.5650],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6136, 0.4117, 0.8700, 0.5167, 0.4188, 0.5083, 0.5147, 0.5495]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.0929, -0.8711, 1.2389, -1.0993, -0.3887, -1.2384, 0.4037, 0.1447],
- [ 0.3426, -0.5333, 1.5412, -0.1081, -0.3541, -0.0805, 0.4267, 0.2252],
- [ 0.4016, -0.5626, 1.8441, -0.3404, -0.4835, -0.6410, 0.5482, 0.0721],
- [-0.2117, -0.9704, 1.2032, -1.0936, -0.4017, -1.2933, 0.2922, 0.1594],
- [ 0.5968, -0.3992, 1.6112, -0.5510, -0.5232, -0.4294, 0.6442, 0.1186],
- [ 0.2000, -0.6920, 1.8410, -0.2265, -0.3198, -0.3455, 0.4740, 0.1843],
- [ 0.3778, -0.5193, 1.7693, 0.1073, -0.1769, 0.0373, 0.4870, 0.1666],
- [ 0.5991, -0.3642, 1.7881, 0.2127, -0.2926, 0.1899, 0.5110, 0.1492]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5746, -0.4153, 1.0917, -1.1620, -0.4037, -1.3082, 0.3234,
- 0.3267],
- [ 0.6454, -0.3960, 1.2764, 0.4470, -0.4210, -0.0842, 1.1057,
- 0.3585],
- [ 0.5807, -0.4378, 1.8249, -0.4691, -0.6289, -0.6385, 0.4104,
- 0.0620],
- [ 0.5532, -0.4264, 0.7626, -1.1466, -0.3979, -1.2928, 0.2494,
- 0.3808],
- [ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.5790, -0.3940, 1.8076, -0.3152, -0.6520, -0.3844, 0.4970,
- 0.3238],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.5482, -0.3841, 1.7326, 0.1005, -0.3517, 0.0620, 0.0912,
- 0.2522]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0595, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0595, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 11.450813557952642
- step: 84
- running loss: 0.13631920902324574
- Train Steps: 84/90 Loss: 0.1363 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.3983, 0.8555, 0.5429, 0.3842, 0.4370, 0.5866, 0.5398],
- [0.6163, 0.4001, 0.8788, 0.5033, 0.4012, 0.4633, 0.5338, 0.5767],
- [0.6127, 0.4084, 0.8700, 0.4467, 0.3987, 0.4317, 0.5013, 0.5471],
- [0.6193, 0.4108, 0.7438, 0.2700, 0.3650, 0.3683, 0.6238, 0.5717],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [ nan, nan, 0.8625, 0.2550, 0.5487, 0.2200, 0.7335, 0.5737],
- [0.6040, 0.4002, 0.7338, 0.2267, 0.3975, 0.2100, 0.5231, 0.4778]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0472, -0.1337, 1.8173, 0.4242, -0.5189, 0.1716, 0.5610, 0.1083],
- [ 0.8067, -0.3118, 1.8637, 0.3343, -0.3867, 0.3309, 0.5161, 0.1508],
- [ 0.3921, -0.5289, 1.6973, -0.0309, -0.4470, -0.0295, 0.2790, 0.2103],
- [ 0.4866, -0.4627, 1.3325, -0.6769, -0.5362, -0.4760, 0.4466, 0.1793],
- [ 0.3842, -0.5916, 1.5074, -0.8420, -0.4493, -0.9451, 0.6339, 0.0836],
- [-0.4744, -1.0623, 1.4592, -0.7937, -0.2200, -0.9876, 0.3152, 0.1966],
- [-0.6824, -1.2032, 1.6402, -0.9928, 0.0528, -1.1821, 0.6461, 0.1668],
- [-0.1324, -0.9212, 1.2158, -0.9944, -0.4907, -1.1888, 0.2543, 0.1415]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5786, -0.4463, 1.6655, 0.2216, -0.5115, -0.2675, 0.4236,
- 0.2075],
- [ 0.5610, -0.4381, 1.7730, 0.0390, -0.4326, -0.1458, 0.1794,
- 0.3777],
- [ 0.5441, -0.3997, 1.7326, -0.2228, -0.4441, -0.2921, 0.0296,
- 0.2409],
- [ 0.5747, -0.3886, 1.1494, -1.0388, -0.6000, -0.5846, 0.5952,
- 0.3546],
- [ 0.6042, -0.4225, 1.5420, -1.2082, -0.4730, -1.0311, 0.6380,
- -0.0220],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [-2.2859, -2.2859, 1.6979, -1.1081, 0.2487, -1.2697, 1.1020,
- 0.3638],
- [ 0.5037, -0.4375, 1.1032, -1.2390, -0.4499, -1.3159, 0.1301,
- -0.0791]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1343, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1343, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 11.58513193950057
- step: 85
- running loss: 0.1362956698764773
- Train Steps: 85/90 Loss: 0.1363 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6196, 0.4068, 0.7645, 0.2234, 0.4575, 0.1737, 0.5926, 0.5284],
- [0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6175, 0.3997, 0.6737, 0.2500, 0.4313, 0.1933, 0.5513, 0.5750],
- [0.6182, 0.3967, 0.8263, 0.3065, 0.3526, 0.4161, 0.6192, 0.5290],
- [0.6300, 0.4102, 0.9088, 0.4433, 0.4088, 0.3067, 0.6820, 0.5540],
- [ nan, nan, 0.8938, 0.2850, 0.4662, 0.3117, 0.7406, 0.5528],
- [0.6230, 0.4113, 0.7213, 0.1983, 0.4325, 0.2367, 0.6262, 0.5400],
- [0.6093, 0.3990, 0.8400, 0.4333, 0.3688, 0.4633, 0.5560, 0.5656]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-6.5229e-02, -8.8510e-01, 1.2439e+00, -9.6002e-01, -4.1075e-01,
- -1.1328e+00, 2.9228e-01, 1.5930e-01],
- [ 5.0083e-01, -4.7344e-01, 1.8331e+00, 1.2776e-01, -1.5367e-01,
- 1.6144e-01, 5.3884e-01, 1.9881e-01],
- [-5.0049e-01, -1.1383e+00, 1.0559e+00, -1.1379e+00, -4.0530e-01,
- -1.2439e+00, 2.0949e-01, 1.9963e-01],
- [ 8.3195e-01, -2.8182e-01, 1.5013e+00, -5.1990e-01, -6.1314e-01,
- -2.9477e-01, 6.0160e-01, 1.3491e-01],
- [ 3.4898e-01, -5.4086e-01, 1.8459e+00, -2.4536e-02, -3.6836e-01,
- -4.6690e-01, 5.0389e-01, 1.1189e-01],
- [-2.3398e-01, -9.3486e-01, 1.8301e+00, -6.1287e-01, -1.3023e-01,
- -8.4830e-01, 6.8406e-01, 1.4688e-01],
- [-2.9074e-01, -9.9740e-01, 1.3294e+00, -8.7116e-01, -3.7710e-01,
- -1.1082e+00, 3.2404e-01, 1.8692e-01],
- [ 9.3682e-01, -1.8088e-01, 1.6402e+00, 1.7329e-03, -5.3351e-01,
- 1.7422e-01, 5.1208e-01, 1.9848e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5761, -0.4070, 1.2452, -1.2541, -0.1725, -1.4835, 0.4511,
- 0.1545],
- [ 0.5371, -0.4252, 1.7037, -0.0688, -0.0342, 0.0620, 0.3758,
- 0.2853],
- [ 0.5663, -0.4396, 0.8261, -1.1312, -0.2940, -1.3929, 0.2603,
- 0.3700],
- [ 0.5697, -0.4538, 1.5308, -0.8703, -0.6572, -0.3639, 0.5739,
- 0.1576],
- [ 0.6240, -0.3912, 1.9115, -0.2382, -0.3979, -0.8694, 0.8644,
- 0.2730],
- [-2.2859, -2.2859, 1.8423, -0.9695, -0.1323, -0.8463, 1.1349,
- 0.2676],
- [ 0.5917, -0.3861, 1.0455, -1.3698, -0.2882, -1.1928, 0.6067,
- 0.2083],
- [ 0.5283, -0.4429, 1.5940, -0.2844, -0.5827, -0.1458, 0.2823,
- 0.3267]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1866, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1866, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 11.77172925695777
- step: 86
- running loss: 0.1368805727553229
- Train Steps: 86/90 Loss: 0.1369 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6189, 0.4049, 0.8888, 0.4417, 0.4213, 0.5200, 0.5988, 0.5633],
- [0.6274, 0.4117, 0.8100, 0.5801, 0.4000, 0.4583, 0.5582, 0.6118],
- [ nan, nan, 0.7097, 0.2346, 0.4250, 0.1850, 0.5175, 0.5583],
- [0.6129, 0.3930, 0.8769, 0.5105, 0.4146, 0.5642, 0.6000, 0.5082],
- [0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822],
- [0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117],
- [0.6122, 0.3993, 0.8738, 0.4667, 0.4517, 0.4879, 0.5155, 0.4927],
- [0.6219, 0.4114, 0.8175, 0.2817, 0.3925, 0.2783, 0.5900, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 4.4188e-01, -5.1116e-01, 1.7300e+00, -2.0806e-01, -3.4683e-01,
- -4.7736e-02, 4.7247e-01, 2.4311e-01],
- [ 7.5987e-01, -3.2086e-01, 1.5680e+00, -9.0686e-04, -4.7657e-01,
- -1.4873e-01, 4.9563e-01, 2.3884e-01],
- [-9.0501e-01, -1.4015e+00, 1.0909e+00, -1.4329e+00, -3.3467e-01,
- -1.6594e+00, 2.6124e-01, 1.6862e-01],
- [ 6.2424e-01, -4.0011e-01, 1.6721e+00, -4.6716e-02, -2.9070e-01,
- 3.5843e-02, 5.2984e-01, 2.0643e-01],
- [ 2.5168e-01, -6.7213e-01, 1.5804e+00, -4.8583e-01, -5.6401e-01,
- -7.9968e-01, 3.3252e-01, 1.5927e-01],
- [ 7.6404e-01, -3.3559e-01, 1.7661e+00, -1.6401e-01, -4.4549e-01,
- -1.6386e-01, 6.5402e-01, 1.4741e-01],
- [ 3.2009e-01, -6.1624e-01, 1.7472e+00, -2.7111e-01, -2.9466e-01,
- -3.7289e-01, 5.0791e-01, 1.5344e-01],
- [-7.9375e-01, -1.3188e+00, 1.3903e+00, -1.2725e+00, -2.4440e-01,
- -1.5361e+00, 3.7710e-01, 1.7771e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5726, -0.4159, 1.8192, -0.2459, -0.3402, 0.1159, 0.4797,
- 0.3161],
- [ 0.6121, -0.3844, 1.4556, 0.3936, -0.4383, -0.1689, 0.2925,
- 0.5401],
- [-2.2859, -2.2859, 0.9922, -1.2021, -0.3229, -1.4314, 0.1044,
- 0.2930],
- [ 0.5450, -0.4706, 1.7643, 0.0722, -0.3708, 0.3200, 0.4854,
- 0.0612],
- [ 0.5697, -0.4113, 1.7499, -0.2767, -0.6404, -0.7155, 0.1576,
- 0.4032],
- [ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774],
- [ 0.5417, -0.4417, 1.7499, -0.1304, -0.1994, -0.0324, 0.0951,
- -0.0099],
- [ 0.5868, -0.3858, 1.4901, -0.9849, -0.4730, -1.0003, 0.4393,
- 0.1852]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1162, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1162, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 11.88794718310237
- step: 87
- running loss: 0.13664307107014217
- Train Steps: 87/90 Loss: 0.1366 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6185, 0.4042, 0.7700, 0.2250, 0.4062, 0.2117, 0.5763, 0.5150],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6037, 0.4020, 0.8300, 0.4033, 0.3575, 0.4883, 0.5647, 0.5631],
- [0.6267, 0.4080, 0.8438, 0.2633, 0.4763, 0.1800, 0.6259, 0.5240],
- [0.6274, 0.4117, 0.8100, 0.5801, 0.4000, 0.4583, 0.5582, 0.6118],
- [0.6180, 0.4064, 0.7200, 0.2467, 0.4275, 0.2367, 0.5963, 0.6217],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6296, 0.4045, 0.9138, 0.4100, 0.4232, 0.4242, 0.7422, 0.5297]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.4403, -1.1100, 1.3071, -1.0556, -0.4015, -1.3357, 0.2999, 0.1572],
- [-0.4792, -1.1614, 1.2105, -1.1959, -0.3678, -1.4565, 0.3273, 0.1607],
- [ 0.5990, -0.3910, 1.5966, -0.3210, -0.4170, 0.0495, 0.5252, 0.2310],
- [-0.2928, -1.0263, 1.6465, -0.8846, -0.2008, -1.1616, 0.5395, 0.1867],
- [ 0.7680, -0.3232, 1.6906, 0.2699, -0.4037, 0.1270, 0.4906, 0.2611],
- [ 0.1373, -0.7692, 1.2821, -0.8456, -0.4277, -0.9613, 0.3662, 0.2293],
- [ 0.2150, -0.6974, 1.6454, -0.4520, -0.5265, -0.5073, 0.3394, 0.2385],
- [ 0.9976, -0.1960, 1.7188, -0.2152, -0.4970, -0.1576, 0.6804, 0.1570]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5707, -0.4189, 1.2707, -1.2467, -0.4095, -1.3082, 0.3758,
- 0.0928],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.5027, -0.4289, 1.5478, -0.4229, -0.6346, -0.0303, 0.3223,
- 0.3148],
- [ 0.6088, -0.4015, 1.6113, -1.0696, -0.0861, -1.4545, 0.6051,
- 0.1343],
- [ 0.6121, -0.3844, 1.4556, 0.3936, -0.4383, -0.1689, 0.2925,
- 0.5401],
- [ 0.5687, -0.4088, 1.0397, -1.1466, -0.3113, -1.1928, 0.4681,
- 0.5855],
- [ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.6224, -0.4177, 1.9346, -0.3921, -0.3314, -0.3264, 1.1422,
- 0.1608]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1034, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1034, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 11.991319220513105
- step: 88
- running loss: 0.13626499114219437
- Train Steps: 88/90 Loss: 0.1363 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6246, 0.4008, 0.8757, 0.5088, 0.4101, 0.5392, 0.6644, 0.5133],
- [0.6199, 0.4112, 0.8475, 0.3717, 0.3550, 0.4350, 0.6063, 0.6083],
- [0.6081, 0.3950, 0.8538, 0.4667, 0.3850, 0.4917, 0.5342, 0.4954],
- [0.6117, 0.4019, 0.8538, 0.4067, 0.3513, 0.3583, 0.5663, 0.5133],
- [0.6182, 0.3930, 0.8841, 0.3892, 0.3556, 0.4967, 0.6222, 0.5279],
- [0.6101, 0.3977, 0.8550, 0.5667, 0.3862, 0.4417, 0.5128, 0.4869],
- [0.6276, 0.4120, 0.8738, 0.3133, 0.4225, 0.2217, 0.6203, 0.4892],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4388, -0.5066, 1.5570, -0.0962, -0.3215, -0.0344, 0.4251, 0.2929],
- [ 0.4409, -0.5333, 1.5423, -0.5185, -0.5618, -0.4258, 0.3752, 0.2538],
- [ 0.4461, -0.5290, 1.4972, -0.4042, -0.4689, -0.3502, 0.4340, 0.2296],
- [ 0.1839, -0.7194, 1.4752, -0.6965, -0.6311, -0.9276, 0.2914, 0.2059],
- [ 0.3816, -0.5760, 1.4603, -0.5821, -0.4492, -0.2598, 0.5169, 0.2633],
- [ 0.4607, -0.5290, 1.5899, -0.0559, -0.5009, -0.4150, 0.3185, 0.2172],
- [-0.2723, -1.0297, 1.5811, -0.9711, -0.3396, -1.3682, 0.4855, 0.1946],
- [-0.1325, -0.9935, 1.5606, -1.2217, -0.1039, -1.4336, 0.7863, 0.1774]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5993, -0.4345, 1.7587, 0.0641, -0.3917, 0.2048, 0.7827,
- 0.0852],
- [ 0.5774, -0.3868, 1.6286, -0.5692, -0.6462, -0.2767, 0.5143,
- 0.5239],
- [ 0.5227, -0.4615, 1.6575, -0.1304, -0.5076, -0.0149, 0.1815,
- 0.0021],
- [ 0.5393, -0.4294, 1.6575, -0.4075, -0.6635, -0.6308, 0.3296,
- 0.0851],
- [ 0.5697, -0.4706, 1.7976, -0.4884, -0.6433, 0.0081, 0.5878,
- 0.1525],
- [ 0.5320, -0.4488, 1.6633, 0.3315, -0.5018, -0.2459, 0.0828,
- -0.0370],
- [ 0.6128, -0.3828, 1.7499, -0.8386, -0.3344, -1.2620, 0.5792,
- -0.0263],
- [ 0.6207, -0.3936, 1.7788, -1.1235, 0.1448, -1.0850, 1.1459,
- 0.1982]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0703, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0703, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 12.061648171395063
- step: 89
- running loss: 0.1355241367572479
- Train Steps: 89/90 Loss: 0.1355 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6199, 0.4071, 0.7600, 0.2117, 0.4037, 0.2767, 0.6138, 0.5550],
- [0.6102, 0.4020, 0.8638, 0.3717, 0.3625, 0.5017, 0.6038, 0.5500],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [0.6277, 0.4036, 0.8688, 0.3617, 0.3925, 0.2600, 0.6132, 0.4742],
- [0.6122, 0.3993, 0.8738, 0.4667, 0.4517, 0.4879, 0.5155, 0.4927]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6735, -0.3933, 1.4704, -0.5424, -0.5828, -0.4557, 0.4849, 0.2398],
- [ 0.2983, -0.6639, 1.7263, -0.3416, -0.2465, -0.5271, 0.5216, 0.2376],
- [-0.2158, -0.9857, 1.3755, -1.1546, -0.3586, -1.3447, 0.3959, 0.2534],
- [ 0.5483, -0.4424, 1.4602, -0.5501, -0.4750, -0.1833, 0.5094, 0.3046],
- [ 0.3909, -0.5643, 1.7396, -0.2520, -0.2310, -0.3395, 0.5488, 0.2893],
- [-0.4585, -1.1134, 1.1996, -1.0510, -0.5404, -1.2248, 0.1798, 0.2508],
- [ 0.7076, -0.4017, 1.5375, -0.7291, -0.5402, -0.9808, 0.5706, 0.1742],
- [ 0.3275, -0.6220, 1.6696, -0.2631, -0.3304, -0.3368, 0.3925, 0.2430]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5367, -0.4294, 1.5709, -0.4999, -0.6693, -0.3075, 0.2455,
- 0.3559],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.5775, -0.4054, 1.2245, -1.3082, -0.4210, -1.0080, 0.5490,
- 0.2776],
- [ 0.5324, -0.4293, 1.7037, -0.5692, -0.6115, 0.0313, 0.5028,
- 0.2545],
- [ 0.5598, -0.4273, 1.7152, -0.1227, -0.0065, 0.1917, 0.5143,
- 0.2853],
- [-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837],
- [ 0.6134, -0.4218, 1.7268, -0.6154, -0.4730, -1.0850, 0.5463,
- -0.0957],
- [ 0.5417, -0.4417, 1.7499, -0.1304, -0.1994, -0.0324, 0.0951,
- -0.0099]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1250, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1250, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 12.186646927148104
- step: 90
- running loss: 0.13540718807942337
- Valid Steps: 10/10 Loss: nan 7.3225
- --------------------------------------------------
- Epoch: 1 Train Loss: 0.1354 Valid Loss: nan
- --------------------------------------------------
- size of train loader is: 90
- torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6179, 0.3961, 0.8347, 0.6020, 0.3887, 0.4624, 0.5714, 0.5373],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6138, 0.4054, 0.8750, 0.4750, 0.4363, 0.5017, 0.5086, 0.5822],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6197, 0.4050, 0.7527, 0.2000, 0.4042, 0.2249, 0.5895, 0.4995],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6097, 0.3988, 0.8650, 0.5250, 0.4213, 0.5200, 0.5675, 0.5050],
- [0.6336, 0.4154, 0.8900, 0.2767, 0.4988, 0.2867, 0.7422, 0.5540]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 7.1007e-01, -3.5610e-01, 1.6399e+00, 6.3848e-02, -5.3525e-01,
- -1.6645e-01, 3.9725e-01, 2.2814e-01],
- [ 5.5852e-02, -7.9860e-01, 1.1793e+00, -1.0058e+00, -5.5704e-01,
- -1.0473e+00, 2.7695e-01, 2.8426e-01],
- [ 3.4867e-01, -5.3798e-01, 1.6127e+00, -2.1721e-01, -3.6062e-01,
- -9.5872e-02, 3.0168e-01, 2.9245e-01],
- [ 1.2011e-01, -7.8341e-01, 1.5370e+00, -1.1360e+00, -2.5911e-01,
- -1.2756e+00, 6.6877e-01, 1.9165e-01],
- [ 2.9767e-01, -6.7336e-01, 1.2427e+00, -1.1123e+00, -5.4911e-01,
- -1.2077e+00, 3.5533e-01, 1.8474e-01],
- [ 2.9968e-01, -6.2822e-01, 1.5641e+00, -6.2807e-01, -5.8927e-01,
- -5.5374e-01, 3.0163e-01, 2.9457e-01],
- [ 5.1429e-01, -4.5790e-01, 1.7073e+00, 6.4440e-03, -3.9337e-01,
- 4.3100e-04, 3.4778e-01, 2.6891e-01],
- [ 2.0770e-01, -7.3136e-01, 1.6545e+00, -9.7071e-01, -1.7980e-01,
- -1.0599e+00, 7.4962e-01, 2.0022e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5680, -0.4562, 1.5697, 0.4947, -0.4904, -0.1503, 0.3536,
- 0.1956],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391],
- [ 0.5491, -0.4132, 1.7557, -0.0919, -0.2709, 0.0313, 0.0635,
- 0.4032],
- [ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.5762, -0.4153, 1.1908, -1.3622, -0.4190, -1.2471, 0.4368,
- 0.0213],
- [ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.5303, -0.4440, 1.7095, 0.1390, -0.3402, 0.1159, 0.3353,
- 0.0467],
- [ 0.6405, -0.3671, 1.8249, -1.0080, 0.0178, -0.9618, 1.1422,
- 0.2730]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0426, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0426, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.04259268939495087
- step: 1
- running loss: 0.04259268939495087
- Train Steps: 1/90 Loss: 0.0426 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6157, 0.4102, 0.8513, 0.3817, 0.3613, 0.3667, 0.5096, 0.5890],
- [0.6084, 0.3981, 0.8588, 0.5233, 0.4600, 0.5367, 0.5680, 0.5006],
- [0.6146, 0.4124, 0.8800, 0.4867, 0.3800, 0.4500, 0.5106, 0.5524],
- [0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [0.6185, 0.4129, 0.8900, 0.4567, 0.3937, 0.5417, 0.5734, 0.5110],
- [0.6276, 0.4235, 0.8888, 0.5333, 0.3800, 0.3117, 0.5427, 0.6164],
- [0.6138, 0.4054, 0.8750, 0.4750, 0.4363, 0.5017, 0.5086, 0.5822],
- [ nan, nan, 0.7192, 0.2346, 0.4037, 0.2050, 0.5138, 0.5650]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4878, -0.4826, 1.4859, -0.7712, -0.6467, -0.7535, 0.3544, 0.2405],
- [ 0.6440, -0.4132, 1.7307, -0.1418, -0.2576, -0.2243, 0.4596, 0.2307],
- [ 0.8317, -0.2595, 1.6605, -0.2190, -0.5446, -0.2266, 0.4281, 0.2645],
- [-0.1442, -0.9106, 1.3206, -1.2534, -0.4152, -1.3048, 0.3776, 0.2055],
- [ 0.4407, -0.5200, 1.7622, -0.3675, -0.3601, -0.1808, 0.4552, 0.2645],
- [ 0.8585, -0.2477, 1.6806, -0.0893, -0.4960, -0.5336, 0.4715, 0.2489],
- [ 0.4703, -0.4759, 1.6254, -0.3660, -0.3193, -0.2449, 0.3914, 0.2745],
- [-0.6149, -1.2198, 1.1038, -1.5154, -0.3264, -1.5851, 0.3474, 0.1998]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5581, -0.3912, 1.6460, -0.5230, -0.6173, -0.5923, 0.0681,
- 0.4348],
- [ 0.5241, -0.4470, 1.6806, 0.1313, -0.1612, 0.1929, 0.3378,
- 0.0261],
- [ 0.5529, -0.3811, 1.7788, -0.0380, -0.5307, -0.2074, 0.0727,
- 0.2657],
- [ 0.5695, -0.3927, 1.3226, -0.9002, -0.4672, -1.1928, 0.1737,
- 0.3700],
- [ 0.5711, -0.3788, 1.8249, -0.1766, -0.4672, 0.2160, 0.3625,
- 0.0742],
- [ 0.6131, -0.3299, 1.8192, 0.1775, -0.5307, -0.8463, 0.2206,
- 0.5612],
- [ 0.5491, -0.4132, 1.7557, -0.0919, -0.2709, 0.0313, 0.0635,
- 0.4032],
- [-2.2859, -2.2859, 1.0361, -1.2021, -0.4210, -1.3390, 0.0871,
- 0.3238]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1098, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1098, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.15242556482553482
- step: 2
- running loss: 0.07621278241276741
- Train Steps: 2/90 Loss: 0.0762 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412],
- [0.6311, 0.3998, 0.7975, 0.5767, 0.3838, 0.4850, 0.7327, 0.5343],
- [0.6186, 0.4013, 0.8191, 0.3188, 0.4279, 0.2060, 0.5767, 0.5411],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6115, 0.3998, 0.7063, 0.2383, 0.4037, 0.1950, 0.5320, 0.4993]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2055, -0.6563, 1.3950, -0.9640, -0.4633, -0.9175, 0.3805, 0.2383],
- [ 0.4255, -0.4887, 1.5916, -0.6542, -0.4395, -0.7613, 0.4005, 0.2200],
- [ 0.5741, -0.4211, 1.5171, -0.2034, -0.3918, -0.0850, 0.3576, 0.2748],
- [ 0.2696, -0.5973, 1.3929, -0.9257, -0.4207, -0.9935, 0.3744, 0.2466],
- [ 0.5596, -0.4324, 1.7364, -0.3859, -0.4203, -0.5341, 0.4266, 0.2516],
- [ 0.6552, -0.3283, 1.7068, -0.2231, -0.4293, -0.4643, 0.4657, 0.2142],
- [ 0.3758, -0.5187, 1.6616, -0.1487, -0.2192, 0.0357, 0.3985, 0.2725],
- [ 0.1727, -0.7338, 1.0763, -1.1915, -0.5032, -1.1693, 0.2439, 0.2383]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6966e-01, -4.4416e-01, 1.3529e+00, -9.5152e-01, -5.7742e-01,
- -7.8011e-01, 5.2533e-01, 1.9310e-01],
- [ 5.7841e-01, -4.0062e-01, 1.7911e+00, -5.7008e-01, -5.1916e-01,
- -1.0331e+00, 4.1374e-01, 2.1391e-01],
- [ 6.2895e-01, -4.3934e-01, 1.3977e+00, 3.7768e-01, -5.1339e-01,
- -4.5727e-02, 1.0984e+00, 1.8214e-01],
- [ 5.7131e-01, -4.3212e-01, 1.4975e+00, -8.1340e-01, -3.0942e-01,
- -1.3345e+00, 3.7786e-01, 2.1339e-01],
- [ 6.0935e-01, -3.9469e-01, 1.8885e+00, -2.9977e-01, -5.7691e-01,
- -6.7698e-01, 6.0670e-01, 1.0054e-01],
- [ 6.5201e-01, -3.6231e-01, 1.8885e+00, 3.1255e-02, -5.5381e-01,
- -5.3841e-01, 6.9257e-01, 1.6611e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 5.3837e-01, -4.3934e-01, 9.7621e-01, -1.1851e+00, -4.2102e-01,
- -1.3852e+00, 1.7122e-01, 2.0118e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0365, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0365, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.1889284811913967
- step: 3
- running loss: 0.06297616039713223
- Train Steps: 3/90 Loss: 0.0630 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6154, 0.4112, 0.7037, 0.2317, 0.4238, 0.1833, 0.5350, 0.5600],
- [0.6114, 0.4018, 0.7213, 0.1967, 0.3763, 0.2700, 0.5875, 0.5533],
- [0.6193, 0.4108, 0.7425, 0.2350, 0.3887, 0.2750, 0.5900, 0.5717],
- [0.6037, 0.4020, 0.8300, 0.4033, 0.3575, 0.4883, 0.5647, 0.5631],
- [0.6339, 0.4112, 0.8838, 0.5067, 0.4000, 0.5433, 0.7549, 0.5378],
- [0.6147, 0.4107, 0.8137, 0.3333, 0.3750, 0.2683, 0.5006, 0.5412],
- [0.6286, 0.4097, 0.8107, 0.2414, 0.4425, 0.2483, 0.6745, 0.5385]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9354, -0.1689, 1.7982, -0.0957, -0.5106, -0.0445, 0.4831, 0.2091],
- [ 0.2110, -0.6863, 1.2094, -1.0299, -0.4531, -1.1977, 0.2558, 0.2339],
- [ 0.4777, -0.5063, 1.2934, -0.9708, -0.5087, -0.9821, 0.3245, 0.2426],
- [ 0.2455, -0.6200, 1.3273, -0.9011, -0.4809, -0.9806, 0.2810, 0.2534],
- [ 0.4969, -0.4011, 1.6330, -0.3698, -0.3892, -0.0085, 0.3747, 0.2756],
- [ 0.8093, -0.2633, 1.9032, 0.2491, -0.2979, 0.3559, 0.4084, 0.2968],
- [ 0.1955, -0.6490, 1.4015, -0.7826, -0.4914, -0.8720, 0.2076, 0.2256],
- [ 0.4164, -0.5204, 1.6887, -0.7912, -0.2706, -1.0754, 0.5404, 0.1997]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.5565, -0.3864, 0.9647, -1.2159, -0.3286, -1.4391, 0.1852,
- 0.3007],
- [ 0.5380, -0.4302, 1.0455, -1.3775, -0.5480, -1.0388, 0.4277,
- 0.2699],
- [ 0.5746, -0.3882, 1.1436, -1.2005, -0.4903, -1.0157, 0.4393,
- 0.3546],
- [ 0.5027, -0.4289, 1.5478, -0.4229, -0.6346, -0.0303, 0.3223,
- 0.3148],
- [ 0.6421, -0.3864, 1.7961, 0.0543, -0.4383, 0.2237, 1.2007,
- 0.1982],
- [ 0.5532, -0.3888, 1.4727, -0.7463, -0.5538, -1.0465, 0.0265,
- 0.2138],
- [ 0.6174, -0.3936, 1.4586, -1.1709, -0.2420, -1.1389, 0.8296,
- 0.2012]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0430, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0430, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.23190448060631752
- step: 4
- running loss: 0.05797612015157938
- Train Steps: 4/90 Loss: 0.0580 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6161, 0.4076, 0.8900, 0.4667, 0.4125, 0.5917, 0.6262, 0.5367],
- [0.6196, 0.4094, 0.7562, 0.2817, 0.3937, 0.3183, 0.6013, 0.6183],
- [0.6037, 0.4020, 0.8300, 0.4033, 0.3575, 0.4883, 0.5647, 0.5631],
- [0.6140, 0.4034, 0.8850, 0.4317, 0.4288, 0.5067, 0.5825, 0.5533],
- [0.6093, 0.3990, 0.8400, 0.4333, 0.3688, 0.4633, 0.5560, 0.5656],
- [0.6264, 0.4055, 0.8425, 0.2767, 0.4425, 0.2767, 0.7050, 0.5586],
- [0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301],
- [0.6157, 0.3956, 0.8323, 0.4138, 0.3479, 0.4431, 0.5914, 0.5131]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7001, -0.2766, 1.6908, -0.1484, -0.3457, -0.0808, 0.4303, 0.2640],
- [ 0.4341, -0.5333, 1.4638, -0.8986, -0.4397, -1.1506, 0.3810, 0.2546],
- [ 0.4200, -0.4447, 1.4699, -0.5509, -0.4799, -0.3041, 0.3056, 0.2709],
- [ 0.5279, -0.4039, 1.6207, -0.2760, -0.3859, -0.1375, 0.2687, 0.2574],
- [ 0.6383, -0.3270, 1.5034, -0.3866, -0.5343, -0.3790, 0.2957, 0.2652],
- [ 0.2794, -0.6039, 1.6532, -1.1591, -0.1907, -1.4350, 0.7113, 0.1612],
- [ 0.4900, -0.4513, 1.6026, -0.0751, -0.3859, -0.1690, 0.2521, 0.2527],
- [ 0.6725, -0.3149, 1.4706, -0.6186, -0.5776, -0.5253, 0.3481, 0.2127]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5598, -0.4032, 1.8249, -0.1304, -0.3806, 0.4470, 0.6067,
- 0.1929],
- [ 0.5759, -0.3948, 1.2072, -0.9849, -0.4672, -0.8156, 0.4912,
- 0.5701],
- [ 0.5027, -0.4289, 1.5478, -0.4229, -0.6346, -0.0303, 0.3223,
- 0.3148],
- [ 0.5499, -0.4225, 1.8018, -0.2921, -0.3055, 0.0543, 0.4046,
- 0.2699],
- [ 0.5283, -0.4429, 1.5940, -0.2844, -0.5827, -0.1458, 0.2823,
- 0.3267],
- [ 0.6075, -0.4129, 1.6055, -1.0080, -0.2420, -1.0080, 0.9704,
- 0.2944],
- [ 0.6010, -0.4562, 1.7198, -0.0090, -0.3464, 0.0108, 0.6294,
- 0.1627],
- [ 0.5581, -0.4586, 1.5586, -0.3747, -0.6792, -0.2391, 0.4455,
- 0.0840]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0299, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0299, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.26177416928112507
- step: 5
- running loss: 0.05235483385622501
- Train Steps: 5/90 Loss: 0.0524 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6263, 0.4030, 0.9000, 0.4767, 0.3800, 0.5167, 0.6415, 0.4771],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6225, 0.4116, 0.8662, 0.3517, 0.3663, 0.3233, 0.5837, 0.5317],
- [0.6127, 0.4119, 0.8650, 0.5300, 0.4512, 0.5250, 0.5260, 0.5461],
- [0.6263, 0.4057, 0.8800, 0.3833, 0.3650, 0.3717, 0.6375, 0.4804],
- [ nan, nan, 0.6992, 0.2791, 0.3950, 0.2383, 0.5483, 0.5819],
- [0.6092, 0.4001, 0.8638, 0.4867, 0.4288, 0.5367, 0.5484, 0.5064],
- [0.6152, 0.4131, 0.6863, 0.2567, 0.3625, 0.3300, 0.5765, 0.5305]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5591, -0.3862, 1.8141, -0.1409, -0.3558, -0.0230, 0.4684, 0.1989],
- [ 0.7180, -0.3285, 1.7612, 0.0262, -0.4315, -0.1326, 0.3433, 0.2333],
- [ 0.6214, -0.3498, 1.5698, -0.8629, -0.4971, -1.0170, 0.4340, 0.2186],
- [ 0.7594, -0.2548, 1.7353, 0.0515, -0.2798, -0.1146, 0.3592, 0.2564],
- [ 0.7539, -0.2979, 1.6140, -0.6767, -0.5285, -0.6289, 0.5362, 0.1763],
- [-0.0267, -0.8053, 1.0535, -1.2585, -0.4485, -1.2836, 0.2414, 0.2479],
- [ 0.6017, -0.3277, 1.7198, -0.1452, -0.3064, -0.0624, 0.3880, 0.2317],
- [ 0.4863, -0.4454, 1.1515, -1.0345, -0.5895, -0.9306, 0.2529, 0.2732]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6072, -0.4247, 1.8711, -0.0842, -0.5307, 0.1005, 0.6771,
- -0.0821],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.5893, -0.3847, 1.7152, -0.6616, -0.5942, -0.7925, 0.4104,
- 0.1698],
- [ 0.5443, -0.3831, 1.7095, 0.1621, -0.2016, 0.1390, 0.1437,
- 0.2364],
- [ 0.6071, -0.4119, 1.7788, -0.5153, -0.6000, -0.5692, 0.6586,
- -0.0670],
- [-2.2859, -2.2859, 0.9438, -0.9967, -0.4614, -1.1851, 0.2468,
- 0.4019],
- [ 0.5278, -0.4377, 1.7037, -0.0380, -0.3055, 0.1929, 0.2473,
- 0.0532],
- [ 0.5557, -0.3779, 0.8838, -1.1004, -0.6115, -0.7617, 0.3769,
- 0.1644]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1336, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1336, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.39533588476479053
- step: 6
- running loss: 0.06588931412746508
- Train Steps: 6/90 Loss: 0.0659 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6277, 0.4057, 0.8300, 0.2650, 0.4363, 0.1850, 0.6140, 0.4823],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679],
- [0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [0.6125, 0.3974, 0.7725, 0.2517, 0.3538, 0.3317, 0.5887, 0.5500],
- [0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650],
- [0.6289, 0.4081, 0.8720, 0.3487, 0.3900, 0.3183, 0.6703, 0.5376],
- [0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117],
- [0.6030, 0.3969, 0.7988, 0.3917, 0.3450, 0.3667, 0.5266, 0.4700]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5827, -0.4379, 1.4441, -1.1022, -0.3418, -1.2180, 0.5851, 0.1748],
- [ 0.6353, -0.2988, 1.7405, 0.2265, -0.2744, 0.2779, 0.4264, 0.2445],
- [ 0.6568, -0.3217, 1.5801, -0.5005, -0.5412, -0.4082, 0.3244, 0.2201],
- [ 0.4810, -0.4567, 1.2555, -0.9256, -0.5145, -0.7963, 0.3975, 0.2130],
- [ 0.6530, -0.3305, 1.5795, -0.3256, -0.5694, -0.2815, 0.2549, 0.2598],
- [ 0.6139, -0.3530, 1.6029, -0.5732, -0.3805, -0.8116, 0.4547, 0.2125],
- [ 0.3853, -0.4860, 1.6758, -0.1242, -0.3653, -0.0897, 0.4411, 0.2086],
- [ 0.5693, -0.3776, 1.4477, -0.5859, -0.5408, -0.5223, 0.3565, 0.1967]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6132, -0.4122, 1.5478, -1.0619, -0.2709, -1.4314, 0.5500,
- -0.0583],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371],
- [ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [ 0.5430, -0.4503, 1.2822, -1.1235, -0.6520, -0.7540, 0.4335,
- 0.2545],
- [ 0.5757, -0.3917, 1.7095, -0.4768, -0.6346, -0.4229, 0.3931,
- 0.3238],
- [ 0.6191, -0.4008, 1.7420, -0.6753, -0.4845, -0.8156, 0.8101,
- 0.1974],
- [ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774],
- [ 0.4992, -0.4525, 1.4035, -0.4768, -0.6924, -0.5923, 0.1465,
- -0.1151]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0207, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0207, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4160845186561346
- step: 7
- running loss: 0.05944064552230494
- Train Steps: 7/90 Loss: 0.0594 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6239, 0.4206, 0.8750, 0.5400, 0.3688, 0.4850, 0.5737, 0.5700],
- [0.6261, 0.3987, 0.8688, 0.4917, 0.4300, 0.5333, 0.7010, 0.5309],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114],
- [0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7021, -0.3496, 1.2926, -1.1388, -0.4333, -1.1794, 0.4480, 0.1941],
- [ 0.9963, -0.1069, 1.7337, 0.1366, -0.6265, -0.0216, 0.4571, 0.1893],
- [ 0.4696, -0.4188, 1.6972, -0.1467, -0.3254, 0.0139, 0.5288, 0.1844],
- [ 0.4513, -0.4183, 1.4989, -0.2108, -0.4803, -0.1647, 0.3797, 0.1903],
- [ 0.6194, -0.3489, 1.4720, -0.8228, -0.5840, -0.7738, 0.4798, 0.2157],
- [ 0.3232, -0.5147, 1.6214, -0.3481, -0.2429, -0.1752, 0.4896, 0.2023],
- [ 0.4407, -0.4461, 1.6146, -0.1038, -0.3282, -0.0575, 0.4005, 0.1977],
- [ 0.4908, -0.4550, 1.4114, -0.8419, -0.5530, -0.9475, 0.3662, 0.1874]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850],
- [ 0.5960, -0.3430, 1.7557, 0.2083, -0.5827, -0.0457, 0.3642,
- 0.3469],
- [ 0.6059, -0.4442, 1.7268, -0.0149, -0.2998, 0.1775, 0.9521,
- 0.1661],
- [ 0.6191, -0.4297, 1.4612, 0.2391, -0.4961, 0.0313, 1.1166,
- 0.1768],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764],
- [ 0.5711, -0.4015, 1.6979, -0.6770, -0.5365, -1.0619, 0.1712,
- 0.1494]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0386, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0386, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4546631034463644
- step: 8
- running loss: 0.05683288793079555
- Train Steps: 8/90 Loss: 0.0568 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6122, 0.4048, 0.7506, 0.3014, 0.3475, 0.3333, 0.5487, 0.5749],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917],
- [0.6286, 0.4055, 0.9000, 0.4717, 0.3763, 0.4683, 0.7018, 0.5494],
- [0.6257, 0.4060, 0.8300, 0.2333, 0.4688, 0.2583, 0.7050, 0.5633],
- [0.6203, 0.4056, 0.8942, 0.4086, 0.3643, 0.3617, 0.5917, 0.5482],
- [0.6263, 0.4029, 0.8900, 0.4933, 0.4075, 0.5183, 0.6406, 0.4758],
- [0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300],
- [0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2553, -0.5819, 1.1939, -0.8524, -0.5313, -0.6417, 0.3589, 0.1984],
- [ 0.7735, -0.2880, 1.4316, -0.4455, -0.6361, -0.5807, 0.2920, 0.2505],
- [ 0.8888, -0.1807, 1.8123, 0.0572, -0.5599, 0.0372, 0.5111, 0.1406],
- [ 0.5415, -0.4451, 1.6313, -0.9964, -0.1940, -1.0505, 0.8056, 0.1340],
- [ 0.7210, -0.2990, 1.6302, -0.2831, -0.5936, -0.3882, 0.4315, 0.1563],
- [ 0.4772, -0.4362, 1.7029, 0.0759, -0.3797, 0.1898, 0.4535, 0.1590],
- [ 0.4588, -0.4792, 1.2327, -0.9956, -0.4738, -0.8281, 0.4887, 0.1862],
- [ 0.5360, -0.3870, 1.7055, -0.0526, -0.3721, 0.0659, 0.5101, 0.1510]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5419, -0.4160, 1.1810, -0.8939, -0.6808, -0.7463, 0.2485,
- 0.3694],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470],
- [ 0.6174, -0.4129, 1.8711, -0.1073, -0.5480, -0.1227, 0.9558,
- 0.2516],
- [ 0.6042, -0.4105, 1.5478, -1.2082, -0.1208, -1.0927, 0.9704,
- 0.3157],
- [ 0.5791, -0.4127, 1.8442, -0.3985, -0.6031, -0.6154, 0.4473,
- 0.2464],
- [ 0.6072, -0.4250, 1.8249, -0.0072, -0.4037, 0.1082, 0.6730,
- -0.0881],
- [ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621],
- [ 0.5604, -0.4620, 1.7961, -0.1997, -0.3229, 0.1082, 0.4104,
- 0.2545]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0231, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0231, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4777647405862808
- step: 9
- running loss: 0.05308497117625342
- Train Steps: 9/90 Loss: 0.0531 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6339, 0.4159, 0.8400, 0.5617, 0.3825, 0.4150, 0.7343, 0.5748],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083],
- [0.6190, 0.4135, 0.8000, 0.4883, 0.3566, 0.3647, 0.5613, 0.5900],
- [0.6264, 0.4049, 0.8988, 0.4633, 0.3813, 0.4983, 0.6326, 0.4843],
- [0.6264, 0.4055, 0.8425, 0.2767, 0.4425, 0.2767, 0.7050, 0.5586],
- [0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505],
- [ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3739, -0.5290, 1.7073, -0.4004, -0.4418, -0.0480, 0.6693, 0.1278],
- [ 0.5947, -0.3743, 1.6106, -0.0960, -0.4672, -0.2712, 0.5061, 0.1534],
- [ 0.6945, -0.3319, 1.6409, 0.2558, -0.4384, 0.1308, 0.4694, 0.1777],
- [ 0.9772, -0.1057, 1.5562, -0.1561, -0.7040, -0.3083, 0.3698, 0.1888],
- [ 0.6909, -0.3311, 1.8099, -0.0290, -0.5085, 0.1348, 0.5433, 0.1238],
- [ 0.5375, -0.4298, 1.6000, -1.0680, -0.1836, -0.9936, 0.8623, 0.1370],
- [ 0.7472, -0.2899, 1.4580, -0.5038, -0.6770, -0.3817, 0.3422, 0.2079],
- [-0.0050, -0.7495, 1.0129, -1.3194, -0.4006, -1.1948, 0.3865, 0.2216]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.6421, -0.3647, 1.5940, 0.3084, -0.5192, -0.3691, 1.1057,
- 0.3692],
- [ 0.6134, -0.3910, 1.4497, 0.3546, -0.3517, -0.0919, 0.3296,
- 0.5239],
- [ 0.5732, -0.3761, 1.4092, -0.0303, -0.6388, -0.6012, 0.3065,
- 0.4393],
- [ 0.6075, -0.4159, 1.8654, -0.1458, -0.5249, 0.0159, 0.6359,
- -0.0490],
- [ 0.6075, -0.4129, 1.6055, -1.0080, -0.2420, -1.0080, 0.9704,
- 0.2944],
- [ 0.5433, -0.4032, 1.6344, -0.4922, -0.5769, -0.5846, 0.0357,
- 0.2567],
- [-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1472, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1472, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6249740868806839
- step: 10
- running loss: 0.06249740868806839
- Train Steps: 10/90 Loss: 0.0625 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679],
- [0.6202, 0.4066, 0.8746, 0.3376, 0.3717, 0.3090, 0.5842, 0.5165],
- [0.6293, 0.4024, 0.8750, 0.5000, 0.4012, 0.5733, 0.7121, 0.5633],
- [ nan, nan, 0.6488, 0.1817, 0.4325, 0.1867, 0.5475, 0.5733],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150],
- [0.6201, 0.4027, 0.8029, 0.2728, 0.4042, 0.2310, 0.5980, 0.5391],
- [0.6266, 0.4070, 0.8712, 0.5600, 0.3713, 0.4783, 0.5775, 0.6100]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5993, -0.4172, 1.2918, -0.8599, -0.4895, -0.8689, 0.4674, 0.2569],
- [ 0.6262, -0.3522, 1.7937, 0.2198, -0.4215, 0.3747, 0.6560, 0.1763],
- [ 0.8618, -0.2339, 1.5645, -0.7135, -0.6392, -0.8297, 0.5180, 0.1674],
- [ 0.5942, -0.3806, 1.8856, 0.2418, -0.3690, 0.4265, 0.7306, 0.1557],
- [-0.1523, -0.8777, 1.0009, -1.3971, -0.3676, -1.3578, 0.4247, 0.2480],
- [ 0.6371, -0.3410, 1.8052, 0.0200, -0.3949, 0.2762, 0.6559, 0.1790],
- [ 0.6024, -0.4038, 1.3672, -1.0748, -0.5515, -1.0605, 0.6216, 0.1504],
- [ 0.6503, -0.3460, 1.6743, 0.2830, -0.5657, 0.1409, 0.4786, 0.1767]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371],
- [ 0.5787, -0.4077, 1.7537, -0.7263, -0.5692, -0.8586, 0.4126,
- 0.1000],
- [ 0.6207, -0.4273, 1.7557, 0.0236, -0.4326, 0.3623, 1.0033,
- 0.3157],
- [-2.2859, -2.2859, 0.7106, -1.4468, -0.2882, -1.4237, 0.2430,
- 0.3623],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928],
- [ 0.5785, -0.4259, 1.4228, -1.0261, -0.4190, -1.2189, 0.4763,
- 0.2043],
- [ 0.6081, -0.4059, 1.7383, 0.3007, -0.5711, -0.0765, 0.3815,
- 0.5316]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1232, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1232, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7481739446520805
- step: 11
- running loss: 0.06801581315018913
- Train Steps: 11/90 Loss: 0.0680 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4079, 0.8025, 0.2500, 0.3763, 0.3217, 0.6125, 0.5533],
- [0.6272, 0.4045, 0.8538, 0.5900, 0.3750, 0.4417, 0.5989, 0.4649],
- [0.6157, 0.4102, 0.8513, 0.3817, 0.3613, 0.3667, 0.5096, 0.5890],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [0.6193, 0.4050, 0.7313, 0.2433, 0.4075, 0.2117, 0.5649, 0.5656],
- [0.6090, 0.4010, 0.7838, 0.3483, 0.3538, 0.3783, 0.5462, 0.5077]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6062, -0.3971, 1.4841, -0.8442, -0.4335, -0.7972, 0.6544, 0.2369],
- [ 0.7315, -0.3394, 1.7088, 0.1553, -0.5659, -0.1784, 0.6413, 0.1370],
- [ 0.6299, -0.3791, 1.5284, -0.3892, -0.6555, -0.3467, 0.4660, 0.2540],
- [ 0.4641, -0.4911, 1.8043, -0.0249, -0.2395, -0.0491, 0.7281, 0.1559],
- [ 0.4449, -0.4664, 1.5657, -0.5493, -0.5251, -0.2868, 0.7594, 0.1848],
- [ 0.3801, -0.5408, 1.7443, 0.1158, -0.3322, 0.1869, 0.5623, 0.2204],
- [ 0.4160, -0.5228, 1.1209, -1.0819, -0.5001, -1.0193, 0.5185, 0.2376],
- [ 0.4419, -0.5029, 1.3971, -0.7442, -0.5796, -0.5155, 0.5558, 0.1858]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5788, -0.4020, 1.4208, -1.1312, -0.5480, -0.8002, 0.5432,
- 0.2699],
- [ 0.6109, -0.4177, 1.6575, 0.4393, -0.5538, -0.2459, 0.4805,
- -0.1385],
- [ 0.5581, -0.3912, 1.6460, -0.5230, -0.6173, -0.5923, 0.0681,
- 0.4348],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391],
- [ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057],
- [ 0.5746, -0.4153, 1.0917, -1.1620, -0.4037, -1.3082, 0.3234,
- 0.3267],
- [ 0.5270, -0.4336, 1.3342, -0.6770, -0.6520, -0.5384, 0.2370,
- 0.0592]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0295, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0295, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7777050696313381
- step: 12
- running loss: 0.06480875580261151
- Train Steps: 12/90 Loss: 0.0648 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6184, 0.4079, 0.8350, 0.3700, 0.3675, 0.2883, 0.5312, 0.5783],
- [0.6109, 0.4009, 0.8725, 0.4700, 0.3550, 0.4167, 0.5650, 0.5117],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896],
- [0.6202, 0.3983, 0.8555, 0.5429, 0.3842, 0.4370, 0.5866, 0.5398],
- [0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136],
- [0.6339, 0.4123, 0.8638, 0.5367, 0.4075, 0.5467, 0.7517, 0.5436]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4202, -0.5173, 1.3836, -0.6753, -0.5700, -0.7469, 0.4485, 0.2560],
- [ 0.4411, -0.5239, 1.6127, -0.2479, -0.5575, -0.2546, 0.5313, 0.2155],
- [ 0.3881, -0.5298, 1.6339, -0.9055, -0.2468, -0.8263, 0.8806, 0.2196],
- [ 0.4624, -0.4736, 1.6928, -0.2386, -0.2085, -0.0825, 0.7356, 0.2267],
- [ 0.3695, -0.5581, 1.3107, -0.6324, -0.6252, -0.6646, 0.4025, 0.3258],
- [ 0.6355, -0.4044, 1.6213, -0.0035, -0.5570, -0.2023, 0.6437, 0.1830],
- [ 0.4304, -0.5043, 1.4906, -0.5608, -0.6256, -0.4607, 0.4872, 0.2247],
- [ 0.6903, -0.3622, 1.7952, 0.1668, -0.4360, 0.3059, 0.8035, 0.2034]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5704, -0.4019, 1.5709, -0.5769, -0.5885, -0.9541, 0.1679,
- 0.3854],
- [ 0.5356, -0.4343, 1.7441, -0.1150, -0.6462, -0.3614, 0.3238,
- 0.0774],
- [ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374],
- [ 0.5786, -0.4463, 1.6655, 0.2216, -0.5115, -0.2675, 0.4236,
- 0.2075],
- [ 0.5235, -0.4273, 1.7499, -0.4306, -0.5827, -0.4614, 0.1651,
- 0.0862],
- [ 0.6421, -0.3816, 1.7037, 0.1929, -0.4037, 0.2391, 1.1861,
- 0.2249]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0215, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0215, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7992481663823128
- step: 13
- running loss: 0.06148062818325483
- Train Steps: 13/90 Loss: 0.0615 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6175, 0.3997, 0.6737, 0.2500, 0.4313, 0.1933, 0.5513, 0.5750],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6143, 0.4040, 0.8237, 0.3333, 0.4075, 0.2117, 0.5137, 0.4973],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [ nan, nan, 0.7335, 0.2569, 0.3788, 0.2667, 0.5066, 0.5578]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.0145, -0.7906, 1.1196, -1.0009, -0.4156, -0.9101, 0.4786, 0.3279],
- [-0.1917, -0.9380, 1.0036, -1.1857, -0.4182, -1.1089, 0.4359, 0.3034],
- [ 0.9721, -0.1997, 1.9877, 0.5234, -0.4360, 0.5324, 0.7571, 0.1728],
- [ 0.9694, -0.2090, 2.0023, 0.2408, -0.5046, 0.1142, 0.8536, 0.1759],
- [ 0.9472, -0.1856, 1.9436, -0.1935, -0.5401, -0.2110, 0.8041, 0.1836],
- [ 0.3610, -0.5303, 1.3561, -0.7856, -0.5148, -0.7644, 0.4557, 0.2544],
- [ 0.7199, -0.3162, 1.7110, -0.1089, -0.3733, -0.3602, 0.6635, 0.2948],
- [-0.4062, -1.0408, 1.1087, -1.0144, -0.4563, -0.8119, 0.3634, 0.2917]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.5663, -0.4396, 0.8261, -1.1312, -0.2940, -1.3929, 0.2603,
- 0.3700],
- [ 0.5539, -0.4031, 1.7168, -0.0594, -0.3748, 0.0543, 0.1390,
- 0.3777],
- [ 0.6454, -0.3719, 1.9115, -0.1304, -0.5076, -0.2844, 1.0033,
- 0.4386],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.5515, -0.4201, 1.5189, -0.7463, -0.4037, -1.3082, 0.0869,
- 0.0111],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822],
- [-2.2859, -2.2859, 1.1020, -1.0994, -0.5365, -1.0542, 0.0542,
- 0.2905]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1652, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1652, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.964469201862812
- step: 14
- running loss: 0.06889065727591515
- Train Steps: 14/90 Loss: 0.0689 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6199, 0.4112, 0.8475, 0.3717, 0.3550, 0.4350, 0.6063, 0.6083],
- [0.6350, 0.4043, 0.8738, 0.5650, 0.3850, 0.4750, 0.6401, 0.4950],
- [0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413],
- [0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [0.6245, 0.4100, 0.7762, 0.2583, 0.4963, 0.1517, 0.5875, 0.5417],
- [0.6239, 0.4206, 0.8750, 0.5400, 0.3688, 0.4850, 0.5737, 0.5700],
- [0.6110, 0.3984, 0.8750, 0.4933, 0.4625, 0.4950, 0.5578, 0.5676]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7869, -0.3033, 1.7974, -0.1969, -0.6868, -0.0957, 0.6042, 0.2605],
- [ 0.8107, -0.2904, 1.8674, 0.4519, -0.6166, 0.1297, 0.6507, 0.2021],
- [-0.2218, -0.9150, 1.0375, -1.0969, -0.4347, -1.0455, 0.3649, 0.3677],
- [ 0.2698, -0.6191, 1.7095, -1.0067, -0.0719, -0.8716, 0.9475, 0.2549],
- [-0.1655, -0.9113, 1.0007, -1.1529, -0.5338, -1.2172, 0.2648, 0.3071],
- [ 0.0616, -0.7328, 1.1565, -1.1089, -0.3360, -1.1049, 0.4130, 0.3369],
- [ 1.0715, -0.1211, 1.9333, 0.5056, -0.6645, 0.2440, 0.6736, 0.2401],
- [ 0.6491, -0.3910, 1.8553, 0.1901, -0.3428, 0.2149, 0.6684, 0.2235]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7742e-01, -3.8684e-01, 1.6286e+00, -5.6921e-01, -6.4619e-01,
- -2.7667e-01, 5.1432e-01, 5.2394e-01],
- [ 6.4706e-01, -4.1832e-01, 1.7499e+00, 3.2379e-01, -5.0762e-01,
- -9.1917e-02, 6.7064e-01, 4.6189e-04],
- [ 5.7131e-01, -3.6712e-01, 8.6651e-01, -1.0696e+00, -3.6905e-01,
- -1.2236e+00, 3.5266e-01, 2.6220e-01],
- [ 6.2401e-01, -3.7675e-01, 1.6575e+00, -1.2851e+00, 2.9492e-01,
- -1.2467e+00, 1.1276e+00, 2.1421e-01],
- [ 5.1085e-01, -4.3164e-01, 1.1436e+00, -1.3467e+00, -3.8637e-01,
- -1.4160e+00, 1.2393e-01, -5.8033e-02],
- [ 5.9850e-01, -3.9207e-01, 1.2995e+00, -1.0927e+00, 6.2356e-03,
- -1.5854e+00, 4.2771e-01, 2.1601e-01],
- [ 5.9601e-01, -3.4305e-01, 1.7557e+00, 2.0831e-01, -5.8268e-01,
- -4.5727e-02, 3.6420e-01, 3.4688e-01],
- [ 5.3637e-01, -4.4573e-01, 1.7557e+00, -7.2363e-03, -1.4965e-01,
- 4.6189e-04, 2.9049e-01, 3.3573e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0739, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0739, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.038370206952095
- step: 15
- running loss: 0.069224680463473
- Train Steps: 15/90 Loss: 0.0692 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6205, 0.4004, 0.8938, 0.4883, 0.3663, 0.5000, 0.6357, 0.4947],
- [0.6240, 0.4217, 0.8150, 0.3133, 0.4425, 0.2650, 0.5650, 0.5817],
- [0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150],
- [0.6180, 0.4064, 0.7200, 0.2467, 0.4275, 0.2367, 0.5963, 0.6217],
- [0.6042, 0.3990, 0.6831, 0.2875, 0.3500, 0.3133, 0.5143, 0.5510],
- [0.6086, 0.3981, 0.8700, 0.4750, 0.4512, 0.5283, 0.5324, 0.5038],
- [0.6272, 0.4045, 0.8538, 0.5900, 0.3750, 0.4417, 0.5989, 0.4649],
- [ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7944, -0.3463, 1.9906, 0.2476, -0.5201, 0.2336, 0.7704, 0.1918],
- [ 0.0689, -0.7012, 1.3575, -0.7715, -0.3078, -0.7171, 0.4185, 0.3937],
- [ 0.3230, -0.5789, 1.3084, -0.9413, -0.4369, -1.0296, 0.4259, 0.3471],
- [ 0.5474, -0.4719, 1.2206, -0.8174, -0.4809, -0.9417, 0.4338, 0.3615],
- [ 0.1219, -0.7330, 1.1250, -0.9224, -0.6118, -0.8752, 0.3285, 0.3161],
- [ 0.5981, -0.4339, 1.8751, 0.1986, -0.4184, 0.1858, 0.5192, 0.2195],
- [ 0.8358, -0.2984, 1.8211, 0.3606, -0.6419, -0.1551, 0.5716, 0.2108],
- [-0.1780, -0.8768, 1.5876, -1.0652, -0.0232, -0.9913, 0.8239, 0.2795]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.8031e-01, -4.3672e-01, 1.8423e+00, -3.0331e-02, -5.9423e-01,
- 2.3557e-02, 6.5034e-01, -9.2270e-04],
- [ 5.9636e-01, -3.3795e-01, 1.4785e+00, -8.3865e-01, -2.4203e-01,
- -1.0619e+00, 3.2379e-01, 4.0077e-01],
- [ 5.7962e-01, -4.3256e-01, 1.4439e+00, -1.1774e+00, -2.9400e-01,
- -1.3390e+00, 3.9307e-01, 9.2841e-02],
- [ 5.6871e-01, -4.0878e-01, 1.0397e+00, -1.1466e+00, -3.1132e-01,
- -1.1928e+00, 4.6813e-01, 5.8553e-01],
- [ 5.0491e-01, -4.4280e-01, 8.6919e-01, -9.5814e-01, -6.6928e-01,
- -8.3865e-01, 8.9698e-02, 2.5891e-01],
- [ 5.2494e-01, -4.4734e-01, 1.7326e+00, -9.1917e-02, -2.0162e-01,
- 1.5443e-01, 1.7328e-01, 4.1158e-02],
- [ 6.1085e-01, -4.1771e-01, 1.6575e+00, 4.3926e-01, -5.5381e-01,
- -2.4588e-01, 4.8055e-01, -1.3847e-01],
- [-2.2859e+00, -2.2859e+00, 1.7557e+00, -1.1466e+00, 8.7067e-02,
- -1.0773e+00, 1.1239e+00, 2.7833e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1397, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1397, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1780213713645935
- step: 16
- running loss: 0.0736263357102871
- Train Steps: 16/90 Loss: 0.0736 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6147, 0.4107, 0.8137, 0.3333, 0.3750, 0.2683, 0.5006, 0.5412],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6276, 0.4095, 0.8237, 0.2250, 0.4662, 0.1783, 0.6171, 0.4869],
- [ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933],
- [0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6184, 0.4079, 0.8350, 0.3700, 0.3675, 0.2883, 0.5312, 0.5783],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3631, -0.5394, 1.4618, -0.3937, -0.5298, -0.5855, 0.3389, 0.2794],
- [ 0.4413, -0.5507, 1.2269, -0.7110, -0.4430, -0.9009, 0.4244, 0.2799],
- [-0.3335, -0.9625, 1.3309, -0.7680, -0.2863, -0.7579, 0.4095, 0.3167],
- [ 0.4255, -0.4960, 1.6453, -0.6700, -0.1697, -0.6607, 0.6740, 0.2752],
- [-0.3251, -0.9842, 1.0067, -0.9940, -0.3619, -1.0681, 0.3570, 0.3390],
- [ 0.5777, -0.4170, 1.6715, -0.5359, -0.3518, -0.6406, 0.6042, 0.2708],
- [ 0.6887, -0.3486, 1.7259, -0.0297, -0.5322, -0.4222, 0.4260, 0.2669],
- [ 0.8123, -0.2894, 1.7652, -0.2682, -0.5185, -0.1183, 0.7801, 0.2465]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5532, -0.3888, 1.4727, -0.7463, -0.5538, -1.0465, 0.0265,
- 0.2138],
- [ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.6127, -0.3944, 1.5189, -1.2467, -0.1323, -1.4622, 0.5646,
- -0.0369],
- [-2.2859, -2.2859, 0.8033, -1.1250, -0.3864, -1.3082, 0.1126,
- 0.4543],
- [ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.5704, -0.4019, 1.5709, -0.5769, -0.5885, -0.9541, 0.1679,
- 0.3854],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2478, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2478, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.425776869058609
- step: 17
- running loss: 0.08386922759168289
- Train Steps: 17/90 Loss: 0.0839 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6164, 0.4102, 0.8850, 0.4867, 0.4213, 0.5983, 0.5609, 0.5038],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6128, 0.4115, 0.7934, 0.3778, 0.3450, 0.4033, 0.5337, 0.5456],
- [ nan, nan, 0.7525, 0.2291, 0.3838, 0.3017, 0.6050, 0.5667],
- [0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334],
- [ nan, nan, 0.7097, 0.2346, 0.4250, 0.1850, 0.5175, 0.5583]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7392, -0.3159, 1.9126, 0.2965, -0.2309, 0.2665, 0.6292, 0.2335],
- [ 0.6183, -0.4341, 1.8354, -0.2402, -0.4850, -0.2214, 0.6888, 0.2186],
- [ 0.7088, -0.3281, 1.7322, -0.1589, -0.4614, -0.2889, 0.5233, 0.2228],
- [ 0.3687, -0.5831, 1.6318, -0.3442, -0.5248, -0.8639, 0.3086, 0.3081],
- [ 0.6640, -0.3452, 1.5032, -0.5834, -0.5535, -0.6198, 0.4556, 0.2848],
- [-0.3263, -0.9667, 1.2706, -1.0857, -0.2673, -1.1992, 0.3529, 0.3348],
- [ 0.3817, -0.5648, 1.2980, -0.9832, -0.4549, -1.1130, 0.4572, 0.2740],
- [-0.7312, -1.2464, 1.0082, -1.2988, -0.2090, -1.4575, 0.2481, 0.3142]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5614, -0.3912, 1.8018, -0.0380, -0.3402, 0.4778, 0.3049,
- 0.0412],
- [ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.5470, -0.3952, 1.6377, -0.4253, -0.6289, -0.8079, 0.0249,
- 0.2116],
- [ 0.5444, -0.3852, 1.3786, -0.5409, -0.6924, -0.4229, 0.1791,
- 0.2341],
- [-2.2859, -2.2859, 1.1898, -1.2278, -0.5134, -0.8925, 0.5085,
- 0.3315],
- [ 0.5730, -0.4562, 1.2195, -1.2440, -0.5497, -0.7711, 0.5704,
- 0.1779],
- [-2.2859, -2.2859, 0.9922, -1.2021, -0.3229, -1.4314, 0.1044,
- 0.2930]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1672, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1672, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5929645597934723
- step: 18
- running loss: 0.08849803109963734
- Train Steps: 18/90 Loss: 0.0885 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285],
- [0.6097, 0.4000, 0.7325, 0.2667, 0.3450, 0.3517, 0.5284, 0.5045],
- [0.6250, 0.4146, 0.8838, 0.3933, 0.3588, 0.4283, 0.6162, 0.5367],
- [0.6275, 0.4050, 0.9038, 0.3767, 0.3838, 0.3533, 0.7074, 0.5575],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7018, -0.3557, 1.7540, -0.1224, -0.4276, -0.0423, 0.5841, 0.2146],
- [ 0.2970, -0.6503, 1.6967, -0.1300, -0.4445, -0.3633, 0.4110, 0.2402],
- [ 0.0251, -0.7716, 1.1079, -1.0232, -0.4226, -1.2824, 0.2213, 0.3022],
- [ 0.3834, -0.5366, 1.4415, -0.5750, -0.4902, -0.5628, 0.3763, 0.2302],
- [-0.1063, -0.8539, 1.1570, -1.0314, -0.4734, -1.0130, 0.2999, 0.2568],
- [ 0.0538, -0.7577, 1.6569, -0.3731, -0.4748, -0.4974, 0.3697, 0.2801],
- [ 0.3470, -0.5624, 1.7853, -0.5192, -0.2951, -0.8880, 0.6386, 0.2389],
- [ 0.1852, -0.6856, 1.3280, -1.0545, -0.2309, -1.3210, 0.3448, 0.3081]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853],
- [ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552],
- [ 0.5303, -0.4384, 1.0975, -1.0542, -0.6924, -0.6616, 0.1548,
- 0.0442],
- [ 0.6009, -0.3710, 1.7961, -0.4691, -0.6289, -0.3075, 0.5605,
- 0.1929],
- [ 0.6125, -0.4153, 1.8885, -0.5461, -0.5134, -0.6539, 0.9814,
- 0.2890],
- [ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0528, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0528, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6457261517643929
- step: 19
- running loss: 0.08661716588233646
- Train Steps: 19/90 Loss: 0.0866 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6113, 0.4088, 0.6859, 0.2208, 0.4363, 0.1700, 0.5188, 0.5533],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6317, 0.4038, 0.8287, 0.5900, 0.3800, 0.4717, 0.6295, 0.4986],
- [0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7591, -0.3409, 1.7338, -0.0023, -0.5642, -0.2221, 0.4435, 0.2436],
- [ 0.3795, -0.5712, 1.7574, -0.1573, -0.3790, -0.1880, 0.3507, 0.2420],
- [-0.8353, -1.2972, 1.2454, -1.2587, -0.2312, -1.3055, 0.2796, 0.2779],
- [-0.5438, -1.1473, 1.0012, -1.3929, -0.3499, -1.5850, 0.1994, 0.2789],
- [ 0.5224, -0.4489, 1.7406, -0.0551, -0.3338, -0.1273, 0.4229, 0.1991],
- [-0.5449, -1.1069, 1.2491, -1.3066, -0.2089, -1.4304, 0.3174, 0.2808],
- [ 0.9422, -0.2005, 1.7710, 0.0972, -0.6246, -0.3440, 0.4153, 0.1871],
- [ 0.5999, -0.4022, 1.3268, -1.1283, -0.6029, -0.9938, 0.4009, 0.2491]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.5377, -0.3978, 0.8824, -1.2663, -0.2709, -1.5007, 0.1102,
- 0.2699],
- [ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698],
- [ 0.6320, -0.4206, 1.5420, 0.4393, -0.5307, -0.1073, 0.6216,
- 0.0171],
- [ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1236, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1236, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7693058922886848
- step: 20
- running loss: 0.08846529461443424
- Train Steps: 20/90 Loss: 0.0885 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6131, 0.4064, 0.8638, 0.5200, 0.4788, 0.4783, 0.5258, 0.5867],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6261, 0.4131, 0.8938, 0.3550, 0.4000, 0.2683, 0.6188, 0.5283],
- [0.6280, 0.4055, 0.8600, 0.5317, 0.3800, 0.4700, 0.6275, 0.5133],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6286, 0.3977, 0.9038, 0.4733, 0.3900, 0.4150, 0.7074, 0.5320],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1886, -0.6836, 1.5729, -0.2674, -0.2233, -0.4119, 0.3165, 0.2492],
- [ 0.4372, -0.5134, 1.6379, -0.3847, -0.5800, -0.9076, 0.2784, 0.1690],
- [-0.0067, -0.8098, 1.6752, -0.9982, -0.3071, -1.3077, 0.4903, 0.2232],
- [ 0.3528, -0.5931, 1.6171, -0.1919, -0.5364, -0.4231, 0.2934, 0.1907],
- [ 0.3657, -0.5674, 1.6048, -0.6359, -0.4923, -0.3539, 0.4614, 0.1826],
- [ 0.1345, -0.6927, 1.1944, -1.0451, -0.4435, -0.9648, 0.2830, 0.3123],
- [ 0.3865, -0.5515, 1.5863, -0.5621, -0.4879, -0.5619, 0.4382, 0.1526],
- [-0.4958, -1.1116, 0.9192, -1.2360, -0.4738, -1.2510, 0.0091, 0.3017]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5462, -0.4090, 1.7037, 0.1159, -0.0746, -0.0765, 0.1427,
- 0.4239],
- [ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.6062, -0.3778, 1.8423, -0.6462, -0.4383, -1.0465, 0.5721,
- 0.1544],
- [ 0.6148, -0.4130, 1.6864, 0.1698, -0.5307, -0.1150, 0.6125,
- 0.0851],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469],
- [ 0.6174, -0.4490, 1.8885, -0.0996, -0.4845, -0.3691, 0.9814,
- 0.1715],
- [ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0934, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0934, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8627125546336174
- step: 21
- running loss: 0.08870059783969607
- Train Steps: 21/90 Loss: 0.0887 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6279, 0.4050, 0.8025, 0.2200, 0.4313, 0.2733, 0.6820, 0.5320],
- [ nan, nan, 0.7625, 0.2433, 0.3713, 0.2867, 0.5235, 0.5220],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837],
- [0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389],
- [0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [ nan, nan, 0.8850, 0.3000, 0.5363, 0.2250, 0.7343, 0.5771],
- [0.6304, 0.4024, 0.8925, 0.4800, 0.3937, 0.4817, 0.7485, 0.5297]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8606, -0.2453, 1.3886, -0.9505, -0.4922, -1.0851, 0.4113, 0.2025],
- [-0.9464, -1.3543, 1.1559, -1.1529, -0.4029, -1.2134, 0.1268, 0.2243],
- [ 0.1395, -0.6876, 1.0224, -0.8792, -0.6390, -0.9361, -0.0147, 0.2747],
- [ 0.2228, -0.6074, 1.3296, -0.9617, -0.5155, -1.1569, 0.2396, 0.2520],
- [ 0.0331, -0.7152, 1.2906, -0.9207, -0.5002, -1.0947, 0.1238, 0.2317],
- [ 0.5004, -0.4776, 1.7450, 0.0990, -0.4102, 0.1276, 0.3315, 0.1553],
- [-0.5480, -1.0970, 1.6360, -1.0426, 0.0103, -1.0901, 0.6953, 0.2123],
- [ 0.7488, -0.3436, 1.7506, 0.0133, -0.5513, 0.1343, 0.4193, 0.1387]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.1413e-01, -4.1527e-01, 1.4208e+00, -1.2697e+00, -2.9400e-01,
- -1.0234e+00, 8.6439e-01, 1.7146e-01],
- [-2.2859e+00, -2.2859e+00, 1.2360e+00, -1.1620e+00, -5.7113e-01,
- -9.6182e-01, 1.3215e-01, 1.2532e-01],
- [ 5.6293e-01, -3.8707e-01, 7.2426e-01, -9.5814e-01, -5.8268e-01,
- -9.8491e-01, 1.2881e-01, 4.1034e-01],
- [ 5.6966e-01, -4.4656e-01, 1.1973e+00, -1.1871e+00, -4.5712e-01,
- -9.9653e-01, 5.2186e-01, 2.0324e-01],
- [ 5.6951e-01, -3.9269e-01, 1.3226e+00, -9.0023e-01, -4.6721e-01,
- -1.1928e+00, 1.7367e-01, 3.6998e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [-2.2859e+00, -2.2859e+00, 1.8018e+00, -9.0023e-01, 1.9099e-01,
- -1.2467e+00, 1.1057e+00, 3.7986e-01],
- [ 6.2566e-01, -4.2731e-01, 1.8365e+00, -6.8822e-02, -4.6721e-01,
- -6.1124e-02, 1.1715e+00, 1.6077e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1541, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1541, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.016780845820904
- step: 22
- running loss: 0.0916718566282229
- Train Steps: 22/90 Loss: 0.0917 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250],
- [0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167],
- [0.6263, 0.4029, 0.8900, 0.4933, 0.4075, 0.5183, 0.6406, 0.4758],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6200, 0.4070, 0.8938, 0.4183, 0.3538, 0.4567, 0.6175, 0.5400],
- [0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [0.6156, 0.4125, 0.8850, 0.4833, 0.4550, 0.5817, 0.5765, 0.5084]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.0417, -0.8324, 1.5706, -0.6496, -0.5061, -0.7902, 0.3200, 0.1959],
- [ 0.6402, -0.3714, 1.5161, -0.6605, -0.4793, -0.3589, 0.4657, 0.1758],
- [ 0.0140, -0.7716, 1.4618, -0.3929, -0.4085, -0.4324, 0.2697, 0.1847],
- [-0.0728, -0.8222, 1.4052, -0.7861, -0.5726, -1.2827, 0.1695, 0.2282],
- [ 0.6133, -0.3940, 1.4564, -0.7158, -0.5758, -0.6346, 0.4125, 0.1667],
- [-0.1707, -0.8710, 1.3833, -0.9728, -0.5769, -1.0311, 0.2213, 0.2043],
- [ 0.1520, -0.6904, 1.5596, -0.5260, -0.2024, -0.6276, 0.3844, 0.1957],
- [ 0.0610, -0.7315, 1.5109, -0.3846, -0.2611, -0.3896, 0.2713, 0.2347]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390],
- [ 0.5886, -0.4253, 1.8654, -0.3460, -0.5480, 0.3623, 0.6587,
- 0.1005],
- [ 0.6072, -0.4250, 1.8249, -0.0072, -0.4037, 0.1082, 0.6730,
- -0.0881],
- [ 0.6016, -0.3633, 1.7152, -0.2228, -0.6115, -0.6385, 0.5028,
- 0.2699],
- [ 0.5778, -0.4061, 1.8423, -0.3537, -0.6520, -0.1766, 0.5663,
- 0.2083],
- [ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [ 0.5598, -0.4273, 1.7152, -0.1227, -0.0065, 0.1917, 0.5143,
- 0.2853],
- [ 0.5573, -0.3808, 1.8018, -0.0534, -0.1843, 0.4008, 0.3769,
- 0.0622]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1489, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1489, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.1656840667128563
- step: 23
- running loss: 0.09416017681360245
- Train Steps: 23/90 Loss: 0.0942 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4079, 0.8025, 0.2500, 0.3763, 0.3217, 0.6125, 0.5533],
- [0.6206, 0.4001, 0.8900, 0.3933, 0.3588, 0.3567, 0.5837, 0.5083],
- [0.6142, 0.3982, 0.8650, 0.4883, 0.3912, 0.4317, 0.5315, 0.5350],
- [0.6277, 0.4118, 0.8988, 0.3833, 0.3950, 0.2650, 0.6290, 0.4938],
- [0.6198, 0.4075, 0.8650, 0.5617, 0.4150, 0.4367, 0.5450, 0.5650],
- [0.6274, 0.4270, 0.8938, 0.4967, 0.3550, 0.4283, 0.5700, 0.5733],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0037, -0.7422, 1.3922, -1.1547, -0.4432, -1.1044, 0.3581, 0.2139],
- [ 0.3885, -0.5133, 1.5515, -0.7567, -0.6069, -0.8293, 0.3334, 0.1393],
- [ 0.2714, -0.6226, 1.5515, -0.2874, -0.5171, -0.2793, 0.2606, 0.1226],
- [-0.1505, -0.8803, 1.5943, -0.8312, -0.3734, -1.0537, 0.4488, 0.1856],
- [ 0.5403, -0.4329, 1.4621, -0.1111, -0.4986, -0.3244, 0.2106, 0.2074],
- [ 0.0936, -0.6823, 1.5448, -0.2891, -0.5000, -0.3389, 0.3101, 0.2221],
- [ 0.5081, -0.4559, 1.6541, -0.3323, -0.2829, 0.0522, 0.4847, 0.1648],
- [-0.2603, -0.9413, 0.9716, -1.3636, -0.4094, -1.4996, 0.1687, 0.2274]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5788, -0.4020, 1.4208, -1.1312, -0.5480, -0.8002, 0.5432,
- 0.2699],
- [ 0.5807, -0.4378, 1.8249, -0.4691, -0.6289, -0.6385, 0.4104,
- 0.0620],
- [ 0.5513, -0.4467, 1.7095, -0.0303, -0.4788, -0.2921, 0.1692,
- 0.1854],
- [ 0.6135, -0.3841, 1.8654, -0.5153, -0.4614, -1.0619, 0.6195,
- -0.0049],
- [ 0.5770, -0.4036, 1.7095, 0.3084, -0.3691, -0.2690, 0.2314,
- 0.3238],
- [ 0.6121, -0.3138, 1.8423, 0.0082, -0.6462, -0.3075, 0.3469,
- 0.3623],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0659, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0659, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.2315543591976166
- step: 24
- running loss: 0.09298143163323402
- Train Steps: 24/90 Loss: 0.0930 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6084, 0.3981, 0.8588, 0.5233, 0.4600, 0.5367, 0.5680, 0.5006],
- [0.6196, 0.4094, 0.7562, 0.2817, 0.3937, 0.3183, 0.6013, 0.6183],
- [0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6257, 0.4167, 0.8775, 0.3433, 0.3563, 0.4133, 0.6200, 0.5667],
- [0.6127, 0.4066, 0.8550, 0.5567, 0.4662, 0.5141, 0.5070, 0.5412],
- [0.6131, 0.4064, 0.8638, 0.5200, 0.4788, 0.4783, 0.5258, 0.5867],
- [0.6279, 0.4050, 0.8025, 0.2200, 0.4313, 0.2733, 0.6820, 0.5320]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2694, -0.6120, 1.6079, -0.1341, -0.3535, -0.1856, 0.2777, 0.1239],
- [-0.1129, -0.8312, 1.4104, -1.0356, -0.4240, -1.0812, 0.3827, 0.2333],
- [ 0.3946, -0.4807, 1.1978, -0.9625, -0.5292, -0.8474, 0.3154, 0.2285],
- [ 0.2245, -0.6238, 1.6883, -0.4121, -0.5650, -0.2460, 0.3334, 0.1264],
- [-0.0742, -0.8103, 1.6389, -0.7168, -0.5823, -0.6310, 0.3868, 0.1623],
- [ 0.3275, -0.5708, 1.5419, -0.0796, -0.3344, -0.2834, 0.2763, 0.1428],
- [ 0.3363, -0.5500, 1.5343, -0.2309, -0.3338, -0.3383, 0.3287, 0.1582],
- [ 0.4521, -0.4659, 1.3048, -1.2154, -0.4436, -1.2450, 0.4739, 0.1569]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5241, -0.4470, 1.6806, 0.1313, -0.1612, 0.1929, 0.3378,
- 0.0261],
- [ 0.5759, -0.3948, 1.2072, -0.9849, -0.4672, -0.8156, 0.4912,
- 0.5701],
- [ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.6040, -0.3614, 1.7672, -0.7001, -0.6404, -0.3768, 0.5778,
- 0.3315],
- [ 0.5443, -0.4079, 1.6633, 0.2853, -0.1325, 0.0888, 0.0558,
- 0.2138],
- [ 0.5462, -0.4090, 1.7037, 0.1159, -0.0746, -0.0765, 0.1427,
- 0.4239],
- [ 0.6141, -0.4153, 1.4208, -1.2697, -0.2940, -1.0234, 0.8644,
- 0.1715]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0605, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0605, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.2920665852725506
- step: 25
- running loss: 0.09168266341090202
- Train Steps: 25/90 Loss: 0.0917 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6265, 0.4251, 0.7113, 0.3550, 0.4375, 0.2117, 0.5587, 0.6118],
- [0.6250, 0.4008, 0.8950, 0.4183, 0.3550, 0.4383, 0.6361, 0.4927],
- [0.6182, 0.3998, 0.8793, 0.4191, 0.3552, 0.4285, 0.6038, 0.5312],
- [0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6099, 0.4030, 0.8638, 0.5117, 0.4983, 0.4965, 0.5086, 0.5388],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [0.6182, 0.3972, 0.8552, 0.5914, 0.3683, 0.4181, 0.5688, 0.5378]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5360, -0.4152, 1.2727, -0.8990, -0.4217, -0.8114, 0.3388, 0.2447],
- [ 0.7213, -0.2711, 1.7282, -0.4758, -0.5359, -0.0893, 0.5664, 0.0697],
- [ 0.5419, -0.4243, 1.8018, -0.3781, -0.5639, -0.1564, 0.3643, 0.1712],
- [-0.1072, -0.8073, 1.2532, -1.0415, -0.3916, -1.0363, 0.3168, 0.2298],
- [ 0.1826, -0.6807, 1.8562, -0.0210, -0.1675, -0.0548, 0.4331, 0.1280],
- [-0.2581, -0.9261, 1.1611, -1.1396, -0.3878, -1.1883, 0.2695, 0.2118],
- [-0.1382, -0.8743, 1.2336, -1.0734, -0.4734, -1.1985, 0.2244, 0.1587],
- [ 0.9495, -0.1562, 1.7437, 0.2298, -0.6145, -0.1484, 0.3448, 0.1113]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6077, -0.3226, 0.9993, -0.6462, -0.2651, -1.3082, 0.2946,
- 0.5401],
- [ 0.6010, -0.4345, 1.8480, -0.3537, -0.6462, -0.2613, 0.6524,
- -0.0099],
- [ 0.5697, -0.4393, 1.7754, -0.3503, -0.6453, -0.3067, 0.5028,
- 0.1677],
- [ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.5309, -0.4246, 1.7037, 0.0774, 0.0158, 0.0075, 0.0635,
- 0.2026],
- [-2.2859, -2.2859, 0.6760, -1.4083, -0.3286, -1.4160, 0.2487,
- 0.3469],
- [ 0.5109, -0.4316, 1.1436, -1.3467, -0.3864, -1.4160, 0.1239,
- -0.0580],
- [ 0.5697, -0.4514, 1.6642, 0.4459, -0.5850, -0.3546, 0.3414,
- 0.1982]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1472, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1472, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.439281862229109
- step: 26
- running loss: 0.09381853316265804
- Train Steps: 26/90 Loss: 0.0938 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6164, 0.4119, 0.7913, 0.2650, 0.3538, 0.3500, 0.5614, 0.5038],
- [0.6207, 0.4110, 0.8738, 0.5000, 0.4800, 0.5633, 0.6300, 0.5433],
- [0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852],
- [0.6339, 0.4159, 0.8400, 0.5617, 0.3825, 0.4150, 0.7343, 0.5748],
- [0.6208, 0.4082, 0.8538, 0.3067, 0.3588, 0.3717, 0.6112, 0.5517],
- [0.6143, 0.4040, 0.8237, 0.3333, 0.4075, 0.2117, 0.5137, 0.4973],
- [0.6286, 0.4078, 0.8063, 0.2267, 0.4788, 0.1533, 0.5953, 0.4913]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7377, -0.2847, 1.2891, -0.9831, -0.6037, -0.6774, 0.3743, 0.1758],
- [ 0.7532, -0.3084, 1.7839, -0.0081, -0.2489, 0.1553, 0.4885, 0.1983],
- [ 0.0920, -0.6706, 1.3227, -0.8929, -0.4635, -0.9294, 0.2370, 0.2070],
- [ 0.4035, -0.5192, 1.7010, 0.0193, -0.2705, 0.0588, 0.4027, 0.1585],
- [ 0.3594, -0.5149, 1.6956, 0.1053, -0.4258, -0.2073, 0.3934, 0.1345],
- [ 0.7228, -0.2703, 1.5759, -0.6418, -0.6070, -0.4731, 0.3995, 0.1768],
- [ 0.1192, -0.6348, 1.3703, -0.8587, -0.4873, -0.9933, 0.2480, 0.1672],
- [-0.3907, -0.9933, 1.3532, -1.0529, -0.2807, -1.1499, 0.4252, 0.2074]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5614, -0.3834, 1.3688, -1.0619, -0.6520, -0.6693, 0.3069,
- 0.0412],
- [ 0.5811, -0.3878, 1.7499, 0.0236, -0.0688, 0.3161, 0.6240,
- 0.2237],
- [ 0.5695, -0.3927, 1.3226, -0.9002, -0.4672, -1.1928, 0.1737,
- 0.3700],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171],
- [ 0.6421, -0.3647, 1.5940, 0.3084, -0.5192, -0.3691, 1.1057,
- 0.3692],
- [ 0.5814, -0.4003, 1.6575, -0.8694, -0.6289, -0.5692, 0.5374,
- 0.2622],
- [ 0.5515, -0.4201, 1.5189, -0.7463, -0.4037, -1.3082, 0.0869,
- 0.0111],
- [ 0.6177, -0.4022, 1.4381, -1.2390, -0.0746, -1.5777, 0.4639,
- -0.0168]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0591, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0591, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.498351190239191
- step: 27
- running loss: 0.09253152556441448
- Train Steps: 27/90 Loss: 0.0925 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6205, 0.4062, 0.8337, 0.2683, 0.3675, 0.4283, 0.6338, 0.5250],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [ nan, nan, 0.6992, 0.2791, 0.3950, 0.2383, 0.5483, 0.5819],
- [0.6267, 0.4094, 0.8712, 0.3083, 0.4400, 0.2267, 0.6250, 0.5200],
- [0.6030, 0.3969, 0.7988, 0.3917, 0.3450, 0.3667, 0.5266, 0.4700],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507],
- [0.6187, 0.4104, 0.7126, 0.2069, 0.3950, 0.2517, 0.5913, 0.5683],
- [ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7374, -0.2478, 1.5541, -0.5682, -0.4088, -0.1455, 0.5582, 0.1738],
- [ 0.8026, -0.1954, 1.4651, -0.5861, -0.5108, -0.2191, 0.4381, 0.1958],
- [-0.3435, -0.9865, 1.1572, -0.7504, -0.3741, -0.8815, 0.1974, 0.2048],
- [ 0.4001, -0.5106, 1.9784, -0.5306, -0.1866, -0.6455, 0.7068, 0.1653],
- [ 0.8539, -0.2140, 1.7453, -0.1278, -0.4997, -0.1058, 0.3985, 0.1125],
- [ 0.6154, -0.3897, 1.2614, -0.7802, -0.4414, -0.8929, 0.3122, 0.2156],
- [ 0.7323, -0.2899, 1.3559, -0.6403, -0.4691, -0.8427, 0.2395, 0.2310],
- [-0.4492, -1.0322, 1.4731, -0.4906, -0.4800, -0.5407, 0.1952, 0.1919]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5799, -0.4099, 1.5651, -1.0465, -0.5885, -0.3075, 0.6413,
- 0.1390],
- [ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545],
- [-2.2859, -2.2859, 0.9438, -0.9967, -0.4614, -1.1851, 0.2468,
- 0.4019],
- [ 0.6089, -0.3948, 1.7383, -0.8617, -0.2536, -1.2390, 0.6009,
- 0.1159],
- [ 0.4992, -0.4525, 1.4035, -0.4768, -0.6924, -0.5923, 0.1465,
- -0.1151],
- [ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576],
- [ 0.5718, -0.3905, 1.0053, -1.3305, -0.4614, -1.1235, 0.4450,
- 0.3392],
- [-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2237, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2237, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.722070474177599
- step: 28
- running loss: 0.09721680264919996
- Train Steps: 28/90 Loss: 0.0972 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6258, 0.4038, 0.8750, 0.4883, 0.3900, 0.5500, 0.6375, 0.5217],
- [0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136],
- [0.6236, 0.3967, 0.8675, 0.5400, 0.3862, 0.4517, 0.5825, 0.5200],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378],
- [0.6200, 0.4024, 0.8390, 0.3139, 0.3525, 0.3833, 0.6162, 0.5383],
- [0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412],
- [0.6286, 0.3977, 0.9038, 0.4733, 0.3900, 0.4150, 0.7074, 0.5320],
- [0.6147, 0.4026, 0.6600, 0.2467, 0.4088, 0.2150, 0.5489, 0.5773]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7305, -0.3132, 1.8139, 0.0944, -0.3673, 0.3312, 0.4564, 0.1943],
- [ 0.4552, -0.4721, 1.6356, -0.4081, -0.5364, -0.3788, 0.2233, 0.1668],
- [ 0.5072, -0.4483, 1.6649, 0.2623, -0.4183, 0.0033, 0.3367, 0.1549],
- [-0.0679, -0.8478, 1.5491, -1.2164, -0.0420, -1.1702, 0.8427, 0.1657],
- [ 0.4734, -0.4277, 1.4086, -0.7942, -0.5250, -0.4327, 0.4632, 0.2055],
- [ 0.7182, -0.3058, 1.7048, -0.6412, -0.4689, -0.8560, 0.4083, 0.2025],
- [ 0.4958, -0.4098, 1.5977, -0.2361, -0.4395, -0.1448, 0.4607, 0.1308],
- [ 0.3944, -0.5354, 1.0374, -1.0903, -0.5026, -1.1602, 0.1620, 0.2851]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6047, -0.4209, 1.7557, -0.0303, -0.4845, 0.2545, 0.6587,
- 0.1236],
- [ 0.5235, -0.4273, 1.7499, -0.4306, -0.5827, -0.4614, 0.1651,
- 0.0862],
- [ 0.5944, -0.4538, 1.7210, 0.2083, -0.5018, -0.1997, 0.4046,
- 0.1159],
- [ 0.6207, -0.3936, 1.7788, -1.1235, 0.1448, -1.0850, 1.1459,
- 0.1982],
- [ 0.5779, -0.4275, 1.5894, -0.8362, -0.6577, -0.5153, 0.5605,
- 0.2006],
- [ 0.5784, -0.4006, 1.7911, -0.5701, -0.5192, -1.0331, 0.4137,
- 0.2139],
- [ 0.6174, -0.4490, 1.8885, -0.0996, -0.4845, -0.3691, 0.9814,
- 0.1715],
- [ 0.5532, -0.4264, 0.7626, -1.1466, -0.3979, -1.2928, 0.2494,
- 0.3808]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0290, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0290, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.751073746010661
- step: 29
- running loss: 0.09486461193140211
- Train Steps: 29/90 Loss: 0.0949 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683],
- [0.6275, 0.4081, 0.8063, 0.2017, 0.4825, 0.1583, 0.6156, 0.4869],
- [0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585],
- [0.6221, 0.4107, 0.7788, 0.3033, 0.3950, 0.2817, 0.6075, 0.5517],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6357, 0.4097, 0.9038, 0.3883, 0.4213, 0.2950, 0.6686, 0.5390],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6250, -0.4144, 1.6957, 0.1613, -0.4815, 0.1147, 0.3846, 0.1633],
- [ 1.0344, -0.0511, 1.5689, 0.0435, -0.5933, -0.2652, 0.2690, 0.2165],
- [ 0.4900, -0.4944, 1.4873, -1.2496, -0.2852, -1.3362, 0.6333, 0.2112],
- [ 0.2213, -0.5748, 1.6522, -0.3171, -0.2605, 0.1559, 0.4833, 0.1932],
- [ 0.6316, -0.3629, 1.5085, -0.9237, -0.4612, -1.0227, 0.4347, 0.2365],
- [ 0.4009, -0.5173, 1.6601, 0.0104, -0.4300, 0.1274, 0.4248, 0.1939],
- [ 0.6442, -0.3294, 1.5468, -0.6170, -0.3791, -0.6776, 0.6063, 0.1621],
- [-0.3326, -0.9680, 1.2587, -1.1487, -0.3353, -1.1027, 0.3654, 0.2544]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.5777, -0.3915, 1.7961, 0.1698, -0.5192, -0.5307, 0.2141,
- 0.3392],
- [ 0.6124, -0.4010, 1.4381, -1.3544, -0.0573, -1.5546, 0.5573,
- -0.0369],
- [ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936],
- [ 0.5875, -0.3888, 1.3111, -0.8848, -0.4614, -0.9849, 0.5201,
- 0.2622],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.6504, -0.3936, 1.8885, -0.4922, -0.3402, -0.9233, 0.8022,
- 0.2035],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0458, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0458, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.796903731301427
- step: 30
- running loss: 0.09323012437671423
- Train Steps: 30/90 Loss: 0.0932 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6128, 0.4116, 0.8450, 0.4583, 0.3675, 0.4867, 0.5337, 0.5446],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412],
- [0.6164, 0.4119, 0.7913, 0.2650, 0.3538, 0.3500, 0.5614, 0.5038],
- [0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6133, 0.4066, 0.6787, 0.2617, 0.3800, 0.2433, 0.5147, 0.5358],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2950, -0.5529, 1.5650, -0.1983, -0.3732, -0.0056, 0.4572, 0.2051],
- [ 0.6665, -0.3533, 1.6722, 0.3198, -0.4098, 0.0561, 0.4334, 0.2532],
- [ 0.3297, -0.5676, 1.8212, -0.9010, -0.2561, -1.0195, 0.8730, 0.1724],
- [ 0.9322, -0.1943, 1.7126, -0.6697, -0.4805, -0.9836, 0.5364, 0.2048],
- [ 0.6783, -0.3507, 1.2963, -1.0498, -0.5918, -0.8010, 0.4516, 0.2145],
- [ 0.4780, -0.4556, 1.7030, -0.1402, -0.4341, -0.1432, 0.4271, 0.2051],
- [ 0.1772, -0.6843, 1.0516, -1.1705, -0.5276, -1.2873, 0.2467, 0.2395],
- [ 0.4062, -0.5089, 1.7335, -0.1380, -0.1525, -0.0088, 0.4933, 0.1989]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5444, -0.3846, 1.6171, -0.1689, -0.5885, -0.0380, 0.1791,
- 0.2296],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007],
- [ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.5784, -0.4006, 1.7911, -0.5701, -0.5192, -1.0331, 0.4137,
- 0.2139],
- [ 0.5614, -0.3834, 1.3688, -1.0619, -0.6520, -0.6693, 0.3069,
- 0.0412],
- [ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.5470, -0.4081, 0.8492, -1.0773, -0.5307, -1.1620, 0.0912,
- 0.1890],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0192, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0192, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.8161120787262917
- step: 31
- running loss: 0.09084232512020296
- Train Steps: 31/90 Loss: 0.0908 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6284, 0.4093, 0.8900, 0.4700, 0.3650, 0.3850, 0.6212, 0.5167],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450],
- [0.6175, 0.4013, 0.8900, 0.4500, 0.4375, 0.5850, 0.6175, 0.5383],
- [0.6164, 0.4119, 0.7913, 0.2650, 0.3538, 0.3500, 0.5614, 0.5038],
- [0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3344, -0.5869, 1.6304, -0.2963, -0.3374, -0.3541, 0.4984, 0.2313],
- [ 0.6908, -0.3503, 1.6515, -0.0700, -0.4126, -0.3634, 0.4649, 0.2478],
- [ 0.7039, -0.3515, 1.7289, -0.5859, -0.5596, -1.0050, 0.6109, 0.1697],
- [ 0.4358, -0.5206, 1.6912, -0.1203, -0.3773, -0.2881, 0.4537, 0.1943],
- [ 0.6527, -0.3829, 1.6514, -0.3966, -0.3452, -0.1804, 0.4861, 0.2402],
- [ 0.2703, -0.5918, 1.6975, -0.4348, -0.2091, -0.3389, 0.6179, 0.2223],
- [ 0.6149, -0.3941, 1.2463, -1.3619, -0.5945, -1.1821, 0.4503, 0.2203],
- [ 0.6631, -0.3669, 1.7404, -0.3028, -0.3693, -0.3110, 0.5155, 0.2463]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6010, -0.4562, 1.7198, -0.0090, -0.3464, 0.0108, 0.6294,
- 0.1627],
- [ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.6164, -0.3956, 1.8249, -0.1150, -0.6000, -0.5076, 0.5836,
- 0.1005],
- [ 0.5778, -0.4389, 1.7107, 0.1192, -0.3921, 0.0815, 0.4741,
- 0.0711],
- [ 0.5960, -0.4102, 1.8018, -0.1612, -0.3344, 0.1159, 0.5490,
- 0.2314],
- [ 0.5664, -0.4321, 1.8249, -0.2074, -0.2651, 0.4162, 0.5663,
- 0.2006],
- [ 0.5614, -0.3834, 1.3688, -1.0619, -0.6520, -0.6693, 0.3069,
- 0.0412],
- [ 0.5991, -0.3803, 1.8018, -0.0534, -0.3460, 0.1852, 0.5374,
- 0.1390]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0467, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0467, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.8628441616892815
- step: 32
- running loss: 0.08946388005279005
- Train Steps: 32/90 Loss: 0.0895 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6222, 0.3937, 0.8350, 0.5617, 0.4138, 0.4600, 0.5800, 0.5233],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960],
- [0.6325, 0.4165, 0.9000, 0.4617, 0.3813, 0.4900, 0.7485, 0.5447],
- [0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927],
- [0.6353, 0.4128, 0.9138, 0.3533, 0.4688, 0.3250, 0.7145, 0.5991],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3982, -0.5270, 1.6897, 0.0661, -0.4512, -0.1298, 0.4194, 0.2139],
- [ 0.4820, -0.4519, 1.0651, -1.1793, -0.3780, -1.2608, 0.3199, 0.3024],
- [ 0.7377, -0.2993, 1.5560, 0.0784, -0.4592, -0.0693, 0.3546, 0.2839],
- [ 0.3434, -0.5464, 1.7874, -0.2388, -0.3171, -0.1256, 0.5441, 0.1799],
- [ 0.4071, -0.4964, 1.8216, -0.1906, -0.4884, 0.0066, 0.5674, 0.1910],
- [ 0.8802, -0.2046, 1.6890, 0.0257, -0.5662, -0.2507, 0.3828, 0.1599],
- [ 0.1629, -0.7121, 1.8712, -0.8241, -0.1683, -0.9695, 0.9181, 0.1917],
- [ 0.9275, -0.1944, 1.2945, -1.3044, -0.3726, -1.3874, 0.5662, 0.2288]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5880, -0.4676, 1.5709, 0.3084, -0.3748, -0.1612, 0.3931,
- 0.1313],
- [ 0.5862, -0.3530, 1.1032, -1.0619, -0.1497, -1.3852, 0.3411,
- 0.3931],
- [ 0.6134, -0.3910, 1.4497, 0.3546, -0.3517, -0.0919, 0.3296,
- 0.5239],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051],
- [ 0.6355, -0.3623, 1.8711, -0.1535, -0.5249, -0.0226, 1.1715,
- 0.2302],
- [ 0.6335, -0.4162, 1.7499, 0.3084, -0.4961, -0.2459, 0.6524,
- -0.0102],
- [ 0.6487, -0.3792, 1.9346, -0.6539, -0.1208, -0.7848, 1.0143,
- 0.4814],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0337, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0337, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.8965813778340816
- step: 33
- running loss: 0.08777519326769945
- Train Steps: 33/90 Loss: 0.0878 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4055, 0.8011, 0.2988, 0.3842, 0.2798, 0.5949, 0.5183],
- [0.6226, 0.4185, 0.8850, 0.5500, 0.3800, 0.4250, 0.5625, 0.5617],
- [0.6109, 0.4041, 0.6975, 0.3167, 0.3513, 0.3383, 0.5153, 0.5319],
- [0.6203, 0.4056, 0.8942, 0.4086, 0.3643, 0.3617, 0.5917, 0.5482],
- [0.6350, 0.4144, 0.9250, 0.4100, 0.4125, 0.3750, 0.7129, 0.5945],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [0.6286, 0.4040, 0.8696, 0.3047, 0.3924, 0.2887, 0.6300, 0.5367]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 7.4253e-01, -3.1317e-01, 1.4131e+00, -8.4927e-01, -4.4791e-01,
- -8.2762e-01, 5.7978e-01, 2.1282e-01],
- [ 4.5485e-01, -4.8451e-01, 1.7093e+00, 3.4834e-01, -4.3732e-01,
- -1.3684e-02, 3.7540e-01, 2.9459e-01],
- [ 3.7311e-01, -5.3037e-01, 1.1646e+00, -8.6670e-01, -5.0887e-01,
- -7.3844e-01, 2.4022e-01, 2.4823e-01],
- [ 7.2810e-01, -3.0743e-01, 1.7642e+00, -2.3315e-01, -4.7777e-01,
- -3.8799e-01, 5.0412e-01, 2.1321e-01],
- [ 2.1975e-01, -6.6211e-01, 1.9491e+00, -2.8295e-01, -1.4412e-01,
- -5.3107e-01, 8.4005e-01, 2.2348e-01],
- [ 3.7371e-01, -5.1912e-01, 1.7898e+00, -9.8038e-04, -4.0995e-01,
- 1.5966e-01, 5.4400e-01, 1.4020e-01],
- [ 8.9009e-01, -2.2979e-01, 1.2286e+00, -9.8838e-01, -5.1678e-01,
- -9.6966e-01, 4.1535e-01, 2.4532e-01],
- [ 8.0319e-01, -2.6611e-01, 1.7041e+00, -7.1392e-01, -3.1660e-01,
- -9.2966e-01, 6.5947e-01, 2.2535e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5781, -0.4129, 1.4142, -0.9057, -0.5115, -0.9937, 0.4621,
- 0.1080],
- [ 0.5897, -0.3527, 1.8018, 0.2545, -0.5307, -0.3229, 0.3122,
- 0.3084],
- [ 0.5359, -0.4193, 0.9358, -0.8232, -0.6635, -0.7232, 0.0943,
- 0.1710],
- [ 0.5791, -0.4127, 1.8442, -0.3985, -0.6031, -0.6154, 0.4473,
- 0.2464],
- [ 0.6471, -0.3719, 1.9866, -0.3921, -0.3806, -0.5538, 1.0070,
- 0.4600],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [ 0.6174, -0.4201, 1.7309, -0.8784, -0.4735, -0.9524, 0.6242,
- 0.1931]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0267, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0267, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.92330539226532
- step: 34
- running loss: 0.0859795703607447
- Train Steps: 34/90 Loss: 0.0860 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6223, 0.3990, 0.8037, 0.2800, 0.4000, 0.2283, 0.5864, 0.5208],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6196, 0.4088, 0.8888, 0.4583, 0.4500, 0.5683, 0.6138, 0.5883],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8903, -0.2314, 1.6684, -0.4563, -0.6001, -0.5134, 0.4911, 0.2531],
- [ 0.6476, -0.3743, 1.3517, -1.0156, -0.4161, -1.1527, 0.6536, 0.2023],
- [ 0.3698, -0.5161, 1.6889, 0.1680, -0.3293, 0.1163, 0.4273, 0.2552],
- [ 0.4036, -0.4773, 1.3534, -0.6551, -0.5117, -0.5085, 0.4268, 0.2266],
- [ 0.5026, -0.4508, 1.7764, 0.0095, -0.2172, 0.1691, 0.5443, 0.2641],
- [ 0.8662, -0.2546, 1.6973, -0.5305, -0.3797, -1.0850, 0.5950, 0.2191],
- [ 0.7662, -0.2932, 1.5427, -0.9673, -0.3746, -1.1312, 0.6072, 0.1884],
- [ 0.2559, -0.5938, 1.7963, 0.2113, -0.3554, 0.0888, 0.5212, 0.1696]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.5885, -0.4429, 1.4266, -0.9926, -0.4383, -1.2313, 0.4228,
- 0.1195],
- [ 0.5539, -0.4031, 1.7168, -0.0594, -0.3748, 0.0543, 0.1390,
- 0.3777],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.5759, -0.3978, 1.8192, -0.1689, -0.2074, 0.3392, 0.5490,
- 0.4316],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.5250, -0.4661, 1.7383, -0.0765, -0.4268, -0.0226, 0.2535,
- 0.2035]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0207, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0207, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.944044752046466
- step: 35
- running loss: 0.08411556434418474
- Train Steps: 35/90 Loss: 0.0841 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [ nan, nan, 0.7225, 0.2167, 0.3987, 0.2283, 0.5427, 0.5181],
- [0.6197, 0.3930, 0.8793, 0.4736, 0.4152, 0.5464, 0.6308, 0.5235],
- [0.6230, 0.4113, 0.7213, 0.1983, 0.4325, 0.2367, 0.6262, 0.5400],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6038, 0.3946, 0.8413, 0.4883, 0.3563, 0.4550, 0.5266, 0.4693]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1132, -0.7020, 1.3995, -0.7614, -0.5315, -0.8818, 0.3390, 0.2145],
- [ 0.6932, -0.3358, 1.7980, -0.1423, -0.4874, -0.1082, 0.4483, 0.2249],
- [ 1.0330, -0.1652, 1.8622, 0.3639, -0.4635, 0.0057, 0.5467, 0.1839],
- [-0.3976, -1.0464, 1.0914, -1.1965, -0.3316, -1.3292, 0.3589, 0.2467],
- [ 0.4669, -0.4691, 1.8673, 0.1160, -0.2885, 0.2232, 0.6384, 0.2342],
- [ 0.8902, -0.2152, 1.3809, -1.1008, -0.2806, -1.3165, 0.6639, 0.2483],
- [ 1.1518, -0.0532, 1.6962, -0.6029, -0.5221, -0.6022, 0.6503, 0.2845],
- [ 0.8269, -0.2339, 1.7614, 0.1012, -0.4519, -0.0218, 0.5402, 0.1910]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.5478, -0.8309, -0.6289, -0.7232, 0.1198,
- 0.1133],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [-2.2859, -2.2859, 1.0513, -1.2851, -0.4441, -1.2313, 0.2206,
- 0.1073],
- [ 0.5762, -0.4706, 1.7754, -0.0984, -0.3680, 0.2380, 0.6277,
- 0.1322],
- [ 0.5917, -0.3861, 1.0455, -1.3698, -0.2882, -1.1928, 0.6067,
- 0.2083],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.5030, -0.4631, 1.5998, -0.0303, -0.6404, -0.1843, 0.1465,
- -0.1181]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2440, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2440, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.1880801748484373
- step: 36
- running loss: 0.08855778263467881
- Train Steps: 36/90 Loss: 0.0886 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183],
- [0.6197, 0.3930, 0.8793, 0.4736, 0.4152, 0.5464, 0.6308, 0.5235],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6197, 0.4090, 0.7825, 0.2500, 0.4200, 0.2483, 0.5988, 0.5667],
- [0.6275, 0.4157, 0.8337, 0.5800, 0.3763, 0.4200, 0.5547, 0.6125],
- [0.6163, 0.4001, 0.8788, 0.5033, 0.4012, 0.4633, 0.5338, 0.5767],
- [0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6768, -0.3240, 1.6868, -1.0501, -0.2776, -1.1492, 0.7312, 0.1656],
- [ 0.6052, -0.3957, 1.7534, -0.1127, -0.5399, -0.3517, 0.5323, 0.1732],
- [ 0.3163, -0.5653, 1.7590, -0.0033, -0.3279, 0.0508, 0.4693, 0.2262],
- [ 0.7730, -0.3122, 1.8314, -0.1909, -0.5424, -0.1122, 0.4518, 0.2226],
- [ 0.7522, -0.3238, 1.3224, -1.1095, -0.3794, -1.1402, 0.5556, 0.2368],
- [ 0.8689, -0.2526, 1.6390, 0.1715, -0.4881, -0.2192, 0.3752, 0.2783],
- [ 0.3152, -0.6392, 1.7184, 0.0382, -0.4561, -0.1316, 0.4029, 0.2143],
- [ 0.4995, -0.4912, 1.5503, -0.8207, -0.5154, -0.7694, 0.6317, 0.1574]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [ 0.5944, -0.4543, 1.8018, 0.0082, -0.6058, -0.4306, 0.4162,
- 0.1082],
- [ 0.5762, -0.4706, 1.7754, -0.0984, -0.3680, 0.2380, 0.6277,
- 0.1322],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.5764, -0.3969, 1.3284, -1.1312, -0.3460, -1.1389, 0.4797,
- 0.3315],
- [ 0.6124, -0.3658, 1.5651, 0.3931, -0.5480, -0.3460, 0.2761,
- 0.5431],
- [ 0.5610, -0.4381, 1.7730, 0.0390, -0.4326, -0.1458, 0.1794,
- 0.3777],
- [ 0.6031, -0.4307, 1.7268, -0.8002, -0.6058, -0.6462, 0.6442,
- -0.0220]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0140, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0140, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.2020612247288227
- step: 37
- running loss: 0.08654219526294116
- Train Steps: 37/90 Loss: 0.0865 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301],
- [0.6197, 0.4091, 0.8800, 0.4783, 0.3538, 0.4767, 0.5950, 0.5550],
- [0.6216, 0.4100, 0.7350, 0.2067, 0.4325, 0.2050, 0.5950, 0.5333],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [0.6034, 0.4011, 0.7350, 0.2533, 0.3438, 0.3367, 0.5516, 0.5084],
- [0.6202, 0.4054, 0.8892, 0.4428, 0.3642, 0.3249, 0.5840, 0.5426]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5374, -0.4509, 1.7941, 0.2094, -0.3937, 0.1123, 0.4918, 0.2022],
- [ 0.6597, -0.3745, 1.7606, 0.0741, -0.4634, 0.1028, 0.4925, 0.2353],
- [ 0.3929, -0.5305, 1.1526, -1.1111, -0.3497, -1.3115, 0.4014, 0.2133],
- [ 0.3941, -0.5861, 1.9533, 0.0592, -0.3937, -0.2195, 0.7192, 0.2054],
- [ 0.7141, -0.3497, 1.5147, -0.8251, -0.4656, -0.9256, 0.4631, 0.2019],
- [ 0.7035, -0.3585, 1.7677, -0.3576, -0.5942, -0.2835, 0.4756, 0.1937],
- [ 0.6302, -0.3811, 1.2863, -0.9902, -0.5425, -0.8486, 0.4420, 0.1782],
- [ 0.4674, -0.5172, 1.7270, -0.2313, -0.5116, -0.5873, 0.4607, 0.1997]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6010, -0.4562, 1.7198, -0.0090, -0.3464, 0.0108, 0.6294,
- 0.1627],
- [ 0.5763, -0.3963, 1.7788, -0.0765, -0.6520, -0.0842, 0.4624,
- 0.2776],
- [ 0.5853, -0.3920, 1.1090, -1.3313, -0.2882, -1.3390, 0.4624,
- 0.1775],
- [ 0.6454, -0.3719, 1.9115, -0.1304, -0.5076, -0.2844, 1.0033,
- 0.4386],
- [ 0.5697, -0.4442, 1.3529, -0.9515, -0.5774, -0.7801, 0.5253,
- 0.1931],
- [ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [ 0.5009, -0.4333, 1.1090, -1.1158, -0.6982, -0.7309, 0.2617,
- 0.0622],
- [ 0.5788, -0.4135, 1.8214, -0.2408, -0.6039, -0.7854, 0.4115,
- 0.2203]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0173, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0173, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.2193711400032043
- step: 38
- running loss: 0.08472029315797906
- Train Steps: 38/90 Loss: 0.0847 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6101, 0.3977, 0.8550, 0.5667, 0.3862, 0.4417, 0.5128, 0.4869],
- [0.6157, 0.3956, 0.8323, 0.4138, 0.3479, 0.4431, 0.5914, 0.5131],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6124, 0.4083, 0.6954, 0.3069, 0.3650, 0.2750, 0.5163, 0.5485],
- [0.6198, 0.3997, 0.8582, 0.5361, 0.4117, 0.5016, 0.5942, 0.5134],
- [0.6198, 0.4114, 0.8263, 0.3283, 0.3550, 0.3583, 0.5813, 0.5667],
- [0.6332, 0.4118, 0.9238, 0.4267, 0.4012, 0.4733, 0.7525, 0.5436],
- [0.6268, 0.4052, 0.8175, 0.2250, 0.4688, 0.1917, 0.6375, 0.5267]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3966, -0.5936, 1.8039, 0.2802, -0.5525, -0.2142, 0.4026, 0.1941],
- [ 0.7885, -0.2987, 1.7053, -0.3514, -0.5758, -0.1134, 0.5165, 0.1591],
- [ 0.6118, -0.3937, 1.6096, -0.7545, -0.3470, -1.0085, 0.4864, 0.1848],
- [ 0.2276, -0.6336, 1.0980, -0.9782, -0.5359, -0.9213, 0.2522, 0.2356],
- [ 0.6053, -0.4369, 1.8010, 0.2543, -0.5330, 0.1212, 0.4244, 0.2200],
- [ 0.6160, -0.4273, 1.6637, -0.5837, -0.6304, -0.6150, 0.4379, 0.2124],
- [ 0.6644, -0.3704, 1.7846, -0.1783, -0.4724, 0.0512, 0.6495, 0.2111],
- [ 0.3931, -0.5582, 1.4735, -1.0682, -0.2781, -1.2448, 0.7007, 0.1874]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5320, -0.4488, 1.6633, 0.3315, -0.5018, -0.2459, 0.0828,
- -0.0370],
- [ 0.5581, -0.4586, 1.5586, -0.3747, -0.6792, -0.2391, 0.4455,
- 0.0840],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.5425, -0.3998, 0.9263, -0.8683, -0.6000, -1.0157, 0.0990,
- 0.2476],
- [ 0.5767, -0.4396, 1.6782, 0.1905, -0.3844, 0.0308, 0.4588,
- 0.0855],
- [ 0.5768, -0.3857, 1.5305, -0.7694, -0.6462, -0.6308, 0.3988,
- 0.3315],
- [ 0.6388, -0.3840, 1.9808, -0.3152, -0.4326, -0.0996, 1.1897,
- 0.2249],
- [ 0.6092, -0.4143, 1.4901, -1.2467, -0.1208, -1.4006, 0.6587,
- 0.1467]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0228, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0228, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.242176616564393
- step: 39
- running loss: 0.08313273375806136
- Train Steps: 39/90 Loss: 0.0831 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [0.6229, 0.4086, 0.7538, 0.2600, 0.4775, 0.1617, 0.5900, 0.5383],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6230, 0.4113, 0.7213, 0.1983, 0.4325, 0.2367, 0.6262, 0.5400],
- [0.6250, 0.4103, 0.8950, 0.4400, 0.3912, 0.5650, 0.6050, 0.5133],
- [0.6161, 0.4076, 0.8900, 0.4667, 0.4125, 0.5917, 0.6262, 0.5367],
- [0.6201, 0.4017, 0.8871, 0.4621, 0.3517, 0.4675, 0.5999, 0.5106],
- [0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 3.5106e-01, -6.2456e-01, 1.8187e+00, -1.4204e-01, -7.0050e-01,
- -4.5938e-01, 4.0855e-01, 1.4211e-01],
- [ 3.8717e-01, -5.6815e-01, 1.1987e+00, -1.1633e+00, -4.1027e-01,
- -1.3726e+00, 3.9880e-01, 2.0288e-01],
- [ 5.8238e-01, -4.1615e-01, 1.8140e+00, -5.7646e-02, -3.4776e-01,
- -5.9229e-03, 4.6542e-01, 2.3383e-01],
- [ 1.1506e-01, -7.4752e-01, 1.2902e+00, -1.1127e+00, -3.6575e-01,
- -1.3568e+00, 4.9368e-01, 2.0120e-01],
- [ 9.3898e-01, -1.8494e-01, 1.8908e+00, 1.1370e-03, -5.2647e-01,
- 2.3662e-01, 5.5223e-01, 1.8537e-01],
- [ 7.6586e-01, -3.0031e-01, 1.8964e+00, 9.3340e-02, -4.1862e-01,
- 3.0666e-01, 5.2751e-01, 2.0029e-01],
- [ 6.4795e-01, -4.0853e-01, 1.7987e+00, -6.2643e-03, -6.3486e-01,
- -1.4099e-01, 4.4951e-01, 1.5382e-01],
- [ 4.4453e-01, -5.2647e-01, 1.3447e+00, -1.0519e+00, -6.0382e-01,
- -9.8575e-01, 5.0882e-01, 1.7739e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [ 0.5911, -0.3984, 1.1956, -1.0850, -0.0804, -1.5392, 0.4393,
- 0.2006],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.5917, -0.3861, 1.0455, -1.3698, -0.2882, -1.1928, 0.6067,
- 0.2083],
- [ 0.6010, -0.3909, 1.8480, -0.2536, -0.4788, 0.3238, 0.5085,
- 0.0851],
- [ 0.5598, -0.4032, 1.8249, -0.1304, -0.3806, 0.4470, 0.6067,
- 0.1929],
- [ 0.5783, -0.4306, 1.8114, -0.1515, -0.6617, -0.1268, 0.4851,
- 0.0727],
- [ 0.5730, -0.4562, 1.2195, -1.2440, -0.5497, -0.7711, 0.5704,
- 0.1779]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0254, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0254, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.2675318624824286
- step: 40
- running loss: 0.08168829656206071
- Train Steps: 40/90 Loss: 0.0817 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6218, 0.4098, 0.7238, 0.1850, 0.4238, 0.2517, 0.6288, 0.5350],
- [0.6120, 0.4014, 0.6863, 0.2817, 0.3700, 0.2783, 0.5513, 0.5667],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767],
- [0.6350, 0.4043, 0.8738, 0.5650, 0.3850, 0.4750, 0.6401, 0.4950],
- [0.6225, 0.4116, 0.8662, 0.3517, 0.3663, 0.3233, 0.5837, 0.5317],
- [0.6128, 0.4116, 0.8450, 0.4583, 0.3675, 0.4867, 0.5337, 0.5446],
- [0.6175, 0.4013, 0.7734, 0.2783, 0.4475, 0.1786, 0.5790, 0.5351]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4367, -0.5225, 1.2960, -1.1648, -0.3793, -1.1430, 0.5874, 0.1746],
- [ 0.1962, -0.6501, 1.1282, -1.0401, -0.5617, -0.9396, 0.2712, 0.2072],
- [ 0.6875, -0.3992, 1.8857, 0.2476, -0.6058, -0.0681, 0.4812, 0.1275],
- [ 0.7143, -0.3665, 1.8605, 0.0773, -0.5132, 0.1704, 0.4499, 0.2356],
- [ 0.6062, -0.4491, 1.9378, 0.2578, -0.6167, -0.0194, 0.5542, 0.1477],
- [ 0.5631, -0.4239, 1.6553, -0.6901, -0.5287, -0.7452, 0.4552, 0.1585],
- [ 0.6069, -0.3986, 1.7356, -0.1607, -0.5416, 0.0315, 0.4627, 0.1705],
- [ 0.2619, -0.6570, 1.2979, -1.1250, -0.3744, -1.2685, 0.4204, 0.1447]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.8614e-01, -3.9292e-01, 1.0570e+00, -1.4314e+00, -3.2864e-01,
- -1.1235e+00, 6.1824e-01, 1.8522e-01],
- [ 5.4099e-01, -4.3210e-01, 8.8383e-01, -9.8491e-01, -5.7691e-01,
- -1.0003e+00, 2.6028e-01, 3.3149e-01],
- [ 6.3949e-01, -4.2132e-01, 1.7037e+00, 3.6228e-01, -4.3256e-01,
- -1.0731e-01, 6.5602e-01, -4.8817e-03],
- [ 5.5393e-01, -4.0308e-01, 1.7168e+00, -5.9398e-02, -3.7483e-01,
- 5.4350e-02, 1.3903e-01, 3.7768e-01],
- [ 6.4706e-01, -4.1832e-01, 1.7499e+00, 3.2379e-01, -5.0762e-01,
- -9.1917e-02, 6.7064e-01, 4.6189e-04],
- [ 5.8932e-01, -3.8468e-01, 1.7152e+00, -6.6159e-01, -5.9423e-01,
- -7.9246e-01, 4.1039e-01, 1.6982e-01],
- [ 5.4440e-01, -3.8460e-01, 1.6171e+00, -1.6890e-01, -5.8845e-01,
- -3.8029e-02, 1.7915e-01, 2.2961e-01],
- [ 5.6637e-01, -4.3212e-01, 1.2862e+00, -1.0003e+00, -2.1894e-01,
- -1.4608e+00, 3.8827e-01, 1.8549e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0188, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0188, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.2863815408200026
- step: 41
- running loss: 0.08015564733707323
- Train Steps: 41/90 Loss: 0.0802 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4098, 0.8575, 0.3617, 0.3700, 0.3550, 0.5988, 0.5667],
- [0.6250, 0.4146, 0.8838, 0.3933, 0.3588, 0.4283, 0.6162, 0.5367],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6262, 0.4052, 0.8888, 0.4700, 0.3675, 0.5117, 0.6350, 0.5233],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6164, 0.4119, 0.7913, 0.2650, 0.3538, 0.3500, 0.5614, 0.5038]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6545, -0.3618, 1.5680, -0.6445, -0.5170, -0.7510, 0.4842, 0.1577],
- [ 0.4752, -0.4913, 1.6541, -0.3822, -0.6434, -0.4164, 0.4551, 0.2095],
- [ 0.3454, -0.5692, 1.5968, -0.1963, -0.6108, -0.2757, 0.2590, 0.1625],
- [-0.3859, -1.0327, 1.4048, -1.1683, -0.1083, -1.1432, 0.6973, 0.1848],
- [ 0.7585, -0.2932, 1.6858, -0.1326, -0.3363, 0.0059, 0.4644, 0.1856],
- [ 0.8664, -0.2708, 1.8608, -0.0368, -0.6589, -0.0560, 0.5618, 0.1448],
- [ 0.7278, -0.3654, 1.6793, 0.0226, -0.5101, -0.1967, 0.3999, 0.1454],
- [ 0.5154, -0.4584, 1.2732, -1.0080, -0.6305, -0.8144, 0.3943, 0.1073]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5783, -0.3933, 1.6748, -0.6154, -0.5769, -0.6462, 0.4797,
- 0.3315],
- [ 0.6009, -0.3710, 1.7961, -0.4691, -0.6289, -0.3075, 0.5605,
- 0.1929],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893],
- [-2.2859, -2.2859, 1.7210, -0.9772, 0.1852, -1.3698, 0.7986,
- 0.3104],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.6063, -0.4142, 1.8192, -0.1150, -0.5885, 0.0774, 0.6471,
- 0.1313],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854],
- [ 0.5614, -0.3834, 1.3688, -1.0619, -0.6520, -0.6693, 0.3069,
- 0.0412]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1001, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1001, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.386511316522956
- step: 42
- running loss: 0.08063122182197514
- Train Steps: 42/90 Loss: 0.0806 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167],
- [0.6226, 0.4098, 0.8912, 0.4100, 0.4025, 0.2383, 0.5763, 0.5367],
- [0.6325, 0.4066, 0.8438, 0.5350, 0.3925, 0.5267, 0.7113, 0.5795],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [ nan, nan, 0.8625, 0.2550, 0.5487, 0.2200, 0.7335, 0.5737],
- [0.6250, 0.4146, 0.8838, 0.3933, 0.3588, 0.4283, 0.6162, 0.5367]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3529, -0.5591, 1.5007, -0.3760, -0.6113, -0.3282, 0.2479, 0.2410],
- [ 0.8080, -0.2771, 1.6025, -0.3582, -0.6192, -0.1207, 0.3971, 0.0940],
- [ 0.5119, -0.4641, 1.4563, -0.7292, -0.5492, -0.8653, 0.3301, 0.0829],
- [ 0.2396, -0.6196, 1.5460, -0.4810, -0.4276, -0.7962, 0.3526, 0.1782],
- [ 0.9781, -0.1953, 1.7784, 0.0812, -0.5292, 0.1354, 0.6026, 0.1393],
- [ 0.7572, -0.3118, 1.6693, -0.2089, -0.5883, -0.2834, 0.4285, 0.1437],
- [-0.4364, -1.0733, 1.4616, -1.2481, -0.0045, -1.1192, 0.8184, 0.1947],
- [ 0.5306, -0.4469, 1.6497, -0.4279, -0.6176, -0.3333, 0.4536, 0.1986]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.5483, -0.4249, 1.5305, -0.7386, -0.6115, -0.8694, 0.3353,
- 0.1005],
- [ 0.5900, -0.3932, 1.8307, -0.3921, -0.4268, -1.1851, 0.3758,
- 0.1929],
- [ 0.6355, -0.4080, 1.6113, 0.1852, -0.4730, 0.1467, 0.9996,
- 0.3905],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [-2.2859, -2.2859, 1.6979, -1.1081, 0.2487, -1.2697, 1.1020,
- 0.3638],
- [ 0.6009, -0.3710, 1.7961, -0.4691, -0.6289, -0.3075, 0.5605,
- 0.1929]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1016, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1016, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.488085037097335
- step: 43
- running loss: 0.08111825667668221
- Train Steps: 43/90 Loss: 0.0811 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6151, 0.4125, 0.8738, 0.4417, 0.3575, 0.3783, 0.5138, 0.5483],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507],
- [0.6196, 0.4090, 0.8450, 0.3900, 0.3575, 0.3933, 0.5975, 0.5583],
- [0.6201, 0.4064, 0.8688, 0.5050, 0.4225, 0.5100, 0.6138, 0.5500],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142],
- [0.6257, 0.4167, 0.8775, 0.3433, 0.3563, 0.4133, 0.6200, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5349, -0.4455, 1.7570, -0.2074, -0.5198, -0.4080, 0.4787, 0.1820],
- [ 0.8963, -0.2477, 1.9448, -0.1026, -0.4083, 0.2798, 0.6832, 0.1334],
- [ 0.3211, -0.5580, 1.6122, -0.2500, -0.5693, -0.4049, 0.2903, 0.1702],
- [ 0.3067, -0.5936, 1.0340, -1.2498, -0.3961, -1.2777, 0.3361, 0.1416],
- [ 0.9243, -0.1990, 1.6087, -0.5908, -0.5360, -0.4200, 0.5517, 0.1284],
- [ 0.6387, -0.3762, 1.7011, 0.0358, -0.4203, 0.0398, 0.3977, 0.1838],
- [-0.7036, -1.2559, 1.3289, -0.8699, -0.4771, -0.9723, 0.2688, 0.1476],
- [ 0.5620, -0.4357, 1.7383, -0.5753, -0.5294, -0.5052, 0.5535, 0.1751]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.5551, -0.3807, 1.7499, -0.2459, -0.6346, -0.5384, 0.0871,
- 0.2468],
- [ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576],
- [ 0.5761, -0.3966, 1.6171, -0.4845, -0.6346, -0.4691, 0.4739,
- 0.2930],
- [ 0.5784, -0.4088, 1.7268, 0.0467, -0.3344, 0.0697, 0.5490,
- 0.2545],
- [-2.2859, -2.2859, 1.5767, -0.7540, -0.6404, -0.7309, 0.1753,
- 0.0893],
- [ 0.6040, -0.3614, 1.7672, -0.7001, -0.6404, -0.3768, 0.5778,
- 0.3315]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0732, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0732, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.561272194609046
- step: 44
- running loss: 0.08093800442293286
- Train Steps: 44/90 Loss: 0.0809 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6233, 0.4091, 0.8100, 0.2950, 0.3563, 0.3883, 0.6013, 0.5200],
- [0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064],
- [ nan, nan, 0.9050, 0.3500, 0.5138, 0.2300, 0.7359, 0.5702],
- [0.6197, 0.3930, 0.8793, 0.4736, 0.4152, 0.5464, 0.6308, 0.5235],
- [0.6268, 0.4061, 0.8350, 0.2433, 0.4575, 0.2283, 0.6350, 0.5300],
- [0.6212, 0.4033, 0.8938, 0.4167, 0.3813, 0.4267, 0.5613, 0.5583],
- [ nan, nan, 0.9088, 0.3783, 0.4562, 0.2617, 0.6741, 0.5575]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5818, -0.3966, 1.3973, -0.8580, -0.6440, -0.5126, 0.4475, 0.1186],
- [ 0.4153, -0.4874, 1.2256, -0.7259, -0.5466, -0.6686, 0.3026, 0.2101],
- [ 0.7514, -0.3059, 1.7006, 0.0987, -0.3783, 0.1060, 0.2963, 0.1680],
- [-0.2648, -0.9652, 1.7160, -0.6842, -0.1627, -0.8070, 0.7399, 0.2166],
- [ 0.5834, -0.4091, 1.7065, -0.0113, -0.5106, 0.1586, 0.4210, 0.1692],
- [ 0.2055, -0.6349, 1.4568, -0.9875, -0.3721, -1.0002, 0.5307, 0.1490],
- [ 0.5841, -0.4346, 1.6850, -0.2511, -0.6876, -0.1978, 0.3221, 0.1884],
- [ 0.0765, -0.7286, 1.7289, -0.5439, -0.2815, -0.7442, 0.6015, 0.1698]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5932, -0.3962, 1.4554, -0.9233, -0.6404, -0.4922, 0.4912,
- 0.1159],
- [ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469],
- [ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532],
- [-2.2859, -2.2859, 1.8942, -0.6693, 0.0871, -1.2236, 1.1130,
- 0.3478],
- [ 0.5762, -0.4706, 1.7754, -0.0984, -0.3680, 0.2380, 0.6277,
- 0.1322],
- [ 0.6093, -0.4104, 1.5709, -1.1620, -0.1727, -1.2313, 0.6471,
- 0.1621],
- [ 0.5832, -0.4231, 1.8423, -0.3614, -0.5249, -0.3152, 0.3065,
- 0.2930],
- [-2.2859, -2.2859, 1.9115, -0.5384, -0.1785, -1.0773, 0.8278,
- 0.2890]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2405, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2405, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.8017283137887716
- step: 45
- running loss: 0.08448285141752826
- Train Steps: 45/90 Loss: 0.0845 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6204, 0.4055, 0.8438, 0.5733, 0.4574, 0.4801, 0.5487, 0.5617],
- [0.6280, 0.4055, 0.8600, 0.5317, 0.3800, 0.4700, 0.6275, 0.5133],
- [0.6261, 0.4131, 0.8938, 0.3550, 0.4000, 0.2683, 0.6188, 0.5283],
- [0.6109, 0.4009, 0.8725, 0.4700, 0.3550, 0.4167, 0.5650, 0.5117],
- [0.6137, 0.4035, 0.8850, 0.4417, 0.3900, 0.4283, 0.5449, 0.5617],
- [0.6124, 0.4075, 0.7696, 0.4153, 0.3475, 0.3767, 0.5157, 0.5427],
- [0.6127, 0.4066, 0.8550, 0.5567, 0.4662, 0.5141, 0.5070, 0.5412],
- [0.6276, 0.4120, 0.8738, 0.3133, 0.4225, 0.2217, 0.6203, 0.4892]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2672, -0.5984, 1.5340, -0.0941, -0.3328, -0.1551, 0.3480, 0.2435],
- [ 0.4768, -0.4793, 1.7141, -0.1435, -0.5566, -0.1459, 0.5068, 0.1785],
- [ 0.1151, -0.7118, 1.6953, -0.8581, -0.3410, -0.9446, 0.6515, 0.1427],
- [ 0.2253, -0.6507, 1.7210, -0.3347, -0.5786, -0.4340, 0.3847, 0.1178],
- [ 0.5995, -0.4095, 1.6505, -0.4597, -0.5058, -0.2441, 0.3787, 0.1836],
- [ 0.4865, -0.4288, 1.3854, -0.7569, -0.5593, -0.5213, 0.3683, 0.1623],
- [ 0.2983, -0.6022, 1.6223, 0.0036, -0.2758, -0.0715, 0.3743, 0.2261],
- [-0.1031, -0.8570, 1.6374, -0.9917, -0.2689, -1.0909, 0.6932, 0.1572]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5795, -0.4129, 1.6113, 0.3623, -0.1733, -0.0684, 0.2487,
- 0.3084],
- [ 0.6148, -0.4130, 1.6864, 0.1698, -0.5307, -0.1150, 0.6125,
- 0.0851],
- [ 0.6062, -0.3778, 1.8423, -0.6462, -0.4383, -1.0465, 0.5721,
- 0.1544],
- [ 0.5356, -0.4343, 1.7441, -0.1150, -0.6462, -0.3614, 0.3238,
- 0.0774],
- [ 0.5488, -0.4221, 1.8018, -0.2459, -0.4845, -0.3075, 0.2309,
- 0.3087],
- [ 0.5427, -0.4035, 1.2688, -0.3675, -0.6808, -0.5461, 0.0959,
- 0.2206],
- [ 0.5443, -0.4079, 1.6633, 0.2853, -0.1325, 0.0888, 0.0558,
- 0.2138],
- [ 0.6128, -0.3828, 1.7499, -0.8386, -0.3344, -1.2620, 0.5792,
- -0.0263]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0429, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0429, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.8446308206766844
- step: 46
- running loss: 0.08357893088427575
- Train Steps: 46/90 Loss: 0.0836 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6364, 0.4165, 0.9088, 0.4367, 0.4075, 0.3150, 0.6448, 0.5297],
- [0.6282, 0.4029, 0.8988, 0.3000, 0.4250, 0.4183, 0.7042, 0.5575],
- [0.6265, 0.4088, 0.8025, 0.1850, 0.4163, 0.2500, 0.6290, 0.4947],
- [0.6157, 0.3956, 0.8323, 0.4138, 0.3479, 0.4431, 0.5914, 0.5131],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [ nan, nan, 0.7412, 0.2200, 0.4450, 0.1517, 0.5312, 0.4983],
- [0.6159, 0.4085, 0.6900, 0.2283, 0.4088, 0.1950, 0.5123, 0.5397],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.1204e-01, -4.0808e-01, 2.0241e+00, -6.4815e-04, -3.0531e-01,
- -2.6063e-01, 6.0652e-01, 2.0491e-01],
- [ 7.8274e-01, -2.8036e-01, 1.8523e+00, -4.5895e-01, -4.4856e-01,
- 9.5163e-03, 7.9724e-01, 1.7100e-01],
- [ 1.6438e-02, -7.3354e-01, 1.6209e+00, -8.2435e-01, -2.8295e-01,
- -8.0990e-01, 6.1467e-01, 1.8458e-01],
- [ 1.1100e+00, -7.7610e-02, 1.8684e+00, -1.1496e-01, -5.9891e-01,
- 1.6606e-01, 4.7673e-01, 1.2712e-01],
- [ 7.8423e-01, -2.5731e-01, 1.6101e+00, -3.6818e-01, -6.1474e-01,
- -1.4655e-01, 3.7293e-01, 1.9924e-01],
- [-1.0913e+00, -1.4849e+00, 1.1627e+00, -9.4262e-01, -2.5918e-01,
- -1.1330e+00, 3.0192e-01, 2.3340e-01],
- [-3.3309e-01, -9.9671e-01, 1.1426e+00, -8.3217e-01, -3.9860e-01,
- -1.0915e+00, 2.8002e-01, 2.2096e-01],
- [-2.7431e-01, -9.2364e-01, 1.1610e+00, -8.5328e-01, -3.1564e-01,
- -1.1378e+00, 2.8685e-01, 2.2268e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.5365e-01, -3.6231e-01, 1.9115e+00, -2.6898e-01, -4.0370e-01,
- -8.3095e-01, 6.9257e-01, 1.6077e-01],
- [ 6.1577e-01, -4.2490e-01, 1.8654e+00, -9.0023e-01, -3.2286e-01,
- -3.5366e-01, 9.6675e-01, 2.8902e-01],
- [ 6.0785e-01, -3.9761e-01, 1.4208e+00, -1.4314e+00, -3.6328e-01,
- -1.1312e+00, 6.1950e-01, -9.2270e-04],
- [ 5.5813e-01, -4.5860e-01, 1.5586e+00, -3.7466e-01, -6.7920e-01,
- -2.3907e-01, 4.4552e-01, 8.4044e-02],
- [ 5.3274e-01, -4.3811e-01, 1.2880e+00, -6.3079e-01, -6.8661e-01,
- -5.3072e-01, 2.6581e-01, 3.4174e-01],
- [-2.2859e+00, -2.2859e+00, 1.1379e+00, -1.2697e+00, -2.3048e-01,
- -1.5854e+00, 1.6790e-01, 1.5858e-02],
- [ 5.5912e-01, -3.9900e-01, 9.0115e-01, -1.2313e+00, -3.9792e-01,
- -1.3852e+00, 8.0445e-02, 2.0706e-01],
- [ 5.5664e-01, -4.1601e-01, 9.9353e-01, -1.3313e+00, -2.8245e-01,
- -1.5161e+00, 2.1441e-01, 1.2532e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1320, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1320, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.9766319822520018
- step: 47
- running loss: 0.08460919111174472
- Train Steps: 47/90 Loss: 0.0846 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6179, 0.4008, 0.7505, 0.2678, 0.4368, 0.1891, 0.5831, 0.5263],
- [ nan, nan, 0.6992, 0.2791, 0.3950, 0.2383, 0.5483, 0.5819],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433],
- [0.6125, 0.3999, 0.8750, 0.4883, 0.4750, 0.4700, 0.5533, 0.5617],
- [ nan, nan, 0.7335, 0.2569, 0.3788, 0.2667, 0.5066, 0.5578],
- [0.6229, 0.4086, 0.7538, 0.2600, 0.4775, 0.1617, 0.5900, 0.5383],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6150, 0.4097, 0.7468, 0.3194, 0.3825, 0.2633, 0.5463, 0.5800]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6122, -0.3670, 1.3447, -0.9112, -0.3654, -1.0974, 0.4136, 0.1788],
- [-0.8619, -1.2983, 1.1162, -0.8752, -0.3778, -1.0457, 0.2680, 0.2437],
- [ 0.9006, -0.2098, 2.0488, 0.0939, -0.2921, 0.4136, 0.6445, 0.1981],
- [ 0.8501, -0.2358, 1.9843, 0.0972, -0.2533, 0.0895, 0.5619, 0.2259],
- [-1.4110, -1.6712, 1.2796, -0.7929, -0.3579, -0.9350, 0.3379, 0.2312],
- [ 0.4885, -0.4263, 1.3314, -1.0387, -0.2906, -1.1694, 0.4948, 0.2056],
- [ 0.9188, -0.1726, 2.0675, -0.0769, -0.5989, 0.1320, 0.6938, 0.0644],
- [-0.4029, -0.9729, 1.3120, -0.6700, -0.4773, -0.8498, 0.2555, 0.2297]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5680, -0.4345, 1.1806, -1.0490, -0.2683, -1.4127, 0.4074,
- 0.1449],
- [-2.2859, -2.2859, 0.9438, -0.9967, -0.4614, -1.1851, 0.2468,
- 0.4019],
- [ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237],
- [ 0.5432, -0.4388, 1.7557, -0.0303, -0.0919, -0.1150, 0.2699,
- 0.3087],
- [-2.2859, -2.2859, 1.1020, -1.0994, -0.5365, -1.0542, 0.0542,
- 0.2905],
- [ 0.5911, -0.3984, 1.1956, -1.0850, -0.0804, -1.5392, 0.4393,
- 0.2006],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.5548, -0.3936, 1.1634, -0.8105, -0.5192, -1.0696, 0.2372,
- 0.3931]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1134, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1134, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.090075312182307
- step: 48
- running loss: 0.0852099023371314
- Train Steps: 48/90 Loss: 0.0852 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517],
- [0.6250, 0.4146, 0.8838, 0.3933, 0.3588, 0.4283, 0.6162, 0.5367],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064],
- [0.6143, 0.4040, 0.8237, 0.3333, 0.4075, 0.2117, 0.5137, 0.4973],
- [0.6164, 0.4102, 0.8850, 0.4867, 0.4213, 0.5983, 0.5609, 0.5038],
- [0.6250, 0.4008, 0.8950, 0.4183, 0.3550, 0.4383, 0.6361, 0.4927],
- [0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.0881, -0.8622, 1.7180, -0.3380, -0.1900, -0.3595, 0.5118, 0.1946],
- [ 0.3039, -0.5632, 1.6433, -0.7080, -0.5210, -0.6785, 0.4957, 0.2307],
- [ 0.1802, -0.7043, 1.6741, -0.2930, -0.1235, -0.5044, 0.3889, 0.2316],
- [-0.6809, -1.1939, 1.2687, -1.0639, -0.3153, -1.4568, 0.3238, 0.1441],
- [ 0.3311, -0.5428, 1.7258, -0.2202, -0.2688, -0.0183, 0.4605, 0.2348],
- [ 0.1304, -0.6649, 1.6293, -0.6603, -0.5442, -0.5658, 0.5650, 0.1014],
- [ 0.3655, -0.5611, 1.7344, -0.1714, -0.4312, -0.2769, 0.4720, 0.1755],
- [ 0.4916, -0.4244, 1.5928, -0.1345, -0.4315, -0.3216, 0.4903, 0.2304]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622],
- [ 0.6009, -0.3710, 1.7961, -0.4691, -0.6289, -0.3075, 0.5605,
- 0.1929],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532],
- [ 0.5515, -0.4201, 1.5189, -0.7463, -0.4037, -1.3082, 0.0869,
- 0.0111],
- [ 0.5614, -0.3912, 1.8018, -0.0380, -0.3402, 0.4778, 0.3049,
- 0.0412],
- [ 0.6010, -0.4345, 1.8480, -0.3537, -0.6462, -0.2613, 0.6524,
- -0.0099],
- [ 0.5783, -0.4363, 1.7724, 0.0432, -0.5153, 0.0871, 0.4840,
- 0.0663],
- [ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0986, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0986, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.188635913655162
- step: 49
- running loss: 0.08548236558479923
- Train Steps: 49/90 Loss: 0.0855 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6124, 0.4075, 0.7696, 0.4153, 0.3475, 0.3767, 0.5157, 0.5427],
- [0.6126, 0.4073, 0.8750, 0.5133, 0.3800, 0.4333, 0.4986, 0.5378],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6022, 0.3994, 0.8025, 0.3350, 0.3350, 0.4400, 0.5565, 0.5025],
- [0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6138, 0.4101, 0.8800, 0.5083, 0.4637, 0.5950, 0.5587, 0.5077],
- [0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3454, -0.4971, 1.4509, -0.5696, -0.5079, -0.5487, 0.3331, 0.1901],
- [ 0.3420, -0.5645, 1.7734, 0.0995, -0.4219, -0.2674, 0.3567, 0.2133],
- [ 0.2438, -0.6110, 1.4486, -0.8155, -0.3154, -1.0944, 0.4986, 0.2272],
- [-0.4310, -1.0179, 1.4841, -0.7465, -0.4986, -0.4605, 0.4811, 0.1436],
- [ 0.0408, -0.7762, 1.8346, -0.0525, -0.3941, -0.1446, 0.5553, 0.1564],
- [-0.1729, -0.8700, 1.3802, -0.9201, -0.1231, -1.1452, 0.4031, 0.2139],
- [ 0.3208, -0.5864, 1.8330, -0.0026, -0.1255, 0.0109, 0.5079, 0.2647],
- [ 0.1492, -0.6696, 1.2125, -1.0467, -0.4508, -1.1560, 0.3488, 0.1378]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5427, -0.4035, 1.2688, -0.3675, -0.6808, -0.5461, 0.0959,
- 0.2206],
- [ 0.5435, -0.4045, 1.7557, 0.0851, -0.5307, -0.2844, 0.0172,
- 0.1980],
- [ 0.5915, -0.3682, 1.2187, -1.2313, -0.4326, -0.9541, 0.5778,
- 0.3777],
- [ 0.4956, -0.4413, 1.4208, -0.7386, -0.7386, -0.2536, 0.2843,
- 0.0351],
- [ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.5491, -0.3918, 1.7788, 0.0620, -0.1439, 0.4624, 0.2946,
- 0.0592],
- [ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0689, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0689, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.257585039362311
- step: 50
- running loss: 0.08515170078724622
- Train Steps: 50/90 Loss: 0.0852 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422],
- [0.6266, 0.4070, 0.8712, 0.5600, 0.3713, 0.4783, 0.5775, 0.6100],
- [0.6275, 0.4008, 0.8047, 0.2484, 0.4037, 0.2777, 0.6506, 0.5212],
- [0.6205, 0.4062, 0.8337, 0.2683, 0.3675, 0.4283, 0.6338, 0.5250],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6262, 0.4052, 0.8888, 0.4700, 0.3675, 0.5117, 0.6350, 0.5233],
- [0.6179, 0.3961, 0.8347, 0.6020, 0.3887, 0.4624, 0.5714, 0.5373],
- [0.6159, 0.4085, 0.6900, 0.2283, 0.4088, 0.1950, 0.5123, 0.5397]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.4041, -1.6769, 1.1390, -0.9196, -0.2693, -1.1782, 0.2023, 0.2169],
- [ 0.6574, -0.3363, 1.7427, 0.2147, -0.4140, 0.0338, 0.3964, 0.2300],
- [ 0.1212, -0.6699, 1.4240, -1.0007, -0.3117, -1.0039, 0.5159, 0.1357],
- [ 0.3221, -0.5317, 1.4546, -0.9382, -0.4403, -0.4426, 0.5357, 0.1844],
- [ 0.6383, -0.3453, 1.7350, -0.3782, -0.4291, -0.7050, 0.3973, 0.1939],
- [ 0.6196, -0.4027, 1.9948, -0.1199, -0.3932, 0.0846, 0.6095, 0.1669],
- [ 0.4998, -0.4693, 1.7146, 0.2622, -0.3820, -0.1580, 0.3382, 0.1745],
- [-0.3319, -0.9927, 1.0219, -1.1324, -0.3122, -1.3981, 0.2109, 0.1964]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.2469, -1.0288, -0.4557, -1.2774, 0.0511,
- 0.2183],
- [ 0.6081, -0.4059, 1.7383, 0.3007, -0.5711, -0.0765, 0.3815,
- 0.5316],
- [ 0.6125, -0.4345, 1.4308, -1.1384, -0.4213, -1.0031, 0.7190,
- 0.1214],
- [ 0.5799, -0.4099, 1.5651, -1.0465, -0.5885, -0.3075, 0.6413,
- 0.1390],
- [ 0.6016, -0.3633, 1.7152, -0.2228, -0.6115, -0.6385, 0.5028,
- 0.2699],
- [ 0.6063, -0.4142, 1.8192, -0.1150, -0.5885, 0.0774, 0.6471,
- 0.1313],
- [ 0.5680, -0.4562, 1.5697, 0.4947, -0.4904, -0.1503, 0.3536,
- 0.1956],
- [ 0.5591, -0.3990, 0.9012, -1.2313, -0.3979, -1.3852, 0.0804,
- 0.2071]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0524, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0524, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.309938298538327
- step: 51
- running loss: 0.08450859408898681
- Train Steps: 51/90 Loss: 0.0845 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [0.6236, 0.4084, 0.7738, 0.2133, 0.3663, 0.3233, 0.5813, 0.5567],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6164, 0.3972, 0.7818, 0.3381, 0.3599, 0.3387, 0.5880, 0.5153],
- [0.6250, 0.4131, 0.8688, 0.2983, 0.4275, 0.2367, 0.6162, 0.5367],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [0.6127, 0.4066, 0.8550, 0.5567, 0.4662, 0.5141, 0.5070, 0.5412]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1343, -0.6719, 1.1848, -0.9330, -0.2622, -1.1929, 0.2911, 0.1972],
- [ 0.1454, -0.6445, 1.3932, -0.7919, -0.4341, -0.7242, 0.3274, 0.2350],
- [ 0.6946, -0.3061, 1.8101, 0.3960, -0.4587, 0.0493, 0.4624, 0.1293],
- [ 0.3389, -0.5140, 1.4280, -0.7080, -0.5626, -0.5696, 0.3538, 0.1557],
- [ 0.1701, -0.6353, 1.6288, -0.7426, -0.2407, -0.7913, 0.5828, 0.1906],
- [ 0.6218, -0.3358, 1.3809, -0.8998, -0.4000, -0.9331, 0.4869, 0.1414],
- [-1.3165, -1.6054, 1.0127, -1.0492, -0.3465, -1.1461, 0.2269, 0.2403],
- [ 0.4412, -0.4863, 1.7145, 0.4136, -0.2517, 0.1877, 0.3192, 0.2372]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [ 0.5946, -0.3995, 1.2880, -1.3005, -0.5942, -0.7925, 0.3988,
- 0.2853],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.5614, -0.4514, 1.3252, -0.7240, -0.6237, -0.7215, 0.4299,
- 0.0942],
- [ 0.6011, -0.3778, 1.7268, -0.9079, -0.3113, -1.1928, 0.5605,
- 0.1929],
- [ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929],
- [-2.2859, -2.2859, 0.8516, -1.3112, -0.4326, -1.2851, 0.0755,
- 0.2930],
- [ 0.5443, -0.4079, 1.6633, 0.2853, -0.1325, 0.0888, 0.0558,
- 0.2138]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0551, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0551, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.365078965201974
- step: 52
- running loss: 0.08394382625388411
- Train Steps: 52/90 Loss: 0.0839 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6152, 0.4131, 0.6863, 0.2567, 0.3625, 0.3300, 0.5765, 0.5305],
- [0.6364, 0.4154, 0.8938, 0.3717, 0.4500, 0.2583, 0.6448, 0.5285],
- [0.6124, 0.4030, 0.8650, 0.4867, 0.4999, 0.5106, 0.5137, 0.5773],
- [ nan, nan, 0.6488, 0.1817, 0.4325, 0.1867, 0.5475, 0.5733],
- [0.6164, 0.4102, 0.8850, 0.4867, 0.4213, 0.5983, 0.5609, 0.5038],
- [0.6299, 0.4008, 0.8450, 0.5350, 0.4213, 0.5000, 0.6350, 0.5100],
- [0.6284, 0.4093, 0.8900, 0.4700, 0.3650, 0.3850, 0.6212, 0.5167],
- [0.6307, 0.4029, 0.8988, 0.4817, 0.3937, 0.3500, 0.7311, 0.5378]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7222, -0.2672, 0.9825, -1.1078, -0.5705, -0.9164, 0.1739, 0.2092],
- [ 0.0665, -0.7308, 1.5910, -0.8096, -0.2316, -1.0253, 0.5274, 0.2099],
- [ 0.3037, -0.5983, 1.5746, -0.1910, -0.2327, -0.1118, 0.2617, 0.2293],
- [-1.2385, -1.5823, 0.9541, -1.2474, -0.2575, -1.4829, 0.2528, 0.2454],
- [ 0.3653, -0.5106, 1.7113, -0.0647, -0.3102, 0.2423, 0.4094, 0.1881],
- [ 0.4853, -0.4565, 1.5947, 0.0082, -0.4378, -0.0843, 0.3777, 0.1922],
- [ 0.8494, -0.2006, 1.7019, -0.1805, -0.6135, -0.5201, 0.4251, 0.0953],
- [ 0.1864, -0.6458, 1.7229, -0.2324, -0.4180, -0.7384, 0.4583, 0.1592]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5557, -0.3779, 0.8838, -1.1004, -0.6115, -0.7617, 0.3769,
- 0.1644],
- [ 0.6537, -0.3671, 1.8423, -0.5692, -0.2074, -1.0927, 0.6926,
- 0.1554],
- [ 0.5428, -0.4244, 1.7095, -0.0380, 0.0232, 0.0725, 0.0866,
- 0.3806],
- [-2.2859, -2.2859, 0.7106, -1.4468, -0.2882, -1.4237, 0.2430,
- 0.3623],
- [ 0.5614, -0.3912, 1.8018, -0.0380, -0.3402, 0.4778, 0.3049,
- 0.0412],
- [ 0.6236, -0.4344, 1.6171, 0.1852, -0.3402, 0.0236, 0.6471,
- 0.0697],
- [ 0.6164, -0.3956, 1.8249, -0.1150, -0.6000, -0.5076, 0.5836,
- 0.1005],
- [ 0.6273, -0.4249, 1.8654, -0.0611, -0.4672, -0.6693, 1.0910,
- 0.1982]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0608, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0608, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.425908403471112
- step: 53
- running loss: 0.08350770572587005
- Train Steps: 53/90 Loss: 0.0835 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6196, 0.4068, 0.7645, 0.2234, 0.4575, 0.1737, 0.5926, 0.5284],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6176, 0.4030, 0.8850, 0.4850, 0.3688, 0.4050, 0.5312, 0.5783],
- [0.6264, 0.4049, 0.8988, 0.4633, 0.3813, 0.4983, 0.6326, 0.4843],
- [0.6109, 0.4015, 0.7668, 0.3639, 0.3513, 0.3667, 0.5200, 0.5641],
- [ nan, nan, 0.6859, 0.2194, 0.4150, 0.1867, 0.5153, 0.5729],
- [0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5744, -0.4358, 1.1659, -1.2056, -0.2736, -1.3638, 0.3496, 0.1837],
- [ 0.7267, -0.3071, 1.6715, 0.2827, -0.4446, -0.0200, 0.4095, 0.1985],
- [ 0.3578, -0.5564, 1.3084, -1.0304, -0.2044, -1.2064, 0.3487, 0.2020],
- [ 0.2623, -0.5900, 1.6264, 0.0475, -0.5414, -0.3029, 0.2797, 0.1594],
- [ 0.3787, -0.5423, 1.8219, -0.1248, -0.5105, -0.0737, 0.4833, 0.1497],
- [ 0.3219, -0.5242, 1.3093, -0.7075, -0.5767, -0.5406, 0.2827, 0.1780],
- [-0.7890, -1.2903, 0.9590, -1.2208, -0.3849, -1.4650, 0.2070, 0.1993],
- [ 0.2771, -0.6158, 1.7014, -0.1671, -0.3850, 0.1569, 0.4584, 0.2011]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5761, -0.4070, 1.2452, -1.2541, -0.1725, -1.4835, 0.4511,
- 0.1545],
- [ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.5667, -0.4246, 1.8018, -0.0457, -0.5827, -0.4152, 0.1679,
- 0.3854],
- [ 0.6075, -0.4159, 1.8654, -0.1458, -0.5249, 0.0159, 0.6359,
- -0.0490],
- [ 0.5357, -0.4315, 1.2557, -0.6051, -0.6635, -0.5923, 0.1159,
- 0.3198],
- [-2.2859, -2.2859, 0.8824, -1.2727, -0.3691, -1.4237, 0.0943,
- 0.3604],
- [ 0.5960, -0.4102, 1.8018, -0.1612, -0.3344, 0.1159, 0.5490,
- 0.2314]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0666, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0666, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.492464052513242
- step: 54
- running loss: 0.08319377875024522
- Train Steps: 54/90 Loss: 0.0832 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6214, 0.4112, 0.7838, 0.2117, 0.3650, 0.3133, 0.5675, 0.5083],
- [0.6198, 0.4114, 0.8263, 0.3283, 0.3550, 0.3583, 0.5813, 0.5667],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6339, 0.4112, 0.8838, 0.5067, 0.4000, 0.5433, 0.7549, 0.5378],
- [ nan, nan, 0.8625, 0.2550, 0.5487, 0.2200, 0.7335, 0.5737],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [0.6175, 0.3997, 0.6737, 0.2500, 0.4313, 0.1933, 0.5513, 0.5750]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9089, -0.1808, 1.2733, -1.0337, -0.5515, -1.1081, 0.3434, 0.1587],
- [ 0.8128, -0.2403, 1.4522, -0.6860, -0.6329, -0.7484, 0.2570, 0.1964],
- [ 1.2244, 0.0209, 1.4750, 0.2158, -0.4895, -0.2091, 0.2452, 0.2366],
- [ 0.7003, -0.3044, 1.5976, -0.0632, -0.4107, 0.0459, 0.3074, 0.2059],
- [ 0.8016, -0.2368, 1.7200, -0.0157, -0.4262, 0.1961, 0.5880, 0.1784],
- [-2.0187, -2.1144, 1.5037, -1.0512, -0.0099, -1.3094, 0.7017, 0.2278],
- [ 0.6363, -0.3475, 1.5620, -0.2526, -0.5205, -0.2222, 0.1970, 0.1762],
- [-0.8353, -1.2941, 0.8914, -1.2110, -0.3824, -1.3947, 0.1707, 0.2402]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5845, -0.3864, 1.3342, -1.3082, -0.6000, -0.8386, 0.3353,
- 0.0620],
- [ 0.5768, -0.3857, 1.5305, -0.7694, -0.6462, -0.6308, 0.3988,
- 0.3315],
- [ 0.5836, -0.3649, 1.7210, 0.3854, -0.3979, -0.2921, 0.3065,
- 0.4470],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.6421, -0.3864, 1.7961, 0.0543, -0.4383, 0.2237, 1.2007,
- 0.1982],
- [-2.2859, -2.2859, 1.6979, -1.1081, 0.2487, -1.2697, 1.1020,
- 0.3638],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893],
- [ 0.5663, -0.4396, 0.8261, -1.1312, -0.2940, -1.3929, 0.2603,
- 0.3700]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0768, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0768, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.569240244105458
- step: 55
- running loss: 0.08307709534737197
- Train Steps: 55/90 Loss: 0.0831 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617],
- [0.6262, 0.4085, 0.8438, 0.3150, 0.4025, 0.2633, 0.6339, 0.4810],
- [0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4381, -0.5051, 1.6501, -0.5635, -0.4440, -0.7391, 0.4992, 0.2375],
- [ 0.8613, -0.2273, 1.4765, -0.8301, -0.5814, -0.6689, 0.3431, 0.1861],
- [ 0.5253, -0.4384, 1.5418, -0.5003, -0.5351, -0.5880, 0.3159, 0.2336],
- [-1.3941, -1.6921, 0.9840, -1.2563, -0.2956, -1.3670, 0.2403, 0.2752],
- [ 0.2707, -0.6181, 1.3609, -1.0859, -0.4104, -1.0531, 0.4897, 0.1458],
- [ 0.4641, -0.4778, 1.4754, 0.0333, -0.1887, -0.2335, 0.3023, 0.2703],
- [ 0.3964, -0.5099, 1.5877, 0.1125, -0.4331, -0.1267, 0.4035, 0.1513],
- [ 1.0365, -0.1044, 1.5411, -0.0426, -0.5323, -0.1892, 0.3891, 0.1199]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753],
- [ 0.5880, -0.3887, 1.8423, -0.3306, -0.6231, -0.5230, 0.4046,
- 0.1544],
- [-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084],
- [ 0.6064, -0.3990, 1.6113, -0.8309, -0.4268, -1.0696, 0.6421,
- -0.0640],
- [ 0.5959, -0.3579, 1.6055, 0.3623, -0.0573, -0.2074, 0.3122,
- 0.4547],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0489, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0489, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.618122825399041
- step: 56
- running loss: 0.08246647902498287
- Train Steps: 56/90 Loss: 0.0825 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6284, 0.4029, 0.8838, 0.3783, 0.3975, 0.2850, 0.6335, 0.5090],
- [0.6038, 0.3946, 0.8413, 0.4883, 0.3563, 0.4550, 0.5266, 0.4693],
- [0.6168, 0.4055, 0.7575, 0.2950, 0.4050, 0.2717, 0.5938, 0.6167],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6263, 0.4057, 0.8800, 0.3833, 0.3650, 0.3717, 0.6375, 0.4804],
- [0.6166, 0.4008, 0.8563, 0.5667, 0.4388, 0.4933, 0.5575, 0.5567],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6339, 0.4102, 0.9088, 0.4767, 0.3925, 0.5283, 0.7509, 0.5390]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1561, -0.7037, 1.4751, -0.8651, -0.4711, -1.0469, 0.5206, 0.1341],
- [ 0.5377, -0.4234, 1.5176, -0.2102, -0.5475, -0.2934, 0.2895, 0.1455],
- [ 0.2270, -0.6552, 1.1592, -1.1152, -0.4722, -1.0442, 0.3479, 0.2583],
- [ 0.4311, -0.5644, 1.5158, -1.1601, -0.2336, -1.4870, 0.4191, 0.1780],
- [ 0.3855, -0.5671, 1.5510, -0.7643, -0.5894, -0.6581, 0.5060, 0.1097],
- [ 0.1805, -0.6948, 1.5721, 0.1061, -0.2787, -0.1696, 0.3229, 0.2565],
- [ 0.5569, -0.4505, 1.4991, 0.0849, -0.4491, -0.2875, 0.2419, 0.2883],
- [ 0.1861, -0.6641, 1.6742, -0.1751, -0.4272, 0.1524, 0.5527, 0.2295]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6165, -0.4249, 1.7961, -0.5384, -0.4499, -0.9695, 0.6401,
- 0.0652],
- [ 0.5030, -0.4631, 1.5998, -0.0303, -0.6404, -0.1843, 0.1465,
- -0.1181],
- [ 0.5631, -0.4129, 1.2129, -0.9233, -0.4152, -1.0311, 0.4566,
- 0.5624],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.6071, -0.4119, 1.7788, -0.5153, -0.6000, -0.5692, 0.6586,
- -0.0670],
- [ 0.5620, -0.4346, 1.6691, 0.3315, -0.2594, -0.0072, 0.2891,
- 0.2853],
- [ 0.5836, -0.3649, 1.7210, 0.3854, -0.3979, -0.2921, 0.3065,
- 0.4470],
- [ 0.6421, -0.3912, 1.9115, -0.0842, -0.4730, 0.1544, 1.1824,
- 0.2035]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0432, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0432, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.661323884502053
- step: 57
- running loss: 0.08177761200880795
- Train Steps: 57/90 Loss: 0.0818 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550],
- [0.6226, 0.4098, 0.8912, 0.4100, 0.4025, 0.2383, 0.5763, 0.5367],
- [0.6201, 0.4017, 0.8871, 0.4621, 0.3517, 0.4675, 0.5999, 0.5106],
- [0.6131, 0.4064, 0.8638, 0.5200, 0.4788, 0.4783, 0.5258, 0.5867],
- [0.6179, 0.4040, 0.7412, 0.1850, 0.3825, 0.2783, 0.5837, 0.5600],
- [0.6260, 0.4133, 0.8800, 0.5117, 0.3713, 0.3283, 0.6223, 0.4967],
- [0.6332, 0.4118, 0.9238, 0.4267, 0.4012, 0.4733, 0.7525, 0.5436],
- [0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0544, -0.7835, 1.6998, -0.2782, -0.1604, -0.2922, 0.4850, 0.1874],
- [ 0.4879, -0.4952, 1.6149, -0.6312, -0.3887, -1.1637, 0.4164, 0.2082],
- [ 0.2649, -0.6053, 1.6451, -0.2102, -0.5783, -0.1651, 0.4630, 0.1315],
- [ 0.2480, -0.6386, 1.6026, 0.0719, -0.2249, -0.1325, 0.3098, 0.2759],
- [ 0.1634, -0.7327, 1.2079, -1.2229, -0.4614, -1.3717, 0.3322, 0.2116],
- [ 1.0088, -0.1769, 1.6499, -0.2045, -0.4546, -0.9317, 0.4750, 0.1453],
- [ 0.2639, -0.6029, 1.6201, -0.3808, -0.5056, -0.0960, 0.6122, 0.1866],
- [ 0.3316, -0.5745, 1.2158, -1.0627, -0.6265, -0.7776, 0.3601, 0.1813]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776],
- [ 0.5900, -0.3932, 1.8307, -0.3921, -0.4268, -1.1851, 0.3758,
- 0.1929],
- [ 0.5783, -0.4306, 1.8114, -0.1515, -0.6617, -0.1268, 0.4851,
- 0.0727],
- [ 0.5462, -0.4090, 1.7037, 0.1159, -0.0746, -0.0765, 0.1427,
- 0.4239],
- [ 0.5680, -0.4201, 1.1379, -1.4314, -0.5192, -1.0003, 0.4104,
- 0.3007],
- [ 0.6054, -0.3767, 1.7788, 0.0774, -0.5711, -0.7694, 0.5887,
- 0.0081],
- [ 0.6388, -0.3840, 1.9808, -0.3152, -0.4326, -0.0996, 1.1897,
- 0.2249],
- [ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0437, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0437, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.704992236569524
- step: 58
- running loss: 0.08112055580292282
- Train Steps: 58/90 Loss: 0.0811 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389],
- [0.6179, 0.4040, 0.7412, 0.1850, 0.3825, 0.2783, 0.5837, 0.5600],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364],
- [ nan, nan, 0.9050, 0.3500, 0.5138, 0.2300, 0.7359, 0.5702],
- [ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6725, -0.3250, 1.3117, -0.8996, -0.4375, -0.9283, 0.3662, 0.2144],
- [ 0.8203, -0.2586, 1.2539, -0.8671, -0.4885, -0.8802, 0.2916, 0.2076],
- [-0.6151, -1.1069, 1.2332, -0.7149, -0.5765, -0.8060, 0.1702, 0.2368],
- [ 1.7519, 0.3353, 1.6793, 0.2937, -0.6515, -0.0736, 0.3457, 0.1722],
- [ 0.9992, -0.1174, 1.3909, -0.8545, -0.3347, -0.9903, 0.4672, 0.1633],
- [-0.7490, -1.2308, 1.8226, -0.4618, -0.1372, -0.7754, 0.7525, 0.2115],
- [-1.7876, -1.9232, 1.5286, -0.8708, -0.1098, -0.9816, 0.6602, 0.2375],
- [ 1.5744, 0.1914, 1.7823, 0.1854, -0.5948, 0.1148, 0.5642, 0.1143]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5697, -0.4466, 1.1973, -1.1871, -0.4571, -0.9965, 0.5219,
- 0.2032],
- [ 0.5680, -0.4201, 1.1379, -1.4314, -0.5192, -1.0003, 0.4104,
- 0.3007],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699],
- [ 0.5902, -0.3493, 1.7961, -0.0072, -0.5942, -0.5615, 0.3180,
- 0.3161],
- [ 0.6158, -0.3960, 1.4092, -1.2774, -0.2074, -1.1851, 0.8491,
- 0.1917],
- [-2.2859, -2.2859, 1.8942, -0.6693, 0.0871, -1.2236, 1.1130,
- 0.3478],
- [-2.2859, -2.2859, 1.7210, -0.9772, 0.1852, -1.3698, 0.7986,
- 0.3104],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2201, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2201, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.925117092207074
- step: 59
- running loss: 0.08347656088486566
- Train Steps: 59/90 Loss: 0.0835 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6265, 0.4091, 0.8950, 0.3533, 0.3600, 0.3967, 0.6295, 0.4901],
- [0.6264, 0.4035, 0.8888, 0.4883, 0.4050, 0.5217, 0.6361, 0.4791],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413],
- [0.6219, 0.4114, 0.8175, 0.2817, 0.3925, 0.2783, 0.5900, 0.5350],
- [0.6271, 0.4005, 0.8450, 0.6067, 0.3850, 0.4983, 0.6069, 0.4649],
- [0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667],
- [0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4033, -0.5310, 1.8569, -0.6054, -0.5117, -0.6812, 0.6229, 0.1313],
- [ 0.4407, -0.5129, 1.8577, 0.1828, -0.4270, 0.1355, 0.5361, 0.1932],
- [-0.1968, -0.9619, 1.9427, -0.6664, -0.1138, -1.0862, 0.8384, 0.1491],
- [-0.0191, -0.8111, 1.3862, -1.2234, -0.2565, -1.2246, 0.4617, 0.2555],
- [ 0.8325, -0.2923, 1.7349, 0.3951, -0.4775, -0.0507, 0.5048, 0.1812],
- [ 0.3294, -0.5901, 1.8064, 0.0931, -0.5397, -0.2080, 0.5408, 0.1618],
- [ 0.2896, -0.6054, 1.0851, -0.9982, -0.5647, -0.8826, 0.1846, 0.2427],
- [ 0.8394, -0.2579, 1.1610, -0.9770, -0.4822, -1.0040, 0.3513, 0.2346]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6077, -0.3965, 1.8480, -0.6539, -0.6231, -0.4537, 0.6216,
- -0.0220],
- [ 0.6074, -0.4223, 1.8192, -0.0303, -0.4152, 0.1236, 0.6524,
- -0.0731],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142],
- [ 0.5868, -0.3858, 1.4901, -0.9849, -0.4730, -1.0003, 0.4393,
- 0.1852],
- [ 0.6105, -0.4360, 1.6171, 0.5162, -0.5076, 0.0159, 0.5171,
- -0.1385],
- [ 0.5944, -0.4543, 1.8018, 0.0082, -0.6058, -0.4306, 0.4162,
- 0.1082],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315],
- [ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0440, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0440, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.969099497422576
- step: 60
- running loss: 0.08281832495704293
- Train Steps: 60/90 Loss: 0.0828 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783],
- [0.6153, 0.4117, 0.8688, 0.5167, 0.4895, 0.5647, 0.5524, 0.5136],
- [0.6219, 0.4114, 0.8175, 0.2817, 0.3925, 0.2783, 0.5900, 0.5350],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650],
- [0.6129, 0.4069, 0.8750, 0.5067, 0.3875, 0.4233, 0.5235, 0.5881],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.1576, -0.9122, 1.2972, -1.2263, -0.1811, -1.4133, 0.4684, 0.2480],
- [ 0.2890, -0.6472, 1.9153, 0.1004, -0.1274, -0.1649, 0.6380, 0.1997],
- [-0.0181, -0.8165, 1.4741, -1.3042, -0.2795, -1.3269, 0.5811, 0.2282],
- [ 0.4858, -0.4523, 1.6892, -0.2371, -0.5700, -0.3723, 0.6010, 0.1366],
- [ 0.8972, -0.2306, 1.8460, 0.3335, -0.4067, 0.0752, 0.6305, 0.1847],
- [ 0.3262, -0.6062, 1.8215, -0.3808, -0.6322, -0.4538, 0.5082, 0.1785],
- [ 0.2143, -0.6786, 1.8186, 0.2153, -0.5068, -0.2337, 0.4954, 0.1994],
- [ 0.7243, -0.3422, 1.3246, -1.1767, -0.5365, -1.1800, 0.4903, 0.1229]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854],
- [ 0.5564, -0.3842, 1.7268, 0.1005, -0.0250, 0.3225, 0.2658,
- 0.0862],
- [ 0.5868, -0.3858, 1.4901, -0.9849, -0.4730, -1.0003, 0.4393,
- 0.1852],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.5742, -0.3792, 1.6460, 0.3084, -0.3460, 0.1467, 0.4162,
- 0.3161],
- [ 0.5757, -0.3917, 1.7095, -0.4768, -0.6346, -0.4229, 0.3931,
- 0.3238],
- [ 0.5452, -0.4067, 1.7557, 0.0543, -0.4961, -0.3306, 0.1323,
- 0.4306],
- [ 0.4997, -0.4446, 1.1610, -0.9772, -0.6058, -1.0311, 0.1404,
- -0.1031]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0569, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0569, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.025980396196246
- step: 61
- running loss: 0.0823931212491188
- Train Steps: 61/90 Loss: 0.0824 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150],
- [0.6289, 0.4032, 0.8419, 0.5446, 0.4075, 0.5017, 0.6312, 0.5117],
- [0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383],
- [0.6229, 0.4066, 0.8513, 0.5717, 0.4500, 0.5050, 0.6025, 0.5350],
- [0.6159, 0.4085, 0.6900, 0.2283, 0.4088, 0.1950, 0.5123, 0.5397],
- [ nan, nan, 0.8463, 0.2550, 0.5850, 0.2133, 0.7129, 0.6072],
- [0.6239, 0.4123, 0.8313, 0.2550, 0.4500, 0.2050, 0.6175, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6747, -0.3336, 1.7131, -0.3043, -0.6373, -0.1198, 0.5301, 0.1279],
- [ 0.3088, -0.5878, 1.4396, -1.0615, -0.3396, -1.2393, 0.4457, 0.2140],
- [ 0.5520, -0.4229, 1.8363, 0.2268, -0.5093, -0.0411, 0.6594, 0.1800],
- [ 0.2341, -0.6258, 1.8258, 0.1728, -0.3747, -0.0613, 0.4904, 0.1651],
- [ 0.6160, -0.3853, 1.7532, 0.3894, -0.3895, -0.0292, 0.4597, 0.1998],
- [-0.1318, -0.8842, 1.0308, -1.2271, -0.5247, -1.3953, 0.2541, 0.2315],
- [-0.7442, -1.3088, 1.8895, -0.8294, -0.0512, -1.0553, 1.0225, 0.2369],
- [ 0.9457, -0.1848, 1.5723, -1.0821, -0.2259, -1.0955, 0.6738, 0.1960]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.2448e-01, -4.3610e-01, 1.5940e+00, -2.9207e-01, -5.4804e-01,
- -9.1917e-02, 2.4319e-01, 5.0176e-02],
- [ 5.7962e-01, -4.3256e-01, 1.4439e+00, -1.1774e+00, -2.9400e-01,
- -1.3390e+00, 3.9307e-01, 9.2841e-02],
- [ 6.1888e-01, -4.2379e-01, 1.6026e+00, 2.2948e-01, -4.0370e-01,
- 3.1255e-02, 6.2979e-01, 7.7444e-02],
- [ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01],
- [ 5.9107e-01, -4.0805e-01, 1.6460e+00, 3.5458e-01, -2.0739e-01,
- 4.6651e-02, 4.9700e-01, 1.8522e-01],
- [ 5.5912e-01, -3.9900e-01, 9.0115e-01, -1.2313e+00, -3.9792e-01,
- -1.3852e+00, 8.0445e-02, 2.0706e-01],
- [-2.2859e+00, -2.2859e+00, 1.6229e+00, -1.1081e+00, 4.1617e-01,
- -1.3005e+00, 1.0070e+00, 5.1879e-01],
- [ 5.9579e-01, -3.8176e-01, 1.5536e+00, -1.1081e+00, -2.0739e-01,
- -1.3390e+00, 5.6628e-01, 2.0831e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0864, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0864, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.112371591851115
- step: 62
- running loss: 0.08245760632017927
- Train Steps: 62/90 Loss: 0.0825 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683],
- [0.6168, 0.4055, 0.7575, 0.2950, 0.4050, 0.2717, 0.5938, 0.6167],
- [0.6196, 0.4094, 0.7562, 0.2817, 0.3937, 0.3183, 0.6013, 0.6183],
- [0.6357, 0.4118, 0.8400, 0.2500, 0.5413, 0.1633, 0.6725, 0.5586],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413],
- [0.6261, 0.3987, 0.8688, 0.4917, 0.4300, 0.5333, 0.7010, 0.5309],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0652, -0.0872, 1.6244, 0.1411, -0.5858, -0.4915, 0.3320, 0.1692],
- [ 0.4764, -0.4995, 1.2932, -0.9137, -0.4796, -0.9494, 0.4467, 0.2394],
- [ 0.5212, -0.4979, 1.4810, -0.8740, -0.4198, -0.9972, 0.4141, 0.2848],
- [-0.5558, -1.2075, 1.6232, -1.1434, 0.0361, -1.3837, 0.8139, 0.2174],
- [ 0.6402, -0.3467, 1.4580, -0.7266, -0.6276, -0.4378, 0.4899, 0.1890],
- [ 0.1559, -0.7151, 1.9275, -0.6109, -0.1726, -1.0339, 0.7443, 0.1235],
- [ 0.2836, -0.6025, 1.9319, 0.1667, -0.3236, 0.1527, 0.6902, 0.1907],
- [ 0.1549, -0.6862, 1.9986, 0.2127, -0.4233, 0.2488, 0.7887, 0.1478]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5777, -0.3915, 1.7961, 0.1698, -0.5192, -0.5307, 0.2141,
- 0.3392],
- [ 0.5631, -0.4129, 1.2129, -0.9233, -0.4152, -1.0311, 0.4566,
- 0.5624],
- [ 0.5759, -0.3948, 1.2072, -0.9849, -0.4672, -0.8156, 0.4912,
- 0.5701],
- [ 0.6504, -0.3840, 1.5940, -1.1312, 0.2141, -1.5315, 0.8205,
- 0.2944],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142],
- [ 0.6059, -0.4442, 1.7268, -0.0149, -0.2998, 0.1775, 0.9521,
- 0.1661],
- [ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0639, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0639, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.176298273727298
- step: 63
- running loss: 0.08216346466233806
- Train Steps: 63/90 Loss: 0.0822 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6221, 0.4107, 0.7788, 0.3033, 0.3950, 0.2817, 0.6075, 0.5517],
- [0.6250, 0.4110, 0.7238, 0.2067, 0.4263, 0.1883, 0.5625, 0.5633],
- [0.6329, 0.4175, 0.8550, 0.2333, 0.5425, 0.2250, 0.7398, 0.5609],
- [0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6198, 0.4075, 0.8650, 0.5617, 0.4150, 0.4367, 0.5450, 0.5650],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550],
- [0.6108, 0.4011, 0.8037, 0.3400, 0.3700, 0.2933, 0.5658, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0356, -0.1234, 1.7094, -0.6482, -0.3317, -0.7847, 0.5696, 0.2211],
- [ 0.3286, -0.6007, 1.2848, -0.8995, -0.3070, -1.1710, 0.4285, 0.2396],
- [-0.8547, -1.3968, 1.9650, -0.7491, 0.1321, -1.1016, 1.1164, 0.2244],
- [ 0.4023, -0.5407, 1.4807, -0.8273, -0.4771, -0.7097, 0.6189, 0.1762],
- [ 0.4463, -0.5124, 1.2960, -0.9030, -0.4845, -0.9337, 0.3351, 0.2077],
- [ 0.2804, -0.6267, 1.8965, 0.6625, -0.3471, 0.0380, 0.4486, 0.2180],
- [ 0.5654, -0.3786, 1.6728, -0.4492, -0.5768, -0.1508, 0.6567, 0.1743],
- [ 0.4768, -0.4687, 1.5823, -0.5242, -0.4902, -0.6029, 0.5122, 0.1763]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5875, -0.3888, 1.3111, -0.8848, -0.4614, -0.9849, 0.5201,
- 0.2622],
- [ 0.6010, -0.3875, 1.0570, -1.3313, -0.3171, -1.4160, 0.3122,
- 0.3161],
- [ 0.6372, -0.3575, 1.6633, -1.2082, 0.2199, -1.2467, 1.1313,
- 0.3050],
- [ 0.5730, -0.4562, 1.2195, -1.2440, -0.5497, -0.7711, 0.5704,
- 0.1779],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.5770, -0.4036, 1.7095, 0.3084, -0.3691, -0.2690, 0.2314,
- 0.3238],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776],
- [ 0.5355, -0.4335, 1.4266, -0.7155, -0.5769, -0.9310, 0.3275,
- 0.3087]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0955, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0955, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.271774662658572
- step: 64
- running loss: 0.08237147910404019
- Train Steps: 64/90 Loss: 0.0824 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6209, 0.3920, 0.8650, 0.5367, 0.4400, 0.5067, 0.6025, 0.4950],
- [0.6277, 0.4013, 0.8888, 0.4767, 0.3600, 0.3567, 0.6148, 0.4672],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6147, 0.4107, 0.8137, 0.3333, 0.3750, 0.2683, 0.5006, 0.5412],
- [0.6059, 0.4002, 0.7562, 0.2767, 0.3538, 0.3033, 0.5529, 0.5455],
- [0.6034, 0.4011, 0.7350, 0.2533, 0.3438, 0.3367, 0.5516, 0.5084],
- [0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6064, 0.3953, 0.8738, 0.4417, 0.3663, 0.4683, 0.5511, 0.5416]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.1920, -0.9985, 1.9063, 0.1103, -0.1318, -0.3085, 0.7094, 0.2498],
- [ 0.7767, -0.3053, 1.7186, -0.3475, -0.4719, -0.7444, 0.6525, 0.1397],
- [ 0.1857, -0.7125, 1.8274, -0.0031, -0.2454, -0.2910, 0.5246, 0.2424],
- [ 0.6512, -0.3863, 1.4643, -0.8827, -0.4335, -1.0586, 0.3989, 0.2542],
- [ 0.4884, -0.4620, 1.2575, -1.1185, -0.4550, -1.0417, 0.4923, 0.2755],
- [ 0.5038, -0.4734, 1.2670, -1.2477, -0.5148, -1.0009, 0.4584, 0.2556],
- [ 0.4376, -0.5049, 1.8492, -0.1855, -0.2348, -0.1066, 0.7327, 0.2066],
- [ 0.5826, -0.4122, 1.8609, -0.2930, -0.4397, -0.3699, 0.7610, 0.1792]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.8199e-01, -4.7544e-01, 1.7095e+00, 1.9292e-01, -2.5358e-01,
- 5.4350e-02, 4.9700e-01, 4.6189e-04],
- [ 6.1322e-01, -4.3241e-01, 1.8192e+00, -8.4219e-02, -6.2309e-01,
- -6.3849e-01, 5.5366e-01, -1.2778e-01],
- [ 5.4348e-01, -4.5974e-01, 1.6575e+00, 1.5858e-02, -3.2286e-01,
- -1.1501e-01, 1.8767e-01, 1.8544e-01],
- [ 5.5319e-01, -3.8879e-01, 1.4727e+00, -7.4627e-01, -5.5381e-01,
- -1.0465e+00, 2.6467e-02, 2.1383e-01],
- [ 5.1288e-01, -4.3741e-01, 1.2072e+00, -1.0080e+00, -6.5196e-01,
- -8.8483e-01, 2.6787e-01, 2.3353e-01],
- [ 5.0092e-01, -4.3333e-01, 1.1090e+00, -1.1158e+00, -6.9815e-01,
- -7.3087e-01, 2.6170e-01, 6.2199e-02],
- [ 5.6039e-01, -4.6197e-01, 1.7961e+00, -1.9969e-01, -3.2286e-01,
- 1.0824e-01, 4.1039e-01, 2.5450e-01],
- [ 5.1490e-01, -4.6028e-01, 1.7499e+00, -2.4588e-01, -5.9423e-01,
- -1.2271e-01, 2.5964e-01, 2.1549e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0448, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0448, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.316538447514176
- step: 65
- running loss: 0.08179289919252579
- Train Steps: 65/90 Loss: 0.0818 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383],
- [0.6258, 0.4038, 0.8750, 0.4883, 0.3900, 0.5500, 0.6375, 0.5217],
- [0.6150, 0.3935, 0.8696, 0.5158, 0.4647, 0.5329, 0.6041, 0.5153],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6185, 0.4079, 0.8838, 0.4617, 0.4838, 0.5650, 0.6175, 0.5850],
- [0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364],
- [0.6284, 0.4093, 0.8900, 0.4700, 0.3650, 0.3850, 0.6212, 0.5167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1470, -0.7047, 1.6310, -0.2381, -0.2549, -0.3992, 0.4727, 0.2435],
- [ 0.4058, -0.5714, 1.8483, -0.1800, -0.3816, -0.1532, 0.7788, 0.2107],
- [ 0.4472, -0.5076, 1.6044, -0.1745, -0.2147, -0.2088, 0.4506, 0.2606],
- [ 0.2987, -0.5943, 1.5517, -0.6556, -0.5261, -0.4434, 0.5252, 0.2261],
- [ 0.4258, -0.5116, 1.7422, -0.3681, -0.1858, -0.1330, 0.6773, 0.2530],
- [ 0.9321, -0.1669, 1.5974, -0.3834, -0.4631, -0.9787, 0.4675, 0.2501],
- [ 0.9310, -0.1953, 1.3346, -1.2929, -0.2924, -1.4419, 0.5972, 0.2600],
- [ 0.1639, -0.7135, 1.7702, -0.3880, -0.5578, -0.8406, 0.6680, 0.1920]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01],
- [ 6.0468e-01, -4.2094e-01, 1.7557e+00, -3.0331e-02, -4.8453e-01,
- 2.5450e-01, 6.5866e-01, 1.2363e-01],
- [ 5.5484e-01, -4.6823e-01, 1.7309e+00, 9.6578e-02, -1.3942e-01,
- 1.7573e-01, 5.0451e-01, 9.4188e-02],
- [ 5.2448e-01, -4.3610e-01, 1.5940e+00, -2.9207e-01, -5.4804e-01,
- -9.1917e-02, 2.4319e-01, 5.0176e-02],
- [ 5.7067e-01, -4.0169e-01, 1.7961e+00, -1.5350e-01, -5.1501e-02,
- 3.2379e-01, 5.6628e-01, 4.1617e-01],
- [ 5.9007e-01, -4.0000e-01, 1.8423e+00, -6.8822e-02, -5.3072e-01,
- -9.2333e-01, 3.6420e-01, 1.8522e-01],
- [ 6.1577e-01, -3.9601e-01, 1.4092e+00, -1.2774e+00, -2.0739e-01,
- -1.1851e+00, 8.4910e-01, 1.9173e-01],
- [ 6.1640e-01, -3.9561e-01, 1.8249e+00, -1.1501e-01, -6.0000e-01,
- -5.0762e-01, 5.8360e-01, 1.0054e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0462, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0462, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.362761614844203
- step: 66
- running loss: 0.0812539638612758
- Train Steps: 66/90 Loss: 0.0813 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4013, 0.8191, 0.3188, 0.4279, 0.2060, 0.5767, 0.5411],
- [ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422],
- [0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309],
- [ nan, nan, 0.7225, 0.2167, 0.3987, 0.2283, 0.5427, 0.5181],
- [0.6202, 0.4064, 0.7879, 0.2179, 0.4567, 0.1725, 0.5955, 0.5478],
- [0.6222, 0.4072, 0.7164, 0.2166, 0.3738, 0.3167, 0.6100, 0.5533],
- [0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.1903, 0.0091, 1.5244, -0.6896, -0.3186, -0.7837, 0.4619, 0.2637],
- [-1.1955, -1.5569, 1.2581, -0.9294, -0.3222, -1.0647, 0.3363, 0.3174],
- [ 1.1021, -0.0334, 1.8661, 0.1296, -0.4194, -0.2605, 0.6152, 0.1854],
- [-1.0808, -1.4725, 1.1546, -1.0496, -0.2942, -1.1167, 0.2463, 0.3367],
- [ 1.2006, -0.0122, 1.3387, -0.9424, -0.2428, -0.9820, 0.4809, 0.2814],
- [ 0.8916, -0.2122, 1.2474, -0.9546, -0.4703, -0.7094, 0.5053, 0.2677],
- [ 0.9518, -0.2066, 1.7941, -0.2452, -0.5559, -0.2133, 0.7859, 0.1155],
- [ 0.6461, -0.3687, 1.8293, 0.2481, -0.3678, 0.1926, 0.6810, 0.1674]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.4321, 1.4975, -0.8134, -0.3094, -1.3345, 0.3779,
- 0.2134],
- [-2.2859, -2.2859, 1.2469, -1.0288, -0.4557, -1.2774, 0.0511,
- 0.2183],
- [ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661],
- [-2.2859, -2.2859, 1.0513, -1.2851, -0.4441, -1.2313, 0.2206,
- 0.1073],
- [ 0.5787, -0.4086, 1.3535, -1.2794, -0.1764, -1.4891, 0.4645,
- 0.2442],
- [ 0.5878, -0.4052, 1.0229, -1.2855, -0.5596, -0.8232, 0.5316,
- 0.2699],
- [ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697],
- [ 0.6273, -0.4393, 1.6402, 0.1313, -0.5076, 0.0467, 1.1532,
- 0.1715]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1144, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1144, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.477159423753619
- step: 67
- running loss: 0.08174864811572566
- Train Steps: 67/90 Loss: 0.0817 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.4106, 0.8700, 0.3717, 0.3588, 0.4967, 0.6038, 0.5167],
- [0.6269, 0.4073, 0.8900, 0.4933, 0.3625, 0.4183, 0.6288, 0.5150],
- [0.6271, 0.4024, 0.9000, 0.3517, 0.3700, 0.4517, 0.6931, 0.5285],
- [0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417],
- [0.6274, 0.4270, 0.8938, 0.4967, 0.3550, 0.4283, 0.5700, 0.5733],
- [0.6325, 0.4066, 0.8438, 0.5350, 0.3925, 0.5267, 0.7113, 0.5795]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7726, -0.2872, 1.5579, -0.6419, -0.4527, -0.1764, 0.6722, 0.1945],
- [ 0.7038, -0.3455, 1.7372, -0.0100, -0.4515, -0.4409, 0.4419, 0.2044],
- [ 0.3806, -0.5373, 1.5902, -0.6628, -0.4155, -0.2866, 0.7564, 0.1726],
- [ 0.3435, -0.5409, 1.6290, -0.5285, -0.3532, -0.7808, 0.4576, 0.3081],
- [-0.3292, -1.0527, 0.9807, -1.2696, -0.3676, -1.4329, 0.1426, 0.3147],
- [ 0.8057, -0.2529, 1.3971, -1.0243, -0.3280, -1.0554, 0.4332, 0.2968],
- [ 0.4678, -0.4743, 1.6984, 0.0821, -0.4409, -0.3279, 0.4225, 0.2544],
- [ 0.9089, -0.2301, 1.7610, 0.1076, -0.3777, 0.0481, 0.7147, 0.1679]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6010, -0.3896, 1.7326, -0.5692, -0.6289, 0.0082, 0.5028,
- 0.1005],
- [ 0.6096, -0.4046, 1.8249, -0.0072, -0.6115, -0.3537, 0.6182,
- 0.0928],
- [ 0.6108, -0.4273, 1.8711, -0.6616, -0.5769, -0.1997, 0.9156,
- 0.1554],
- [ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928],
- [-2.2859, -2.2859, 1.0712, -1.2085, -0.3806, -1.3929, 0.0755,
- 0.2006],
- [ 0.5977, -0.3792, 1.2995, -1.0311, -0.5192, -0.8386, 0.5836,
- 0.2160],
- [ 0.6121, -0.3138, 1.8423, 0.0082, -0.6462, -0.3075, 0.3469,
- 0.3623],
- [ 0.6355, -0.4080, 1.6113, 0.1852, -0.4730, 0.1467, 0.9996,
- 0.3905]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1032, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1032, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.580386197194457
- step: 68
- running loss: 0.08206450289991848
- Train Steps: 68/90 Loss: 0.0821 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6277, 0.4083, 0.8350, 0.2717, 0.4562, 0.1800, 0.5918, 0.4878],
- [0.6059, 0.4002, 0.7562, 0.2767, 0.3538, 0.3033, 0.5529, 0.5455],
- [0.6142, 0.4127, 0.7575, 0.3067, 0.3438, 0.4383, 0.5778, 0.5207],
- [0.6189, 0.4049, 0.8888, 0.4417, 0.4213, 0.5200, 0.5988, 0.5633],
- [0.6357, 0.4118, 0.8400, 0.2500, 0.5413, 0.1633, 0.6725, 0.5586],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550],
- [0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0113, -0.7472, 1.4550, -0.8894, -0.2580, -0.9753, 0.4123, 0.2772],
- [ 0.4000, -0.5421, 1.2346, -0.7698, -0.5962, -0.8030, 0.3929, 0.2342],
- [ 0.3309, -0.5806, 1.3000, -0.6666, -0.6106, -0.3164, 0.3896, 0.2519],
- [ 0.7557, -0.3389, 1.8301, 0.0937, -0.4879, 0.2119, 0.5420, 0.1904],
- [ 0.4026, -0.5461, 1.5781, -1.0008, -0.0243, -1.1922, 0.8809, 0.2303],
- [ 0.6067, -0.4049, 1.7667, 0.0835, -0.2052, -0.0215, 0.5257, 0.1727],
- [ 0.5976, -0.4035, 1.4973, -0.7421, -0.5810, -0.9208, 0.4500, 0.2530],
- [ 0.6578, -0.3394, 1.3067, -0.5173, -0.5163, -0.6640, 0.3923, 0.2634]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6132, -0.4002, 1.5709, -1.0311, -0.1785, -1.4545, 0.4474,
- -0.0328],
- [ 0.5129, -0.4374, 1.2072, -1.0080, -0.6520, -0.8848, 0.2679,
- 0.2335],
- [ 0.5509, -0.3798, 1.2129, -0.8694, -0.6982, -0.2613, 0.3830,
- 0.1193],
- [ 0.5726, -0.4159, 1.8192, -0.2459, -0.3402, 0.1159, 0.4797,
- 0.3161],
- [ 0.6504, -0.3840, 1.5940, -1.1312, 0.2141, -1.5315, 0.8205,
- 0.2944],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776],
- [ 0.5697, -0.4442, 1.3529, -0.9515, -0.5774, -0.7801, 0.5253,
- 0.1931],
- [ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0307, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0307, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.611077474430203
- step: 69
- running loss: 0.08131996339753918
- Train Steps: 69/90 Loss: 0.0813 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [0.6149, 0.4054, 0.6713, 0.2333, 0.4025, 0.2017, 0.5213, 0.5695],
- [0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517],
- [0.6268, 0.4061, 0.8350, 0.2433, 0.4575, 0.2283, 0.6350, 0.5300],
- [0.6165, 0.4106, 0.7575, 0.1733, 0.3838, 0.2650, 0.5680, 0.5116],
- [0.6218, 0.4137, 0.7263, 0.2233, 0.4075, 0.2650, 0.6212, 0.5783],
- [0.6226, 0.4125, 0.8800, 0.4900, 0.4512, 0.5600, 0.6275, 0.5517],
- [0.6329, 0.4175, 0.8550, 0.2333, 0.5425, 0.2250, 0.7398, 0.5609]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3484, -0.5802, 1.6820, -0.0106, -0.6042, -0.0191, 0.2717, 0.2010],
- [-0.0371, -0.8269, 1.0462, -1.0266, -0.5768, -1.1250, 0.1318, 0.3113],
- [ 0.2505, -0.6367, 1.7123, 0.0646, -0.3241, 0.1082, 0.4883, 0.1706],
- [ 0.7437, -0.2872, 1.5725, -0.9677, -0.3447, -1.0602, 0.6722, 0.2208],
- [ 0.6053, -0.4160, 1.2632, -0.9159, -0.6230, -0.9971, 0.3718, 0.2289],
- [ 0.5458, -0.4351, 1.2529, -0.7791, -0.5063, -0.8925, 0.3689, 0.3058],
- [ 0.9709, -0.1950, 1.7543, 0.1357, -0.3991, 0.3494, 0.5204, 0.2254],
- [ 0.2469, -0.6231, 1.6615, -1.0366, -0.0289, -1.1851, 1.0284, 0.2094]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893],
- [ 0.5544, -0.4133, 0.8145, -1.2082, -0.4268, -1.3544, 0.1221,
- 0.3446],
- [ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622],
- [ 0.6093, -0.4104, 1.5709, -1.1620, -0.1727, -1.2313, 0.6471,
- 0.1621],
- [ 0.5618, -0.3895, 1.2129, -1.4853, -0.5134, -1.0619, 0.3378,
- 0.0772],
- [ 0.5863, -0.3749, 1.0686, -1.2543, -0.4037, -1.0619, 0.5836,
- 0.3854],
- [ 0.5897, -0.3804, 1.7788, -0.0226, -0.2016, 0.3007, 0.6125,
- 0.2622],
- [ 0.6372, -0.3575, 1.6633, -1.2082, 0.2199, -1.2467, 1.1313,
- 0.3050]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0399, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0399, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.6510243806988
- step: 70
- running loss: 0.08072891972426857
- Train Steps: 70/90 Loss: 0.0807 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [ nan, nan, 0.7425, 0.2117, 0.3937, 0.2433, 0.5438, 0.5567],
- [0.6064, 0.3953, 0.8738, 0.4417, 0.3663, 0.4683, 0.5511, 0.5416],
- [0.6275, 0.4024, 0.8600, 0.2283, 0.5350, 0.1800, 0.7074, 0.5413],
- [0.6101, 0.4042, 0.7775, 0.2617, 0.3713, 0.2817, 0.5440, 0.5650],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6906, -0.3369, 1.5061, -0.8076, -0.6131, -0.6987, 0.4593, 0.2867],
- [ 1.1220, -0.1122, 1.6284, 0.0842, -0.5748, -0.0547, 0.4497, 0.1736],
- [-1.1066, -1.4997, 1.2091, -1.2279, -0.3574, -1.2843, 0.2825, 0.3239],
- [ 0.8739, -0.2342, 1.6656, -0.2235, -0.6230, -0.1619, 0.4995, 0.1426],
- [ 0.1316, -0.6897, 1.6279, -1.2489, 0.0040, -1.3089, 1.0166, 0.2204],
- [-0.0195, -0.7876, 1.2561, -1.1085, -0.4725, -1.0848, 0.1783, 0.2908],
- [ 1.0028, -0.1599, 1.6230, -0.1222, -0.6060, -0.2397, 0.3403, 0.1853],
- [ 1.0546, -0.1132, 1.6749, -0.0623, -0.2407, 0.1899, 0.5196, 0.2179]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [-2.2859, -2.2859, 1.1436, -1.3082, -0.4672, -1.1620, 0.2256,
- 0.2853],
- [ 0.5149, -0.4603, 1.7499, -0.2459, -0.5942, -0.1227, 0.2596,
- 0.2155],
- [ 0.6125, -0.4273, 1.6864, -1.2313, 0.1852, -1.4545, 0.9814,
- 0.2142],
- [ 0.5320, -0.4189, 1.3053, -1.0773, -0.5711, -0.9849, 0.2267,
- 0.3237],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0682, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0682, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.719259122386575
- step: 71
- running loss: 0.08055294538572641
- Train Steps: 71/90 Loss: 0.0806 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6226, 0.4098, 0.8912, 0.4100, 0.4025, 0.2383, 0.5763, 0.5367],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519],
- [0.6267, 0.4065, 0.8313, 0.2467, 0.4788, 0.1733, 0.6312, 0.5133],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6179, 0.3961, 0.8347, 0.6020, 0.3887, 0.4624, 0.5714, 0.5373],
- [0.6353, 0.4128, 0.9138, 0.3533, 0.4688, 0.3250, 0.7145, 0.5991]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3293, -0.5473, 1.5970, -0.7552, -0.4687, -1.1431, 0.3605, 0.2590],
- [ 0.5273, -0.4548, 1.5444, -0.1336, -0.3016, -0.1362, 0.2556, 0.2522],
- [ 0.5754, -0.4633, 1.3858, -1.3929, -0.2541, -1.4203, 0.6970, 0.2267],
- [ 0.3987, -0.5329, 1.1977, -1.1102, -0.6819, -0.5298, 0.3855, 0.2635],
- [ 0.7827, -0.3052, 1.5950, -0.4448, -0.5119, 0.0966, 0.5064, 0.1696],
- [ 0.7617, -0.3088, 1.5791, 0.0172, -0.4719, 0.0445, 0.3501, 0.2548],
- [ 0.5747, -0.4432, 1.4921, -0.0964, -0.5980, -0.3201, 0.3020, 0.2039],
- [-0.0334, -0.8246, 1.8454, -0.9392, -0.1398, -1.1044, 0.9246, 0.2017]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5900, -0.3932, 1.8307, -0.3921, -0.4268, -1.1851, 0.3758,
- 0.1929],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634],
- [ 0.6085, -0.4084, 1.5536, -1.1466, -0.0746, -1.4853, 0.6298,
- 0.0851],
- [ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.5742, -0.3792, 1.6460, 0.3084, -0.3460, 0.1467, 0.4162,
- 0.3161],
- [ 0.5680, -0.4562, 1.5697, 0.4947, -0.4904, -0.1503, 0.3536,
- 0.1956],
- [ 0.6487, -0.3792, 1.9346, -0.6539, -0.1208, -0.7848, 1.0143,
- 0.4814]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0389, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0389, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.758151488378644
- step: 72
- running loss: 0.07997432622748117
- Train Steps: 72/90 Loss: 0.0800 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6230, 0.4113, 0.7213, 0.1983, 0.4325, 0.2367, 0.6262, 0.5400],
- [0.6204, 0.4055, 0.8438, 0.5733, 0.4574, 0.4801, 0.5487, 0.5617],
- [0.6042, 0.3990, 0.6831, 0.2875, 0.3500, 0.3133, 0.5143, 0.5510],
- [0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901],
- [0.6152, 0.4131, 0.6863, 0.2567, 0.3625, 0.3300, 0.5765, 0.5305]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7339, -0.3342, 1.6868, -0.8073, -0.5287, -0.6222, 0.6321, 0.2477],
- [ 0.2298, -0.6195, 1.3810, -1.2579, -0.2440, -1.3400, 0.6141, 0.2401],
- [ 0.6787, -0.3341, 1.6653, 0.1779, -0.2311, -0.0670, 0.4532, 0.2377],
- [ 0.1206, -0.7352, 1.1463, -1.0930, -0.5663, -0.8224, 0.2956, 0.2897],
- [ 0.6156, -0.3924, 1.8099, -0.0031, -0.2276, 0.0718, 0.4336, 0.1735],
- [ 0.9364, -0.2195, 1.8013, -0.1140, -0.5267, -0.1311, 0.5028, 0.1453],
- [-0.1940, -0.9203, 1.4506, -1.0456, -0.4763, -1.1710, 0.3304, 0.2000],
- [ 0.4077, -0.5119, 1.1473, -1.1172, -0.5598, -0.7982, 0.3682, 0.2957]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.5917, -0.3861, 1.0455, -1.3698, -0.2882, -1.1928, 0.6067,
- 0.2083],
- [ 0.5795, -0.4129, 1.6113, 0.3623, -0.1733, -0.0684, 0.2487,
- 0.3084],
- [ 0.5049, -0.4428, 0.8692, -0.9581, -0.6693, -0.8386, 0.0897,
- 0.2589],
- [ 0.5515, -0.4225, 1.7788, -0.0534, -0.2016, 0.1929, 0.1568,
- 0.0682],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220],
- [ 0.5557, -0.3779, 0.8838, -1.1004, -0.6115, -0.7617, 0.3769,
- 0.1644]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0385, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0385, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.796630242839456
- step: 73
- running loss: 0.0794058937375268
- Train Steps: 73/90 Loss: 0.0794 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [0.6261, 0.3987, 0.8688, 0.4917, 0.4300, 0.5333, 0.7010, 0.5309],
- [0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783],
- [0.6261, 0.4066, 0.8325, 0.2150, 0.4763, 0.2667, 0.7002, 0.5633],
- [0.6273, 0.4100, 0.7137, 0.2133, 0.4000, 0.2650, 0.6075, 0.5633],
- [0.6185, 0.4079, 0.8838, 0.4617, 0.4838, 0.5650, 0.6175, 0.5850],
- [0.6226, 0.4001, 0.8438, 0.5733, 0.3862, 0.4250, 0.5750, 0.5283],
- [0.6271, 0.4024, 0.9000, 0.3517, 0.3700, 0.4517, 0.6931, 0.5285]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2847, -0.5792, 1.3632, -0.7519, -0.6296, -0.4213, 0.2320, 0.2479],
- [ 0.9402, -0.1907, 1.7925, -0.0052, -0.3164, 0.1891, 0.5462, 0.1902],
- [-0.1207, -0.9044, 1.0360, -1.2763, -0.4952, -1.2274, 0.1613, 0.3052],
- [ 0.5824, -0.4396, 1.6488, -1.2598, -0.1587, -1.4267, 0.8792, 0.1771],
- [-0.1792, -0.8918, 1.1804, -1.4088, -0.4414, -1.3778, 0.3201, 0.2816],
- [ 0.9521, -0.1891, 1.8129, -0.1594, -0.1669, 0.0568, 0.5431, 0.2250],
- [ 0.5457, -0.4516, 1.6558, 0.1544, -0.5624, -0.3605, 0.2724, 0.1758],
- [ 0.7240, -0.3286, 1.6456, -0.6170, -0.5346, -0.1263, 0.6180, 0.1891]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386],
- [ 0.6059, -0.4442, 1.7268, -0.0149, -0.2998, 0.1775, 0.9521,
- 0.1661],
- [ 0.5483, -0.3991, 0.8030, -1.2159, -0.5018, -1.1928, 0.2624,
- 0.3852],
- [ 0.6059, -0.4080, 1.5594, -1.2928, -0.0861, -1.0542, 0.9485,
- 0.3157],
- [ 0.6115, -0.3924, 1.0109, -1.3005, -0.4383, -1.0619, 0.5201,
- 0.3161],
- [ 0.5707, -0.4017, 1.7961, -0.1535, -0.0515, 0.3238, 0.5663,
- 0.4162],
- [ 0.5900, -0.4377, 1.6113, 0.3623, -0.5018, -0.3229, 0.3700,
- 0.1544],
- [ 0.6108, -0.4273, 1.8711, -0.6616, -0.5769, -0.1997, 0.9156,
- 0.1554]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0481, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0481, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.844778308644891
- step: 74
- running loss: 0.07898349065736339
- Train Steps: 74/90 Loss: 0.0790 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6093, 0.3990, 0.8400, 0.4333, 0.3688, 0.4633, 0.5560, 0.5656],
- [0.6353, 0.4128, 0.8488, 0.2600, 0.5525, 0.1616, 0.6694, 0.5540],
- [0.6236, 0.4084, 0.7738, 0.2133, 0.3663, 0.3233, 0.5813, 0.5567],
- [ nan, nan, 0.7515, 0.2708, 0.3987, 0.2267, 0.5162, 0.5567],
- [0.6064, 0.4019, 0.8650, 0.4517, 0.4037, 0.5367, 0.5703, 0.5609],
- [0.6218, 0.4137, 0.7263, 0.2233, 0.4075, 0.2650, 0.6212, 0.5783],
- [0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6153, 0.4119, 0.8463, 0.3833, 0.3600, 0.3200, 0.5106, 0.5563]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6634, -0.3417, 1.6444, -0.0885, -0.5261, 0.1245, 0.4036, 0.2044],
- [ 0.0769, -0.6972, 1.6346, -1.2106, 0.0788, -1.1002, 0.9462, 0.1777],
- [ 0.7241, -0.3058, 1.3758, -0.9954, -0.4782, -0.8373, 0.4019, 0.2229],
- [-0.9479, -1.3831, 1.1639, -1.1251, -0.3941, -1.1239, 0.1696, 0.2574],
- [ 0.7407, -0.2983, 1.7178, 0.0877, -0.3567, 0.4966, 0.5218, 0.1839],
- [ 0.7174, -0.3030, 1.2691, -0.8878, -0.4343, -0.9197, 0.4034, 0.2610],
- [ 0.7942, -0.2631, 1.2833, -0.8850, -0.3832, -0.8959, 0.4042, 0.2976],
- [ 0.4527, -0.4678, 1.5730, -0.3388, -0.6123, -0.5245, 0.2143, 0.1994]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5283, -0.4429, 1.5940, -0.2844, -0.5827, -0.1458, 0.2823,
- 0.3267],
- [ 0.6487, -0.3792, 1.6344, -1.0850, 0.2659, -1.5397, 0.8059,
- 0.2730],
- [ 0.5946, -0.3995, 1.2880, -1.3005, -0.5942, -0.7925, 0.3988,
- 0.2853],
- [-2.2859, -2.2859, 1.1854, -1.0352, -0.4441, -1.2390, 0.0986,
- 0.2853],
- [ 0.5151, -0.4296, 1.7095, -0.1997, -0.4210, 0.1929, 0.3484,
- 0.3047],
- [ 0.5863, -0.3749, 1.0686, -1.2543, -0.4037, -1.0619, 0.5836,
- 0.3854],
- [ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084],
- [ 0.5561, -0.3834, 1.6229, -0.5153, -0.6231, -0.8079, 0.0727,
- 0.2837]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0734, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0734, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.9181370083242655
- step: 75
- running loss: 0.07890849344432355
- Train Steps: 75/90 Loss: 0.0789 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892],
- [0.6185, 0.4129, 0.8900, 0.4567, 0.3937, 0.5417, 0.5734, 0.5110],
- [0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183],
- [0.6211, 0.3993, 0.7650, 0.1933, 0.4575, 0.1550, 0.5965, 0.4895],
- [0.6219, 0.3934, 0.8688, 0.5267, 0.4313, 0.4967, 0.5988, 0.4983],
- [0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650],
- [0.6182, 0.3967, 0.8263, 0.3065, 0.3526, 0.4161, 0.6192, 0.5290],
- [ nan, nan, 0.6488, 0.1817, 0.4325, 0.1867, 0.5475, 0.5733]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4961, -0.4331, 1.5760, -0.2348, -0.5834, -0.4121, 0.2109, 0.2769],
- [ 0.7604, -0.2908, 1.7494, -0.1816, -0.3328, 0.1616, 0.4631, 0.2463],
- [ 0.6928, -0.3400, 1.6568, -0.1040, -0.4998, -0.2940, 0.4876, 0.1865],
- [ 0.5090, -0.4994, 1.2263, -1.5922, -0.1983, -1.5729, 0.5735, 0.2009],
- [ 0.4650, -0.4823, 1.6588, -0.0199, -0.2635, -0.0374, 0.3917, 0.1881],
- [ 0.7070, -0.3193, 1.6073, -0.4942, -0.5277, -0.3145, 0.2996, 0.2586],
- [ 0.6871, -0.3422, 1.4430, -0.9179, -0.5518, -0.4318, 0.6278, 0.2200],
- [-0.7194, -1.2560, 1.0205, -1.5431, -0.1226, -1.5735, 0.3658, 0.3317]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5454, -0.4053, 1.6633, -0.1766, -0.6058, -0.5923, 0.1577,
- 0.4357],
- [ 0.5711, -0.3788, 1.8249, -0.1766, -0.4672, 0.2160, 0.3625,
- 0.0742],
- [ 0.5944, -0.4543, 1.8018, 0.0082, -0.6058, -0.4306, 0.4162,
- 0.1082],
- [ 0.5828, -0.4417, 1.2476, -1.3929, -0.1727, -1.5700, 0.4694,
- -0.0248],
- [ 0.5864, -0.4690, 1.7268, 0.1467, -0.2940, 0.0082, 0.4797,
- 0.0159],
- [ 0.5757, -0.3917, 1.7095, -0.4768, -0.6346, -0.4229, 0.3931,
- 0.3238],
- [ 0.5697, -0.4538, 1.5308, -0.8703, -0.6572, -0.3639, 0.5739,
- 0.1576],
- [-2.2859, -2.2859, 0.7106, -1.4468, -0.2882, -1.4237, 0.2430,
- 0.3623]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0671, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0671, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.985258931294084
- step: 76
- running loss: 0.07875340699071162
- Train Steps: 76/90 Loss: 0.0788 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6261, 0.4066, 0.8325, 0.2150, 0.4763, 0.2667, 0.7002, 0.5633],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6090, 0.4010, 0.7838, 0.3483, 0.3538, 0.3783, 0.5462, 0.5077]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2429, -0.6192, 1.5609, 0.0381, -0.3987, 0.1600, 0.2758, 0.2319],
- [ 0.6422, -0.3465, 1.4123, -0.8676, -0.6071, -0.7099, 0.3021, 0.2425],
- [ 0.4155, -0.4701, 1.5756, -1.0316, -0.1855, -0.9949, 0.7275, 0.2318],
- [ 0.2456, -0.6119, 1.5407, -0.1703, -0.4710, 0.0538, 0.3149, 0.2587],
- [ 0.4306, -0.5004, 1.4961, -0.8191, -0.3195, -1.1378, 0.4181, 0.2407],
- [ 0.6043, -0.4138, 1.4270, -1.2980, -0.1460, -1.2675, 0.8263, 0.2275],
- [ 0.3764, -0.5286, 1.5590, 0.0285, -0.3335, 0.1087, 0.2864, 0.2451],
- [ 0.3725, -0.5430, 1.1990, -0.9545, -0.6115, -0.5813, 0.2128, 0.2504]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5783, -0.4363, 1.7724, 0.0432, -0.5153, 0.0871, 0.4840,
- 0.0663],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753],
- [ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.5368, -0.4406, 1.7730, -0.1766, -0.5249, -0.0534, 0.2314,
- 0.3469],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.6059, -0.4080, 1.5594, -1.2928, -0.0861, -1.0542, 0.9485,
- 0.3157],
- [ 0.5778, -0.4389, 1.7107, 0.1192, -0.3921, 0.0815, 0.4741,
- 0.0711],
- [ 0.5270, -0.4336, 1.3342, -0.6770, -0.6520, -0.5384, 0.2370,
- 0.0592]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0258, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0258, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.011011159047484
- step: 77
- running loss: 0.07806507998762967
- Train Steps: 77/90 Loss: 0.0781 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332],
- [0.6201, 0.4027, 0.8029, 0.2728, 0.4042, 0.2310, 0.5980, 0.5391],
- [0.6325, 0.4066, 0.8438, 0.5350, 0.3925, 0.5267, 0.7113, 0.5795],
- [0.6205, 0.4062, 0.8337, 0.2683, 0.3675, 0.4283, 0.6338, 0.5250],
- [0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6207, 0.4110, 0.8738, 0.5000, 0.4800, 0.5633, 0.6300, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4532, -0.4790, 1.3550, -0.6564, -0.5215, -0.4282, 0.2318, 0.2345],
- [ 0.3515, -0.5440, 1.4068, -1.3205, -0.1814, -1.4695, 0.5088, 0.2038],
- [ 0.1470, -0.6419, 1.4864, -0.0764, -0.4137, -0.0734, 0.2511, 0.2779],
- [ 0.4911, -0.4676, 1.3029, -1.1856, -0.4020, -1.2807, 0.5175, 0.2134],
- [ 0.4871, -0.4626, 1.7018, 0.0512, -0.3980, 0.0687, 0.4746, 0.2630],
- [ 0.3120, -0.5893, 1.3329, -0.9873, -0.5125, -0.4713, 0.4975, 0.2488],
- [ 0.5257, -0.4181, 1.4685, -0.6656, -0.6150, -0.5175, 0.4017, 0.2125],
- [ 0.6028, -0.3793, 1.7381, -0.1580, -0.0723, -0.1443, 0.4997, 0.2906]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552],
- [ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807],
- [ 0.6191, -0.4297, 1.4612, 0.2391, -0.4961, 0.0313, 1.1166,
- 0.1768],
- [ 0.5785, -0.4259, 1.4228, -1.0261, -0.4190, -1.2189, 0.4763,
- 0.2043],
- [ 0.6355, -0.4080, 1.6113, 0.1852, -0.4730, 0.1467, 0.9996,
- 0.3905],
- [ 0.5799, -0.4099, 1.5651, -1.0465, -0.5885, -0.3075, 0.6413,
- 0.1390],
- [ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.5811, -0.3878, 1.7499, 0.0236, -0.0688, 0.3161, 0.6240,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0425, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0425, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.053500270470977
- step: 78
- running loss: 0.07760897782655099
- Train Steps: 78/90 Loss: 0.0776 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6109, 0.4041, 0.6975, 0.3167, 0.3513, 0.3383, 0.5153, 0.5319],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6216, 0.4100, 0.7350, 0.2067, 0.4325, 0.2050, 0.5950, 0.5333],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550],
- [0.6110, 0.3984, 0.8750, 0.4933, 0.4625, 0.4950, 0.5578, 0.5676]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4413, -0.4662, 1.6231, -0.0098, -0.5385, -0.2463, 0.3870, 0.2384],
- [-0.1857, -0.8836, 1.1719, -1.4335, -0.3002, -1.3386, 0.4504, 0.2599],
- [ 0.3408, -0.5526, 1.1232, -1.1451, -0.6713, -0.8068, 0.2262, 0.2412],
- [ 0.4423, -0.5052, 1.7007, 0.0671, -0.4582, -0.0958, 0.4439, 0.1918],
- [ 0.5977, -0.3991, 1.6609, -0.2335, -0.4152, -0.0203, 0.4947, 0.2128],
- [ 0.4420, -0.4872, 1.0551, -1.5288, -0.4055, -1.4832, 0.4326, 0.2587],
- [ 0.4850, -0.4208, 1.7230, -0.2698, -0.1252, -0.2177, 0.5447, 0.2066],
- [ 0.7261, -0.3160, 1.7007, -0.2241, -0.2409, -0.1268, 0.4922, 0.2211]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.1149e-01, -3.7244e-01, 1.7557e+00, 3.4688e-01, -4.4411e-01,
- -1.0731e-01, 4.9122e-01, 2.3911e-01],
- [ 5.8487e-01, -3.8360e-01, 1.2649e+00, -1.2236e+00, -3.4596e-01,
- -1.2313e+00, 4.5081e-01, 1.6982e-01],
- [ 5.3591e-01, -4.1932e-01, 9.3580e-01, -8.2325e-01, -6.6351e-01,
- -7.2317e-01, 9.4325e-02, 1.7099e-01],
- [ 5.7748e-01, -4.6066e-01, 1.6741e+00, 1.9623e-01, -4.0362e-01,
- -1.2115e-01, 4.5876e-01, 1.9786e-01],
- [ 5.6039e-01, -4.6197e-01, 1.7961e+00, -1.9969e-01, -3.2286e-01,
- 1.0824e-01, 4.1039e-01, 2.5450e-01],
- [ 5.8528e-01, -3.9199e-01, 1.1090e+00, -1.3313e+00, -2.8822e-01,
- -1.3390e+00, 4.6236e-01, 1.7752e-01],
- [ 5.3508e-01, -4.3212e-01, 1.7326e+00, -1.3811e-01, 4.6329e-02,
- 1.0491e-01, 4.1617e-01, 2.7760e-01],
- [ 5.3637e-01, -4.4573e-01, 1.7557e+00, -7.2363e-03, -1.4965e-01,
- 4.6189e-04, 2.9049e-01, 3.3573e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0302, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0302, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.083718063309789
- step: 79
- running loss: 0.07700908940898467
- Train Steps: 79/90 Loss: 0.0770 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837],
- [0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973],
- [0.6296, 0.4045, 0.9138, 0.4100, 0.4232, 0.4242, 0.7422, 0.5297],
- [0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6160, 0.4093, 0.8314, 0.4417, 0.3675, 0.4583, 0.5250, 0.5808],
- [0.6125, 0.3974, 0.7725, 0.2517, 0.3538, 0.3317, 0.5887, 0.5500],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1627, -0.6941, 0.9786, -1.1865, -0.5091, -0.9566, 0.1641, 0.3193],
- [ 0.4703, -0.4958, 1.7074, 0.0998, -0.4157, -0.0632, 0.4524, 0.1856],
- [ 0.6864, -0.3392, 1.5980, -0.5553, -0.4134, -0.3026, 0.6323, 0.1914],
- [ 0.4189, -0.4609, 1.6604, -0.2478, -0.3532, -0.0982, 0.3636, 0.2113],
- [ 0.6215, -0.3834, 1.6149, -0.8199, -0.2564, -1.1783, 0.5599, 0.1797],
- [ 0.3355, -0.5244, 1.5251, -0.3258, -0.3835, -0.0800, 0.3727, 0.2703],
- [ 0.4983, -0.4797, 1.2142, -1.1775, -0.5485, -0.8428, 0.4820, 0.2132],
- [ 0.3679, -0.5232, 1.6810, -0.1140, -0.4568, -0.6179, 0.4762, 0.1681]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103],
- [ 0.6240, -0.4321, 1.8423, 0.1852, -0.5885, -0.1612, 0.6962,
- 0.0111],
- [ 0.6224, -0.4177, 1.9346, -0.3921, -0.3314, -0.3264, 1.1422,
- 0.1608],
- [ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.5592, -0.3956, 1.5543, -0.2456, -0.5885, -0.1689, 0.1392,
- 0.3968],
- [ 0.5430, -0.4503, 1.2822, -1.1235, -0.6520, -0.7540, 0.4335,
- 0.2545],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0294, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0294, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.113160545006394
- step: 80
- running loss: 0.07641450681257993
- Train Steps: 80/90 Loss: 0.0764 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6361, 0.4102, 0.8650, 0.2983, 0.4888, 0.2000, 0.6702, 0.5459],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667],
- [0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [0.6196, 0.4090, 0.8450, 0.3900, 0.3575, 0.3933, 0.5975, 0.5583],
- [0.6189, 0.4029, 0.8375, 0.5767, 0.4745, 0.4829, 0.5551, 0.5598],
- [0.6204, 0.4055, 0.8438, 0.5733, 0.4574, 0.4801, 0.5487, 0.5617],
- [0.6126, 0.4067, 0.8638, 0.5383, 0.4188, 0.4850, 0.5016, 0.5392]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6616, -0.3725, 1.5009, -1.1986, -0.1821, -1.1744, 0.8349, 0.1502],
- [ 0.5140, -0.4440, 1.6731, 0.1572, -0.5335, -0.1048, 0.4126, 0.1451],
- [ 0.2424, -0.6267, 1.0445, -1.1448, -0.6994, -0.8297, 0.1809, 0.2472],
- [ 0.5195, -0.4319, 1.5449, -1.2729, -0.2066, -1.1167, 0.6421, 0.1443],
- [ 0.6524, -0.3687, 1.5164, -0.6111, -0.6951, -0.3538, 0.4975, 0.2012],
- [ 0.4303, -0.4790, 1.5850, 0.0598, -0.2176, -0.2040, 0.3833, 0.2430],
- [ 0.2529, -0.5771, 1.5279, 0.0467, -0.2950, -0.1728, 0.3096, 0.2634],
- [ 0.3993, -0.4989, 1.6358, 0.0904, -0.4416, -0.0155, 0.3070, 0.2172]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6520, -0.3912, 1.7095, -0.9079, -0.0284, -1.3621, 0.8096,
- 0.2356],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315],
- [ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [ 0.5761, -0.3966, 1.6171, -0.4845, -0.6346, -0.4691, 0.4739,
- 0.2930],
- [ 0.5726, -0.4249, 1.5824, 0.3777, -0.0942, -0.0556, 0.2782,
- 0.2997],
- [ 0.5795, -0.4129, 1.6113, 0.3623, -0.1733, -0.0684, 0.2487,
- 0.3084],
- [ 0.5436, -0.4076, 1.7037, 0.2006, -0.3517, -0.0457, 0.0311,
- 0.2048]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0210, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0210, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.13416051492095
- step: 81
- running loss: 0.07573037672741914
- Train Steps: 81/90 Loss: 0.0757 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7412, 0.2200, 0.4450, 0.1517, 0.5312, 0.4983],
- [0.6286, 0.4040, 0.8696, 0.3047, 0.3924, 0.2887, 0.6300, 0.5367],
- [0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672],
- [0.6122, 0.4006, 0.8850, 0.4217, 0.4088, 0.5517, 0.6063, 0.5517],
- [0.6250, 0.4116, 0.8700, 0.4850, 0.4650, 0.5567, 0.6088, 0.5183],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6275, 0.4008, 0.8047, 0.2484, 0.4037, 0.2777, 0.6506, 0.5212],
- [0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.8470, -1.3406, 0.9710, -1.2401, -0.3874, -1.3271, 0.1391, 0.1894],
- [ 0.9333, -0.1303, 1.5740, -0.8103, -0.3536, -0.8830, 0.5989, 0.1798],
- [ 0.9719, -0.1713, 1.7116, 0.3834, -0.6196, -0.0734, 0.4194, 0.1456],
- [ 0.8045, -0.2672, 1.7701, -0.0866, -0.4186, 0.3582, 0.5741, 0.2192],
- [ 0.7323, -0.2955, 1.8138, 0.0565, -0.1777, 0.2783, 0.5645, 0.2337],
- [-0.4810, -1.0622, 1.0033, -1.1718, -0.4243, -1.2152, 0.1338, 0.2037],
- [ 0.8303, -0.2347, 1.4033, -0.9560, -0.4656, -0.8805, 0.6346, 0.1497],
- [ 0.6676, -0.2687, 1.7781, 0.0762, -0.3945, -0.3554, 0.4669, 0.1738]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.1379, -1.2697, -0.2305, -1.5854, 0.1679,
- 0.0159],
- [ 0.6174, -0.4201, 1.7309, -0.8784, -0.4735, -0.9524, 0.6242,
- 0.1931],
- [ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278],
- [ 0.5417, -0.4355, 1.8018, -0.3383, -0.3979, 0.2622, 0.5143,
- 0.2622],
- [ 0.6012, -0.3846, 1.7326, -0.0457, -0.1381, 0.2853, 0.5259,
- 0.1082],
- [ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.6125, -0.4345, 1.4308, -1.1384, -0.4213, -1.0031, 0.7190,
- 0.1214],
- [ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0915, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0915, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.2256993018090725
- step: 82
- running loss: 0.07592316221718381
- Train Steps: 82/90 Loss: 0.0759 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [0.6156, 0.4125, 0.8850, 0.4833, 0.4550, 0.5817, 0.5765, 0.5084],
- [0.6125, 0.3983, 0.8750, 0.4867, 0.4275, 0.4783, 0.5413, 0.5717],
- [0.6179, 0.4082, 0.6688, 0.2667, 0.3588, 0.3317, 0.5750, 0.5783],
- [0.6261, 0.3987, 0.8688, 0.4917, 0.4300, 0.5333, 0.7010, 0.5309],
- [0.6164, 0.4102, 0.8850, 0.4867, 0.4213, 0.5983, 0.5609, 0.5038],
- [0.6262, 0.4163, 0.8850, 0.5183, 0.3763, 0.4150, 0.6025, 0.5500]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.4372, -1.0773, 0.9684, -1.1526, -0.4889, -1.4327, 0.1665, 0.1314],
- [ 0.8245, -0.2356, 1.2412, -1.2070, -0.3522, -1.4770, 0.4970, 0.1102],
- [ 0.6625, -0.3387, 1.8488, 0.0791, -0.2400, 0.1239, 0.5469, 0.1703],
- [ 0.6149, -0.3869, 1.8176, 0.0247, -0.3495, -0.1160, 0.5422, 0.1320],
- [ 0.5138, -0.4426, 1.1079, -0.9809, -0.7097, -0.8575, 0.2407, 0.2462],
- [ 0.6288, -0.3711, 1.8621, 0.0934, -0.3120, 0.1545, 0.6857, 0.1629],
- [ 0.4593, -0.4756, 1.8160, 0.1128, -0.2644, 0.2836, 0.5064, 0.1714],
- [ 0.5796, -0.3572, 1.7196, 0.0223, -0.5261, -0.5082, 0.3354, 0.1745]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [ 0.5573, -0.3808, 1.8018, -0.0534, -0.1843, 0.4008, 0.3769,
- 0.0622],
- [ 0.5432, -0.4462, 1.7557, -0.0380, -0.3113, -0.0765, 0.2141,
- 0.3546],
- [ 0.5680, -0.4005, 0.8030, -1.0542, -0.6289, -0.7540, 0.3700,
- 0.3854],
- [ 0.6059, -0.4442, 1.7268, -0.0149, -0.2998, 0.1775, 0.9521,
- 0.1661],
- [ 0.5614, -0.3912, 1.8018, -0.0380, -0.3402, 0.4778, 0.3049,
- 0.0412],
- [ 0.6066, -0.3632, 1.8018, 0.1082, -0.5480, -0.3691, 0.4970,
- 0.2545]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0369, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0369, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.262647982686758
- step: 83
- running loss: 0.07545359015285251
- Train Steps: 83/90 Loss: 0.0755 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433],
- [0.6276, 0.4120, 0.8738, 0.3133, 0.4225, 0.2217, 0.6203, 0.4892],
- [0.6124, 0.4075, 0.7696, 0.4153, 0.3475, 0.3767, 0.5157, 0.5427],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6339, 0.4149, 0.8800, 0.5000, 0.3900, 0.5283, 0.7541, 0.5424],
- [0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760],
- [0.6206, 0.4001, 0.8900, 0.3933, 0.3588, 0.3567, 0.5837, 0.5083]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5588, -0.4253, 1.3032, -0.8714, -0.5930, -0.6349, 0.4121, 0.1612],
- [ 0.5840, -0.3910, 1.6718, -0.5996, -0.2422, -0.9303, 0.5476, 0.1022],
- [ 0.5503, -0.4172, 1.4038, -0.4003, -0.5918, -0.4394, 0.2558, 0.1828],
- [ 0.0503, -0.7139, 1.2194, -0.9917, -0.2453, -1.0379, 0.3115, 0.1644],
- [ 0.6324, -0.3348, 1.6029, -0.8426, -0.1930, -0.8667, 0.6539, 0.1151],
- [ 0.6758, -0.3652, 1.8777, 0.4324, -0.4339, 0.3469, 0.6145, 0.1447],
- [ 0.5173, -0.4611, 1.6718, 0.3990, -0.4012, 0.1117, 0.4887, 0.2058],
- [ 0.4877, -0.4613, 1.7446, -0.1365, -0.5940, -0.5164, 0.3340, 0.0479]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237],
- [ 0.6128, -0.3828, 1.7499, -0.8386, -0.3344, -1.2620, 0.5792,
- -0.0263],
- [ 0.5427, -0.4035, 1.2688, -0.3675, -0.6808, -0.5461, 0.0959,
- 0.2206],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698],
- [ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [ 0.6421, -0.3695, 1.7788, 0.0236, -0.4845, 0.1544, 1.1971,
- 0.2196],
- [ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745],
- [ 0.5807, -0.4378, 1.8249, -0.4691, -0.6289, -0.6385, 0.4104,
- 0.0620]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0327, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0327, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.2953284196555614
- step: 84
- running loss: 0.0749443859482805
- Train Steps: 84/90 Loss: 0.0749 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6171, 0.4127, 0.8900, 0.4800, 0.4325, 0.5783, 0.5769, 0.5090],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6277, 0.4057, 0.8300, 0.2650, 0.4363, 0.1850, 0.6140, 0.4823],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6280, 0.4101, 0.9050, 0.4533, 0.3775, 0.3217, 0.6338, 0.4915],
- [0.6264, 0.3972, 0.8853, 0.4771, 0.3853, 0.4511, 0.6293, 0.5334],
- [0.6075, 0.4000, 0.8513, 0.5183, 0.4510, 0.5329, 0.5180, 0.5280],
- [0.6167, 0.4048, 0.6831, 0.3639, 0.3763, 0.3017, 0.5700, 0.5883]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5941, -0.4105, 1.7887, 0.0018, -0.3170, 0.2443, 0.5468, 0.1424],
- [ 0.8466, -0.2356, 1.7616, -0.3460, -0.4721, -0.6073, 0.5775, 0.1030],
- [ 0.6348, -0.4047, 1.4215, -1.2086, -0.3035, -1.2415, 0.7195, 0.0656],
- [ 0.6285, -0.3556, 1.6495, 0.1077, -0.5253, -0.5284, 0.3942, 0.1090],
- [ 0.5837, -0.3673, 1.7452, -0.1876, -0.4923, -0.7255, 0.4623, 0.0394],
- [ 0.6060, -0.3984, 1.7194, -0.0774, -0.5157, -0.1249, 0.5227, 0.1249],
- [ 0.3936, -0.5132, 1.6742, 0.0635, -0.1754, 0.0217, 0.4127, 0.1566],
- [-0.0444, -0.7899, 1.1317, -0.8592, -0.4773, -0.8693, 0.1572, 0.2092]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5645, -0.3797, 1.8249, -0.0688, -0.2882, 0.3854, 0.3789,
- 0.0652],
- [ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.6132, -0.4122, 1.5478, -1.0619, -0.2709, -1.4314, 0.5500,
- -0.0583],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698],
- [ 0.6148, -0.3918, 1.8942, -0.1920, -0.5423, -0.8002, 0.6414,
- -0.0156],
- [ 0.6075, -0.4514, 1.8032, -0.0822, -0.5061, -0.2023, 0.6208,
- 0.1779],
- [ 0.5202, -0.4382, 1.6460, 0.1082, -0.2029, 0.1754, 0.1067,
- 0.1530],
- [ 0.5626, -0.4162, 0.8692, -0.6051, -0.5480, -0.8925, 0.3469,
- 0.4316]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.315675836056471
- step: 85
- running loss: 0.0743020686594879
- Train Steps: 85/90 Loss: 0.0743 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250],
- [0.6093, 0.3990, 0.8400, 0.4333, 0.3688, 0.4633, 0.5560, 0.5656],
- [0.6357, 0.4159, 0.8788, 0.5583, 0.3638, 0.4433, 0.6488, 0.5297],
- [0.6205, 0.4004, 0.8938, 0.4883, 0.3663, 0.5000, 0.6357, 0.4947],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6127, 0.4119, 0.8650, 0.5300, 0.4512, 0.5250, 0.5260, 0.5461],
- [0.6127, 0.4118, 0.8650, 0.5083, 0.4088, 0.5367, 0.5300, 0.5456]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0889, -0.0847, 1.4050, -1.0740, -0.3015, -1.3084, 0.4984, 0.0900],
- [ 0.7538, -0.3029, 1.8606, 0.0660, -0.4251, 0.0576, 0.5933, 0.1187],
- [ 0.3395, -0.5567, 1.6117, -0.3520, -0.5353, -0.2820, 0.4529, 0.1650],
- [ 0.7772, -0.2671, 1.8426, 0.1250, -0.5436, -0.4465, 0.5795, 0.0673],
- [ 0.4460, -0.5134, 1.7618, -0.0583, -0.5205, -0.1142, 0.5861, 0.0676],
- [-0.0893, -0.8130, 1.1007, -1.2895, -0.3983, -1.5112, 0.2485, 0.0711],
- [ 0.5626, -0.4279, 1.7289, 0.1548, -0.2305, -0.0760, 0.4865, 0.1444],
- [ 0.4831, -0.4688, 1.6880, 0.0459, -0.3398, -0.0117, 0.4747, 0.1757]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7679e-01, -4.0308e-01, 1.3838e+00, -1.1527e+00, -2.1876e-01,
- -1.4216e+00, 4.3790e-01, 1.8502e-01],
- [ 5.9913e-01, -3.8029e-01, 1.8018e+00, -5.3426e-02, -3.4596e-01,
- 1.8522e-01, 5.3741e-01, 1.3903e-01],
- [ 5.2835e-01, -4.4288e-01, 1.5940e+00, -2.8437e-01, -5.8268e-01,
- -1.4580e-01, 2.8226e-01, 3.2671e-01],
- [ 6.5036e-01, -3.6471e-01, 1.7730e+00, 2.9299e-01, -6.0577e-01,
- -2.3818e-01, 7.1085e-01, 1.6077e-01],
- [ 5.8031e-01, -4.3672e-01, 1.8423e+00, -3.0331e-02, -5.9423e-01,
- 2.3557e-02, 6.5034e-01, -9.2270e-04],
- [ 5.6761e-01, -4.1124e-01, 1.1898e+00, -1.2467e+00, -2.9400e-01,
- -1.4622e+00, 2.1029e-01, 1.3434e-01],
- [ 5.4428e-01, -3.8314e-01, 1.7095e+00, 1.6212e-01, -2.0162e-01,
- 1.3903e-01, 1.4368e-01, 2.3637e-01],
- [ 5.4428e-01, -3.8399e-01, 1.7095e+00, 6.2048e-02, -3.9792e-01,
- 1.9292e-01, 1.6218e-01, 2.3412e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0275, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0275, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.343135943636298
- step: 86
- running loss: 0.07375739469344533
- Train Steps: 86/90 Loss: 0.0738 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6250, 0.4008, 0.8950, 0.4183, 0.3550, 0.4383, 0.6361, 0.4927],
- [0.6236, 0.3967, 0.8675, 0.5400, 0.3862, 0.4517, 0.5825, 0.5200],
- [0.6190, 0.4135, 0.8000, 0.4883, 0.3566, 0.3647, 0.5613, 0.5900],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [0.6202, 0.4079, 0.8025, 0.2500, 0.3763, 0.3217, 0.6125, 0.5533]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3789, -0.5709, 1.7497, -0.0941, -0.4350, -0.0917, 0.5710, 0.1533],
- [ 0.6201, -0.4236, 1.7590, -0.4153, -0.6013, -0.2876, 0.6609, 0.0435],
- [ 0.5135, -0.4883, 1.8328, 0.1927, -0.4158, -0.2144, 0.5694, 0.0815],
- [ 0.7109, -0.2940, 1.6941, -0.1075, -0.5754, -0.5636, 0.3950, 0.1171],
- [ 0.5652, -0.4250, 1.7889, 0.1220, -0.4005, -0.2076, 0.5230, 0.0870],
- [ 0.5411, -0.4651, 1.1210, -1.0702, -0.5344, -1.0046, 0.2457, 0.1836],
- [ 0.5682, -0.4166, 1.6892, -0.0446, -0.2701, -0.6571, 0.4846, 0.1540],
- [ 0.8239, -0.2596, 1.5885, -0.9988, -0.3349, -0.9175, 0.5501, 0.0685]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.6010, -0.4345, 1.8480, -0.3537, -0.6462, -0.2613, 0.6524,
- -0.0099],
- [ 0.5944, -0.4538, 1.7210, 0.2083, -0.5018, -0.1997, 0.4046,
- 0.1159],
- [ 0.5732, -0.3761, 1.4092, -0.0303, -0.6388, -0.6012, 0.3065,
- 0.4393],
- [ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391],
- [ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762],
- [ 0.5788, -0.4020, 1.4208, -1.1312, -0.5480, -0.8002, 0.5432,
- 0.2699]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0253, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0253, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.368460373952985
- step: 87
- running loss: 0.07320069395348258
- Train Steps: 87/90 Loss: 0.0732 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6110, 0.3984, 0.8750, 0.4933, 0.4625, 0.4950, 0.5578, 0.5676],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6202, 0.4054, 0.8892, 0.4428, 0.3642, 0.3249, 0.5840, 0.5426],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726],
- [0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136],
- [0.6113, 0.4006, 0.8700, 0.5350, 0.3638, 0.3767, 0.5097, 0.4882]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3796, -0.5730, 1.8118, 0.0626, -0.1858, 0.1095, 0.5429, 0.1698],
- [ 0.2873, -0.6146, 1.8132, 0.1036, -0.1824, 0.1075, 0.5311, 0.1494],
- [ 0.6287, -0.3633, 1.7577, -0.1201, -0.5401, -0.5168, 0.4512, 0.1066],
- [ 0.7294, -0.3134, 1.3254, -0.9326, -0.5984, -0.9560, 0.4578, 0.1106],
- [ 0.6293, -0.3388, 1.7535, 0.0480, -0.4836, -0.6253, 0.4843, 0.1036],
- [ 0.7504, -0.3159, 1.5468, -0.8603, -0.3524, -0.7077, 0.5296, 0.1618],
- [ 0.6767, -0.3592, 1.1190, -1.0683, -0.5741, -1.1130, 0.3685, 0.1548],
- [ 0.5433, -0.4318, 1.8166, 0.2886, -0.6087, -0.4237, 0.5091, 0.0742]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3637e-01, -4.4573e-01, 1.7557e+00, -7.2363e-03, -1.4965e-01,
- 4.6189e-04, 2.9049e-01, 3.3573e-01],
- [ 5.3585e-01, -4.3703e-01, 1.7095e+00, -3.0331e-02, -8.0370e-02,
- -3.8029e-02, 1.0439e-01, 3.3918e-01],
- [ 5.7875e-01, -4.1347e-01, 1.8214e+00, -2.4075e-01, -6.0389e-01,
- -7.8543e-01, 4.1155e-01, 2.2033e-01],
- [ 4.9971e-01, -4.4465e-01, 1.1610e+00, -9.7721e-01, -6.0577e-01,
- -1.0311e+00, 1.4038e-01, -1.0312e-01],
- [ 5.9007e-01, -4.0000e-01, 1.8423e+00, -6.8822e-02, -5.3072e-01,
- -9.2333e-01, 3.6420e-01, 1.8522e-01],
- [ 5.7783e-01, -3.9299e-01, 1.5189e+00, -9.3872e-01, -4.3256e-01,
- -9.1563e-01, 4.8545e-01, 3.3918e-01],
- [ 5.0531e-01, -4.2810e-01, 8.9538e-01, -1.3698e+00, -5.4226e-01,
- -1.1389e+00, 2.4525e-01, 8.6245e-02],
- [ 5.3788e-01, -4.3580e-01, 1.7326e+00, 1.8522e-01, -6.0577e-01,
- -5.4611e-01, 6.8408e-02, -3.0981e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0248, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0248, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.393300803378224
- step: 88
- running loss: 0.07265114549293437
- Train Steps: 88/90 Loss: 0.0727 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533],
- [0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585],
- [0.6127, 0.4066, 0.8550, 0.5567, 0.4662, 0.5141, 0.5070, 0.5412],
- [0.6293, 0.4024, 0.8750, 0.5000, 0.4012, 0.5733, 0.7121, 0.5633],
- [0.6289, 0.4024, 0.9088, 0.4567, 0.3937, 0.5633, 0.7058, 0.5609]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6858, -0.3454, 1.5921, -0.3030, -0.6412, -0.4857, 0.3554, 0.1860],
- [ 0.6335, -0.4234, 1.8358, 0.0389, -0.5274, 0.0525, 0.6177, 0.1727],
- [ 0.4526, -0.4640, 1.5374, -1.1822, -0.0993, -1.4783, 0.6228, 0.0749],
- [ 0.0231, -0.7600, 1.2551, -0.8862, -0.6108, -1.3019, 0.1301, 0.1329],
- [ 0.7427, -0.3045, 1.7052, -0.2803, -0.4116, -0.1438, 0.4753, 0.1974],
- [ 0.8011, -0.2826, 1.6989, 0.2847, -0.3568, -0.3909, 0.3260, 0.1932],
- [ 0.8804, -0.2614, 1.8125, 0.1778, -0.4982, 0.1325, 0.6062, 0.2211],
- [ 0.7056, -0.3785, 1.8138, -0.0180, -0.5265, 0.0883, 0.6572, 0.2042]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554],
- [-2.2859, -2.2859, 1.7210, -0.9772, 0.1852, -1.3698, 0.7986,
- 0.3104],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699],
- [ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936],
- [ 0.5443, -0.4079, 1.6633, 0.2853, -0.1325, 0.0888, 0.0558,
- 0.2138],
- [ 0.6207, -0.4273, 1.7557, 0.0236, -0.4326, 0.3623, 1.0033,
- 0.3157],
- [ 0.6191, -0.4273, 1.9115, -0.1766, -0.4672, 0.3161, 0.9741,
- 0.3050]]], device='cuda:0')
- loss_train_step before backward: tensor(0.3238, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.3238, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.717064084485173
- step: 89
- running loss: 0.07547263016275475
- Train Steps: 89/90 Loss: 0.0755 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [0.6193, 0.4108, 0.7425, 0.2350, 0.3887, 0.2750, 0.5900, 0.5717],
- [0.6332, 0.4118, 0.9238, 0.4267, 0.4012, 0.4733, 0.7525, 0.5436],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6132, 0.4037, 0.6963, 0.2217, 0.4100, 0.1950, 0.5395, 0.5175],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [ nan, nan, 0.6859, 0.2194, 0.4150, 0.1867, 0.5153, 0.5729],
- [0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6784, -0.3806, 1.9874, 0.0074, -0.5508, 0.0863, 0.5777, 0.2291],
- [ 0.4791, -0.4740, 1.2944, -0.7627, -0.4662, -0.9881, 0.2634, 0.2488],
- [ 0.7636, -0.3399, 1.8175, -0.1967, -0.4223, 0.0778, 0.7374, 0.2327],
- [ 0.6685, -0.3923, 1.8977, 0.5432, -0.4429, -0.0133, 0.5661, 0.1591],
- [ 0.6327, -0.3888, 1.1758, -0.9603, -0.4457, -1.3570, 0.2291, 0.1677],
- [ 0.5583, -0.4858, 1.8936, 0.0071, -0.5720, 0.0042, 0.5830, 0.1419],
- [-0.0687, -0.8334, 1.0642, -1.0043, -0.3935, -1.2683, 0.1451, 0.2017],
- [ 0.8477, -0.2481, 1.7314, -0.2783, -0.5498, -0.6370, 0.5573, 0.1633]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [ 0.5746, -0.3882, 1.1436, -1.2005, -0.4903, -1.0157, 0.4393,
- 0.3546],
- [ 0.6388, -0.3840, 1.9808, -0.3152, -0.4326, -0.0996, 1.1897,
- 0.2249],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.5465, -0.4214, 0.9300, -1.2620, -0.3921, -1.3852, 0.2062,
- 0.1043],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [-2.2859, -2.2859, 0.8824, -1.2727, -0.3691, -1.4237, 0.0943,
- 0.3604],
- [ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1345, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1345, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 6.85155183263123
- step: 90
- running loss: 0.07612835369590255
- Valid Steps: 10/10 Loss: nan 61
- --------------------------------------------------
- Epoch: 2 Train Loss: 0.0761 Valid Loss: nan
- --------------------------------------------------
- size of train loader is: 90
- torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4153, 0.9000, 0.4533, 0.4025, 0.2633, 0.6223, 0.4967],
- [0.6350, 0.4144, 0.9250, 0.4100, 0.4125, 0.3750, 0.7129, 0.5945],
- [0.6200, 0.4024, 0.8390, 0.3139, 0.3525, 0.3833, 0.6162, 0.5383],
- [0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413],
- [0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6166, 0.4008, 0.8563, 0.5667, 0.4388, 0.4933, 0.5575, 0.5567],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4644, -0.4431, 1.6816, -0.0788, -0.5316, -0.8118, 0.3678, 0.1957],
- [ 0.3627, -0.5644, 1.7695, -0.1866, -0.3259, -0.4964, 0.6251, 0.2581],
- [ 0.7497, -0.3301, 1.4605, -0.7863, -0.7153, -0.5758, 0.6303, 0.2166],
- [ 0.5202, -0.4933, 1.6469, -0.0964, -0.5065, 0.0323, 0.4776, 0.2630],
- [ 0.3867, -0.5193, 1.5639, -0.8843, -0.0533, -0.8858, 0.7233, 0.2240],
- [ 0.3793, -0.5255, 1.4437, -0.7890, -0.4778, -0.8875, 0.3171, 0.1730],
- [ 0.6539, -0.3837, 1.6159, 0.2937, -0.4373, -0.0220, 0.4100, 0.2954],
- [ 0.3520, -0.5612, 1.6328, -0.0203, -0.6282, -0.1089, 0.2260, 0.2060]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6055, -0.3676, 1.8711, -0.1920, -0.4268, -1.0696, 0.5887,
- 0.0081],
- [ 0.6471, -0.3719, 1.9866, -0.3921, -0.3806, -0.5538, 1.0070,
- 0.4600],
- [ 0.5779, -0.4275, 1.5894, -0.8362, -0.6577, -0.5153, 0.5605,
- 0.2006],
- [ 0.5604, -0.4620, 1.7961, -0.1997, -0.3229, 0.1082, 0.4104,
- 0.2545],
- [ 0.6240, -0.3768, 1.6575, -1.2851, 0.2949, -1.2467, 1.1276,
- 0.2142],
- [ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.5620, -0.4346, 1.6691, 0.3315, -0.2594, -0.0072, 0.2891,
- 0.2853],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0298, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0298, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.02976425550878048
- step: 1
- running loss: 0.02976425550878048
- Train Steps: 1/90 Loss: 0.0298 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6350, 0.4043, 0.8738, 0.5650, 0.3850, 0.4750, 0.6401, 0.4950],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2171, -0.6386, 1.4917, -0.9178, -0.3628, -0.8859, 0.3591, 0.1758],
- [ 0.4784, -0.4798, 1.6804, 0.0476, -0.5666, -0.5235, 0.3980, 0.2267],
- [ 0.2964, -0.6088, 1.7696, -0.4092, -0.2328, -0.7607, 0.5548, 0.2144],
- [ 0.5832, -0.4517, 1.6893, -0.2107, -0.5384, -0.1259, 0.5803, 0.2860],
- [ 0.6926, -0.3706, 1.8076, 0.1113, -0.5554, -0.0664, 0.6463, 0.2072],
- [ 0.3910, -0.5487, 1.6927, -0.3536, -0.6326, -0.4973, 0.4058, 0.2500],
- [ 0.3494, -0.5530, 1.1658, -0.9490, -0.4045, -0.9441, 0.3029, 0.3491],
- [ 0.7162, -0.3370, 1.7149, 0.1530, -0.5052, -0.0180, 0.5345, 0.3138]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7997e-01, -4.3118e-01, 1.5709e+00, -1.0311e+00, -4.4411e-01,
- -1.1081e+00, 3.8730e-01, 8.5142e-02],
- [ 5.9013e-01, -4.1570e-01, 1.7557e+00, 1.9292e-01, -5.4226e-01,
- -5.9230e-01, 3.5843e-01, 1.6982e-01],
- [ 6.2236e-01, -4.3453e-01, 1.9404e+00, -2.9207e-01, -3.1709e-01,
- -8.7714e-01, 1.0655e+00, 2.1421e-01],
- [ 5.7131e-01, -4.1045e-01, 1.7557e+00, 4.6651e-02, -6.5196e-01,
- -2.6898e-01, 3.9885e-01, 5.2394e-01],
- [ 6.4706e-01, -4.1832e-01, 1.7499e+00, 3.2379e-01, -5.0762e-01,
- -9.1917e-02, 6.7064e-01, 4.6189e-04],
- [ 5.8799e-01, -3.8868e-01, 1.8423e+00, -3.3056e-01, -6.2309e-01,
- -5.2302e-01, 4.0462e-01, 1.5443e-01],
- [ 5.7460e-01, -4.0208e-01, 1.0801e+00, -1.1312e+00, -3.2286e-01,
- -1.1081e+00, 4.8034e-01, 6.0842e-01],
- [ 5.8909e-01, -3.5574e-01, 1.7326e+00, 3.3918e-01, -4.2102e-01,
- -1.2271e-01, 3.2379e-01, 3.0069e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0238, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0238, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.053598225116729736
- step: 2
- running loss: 0.026799112558364868
- Train Steps: 2/90 Loss: 0.0268 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6218, 0.4098, 0.7238, 0.1850, 0.4238, 0.2517, 0.6288, 0.5350],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6222, 0.3937, 0.8350, 0.5617, 0.4138, 0.4600, 0.5800, 0.5233],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [0.6275, 0.4081, 0.8063, 0.2017, 0.4825, 0.1583, 0.6156, 0.4869],
- [0.6101, 0.4042, 0.7775, 0.2617, 0.3713, 0.2817, 0.5440, 0.5650],
- [0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4246, -0.5008, 1.2521, -0.9797, -0.3414, -1.0541, 0.5136, 0.3221],
- [-0.0271, -0.7760, 1.0477, -0.9932, -0.3399, -1.3101, 0.2273, 0.2716],
- [ 0.7331, -0.3530, 1.8778, 0.4137, -0.4636, 0.0050, 0.6195, 0.2694],
- [ 0.5305, -0.4416, 1.7704, -0.2950, -0.6993, -0.4477, 0.4500, 0.2283],
- [ 0.0741, -0.7198, 1.4817, -0.8829, -0.1518, -1.0529, 0.5162, 0.2382],
- [-0.0500, -0.7776, 1.3050, -0.7022, -0.4658, -0.7708, 0.1221, 0.3052],
- [ 0.6535, -0.3872, 2.0202, -0.0684, -0.6126, 0.1059, 0.6727, 0.2721],
- [ 0.8910, -0.2266, 1.7665, 0.1437, -0.5468, 0.1158, 0.6713, 0.2996]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5861, -0.3929, 1.0570, -1.4314, -0.3286, -1.1235, 0.6182,
- 0.1852],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.5880, -0.4676, 1.5709, 0.3084, -0.3748, -0.1612, 0.3931,
- 0.1313],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753],
- [ 0.6124, -0.4010, 1.4381, -1.3544, -0.0573, -1.5546, 0.5573,
- -0.0369],
- [ 0.5320, -0.4189, 1.3053, -1.0773, -0.5711, -0.9849, 0.2267,
- 0.3237],
- [ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [ 0.6191, -0.4297, 1.4612, 0.2391, -0.4961, 0.0313, 1.1166,
- 0.1768]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0572, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0572, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.11081135272979736
- step: 3
- running loss: 0.03693711757659912
- Train Steps: 3/90 Loss: 0.0369 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6177, 0.4086, 0.8738, 0.3950, 0.3775, 0.5600, 0.6225, 0.5700],
- [0.6162, 0.4014, 0.8800, 0.5333, 0.3750, 0.4817, 0.5988, 0.5283],
- [0.6286, 0.4060, 0.9188, 0.4333, 0.3675, 0.4167, 0.7034, 0.5528],
- [0.6109, 0.4009, 0.8725, 0.4700, 0.3550, 0.4167, 0.5650, 0.5117],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383],
- [0.6325, 0.4066, 0.8438, 0.5350, 0.3925, 0.5267, 0.7113, 0.5795]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3962, -0.5233, 1.6373, -0.6012, -0.6109, -0.6016, 0.4131, 0.2866],
- [ 0.8978, -0.2230, 1.7190, -0.6673, -0.4464, 0.1228, 0.8288, 0.3088],
- [ 0.8697, -0.2394, 1.7297, 0.0657, -0.4729, -0.1425, 0.5340, 0.2836],
- [ 0.4009, -0.5431, 1.8679, -0.2984, -0.4186, -0.5946, 0.6249, 0.2787],
- [ 0.3967, -0.5239, 1.7884, -0.1002, -0.5768, -0.4514, 0.3042, 0.2203],
- [ 0.2635, -0.5390, 1.5640, -0.2989, -0.2955, -0.9008, 0.3495, 0.3293],
- [-0.8458, -1.3516, 0.9245, -1.1352, -0.3281, -1.4677, 0.0543, 0.2653],
- [ 0.8833, -0.2316, 1.8017, -0.0581, -0.4491, -0.0319, 0.8155, 0.3043]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.5671, -0.3988, 1.7499, -0.4614, -0.5423, 0.3007, 0.5894,
- 0.3469],
- [ 0.5603, -0.4319, 1.7788, 0.1775, -0.5538, -0.0611, 0.4797,
- 0.1544],
- [ 0.6174, -0.4105, 1.9577, -0.2844, -0.5885, -0.3614, 0.9631,
- 0.2676],
- [ 0.5356, -0.4343, 1.7441, -0.1150, -0.6462, -0.3614, 0.3238,
- 0.0774],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822],
- [-2.2859, -2.2859, 1.0712, -1.2085, -0.3806, -1.3929, 0.0755,
- 0.2006],
- [ 0.6355, -0.4080, 1.6113, 0.1852, -0.4730, 0.1467, 0.9996,
- 0.3905]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0703, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0703, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.18106389790773392
- step: 4
- running loss: 0.04526597447693348
- Train Steps: 4/90 Loss: 0.0453 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6229, 0.4066, 0.7612, 0.2967, 0.4250, 0.2167, 0.5925, 0.5350],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6201, 0.4017, 0.8871, 0.4621, 0.3517, 0.4675, 0.5999, 0.5106],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6286, 0.4086, 0.8408, 0.2801, 0.4163, 0.2800, 0.6725, 0.5393],
- [0.6275, 0.4048, 0.8488, 0.2883, 0.4463, 0.2033, 0.6321, 0.5155],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4974, -0.4397, 1.3189, -0.9185, -0.3947, -0.9882, 0.4938, 0.2704],
- [-1.2384, -1.5859, 1.0754, -0.9062, -0.3067, -1.0850, 0.0830, 0.2715],
- [ 0.8753, -0.2470, 1.9504, 0.1446, -0.6124, 0.1460, 0.6680, 0.2374],
- [ 0.4194, -0.4939, 1.1516, -0.8221, -0.5055, -0.9677, 0.3401, 0.3332],
- [ 0.2939, -0.5620, 1.6364, -0.6533, -0.2373, -0.7871, 0.6217, 0.2830],
- [ 0.6833, -0.3461, 1.5232, -0.9011, -0.2480, -1.0147, 0.7707, 0.2400],
- [ 0.9637, -0.1672, 2.0570, 0.1913, -0.6407, 0.2832, 0.6602, 0.2757],
- [ 0.3353, -0.5188, 1.7350, -0.2497, -0.4663, -0.4829, 0.4751, 0.3412]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5911, -0.4080, 1.2303, -0.9156, -0.3229, -1.2851, 0.4508,
- 0.1852],
- [-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373],
- [ 0.5783, -0.4306, 1.8114, -0.1515, -0.6617, -0.1268, 0.4851,
- 0.0727],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.6174, -0.3984, 1.5975, -0.9921, -0.3633, -0.9926, 0.8205,
- 0.2050],
- [ 0.6126, -0.4161, 1.6344, -0.9541, -0.2247, -1.3467, 0.6339,
- 0.0953],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0653, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0653, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.24632898718118668
- step: 5
- running loss: 0.04926579743623734
- Train Steps: 5/90 Loss: 0.0493 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6230, 0.4113, 0.7213, 0.1983, 0.4325, 0.2367, 0.6262, 0.5400],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167],
- [0.6193, 0.4050, 0.7313, 0.2433, 0.4075, 0.2117, 0.5649, 0.5656],
- [0.6166, 0.4008, 0.8563, 0.5667, 0.4388, 0.4933, 0.5575, 0.5567],
- [0.6132, 0.4118, 0.8200, 0.3633, 0.3563, 0.5400, 0.5787, 0.5136],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378],
- [0.6165, 0.4106, 0.7575, 0.1733, 0.3838, 0.2650, 0.5680, 0.5116],
- [0.6258, 0.4143, 0.8525, 0.2617, 0.4950, 0.1667, 0.6219, 0.4967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.0326, -0.7844, 1.3051, -0.8212, -0.3678, -1.0649, 0.3889, 0.3106],
- [ 0.4314, -0.4934, 1.6887, -0.4932, -0.6464, -0.7139, 0.3917, 0.2290],
- [ 0.2542, -0.5932, 1.1377, -0.9746, -0.4692, -1.0381, 0.3510, 0.3077],
- [ 0.7676, -0.3140, 1.8666, 0.5899, -0.4083, 0.2689, 0.5522, 0.3187],
- [ 0.6571, -0.3569, 1.7394, -0.4057, -0.6427, 0.3132, 0.6880, 0.2942],
- [ 0.4241, -0.5237, 1.5921, -1.0009, -0.0236, -1.0447, 0.9168, 0.2845],
- [ 0.3779, -0.5320, 1.3481, -0.8482, -0.5710, -1.0561, 0.4776, 0.2223],
- [-0.1864, -0.8626, 1.5975, -0.7790, -0.1724, -0.9359, 0.5434, 0.2635]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.9169e-01, -3.8607e-01, 1.0455e+00, -1.3698e+00, -2.8822e-01,
- -1.1928e+00, 6.0670e-01, 2.0831e-01],
- [ 5.4825e-01, -4.2490e-01, 1.5305e+00, -7.3857e-01, -6.1155e-01,
- -8.6944e-01, 3.3533e-01, 1.0054e-01],
- [ 5.7460e-01, -4.1527e-01, 1.0917e+00, -1.1620e+00, -4.0370e-01,
- -1.3082e+00, 3.2339e-01, 3.2671e-01],
- [ 5.6195e-01, -4.3457e-01, 1.6691e+00, 3.3149e-01, -2.5935e-01,
- -7.2363e-03, 2.8915e-01, 2.8530e-01],
- [ 5.4660e-01, -3.8397e-01, 1.5016e+00, -6.0770e-01, -6.4042e-01,
- 2.0831e-01, 3.8714e-01, 8.6245e-02],
- [ 6.2072e-01, -3.9360e-01, 1.7788e+00, -1.1235e+00, 1.4480e-01,
- -1.0850e+00, 1.1459e+00, 1.9818e-01],
- [ 5.6184e-01, -3.8945e-01, 1.2129e+00, -1.4853e+00, -5.1339e-01,
- -1.0619e+00, 3.3778e-01, 7.7228e-02],
- [ 6.0479e-01, -3.7229e-01, 1.6517e+00, -1.0773e+00, 4.6189e-04,
- -1.5161e+00, 5.8660e-01, 8.0947e-03]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0639, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0639, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3102681040763855
- step: 6
- running loss: 0.05171135067939758
- Train Steps: 6/90 Loss: 0.0517 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [ nan, nan, 0.6469, 0.1943, 0.4025, 0.2000, 0.5125, 0.5533],
- [0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892],
- [0.6147, 0.4112, 0.7988, 0.3200, 0.3775, 0.2767, 0.5150, 0.5550],
- [0.6205, 0.4062, 0.8337, 0.2683, 0.3675, 0.4283, 0.6338, 0.5250],
- [0.6189, 0.4033, 0.8650, 0.5267, 0.4487, 0.5150, 0.5925, 0.5050],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683],
- [0.6127, 0.4118, 0.8650, 0.5083, 0.4088, 0.5367, 0.5300, 0.5456]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0117, -0.1618, 1.9431, 0.1343, -0.5366, -0.3326, 0.7663, 0.1990],
- [-1.0768, -1.5103, 0.9567, -1.3619, -0.3016, -1.6007, 0.2505, 0.2646],
- [ 0.7019, -0.3086, 1.7838, -0.1395, -0.7029, -0.5296, 0.4112, 0.2605],
- [-0.0792, -0.8122, 1.4107, -0.9500, -0.5097, -1.0802, 0.2752, 0.2552],
- [ 0.5814, -0.4038, 1.4747, -1.1459, -0.5425, -0.5329, 0.7750, 0.2706],
- [ 0.6678, -0.3636, 1.8524, -0.0345, -0.2189, -0.0571, 0.5808, 0.2862],
- [ 0.3820, -0.5253, 1.6017, -1.1825, -0.3065, -1.0830, 0.6300, 0.2799],
- [ 0.6951, -0.3400, 1.8050, -0.0378, -0.3050, 0.0485, 0.5817, 0.3112]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [-2.2859, -2.2859, 0.7023, -1.3883, -0.4268, -1.3621, 0.0813,
- 0.2699],
- [ 0.5454, -0.4053, 1.6633, -0.1766, -0.6058, -0.5923, 0.1577,
- 0.4357],
- [ 0.5532, -0.3864, 1.4035, -0.8079, -0.5423, -1.0080, 0.0928,
- 0.2776],
- [ 0.5799, -0.4099, 1.5651, -1.0465, -0.5885, -0.3075, 0.6413,
- 0.1390],
- [ 0.5726, -0.4230, 1.7095, 0.1467, -0.2132, 0.0928, 0.4508,
- 0.0467],
- [ 0.5778, -0.3930, 1.5189, -0.9387, -0.4326, -0.9156, 0.4855,
- 0.3392],
- [ 0.5443, -0.3840, 1.7095, 0.0620, -0.3979, 0.1929, 0.1622,
- 0.2341]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0636, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0636, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.37389400601387024
- step: 7
- running loss: 0.05341342943055289
- Train Steps: 7/90 Loss: 0.0534 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783],
- [0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6161, 0.4024, 0.8838, 0.4583, 0.3688, 0.3733, 0.5311, 0.5344],
- [0.6200, 0.3913, 0.8788, 0.5217, 0.4075, 0.5100, 0.6060, 0.4913],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567],
- [0.6271, 0.4040, 0.9000, 0.3250, 0.4938, 0.2300, 0.7192, 0.5413]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0096, -0.7602, 0.9648, -1.2757, -0.4615, -1.1769, 0.3001, 0.3017],
- [ 0.2265, -0.6059, 1.3263, -1.2305, -0.4143, -1.1986, 0.5272, 0.2549],
- [ 0.4254, -0.4732, 1.6860, 0.2153, -0.4557, -0.1081, 0.4277, 0.3285],
- [ 0.9220, -0.1908, 1.9226, -0.0915, -0.6569, -0.3724, 0.5336, 0.1467],
- [ 0.7344, -0.3511, 1.9044, 0.0650, -0.4269, 0.1083, 0.6133, 0.1700],
- [ 0.4440, -0.4838, 1.4899, -0.6572, -0.6243, -0.7794, 0.3746, 0.2894],
- [-0.5248, -1.0886, 1.2168, -1.2470, -0.2981, -1.0941, 0.3924, 0.3101],
- [ 0.3848, -0.5339, 1.8826, -1.0373, 0.0069, -1.0555, 1.0492, 0.1640]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5483, -0.3991, 0.8030, -1.2159, -0.5018, -1.1928, 0.2624,
- 0.3852],
- [ 0.5697, -0.4466, 1.1973, -1.1871, -0.4571, -0.9965, 0.5219,
- 0.2032],
- [ 0.5836, -0.3649, 1.7210, 0.3854, -0.3979, -0.2921, 0.3065,
- 0.4470],
- [ 0.5598, -0.4273, 1.7961, -0.1689, -0.5827, -0.5615, 0.1671,
- 0.1824],
- [ 0.5776, -0.4784, 1.7730, 0.1236, -0.4037, 0.0697, 0.5132,
- -0.0168],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853],
- [ 0.6108, -0.4201, 1.8711, -0.7848, -0.0053, -1.2236, 1.0362,
- 0.2142]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0577, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0577, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.43161700665950775
- step: 8
- running loss: 0.05395212583243847
- Train Steps: 8/90 Loss: 0.0540 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6284, 0.4127, 0.8538, 0.5867, 0.4363, 0.5083, 0.6038, 0.5433],
- [0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412],
- [0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [0.6245, 0.4100, 0.7762, 0.2583, 0.4963, 0.1517, 0.5875, 0.5417],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6265, 0.4251, 0.7113, 0.3550, 0.4375, 0.2117, 0.5587, 0.6118],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4623, -0.4913, 1.8371, 0.3857, -0.4122, 0.2493, 0.5271, 0.1908],
- [ 0.6949, -0.3453, 1.8927, -0.5485, -0.4945, -0.7875, 0.6493, 0.1366],
- [ 0.2863, -0.5883, 1.2984, -0.9925, -0.5895, -0.7914, 0.3332, 0.1886],
- [-0.0663, -0.7860, 1.2408, -1.1712, -0.1767, -1.2552, 0.4436, 0.2503],
- [ 0.0550, -0.7215, 1.0977, -1.0060, -0.5058, -1.0154, 0.3654, 0.2937],
- [ 0.2194, -0.6018, 1.1889, -0.9221, -0.3864, -0.8253, 0.3989, 0.3544],
- [ 0.7135, -0.3359, 2.0395, -0.5939, -0.1271, -0.8327, 0.9730, 0.1247],
- [ 0.1684, -0.6724, 1.2012, -1.1064, -0.4767, -0.9897, 0.3972, 0.2452]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6166, -0.3795, 1.6575, 0.4239, -0.2709, 0.0620, 0.5028,
- 0.2237],
- [ 0.5784, -0.4006, 1.7911, -0.5701, -0.5192, -1.0331, 0.4137,
- 0.2139],
- [ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141],
- [ 0.5985, -0.3921, 1.2995, -1.0927, 0.0062, -1.5854, 0.4277,
- 0.2160],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391],
- [ 0.6077, -0.3226, 0.9993, -0.6462, -0.2651, -1.3082, 0.2946,
- 0.5401],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142],
- [ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0441, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0441, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4756833612918854
- step: 9
- running loss: 0.05285370681020948
- Train Steps: 9/90 Loss: 0.0529 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [0.6275, 0.4050, 0.9038, 0.3767, 0.3838, 0.3533, 0.7074, 0.5575],
- [0.6193, 0.4108, 0.7438, 0.2700, 0.3650, 0.3683, 0.6238, 0.5717],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2563, -0.6047, 1.2636, -1.1187, -0.5007, -0.8370, 0.3980, 0.2478],
- [ 0.4033, -0.5214, 1.6198, -0.9454, -0.4773, -0.9102, 0.5505, 0.2541],
- [ 0.1984, -0.6554, 1.7125, 0.0773, -0.3777, -0.4354, 0.3894, 0.1948],
- [ 0.6152, -0.3854, 1.7423, -0.4495, -0.5086, -0.2344, 0.5880, 0.1688],
- [ 0.7010, -0.3407, 1.9443, -0.7245, -0.2994, -1.1247, 0.8264, 0.1196],
- [ 0.2452, -0.5995, 1.1966, -1.1032, -0.5102, -0.7389, 0.3625, 0.2906],
- [ 0.0631, -0.7514, 1.0383, -1.2927, -0.3640, -1.4295, 0.3175, 0.2634],
- [ 0.5745, -0.3833, 1.5549, -0.1126, -0.4069, -0.2537, 0.5014, 0.2783]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [ 0.6125, -0.4153, 1.8885, -0.5461, -0.5134, -0.6539, 0.9814,
- 0.2890],
- [ 0.5747, -0.3886, 1.1494, -1.0388, -0.6000, -0.5846, 0.5952,
- 0.3546],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0468, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0468, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5225014016032219
- step: 10
- running loss: 0.05225014016032219
- Train Steps: 10/90 Loss: 0.0523 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6260, 0.4253, 0.8938, 0.4400, 0.3675, 0.3650, 0.5675, 0.5767],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6293, 0.4024, 0.8750, 0.5000, 0.4012, 0.5733, 0.7121, 0.5633],
- [0.6273, 0.4100, 0.7137, 0.2133, 0.4000, 0.2650, 0.6075, 0.5633],
- [0.6277, 0.4118, 0.8988, 0.3833, 0.3950, 0.2650, 0.6290, 0.4938],
- [0.6214, 0.4175, 0.7300, 0.2883, 0.4338, 0.2167, 0.5698, 0.5773]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.4314, -1.0916, 0.9281, -1.1852, -0.4754, -1.1881, 0.1755, 0.3128],
- [ 0.6351, -0.3751, 1.7415, 0.0037, -0.3866, -0.0694, 0.4654, 0.1710],
- [ 0.9734, -0.1112, 1.7456, -0.4610, -0.5858, -0.7201, 0.5528, 0.2130],
- [ 0.7597, -0.2628, 1.6129, -0.4537, -0.5275, -0.2665, 0.4179, 0.2051],
- [ 0.7301, -0.2926, 1.8193, -0.0412, -0.3160, 0.2844, 0.7483, 0.2097],
- [-0.0348, -0.7843, 1.0376, -1.5226, -0.3714, -1.4824, 0.4634, 0.2735],
- [ 0.3638, -0.5736, 1.8100, -0.7922, -0.3260, -1.3180, 0.7065, 0.1236],
- [ 0.1663, -0.6514, 1.0272, -1.2652, -0.3324, -1.2683, 0.3238, 0.3170]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085],
- [ 0.5778, -0.4389, 1.7107, 0.1192, -0.3921, 0.0815, 0.4741,
- 0.0711],
- [ 0.6058, -0.3216, 1.8423, -0.2536, -0.5885, -0.6000, 0.3353,
- 0.3777],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.6207, -0.4273, 1.7557, 0.0236, -0.4326, 0.3623, 1.0033,
- 0.3157],
- [ 0.6115, -0.3924, 1.0109, -1.3005, -0.4383, -1.0619, 0.5201,
- 0.3161],
- [ 0.6135, -0.3841, 1.8654, -0.5153, -0.4614, -1.0619, 0.6195,
- -0.0049],
- [ 0.5841, -0.3574, 1.0859, -0.9541, -0.2824, -1.2851, 0.3460,
- 0.3808]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0607, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0607, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5832267887890339
- step: 11
- running loss: 0.05302061716263944
- Train Steps: 11/90 Loss: 0.0530 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6175, 0.4013, 0.8900, 0.4500, 0.4375, 0.5850, 0.6175, 0.5383],
- [0.6182, 0.3972, 0.8552, 0.5914, 0.3683, 0.4181, 0.5688, 0.5378],
- [0.6263, 0.4057, 0.8800, 0.3833, 0.3650, 0.3717, 0.6375, 0.4804],
- [0.6222, 0.3937, 0.8350, 0.5617, 0.4138, 0.4600, 0.5800, 0.5233],
- [0.6266, 0.4070, 0.8712, 0.5600, 0.3713, 0.4783, 0.5775, 0.6100],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413],
- [0.6229, 0.4066, 0.7612, 0.2967, 0.4250, 0.2167, 0.5925, 0.5350],
- [0.6353, 0.4128, 0.8488, 0.2600, 0.5525, 0.1616, 0.6694, 0.5540]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6115, -0.3336, 1.6059, -0.4732, -0.3632, 0.0570, 0.5158, 0.2598],
- [ 0.6079, -0.3892, 1.4494, -0.0570, -0.6873, -0.5817, 0.3053, 0.2103],
- [ 0.7655, -0.2788, 1.4467, -0.8692, -0.6469, -0.6972, 0.4732, 0.1591],
- [ 0.3337, -0.5651, 1.4831, -0.1191, -0.5095, -0.3914, 0.3344, 0.2294],
- [ 0.4696, -0.4529, 1.4901, 0.0385, -0.6042, -0.0918, 0.3001, 0.2966],
- [ 0.6387, -0.3689, 1.6943, -1.0324, -0.1716, -1.3331, 0.7935, 0.1650],
- [ 0.3009, -0.5571, 1.0399, -1.4437, -0.4154, -1.3370, 0.4036, 0.2582],
- [-0.2349, -0.9462, 1.4178, -1.5820, 0.0318, -1.4545, 0.7773, 0.2556]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5664, -0.4321, 1.8249, -0.2074, -0.2651, 0.4162, 0.5663,
- 0.2006],
- [ 0.5697, -0.4514, 1.6642, 0.4459, -0.5850, -0.3546, 0.3414,
- 0.1982],
- [ 0.6071, -0.4119, 1.7788, -0.5153, -0.6000, -0.5692, 0.6586,
- -0.0670],
- [ 0.5880, -0.4676, 1.5709, 0.3084, -0.3748, -0.1612, 0.3931,
- 0.1313],
- [ 0.6081, -0.4059, 1.7383, 0.3007, -0.5711, -0.0765, 0.3815,
- 0.5316],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142],
- [ 0.5911, -0.4080, 1.2303, -0.9156, -0.3229, -1.2851, 0.4508,
- 0.1852],
- [ 0.6487, -0.3792, 1.6344, -1.0850, 0.2659, -1.5397, 0.8059,
- 0.2730]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0612, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0612, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6444090195000172
- step: 12
- running loss: 0.05370075162500143
- Train Steps: 12/90 Loss: 0.0537 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6145, 0.4008, 0.8750, 0.5383, 0.3975, 0.4650, 0.5563, 0.5533],
- [0.6124, 0.4083, 0.6954, 0.3069, 0.3650, 0.2750, 0.5163, 0.5485],
- [0.6030, 0.3969, 0.7988, 0.3917, 0.3450, 0.3667, 0.5266, 0.4700],
- [0.6272, 0.4120, 0.9038, 0.4117, 0.3725, 0.3200, 0.6175, 0.5250],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6371, 0.4092, 0.8337, 0.5850, 0.3950, 0.5117, 0.6559, 0.5262],
- [0.6308, 0.3990, 0.8688, 0.5183, 0.3950, 0.4983, 0.6388, 0.5133]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3441, -0.5397, 1.5219, 0.0440, -0.4011, -0.1755, 0.2975, 0.2755],
- [ 0.1604, -0.6528, 0.9091, -1.3186, -0.5518, -1.0986, 0.2071, 0.3260],
- [ 0.6327, -0.3453, 1.3894, -0.7139, -0.6229, -0.6713, 0.3165, 0.2111],
- [ 0.5513, -0.4404, 1.7052, -0.7359, -0.4866, -1.0530, 0.6075, 0.1834],
- [ 0.4135, -0.5046, 1.4512, -0.8828, -0.2866, -0.9949, 0.6271, 0.1815],
- [ 0.3910, -0.5328, 1.3725, -1.4320, -0.0645, -1.5060, 0.8007, 0.2209],
- [ 0.7513, -0.2595, 1.5456, -0.0458, -0.5058, -0.1437, 0.5182, 0.2074],
- [ 0.5279, -0.4191, 1.5979, -0.1190, -0.4333, -0.1138, 0.4706, 0.2032]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5526, -0.4347, 1.7557, 0.2006, -0.4499, -0.1381, 0.2834,
- 0.2699],
- [ 0.5425, -0.3998, 0.9263, -0.8683, -0.6000, -1.0157, 0.0990,
- 0.2476],
- [ 0.4992, -0.4525, 1.4035, -0.4768, -0.6924, -0.5923, 0.1465,
- -0.1151],
- [ 0.6111, -0.3828, 1.8885, -0.3844, -0.5654, -0.8079, 0.5663,
- 0.1390],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.6569, -0.3960, 1.5651, 0.4162, -0.4614, 0.0774, 0.7438,
- 0.1447],
- [ 0.6275, -0.4430, 1.7268, 0.1082, -0.4614, 0.0159, 0.6644,
- 0.0851]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0372, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0372, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6816200353205204
- step: 13
- running loss: 0.0524323104092708
- Train Steps: 13/90 Loss: 0.0524 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917],
- [0.6276, 0.4095, 0.8237, 0.2250, 0.4662, 0.1783, 0.6171, 0.4869],
- [0.6187, 0.4104, 0.7126, 0.2069, 0.3950, 0.2517, 0.5913, 0.5683],
- [ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617],
- [0.6069, 0.3975, 0.8625, 0.5083, 0.4388, 0.5483, 0.5650, 0.4967],
- [0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268],
- [0.6229, 0.4066, 0.7612, 0.2967, 0.4250, 0.2167, 0.5925, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6906, -0.3250, 1.7244, 0.2441, -0.6534, -0.5400, 0.4928, 0.0983],
- [ 0.7984, -0.2507, 1.3635, -0.5323, -0.6371, -0.7391, 0.3126, 0.2900],
- [ 0.6796, -0.2984, 1.4948, -1.3600, -0.1548, -1.2440, 0.7837, 0.1997],
- [ 0.6440, -0.3274, 1.0033, -1.1558, -0.4957, -1.1206, 0.3775, 0.2980],
- [-0.6363, -1.1912, 0.9427, -1.3059, -0.3338, -1.3175, 0.2988, 0.2964],
- [ 0.5409, -0.4155, 1.6573, 0.1337, -0.2963, 0.1204, 0.4237, 0.1673],
- [ 0.4925, -0.4208, 1.6593, -0.0255, -0.3210, 0.0899, 0.5519, 0.1780],
- [ 0.5728, -0.3631, 1.1779, -1.0559, -0.3516, -1.1277, 0.5243, 0.2186]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470],
- [ 0.6127, -0.3944, 1.5189, -1.2467, -0.1323, -1.4622, 0.5646,
- -0.0369],
- [ 0.5718, -0.3905, 1.0053, -1.3305, -0.4614, -1.1235, 0.4450,
- 0.3392],
- [-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084],
- [ 0.5174, -0.4497, 1.6979, 0.0620, -0.2594, 0.2468, 0.3238,
- 0.0082],
- [ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474],
- [ 0.5911, -0.4080, 1.2303, -0.9156, -0.3229, -1.2851, 0.4508,
- 0.1852]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0724, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0724, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7540136612951756
- step: 14
- running loss: 0.053858118663941114
- Train Steps: 14/90 Loss: 0.0539 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467],
- [0.6037, 0.4020, 0.8300, 0.4033, 0.3575, 0.4883, 0.5647, 0.5631],
- [0.6260, 0.4253, 0.8938, 0.4400, 0.3675, 0.3650, 0.5675, 0.5767],
- [0.6182, 0.3967, 0.8263, 0.3065, 0.3526, 0.4161, 0.6192, 0.5290],
- [0.6364, 0.4144, 0.8625, 0.3083, 0.4913, 0.2000, 0.6448, 0.5274],
- [0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6262, 0.4163, 0.8850, 0.5183, 0.3763, 0.4150, 0.6025, 0.5500],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 3.8594e-01, -4.9731e-01, 1.3426e+00, -7.9972e-01, -5.6520e-01,
- -5.4576e-01, 5.2629e-01, 1.8728e-01],
- [ 4.3697e-01, -4.5928e-01, 1.3931e+00, -5.5027e-01, -4.7881e-01,
- -1.3916e-01, 3.5979e-01, 2.4473e-01],
- [ 8.3286e-01, -2.0214e-01, 1.5449e+00, -3.9255e-01, -5.2480e-01,
- -7.8448e-01, 4.5361e-01, 2.3245e-01],
- [ 5.0168e-01, -4.3097e-01, 1.3078e+00, -9.1020e-01, -5.8755e-01,
- -6.2334e-01, 5.8320e-01, 1.7699e-01],
- [ 7.1887e-01, -3.1333e-01, 1.5627e+00, -1.1709e+00, -4.1819e-04,
- -1.5454e+00, 8.5491e-01, 2.0092e-01],
- [ 4.4513e-01, -4.4595e-01, 1.5378e+00, -2.1944e-01, -4.1386e-01,
- -3.5445e-01, 3.4656e-01, 1.7412e-01],
- [ 6.1162e-01, -3.6164e-01, 1.5413e+00, -1.8645e-01, -4.5866e-01,
- -7.5103e-01, 3.8984e-01, 2.1871e-01],
- [ 2.9098e-01, -5.9695e-01, 1.4410e+00, 1.1175e-01, -3.4529e-01,
- -4.0318e-01, 3.0012e-01, 1.9724e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391],
- [ 0.5027, -0.4289, 1.5478, -0.4229, -0.6346, -0.0303, 0.3223,
- 0.3148],
- [ 0.6058, -0.3216, 1.8423, -0.2536, -0.5885, -0.6000, 0.3353,
- 0.3777],
- [ 0.5697, -0.4538, 1.5308, -0.8703, -0.6572, -0.3639, 0.5739,
- 0.1576],
- [ 0.6537, -0.3719, 1.6979, -0.8617, -0.0169, -1.3621, 0.6926,
- 0.1501],
- [ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.6066, -0.3632, 1.8018, 0.1082, -0.5480, -0.3691, 0.4970,
- 0.2545],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0246, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0246, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7785959504544735
- step: 15
- running loss: 0.0519063966969649
- Train Steps: 15/90 Loss: 0.0519 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6022, 0.3994, 0.8025, 0.3350, 0.3350, 0.4400, 0.5565, 0.5025],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413],
- [0.6097, 0.4000, 0.7325, 0.2667, 0.3450, 0.3517, 0.5284, 0.5045],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6122, 0.4006, 0.8850, 0.4217, 0.4088, 0.5517, 0.6063, 0.5517],
- [0.6129, 0.3930, 0.8769, 0.5105, 0.4146, 0.5642, 0.6000, 0.5082],
- [0.6199, 0.4015, 0.8716, 0.5228, 0.3833, 0.4772, 0.5883, 0.5148],
- [0.6261, 0.4029, 0.8720, 0.3364, 0.3665, 0.3753, 0.6531, 0.5183]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3495, -0.5137, 1.2867, -0.7977, -0.5919, -0.4932, 0.3754, 0.2001],
- [ 0.7947, -0.2719, 1.7608, -0.7762, -0.0301, -1.4611, 0.8805, 0.1307],
- [ 0.0306, -0.7537, 1.0149, -1.0745, -0.5246, -0.9820, 0.2472, 0.2447],
- [ 0.6324, -0.3694, 1.6234, 0.0647, -0.5520, -0.8712, 0.4582, 0.1424],
- [ 0.7833, -0.2346, 1.5707, -0.3463, -0.3770, 0.0528, 0.5279, 0.2255],
- [ 0.7305, -0.2862, 1.5712, 0.0126, -0.2571, 0.0699, 0.4072, 0.2189],
- [ 0.4863, -0.4808, 1.5423, 0.0540, -0.5026, -0.2989, 0.2758, 0.1930],
- [ 0.5454, -0.4141, 1.3707, -0.7669, -0.4660, -0.6807, 0.5137, 0.2013]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.4956, -0.4413, 1.4208, -0.7386, -0.7386, -0.2536, 0.2843,
- 0.0351],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142],
- [ 0.5303, -0.4384, 1.0975, -1.0542, -0.6924, -0.6616, 0.1548,
- 0.0442],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.5417, -0.4355, 1.8018, -0.3383, -0.3979, 0.2622, 0.5143,
- 0.2622],
- [ 0.5450, -0.4706, 1.7643, 0.0722, -0.3708, 0.3200, 0.4854,
- 0.0612],
- [ 0.5773, -0.4316, 1.7399, 0.1287, -0.5153, -0.0817, 0.4313,
- 0.0919],
- [ 0.6059, -0.4249, 1.7420, -0.7321, -0.5931, -0.5523, 0.7307,
- 0.1080]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0321, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0321, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8107367865741253
- step: 16
- running loss: 0.05067104916088283
- Train Steps: 16/90 Loss: 0.0507 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550],
- [0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933],
- [0.6264, 0.4049, 0.8988, 0.4633, 0.3813, 0.4983, 0.6326, 0.4843],
- [0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517],
- [0.6097, 0.4000, 0.7325, 0.2667, 0.3450, 0.3517, 0.5284, 0.5045],
- [0.6087, 0.3951, 0.8387, 0.5833, 0.4188, 0.4933, 0.5146, 0.4830],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6204, 0.4055, 0.8438, 0.5733, 0.4574, 0.4801, 0.5487, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6864, -0.3161, 1.3957, -0.8741, -0.6393, -0.4115, 0.5721, 0.1944],
- [ 0.6869, -0.2809, 1.5808, 0.1829, -0.1508, -0.4284, 0.3990, 0.2674],
- [ 1.0103, -0.1350, 1.8808, -0.1997, -0.6168, -0.1198, 0.6180, 0.0456],
- [ 0.4554, -0.4768, 1.7386, -0.1356, -0.2300, -0.1834, 0.4971, 0.1328],
- [ 0.2051, -0.6472, 1.1485, -1.0484, -0.5905, -0.9150, 0.3315, 0.1749],
- [ 0.6647, -0.3565, 1.6234, 0.2364, -0.4079, -0.3115, 0.3650, 0.1571],
- [ 0.0915, -0.7536, 0.9806, -1.4417, -0.3319, -1.7779, 0.4103, 0.1761],
- [ 0.6232, -0.3394, 1.5547, 0.1898, -0.2631, -0.2693, 0.3681, 0.2310]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776],
- [ 0.5959, -0.3579, 1.6055, 0.3623, -0.0573, -0.2074, 0.3122,
- 0.4547],
- [ 0.6075, -0.4159, 1.8654, -0.1458, -0.5249, 0.0159, 0.6359,
- -0.0490],
- [ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622],
- [ 0.5303, -0.4384, 1.0975, -1.0542, -0.6924, -0.6616, 0.1548,
- 0.0442],
- [ 0.5258, -0.4610, 1.5882, 0.4085, -0.3517, -0.0072, 0.0910,
- -0.0550],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.5795, -0.4129, 1.6113, 0.3623, -0.1733, -0.0684, 0.2487,
- 0.3084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0270, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0270, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8377538155764341
- step: 17
- running loss: 0.04927963621037848
- Train Steps: 17/90 Loss: 0.0493 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6115, 0.3998, 0.7063, 0.2383, 0.4037, 0.1950, 0.5320, 0.4993],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6222, 0.3937, 0.8350, 0.5617, 0.4138, 0.4600, 0.5800, 0.5233],
- [0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6150, 0.3935, 0.8696, 0.5158, 0.4647, 0.5329, 0.6041, 0.5153]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4288, -0.4854, 1.6467, 0.1719, -0.4551, 0.0582, 0.4286, 0.1805],
- [ 0.5306, -0.4610, 0.9986, -1.3014, -0.4066, -1.3574, 0.3123, 0.1790],
- [ 0.7175, -0.2925, 1.6305, -0.1981, -0.5236, -0.6448, 0.4414, 0.1654],
- [ 0.5649, -0.4179, 1.0909, -1.1380, -0.4752, -1.0641, 0.3576, 0.1617],
- [ 0.4000, -0.5173, 1.6446, 0.3024, -0.3259, -0.0621, 0.4557, 0.1149],
- [ 0.5420, -0.4195, 1.7541, 0.0212, -0.4687, -0.2140, 0.5426, 0.1165],
- [ 0.5727, -0.4093, 1.5485, -0.5428, -0.4685, -0.9696, 0.3440, 0.1536],
- [ 0.6472, -0.3293, 1.6709, 0.1153, -0.1787, 0.1713, 0.4548, 0.1555]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5384, -0.4393, 0.9762, -1.1851, -0.4210, -1.3852, 0.1712,
- 0.0201],
- [ 0.6016, -0.3633, 1.7152, -0.2228, -0.6115, -0.6385, 0.5028,
- 0.2699],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.5880, -0.4676, 1.5709, 0.3084, -0.3748, -0.1612, 0.3931,
- 0.1313],
- [ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499],
- [ 0.5485, -0.4209, 1.6691, -0.4152, -0.5249, -1.1081, 0.0725,
- 0.0021],
- [ 0.5548, -0.4682, 1.7309, 0.0966, -0.1394, 0.1757, 0.5045,
- 0.0942]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0131, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0131, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8508864175528288
- step: 18
- running loss: 0.04727146764182382
- Train Steps: 18/90 Loss: 0.0473 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6143, 0.4040, 0.8237, 0.3333, 0.4075, 0.2117, 0.5137, 0.4973],
- [0.6226, 0.4098, 0.8912, 0.4100, 0.4025, 0.2383, 0.5763, 0.5367],
- [0.6097, 0.4024, 0.8488, 0.3717, 0.3875, 0.5517, 0.5836, 0.5591],
- [0.6339, 0.4118, 0.7988, 0.5800, 0.3912, 0.4583, 0.7343, 0.5760],
- [0.6198, 0.4164, 0.8700, 0.5067, 0.4625, 0.5650, 0.5464, 0.5197],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896],
- [0.6275, 0.4157, 0.8337, 0.5800, 0.3763, 0.4200, 0.5547, 0.6125]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4882, -0.4754, 1.3462, -1.0380, -0.4194, -1.2863, 0.3462, 0.1151],
- [ 0.6720, -0.3324, 1.6458, -0.4131, -0.3798, -1.0912, 0.4214, 0.1275],
- [ 0.5824, -0.3736, 1.5879, -0.6035, -0.3317, 0.1944, 0.6438, 0.1732],
- [ 0.5314, -0.4397, 1.4854, -0.0471, -0.4339, -0.2989, 0.4611, 0.1603],
- [ 0.3135, -0.5562, 1.6645, 0.0351, -0.2306, 0.0211, 0.3456, 0.1590],
- [ 0.6142, -0.3721, 1.6466, 0.2753, -0.4500, -0.0996, 0.4397, 0.0354],
- [ 0.5489, -0.4391, 1.4079, -0.5499, -0.6380, -0.7958, 0.2248, 0.2013],
- [ 0.6976, -0.3084, 1.5207, 0.3261, -0.4993, -0.1174, 0.3380, 0.2032]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5515, -0.4201, 1.5189, -0.7463, -0.4037, -1.3082, 0.0869,
- 0.0111],
- [ 0.5900, -0.3932, 1.8307, -0.3921, -0.4268, -1.1851, 0.3758,
- 0.1929],
- [ 0.5301, -0.4273, 1.6344, -0.5692, -0.4961, 0.2622, 0.4098,
- 0.2966],
- [ 0.6421, -0.3840, 1.4035, 0.3931, -0.4788, -0.1689, 1.1057,
- 0.3745],
- [ 0.5770, -0.3624, 1.7326, 0.0543, -0.1497, 0.3238, 0.2378,
- 0.1146],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374],
- [ 0.6124, -0.3658, 1.5651, 0.3931, -0.5480, -0.3460, 0.2761,
- 0.5431]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0252, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0252, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8761011473834515
- step: 19
- running loss: 0.046110586704392185
- Train Steps: 19/90 Loss: 0.0461 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6196, 0.4088, 0.8888, 0.4583, 0.4500, 0.5683, 0.6138, 0.5883],
- [0.6216, 0.4167, 0.8588, 0.5583, 0.3975, 0.5167, 0.5775, 0.5667],
- [0.6133, 0.4066, 0.6787, 0.2617, 0.3800, 0.2433, 0.5147, 0.5358],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960],
- [0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6196, 0.4090, 0.8450, 0.3900, 0.3575, 0.3933, 0.5975, 0.5583],
- [0.6275, 0.4071, 0.8075, 0.2100, 0.4700, 0.1533, 0.6148, 0.4834]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7217, -0.2845, 1.9075, 0.2389, -0.2370, 0.5157, 0.5562, 0.1797],
- [ 0.6534, -0.3390, 1.8560, 0.8615, -0.4630, 0.5337, 0.4289, 0.1413],
- [ 0.4276, -0.5171, 1.1000, -0.8050, -0.5874, -1.0325, 0.1186, 0.1669],
- [ 0.5049, -0.4677, 1.8860, 0.3048, -0.2772, 0.1354, 0.3989, 0.0883],
- [ 0.4303, -0.5046, 1.0362, -0.7526, -0.5927, -0.9086, 0.1313, 0.2303],
- [ 0.2386, -0.6472, 1.1012, -0.9978, -0.3905, -1.3685, 0.2712, 0.1517],
- [ 0.6727, -0.3058, 1.6422, -0.0121, -0.6491, -0.0651, 0.3661, 0.1395],
- [ 0.7852, -0.3123, 1.5772, -0.9977, -0.1710, -1.3506, 0.8036, 0.0506]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5759, -0.3978, 1.8192, -0.1689, -0.2074, 0.3392, 0.5490,
- 0.4316],
- [ 0.5853, -0.3614, 1.6806, 0.2930, -0.4499, 0.1005, 0.3815,
- 0.3315],
- [ 0.5470, -0.4081, 0.8492, -1.0773, -0.5307, -1.1620, 0.0912,
- 0.1890],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051],
- [ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.5761, -0.3966, 1.6171, -0.4845, -0.6346, -0.4691, 0.4739,
- 0.2930],
- [ 0.6126, -0.4057, 1.4439, -1.3159, -0.1150, -1.5777, 0.5537,
- -0.0530]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0413, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0413, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9173775799572468
- step: 20
- running loss: 0.045868878997862336
- Train Steps: 20/90 Loss: 0.0459 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6264, 0.3972, 0.8853, 0.4771, 0.3853, 0.4511, 0.6293, 0.5334],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6147, 0.4081, 0.8538, 0.3400, 0.3663, 0.3150, 0.5142, 0.4875],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617],
- [0.6203, 0.4096, 0.8862, 0.4267, 0.3538, 0.4117, 0.6025, 0.5650],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [ nan, nan, 0.7850, 0.2700, 0.4288, 0.1717, 0.5199, 0.4999]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7893, -0.2475, 1.7366, 0.2809, -0.4846, 0.0459, 0.5071, 0.1226],
- [ 0.5180, -0.4447, 1.6718, 0.4874, -0.3442, 0.0526, 0.3643, 0.1179],
- [ 1.0198, -0.1212, 1.6432, -0.3591, -0.6406, -0.6661, 0.2950, 0.1079],
- [ 0.9893, -0.1316, 1.7666, 0.0423, -0.4163, 0.3851, 0.7108, 0.1294],
- [-0.4476, -1.0955, 1.0936, -1.0599, -0.2677, -1.2739, 0.2665, 0.2393],
- [ 1.0255, -0.0901, 1.7566, -0.0326, -0.5185, -0.2372, 0.4457, 0.1847],
- [ 0.7385, -0.2714, 1.6558, 0.0937, -0.4627, 0.0625, 0.1644, 0.1464],
- [-0.2213, -0.9596, 1.1754, -1.1070, -0.3095, -1.3147, 0.2532, 0.1814]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6075, -0.4514, 1.8032, -0.0822, -0.5061, -0.2023, 0.6208,
- 0.1779],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.5532, -0.4008, 1.6575, -0.7155, -0.5942, -0.8309, 0.0890,
- -0.0340],
- [ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715],
- [-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084],
- [ 0.5790, -0.3940, 1.8076, -0.3152, -0.6520, -0.3844, 0.4970,
- 0.3238],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893],
- [-2.2859, -2.2859, 1.3400, -1.0388, -0.3055, -1.4930, 0.1157,
- 0.0231]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2049, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2049, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.122323576360941
- step: 21
- running loss: 0.05344397982671147
- Train Steps: 21/90 Loss: 0.0534 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6245, 0.4115, 0.8700, 0.4883, 0.4625, 0.5517, 0.6100, 0.5217],
- [0.6258, 0.4143, 0.8525, 0.2617, 0.4950, 0.1667, 0.6219, 0.4967],
- [0.6250, 0.4106, 0.8700, 0.3717, 0.3588, 0.4967, 0.6038, 0.5167],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [0.6176, 0.3911, 0.8738, 0.4217, 0.3488, 0.4033, 0.6025, 0.4817],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6113, 0.4006, 0.8700, 0.5350, 0.3638, 0.3767, 0.5097, 0.4882],
- [ nan, nan, 0.7412, 0.2200, 0.4450, 0.1517, 0.5312, 0.4983]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8088, -0.2508, 1.7559, 0.1688, -0.2472, 0.3217, 0.4909, 0.2069],
- [ 0.9829, -0.1435, 1.6416, -0.8073, -0.1479, -1.0794, 0.5959, 0.1515],
- [ 0.8976, -0.1853, 1.6383, -0.2905, -0.5018, 0.1907, 0.5584, 0.1761],
- [ 0.5843, -0.3556, 1.3610, -0.4377, -0.6175, -0.2176, 0.2037, 0.2051],
- [ 0.6115, -0.3894, 1.6423, -0.0639, -0.6122, -0.2054, 0.3690, 0.0912],
- [ 0.8306, -0.2088, 1.6910, 0.3271, -0.6185, -0.2251, 0.2638, 0.2357],
- [ 0.4336, -0.5279, 1.7140, 0.4916, -0.5995, -0.3352, 0.2393, 0.1133],
- [-0.9037, -1.4468, 1.1021, -1.1638, -0.2849, -1.4912, 0.2202, 0.1359]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.9873e-01, -3.8522e-01, 1.7326e+00, -3.0331e-02, -1.4965e-01,
- 2.6220e-01, 5.3164e-01, 1.2363e-01],
- [ 6.0479e-01, -3.7229e-01, 1.6517e+00, -1.0773e+00, 4.6189e-04,
- -1.5161e+00, 5.8660e-01, 8.0947e-03],
- [ 6.0098e-01, -3.8961e-01, 1.7326e+00, -5.6921e-01, -6.2887e-01,
- 8.1601e-03, 5.0277e-01, 1.0054e-01],
- [ 5.0762e-01, -4.4426e-01, 1.2337e+00, -5.0235e-01, -6.8083e-01,
- -3.6135e-01, 8.6614e-02, 2.3862e-01],
- [ 5.6692e-01, -4.7937e-01, 1.7499e+00, -3.3826e-01, -6.7506e-01,
- -4.2294e-01, 4.9700e-01, -6.1124e-02],
- [ 5.9024e-01, -3.4927e-01, 1.7961e+00, -7.2363e-03, -5.9423e-01,
- -5.6151e-01, 3.1801e-01, 3.1609e-01],
- [ 5.3788e-01, -4.3580e-01, 1.7326e+00, 1.8522e-01, -6.0577e-01,
- -5.4611e-01, 6.8408e-02, -3.0981e-02],
- [-2.2859e+00, -2.2859e+00, 1.1379e+00, -1.2697e+00, -2.3048e-01,
- -1.5854e+00, 1.6790e-01, 1.5858e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0680, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0680, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1902863197028637
- step: 22
- running loss: 0.05410392362285744
- Train Steps: 22/90 Loss: 0.0541 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6137, 0.4035, 0.8850, 0.4417, 0.3900, 0.4283, 0.5449, 0.5617],
- [0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334],
- [0.6346, 0.4092, 0.7712, 0.5917, 0.4037, 0.4767, 0.7343, 0.5725],
- [0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272],
- [0.6156, 0.4125, 0.8850, 0.4833, 0.4550, 0.5817, 0.5765, 0.5084],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550],
- [0.6175, 0.4013, 0.8900, 0.4500, 0.4375, 0.5850, 0.6175, 0.5383]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5841, -0.4228, 1.7081, -0.6441, -0.2441, -1.1153, 0.5499, 0.0982],
- [ 0.4278, -0.5337, 1.6374, -0.0478, -0.6051, -0.2112, 0.1915, 0.1604],
- [ 0.6027, -0.4234, 1.2634, -0.8264, -0.6503, -0.8771, 0.4061, 0.1357],
- [ 0.4495, -0.5041, 1.4335, 0.1495, -0.5340, -0.1502, 0.3682, 0.2350],
- [ 0.5330, -0.4750, 1.5403, -0.4251, -0.6036, -0.9126, 0.1916, 0.1295],
- [ 0.4893, -0.4719, 1.7569, 0.1496, -0.3485, 0.2388, 0.4058, 0.1906],
- [ 0.4003, -0.5475, 1.7447, -0.0725, -0.2012, -0.1510, 0.3865, 0.1682],
- [ 0.4466, -0.5168, 1.7576, 0.0200, -0.3755, 0.2759, 0.4909, 0.1671]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.5488, -0.4221, 1.8018, -0.2459, -0.4845, -0.3075, 0.2309,
- 0.3087],
- [ 0.5730, -0.4562, 1.2195, -1.2440, -0.5497, -0.7711, 0.5704,
- 0.1779],
- [ 0.6454, -0.3960, 1.2764, 0.4470, -0.4210, -0.0842, 1.1057,
- 0.3585],
- [ 0.5711, -0.4015, 1.6979, -0.6770, -0.5365, -1.0619, 0.1712,
- 0.1494],
- [ 0.5573, -0.3808, 1.8018, -0.0534, -0.1843, 0.4008, 0.3769,
- 0.0622],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776],
- [ 0.5664, -0.4321, 1.8249, -0.2074, -0.2651, 0.4162, 0.5663,
- 0.2006]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0284, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0284, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2187356799840927
- step: 23
- running loss: 0.05298850782539533
- Train Steps: 23/90 Loss: 0.0530 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6203, 0.4078, 0.8800, 0.5083, 0.3900, 0.5000, 0.6100, 0.5583],
- [0.6265, 0.4088, 0.8025, 0.1850, 0.4163, 0.2500, 0.6290, 0.4947],
- [0.6268, 0.4102, 0.8938, 0.3667, 0.4025, 0.2833, 0.6275, 0.5183],
- [0.6166, 0.4008, 0.8563, 0.5667, 0.4388, 0.4933, 0.5575, 0.5567],
- [0.6136, 0.4117, 0.8700, 0.5167, 0.4188, 0.5083, 0.5147, 0.5495],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6201, 0.4098, 0.8575, 0.3617, 0.3700, 0.3550, 0.5988, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6472, -0.3689, 1.6346, -0.8492, -0.3853, -0.9779, 0.5521, 0.1281],
- [ 0.3331, -0.6111, 1.7036, 0.2330, -0.5683, 0.1859, 0.3486, 0.1685],
- [ 0.6470, -0.3728, 1.4712, -1.0258, -0.3932, -1.1447, 0.4923, 0.0939],
- [ 0.5844, -0.4290, 1.7303, -0.4750, -0.4808, -0.8322, 0.4734, 0.1240],
- [ 0.2903, -0.6353, 1.5557, 0.3560, -0.3045, 0.0172, 0.2414, 0.2126],
- [ 0.2699, -0.6419, 1.6350, 0.2703, -0.4484, 0.1321, 0.2392, 0.1861],
- [ 0.1457, -0.7198, 1.6238, -0.0021, -0.3622, 0.0578, 0.3708, 0.1807],
- [ 0.7875, -0.2717, 1.6199, -0.5207, -0.6258, -0.5832, 0.3459, 0.1732]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0837e-01, -4.0762e-01, 1.6806e+00, -9.6182e-01, -2.9977e-01,
- -9.6952e-01, 6.3557e-01, 1.4673e-01],
- [ 5.7939e-01, -4.0231e-01, 1.7788e+00, 6.2048e-02, -4.8453e-01,
- 2.3557e-02, 5.3164e-01, 2.9299e-01],
- [ 6.0785e-01, -3.9761e-01, 1.4208e+00, -1.4314e+00, -3.6328e-01,
- -1.1312e+00, 6.1950e-01, -9.2270e-04],
- [ 6.0918e-01, -3.9130e-01, 1.8423e+00, -5.9230e-01, -4.2679e-01,
- -9.7721e-01, 6.1247e-01, 1.0824e-01],
- [ 5.6195e-01, -4.3457e-01, 1.6691e+00, 3.3149e-01, -2.5935e-01,
- -7.2363e-03, 2.8915e-01, 2.8530e-01],
- [ 5.4821e-01, -3.8414e-01, 1.7326e+00, 1.0054e-01, -3.5173e-01,
- 6.2048e-02, 9.1240e-02, 2.5215e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 5.7829e-01, -3.9330e-01, 1.6748e+00, -6.1540e-01, -5.7691e-01,
- -6.4619e-01, 4.7968e-01, 3.3149e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2390678264200687
- step: 24
- running loss: 0.051627826100836195
- Train Steps: 24/90 Loss: 0.0516 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4270, 0.8938, 0.4967, 0.3550, 0.4283, 0.5700, 0.5733],
- [0.6236, 0.4084, 0.7738, 0.2133, 0.3663, 0.3233, 0.5813, 0.5567],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6145, 0.4007, 0.8775, 0.4533, 0.4562, 0.5533, 0.6088, 0.5533],
- [0.6182, 0.3972, 0.8720, 0.5527, 0.3638, 0.3582, 0.5696, 0.5395]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1949, -0.6706, 1.7265, 0.0499, -0.5140, -0.1403, 0.4085, 0.2467],
- [ 0.7997, -0.3032, 1.3810, -1.0131, -0.5489, -0.8264, 0.3527, 0.1973],
- [ 0.3795, -0.5790, 1.7047, -0.0913, -0.1195, -0.1106, 0.3014, 0.1788],
- [ 0.8255, -0.2797, 1.6323, -0.6465, -0.6656, -0.5635, 0.3617, 0.1430],
- [ 0.2209, -0.6711, 1.5705, -0.1209, -0.6546, -0.3792, 0.2130, 0.2171],
- [ 0.3898, -0.5788, 1.8787, -0.3252, -0.2116, -0.8951, 0.6498, 0.0578],
- [ 0.4747, -0.5116, 1.7857, -0.2169, -0.2496, 0.1502, 0.5376, 0.1775],
- [ 0.3268, -0.6048, 1.6250, 0.1335, -0.5727, -0.5164, 0.2695, 0.1240]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6121, -0.3138, 1.8423, 0.0082, -0.6462, -0.3075, 0.3469,
- 0.3623],
- [ 0.5946, -0.3995, 1.2880, -1.3005, -0.5942, -0.7925, 0.3988,
- 0.2853],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753],
- [ 0.5454, -0.4053, 1.6633, -0.1766, -0.6058, -0.5923, 0.1577,
- 0.4357],
- [ 0.6224, -0.4345, 1.9404, -0.2921, -0.3171, -0.8771, 1.0655,
- 0.2142],
- [ 0.5526, -0.4351, 1.7672, -0.1920, -0.1785, 0.2699, 0.5259,
- 0.2699],
- [ 0.5697, -0.4514, 1.7420, 0.2672, -0.6055, -0.6312, 0.3449,
- 0.2058]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0248, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0248, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2638748027384281
- step: 25
- running loss: 0.05055499210953712
- Train Steps: 25/90 Loss: 0.0506 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6172, 0.4055, 0.8175, 0.2650, 0.3550, 0.3683, 0.5787, 0.5550],
- [ nan, nan, 0.6469, 0.1943, 0.4025, 0.2000, 0.5125, 0.5533],
- [0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006],
- [0.6200, 0.4059, 0.8700, 0.4900, 0.4163, 0.5000, 0.6162, 0.5467],
- [0.6198, 0.4115, 0.7762, 0.2717, 0.3713, 0.3200, 0.5837, 0.5683],
- [0.6161, 0.4024, 0.8838, 0.4583, 0.3688, 0.3733, 0.5311, 0.5344],
- [0.6264, 0.4055, 0.8425, 0.2767, 0.4425, 0.2767, 0.7050, 0.5586],
- [0.6261, 0.4066, 0.8325, 0.2150, 0.4763, 0.2667, 0.7002, 0.5633]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7765, -0.3130, 1.6100, -0.5837, -0.6955, -0.3657, 0.2162, 0.2099],
- [-1.2546, -1.6816, 1.0257, -1.0399, -0.3609, -1.2230, 0.1650, 0.1903],
- [ 0.6993, -0.4141, 1.8532, 0.5163, -0.5222, 0.1399, 0.4955, 0.1509],
- [ 0.7386, -0.3365, 1.8392, 0.0971, -0.3573, 0.2887, 0.4597, 0.2088],
- [ 0.6512, -0.3750, 1.5533, -0.6460, -0.5720, -0.5968, 0.2478, 0.2290],
- [ 0.5814, -0.4463, 1.8323, 0.1737, -0.6186, -0.1798, 0.2413, 0.1486],
- [ 0.5449, -0.4473, 1.8061, -0.7797, -0.1826, -0.8444, 0.7036, 0.1541],
- [ 0.8164, -0.3197, 1.7249, -0.7490, -0.1492, -1.0003, 0.7974, 0.0999]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5647, -0.4129, 1.4901, -1.0619, -0.6462, -0.5846, 0.3873,
- 0.2776],
- [-2.2859, -2.2859, 0.7023, -1.3883, -0.4268, -1.3621, 0.0813,
- 0.2699],
- [ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261],
- [ 0.5776, -0.4109, 1.7326, -0.0226, -0.3633, 0.0236, 0.5605,
- 0.2391],
- [ 0.5768, -0.3852, 1.2995, -1.0311, -0.5711, -0.8079, 0.4104,
- 0.3392],
- [ 0.5598, -0.4273, 1.7961, -0.1689, -0.5827, -0.5615, 0.1671,
- 0.1824],
- [ 0.6075, -0.4129, 1.6055, -1.0080, -0.2420, -1.0080, 0.9704,
- 0.2944],
- [ 0.6059, -0.4080, 1.5594, -1.2928, -0.0861, -1.0542, 0.9485,
- 0.3157]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0563, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0563, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3201934099197388
- step: 26
- running loss: 0.05077666961229765
- Train Steps: 26/90 Loss: 0.0508 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6198, 0.4075, 0.8650, 0.5617, 0.4150, 0.4367, 0.5450, 0.5650],
- [0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6136, 0.4117, 0.8700, 0.5167, 0.4188, 0.5083, 0.5147, 0.5495],
- [0.6273, 0.4100, 0.7137, 0.2133, 0.4000, 0.2650, 0.6075, 0.5633],
- [0.6296, 0.4076, 0.8400, 0.5583, 0.3700, 0.4367, 0.6876, 0.5494]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4843, -0.4923, 1.8988, -0.3992, -0.2177, 0.0468, 0.5391, 0.2222],
- [ 0.4154, -0.5881, 1.2895, -1.1669, -0.4763, -1.3328, 0.2543, 0.1306],
- [ 0.4518, -0.5199, 1.7362, 0.0782, -0.4067, -0.2372, 0.3323, 0.2551],
- [ 0.5852, -0.4918, 1.9120, -0.1694, -0.4795, 0.0024, 0.5392, 0.1263],
- [ 0.6552, -0.4101, 1.8002, 0.1895, -0.3935, -0.2230, 0.4755, 0.2813],
- [ 0.5180, -0.4848, 1.8587, 0.0052, -0.4058, 0.0462, 0.3925, 0.1988],
- [ 0.1950, -0.6670, 1.2533, -1.3154, -0.4737, -1.2690, 0.4056, 0.1900],
- [ 0.2802, -0.6517, 1.8728, -0.1009, -0.4746, -0.4945, 0.5519, 0.1651]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.5598, -0.4201, 1.1898, -1.3005, -0.3806, -1.3313, 0.3873,
- 0.0774],
- [ 0.5770, -0.4036, 1.7095, 0.3084, -0.3691, -0.2690, 0.2314,
- 0.3238],
- [ 0.5783, -0.4363, 1.7724, 0.0432, -0.5153, 0.0871, 0.4840,
- 0.0663],
- [ 0.6038, -0.3464, 1.7037, 0.3931, -0.4441, -0.2613, 0.3007,
- 0.4624],
- [ 0.5482, -0.3841, 1.7326, 0.1005, -0.3517, 0.0620, 0.0912,
- 0.2522],
- [ 0.6115, -0.3924, 1.0109, -1.3005, -0.4383, -1.0619, 0.5201,
- 0.3161],
- [ 0.6224, -0.4032, 1.5940, 0.2930, -0.5769, -0.2690, 0.8900,
- 0.2516]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0273, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0273, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3474591448903084
- step: 27
- running loss: 0.049905894255196606
- Train Steps: 27/90 Loss: 0.0499 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6145, 0.4007, 0.8775, 0.4533, 0.4562, 0.5533, 0.6088, 0.5533],
- [0.6224, 0.3964, 0.8225, 0.5717, 0.4150, 0.4617, 0.5775, 0.5267],
- [0.6161, 0.4024, 0.8838, 0.4583, 0.3688, 0.3733, 0.5311, 0.5344],
- [0.6364, 0.4144, 0.8625, 0.3083, 0.4913, 0.2000, 0.6448, 0.5274],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6099, 0.4030, 0.8638, 0.5117, 0.4983, 0.4965, 0.5086, 0.5388],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4047, -0.5641, 1.8557, -0.3915, -0.3121, 0.0780, 0.5668, 0.2369],
- [ 0.5390, -0.4681, 1.7114, -0.0121, -0.5081, -0.2583, 0.4403, 0.2166],
- [ 0.3382, -0.6112, 1.8243, -0.2912, -0.6956, -0.5718, 0.3748, 0.1359],
- [ 0.1285, -0.7148, 1.7671, -0.9421, -0.1662, -1.1593, 0.6499, 0.1509],
- [ 0.5752, -0.4616, 1.7676, -0.2336, -0.2807, -0.1014, 0.4329, 0.2863],
- [ 0.3819, -0.5542, 1.1212, -1.1021, -0.6073, -1.2107, 0.3160, 0.2424],
- [ 0.5454, -0.4809, 1.7989, -0.1910, -0.1672, -0.2807, 0.4104, 0.2157],
- [ 0.6235, -0.4284, 1.7667, 0.0837, -0.4849, -0.0909, 0.5290, 0.3062]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5526, -0.4351, 1.7672, -0.1920, -0.1785, 0.2699, 0.5259,
- 0.2699],
- [ 0.5891, -0.4550, 1.5132, 0.3546, -0.3691, -0.1535, 0.3815,
- 0.1467],
- [ 0.5598, -0.4273, 1.7961, -0.1689, -0.5827, -0.5615, 0.1671,
- 0.1824],
- [ 0.6537, -0.3719, 1.6979, -0.8617, -0.0169, -1.3621, 0.6926,
- 0.1501],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391],
- [ 0.5309, -0.4246, 1.7037, 0.0774, 0.0158, 0.0075, 0.0635,
- 0.2026],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0284, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0284, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3758654911071062
- step: 28
- running loss: 0.04913805325382522
- Train Steps: 28/90 Loss: 0.0491 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6124, 0.4083, 0.6954, 0.3069, 0.3650, 0.2750, 0.5163, 0.5485],
- [0.6201, 0.4116, 0.8725, 0.4733, 0.3700, 0.3217, 0.5386, 0.5767],
- [0.6200, 0.4055, 0.8011, 0.2988, 0.3842, 0.2798, 0.5949, 0.5183],
- [ nan, nan, 0.7240, 0.2722, 0.3900, 0.2567, 0.5168, 0.5933],
- [0.6171, 0.4127, 0.8900, 0.4800, 0.4325, 0.5783, 0.5769, 0.5090],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [0.6226, 0.4001, 0.8438, 0.5733, 0.3862, 0.4250, 0.5750, 0.5283],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4980, -0.4510, 1.1881, -0.9858, -0.5650, -0.8815, 0.2687, 0.2701],
- [ 0.6081, -0.3681, 1.7448, -0.0104, -0.5931, -0.5104, 0.2804, 0.2786],
- [ 0.8465, -0.2756, 1.5485, -0.9244, -0.5099, -0.8537, 0.6211, 0.1732],
- [-1.4354, -1.7735, 1.1950, -0.9369, -0.3766, -1.0535, 0.1421, 0.2662],
- [ 0.8859, -0.2152, 1.9825, 0.0037, -0.3014, 0.4025, 0.5873, 0.2754],
- [ 0.9285, -0.2262, 1.8356, -0.0805, -0.3351, 0.0737, 0.4443, 0.3068],
- [ 0.5997, -0.4376, 1.7979, 0.2760, -0.4896, -0.1813, 0.4074, 0.2335],
- [ 0.5675, -0.4697, 1.8163, -1.0207, 0.0638, -1.0853, 1.0176, 0.1437]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5425, -0.3998, 0.9263, -0.8683, -0.6000, -1.0157, 0.0990,
- 0.2476],
- [ 0.5781, -0.3848, 1.7441, -0.0996, -0.5769, -0.8002, 0.2021,
- 0.3778],
- [ 0.5781, -0.4129, 1.4142, -0.9057, -0.5115, -0.9937, 0.4621,
- 0.1080],
- [-2.2859, -2.2859, 1.0580, -1.0288, -0.4845, -1.1004, 0.1011,
- 0.4543],
- [ 0.5645, -0.3797, 1.8249, -0.0688, -0.2882, 0.3854, 0.3789,
- 0.0652],
- [ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057],
- [ 0.5900, -0.4377, 1.6113, 0.3623, -0.5018, -0.3229, 0.3700,
- 0.1544],
- [ 0.6207, -0.3936, 1.7788, -1.1235, 0.1448, -1.0850, 1.1459,
- 0.1982]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0327, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0327, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4085994269698858
- step: 29
- running loss: 0.048572394033444335
- Train Steps: 29/90 Loss: 0.0486 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6176, 0.3911, 0.8738, 0.4217, 0.3488, 0.4033, 0.6025, 0.4817],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6147, 0.4026, 0.6600, 0.2467, 0.4088, 0.2150, 0.5489, 0.5773],
- [0.6102, 0.3999, 0.8750, 0.5133, 0.3825, 0.4750, 0.5637, 0.5083],
- [0.6189, 0.4049, 0.8888, 0.4417, 0.4213, 0.5200, 0.5988, 0.5633],
- [0.6179, 0.4082, 0.6688, 0.2667, 0.3588, 0.3317, 0.5750, 0.5783],
- [0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8314, -0.2732, 1.7540, -0.4890, -0.5828, -0.5468, 0.6514, 0.1730],
- [ 0.6223, -0.3878, 1.9407, -0.0566, -0.0362, 0.2270, 0.7201, 0.3392],
- [-0.0648, -0.8505, 0.9972, -1.0496, -0.3593, -1.3296, 0.2686, 0.3011],
- [ 0.6182, -0.4060, 1.8672, 0.1088, -0.4125, -0.1731, 0.4656, 0.2312],
- [ 0.7090, -0.3358, 1.9056, -0.3081, -0.2719, 0.1284, 0.6481, 0.3014],
- [ 0.5278, -0.4470, 1.1147, -0.9866, -0.5895, -0.8632, 0.3399, 0.3552],
- [-0.2827, -0.9884, 1.6530, -0.4936, -0.3237, -1.1280, 0.2926, 0.1953],
- [ 0.4771, -0.4732, 1.8016, -0.2574, -0.3934, -0.2234, 0.3285, 0.2492]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5669, -0.4794, 1.7499, -0.3383, -0.6751, -0.4229, 0.4970,
- -0.0611],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.5532, -0.4264, 0.7626, -1.1466, -0.3979, -1.2928, 0.2494,
- 0.3808],
- [ 0.5326, -0.4390, 1.7557, 0.0851, -0.5192, -0.0919, 0.3180,
- 0.0620],
- [ 0.5726, -0.4159, 1.8192, -0.2459, -0.3402, 0.1159, 0.4797,
- 0.3161],
- [ 0.5680, -0.4005, 0.8030, -1.0542, -0.6289, -0.7540, 0.3700,
- 0.3854],
- [ 0.5485, -0.4209, 1.6691, -0.4152, -0.5249, -1.1081, 0.0725,
- 0.0021],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0391, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0391, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.447685481980443
- step: 30
- running loss: 0.04825618273268143
- Train Steps: 30/90 Loss: 0.0483 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6179, 0.4008, 0.8600, 0.4015, 0.3932, 0.2515, 0.5711, 0.5438],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667],
- [0.6200, 0.4039, 0.8880, 0.4799, 0.3625, 0.4285, 0.5866, 0.5148],
- [0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [0.6307, 0.4029, 0.8650, 0.5200, 0.3763, 0.4017, 0.7311, 0.5366],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4710, -0.5241, 1.6559, -0.7972, -0.2771, -0.8978, 0.7490, 0.1722],
- [ 0.2822, -0.5709, 1.4792, -0.7684, -0.3218, -1.0572, 0.2195, 0.2607],
- [ 0.3433, -0.5362, 1.0719, -1.1185, -0.5896, -0.8732, 0.2522, 0.3263],
- [ 0.3159, -0.5916, 1.7861, -0.2359, -0.5078, -0.4310, 0.3561, 0.2378],
- [ 0.4398, -0.4827, 1.7726, -0.2350, -0.0829, 0.0155, 0.4125, 0.3373],
- [ 0.3263, -0.5749, 1.7963, -0.0346, -0.3689, -0.5563, 0.4905, 0.1943],
- [ 0.6169, -0.3935, 1.6085, -0.0899, -0.3845, -0.1183, 0.6086, 0.3813],
- [ 0.6808, -0.3486, 1.7518, -0.1970, -0.3165, -0.0122, 0.5884, 0.2809]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5680, -0.4345, 1.6864, -0.4315, -0.4698, -1.1241, 0.3518,
- 0.2261],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315],
- [ 0.5781, -0.4205, 1.8159, -0.0691, -0.6116, -0.3069, 0.4236,
- 0.0919],
- [ 0.5505, -0.4207, 1.7095, -0.0534, -0.0509, 0.1050, 0.3873,
- 0.3007],
- [ 0.6273, -0.4249, 1.7095, 0.1159, -0.5480, -0.4306, 1.0910,
- 0.1928],
- [ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478],
- [ 0.6010, -0.4562, 1.7198, -0.0090, -0.3464, 0.0108, 0.6294,
- 0.1627]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0302, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0302, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.477879423648119
- step: 31
- running loss: 0.04767352979510061
- Train Steps: 31/90 Loss: 0.0477 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6179, 0.4082, 0.6688, 0.2667, 0.3588, 0.3317, 0.5750, 0.5783],
- [0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [0.6272, 0.4120, 0.9038, 0.4117, 0.3725, 0.3200, 0.6175, 0.5250],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960],
- [0.6179, 0.4040, 0.7412, 0.1850, 0.3825, 0.2783, 0.5837, 0.5600],
- [0.6129, 0.3930, 0.8769, 0.5105, 0.4146, 0.5642, 0.6000, 0.5082],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550],
- [0.6271, 0.4040, 0.9000, 0.3250, 0.4938, 0.2300, 0.7192, 0.5413]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5117, -0.4196, 0.9786, -0.9416, -0.6249, -0.8022, 0.2050, 0.3726],
- [ 0.5619, -0.3987, 1.7834, 0.0175, -0.4419, -0.3582, 0.4923, 0.2641],
- [ 0.3236, -0.5390, 1.8023, -0.3149, -0.4430, -0.8161, 0.4877, 0.2087],
- [ 0.4976, -0.4769, 1.7585, 0.0139, -0.1965, 0.0074, 0.3728, 0.2565],
- [ 0.4925, -0.4334, 1.1824, -1.0599, -0.4519, -1.0876, 0.3643, 0.2796],
- [ 0.7676, -0.2882, 1.7267, 0.1517, -0.2220, 0.2903, 0.4297, 0.2950],
- [ 0.3197, -0.5553, 1.3923, -0.9086, -0.5696, -0.3927, 0.5116, 0.3125],
- [-0.0600, -0.8208, 1.8714, -0.7073, 0.0863, -0.9754, 0.9008, 0.1728]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5680, -0.4005, 0.8030, -1.0542, -0.6289, -0.7540, 0.3700,
- 0.3854],
- [ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499],
- [ 0.6111, -0.3828, 1.8885, -0.3844, -0.5654, -0.8079, 0.5663,
- 0.1390],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051],
- [ 0.5680, -0.4201, 1.1379, -1.4314, -0.5192, -1.0003, 0.4104,
- 0.3007],
- [ 0.5450, -0.4706, 1.7643, 0.0722, -0.3708, 0.3200, 0.4854,
- 0.0612],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776],
- [ 0.6108, -0.4201, 1.8711, -0.7848, -0.0053, -1.2236, 1.0362,
- 0.2142]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0254, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0254, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5033203139901161
- step: 32
- running loss: 0.04697875981219113
- Train Steps: 32/90 Loss: 0.0470 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4008, 0.8047, 0.2484, 0.4037, 0.2777, 0.6506, 0.5212],
- [0.6276, 0.4120, 0.8738, 0.3133, 0.4225, 0.2217, 0.6203, 0.4892],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6128, 0.4116, 0.8450, 0.4583, 0.3675, 0.4867, 0.5337, 0.5446],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142],
- [0.6150, 0.3935, 0.8696, 0.5158, 0.4647, 0.5329, 0.6041, 0.5153],
- [0.6126, 0.4039, 0.8237, 0.3967, 0.3625, 0.3600, 0.5894, 0.6138],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6046, -0.4041, 1.3856, -1.1072, -0.3421, -1.0320, 0.7189, 0.1829],
- [ 0.3036, -0.5672, 1.7304, -0.7838, -0.2106, -1.1523, 0.6788, 0.1557],
- [ 0.8693, -0.1994, 1.6628, 0.1078, -0.4714, -0.5727, 0.3917, 0.2545],
- [ 0.4444, -0.4605, 1.5921, -0.3418, -0.3893, -0.0181, 0.3984, 0.3219],
- [-0.8497, -1.3179, 1.3370, -0.8653, -0.4921, -0.9819, 0.1373, 0.2156],
- [ 0.7128, -0.3160, 1.6909, 0.0323, -0.1249, 0.1409, 0.4211, 0.3112],
- [ 0.8694, -0.2089, 1.4843, -0.6585, -0.5412, -0.5137, 0.5688, 0.3325],
- [ 0.6722, -0.3150, 1.5615, -0.0606, -0.3911, -0.0069, 0.5004, 0.3152]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4345, 1.4308, -1.1384, -0.4213, -1.0031, 0.7190,
- 0.1214],
- [ 0.6128, -0.3828, 1.7499, -0.8386, -0.3344, -1.2620, 0.5792,
- -0.0263],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698],
- [ 0.5444, -0.3846, 1.6171, -0.1689, -0.5885, -0.0380, 0.1791,
- 0.2296],
- [-2.2859, -2.2859, 1.5767, -0.7540, -0.6404, -0.7309, 0.1753,
- 0.0893],
- [ 0.5548, -0.4682, 1.7309, 0.0966, -0.1394, 0.1757, 0.5045,
- 0.0942],
- [ 0.5438, -0.4206, 1.5189, -0.4537, -0.6115, -0.6231, 0.4365,
- 0.5491],
- [ 0.6191, -0.4297, 1.4612, 0.2391, -0.4961, 0.0313, 1.1166,
- 0.1768]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0719, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0719, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.575185589492321
- step: 33
- running loss: 0.047732896651282455
- Train Steps: 33/90 Loss: 0.0477 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6228, 0.4004, 0.8750, 0.5250, 0.3825, 0.5233, 0.6362, 0.5000],
- [0.6068, 0.3963, 0.8650, 0.4317, 0.4037, 0.5083, 0.5253, 0.4999],
- [0.6299, 0.4008, 0.8450, 0.5350, 0.4213, 0.5000, 0.6350, 0.5100],
- [ nan, nan, 0.7553, 0.2722, 0.3875, 0.2550, 0.5125, 0.5633],
- [0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650],
- [0.6222, 0.4169, 0.8638, 0.5650, 0.4313, 0.4783, 0.5637, 0.5633],
- [0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383],
- [0.6270, 0.4267, 0.7150, 0.3317, 0.4900, 0.1817, 0.5560, 0.6183]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9045, -0.2045, 1.7665, -0.0190, -0.4189, -0.0906, 0.6821, 0.1926],
- [ 0.6531, -0.3369, 1.7089, -0.5142, -0.4372, -0.1780, 0.5593, 0.1922],
- [ 0.9902, -0.1364, 1.6734, -0.0408, -0.3670, -0.1510, 0.6068, 0.2448],
- [-1.6674, -1.8711, 1.1930, -1.0522, -0.3653, -1.3092, 0.2291, 0.2265],
- [ 0.6062, -0.3637, 1.6419, -0.6272, -0.6213, -0.5816, 0.3713, 0.2625],
- [ 1.0549, -0.0778, 1.6522, 0.2349, -0.3027, -0.0812, 0.4714, 0.3484],
- [ 0.8803, -0.1778, 1.6896, -0.1564, -0.2772, -0.2500, 0.4931, 0.2272],
- [ 0.2314, -0.5895, 0.9697, -1.2898, -0.2493, -1.2955, 0.3786, 0.3206]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.9082e-01, -4.3664e-01, 1.7557e+00, 1.3903e-01, -5.1917e-01,
- 1.3133e-01, 6.5289e-01, 2.3557e-02],
- [ 5.1680e-01, -4.5558e-01, 1.7095e+00, -2.9207e-01, -4.2102e-01,
- 6.2048e-02, 1.4038e-01, 2.3124e-02],
- [ 6.2361e-01, -4.3441e-01, 1.6171e+00, 1.8522e-01, -3.4018e-01,
- 2.3557e-02, 6.4711e-01, 6.9746e-02],
- [-2.2859e+00, -2.2859e+00, 1.2030e+00, -1.0288e+00, -4.9607e-01,
- -1.1081e+00, 8.1293e-02, 3.1609e-01],
- [ 5.7569e-01, -3.9169e-01, 1.7095e+00, -4.7683e-01, -6.3464e-01,
- -4.2294e-01, 3.9307e-01, 3.2379e-01],
- [ 5.8799e-01, -3.6051e-01, 1.7037e+00, 3.2379e-01, -2.9400e-01,
- -7.6520e-02, 3.1801e-01, 3.1609e-01],
- [ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01],
- [ 6.1010e-01, -3.1524e-01, 1.0166e+00, -7.5396e-01, -2.2633e-02,
- -1.4468e+00, 2.8226e-01, 5.7018e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0464, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0464, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6215668469667435
- step: 34
- running loss: 0.047693142557845396
- Train Steps: 34/90 Loss: 0.0477 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6196, 0.4094, 0.7562, 0.2817, 0.3937, 0.3183, 0.6013, 0.6183],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [ nan, nan, 0.7850, 0.2700, 0.4288, 0.1717, 0.5199, 0.4999],
- [0.6126, 0.4067, 0.8638, 0.5383, 0.4188, 0.4850, 0.5016, 0.5392],
- [0.6179, 0.4082, 0.6688, 0.2667, 0.3588, 0.3317, 0.5750, 0.5783],
- [0.6212, 0.4033, 0.8938, 0.4167, 0.3813, 0.4267, 0.5613, 0.5583],
- [0.6336, 0.4154, 0.8900, 0.2767, 0.4988, 0.2867, 0.7422, 0.5540],
- [ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8166, -0.1899, 1.3306, -0.7404, -0.4580, -0.7054, 0.4630, 0.3116],
- [ 1.0022, -0.1160, 1.6696, 0.1123, -0.4108, 0.1231, 0.3683, 0.2487],
- [-1.8722, -1.9516, 1.0990, -0.9600, -0.3088, -1.2137, 0.1891, 0.1847],
- [ 1.2242, 0.0453, 1.6560, 0.5276, -0.4548, 0.1647, 0.3594, 0.2172],
- [ 0.9550, -0.1220, 0.9757, -0.8584, -0.7268, -0.6853, 0.3104, 0.3065],
- [ 0.8092, -0.2271, 1.7159, -0.2274, -0.6485, -0.1693, 0.3869, 0.2246],
- [ 0.9884, -0.1324, 1.6578, -0.7221, -0.1089, -0.8412, 0.9937, 0.1319],
- [-0.7703, -1.1830, 1.6248, -0.8926, 0.0598, -0.9897, 0.8520, 0.1865]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5759, -0.3948, 1.2072, -0.9849, -0.4672, -0.8156, 0.4912,
- 0.5701],
- [ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057],
- [-2.2859, -2.2859, 1.3400, -1.0388, -0.3055, -1.4930, 0.1157,
- 0.0231],
- [ 0.5436, -0.4076, 1.7037, 0.2006, -0.3517, -0.0457, 0.0311,
- 0.2048],
- [ 0.5680, -0.4005, 0.8030, -1.0542, -0.6289, -0.7540, 0.3700,
- 0.3854],
- [ 0.5832, -0.4231, 1.8423, -0.3614, -0.5249, -0.3152, 0.3065,
- 0.2930],
- [ 0.6405, -0.3671, 1.8249, -1.0080, 0.0178, -0.9618, 1.1422,
- 0.2730],
- [-2.2859, -2.2859, 1.7557, -1.1466, 0.0871, -1.0773, 1.1239,
- 0.2783]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1038, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1038, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.725327029824257
- step: 35
- running loss: 0.04929505799497877
- Train Steps: 35/90 Loss: 0.0493 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4075, 0.8650, 0.5617, 0.4150, 0.4367, 0.5450, 0.5650],
- [0.6173, 0.4114, 0.7325, 0.2500, 0.4213, 0.1917, 0.5338, 0.5700],
- [0.6097, 0.3988, 0.8650, 0.5250, 0.4213, 0.5200, 0.5675, 0.5050],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6068, 0.3963, 0.8650, 0.4317, 0.4037, 0.5083, 0.5253, 0.4999],
- [0.6182, 0.3930, 0.8841, 0.3892, 0.3556, 0.4967, 0.6222, 0.5279],
- [ nan, nan, 0.7553, 0.2722, 0.3875, 0.2550, 0.5125, 0.5633],
- [0.6127, 0.4115, 0.7163, 0.2883, 0.3625, 0.2950, 0.5327, 0.5495]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0376, -0.0549, 1.6175, 0.2950, -0.3642, -0.3018, 0.4173, 0.2902],
- [-1.3129, -1.5726, 1.0380, -1.1441, -0.2995, -1.3562, 0.2976, 0.2221],
- [ 1.0837, -0.0560, 1.7402, 0.1596, -0.3138, 0.0089, 0.4249, 0.1761],
- [ 1.2470, 0.0356, 1.7279, 0.3775, -0.3578, 0.1207, 0.5581, 0.2648],
- [ 0.7240, -0.2872, 1.7171, -0.3920, -0.4311, -0.0944, 0.5612, 0.1505],
- [ 0.7184, -0.3035, 1.6816, -0.6363, -0.5410, -0.1442, 0.8086, 0.2079],
- [-1.6307, -1.8106, 1.2022, -0.9604, -0.3940, -1.2524, 0.2497, 0.2113],
- [ 0.9433, -0.1526, 1.0979, -1.1195, -0.5620, -0.9940, 0.4218, 0.2257]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5770, -0.4036, 1.7095, 0.3084, -0.3691, -0.2690, 0.2314,
- 0.3238],
- [ 0.5652, -0.3858, 1.0975, -1.1312, -0.3402, -1.4006, 0.1794,
- 0.3469],
- [ 0.5303, -0.4440, 1.7095, 0.1390, -0.3402, 0.1159, 0.3353,
- 0.0467],
- [ 0.5742, -0.3792, 1.6460, 0.3084, -0.3460, 0.1467, 0.4162,
- 0.3161],
- [ 0.5168, -0.4556, 1.7095, -0.2921, -0.4210, 0.0620, 0.1404,
- 0.0231],
- [ 0.5697, -0.4706, 1.7976, -0.4884, -0.6433, 0.0081, 0.5878,
- 0.1525],
- [-2.2859, -2.2859, 1.2030, -1.0288, -0.4961, -1.1081, 0.0813,
- 0.3161],
- [ 0.5442, -0.3855, 1.0224, -0.9541, -0.6115, -0.9233, 0.1745,
- 0.2522]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1254, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1254, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8506858050823212
- step: 36
- running loss: 0.05140793903006448
- Train Steps: 36/90 Loss: 0.0514 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6364, 0.4165, 0.9088, 0.4367, 0.4075, 0.3150, 0.6448, 0.5297],
- [0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6097, 0.4024, 0.8488, 0.3717, 0.3875, 0.5517, 0.5836, 0.5591],
- [ nan, nan, 0.8900, 0.3217, 0.5038, 0.2233, 0.6694, 0.5609],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6093, 0.3990, 0.8400, 0.4333, 0.3688, 0.4633, 0.5560, 0.5656],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3210, -0.4926, 1.6567, -0.4000, -0.3599, -1.0264, 0.4894, 0.1499],
- [ 0.6571, -0.3596, 1.0842, -1.2060, -0.6313, -1.0385, 0.5151, 0.1963],
- [ 0.5058, -0.4447, 1.5437, 0.0189, -0.4176, -0.1923, 0.2316, 0.1914],
- [ 0.1817, -0.6537, 1.4349, -0.7227, -0.4305, 0.0920, 0.4942, 0.2824],
- [-1.3286, -1.6251, 1.6110, -0.9346, 0.0318, -1.2953, 0.6881, 0.1814],
- [ 0.9706, -0.1940, 1.5802, 0.2639, -0.6015, -0.3286, 0.4616, 0.1453],
- [ 0.5066, -0.4450, 1.4328, -0.3905, -0.5788, -0.2500, 0.2859, 0.2596],
- [ 0.6623, -0.3516, 1.6435, 0.1237, -0.3628, 0.2921, 0.5939, 0.2574]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6537, -0.3623, 1.9115, -0.2690, -0.4037, -0.8309, 0.6926,
- 0.1608],
- [ 0.5730, -0.4562, 1.2195, -1.2440, -0.5497, -0.7711, 0.5704,
- 0.1779],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854],
- [ 0.5301, -0.4273, 1.6344, -0.5692, -0.4961, 0.2622, 0.4098,
- 0.2966],
- [-2.2859, -2.2859, 1.8249, -0.8002, 0.0409, -1.2543, 0.8059,
- 0.3050],
- [ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [ 0.5283, -0.4429, 1.5940, -0.2844, -0.5827, -0.1458, 0.2823,
- 0.3267],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0419, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0419, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8925394713878632
- step: 37
- running loss: 0.051149715442915224
- Train Steps: 37/90 Loss: 0.0511 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6200, 0.4086, 0.8414, 0.2974, 0.4117, 0.2274, 0.5869, 0.5391],
- [0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901],
- [0.6128, 0.4118, 0.8638, 0.5333, 0.4625, 0.5267, 0.5193, 0.5475],
- [0.6339, 0.4118, 0.7988, 0.5800, 0.3912, 0.4583, 0.7343, 0.5760],
- [0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6172, 0.4055, 0.8175, 0.2650, 0.3550, 0.3683, 0.5787, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1391, -0.6635, 1.4068, -1.0223, -0.4018, -1.1637, 0.4464, 0.0945],
- [ 0.4628, -0.4339, 1.3757, -0.9598, -0.3049, -1.1301, 0.4751, 0.1769],
- [ 0.4638, -0.5336, 1.4785, -0.6946, -0.6105, -0.6798, 0.6793, 0.1371],
- [ 0.3253, -0.5462, 1.5459, 0.2808, -0.2164, 0.0366, 0.2902, 0.2458],
- [ 0.2621, -0.6162, 1.4384, 0.0273, -0.4463, -0.3500, 0.6303, 0.2597],
- [ 0.2308, -0.6349, 1.5872, -0.2894, -0.2790, 0.2705, 0.4701, 0.2369],
- [ 0.0074, -0.7388, 1.5592, 0.3414, -0.4608, -0.2093, 0.3406, 0.1944],
- [ 0.0809, -0.7228, 1.3165, -0.9145, -0.6861, -0.7330, 0.3129, 0.2097]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.5776, -0.3987, 1.6005, -0.9121, -0.3844, -1.2358, 0.4247,
- 0.2043],
- [ 0.6031, -0.4307, 1.7268, -0.8002, -0.6058, -0.6462, 0.6442,
- -0.0220],
- [ 0.5447, -0.3838, 1.7037, 0.1775, -0.1497, 0.1467, 0.1128,
- 0.2431],
- [ 0.6421, -0.3840, 1.4035, 0.3931, -0.4788, -0.1689, 1.1057,
- 0.3745],
- [ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936],
- [ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391],
- [ 0.5647, -0.4129, 1.4901, -1.0619, -0.6462, -0.5846, 0.3873,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0379, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0379, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.9304494857788086
- step: 38
- running loss: 0.05080130225733707
- Train Steps: 38/90 Loss: 0.0508 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6271, 0.4020, 0.8375, 0.6083, 0.3925, 0.4867, 0.6037, 0.4626],
- [0.6200, 0.4070, 0.8938, 0.4183, 0.3538, 0.4567, 0.6175, 0.5400],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [0.6212, 0.4033, 0.8938, 0.4167, 0.3813, 0.4267, 0.5613, 0.5583],
- [0.6199, 0.4112, 0.8475, 0.3717, 0.3550, 0.4350, 0.6063, 0.6083],
- [0.6131, 0.4037, 0.6907, 0.2819, 0.3688, 0.2700, 0.5217, 0.5680],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6216, 0.4167, 0.8588, 0.5583, 0.3975, 0.5167, 0.5775, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3057, -0.5980, 1.6707, 0.3744, -0.3315, -0.2668, 0.4939, 0.1397],
- [ 0.3730, -0.5857, 1.6908, -0.3420, -0.5077, -0.1843, 0.5378, 0.1543],
- [ 0.3094, -0.5495, 1.2988, -0.7486, -0.5875, -0.4744, 0.3223, 0.2321],
- [ 0.0189, -0.7939, 1.7198, -0.3256, -0.4821, -0.3460, 0.3194, 0.2208],
- [-0.0583, -0.8413, 1.6697, -0.4644, -0.5157, -0.3376, 0.4319, 0.2579],
- [-0.0959, -0.8424, 0.9313, -1.0887, -0.5208, -1.0755, 0.2619, 0.2573],
- [ 0.4857, -0.5217, 1.4339, -1.0939, -0.3772, -1.2367, 0.7471, 0.0930],
- [ 0.5980, -0.3976, 1.7367, 0.5304, -0.3018, 0.2016, 0.4894, 0.2275]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6105, -0.4293, 1.5824, 0.5239, -0.4730, -0.0380, 0.5025,
- -0.1492],
- [ 0.5778, -0.4061, 1.8423, -0.3537, -0.6520, -0.1766, 0.5663,
- 0.2083],
- [ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386],
- [ 0.5832, -0.4231, 1.8423, -0.3614, -0.5249, -0.3152, 0.3065,
- 0.2930],
- [ 0.5774, -0.3868, 1.6286, -0.5692, -0.6462, -0.2767, 0.5143,
- 0.5239],
- [ 0.5459, -0.4215, 0.9043, -0.9838, -0.5827, -1.0388, 0.1236,
- 0.3378],
- [ 0.6042, -0.4225, 1.5420, -1.2082, -0.4730, -1.0311, 0.6380,
- -0.0220],
- [ 0.5853, -0.3614, 1.6806, 0.2930, -0.4499, 0.1005, 0.3815,
- 0.3315]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0421, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0421, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.9725742973387241
- step: 39
- running loss: 0.05057882813689036
- Train Steps: 39/90 Loss: 0.0506 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8850, 0.3000, 0.5363, 0.2250, 0.7343, 0.5771],
- [0.6185, 0.4079, 0.8838, 0.4617, 0.4838, 0.5650, 0.6175, 0.5850],
- [0.6282, 0.4029, 0.8988, 0.3000, 0.4250, 0.4183, 0.7042, 0.5575],
- [ nan, nan, 0.8938, 0.2850, 0.4662, 0.3117, 0.7406, 0.5528],
- [0.6147, 0.4112, 0.7988, 0.3200, 0.3775, 0.2767, 0.5150, 0.5550],
- [0.6135, 0.4115, 0.8838, 0.4667, 0.4288, 0.6050, 0.5778, 0.5097],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507],
- [0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.5030, -1.7617, 1.5962, -0.7638, -0.0465, -1.1517, 0.7468, 0.2570],
- [ 0.6592, -0.3707, 1.6703, 0.0753, -0.2731, 0.2695, 0.3758, 0.2453],
- [ 0.8878, -0.2623, 1.4946, -0.6248, -0.5640, -0.3283, 0.7205, 0.1871],
- [-0.2709, -0.9534, 1.8355, -0.6016, -0.2469, -1.0011, 0.8533, 0.1493],
- [ 0.1519, -0.6594, 1.1395, -0.7464, -0.7456, -0.9452, -0.0113, 0.1914],
- [ 0.5785, -0.4321, 1.6872, 0.1249, -0.4040, 0.3933, 0.3312, 0.1839],
- [ 0.5743, -0.4212, 0.8302, -1.1150, -0.6334, -1.2068, 0.3029, 0.2081],
- [ 0.6235, -0.3960, 1.6506, 0.2356, -0.4930, 0.2425, 0.3186, 0.1754]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.8018, -0.9002, 0.1910, -1.2467, 1.1057,
- 0.3799],
- [ 0.5707, -0.4017, 1.7961, -0.1535, -0.0515, 0.3238, 0.5663,
- 0.4162],
- [ 0.6158, -0.4249, 1.8654, -0.9002, -0.3229, -0.3537, 0.9667,
- 0.2890],
- [-2.2859, -2.2859, 1.8423, -0.9695, -0.1323, -0.8463, 1.1349,
- 0.2676],
- [ 0.5532, -0.3864, 1.4035, -0.8079, -0.5423, -1.0080, 0.0928,
- 0.2776],
- [ 0.5477, -0.3851, 1.7961, -0.1304, -0.3055, 0.5085, 0.3830,
- 0.0682],
- [ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576],
- [ 0.5991, -0.3803, 1.8018, -0.0534, -0.3460, 0.1852, 0.5374,
- 0.1390]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1352, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1352, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.107771832495928
- step: 40
- running loss: 0.05269429581239819
- Train Steps: 40/90 Loss: 0.0527 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383],
- [0.6095, 0.4002, 0.8533, 0.5168, 0.5031, 0.5094, 0.5125, 0.5433],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505],
- [0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567],
- [0.6200, 0.4049, 0.8638, 0.5617, 0.4125, 0.5100, 0.6013, 0.5317],
- [ nan, nan, 0.7335, 0.2569, 0.3788, 0.2667, 0.5066, 0.5578]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.4297, -1.6886, 0.9586, -1.0350, -0.4966, -1.1245, 0.2422, 0.2725],
- [ 0.7484, -0.3261, 1.7147, 0.2348, -0.1303, 0.2234, 0.4167, 0.1816],
- [ 0.9006, -0.1798, 1.8771, -0.0190, -0.2798, -0.6128, 0.7785, 0.1212],
- [ 0.9955, -0.1387, 1.5056, -0.5418, -0.7577, -0.3477, 0.2791, 0.1715],
- [ 1.1263, -0.0555, 1.6218, -0.5745, -0.5147, -0.7123, 0.5116, 0.1374],
- [ 0.1350, -0.6184, 1.1729, -1.1085, -0.3779, -0.8162, 0.4339, 0.3186],
- [ 0.9295, -0.2291, 1.6967, 0.4022, -0.3601, 0.3717, 0.5515, 0.1648],
- [-2.0216, -2.0805, 1.1421, -0.9266, -0.5279, -0.9488, 0.2146, 0.2698]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.0712, -1.2085, -0.3806, -1.3929, 0.0755,
- 0.2006],
- [ 0.5295, -0.4373, 1.6553, 0.1011, 0.0380, 0.0671, 0.0813,
- 0.2237],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.5433, -0.4032, 1.6344, -0.4922, -0.5769, -0.5846, 0.0357,
- 0.2567],
- [ 0.5784, -0.4006, 1.7911, -0.5701, -0.5192, -1.0331, 0.4137,
- 0.2139],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853],
- [ 0.5776, -0.4159, 1.7037, 0.3084, -0.3806, 0.0697, 0.4912,
- 0.1698],
- [-2.2859, -2.2859, 1.1020, -1.0994, -0.5365, -1.0542, 0.0542,
- 0.2905]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0592, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0592, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.1670092418789864
- step: 41
- running loss: 0.05285388394826796
- Train Steps: 41/90 Loss: 0.0529 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [0.6113, 0.4006, 0.8700, 0.5350, 0.3638, 0.3767, 0.5097, 0.4882],
- [0.6266, 0.4070, 0.8712, 0.5600, 0.3713, 0.4783, 0.5775, 0.6100],
- [0.6226, 0.4125, 0.8800, 0.4900, 0.4512, 0.5600, 0.6275, 0.5517],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6236, 0.3967, 0.8675, 0.5400, 0.3862, 0.4517, 0.5825, 0.5200],
- [0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.3570, -1.0477, 1.9500, -0.7166, -0.2661, -0.7335, 0.7896, 0.2005],
- [ 0.1600, -0.7055, 1.7179, -0.2601, -0.1441, 0.0563, 0.3919, 0.2460],
- [ 0.2115, -0.6359, 1.6255, -0.0376, -0.6835, -0.6661, 0.2499, 0.1468],
- [-0.0534, -0.8461, 1.5375, 0.2503, -0.5311, -0.0212, 0.3671, 0.3205],
- [ 0.3429, -0.5866, 1.6651, -0.2636, -0.2702, 0.2513, 0.4242, 0.2724],
- [ 0.3025, -0.6347, 1.5513, -0.8119, -0.4023, -0.8750, 0.6761, 0.1629],
- [ 0.1845, -0.6954, 1.6102, 0.1688, -0.5439, -0.2238, 0.3499, 0.2069],
- [ 0.6012, -0.4584, 1.0800, -1.4243, -0.6784, -1.1147, 0.3999, 0.1815]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6388, -0.3623, 1.9173, -0.7386, -0.3517, -0.5846, 1.1495,
- 0.2676],
- [ 0.5598, -0.4273, 1.7152, -0.1227, -0.0065, 0.1917, 0.5143,
- 0.2853],
- [ 0.5379, -0.4358, 1.7326, 0.1852, -0.6058, -0.5461, 0.0684,
- -0.0310],
- [ 0.6081, -0.4059, 1.7383, 0.3007, -0.5711, -0.0765, 0.3815,
- 0.5316],
- [ 0.5897, -0.3804, 1.7788, -0.0226, -0.2016, 0.3007, 0.6125,
- 0.2622],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5944, -0.4538, 1.7210, 0.2083, -0.5018, -0.1997, 0.4046,
- 0.1159],
- [ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0622, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0622, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.229224521666765
- step: 42
- running loss: 0.05307677432539917
- Train Steps: 42/90 Loss: 0.0531 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6203, 0.4056, 0.8942, 0.4086, 0.3643, 0.3617, 0.5917, 0.5482],
- [0.6143, 0.4040, 0.8237, 0.3333, 0.4075, 0.2117, 0.5137, 0.4973],
- [0.6280, 0.4055, 0.8600, 0.5317, 0.3800, 0.4700, 0.6275, 0.5133],
- [0.6145, 0.4007, 0.8775, 0.4533, 0.4562, 0.5533, 0.6088, 0.5533],
- [0.6115, 0.4081, 0.6725, 0.2433, 0.4088, 0.1933, 0.5167, 0.5544],
- [0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6111, 0.4033, 0.8300, 0.3267, 0.3588, 0.3333, 0.5444, 0.5637],
- [0.6064, 0.3953, 0.8738, 0.4417, 0.3663, 0.4683, 0.5511, 0.5416]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2737, -0.6350, 1.8644, -0.3020, -0.5419, -0.4866, 0.5516, 0.1753],
- [-0.2903, -0.9783, 1.4274, -0.8604, -0.4192, -1.0997, 0.3088, 0.1842],
- [ 0.2797, -0.6562, 1.8521, 0.2928, -0.4158, 0.0558, 0.6270, 0.1605],
- [ 0.1652, -0.7392, 1.9001, -0.0229, -0.1119, 0.4007, 0.5122, 0.2274],
- [ 0.1744, -0.7025, 0.9219, -1.1211, -0.4517, -1.2485, 0.2254, 0.2856],
- [ 0.4882, -0.4664, 1.4131, -0.6096, -0.4171, -0.6723, 0.5737, 0.3040],
- [ 0.2635, -0.6419, 1.5242, -0.7674, -0.6309, -0.7327, 0.2655, 0.2337],
- [ 0.0690, -0.8091, 1.8354, -0.1112, -0.5163, 0.0057, 0.4869, 0.1616]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5791, -0.4127, 1.8442, -0.3985, -0.6031, -0.6154, 0.4473,
- 0.2464],
- [ 0.5515, -0.4201, 1.5189, -0.7463, -0.4037, -1.3082, 0.0869,
- 0.0111],
- [ 0.6148, -0.4130, 1.6864, 0.1698, -0.5307, -0.1150, 0.6125,
- 0.0851],
- [ 0.5526, -0.4351, 1.7672, -0.1920, -0.1785, 0.2699, 0.5259,
- 0.2699],
- [ 0.5385, -0.4010, 0.8203, -1.1620, -0.3979, -1.3929, 0.1005,
- 0.2747],
- [ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469],
- [ 0.5366, -0.4232, 1.5478, -0.7771, -0.6289, -0.7463, 0.2288,
- 0.3177],
- [ 0.5149, -0.4603, 1.7499, -0.2459, -0.5942, -0.1227, 0.2596,
- 0.2155]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0456, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0456, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.2748365253210068
- step: 43
- running loss: 0.052903175007465275
- Train Steps: 43/90 Loss: 0.0529 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6157, 0.4102, 0.8513, 0.3817, 0.3613, 0.3667, 0.5096, 0.5890],
- [0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578],
- [0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717],
- [0.6261, 0.4029, 0.8720, 0.3364, 0.3665, 0.3753, 0.6531, 0.5183],
- [0.6268, 0.4052, 0.8175, 0.2250, 0.4688, 0.1917, 0.6375, 0.5267],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726],
- [0.6139, 0.4019, 0.7137, 0.2150, 0.4375, 0.1533, 0.5293, 0.5006],
- [0.6222, 0.4072, 0.7164, 0.2166, 0.3738, 0.3167, 0.6100, 0.5533]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5413, -0.4571, 1.9170, 0.0479, -0.6285, 0.0270, 0.2009, 0.2187],
- [-0.0423, -0.8500, 1.2509, -0.7857, -0.3819, -0.8803, 0.3350, 0.2688],
- [ 0.3422, -0.5660, 1.1678, -0.6439, -0.4835, -0.6990, 0.2194, 0.2989],
- [ 0.1666, -0.7358, 1.9808, -0.2059, -0.4849, 0.0364, 0.7351, 0.1599],
- [ 0.1013, -0.7613, 1.7859, -0.6040, -0.0864, -0.9437, 0.7968, 0.1880],
- [ 0.3649, -0.5743, 1.5560, -0.5668, -0.5240, -0.5518, 0.4174, 0.1397],
- [-0.6398, -1.2337, 1.2423, -0.8344, -0.2962, -1.1112, 0.2853, 0.2156],
- [ 0.5759, -0.4420, 1.3873, -0.7166, -0.5027, -0.4597, 0.4913, 0.2565]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5581, -0.3912, 1.6460, -0.5230, -0.6173, -0.5923, 0.0681,
- 0.4348],
- [ 0.5232, -0.4189, 0.9358, -1.4006, -0.3921, -1.3698, 0.2555,
- 0.2906],
- [ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546],
- [ 0.6059, -0.4249, 1.7420, -0.7321, -0.5931, -0.5523, 0.7307,
- 0.1080],
- [ 0.6092, -0.4143, 1.4901, -1.2467, -0.1208, -1.4006, 0.6587,
- 0.1467],
- [ 0.4997, -0.4446, 1.1610, -0.9772, -0.6058, -1.0311, 0.1404,
- -0.1031],
- [ 0.5496, -0.4295, 1.0109, -1.2928, -0.2651, -1.5777, 0.1589,
- 0.0261],
- [ 0.5878, -0.4052, 1.0229, -1.2855, -0.5596, -0.8232, 0.5316,
- 0.2699]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1343, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1343, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.4091132134199142
- step: 44
- running loss: 0.054752573032270775
- Train Steps: 44/90 Loss: 0.0548 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6265, 0.4251, 0.7113, 0.3550, 0.4375, 0.2117, 0.5587, 0.6118],
- [0.6188, 0.4099, 0.7400, 0.2433, 0.3962, 0.2750, 0.6162, 0.5467],
- [0.6097, 0.3988, 0.8650, 0.5250, 0.4213, 0.5200, 0.5675, 0.5050],
- [0.6093, 0.3990, 0.8400, 0.4333, 0.3688, 0.4633, 0.5560, 0.5656],
- [0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301],
- [0.6201, 0.4151, 0.8588, 0.5467, 0.3700, 0.3950, 0.5637, 0.5933],
- [0.6117, 0.4018, 0.6562, 0.1967, 0.3738, 0.2550, 0.5280, 0.5103],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4334, -0.5116, 1.3480, -0.8113, -0.3142, -1.0172, 0.4388, 0.3419],
- [ 0.4893, -0.5194, 1.3963, -1.1550, -0.5044, -1.0069, 0.5392, 0.1921],
- [ 0.0556, -0.8011, 2.0050, 0.2513, -0.3090, 0.1978, 0.3072, 0.1363],
- [ 0.0547, -0.7857, 1.8429, -0.2424, -0.5380, -0.0357, 0.3545, 0.2218],
- [ 0.0402, -0.8175, 1.9599, 0.0763, -0.3227, 0.1221, 0.5820, 0.1758],
- [ 0.4812, -0.4551, 1.8308, 0.2619, -0.5223, -0.2912, 0.3599, 0.2569],
- [-0.0621, -0.8441, 1.1563, -1.2337, -0.5207, -1.1930, 0.2766, 0.2215],
- [ 0.6473, -0.4163, 1.2970, -1.1117, -0.4648, -1.1489, 0.4737, 0.2342]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6077, -0.3226, 0.9993, -0.6462, -0.2651, -1.3082, 0.2946,
- 0.5401],
- [ 0.5725, -0.3926, 1.1321, -1.1620, -0.4557, -1.0157, 0.5605,
- 0.2391],
- [ 0.5303, -0.4440, 1.7095, 0.1390, -0.3402, 0.1159, 0.3353,
- 0.0467],
- [ 0.5283, -0.4429, 1.5940, -0.2844, -0.5827, -0.1458, 0.2823,
- 0.3267],
- [ 0.6010, -0.4562, 1.7198, -0.0090, -0.3464, 0.0108, 0.6294,
- 0.1627],
- [ 0.5785, -0.3687, 1.6806, 0.2391, -0.5769, -0.4614, 0.3180,
- 0.4547],
- [ 0.5393, -0.4299, 0.7453, -1.3775, -0.5596, -1.1081, 0.1527,
- 0.0712],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0466, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0466, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.45572542399168
- step: 45
- running loss: 0.054571676088704
- Train Steps: 45/90 Loss: 0.0546 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6266, 0.4101, 0.8350, 0.2333, 0.3950, 0.2950, 0.6264, 0.4921],
- [0.6236, 0.3967, 0.8675, 0.5400, 0.3862, 0.4517, 0.5825, 0.5200],
- [0.6097, 0.3988, 0.8650, 0.5250, 0.4213, 0.5200, 0.5675, 0.5050],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6286, 0.4086, 0.8408, 0.2801, 0.4163, 0.2800, 0.6725, 0.5393],
- [0.6188, 0.4099, 0.7400, 0.2433, 0.3962, 0.2750, 0.6162, 0.5467],
- [0.6339, 0.4123, 0.8638, 0.5367, 0.4075, 0.5467, 0.7517, 0.5436]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0486, -0.7520, 1.5484, -1.2891, -0.3776, -1.1524, 0.5029, 0.2081],
- [ 0.1276, -0.7314, 1.7045, 0.1468, -0.5277, -0.3232, 0.3239, 0.1693],
- [ 0.1799, -0.6887, 1.7231, -0.0090, -0.3742, 0.0133, 0.1609, 0.1769],
- [ 0.4219, -0.5362, 1.5284, -0.9328, -0.6049, -0.6821, 0.3582, 0.3489],
- [ 0.5471, -0.4507, 1.6436, 0.3289, -0.4051, -0.3247, 0.3472, 0.3031],
- [ 0.4886, -0.4759, 1.6206, -1.0411, -0.2340, -1.1404, 0.6254, 0.2247],
- [ 0.5113, -0.4774, 1.1860, -1.2704, -0.5188, -1.0736, 0.4048, 0.2197],
- [ 0.2363, -0.6918, 1.8009, -0.0398, -0.4113, 0.2251, 0.6258, 0.2378]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6081, -0.3918, 1.5709, -1.2082, -0.4614, -0.9233, 0.6072,
- -0.0129],
- [ 0.5944, -0.4538, 1.7210, 0.2083, -0.5018, -0.1997, 0.4046,
- 0.1159],
- [ 0.5303, -0.4440, 1.7095, 0.1390, -0.3402, 0.1159, 0.3353,
- 0.0467],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.6038, -0.3464, 1.7037, 0.3931, -0.4441, -0.2613, 0.3007,
- 0.4624],
- [ 0.6174, -0.3984, 1.5975, -0.9921, -0.3633, -0.9926, 0.8205,
- 0.2050],
- [ 0.5725, -0.3926, 1.1321, -1.1620, -0.4557, -1.0157, 0.5605,
- 0.2391],
- [ 0.6421, -0.3816, 1.7037, 0.1929, -0.4037, 0.2391, 1.1861,
- 0.2249]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0342, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0342, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.489884562790394
- step: 46
- running loss: 0.05412792527805204
- Train Steps: 46/90 Loss: 0.0541 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6308, 0.3990, 0.8688, 0.5183, 0.3950, 0.4983, 0.6388, 0.5133],
- [0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300],
- [0.6216, 0.4099, 0.7225, 0.2033, 0.4188, 0.2217, 0.5975, 0.5283],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6154, 0.4112, 0.7037, 0.2317, 0.4238, 0.1833, 0.5350, 0.5600],
- [0.6161, 0.4024, 0.8838, 0.4583, 0.3688, 0.3733, 0.5311, 0.5344],
- [0.6058, 0.3986, 0.8324, 0.4626, 0.3838, 0.4983, 0.5147, 0.5466],
- [0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2156, -0.7264, 1.8884, 0.1304, -0.4089, 0.0797, 0.6120, 0.1767],
- [ 0.5065, -0.4851, 1.3740, -1.1749, -0.4529, -0.8285, 0.6044, 0.2467],
- [ 0.7207, -0.3375, 1.1234, -1.1218, -0.3663, -1.1669, 0.2888, 0.2777],
- [ 0.5620, -0.4515, 1.7631, -0.4039, -0.5657, -0.5033, 0.4060, 0.2434],
- [ 0.3018, -0.6048, 1.0069, -1.1920, -0.3891, -1.2506, 0.2564, 0.3108],
- [ 0.4506, -0.5106, 1.7971, -0.1529, -0.6035, -0.4831, 0.2787, 0.1326],
- [ 0.1461, -0.7076, 1.6684, -0.1707, -0.4278, 0.0296, 0.3463, 0.2320],
- [ 0.3512, -0.5647, 1.8268, -0.5372, -0.2307, -1.0289, 0.5388, 0.2037]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6275, -0.4430, 1.7268, 0.1082, -0.4614, 0.0159, 0.6644,
- 0.0851],
- [ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621],
- [ 0.5850, -0.3925, 1.0513, -1.3467, -0.3517, -1.2620, 0.4739,
- 0.1544],
- [ 0.5880, -0.3887, 1.8423, -0.3306, -0.6231, -0.5230, 0.4046,
- 0.1544],
- [ 0.5565, -0.3864, 0.9647, -1.2159, -0.3286, -1.4391, 0.1852,
- 0.3007],
- [ 0.5598, -0.4273, 1.7961, -0.1689, -0.5827, -0.5615, 0.1671,
- 0.1824],
- [ 0.5124, -0.4446, 1.5587, -0.1493, -0.5134, 0.0159, 0.0912,
- 0.2386],
- [ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0191, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0191, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5090342573821545
- step: 47
- running loss: 0.053383707603875626
- Train Steps: 47/90 Loss: 0.0534 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6135, 0.4115, 0.8838, 0.4667, 0.4288, 0.6050, 0.5778, 0.5097],
- [0.6353, 0.4128, 0.8488, 0.2600, 0.5525, 0.1616, 0.6694, 0.5540],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [0.6133, 0.4066, 0.6787, 0.2617, 0.3800, 0.2433, 0.5147, 0.5358],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6261, 0.4066, 0.8325, 0.2150, 0.4763, 0.2667, 0.7002, 0.5633],
- [0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8475, -0.2794, 1.7623, -0.0957, -0.4256, 0.2351, 0.4581, 0.2182],
- [-0.2862, -0.9827, 1.5954, -1.0876, -0.1128, -1.1886, 0.7696, 0.2542],
- [ 0.5297, -0.4046, 1.3062, -0.6987, -0.7591, -0.5133, 0.2072, 0.2336],
- [ 0.7029, -0.3222, 0.8991, -1.0768, -0.5859, -1.2088, 0.0907, 0.2763],
- [ 0.4878, -0.4625, 1.5844, -0.8308, -0.2686, -1.0829, 0.5105, 0.2126],
- [ 0.3900, -0.5851, 1.5981, -1.0304, -0.2450, -1.2939, 0.9065, 0.1962],
- [ 0.4773, -0.5220, 1.6869, -0.1609, -0.5247, 0.0040, 0.3232, 0.2207],
- [ 0.4895, -0.4908, 1.6441, 0.0550, -0.2706, -0.1646, 0.1716, 0.2209]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5477, -0.3851, 1.7961, -0.1304, -0.3055, 0.5085, 0.3830,
- 0.0682],
- [ 0.6487, -0.3792, 1.6344, -1.0850, 0.2659, -1.5397, 0.8059,
- 0.2730],
- [ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386],
- [ 0.5470, -0.4081, 0.8492, -1.0773, -0.5307, -1.1620, 0.0912,
- 0.1890],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.6059, -0.4080, 1.5594, -1.2928, -0.0861, -1.0542, 0.9485,
- 0.3157],
- [ 0.5604, -0.4620, 1.7961, -0.1997, -0.3229, 0.1082, 0.4104,
- 0.2545],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0418, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0418, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.550849635154009
- step: 48
- running loss: 0.05314270073237518
- Train Steps: 48/90 Loss: 0.0531 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.4008, 0.8950, 0.4183, 0.3550, 0.4383, 0.6361, 0.4927],
- [0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760],
- [0.6202, 0.4064, 0.7879, 0.2179, 0.4567, 0.1725, 0.5955, 0.5478],
- [0.6211, 0.4069, 0.8750, 0.5117, 0.4150, 0.4900, 0.5875, 0.5167],
- [0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973],
- [0.6086, 0.3998, 0.8788, 0.4450, 0.4025, 0.4650, 0.5306, 0.5103],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6204, 0.4055, 0.8438, 0.5733, 0.4574, 0.4801, 0.5487, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5748, -0.4237, 1.6063, -0.8094, -0.6753, -0.6006, 0.4739, 0.1426],
- [ 0.6394, -0.3932, 1.5600, -0.2398, -0.4541, -0.4561, 0.6120, 0.2591],
- [ 0.7896, -0.2924, 1.2072, -1.4725, -0.2467, -1.6366, 0.4556, 0.2659],
- [ 0.6717, -0.3560, 1.6344, -0.4148, -0.4480, -0.3918, 0.3761, 0.2047],
- [ 0.5630, -0.4586, 1.6663, -0.2600, -0.5572, -0.5696, 0.4639, 0.1523],
- [ 0.3004, -0.6163, 1.5867, -0.7224, -0.5165, -0.5790, 0.2084, 0.2126],
- [ 0.4216, -0.5652, 1.7460, -0.4298, -0.1611, -0.1235, 0.5190, 0.2736],
- [ 0.5407, -0.4184, 1.4882, -0.1265, -0.2424, -0.5134, 0.2740, 0.2863]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6010, -0.4345, 1.8480, -0.3537, -0.6462, -0.2613, 0.6524,
- -0.0099],
- [ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745],
- [ 0.5787, -0.4086, 1.3535, -1.2794, -0.1764, -1.4891, 0.4645,
- 0.2442],
- [ 0.5828, -0.4066, 1.7557, 0.0774, -0.3691, -0.0226, 0.4277,
- 0.1005],
- [ 0.6240, -0.4321, 1.8423, 0.1852, -0.5885, -0.1612, 0.6962,
- 0.0111],
- [ 0.5253, -0.4392, 1.7730, -0.2305, -0.4268, -0.1381, 0.1651,
- 0.0712],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.5795, -0.4129, 1.6113, 0.3623, -0.1733, -0.0684, 0.2487,
- 0.3084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0571, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0571, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.607935193926096
- step: 49
- running loss: 0.05322316722298155
- Train Steps: 49/90 Loss: 0.0532 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6093, 0.3990, 0.8400, 0.4333, 0.3688, 0.4633, 0.5560, 0.5656],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [ nan, nan, 0.6859, 0.2194, 0.4150, 0.1867, 0.5153, 0.5729],
- [0.6201, 0.4098, 0.8575, 0.3617, 0.3700, 0.3550, 0.5988, 0.5667],
- [0.6150, 0.3935, 0.8696, 0.5158, 0.4647, 0.5329, 0.6041, 0.5153],
- [0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300],
- [0.6184, 0.4079, 0.8350, 0.3700, 0.3675, 0.2883, 0.5312, 0.5783],
- [0.6264, 0.3972, 0.8853, 0.4771, 0.3853, 0.4511, 0.6293, 0.5334]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6072, -0.3902, 1.6371, -0.2984, -0.5227, -0.1801, 0.3244, 0.2093],
- [ 0.7488, -0.2995, 1.2322, -1.1851, -0.3223, -1.2072, 0.5751, 0.2034],
- [-0.4687, -1.0840, 0.9513, -1.2575, -0.3393, -1.3697, 0.1948, 0.2901],
- [ 1.0454, -0.1078, 1.6811, -0.6875, -0.4405, -0.7529, 0.4998, 0.2144],
- [ 0.6249, -0.4240, 1.7408, 0.0835, -0.1700, 0.0287, 0.4339, 0.1851],
- [ 0.5893, -0.3850, 1.3182, -1.1520, -0.3977, -0.9190, 0.5694, 0.2199],
- [ 1.0067, -0.1159, 1.5522, -0.5882, -0.5079, -0.9046, 0.2085, 0.2066],
- [ 0.7296, -0.3669, 1.8127, -0.0611, -0.4475, -0.2764, 0.6066, 0.1349]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5283, -0.4429, 1.5940, -0.2844, -0.5827, -0.1458, 0.2823,
- 0.3267],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [-2.2859, -2.2859, 0.8824, -1.2727, -0.3691, -1.4237, 0.0943,
- 0.3604],
- [ 0.5783, -0.3933, 1.6748, -0.6154, -0.5769, -0.6462, 0.4797,
- 0.3315],
- [ 0.5548, -0.4682, 1.7309, 0.0966, -0.1394, 0.1757, 0.5045,
- 0.0942],
- [ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621],
- [ 0.5704, -0.4019, 1.5709, -0.5769, -0.5885, -0.9541, 0.1679,
- 0.3854],
- [ 0.6075, -0.4514, 1.8032, -0.0822, -0.5061, -0.2023, 0.6208,
- 0.1779]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0894, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0894, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.6973820067942142
- step: 50
- running loss: 0.05394764013588429
- Train Steps: 50/90 Loss: 0.0539 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6097, 0.3988, 0.8650, 0.5250, 0.4213, 0.5200, 0.5675, 0.5050],
- [0.6282, 0.4029, 0.8988, 0.3000, 0.4250, 0.4183, 0.7042, 0.5575],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268],
- [0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6157, 0.4102, 0.8513, 0.3817, 0.3613, 0.3667, 0.5096, 0.5890],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7791, -0.2850, 1.6467, -0.0968, -0.3366, -0.3221, 0.2981, 0.1525],
- [ 0.5286, -0.4658, 1.6174, -0.9831, -0.3909, -0.7397, 0.8328, 0.1719],
- [ 0.4610, -0.5079, 1.6431, -0.6665, -0.5766, -0.4751, 0.6097, 0.1839],
- [ 0.6320, -0.3769, 1.6806, -0.2920, -0.2413, -0.3868, 0.5874, 0.1817],
- [ 0.5876, -0.3599, 0.9087, -1.2984, -0.4308, -1.4225, 0.2108, 0.2846],
- [ 0.6575, -0.3667, 1.7049, -0.3855, -0.1634, -0.2377, 0.4880, 0.2273],
- [ 0.9322, -0.1638, 1.4860, -0.7307, -0.6229, -0.9279, 0.2114, 0.2237],
- [ 0.5982, -0.3987, 1.6030, -0.1148, -0.2391, -0.3081, 0.3186, 0.1958]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5303, -0.4440, 1.7095, 0.1390, -0.3402, 0.1159, 0.3353,
- 0.0467],
- [ 0.6158, -0.4249, 1.8654, -0.9002, -0.3229, -0.3537, 0.9667,
- 0.2890],
- [ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474],
- [ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.5581, -0.3912, 1.6460, -0.5230, -0.6173, -0.5923, 0.0681,
- 0.4348],
- [ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0397, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0397, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.7370548360049725
- step: 51
- running loss: 0.05366774188245044
- Train Steps: 51/90 Loss: 0.0537 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6184, 0.4079, 0.8350, 0.3700, 0.3675, 0.2883, 0.5312, 0.5783],
- [0.6292, 0.4010, 0.8988, 0.4800, 0.3638, 0.3817, 0.6357, 0.5051],
- [0.6339, 0.4102, 0.9088, 0.4767, 0.3925, 0.5283, 0.7509, 0.5390],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6269, 0.4073, 0.8900, 0.4933, 0.3625, 0.4183, 0.6288, 0.5150],
- [0.6296, 0.4076, 0.8400, 0.5583, 0.3700, 0.4367, 0.6876, 0.5494],
- [0.6289, 0.4024, 0.9088, 0.4567, 0.3937, 0.5633, 0.7058, 0.5609]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8567, -0.1718, 1.3547, -0.7763, -0.4955, -0.9446, 0.0881, 0.2427],
- [ 0.5651, -0.4153, 1.5431, -0.3089, -0.4450, -0.5726, 0.3844, 0.1504],
- [ 0.6591, -0.3766, 1.6432, -0.2126, -0.3674, 0.1996, 0.5802, 0.2341],
- [ 0.4659, -0.4568, 1.6246, -1.0287, -0.2502, -1.0492, 0.6522, 0.2027],
- [ 0.4960, -0.4170, 1.1506, -1.3285, -0.1680, -1.3687, 0.4071, 0.2298],
- [ 0.8298, -0.2185, 1.5897, -0.2481, -0.4702, -0.4676, 0.2961, 0.1988],
- [ 0.5850, -0.4000, 1.5434, -0.1594, -0.4074, -0.6108, 0.4559, 0.1936],
- [ 0.7825, -0.3099, 1.6470, -0.3506, -0.3493, 0.2103, 0.7165, 0.2178]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5704, -0.4019, 1.5709, -0.5769, -0.5885, -0.9541, 0.1679,
- 0.3854],
- [ 0.6204, -0.4336, 1.8654, -0.0688, -0.6058, -0.5230, 0.6503,
- 0.0472],
- [ 0.6421, -0.3912, 1.9115, -0.0842, -0.4730, 0.1544, 1.1824,
- 0.2035],
- [ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.6096, -0.4046, 1.8249, -0.0072, -0.6115, -0.3537, 0.6182,
- 0.0928],
- [ 0.6224, -0.4032, 1.5940, 0.2930, -0.5769, -0.2690, 0.8900,
- 0.2516],
- [ 0.6191, -0.4273, 1.9115, -0.1766, -0.4672, 0.3161, 0.9741,
- 0.3050]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0385, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0385, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.775506906211376
- step: 52
- running loss: 0.05337513281175724
- Train Steps: 52/90 Loss: 0.0534 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6207, 0.4110, 0.8738, 0.5000, 0.4800, 0.5633, 0.6300, 0.5433],
- [0.6189, 0.4049, 0.8888, 0.4417, 0.4213, 0.5200, 0.5988, 0.5633],
- [0.6263, 0.4057, 0.8800, 0.3833, 0.3650, 0.3717, 0.6375, 0.4804],
- [ nan, nan, 0.8625, 0.2550, 0.5487, 0.2200, 0.7335, 0.5737],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [ nan, nan, 0.6469, 0.1943, 0.4025, 0.2000, 0.5125, 0.5533],
- [0.6109, 0.4041, 0.6975, 0.3167, 0.3513, 0.3383, 0.5153, 0.5319],
- [0.6260, 0.4153, 0.9000, 0.4533, 0.4025, 0.2633, 0.6223, 0.4967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.2758, -0.0035, 1.7499, -0.0705, -0.1819, 0.2553, 0.6336, 0.2368],
- [ 1.0472, -0.1138, 1.7491, -0.2063, -0.3777, 0.2831, 0.5343, 0.2114],
- [ 1.0555, -0.0865, 1.6605, -0.5979, -0.5562, -0.4504, 0.5985, 0.0961],
- [-0.9829, -1.4003, 1.5955, -1.0695, 0.0910, -1.2272, 0.8067, 0.2447],
- [ 1.1451, -0.0027, 1.6130, 0.1069, -0.5170, -0.5456, 0.3886, 0.1717],
- [-0.4768, -1.0166, 0.8050, -1.3387, -0.3480, -1.3481, 0.2209, 0.2965],
- [ 0.7727, -0.2133, 1.0725, -1.0082, -0.5601, -0.6336, 0.1684, 0.2660],
- [ 1.1011, 0.0046, 1.6445, -0.3508, -0.3602, -0.9335, 0.4613, 0.1539]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5811, -0.3878, 1.7499, 0.0236, -0.0688, 0.3161, 0.6240,
- 0.2237],
- [ 0.5726, -0.4159, 1.8192, -0.2459, -0.3402, 0.1159, 0.4797,
- 0.3161],
- [ 0.6071, -0.4119, 1.7788, -0.5153, -0.6000, -0.5692, 0.6586,
- -0.0670],
- [-2.2859, -2.2859, 1.6979, -1.1081, 0.2487, -1.2697, 1.1020,
- 0.3638],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698],
- [-2.2859, -2.2859, 0.7023, -1.3883, -0.4268, -1.3621, 0.0813,
- 0.2699],
- [ 0.5359, -0.4193, 0.9358, -0.8232, -0.6635, -0.7232, 0.0943,
- 0.1710],
- [ 0.6055, -0.3676, 1.8711, -0.1920, -0.4268, -1.0696, 0.5887,
- 0.0081]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1587, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1587, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.9342208728194237
- step: 53
- running loss: 0.05536265797772497
- Train Steps: 53/90 Loss: 0.0554 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6307, 0.4045, 0.8025, 0.5833, 0.3775, 0.4867, 0.6892, 0.5459],
- [0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [0.6339, 0.4149, 0.8800, 0.5000, 0.3900, 0.5283, 0.7541, 0.5424],
- [0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650],
- [0.6109, 0.4015, 0.7668, 0.3639, 0.3513, 0.3667, 0.5200, 0.5641],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3557, -0.4607, 1.3432, -0.7182, -0.4009, -1.1316, 0.1919, 0.2739],
- [ 0.6425, -0.3514, 1.5266, -0.1332, -0.3826, -0.3711, 0.5698, 0.1939],
- [ 0.3685, -0.5062, 1.3575, -1.2130, -0.1406, -1.5512, 0.6332, 0.1769],
- [ 0.6780, -0.3683, 1.8085, -0.1522, -0.3207, 0.1319, 0.8006, 0.1867],
- [ 0.9286, -0.1924, 1.7689, 0.0137, -0.3892, 0.0995, 0.7076, 0.1731],
- [ 0.7650, -0.2433, 1.5615, -0.4926, -0.5390, -0.5534, 0.2164, 0.2737],
- [ 0.5087, -0.3998, 1.2703, -0.8557, -0.5282, -0.6876, 0.2457, 0.2515],
- [ 0.8064, -0.2767, 1.7967, -0.1745, -0.3430, 0.2104, 0.6670, 0.1899]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.6273, -0.4177, 1.4208, 0.4085, -0.5423, -0.0380, 0.8973,
- 0.2356],
- [ 0.6158, -0.3960, 1.4092, -1.2774, -0.2074, -1.1851, 0.8491,
- 0.1917],
- [ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554],
- [ 0.6421, -0.3695, 1.7788, 0.0236, -0.4845, 0.1544, 1.1971,
- 0.2196],
- [ 0.5757, -0.3917, 1.7095, -0.4768, -0.6346, -0.4229, 0.3931,
- 0.3238],
- [ 0.5357, -0.4315, 1.2557, -0.6051, -0.6635, -0.5923, 0.1159,
- 0.3198],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0291, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0291, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.9632861856371164
- step: 54
- running loss: 0.054875670104391046
- Train Steps: 54/90 Loss: 0.0549 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6270, 0.4267, 0.7150, 0.3317, 0.4900, 0.1817, 0.5560, 0.6183],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6122, 0.4048, 0.7506, 0.3014, 0.3475, 0.3333, 0.5487, 0.5749],
- [0.6166, 0.4008, 0.8563, 0.5667, 0.4388, 0.4933, 0.5575, 0.5567],
- [0.6145, 0.4008, 0.8750, 0.5383, 0.3975, 0.4650, 0.5563, 0.5533],
- [0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194],
- [0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4483, -0.4180, 1.0823, -1.1483, -0.1529, -1.1096, 0.4683, 0.3558],
- [ 0.5588, -0.3981, 1.7192, -0.2750, -0.4433, -0.2098, 0.4805, 0.2380],
- [ 0.4985, -0.4167, 1.3104, -0.8960, -0.5438, -0.5640, 0.4597, 0.2455],
- [ 0.8661, -0.2295, 1.7761, 0.4190, -0.2540, 0.1397, 0.5771, 0.2036],
- [ 0.8158, -0.2632, 1.8079, 0.3212, -0.3979, 0.0774, 0.5858, 0.1922],
- [ 0.8015, -0.2651, 1.9091, -0.0199, -0.4642, -0.2573, 0.7678, 0.1320],
- [ 0.0030, -0.7220, 1.4453, -0.7867, -0.5837, -0.7954, 0.2402, 0.1659],
- [ 0.5133, -0.4323, 1.2798, -1.3046, -0.2583, -1.3138, 0.6630, 0.1580]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6101, -0.3152, 1.0166, -0.7540, -0.0226, -1.4468, 0.2823,
- 0.5702],
- [ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145],
- [ 0.5419, -0.4160, 1.1810, -0.8939, -0.6808, -0.7463, 0.2485,
- 0.3694],
- [ 0.5620, -0.4346, 1.6691, 0.3315, -0.2594, -0.0072, 0.2891,
- 0.2853],
- [ 0.5526, -0.4347, 1.7557, 0.2006, -0.4499, -0.1381, 0.2834,
- 0.2699],
- [ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499],
- [-2.2859, -2.2859, 1.5478, -0.8309, -0.6289, -0.7232, 0.1198,
- 0.1133],
- [ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1432, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1432, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.106526607647538
- step: 55
- running loss: 0.05648230195722797
- Train Steps: 55/90 Loss: 0.0565 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6260, 0.4153, 0.9000, 0.4533, 0.4025, 0.2633, 0.6223, 0.4967],
- [0.6193, 0.4108, 0.7425, 0.2350, 0.3887, 0.2750, 0.5900, 0.5717],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [0.6193, 0.4050, 0.7313, 0.2433, 0.4075, 0.2117, 0.5649, 0.5656],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837],
- [ nan, nan, 0.7725, 0.2611, 0.3675, 0.2733, 0.5413, 0.5167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0553e+00, -1.5796e-01, 2.0803e+00, 3.1226e-01, -3.8757e-01,
- 6.7862e-01, 8.9026e-01, 1.5629e-01],
- [ 1.4193e-01, -6.1169e-01, 1.3132e+00, -9.7362e-01, -2.6433e-01,
- -1.0044e+00, 5.2136e-01, 2.8896e-01],
- [ 9.9363e-01, -7.4630e-02, 1.8583e+00, 3.2327e-02, -3.5001e-01,
- -7.8548e-01, 6.0284e-01, 1.4803e-01],
- [ 5.2145e-01, -3.7143e-01, 1.2435e+00, -8.4906e-01, -4.3652e-01,
- -8.5257e-01, 4.4860e-01, 3.1134e-01],
- [ 1.3241e+00, -6.1965e-04, 2.0147e+00, 5.9596e-02, -4.7098e-01,
- 5.4668e-01, 9.9321e-01, 1.6055e-01],
- [ 4.0170e-01, -4.5806e-01, 1.1629e+00, -9.4310e-01, -3.3796e-01,
- -9.8315e-01, 4.4373e-01, 2.4660e-01],
- [ 5.9641e-01, -3.5358e-01, 1.0719e+00, -7.0736e-01, -5.0569e-01,
- -7.0185e-01, 2.7299e-01, 3.3588e-01],
- [-1.0682e+00, -1.4444e+00, 1.2887e+00, -8.3251e-01, -4.0688e-01,
- -9.9458e-01, 2.4550e-01, 2.2370e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698],
- [ 0.6055, -0.3676, 1.8711, -0.1920, -0.4268, -1.0696, 0.5887,
- 0.0081],
- [ 0.5746, -0.3882, 1.1436, -1.2005, -0.4903, -1.0157, 0.4393,
- 0.3546],
- [ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715],
- [ 0.5746, -0.4153, 1.0917, -1.1620, -0.4037, -1.3082, 0.3234,
- 0.3267],
- [ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103],
- [-2.2859, -2.2859, 1.2820, -1.0801, -0.5885, -1.0234, 0.2141,
- 0.1005]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0860, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0860, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.1925710681825876
- step: 56
- running loss: 0.05701019764611764
- Train Steps: 56/90 Loss: 0.0570 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6272, 0.4045, 0.8538, 0.5900, 0.3750, 0.4417, 0.5989, 0.4649],
- [0.6270, 0.4267, 0.7150, 0.3317, 0.4900, 0.1817, 0.5560, 0.6183],
- [0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6274, 0.4003, 0.8638, 0.5967, 0.3688, 0.4900, 0.6108, 0.4661],
- [0.6144, 0.4032, 0.8563, 0.3283, 0.3525, 0.4200, 0.5775, 0.5583],
- [0.6267, 0.4065, 0.8313, 0.2467, 0.4788, 0.1733, 0.6312, 0.5133],
- [0.6132, 0.4037, 0.6963, 0.2217, 0.4100, 0.1950, 0.5395, 0.5175]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7179, -0.3238, 1.7458, 0.3163, -0.5806, -0.1069, 0.5669, 0.1582],
- [ 0.0706, -0.6531, 0.9820, -1.0993, -0.2456, -1.0041, 0.3436, 0.3978],
- [ 0.5253, -0.4076, 1.7337, 0.1074, -0.3820, 0.2137, 0.4023, 0.2355],
- [ 0.5145, -0.4474, 2.0043, -0.2206, -0.2732, -0.6411, 0.8709, 0.1705],
- [ 0.6235, -0.4090, 1.7221, 0.4188, -0.5392, 0.1507, 0.6613, 0.1973],
- [ 0.6203, -0.3780, 1.6743, -0.5646, -0.6705, -0.1017, 0.4486, 0.2820],
- [ 0.3270, -0.5656, 1.5257, -1.1182, -0.2016, -1.1584, 0.8714, 0.2187],
- [ 0.1576, -0.6544, 1.0196, -1.1413, -0.4234, -1.1670, 0.3124, 0.2487]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.1085e-01, -4.1771e-01, 1.6575e+00, 4.3926e-01, -5.5381e-01,
- -2.4588e-01, 4.8055e-01, -1.3847e-01],
- [ 6.1010e-01, -3.1524e-01, 1.0166e+00, -7.5396e-01, -2.2633e-02,
- -1.4468e+00, 2.8226e-01, 5.7018e-01],
- [ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01],
- [ 6.2236e-01, -4.3453e-01, 1.9404e+00, -2.9207e-01, -3.1709e-01,
- -8.7714e-01, 1.0655e+00, 2.1421e-01],
- [ 6.1201e-01, -4.3711e-01, 1.7037e+00, 4.7005e-01, -5.8268e-01,
- -2.2633e-02, 5.3538e-01, -1.3313e-01],
- [ 5.5196e-01, -4.2371e-01, 1.6691e+00, -7.6936e-01, -6.5774e-01,
- -3.4596e-01, 3.8152e-01, 2.9299e-01],
- [ 6.0855e-01, -4.0839e-01, 1.5536e+00, -1.1466e+00, -7.4596e-02,
- -1.4853e+00, 6.2979e-01, 8.5142e-02],
- [ 5.4648e-01, -4.2140e-01, 9.3002e-01, -1.2620e+00, -3.9215e-01,
- -1.3852e+00, 2.0618e-01, 1.0428e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0330, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0330, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.2255628500133753
- step: 57
- running loss: 0.056588821930059215
- Train Steps: 57/90 Loss: 0.0566 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6364, 0.4092, 0.8525, 0.5633, 0.3875, 0.4950, 0.6599, 0.5285],
- [0.6222, 0.4169, 0.8638, 0.5650, 0.4313, 0.4783, 0.5637, 0.5633],
- [0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272],
- [0.6277, 0.4118, 0.8988, 0.3833, 0.3950, 0.2650, 0.6290, 0.4938],
- [0.6201, 0.4151, 0.8588, 0.5467, 0.3700, 0.3950, 0.5637, 0.5933],
- [0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6276, 0.4120, 0.8738, 0.3133, 0.4225, 0.2217, 0.6203, 0.4892],
- [0.6197, 0.3930, 0.8793, 0.4736, 0.4152, 0.5464, 0.6308, 0.5235]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6005, -0.4291, 1.6331, 0.1981, -0.4927, 0.1141, 0.6502, 0.2158],
- [ 0.3800, -0.5244, 1.5364, 0.2919, -0.3730, 0.0861, 0.3863, 0.3102],
- [ 0.3911, -0.4867, 1.4536, -0.7373, -0.5116, -0.8875, 0.3204, 0.2352],
- [ 0.3081, -0.5687, 1.7045, -0.6232, -0.4642, -0.9397, 0.6238, 0.1902],
- [ 0.3725, -0.4843, 1.4808, 0.0960, -0.5485, -0.2804, 0.3564, 0.3270],
- [ 0.3914, -0.4865, 1.5987, -1.1421, -0.3147, -0.9816, 0.8374, 0.2485],
- [ 0.4910, -0.4554, 1.6362, -0.9180, -0.3677, -1.0994, 0.7497, 0.2050],
- [ 0.3051, -0.6135, 1.6622, -0.1353, -0.3918, 0.2559, 0.6588, 0.2314]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.5365e-01, -3.9601e-01, 1.6517e+00, 3.1609e-01, -4.9607e-01,
- 4.6189e-04, 7.6203e-01, 1.5543e-01],
- [ 5.8799e-01, -3.6051e-01, 1.7037e+00, 3.2379e-01, -2.9400e-01,
- -7.6520e-02, 3.1801e-01, 3.1609e-01],
- [ 5.7113e-01, -4.0146e-01, 1.6979e+00, -6.7698e-01, -5.3649e-01,
- -1.0619e+00, 1.7122e-01, 1.4937e-01],
- [ 6.1351e-01, -3.8406e-01, 1.8654e+00, -5.1532e-01, -4.6143e-01,
- -1.0619e+00, 6.1946e-01, -4.8817e-03],
- [ 5.7852e-01, -3.6867e-01, 1.6806e+00, 2.3911e-01, -5.7691e-01,
- -4.6143e-01, 3.1801e-01, 4.5466e-01],
- [ 6.0837e-01, -4.0762e-01, 1.6806e+00, -9.6182e-01, -2.9977e-01,
- -9.6952e-01, 6.3557e-01, 1.4673e-01],
- [ 6.1282e-01, -3.8283e-01, 1.7499e+00, -8.3865e-01, -3.3441e-01,
- -1.2620e+00, 5.7925e-01, -2.6256e-02],
- [ 5.7625e-01, -4.7064e-01, 1.7754e+00, -9.8417e-02, -3.6803e-01,
- 2.3803e-01, 6.2770e-01, 1.3223e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0179, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0179, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.2434706911444664
- step: 58
- running loss: 0.05592190846800804
- Train Steps: 58/90 Loss: 0.0559 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6164, 0.3956, 0.8757, 0.5088, 0.4300, 0.5320, 0.6022, 0.5202],
- [0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332],
- [0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517],
- [0.6175, 0.4013, 0.8900, 0.4500, 0.4375, 0.5850, 0.6175, 0.5383],
- [0.6113, 0.4088, 0.6859, 0.2208, 0.4363, 0.1700, 0.5188, 0.5533],
- [0.6125, 0.3999, 0.8750, 0.4883, 0.4750, 0.4700, 0.5533, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6097, -0.3595, 1.6705, -0.0305, -0.4489, -0.0061, 0.5127, 0.2028],
- [ 0.6639, -0.3332, 1.6342, -0.4861, -0.7198, -0.5016, 0.3668, 0.2877],
- [ 0.5705, -0.3734, 1.9512, -0.2589, -0.3449, -1.1658, 0.9691, 0.1807],
- [ 0.6820, -0.3269, 1.5413, -0.0517, -0.5332, -0.0984, 0.6546, 0.2289],
- [ 0.2860, -0.6007, 1.7352, -0.1885, -0.3268, -0.1205, 0.5123, 0.2239],
- [ 0.6428, -0.3875, 1.7646, -0.3212, -0.3645, 0.1299, 0.7426, 0.2294],
- [-1.0394, -1.4764, 0.8833, -1.3383, -0.4541, -1.6894, 0.2561, 0.2452],
- [ 0.6094, -0.3865, 1.7440, -0.0939, -0.2608, -0.1809, 0.4635, 0.2568]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5614, -0.4586, 1.7587, 0.0641, -0.2998, 0.1712, 0.4958,
- 0.1170],
- [ 0.5757, -0.3917, 1.7095, -0.4768, -0.6346, -0.4229, 0.3931,
- 0.3238],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.6191, -0.4297, 1.4612, 0.2391, -0.4961, 0.0313, 1.1166,
- 0.1768],
- [ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622],
- [ 0.5664, -0.4321, 1.8249, -0.2074, -0.2651, 0.4162, 0.5663,
- 0.2006],
- [ 0.5377, -0.3978, 0.8824, -1.2663, -0.2709, -1.5007, 0.1102,
- 0.2699],
- [ 0.5432, -0.4388, 1.7557, -0.0303, -0.0919, -0.1150, 0.2699,
- 0.3087]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0746, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0746, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.3181116357445717
- step: 59
- running loss: 0.05623918026685715
- Train Steps: 59/90 Loss: 0.0562 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136],
- [0.6193, 0.4034, 0.7757, 0.2347, 0.3733, 0.2919, 0.5930, 0.4926],
- [0.6186, 0.4013, 0.8191, 0.3188, 0.4279, 0.2060, 0.5767, 0.5411],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6201, 0.4064, 0.8688, 0.5050, 0.4225, 0.5100, 0.6138, 0.5500],
- [0.6147, 0.4026, 0.6600, 0.2467, 0.4088, 0.2150, 0.5489, 0.5773],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5722, -0.3972, 1.2615, -0.8737, -0.5309, -0.9137, 0.4895, 0.1950],
- [ 0.2688, -0.6062, 1.5072, -0.8471, -0.5606, -0.7359, 0.5700, 0.1372],
- [ 0.5582, -0.3715, 1.6745, -0.5818, -0.3719, -1.0065, 0.5898, 0.1881],
- [ 0.4482, -0.5203, 2.0914, 0.4037, -0.2245, 0.3096, 0.6900, 0.1714],
- [ 0.6675, -0.3792, 2.0147, 0.5004, -0.4290, 0.4757, 0.7432, 0.1545],
- [ 0.7220, -0.3007, 1.1824, -0.7761, -0.4194, -1.0112, 0.4273, 0.3017],
- [-1.0870, -1.5275, 1.1445, -0.9036, -0.3555, -1.3402, 0.3323, 0.2497],
- [ 0.6270, -0.3712, 1.2397, -0.6168, -0.5985, -0.6370, 0.3945, 0.3137]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862],
- [ 0.5746, -0.4225, 1.2968, -1.2019, -0.5615, -0.9374, 0.4533,
- -0.0108],
- [ 0.5713, -0.4321, 1.4975, -0.8134, -0.3094, -1.3345, 0.3779,
- 0.2134],
- [ 0.5359, -0.4370, 1.7095, -0.0303, -0.0804, -0.0380, 0.1044,
- 0.3392],
- [ 0.5784, -0.4088, 1.7268, 0.0467, -0.3344, 0.0697, 0.5490,
- 0.2545],
- [ 0.5532, -0.4264, 0.7626, -1.1466, -0.3979, -1.2928, 0.2494,
- 0.3808],
- [-2.2859, -2.2859, 0.6760, -1.4083, -0.3286, -1.4160, 0.2487,
- 0.3469],
- [ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0950, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0950, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.413125194609165
- step: 60
- running loss: 0.05688541991015275
- Train Steps: 60/90 Loss: 0.0569 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507],
- [0.6286, 0.4055, 0.9000, 0.4717, 0.3763, 0.4683, 0.7018, 0.5494],
- [0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [0.6060, 0.3924, 0.8450, 0.5717, 0.4200, 0.5217, 0.5253, 0.4752],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4477, -0.4989, 1.0241, -1.1866, -0.4538, -1.1541, 0.3545, 0.2349],
- [ 0.5304, -0.4797, 1.9840, -0.0666, -0.5513, -0.0591, 0.9308, 0.1303],
- [ 0.3024, -0.5637, 1.5836, -0.9812, -0.3989, -1.0223, 0.4467, 0.1653],
- [ 0.1767, -0.6606, 1.9765, -0.0760, -0.2892, -0.9039, 0.8481, 0.2020],
- [ 0.3131, -0.5527, 1.3676, -0.6091, -0.6676, -0.2343, 0.2590, 0.2547],
- [ 0.3597, -0.5725, 1.6503, 0.4191, -0.3494, 0.0670, 0.4046, 0.2217],
- [ 0.6185, -0.4473, 1.8138, 0.3166, -0.5707, -0.0937, 0.6552, 0.1352],
- [ 0.1686, -0.6116, 1.1855, -1.0051, -0.2213, -1.1281, 0.3396, 0.3605]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576],
- [ 0.6174, -0.4129, 1.8711, -0.1073, -0.5480, -0.1227, 0.9558,
- 0.2516],
- [ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386],
- [ 0.5132, -0.4736, 1.6171, 0.3546, -0.3460, 0.1236, 0.1404,
- -0.0911],
- [ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0200, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0200, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.433107813820243
- step: 61
- running loss: 0.05628045596426628
- Train Steps: 61/90 Loss: 0.0563 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6296, 0.4060, 0.9100, 0.3267, 0.4726, 0.3367, 0.7446, 0.5355],
- [0.6202, 0.4064, 0.7879, 0.2179, 0.4567, 0.1725, 0.5955, 0.5478],
- [0.6147, 0.4026, 0.6600, 0.2467, 0.4088, 0.2150, 0.5489, 0.5773],
- [0.6164, 0.3972, 0.7818, 0.3381, 0.3599, 0.3387, 0.5880, 0.5153],
- [0.6147, 0.4112, 0.7988, 0.3200, 0.3775, 0.2767, 0.5150, 0.5550],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1825, -0.7061, 1.7562, -0.5810, -0.2417, -0.6851, 0.8891, 0.1766],
- [ 0.5891, -0.4146, 1.3659, -0.8705, -0.2362, -1.3972, 0.5131, 0.2131],
- [ 0.7430, -0.3105, 1.0501, -0.8250, -0.3662, -1.2392, 0.3054, 0.2914],
- [ 0.6278, -0.3860, 1.5139, -0.6035, -0.6098, -0.5631, 0.4918, 0.1655],
- [ 0.0770, -0.7091, 1.4412, -0.5819, -0.5632, -0.9197, 0.1810, 0.1949],
- [ 0.3116, -0.6728, 1.9799, 0.2008, -0.3828, 0.4249, 0.8613, 0.1679],
- [-0.1072, -0.8606, 1.7340, -0.0695, -0.4591, -0.2890, 0.4195, 0.2378],
- [ 0.4073, -0.5107, 1.4580, -0.4866, -0.6220, -0.3523, 0.3635, 0.2498]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6224, -0.4105, 1.9173, -0.7771, -0.1030, -0.7308, 1.1532,
- 0.1875],
- [ 0.5787, -0.4086, 1.3535, -1.2794, -0.1764, -1.4891, 0.4645,
- 0.2442],
- [ 0.5532, -0.4264, 0.7626, -1.1466, -0.3979, -1.2928, 0.2494,
- 0.3808],
- [ 0.5614, -0.4514, 1.3252, -0.7240, -0.6237, -0.7215, 0.4299,
- 0.0942],
- [ 0.5532, -0.3864, 1.4035, -0.8079, -0.5423, -1.0080, 0.0928,
- 0.2776],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0419, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0419, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.4750487077981234
- step: 62
- running loss: 0.056049172706421346
- Train Steps: 62/90 Loss: 0.0560 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [0.6264, 0.4049, 0.8988, 0.4633, 0.3813, 0.4983, 0.6326, 0.4843],
- [0.6203, 0.4021, 0.8780, 0.5031, 0.3667, 0.3882, 0.5842, 0.5405],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6064, 0.4019, 0.8650, 0.4517, 0.4037, 0.5367, 0.5703, 0.5609],
- [0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833],
- [0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5372, -0.4427, 1.7632, 0.0029, -0.1842, -0.0286, 0.4543, 0.2477],
- [ 0.7022, -0.3753, 2.0274, -0.0909, -0.5449, -0.0211, 0.7920, 0.0436],
- [ 0.8202, -0.2692, 1.8586, 0.2130, -0.6447, -0.3823, 0.6089, 0.0845],
- [-1.2660, -1.6953, 0.9615, -1.1883, -0.3203, -1.5984, 0.1894, 0.2762],
- [ 0.4728, -0.4804, 1.6838, -0.2210, -0.3768, 0.2081, 0.5723, 0.2581],
- [ 0.0046, -0.7779, 1.0222, -0.8906, -0.4931, -1.0298, 0.2306, 0.3229],
- [ 1.1376, -0.0465, 1.2655, -0.9522, -0.3285, -1.1190, 0.5421, 0.3597],
- [ 0.4906, -0.4482, 1.6257, -0.5826, -0.4980, -1.0188, 0.3590, 0.1793]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5505, -0.4207, 1.7095, -0.0534, -0.0509, 0.1050, 0.3873,
- 0.3007],
- [ 0.6075, -0.4159, 1.8654, -0.1458, -0.5249, 0.0159, 0.6359,
- -0.0490],
- [ 0.5791, -0.4289, 1.7694, 0.0379, -0.5923, -0.4927, 0.4126,
- 0.2107],
- [-2.2859, -2.2859, 0.6760, -1.4083, -0.3286, -1.4160, 0.2487,
- 0.3469],
- [ 0.5151, -0.4296, 1.7095, -0.1997, -0.4210, 0.1929, 0.3484,
- 0.3047],
- [ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085],
- [ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084],
- [ 0.5711, -0.4015, 1.6979, -0.6770, -0.5365, -1.0619, 0.1712,
- 0.1494]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0488, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0488, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.523830173537135
- step: 63
- running loss: 0.055933812278367225
- Train Steps: 63/90 Loss: 0.0559 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6153, 0.4117, 0.8688, 0.5167, 0.4895, 0.5647, 0.5524, 0.5136],
- [0.6197, 0.4118, 0.8688, 0.5517, 0.4037, 0.5233, 0.5875, 0.5600],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6280, 0.4055, 0.8600, 0.5317, 0.3800, 0.4700, 0.6275, 0.5133],
- [0.6128, 0.4084, 0.8738, 0.4683, 0.3613, 0.3700, 0.4960, 0.5397],
- [0.6115, 0.3998, 0.7063, 0.2383, 0.4037, 0.1950, 0.5320, 0.4993],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6161, 0.4024, 0.8838, 0.4583, 0.3688, 0.3733, 0.5311, 0.5344]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2926, -0.6040, 1.6742, -0.2913, -0.0972, -0.4117, 0.4652, 0.2531],
- [ 0.4361, -0.5205, 1.5725, 0.0129, -0.3745, -0.1130, 0.4582, 0.2891],
- [ 0.4407, -0.5717, 1.6980, -0.5028, -0.3800, 0.0268, 0.6957, 0.2434],
- [ 0.6617, -0.4029, 1.7236, -0.1642, -0.4997, -0.3592, 0.7090, 0.1786],
- [ 0.1602, -0.6668, 1.5842, -0.4179, -0.6784, -0.8125, 0.2578, 0.2007],
- [ 0.6501, -0.3948, 0.9773, -1.4541, -0.3942, -1.6491, 0.1857, 0.2270],
- [ 0.2614, -0.6123, 1.6131, -0.0798, -0.4499, -0.5095, 0.4385, 0.2353],
- [ 0.2661, -0.6166, 1.6872, -0.4090, -0.5893, -0.7739, 0.3292, 0.1672]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5564, -0.3842, 1.7268, 0.1005, -0.0250, 0.3225, 0.2658,
- 0.0862],
- [ 0.5762, -0.3840, 1.7268, 0.2622, -0.4210, 0.1313, 0.4277,
- 0.3007],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.6148, -0.4130, 1.6864, 0.1698, -0.5307, -0.1150, 0.6125,
- 0.0851],
- [ 0.5443, -0.3994, 1.7499, -0.1227, -0.6173, -0.5769, 0.0049,
- 0.2071],
- [ 0.5384, -0.4393, 0.9762, -1.1851, -0.4210, -1.3852, 0.1712,
- 0.0201],
- [ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391],
- [ 0.5598, -0.4273, 1.7961, -0.1689, -0.5827, -0.5615, 0.1671,
- 0.1824]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0479, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0479, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.5717637445777655
- step: 64
- running loss: 0.055808808509027585
- Train Steps: 64/90 Loss: 0.0558 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578],
- [0.6110, 0.4047, 0.8700, 0.4483, 0.3713, 0.3967, 0.5088, 0.5517],
- [0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6203, 0.4096, 0.8862, 0.4267, 0.3538, 0.4117, 0.6025, 0.5650],
- [0.6030, 0.3969, 0.7988, 0.3917, 0.3450, 0.3667, 0.5266, 0.4700],
- [0.6346, 0.4092, 0.7712, 0.5917, 0.4037, 0.4767, 0.7343, 0.5725],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667],
- [0.6264, 0.4035, 0.8888, 0.4883, 0.4050, 0.5217, 0.6361, 0.4791]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1179, -0.7341, 0.9864, -1.3143, -0.3100, -1.6252, 0.2862, 0.2487],
- [-0.0459, -0.8347, 1.5879, -0.2601, -0.5802, -0.5579, 0.1200, 0.2064],
- [ 0.1300, -0.7234, 1.6999, -0.2421, -0.2803, -0.0950, 0.4022, 0.2162],
- [ 0.7086, -0.3531, 1.8816, -0.3375, -0.4554, -0.5277, 0.5610, 0.2529],
- [ 0.6942, -0.3544, 1.5433, -0.5519, -0.6117, -0.6795, 0.2996, 0.1721],
- [ 0.4877, -0.4784, 1.4619, -0.0209, -0.3364, -0.2908, 0.5970, 0.2885],
- [ 0.5985, -0.3888, 1.0590, -1.0507, -0.5366, -0.9573, 0.2380, 0.2933],
- [ 0.4173, -0.5434, 1.8164, -0.0607, -0.3924, -0.0882, 0.5622, 0.1204]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5232, -0.4189, 0.9358, -1.4006, -0.3921, -1.3698, 0.2555,
- 0.2906],
- [ 0.5363, -0.4168, 1.7326, -0.2151, -0.5711, -0.4537, 0.0640,
- 0.2622],
- [ 0.5604, -0.4620, 1.7961, -0.1997, -0.3229, 0.1082, 0.4104,
- 0.2545],
- [ 0.5790, -0.3940, 1.8076, -0.3152, -0.6520, -0.3844, 0.4970,
- 0.3238],
- [ 0.4992, -0.4525, 1.4035, -0.4768, -0.6924, -0.5923, 0.1465,
- -0.1151],
- [ 0.6454, -0.3960, 1.2764, 0.4470, -0.4210, -0.0842, 1.1057,
- 0.3585],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315],
- [ 0.6074, -0.4223, 1.8192, -0.0303, -0.4152, 0.1236, 0.6524,
- -0.0731]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0361, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0361, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.6078634057193995
- step: 65
- running loss: 0.05550559085722153
- Train Steps: 65/90 Loss: 0.0555 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6269, 0.4073, 0.8900, 0.4933, 0.3625, 0.4183, 0.6288, 0.5150],
- [0.6171, 0.4127, 0.8900, 0.4800, 0.4325, 0.5783, 0.5769, 0.5090],
- [0.6128, 0.4118, 0.8638, 0.5333, 0.4625, 0.5267, 0.5193, 0.5475],
- [0.6122, 0.4006, 0.8850, 0.4217, 0.4088, 0.5517, 0.6063, 0.5517],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6350, 0.4043, 0.8738, 0.5650, 0.3850, 0.4750, 0.6401, 0.4950]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6163, -0.4138, 1.7250, -0.3640, -0.5013, -0.7299, 0.5247, 0.1902],
- [ 0.5455, -0.4279, 1.6308, -0.4267, -0.2563, -0.1842, 0.3521, 0.2184],
- [ 0.4818, -0.4494, 1.5292, -0.1557, -0.1983, -0.3982, 0.2413, 0.2642],
- [ 0.6494, -0.3782, 1.5309, -0.6403, -0.3890, -0.0985, 0.4624, 0.2583],
- [ 0.2372, -0.6260, 1.3226, -0.8351, -0.6079, -0.6038, 0.2684, 0.2611],
- [-0.0703, -0.8515, 1.4496, -0.6823, -0.6232, -1.0105, 0.1126, 0.2541],
- [ 0.3390, -0.5573, 1.5586, -0.0887, -0.4338, -0.5858, 0.3697, 0.2282],
- [ 0.6466, -0.4126, 1.6265, -0.0297, -0.4755, -0.4721, 0.5771, 0.1254]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0964e-01, -4.0462e-01, 1.8249e+00, -7.2363e-03, -6.1155e-01,
- -3.5366e-01, 6.1824e-01, 9.2841e-02],
- [ 5.6449e-01, -3.7968e-01, 1.8249e+00, -6.8822e-02, -2.8822e-01,
- 3.8537e-01, 3.7891e-01, 6.5205e-02],
- [ 5.4475e-01, -3.8383e-01, 1.7037e+00, 1.7752e-01, -1.4965e-01,
- 1.4673e-01, 1.1283e-01, 2.4313e-01],
- [ 5.4169e-01, -4.3549e-01, 1.8018e+00, -3.3826e-01, -3.9792e-01,
- 2.6220e-01, 5.1432e-01, 2.6220e-01],
- [ 5.3672e-01, -4.2941e-01, 1.5709e+00, -4.9992e-01, -6.6928e-01,
- -3.0747e-01, 2.4546e-01, 3.5585e-01],
- [ 5.6969e-01, -4.1132e-01, 1.7499e+00, -2.7667e-01, -6.4042e-01,
- -7.1547e-01, 1.5756e-01, 4.0319e-01],
- [ 6.1149e-01, -3.7244e-01, 1.7557e+00, 3.4688e-01, -4.4411e-01,
- -1.0731e-01, 4.9122e-01, 2.3911e-01],
- [ 6.4706e-01, -4.1832e-01, 1.7499e+00, 3.2379e-01, -5.0762e-01,
- -9.1917e-02, 6.7064e-01, 4.6189e-04]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0605, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0605, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.6683297883719206
- step: 66
- running loss: 0.055580754369271526
- Train Steps: 66/90 Loss: 0.0556 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6126, 0.4067, 0.8638, 0.5383, 0.4188, 0.4850, 0.5016, 0.5392],
- [0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467],
- [0.6131, 0.4064, 0.8638, 0.5200, 0.4788, 0.4783, 0.5258, 0.5867],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6202, 0.4054, 0.8892, 0.4428, 0.3642, 0.3249, 0.5840, 0.5426],
- [0.6034, 0.4011, 0.7350, 0.2533, 0.3438, 0.3367, 0.5516, 0.5084],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6224, 0.4061, 0.8988, 0.4300, 0.3838, 0.4750, 0.6112, 0.5483]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3850, -0.5190, 1.6111, 0.1055, -0.3750, -0.2330, 0.2022, 0.1973],
- [ 0.3577, -0.5619, 1.4013, -1.0461, -0.6647, -0.5698, 0.4761, 0.2041],
- [ 0.6324, -0.3384, 1.5729, -0.1080, -0.1719, -0.2944, 0.2511, 0.2831],
- [ 0.1516, -0.6485, 1.5047, 0.1610, -0.4048, -0.4003, 0.2617, 0.3252],
- [ 0.3335, -0.5744, 1.6998, -0.5424, -0.5618, -0.9373, 0.3913, 0.1944],
- [ 0.6777, -0.3749, 1.0782, -1.4071, -0.6146, -1.0976, 0.3684, 0.1518],
- [ 0.3836, -0.5650, 1.5613, 0.1353, -0.2767, -0.2953, 0.2244, 0.2116],
- [ 0.7497, -0.3079, 1.7789, -0.4326, -0.5525, -0.0923, 0.4460, 0.1365]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5436, -0.4076, 1.7037, 0.2006, -0.3517, -0.0457, 0.0311,
- 0.2048],
- [ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391],
- [ 0.5462, -0.4090, 1.7037, 0.1159, -0.0746, -0.0765, 0.1427,
- 0.4239],
- [ 0.5836, -0.3649, 1.7210, 0.3854, -0.3979, -0.2921, 0.3065,
- 0.4470],
- [ 0.5788, -0.4135, 1.8214, -0.2408, -0.6039, -0.7854, 0.4115,
- 0.2203],
- [ 0.5009, -0.4333, 1.1090, -1.1158, -0.6982, -0.7309, 0.2617,
- 0.0622],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610],
- [ 0.5888, -0.4101, 1.8654, -0.2998, -0.5134, -0.0919, 0.5374,
- 0.2468]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0281, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0281, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.6964302863925695
- step: 67
- running loss: 0.05517060128944134
- Train Steps: 67/90 Loss: 0.0552 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6260, 0.4253, 0.8938, 0.4400, 0.3675, 0.3650, 0.5675, 0.5767],
- [ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617],
- [ nan, nan, 0.8938, 0.2850, 0.4662, 0.3117, 0.7406, 0.5528],
- [0.6197, 0.4050, 0.7527, 0.2000, 0.4042, 0.2249, 0.5895, 0.4995],
- [ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194],
- [0.6198, 0.4115, 0.7762, 0.2717, 0.3713, 0.3200, 0.5837, 0.5683],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0962, -0.0146, 1.2800, -0.4273, -0.4927, -0.5402, 0.3793, 0.2654],
- [ 1.1303, 0.0088, 1.7883, 0.1866, -0.6208, -0.2242, 0.3961, 0.1948],
- [-0.9555, -1.3945, 0.9320, -1.0741, -0.2958, -1.0923, 0.1046, 0.3117],
- [-0.2414, -0.9752, 1.8767, -0.6888, -0.1368, -0.7230, 0.9422, 0.2153],
- [ 1.2111, 0.0026, 1.0614, -0.9498, -0.4365, -1.0711, 0.2465, 0.1665],
- [-0.9628, -1.4400, 1.3093, -0.5955, -0.5826, -0.5954, -0.0528, 0.1944],
- [ 0.7971, -0.2340, 1.3919, -0.7441, -0.5827, -0.6364, 0.2316, 0.2503],
- [ 1.2873, 0.0838, 1.7751, 0.3542, -0.3632, 0.3964, 0.1676, 0.1421]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469],
- [ 0.6058, -0.3216, 1.8423, -0.2536, -0.5885, -0.6000, 0.3353,
- 0.3777],
- [-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084],
- [-2.2859, -2.2859, 1.8423, -0.9695, -0.1323, -0.8463, 1.1349,
- 0.2676],
- [ 0.5762, -0.4153, 1.1908, -1.3622, -0.4190, -1.2471, 0.4368,
- 0.0213],
- [-2.2859, -2.2859, 1.5478, -0.8309, -0.6289, -0.7232, 0.1198,
- 0.1133],
- [ 0.5768, -0.3852, 1.2995, -1.0311, -0.5711, -0.8079, 0.4104,
- 0.3392],
- [ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2376, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2376, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.9340319838374853
- step: 68
- running loss: 0.05785341152702184
- Train Steps: 68/90 Loss: 0.0579 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6273, 0.4100, 0.7137, 0.2133, 0.4000, 0.2650, 0.6075, 0.5633],
- [0.6126, 0.4067, 0.8638, 0.5383, 0.4188, 0.4850, 0.5016, 0.5392],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683],
- [0.6201, 0.4116, 0.8725, 0.4733, 0.3700, 0.3217, 0.5386, 0.5767],
- [ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6175, 0.3997, 0.6737, 0.2500, 0.4313, 0.1933, 0.5513, 0.5750],
- [0.6308, 0.3990, 0.8688, 0.5183, 0.3950, 0.4983, 0.6388, 0.5133]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9220, -0.1580, 1.0716, -1.1080, -0.4721, -0.9665, 0.4439, 0.2881],
- [ 0.9092, -0.1540, 1.7848, 0.5763, -0.4562, 0.3608, 0.1721, 0.1350],
- [ 1.0781, -0.0502, 1.5693, -0.8404, -0.4444, -0.5808, 0.4128, 0.2579],
- [ 0.7694, -0.1921, 1.6814, 0.1707, -0.6954, -0.4038, 0.1699, 0.1999],
- [-1.3740, -1.7090, 1.0674, -0.9417, -0.4480, -0.9951, 0.0507, 0.2347],
- [ 0.7294, -0.2306, 1.4294, -0.7451, -0.2152, -0.9191, 0.3202, 0.2228],
- [-0.9718, -1.4156, 0.8144, -1.1385, -0.3027, -1.2208, 0.0989, 0.3416],
- [ 1.0325, -0.1115, 1.8781, 0.3365, -0.4912, 0.4340, 0.5514, 0.0747]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6115, -0.3924, 1.0109, -1.3005, -0.4383, -1.0619, 0.5201,
- 0.3161],
- [ 0.5436, -0.4076, 1.7037, 0.2006, -0.3517, -0.0457, 0.0311,
- 0.2048],
- [ 0.5778, -0.3930, 1.5189, -0.9387, -0.4326, -0.9156, 0.4855,
- 0.3392],
- [ 0.5781, -0.3848, 1.7441, -0.0996, -0.5769, -0.8002, 0.2021,
- 0.3778],
- [-2.2859, -2.2859, 1.2469, -1.0288, -0.4557, -1.2774, 0.0511,
- 0.2183],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.5663, -0.4396, 0.8261, -1.1312, -0.2940, -1.3929, 0.2603,
- 0.3700],
- [ 0.6275, -0.4430, 1.7268, 0.1082, -0.4614, 0.0159, 0.6644,
- 0.0851]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1133, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1133, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.047340488061309
- step: 69
- running loss: 0.05865710852262766
- Train Steps: 69/90 Loss: 0.0587 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6085, 0.4008, 0.8588, 0.5200, 0.4959, 0.4977, 0.5175, 0.5283],
- [0.6162, 0.4014, 0.8800, 0.5333, 0.3750, 0.4817, 0.5988, 0.5283],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6183, 0.4076, 0.8838, 0.4517, 0.3813, 0.4483, 0.5775, 0.5633],
- [0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585],
- [0.6262, 0.4163, 0.8850, 0.5183, 0.3763, 0.4150, 0.6025, 0.5500],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5268, -0.4157, 1.5812, -0.0265, -0.1701, -0.2196, 0.0827, 0.2199],
- [ 0.4279, -0.4902, 1.4951, 0.0641, -0.5407, -0.0932, 0.2282, 0.2088],
- [ 0.0935, -0.7143, 1.5847, -0.2925, -0.2550, -0.1841, 0.1801, 0.1760],
- [ 0.5612, -0.4071, 1.5233, -0.3984, -0.5938, -0.1783, 0.2074, 0.2572],
- [ 0.4294, -0.4534, 1.4494, -0.5346, -0.3546, 0.1353, 0.2257, 0.2259],
- [ 0.4408, -0.4619, 1.5718, -0.1829, -0.6354, -0.6182, 0.2701, 0.2355],
- [ 0.2905, -0.6043, 1.6711, -1.0866, -0.3326, -1.1208, 0.8237, 0.2227],
- [ 0.6131, -0.3707, 1.5938, 0.0196, -0.6678, -0.7217, 0.3395, 0.1753]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5245, -0.4347, 1.6806, 0.1159, 0.0046, 0.0129, 0.1044,
- 0.1544],
- [ 0.5603, -0.4319, 1.7788, 0.1775, -0.5538, -0.0611, 0.4797,
- 0.1544],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.5700, -0.4032, 1.7961, -0.1997, -0.5249, -0.2151, 0.3815,
- 0.3161],
- [ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936],
- [ 0.6066, -0.3632, 1.8018, 0.1082, -0.5480, -0.3691, 0.4970,
- 0.2545],
- [ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0292, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0292, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.076580006629229
- step: 70
- running loss: 0.05823685723756041
- Train Steps: 70/90 Loss: 0.0582 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6147, 0.4107, 0.8137, 0.3333, 0.3750, 0.2683, 0.5006, 0.5412],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6086, 0.3998, 0.8788, 0.4450, 0.4025, 0.4650, 0.5306, 0.5103],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6162, 0.4014, 0.8800, 0.5333, 0.3750, 0.4817, 0.5988, 0.5283],
- [0.6126, 0.4073, 0.8750, 0.5133, 0.3800, 0.4333, 0.4986, 0.5378],
- [0.6336, 0.4086, 0.8900, 0.3950, 0.3900, 0.2950, 0.6504, 0.5066],
- [0.6127, 0.4066, 0.8550, 0.5567, 0.4662, 0.5141, 0.5070, 0.5412]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0387, -0.7622, 1.2751, -0.9462, -0.5724, -1.0365, 0.1144, 0.2447],
- [ 0.3928, -0.5237, 1.6429, -0.0807, -0.3716, -0.0862, 0.2124, 0.1810],
- [ 0.3224, -0.5755, 1.7140, -0.3758, -0.4884, -0.1473, 0.1929, 0.1669],
- [ 0.5737, -0.3796, 1.5390, -0.4548, -0.5041, -0.2859, 0.1710, 0.2812],
- [ 0.4217, -0.4986, 1.6079, 0.0801, -0.4330, -0.0176, 0.3749, 0.2277],
- [ 0.2513, -0.5825, 1.6658, 0.0256, -0.5307, -0.3036, 0.2110, 0.2185],
- [ 0.7274, -0.3245, 1.5881, -0.7377, -0.4621, -1.0521, 0.7483, 0.1522],
- [ 0.5020, -0.4279, 1.5960, 0.1977, -0.1573, -0.0532, 0.2000, 0.2492]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5532, -0.3888, 1.4727, -0.7463, -0.5538, -1.0465, 0.0265,
- 0.2138],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854],
- [ 0.5253, -0.4392, 1.7730, -0.2305, -0.4268, -0.1381, 0.1651,
- 0.0712],
- [ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145],
- [ 0.5603, -0.4319, 1.7788, 0.1775, -0.5538, -0.0611, 0.4797,
- 0.1544],
- [ 0.5435, -0.4045, 1.7557, 0.0851, -0.5307, -0.2844, 0.0172,
- 0.1980],
- [ 0.6405, -0.3984, 1.8249, -0.4614, -0.4845, -0.9233, 0.7182,
- 0.0539],
- [ 0.5443, -0.4079, 1.6633, 0.2853, -0.1325, 0.0888, 0.0558,
- 0.2138]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0193, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0193, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.095911048352718
- step: 71
- running loss: 0.05768888800496787
- Train Steps: 71/90 Loss: 0.0577 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6277, 0.4013, 0.8888, 0.4767, 0.3600, 0.3567, 0.6148, 0.4672],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6151, 0.4058, 0.7068, 0.2680, 0.3400, 0.4083, 0.5775, 0.5733],
- [0.6222, 0.4169, 0.8638, 0.5650, 0.4313, 0.4783, 0.5637, 0.5633],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5969, -0.4272, 1.8214, -0.2437, -0.6127, -0.7385, 0.4143, 0.1130],
- [ 0.2856, -0.6078, 1.3131, -1.0711, -0.6239, -0.5276, 0.3054, 0.2557],
- [ 0.5606, -0.4006, 1.7808, 0.4420, -0.3751, -0.0319, 0.2513, 0.2444],
- [ 0.3458, -0.5758, 1.1444, -1.0937, -0.6510, -0.5539, 0.1815, 0.3004],
- [ 0.5171, -0.4272, 1.7789, 0.4450, -0.2649, 0.0206, 0.1874, 0.2545],
- [ 0.3801, -0.5404, 1.8297, -0.2141, -0.4082, 0.2344, 0.2679, 0.1173],
- [ 0.0454, -0.7213, 1.2479, -0.9528, -0.0633, -1.2849, 0.2315, 0.3592],
- [ 0.6147, -0.4189, 1.8836, 0.2746, -0.5885, -0.2440, 0.4363, 0.1093]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6132, -0.4324, 1.8192, -0.0842, -0.6231, -0.6385, 0.5537,
- -0.1278],
- [ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007],
- [ 0.5552, -0.4113, 0.9790, -1.0480, -0.7155, -0.3998, 0.3815,
- 0.3623],
- [ 0.5880, -0.3605, 1.7037, 0.3238, -0.2940, -0.0765, 0.3180,
- 0.3161],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.11814627237618
- step: 72
- running loss: 0.05719647600522472
- Train Steps: 72/90 Loss: 0.0572 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6280, 0.4101, 0.9050, 0.4533, 0.3775, 0.3217, 0.6338, 0.4915],
- [0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097],
- [0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450],
- [ nan, nan, 0.6488, 0.1817, 0.4325, 0.1867, 0.5475, 0.5733],
- [0.6150, 0.4097, 0.7468, 0.3194, 0.3825, 0.2633, 0.5463, 0.5800],
- [0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272],
- [0.6124, 0.4069, 0.8314, 0.5001, 0.3738, 0.4650, 0.5167, 0.5402],
- [0.6346, 0.4165, 0.9138, 0.3983, 0.3875, 0.4317, 0.7469, 0.5471]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6401, -0.3275, 1.9039, 0.0135, -0.5674, -0.7047, 0.5180, 0.0962],
- [ 0.5311, -0.3679, 1.7335, 0.1201, -0.2505, 0.2096, 0.0463, 0.1490],
- [ 0.7563, -0.2695, 1.7467, -0.0610, -0.3387, 0.3851, 0.3857, 0.1715],
- [-1.4279, -1.7449, 0.9868, -1.1764, -0.1548, -1.2947, 0.2669, 0.3564],
- [ 0.2403, -0.5642, 1.0672, -0.7588, -0.5522, -0.9347, 0.0379, 0.3111],
- [ 0.7850, -0.2328, 1.5708, -0.5475, -0.5102, -0.8937, 0.2295, 0.2070],
- [ 0.9283, -0.1462, 1.5625, 0.1236, -0.4814, -0.0100, 0.1466, 0.2467],
- [ 0.2391, -0.6283, 1.9438, -0.2441, -0.3081, -0.1375, 0.7502, 0.2023]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6148, -0.3918, 1.8942, -0.1920, -0.5423, -0.8002, 0.6414,
- -0.0156],
- [ 0.5515, -0.4225, 1.7788, -0.0534, -0.2016, 0.1929, 0.1568,
- 0.0682],
- [ 0.5960, -0.4102, 1.8018, -0.1612, -0.3344, 0.1159, 0.5490,
- 0.2314],
- [-2.2859, -2.2859, 0.7106, -1.4468, -0.2882, -1.4237, 0.2430,
- 0.3623],
- [ 0.5548, -0.3936, 1.1634, -0.8105, -0.5192, -1.0696, 0.2372,
- 0.3931],
- [ 0.5711, -0.4015, 1.6979, -0.6770, -0.5365, -1.0619, 0.1712,
- 0.1494],
- [ 0.5425, -0.4067, 1.5543, 0.0241, -0.5596, -0.1381, 0.1005,
- 0.2093],
- [ 0.6454, -0.3623, 1.9346, -0.4460, -0.4961, -0.2921, 1.1642,
- 0.2409]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0410, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0410, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.15917850472033
- step: 73
- running loss: 0.05697504800986754
- Train Steps: 73/90 Loss: 0.0570 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6218, 0.4137, 0.7263, 0.2233, 0.4075, 0.2650, 0.6212, 0.5783],
- [0.6200, 0.4039, 0.8880, 0.4799, 0.3625, 0.4285, 0.5866, 0.5148],
- [0.6300, 0.4102, 0.9088, 0.4433, 0.4088, 0.3067, 0.6820, 0.5540],
- [0.6264, 0.3972, 0.8853, 0.4771, 0.3853, 0.4511, 0.6293, 0.5334],
- [0.6153, 0.4117, 0.8688, 0.5167, 0.4895, 0.5647, 0.5524, 0.5136]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0255, -0.7414, 1.0145, -1.0360, -0.3244, -1.0102, 0.2996, 0.3519],
- [ 0.1935, -0.6416, 0.8982, -0.9045, -0.4853, -0.9969, 0.0657, 0.2896],
- [ 0.5508, -0.4163, 1.8753, -0.2968, -0.3653, -0.9057, 0.3999, 0.1318],
- [ 0.9391, -0.2031, 1.1864, -0.7964, -0.4275, -0.8836, 0.3701, 0.3034],
- [ 0.3174, -0.5763, 1.8704, 0.0961, -0.6383, -0.0635, 0.1696, 0.1344],
- [ 0.0848, -0.7070, 2.0174, -0.0102, -0.3566, -0.5104, 0.5992, 0.1564],
- [ 0.4316, -0.5112, 1.8690, 0.0852, -0.5426, 0.1211, 0.4263, 0.1277],
- [ 0.3346, -0.5371, 1.7998, 0.2516, -0.0975, 0.4123, 0.1748, 0.1796]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.5863, -0.3749, 1.0686, -1.2543, -0.4037, -1.0619, 0.5836,
- 0.3854],
- [ 0.5781, -0.4205, 1.8159, -0.0691, -0.6116, -0.3069, 0.4236,
- 0.0919],
- [ 0.6240, -0.3912, 1.9115, -0.2382, -0.3979, -0.8694, 0.8644,
- 0.2730],
- [ 0.6075, -0.4514, 1.8032, -0.0822, -0.5061, -0.2023, 0.6208,
- 0.1779],
- [ 0.5564, -0.3842, 1.7268, 0.1005, -0.0250, 0.3225, 0.2658,
- 0.0862]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0431, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0431, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.202301474288106
- step: 74
- running loss: 0.05678785776065008
- Train Steps: 74/90 Loss: 0.0568 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6282, 0.4034, 0.7830, 0.2080, 0.4532, 0.2080, 0.6404, 0.5323],
- [0.6339, 0.4149, 0.8800, 0.5000, 0.3900, 0.5283, 0.7541, 0.5424],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6129, 0.4069, 0.8750, 0.5067, 0.3875, 0.4233, 0.5235, 0.5881],
- [0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7725, -0.2617, 1.3868, -1.0722, -0.2079, -1.3805, 0.5797, 0.2321],
- [ 0.7794, -0.2949, 1.9241, 0.0387, -0.4490, 0.2553, 0.8479, 0.1340],
- [ 0.8670, -0.1966, 1.7308, 0.3913, -0.4143, -0.0618, 0.3734, 0.2194],
- [ 0.5301, -0.4200, 1.6865, -0.3245, -0.6355, -0.6120, 0.1663, 0.2268],
- [ 0.5905, -0.3883, 1.7943, -0.0852, -0.4299, 0.0460, 0.2930, 0.1580],
- [-1.6482, -1.9028, 1.1660, -1.1783, -0.2913, -1.2421, 0.1438, 0.2935],
- [ 0.5850, -0.3715, 1.7286, 0.1431, -0.5558, -0.2252, 0.1153, 0.2125],
- [ 0.5957, -0.3554, 1.7312, -0.0205, -0.2870, -0.0232, 0.1938, 0.1764]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.1577e-01, -4.2249e-01, 1.3307e+00, -1.3253e+00, -1.9244e-01,
- -1.3252e+00, 6.7213e-01, 1.7271e-01],
- [ 6.4212e-01, -3.6953e-01, 1.7788e+00, 2.3557e-02, -4.8453e-01,
- 1.5443e-01, 1.1971e+00, 2.1955e-01],
- [ 5.8909e-01, -3.5574e-01, 1.7326e+00, 3.3918e-01, -4.2102e-01,
- -1.2271e-01, 3.2379e-01, 3.0069e-01],
- [ 5.6969e-01, -4.1132e-01, 1.7499e+00, -2.7667e-01, -6.4042e-01,
- -7.1547e-01, 1.5756e-01, 4.0319e-01],
- [ 5.2500e-01, -4.6613e-01, 1.7383e+00, -7.6520e-02, -4.2679e-01,
- -2.2633e-02, 2.5348e-01, 2.0347e-01],
- [-2.2859e+00, -2.2859e+00, 9.0115e-01, -1.4006e+00, -4.6721e-01,
- -1.1928e+00, 1.3421e-01, 1.3734e-01],
- [ 5.4515e-01, -4.0670e-01, 1.7557e+00, 5.4350e-02, -4.9607e-01,
- -3.3056e-01, 1.3228e-01, 4.3063e-01],
- [ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0209, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0209, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.223247792571783
- step: 75
- running loss: 0.056309970567623775
- Train Steps: 75/90 Loss: 0.0563 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6199, 0.4065, 0.7598, 0.2385, 0.4317, 0.1981, 0.5933, 0.5221],
- [0.6274, 0.4117, 0.8100, 0.5801, 0.4000, 0.4583, 0.5582, 0.6118],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167],
- [0.6277, 0.4118, 0.8988, 0.3833, 0.3950, 0.2650, 0.6290, 0.4938],
- [ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552],
- [0.6276, 0.4120, 0.8738, 0.3133, 0.4225, 0.2217, 0.6203, 0.4892],
- [0.6224, 0.4097, 0.7438, 0.2267, 0.3850, 0.2850, 0.5988, 0.5250],
- [0.6216, 0.4100, 0.7350, 0.2067, 0.4325, 0.2050, 0.5950, 0.5333]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7325, -0.3244, 1.1943, -0.8618, -0.3379, -0.9226, 0.3813, 0.1958],
- [ 0.6158, -0.3913, 1.6816, 0.5930, -0.4862, 0.2030, 0.3024, 0.2457],
- [ 0.6307, -0.3547, 1.6683, -0.4211, -0.6285, -0.4675, 0.1638, 0.1631],
- [ 0.6086, -0.3802, 1.9739, -0.0922, -0.4960, -0.5134, 0.4618, 0.1108],
- [-1.6262, -1.8678, 1.7056, -0.6897, 0.0506, -0.6270, 0.7561, 0.2796],
- [ 0.5644, -0.3997, 1.8604, -0.3902, -0.3610, -0.6911, 0.5889, 0.1362],
- [ 0.6110, -0.4007, 1.1670, -0.8371, -0.5049, -0.6734, 0.3403, 0.2276],
- [ 0.6246, -0.3696, 1.0815, -0.8941, -0.3092, -1.0043, 0.2052, 0.3066]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5774, -0.4082, 1.2235, -1.1844, -0.2919, -1.3709, 0.4544,
- 0.1256],
- [ 0.6121, -0.3844, 1.4556, 0.3936, -0.4383, -0.1689, 0.2925,
- 0.5401],
- [ 0.5483, -0.4249, 1.5305, -0.7386, -0.6115, -0.8694, 0.3353,
- 0.1005],
- [ 0.6135, -0.3841, 1.8654, -0.5153, -0.4614, -1.0619, 0.6195,
- -0.0049],
- [-2.2859, -2.2859, 1.7557, -1.1466, 0.0871, -1.0773, 1.1239,
- 0.2783],
- [ 0.6128, -0.3828, 1.7499, -0.8386, -0.3344, -1.2620, 0.5792,
- -0.0263],
- [ 0.5891, -0.3937, 1.1494, -1.2390, -0.5076, -0.9695, 0.4797,
- 0.1390],
- [ 0.5853, -0.3920, 1.1090, -1.3313, -0.2882, -1.3390, 0.4624,
- 0.1775]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0613, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0613, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.284526634961367
- step: 76
- running loss: 0.05637535046001798
- Train Steps: 76/90 Loss: 0.0564 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6248, 0.4185, 0.8500, 0.5767, 0.4463, 0.4550, 0.5613, 0.5917],
- [0.6147, 0.4026, 0.6600, 0.2467, 0.4088, 0.2150, 0.5489, 0.5773],
- [0.6225, 0.4116, 0.8662, 0.3517, 0.3663, 0.3233, 0.5837, 0.5317],
- [0.6117, 0.4019, 0.8538, 0.4067, 0.3513, 0.3583, 0.5663, 0.5133],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000],
- [0.6304, 0.4024, 0.8925, 0.4800, 0.3937, 0.4817, 0.7485, 0.5297],
- [0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833],
- [0.6179, 0.3993, 0.8925, 0.4789, 0.3879, 0.4900, 0.6041, 0.5279]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5418, -0.4318, 1.7813, 0.4587, -0.0794, -0.2210, 0.4923, 0.2903],
- [ 0.6229, -0.4087, 0.9010, -1.1260, -0.3065, -1.4490, 0.2739, 0.3452],
- [ 0.7011, -0.3479, 1.7164, -0.6893, -0.4533, -0.8815, 0.4622, 0.2529],
- [ 0.3350, -0.5665, 1.7980, -0.4537, -0.5948, -0.7768, 0.2783, 0.1598],
- [ 0.2740, -0.6352, 1.6905, -0.5705, -0.6383, -0.4217, 0.5188, 0.1312],
- [ 0.2169, -0.6774, 1.7397, -0.1561, -0.3435, -0.0342, 0.6219, 0.1825],
- [ 0.1279, -0.7503, 1.7811, -0.0669, -0.4797, -0.1236, 0.4468, 0.1145],
- [ 0.4340, -0.5311, 1.8522, -0.1038, -0.4461, -0.0288, 0.4235, 0.1248]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5999, -0.3530, 1.6402, 0.3777, -0.2247, -0.1843, 0.3065,
- 0.4470],
- [ 0.5532, -0.4264, 0.7626, -1.1466, -0.3979, -1.2928, 0.2494,
- 0.3808],
- [ 0.5893, -0.3847, 1.7152, -0.6616, -0.5942, -0.7925, 0.4104,
- 0.1698],
- [ 0.5393, -0.4294, 1.6575, -0.4075, -0.6635, -0.6308, 0.3296,
- 0.0851],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236],
- [ 0.6257, -0.4273, 1.8365, -0.0688, -0.4672, -0.0611, 1.1715,
- 0.1608],
- [ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534],
- [ 0.5680, -0.4417, 1.8365, -0.0740, -0.4941, -0.0227, 0.5045,
- 0.1525]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0220, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0220, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.306571505963802
- step: 77
- running loss: 0.055929500077451975
- Train Steps: 77/90 Loss: 0.0559 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4027, 0.8029, 0.2728, 0.4042, 0.2310, 0.5980, 0.5391],
- [0.6257, 0.4167, 0.8775, 0.3433, 0.3563, 0.4133, 0.6200, 0.5667],
- [0.6240, 0.4217, 0.8150, 0.3133, 0.4425, 0.2650, 0.5650, 0.5817],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6222, 0.4072, 0.7164, 0.2166, 0.3738, 0.3167, 0.6100, 0.5533],
- [0.6250, 0.4146, 0.8838, 0.3933, 0.3588, 0.4283, 0.6162, 0.5367],
- [0.6197, 0.3986, 0.8800, 0.4617, 0.4188, 0.4783, 0.5687, 0.5550],
- [0.6188, 0.4099, 0.7400, 0.2433, 0.3962, 0.2750, 0.6162, 0.5467]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2123, -0.6681, 1.4267, -0.7008, -0.3961, -1.0789, 0.4470, 0.1575],
- [ 0.6287, -0.4280, 1.9480, -0.2461, -0.6259, -0.2741, 0.5778, 0.1985],
- [ 0.2384, -0.6014, 1.5249, -0.6004, -0.1790, -0.7868, 0.4314, 0.3348],
- [ 0.2662, -0.6620, 1.6252, -0.7981, -0.0459, -1.2227, 0.8659, 0.1341],
- [ 0.3869, -0.5725, 1.1212, -0.8591, -0.5330, -0.8021, 0.4060, 0.2322],
- [ 0.6979, -0.3889, 1.9113, -0.0834, -0.5975, -0.2396, 0.4525, 0.2011],
- [ 0.1241, -0.7364, 1.8760, 0.2413, -0.3607, 0.2421, 0.2625, 0.1373],
- [ 0.4837, -0.5007, 1.1885, -0.8307, -0.4771, -0.9443, 0.4297, 0.1917]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5785, -0.4259, 1.4228, -1.0261, -0.4190, -1.2189, 0.4763,
- 0.2043],
- [ 0.6040, -0.3614, 1.7672, -0.7001, -0.6404, -0.3768, 0.5778,
- 0.3315],
- [ 0.5964, -0.3380, 1.4785, -0.8386, -0.2420, -1.0619, 0.3238,
- 0.4008],
- [ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.5878, -0.4052, 1.0229, -1.2855, -0.5596, -0.8232, 0.5316,
- 0.2699],
- [ 0.6009, -0.3710, 1.7961, -0.4691, -0.6289, -0.3075, 0.5605,
- 0.1929],
- [ 0.5763, -0.4450, 1.7788, -0.1535, -0.3517, -0.0765, 0.3411,
- 0.2776],
- [ 0.5725, -0.3926, 1.1321, -1.1620, -0.4557, -1.0157, 0.5605,
- 0.2391]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0397, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0397, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.346283700317144
- step: 78
- running loss: 0.05572158590150185
- Train Steps: 78/90 Loss: 0.0557 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585],
- [0.6135, 0.4115, 0.8838, 0.4667, 0.4288, 0.6050, 0.5778, 0.5097],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6165, 0.4106, 0.7575, 0.1733, 0.3838, 0.2650, 0.5680, 0.5116],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2503, -0.6194, 1.6771, -0.5663, -0.2092, -0.0134, 0.4559, 0.2668],
- [ 0.4025, -0.5215, 1.7636, -0.3499, -0.2321, 0.0510, 0.4189, 0.2570],
- [ 0.3624, -0.5574, 1.6009, -0.4521, -0.5776, -0.7097, 0.4002, 0.2726],
- [ 0.4997, -0.5109, 1.9008, -0.2579, -0.4983, -0.6274, 0.6899, 0.2027],
- [ 0.3547, -0.6100, 1.7118, -0.0586, -0.3042, -0.1724, 0.6228, 0.1929],
- [ 0.8087, -0.3421, 1.1945, -1.4183, -0.5040, -1.5953, 0.5418, 0.1857],
- [ 0.5330, -0.4932, 1.7886, 0.0924, -0.4707, -0.4936, 0.5452, 0.1295],
- [ 0.4743, -0.5207, 1.8230, 0.0724, -0.5959, -0.6447, 0.4366, 0.1072]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936],
- [ 0.5477, -0.3851, 1.7961, -0.1304, -0.3055, 0.5085, 0.3830,
- 0.0682],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499],
- [ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.5618, -0.3895, 1.2129, -1.4853, -0.5134, -1.0619, 0.3378,
- 0.0772],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.6335, -0.4162, 1.7499, 0.3084, -0.4961, -0.2459, 0.6524,
- -0.0102]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0373, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0373, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.3835613541305065
- step: 79
- running loss: 0.055488118406715276
- Train Steps: 79/90 Loss: 0.0555 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6332, 0.4128, 0.9200, 0.3517, 0.4400, 0.3833, 0.7461, 0.5494],
- [0.6136, 0.4060, 0.8025, 0.3217, 0.3650, 0.3000, 0.5060, 0.5646],
- [0.6233, 0.4091, 0.8100, 0.2950, 0.3563, 0.3883, 0.6013, 0.5200],
- [0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [0.6274, 0.4087, 0.8375, 0.5700, 0.4025, 0.4800, 0.5700, 0.6117],
- [0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300],
- [0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2519, -0.6683, 1.8736, -0.7073, -0.3280, -0.7509, 1.0281, 0.1443],
- [ 0.2991, -0.6456, 1.4643, -0.8791, -0.6811, -1.1794, 0.2277, 0.1704],
- [ 0.4103, -0.5468, 1.4818, -1.0196, -0.6254, -0.7361, 0.6622, 0.1772],
- [ 0.6202, -0.4099, 1.7969, 0.1244, -0.5959, -0.7569, 0.3795, 0.1859],
- [ 0.4177, -0.5366, 1.7767, 0.0455, -0.1893, -0.0683, 0.3880, 0.1860],
- [ 0.6376, -0.4223, 1.7051, 0.2728, -0.3978, -0.2228, 0.6107, 0.2835],
- [ 0.7116, -0.3657, 1.2793, -1.2706, -0.4624, -1.1501, 0.7176, 0.1937],
- [ 0.3611, -0.5519, 1.7906, -0.4244, -0.2106, 0.0961, 0.5442, 0.1985]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6388, -0.3792, 1.9635, -0.6616, -0.2536, -0.5153, 1.1605,
- 0.2516],
- [ 0.5483, -0.4105, 1.4208, -0.8002, -0.6000, -0.9002, 0.0511,
- 0.3220],
- [ 0.5932, -0.3962, 1.4554, -0.9233, -0.6404, -0.4922, 0.4912,
- 0.1159],
- [ 0.5777, -0.3915, 1.7961, 0.1698, -0.5192, -0.5307, 0.2141,
- 0.3392],
- [ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386],
- [ 0.6118, -0.3983, 1.5824, 0.3469, -0.4268, -0.0688, 0.3469,
- 0.5393],
- [ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621],
- [ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0244, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0244, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.407923875376582
- step: 80
- running loss: 0.055099048442207275
- Train Steps: 80/90 Loss: 0.0551 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6261, 0.3987, 0.8688, 0.4917, 0.4300, 0.5333, 0.7010, 0.5309],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6127, 0.4119, 0.8650, 0.5300, 0.4512, 0.5250, 0.5260, 0.5461],
- [0.6100, 0.4071, 0.7601, 0.3444, 0.3400, 0.4117, 0.5625, 0.5617],
- [0.6179, 0.4082, 0.6688, 0.2667, 0.3588, 0.3317, 0.5750, 0.5783],
- [0.6205, 0.4062, 0.8337, 0.2683, 0.3675, 0.4283, 0.6338, 0.5250],
- [0.6185, 0.4129, 0.8900, 0.4567, 0.3937, 0.5417, 0.5734, 0.5110]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 3.5290e-01, -6.1397e-01, 1.9549e+00, -5.3336e-03, -2.9136e-01,
- -7.2324e-02, 9.3207e-01, 1.6320e-01],
- [ 2.8469e-01, -6.2809e-01, 1.9445e+00, -2.0434e-04, -1.3079e-01,
- -2.2070e-02, 6.3742e-01, 2.1544e-01],
- [ 3.5060e-01, -5.5138e-01, 1.1136e+00, -1.1509e+00, -2.4896e-01,
- -1.5926e+00, 4.1498e-01, 2.8779e-01],
- [ 6.6889e-01, -3.9197e-01, 1.8542e+00, 2.6315e-01, -2.2090e-01,
- -2.3001e-01, 3.9280e-01, 1.7063e-01],
- [ 4.6131e-01, -4.7106e-01, 1.4068e+00, -8.2631e-01, -7.4783e-01,
- -6.5008e-01, 4.4228e-01, 2.2298e-01],
- [ 8.3234e-01, -2.7670e-01, 1.0542e+00, -1.0518e+00, -7.1007e-01,
- -1.0961e+00, 3.9934e-01, 2.9696e-01],
- [ 2.8947e-01, -6.0541e-01, 1.5527e+00, -9.9893e-01, -6.6533e-01,
- -6.2536e-01, 7.8111e-01, 1.6534e-01],
- [ 6.4313e-01, -4.2306e-01, 2.0376e+00, -1.2549e-01, -4.8717e-01,
- -1.5416e-01, 5.7382e-01, 1.1506e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6059, -0.4442, 1.7268, -0.0149, -0.2998, 0.1775, 0.9521,
- 0.1661],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.5862, -0.3530, 1.1032, -1.0619, -0.1497, -1.3852, 0.3411,
- 0.3931],
- [ 0.5443, -0.3831, 1.7095, 0.1621, -0.2016, 0.1390, 0.1437,
- 0.2364],
- [ 0.5318, -0.4056, 1.2249, -0.6949, -0.7155, -0.3844, 0.3122,
- 0.3084],
- [ 0.5680, -0.4005, 0.8030, -1.0542, -0.6289, -0.7540, 0.3700,
- 0.3854],
- [ 0.5799, -0.4099, 1.5651, -1.0465, -0.5885, -0.3075, 0.6413,
- 0.1390],
- [ 0.5711, -0.3788, 1.8249, -0.1766, -0.4672, 0.2160, 0.3625,
- 0.0742]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0296, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0296, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.4374978970736265
- step: 81
- running loss: 0.05478392465522996
- Train Steps: 81/90 Loss: 0.0548 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.9088, 0.3783, 0.4562, 0.2617, 0.6741, 0.5575],
- [0.6275, 0.4071, 0.8075, 0.2100, 0.4700, 0.1533, 0.6148, 0.4834],
- [0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901],
- [0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6152, 0.4131, 0.6863, 0.2567, 0.3625, 0.3300, 0.5765, 0.5305],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064],
- [0.6075, 0.4000, 0.8513, 0.5183, 0.4510, 0.5329, 0.5180, 0.5280],
- [0.6117, 0.4018, 0.6562, 0.1967, 0.3738, 0.2550, 0.5280, 0.5103]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1934, -0.6862, 2.0995, -0.3841, -0.2928, -0.8127, 1.0653, 0.1940],
- [ 0.3868, -0.5774, 1.4899, -1.2743, -0.2030, -1.3073, 0.9704, 0.1167],
- [ 0.1129, -0.7319, 1.4276, -0.8536, -0.6659, -0.9510, 0.3433, 0.1963],
- [ 0.9127, -0.1787, 1.4410, -0.5508, -0.5565, -0.6560, 0.6148, 0.2726],
- [ 0.6915, -0.3389, 1.0534, -1.0535, -0.6616, -0.7224, 0.4801, 0.2981],
- [ 0.5534, -0.4577, 1.8698, 0.1757, -0.1685, 0.1346, 0.3258, 0.1852],
- [ 0.5698, -0.4565, 1.8043, 0.2670, -0.2506, 0.2621, 0.3408, 0.1853],
- [ 0.3866, -0.5505, 0.9395, -1.1604, -0.5641, -1.0599, 0.3823, 0.2258]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.9115, -0.5384, -0.1785, -1.0773, 0.8278,
- 0.2890],
- [ 0.6126, -0.4057, 1.4439, -1.3159, -0.1150, -1.5777, 0.5537,
- -0.0530],
- [ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220],
- [ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469],
- [ 0.5557, -0.3779, 0.8838, -1.1004, -0.6115, -0.7617, 0.3769,
- 0.1644],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532],
- [ 0.5202, -0.4382, 1.6460, 0.1082, -0.2029, 0.1754, 0.1067,
- 0.1530],
- [ 0.5393, -0.4299, 0.7453, -1.3775, -0.5596, -1.1081, 0.1527,
- 0.0712]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1651, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1651, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.602609543129802
- step: 82
- running loss: 0.05612938467231465
- Train Steps: 82/90 Loss: 0.0561 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6219, 0.4114, 0.8175, 0.2817, 0.3925, 0.2783, 0.5900, 0.5350],
- [0.6153, 0.4117, 0.8688, 0.5167, 0.4895, 0.5647, 0.5524, 0.5136],
- [ nan, nan, 0.8463, 0.2550, 0.5850, 0.2133, 0.7129, 0.6072],
- [0.6182, 0.3972, 0.8552, 0.5914, 0.3683, 0.4181, 0.5688, 0.5378],
- [0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117],
- [0.6286, 0.4078, 0.8063, 0.2267, 0.4788, 0.1533, 0.5953, 0.4913],
- [0.6157, 0.3956, 0.8323, 0.4138, 0.3479, 0.4431, 0.5914, 0.5131],
- [ nan, nan, 0.9050, 0.3500, 0.5138, 0.2300, 0.7359, 0.5702]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6205, -0.3294, 1.2714, -1.1643, -0.4718, -0.8882, 0.4508, 0.2994],
- [ 0.8339, -0.2433, 1.6666, -0.0028, -0.2328, 0.1562, 0.3314, 0.2164],
- [-0.2367, -0.9609, 1.5820, -1.1476, -0.0225, -0.9568, 1.1855, 0.2878],
- [ 1.3021, 0.0271, 1.6371, 0.3666, -0.7279, -0.3856, 0.4049, 0.1173],
- [ 0.8664, -0.2550, 1.7417, -0.1421, -0.6307, -0.0862, 0.6780, 0.1225],
- [ 0.2749, -0.5678, 1.1647, -1.2503, -0.3124, -1.2384, 0.3588, 0.2341],
- [ 1.0003, -0.1206, 1.5467, -0.5714, -0.7366, -0.1394, 0.3882, 0.1260],
- [-0.8779, -1.3588, 1.7350, -0.7833, -0.1671, -0.9548, 0.8598, 0.2712]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5868, -0.3858, 1.4901, -0.9849, -0.4730, -1.0003, 0.4393,
- 0.1852],
- [ 0.5564, -0.3842, 1.7268, 0.1005, -0.0250, 0.3225, 0.2658,
- 0.0862],
- [-2.2859, -2.2859, 1.6229, -1.1081, 0.4162, -1.3005, 1.0070,
- 0.5188],
- [ 0.5697, -0.4514, 1.6642, 0.4459, -0.5850, -0.3546, 0.3414,
- 0.1982],
- [ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774],
- [ 0.6177, -0.4022, 1.4381, -1.2390, -0.0746, -1.5777, 0.4639,
- -0.0168],
- [ 0.5581, -0.4586, 1.5586, -0.3747, -0.6792, -0.2391, 0.4455,
- 0.0840],
- [-2.2859, -2.2859, 1.8942, -0.6693, 0.0871, -1.2236, 1.1130,
- 0.3478]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1799, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1799, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.782462088391185
- step: 83
- running loss: 0.05762002516133958
- Train Steps: 83/90 Loss: 0.0576 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7225, 0.2167, 0.3987, 0.2283, 0.5427, 0.5181],
- [0.6203, 0.4072, 0.8892, 0.3523, 0.3783, 0.3017, 0.5898, 0.5478],
- [0.6168, 0.4029, 0.8523, 0.3417, 0.3588, 0.5000, 0.6125, 0.5400],
- [0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467],
- [0.6261, 0.3987, 0.8688, 0.4917, 0.4300, 0.5333, 0.7010, 0.5309],
- [0.6275, 0.4081, 0.8063, 0.2017, 0.4825, 0.1583, 0.6156, 0.4869],
- [0.6246, 0.4008, 0.8757, 0.5088, 0.4101, 0.5392, 0.6644, 0.5133],
- [0.6223, 0.4130, 0.8100, 0.2983, 0.3525, 0.3900, 0.5694, 0.5103]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.6758, -1.8790, 0.9515, -1.1832, -0.3921, -1.2005, 0.1190, 0.2377],
- [ 0.6928, -0.3176, 1.7793, -0.5673, -0.5725, -0.8589, 0.5203, 0.2073],
- [ 0.6950, -0.3005, 1.5411, -0.6932, -0.5098, -0.0067, 0.7635, 0.2678],
- [ 0.8057, -0.2322, 1.5274, -0.7015, -0.6557, -0.4211, 0.5909, 0.1877],
- [ 0.8430, -0.2581, 1.7463, 0.0786, -0.2258, 0.1675, 0.8384, 0.2404],
- [ 0.8383, -0.2479, 1.3780, -1.2788, -0.1072, -1.4723, 0.6900, 0.1429],
- [ 0.7669, -0.3051, 1.6815, 0.1584, -0.2706, 0.1765, 0.6218, 0.2041],
- [ 0.6534, -0.3386, 1.4837, -0.7632, -0.6079, -0.6089, 0.3371, 0.2506]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.0513, -1.2851, -0.4441, -1.2313, 0.2206,
- 0.1073],
- [ 0.5792, -0.4052, 1.8214, -0.6587, -0.5384, -0.8924, 0.4381,
- 0.2442],
- [ 0.5631, -0.4249, 1.6509, -0.7078, -0.6289, 0.0236, 0.5432,
- 0.2083],
- [ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391],
- [ 0.6059, -0.4442, 1.7268, -0.0149, -0.2998, 0.1775, 0.9521,
- 0.1661],
- [ 0.6124, -0.4010, 1.4381, -1.3544, -0.0573, -1.5546, 0.5573,
- -0.0369],
- [ 0.5993, -0.4345, 1.7587, 0.0641, -0.3917, 0.2048, 0.7827,
- 0.0852],
- [ 0.5886, -0.3784, 1.4554, -0.9079, -0.6577, -0.4845, 0.3440,
- 0.0712]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0214, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0214, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.803826930001378
- step: 84
- running loss: 0.057188415833349736
- Train Steps: 84/90 Loss: 0.0572 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892],
- [0.6270, 0.4267, 0.7150, 0.3317, 0.4900, 0.1817, 0.5560, 0.6183],
- [0.6160, 0.4093, 0.8314, 0.4417, 0.3675, 0.4583, 0.5250, 0.5808],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6128, 0.4115, 0.7934, 0.3778, 0.3450, 0.4033, 0.5337, 0.5456],
- [0.6240, 0.4217, 0.8150, 0.3133, 0.4425, 0.2650, 0.5650, 0.5817],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [0.6203, 0.4076, 0.8611, 0.2878, 0.4050, 0.2554, 0.5907, 0.5496]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5288, -0.4258, 1.6598, 0.0234, -0.6744, -0.2807, 0.2428, 0.2266],
- [ 0.9124, -0.1514, 0.9119, -1.0870, -0.1578, -1.0845, 0.3587, 0.3047],
- [ 0.5259, -0.4405, 1.5958, -0.1026, -0.4962, 0.0710, 0.4900, 0.2501],
- [ 0.4467, -0.4996, 1.2111, -1.1375, -0.1979, -1.1565, 0.5828, 0.1334],
- [ 0.8034, -0.2387, 1.4822, -0.5721, -0.6348, -0.1920, 0.4950, 0.1476],
- [ 0.0750, -0.6879, 1.3695, -0.9216, -0.2341, -0.8327, 0.4966, 0.2875],
- [-0.2534, -0.9799, 1.8873, -0.7545, -0.4268, -0.6145, 1.0349, 0.0990],
- [ 0.2832, -0.5986, 1.6411, -0.9750, -0.3327, -0.7753, 0.6956, 0.1492]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5454, -0.4053, 1.6633, -0.1766, -0.6058, -0.5923, 0.1577,
- 0.4357],
- [ 0.6101, -0.3152, 1.0166, -0.7540, -0.0226, -1.4468, 0.2823,
- 0.5702],
- [ 0.5592, -0.3956, 1.5543, -0.2456, -0.5885, -0.1689, 0.1392,
- 0.3968],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.5444, -0.3852, 1.3786, -0.5409, -0.6924, -0.4229, 0.1791,
- 0.2341],
- [ 0.5964, -0.3380, 1.4785, -0.8386, -0.2420, -1.0619, 0.3238,
- 0.4008],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390],
- [ 0.5790, -0.4031, 1.6915, -0.9564, -0.4152, -1.1063, 0.4425,
- 0.2528]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0526, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0526, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.856406262144446
- step: 85
- running loss: 0.057134191319346425
- Train Steps: 85/90 Loss: 0.0571 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [0.6168, 0.4081, 0.8438, 0.3367, 0.3500, 0.4667, 0.6212, 0.5400],
- [0.6196, 0.4068, 0.7645, 0.2234, 0.4575, 0.1737, 0.5926, 0.5284],
- [0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6187, 0.4104, 0.7126, 0.2069, 0.3950, 0.2517, 0.5913, 0.5683],
- [0.6171, 0.4127, 0.8900, 0.4800, 0.4325, 0.5783, 0.5769, 0.5090],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.3328e-01, -6.7352e-01, 1.6691e+00, -2.4416e-01, -1.3594e-01,
- -3.2876e-04, 4.3535e-01, 2.3712e-01],
- [ 5.2176e-01, -4.1727e-01, 1.4926e+00, -8.6348e-01, -5.5333e-01,
- -1.8318e-01, 7.7127e-01, 2.5558e-01],
- [ 9.9327e-01, -1.1433e-01, 1.0954e+00, -1.3476e+00, -2.7377e-01,
- -1.3365e+00, 3.8144e-01, 1.8049e-01],
- [-8.2795e-01, -1.3359e+00, 1.9219e+00, -7.7653e-01, -3.3671e-01,
- -8.4426e-01, 9.5188e-01, 1.5540e-01],
- [ 4.9690e-01, -4.4354e-01, 1.7715e+00, 3.1300e-02, -6.5161e-01,
- -6.7309e-01, 5.1132e-01, 5.7911e-02],
- [ 7.3540e-01, -2.5406e-01, 9.1125e-01, -1.3418e+00, -4.7364e-01,
- -1.1851e+00, 3.1099e-01, 2.4200e-01],
- [ 4.7442e-01, -4.5498e-01, 1.7000e+00, -1.5574e-01, -2.9064e-01,
- 1.7565e-01, 4.3889e-01, 2.0958e-01],
- [ 6.5780e-01, -3.2612e-01, 1.6180e+00, -1.8878e-01, -3.5431e-01,
- 1.8572e-02, 3.9815e-01, 2.2789e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5598, -0.4273, 1.7152, -0.1227, -0.0065, 0.1917, 0.5143,
- 0.2853],
- [ 0.5631, -0.4008, 1.6113, -0.7309, -0.6693, -0.1304, 0.5836,
- 0.2083],
- [ 0.5761, -0.4070, 1.2452, -1.2541, -0.1725, -1.4835, 0.4511,
- 0.1545],
- [ 0.6388, -0.3623, 1.9173, -0.7386, -0.3517, -0.5846, 1.1495,
- 0.2676],
- [ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.5718, -0.3905, 1.0053, -1.3305, -0.4614, -1.1235, 0.4450,
- 0.3392],
- [ 0.5645, -0.3797, 1.8249, -0.0688, -0.2882, 0.3854, 0.3789,
- 0.0652],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0671, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0671, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.923488324508071
- step: 86
- running loss: 0.05724986423846594
- Train Steps: 86/90 Loss: 0.0572 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683],
- [0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6679, -0.3393, 1.1044, -1.2029, -0.5126, -0.9637, 0.2172, 0.1473],
- [ 0.2462, -0.6525, 1.7013, 0.3707, -0.3928, 0.1506, 0.3154, 0.2182],
- [ 0.2769, -0.6023, 1.5248, -1.0627, -0.4206, -0.9832, 0.6722, 0.0666],
- [ 0.6292, -0.3429, 1.2376, -1.0654, -0.3068, -0.9931, 0.4548, 0.3508],
- [-0.0074, -0.7957, 1.7611, -0.6247, -0.3023, -0.7269, 0.8707, 0.0755],
- [ 0.5369, -0.4352, 1.6622, -0.9727, -0.4015, -0.7603, 0.5024, 0.2316],
- [ 0.2221, -0.6411, 1.7975, -0.0278, -0.1875, 0.1898, 0.6727, 0.1995],
- [ 0.5766, -0.3768, 1.0787, -1.0031, -0.5580, -0.6710, 0.2390, 0.2642]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.5079, -0.4714, 1.6575, 0.2237, -0.4961, 0.0774, 0.1465,
- -0.1061],
- [ 0.6042, -0.4225, 1.5420, -1.2082, -0.4730, -1.0311, 0.6380,
- -0.0220],
- [ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5778, -0.3930, 1.5189, -0.9387, -0.4326, -0.9156, 0.4855,
- 0.3392],
- [ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0238, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0238, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.947322266176343
- step: 87
- running loss: 0.05686577317444073
- Train Steps: 87/90 Loss: 0.0569 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6307, 0.4029, 0.8650, 0.5200, 0.3763, 0.4017, 0.7311, 0.5366],
- [0.6357, 0.4097, 0.9038, 0.3883, 0.4213, 0.2950, 0.6686, 0.5390],
- [0.6205, 0.4012, 0.8675, 0.3283, 0.3713, 0.3050, 0.5813, 0.5117],
- [0.6159, 0.4085, 0.6900, 0.2283, 0.4088, 0.1950, 0.5123, 0.5397],
- [0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7241, -0.3271, 1.7330, 0.0022, -0.4415, -0.4059, 0.5559, 0.1113],
- [ 0.8021, -0.2346, 1.5974, -0.6172, -0.2946, -0.7826, 0.7004, 0.1298],
- [ 0.5272, -0.4370, 1.6671, -0.8570, -0.4961, -0.7450, 0.4616, 0.1232],
- [-0.0215, -0.7598, 0.7726, -1.3853, -0.3094, -1.2399, 0.1006, 0.2363],
- [ 0.6110, -0.3637, 1.6312, -1.1276, -0.2190, -0.9056, 0.7891, 0.1843],
- [ 0.5471, -0.3935, 1.6186, -0.3462, -0.4808, -0.3456, 0.2056, 0.2678],
- [ 1.0903, -0.1401, 1.7994, -0.1475, -0.3498, 0.5473, 0.7149, 0.1603],
- [-1.3033, -1.6128, 1.1779, -0.9719, -0.4504, -0.8305, 0.2302, 0.2690]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6273, -0.4249, 1.7095, 0.1159, -0.5480, -0.4306, 1.0910,
- 0.1928],
- [ 0.6504, -0.3936, 1.8885, -0.4922, -0.3402, -0.9233, 0.8022,
- 0.2035],
- [ 0.5799, -0.4329, 1.7210, -0.7694, -0.5711, -0.8771, 0.3988,
- 0.0774],
- [ 0.5591, -0.3990, 0.9012, -1.2313, -0.3979, -1.3852, 0.0804,
- 0.2071],
- [ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0496, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0496, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.99695965833962
- step: 88
- running loss: 0.056783632481132044
- Train Steps: 88/90 Loss: 0.0568 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6129, 0.4069, 0.8750, 0.5067, 0.3875, 0.4233, 0.5235, 0.5881],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [0.6261, 0.4045, 0.8865, 0.5369, 0.3895, 0.4859, 0.6683, 0.5249],
- [0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5584, -0.4093, 1.1146, -1.4048, -0.2779, -1.3822, 0.1954, 0.1406],
- [ 0.4821, -0.4613, 1.6472, -0.0517, -0.4324, -0.3837, 0.1814, 0.2442],
- [ 0.4873, -0.4168, 1.3685, -0.8206, -0.5818, -0.4522, 0.2436, 0.1914],
- [ 0.3995, -0.5603, 1.7979, -0.0745, -0.4099, -0.0866, 0.7555, 0.1212],
- [ 0.0065, -0.7383, 0.9568, -1.1590, -0.3999, -1.0661, 0.2975, 0.3177],
- [ 0.6278, -0.4072, 1.7102, -0.8546, -0.5490, -0.6138, 0.4779, 0.2245],
- [ 0.0327, -0.7839, 1.8877, -0.2893, -0.2485, -0.4808, 0.8511, 0.2126],
- [ 0.7731, -0.3011, 1.7908, -0.3452, -0.3399, 0.2271, 0.5904, 0.0849]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5598, -0.4201, 1.1898, -1.3005, -0.3806, -1.3313, 0.3873,
- 0.0774],
- [ 0.5452, -0.4067, 1.7557, 0.0543, -0.4961, -0.3306, 0.1323,
- 0.4306],
- [ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386],
- [ 0.6059, -0.4177, 1.8087, 0.1941, -0.4868, -0.0414, 0.8010,
- 0.1385],
- [ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085],
- [ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.6454, -0.3719, 1.9115, -0.1304, -0.5076, -0.2844, 1.0033,
- 0.4386],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0316, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0316, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.028513407334685
- step: 89
- running loss: 0.05650015064420995
- Train Steps: 89/90 Loss: 0.0565 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6129, 0.4114, 0.6950, 0.2467, 0.4050, 0.2133, 0.5363, 0.5550],
- [0.6264, 0.4049, 0.8988, 0.4633, 0.3813, 0.4983, 0.6326, 0.4843],
- [0.6271, 0.4020, 0.8375, 0.6083, 0.3925, 0.4867, 0.6037, 0.4626],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767],
- [0.6339, 0.4123, 0.8638, 0.5367, 0.4075, 0.5467, 0.7517, 0.5436],
- [0.6241, 0.4143, 0.8938, 0.4650, 0.4075, 0.5350, 0.6250, 0.5550],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4825, -0.4272, 0.9730, -1.4118, -0.4009, -1.3373, 0.0767, 0.2495],
- [ 0.4915, -0.4617, 1.9515, -0.4588, -0.5543, -0.2200, 0.6500, 0.0742],
- [ 0.3416, -0.6033, 1.6379, -0.0055, -0.4627, -0.4151, 0.4429, 0.1592],
- [ 0.4801, -0.4831, 1.6780, -0.1948, -0.3423, -0.0419, 0.2214, 0.2790],
- [ 0.6173, -0.4038, 1.7523, -0.1950, -0.3700, 0.1127, 0.7578, 0.2431],
- [ 0.5661, -0.4204, 1.8014, -0.4218, -0.4077, 0.0485, 0.4907, 0.2373],
- [ 0.0555, -0.7193, 0.9680, -1.5541, -0.2692, -1.6329, 0.2577, 0.1827],
- [ 0.4184, -0.5378, 1.6813, -0.1549, -0.4171, -0.3171, 0.5697, 0.1647]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5448, -0.3859, 0.9242, -1.1466, -0.4152, -1.3005, 0.1910,
- 0.2776],
- [ 0.6075, -0.4159, 1.8654, -0.1458, -0.5249, 0.0159, 0.6359,
- -0.0490],
- [ 0.6105, -0.4293, 1.5824, 0.5239, -0.4730, -0.0380, 0.5025,
- -0.1492],
- [ 0.5539, -0.4031, 1.7168, -0.0594, -0.3748, 0.0543, 0.1390,
- 0.3777],
- [ 0.6421, -0.3816, 1.7037, 0.1929, -0.4037, 0.2391, 1.1861,
- 0.2249],
- [ 0.5968, -0.3725, 1.8423, -0.1381, -0.4037, 0.1852, 0.6009,
- 0.2776],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0333, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0333, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 5.0618322510272264
- step: 90
- running loss: 0.05624258056696918
- Valid Steps: 10/10 Loss: nan 62
- --------------------------------------------------
- Epoch: 3 Train Loss: 0.0562 Valid Loss: nan
- --------------------------------------------------
- size of train loader is: 90
- torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6136, 0.4117, 0.8700, 0.5167, 0.4188, 0.5083, 0.5147, 0.5495],
- [0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6257, 0.4024, 0.8612, 0.5352, 0.4361, 0.5253, 0.6680, 0.5166],
- [ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194],
- [0.6296, 0.4060, 0.9100, 0.3267, 0.4726, 0.3367, 0.7446, 0.5355],
- [0.6268, 0.4029, 0.8500, 0.2683, 0.3937, 0.3500, 0.6860, 0.5297],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7934, -0.2856, 1.6860, 0.1092, -0.3487, 0.0531, 0.2048, 0.2442],
- [ 0.6889, -0.3477, 1.0870, -1.3416, -0.3528, -1.3638, 0.1548, 0.2013],
- [ 0.5929, -0.4533, 1.6458, -0.0300, -0.2567, 0.1042, 0.5948, 0.2456],
- [-1.2841, -1.6451, 1.3930, -0.9300, -0.5634, -0.8736, 0.1785, 0.2065],
- [ 0.2508, -0.6173, 1.6443, -0.9053, -0.2038, -0.8371, 0.8204, 0.1669],
- [ 0.7218, -0.3298, 1.5397, -1.0182, -0.5050, -0.7588, 0.8013, 0.1396],
- [ 0.9012, -0.2219, 1.7789, -0.3461, -0.6196, -0.1334, 0.4337, 0.0805],
- [ 0.8209, -0.2396, 1.5937, -0.2505, -0.5622, -0.4064, 0.3184, 0.2603]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5482, -0.3841, 1.7326, 0.1005, -0.3517, 0.0620, 0.0912,
- 0.2522],
- [ 0.5598, -0.4201, 1.1898, -1.3005, -0.3806, -1.3313, 0.3873,
- 0.0774],
- [ 0.6042, -0.4273, 1.6920, 0.1860, -0.2717, 0.1406, 0.7997,
- 0.1004],
- [-2.2859, -2.2859, 1.5478, -0.8309, -0.6289, -0.7232, 0.1198,
- 0.1133],
- [ 0.6224, -0.4105, 1.9173, -0.7771, -0.1030, -0.7308, 1.1532,
- 0.1875],
- [ 0.6092, -0.4249, 1.6402, -1.0465, -0.4672, -0.6693, 0.8827,
- 0.1608],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0408, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0408, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.0408310666680336
- step: 1
- running loss: 0.0408310666680336
- Train Steps: 1/90 Loss: 0.0408 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6203, 0.4096, 0.8862, 0.4267, 0.3538, 0.4117, 0.6025, 0.5650],
- [ nan, nan, 0.6935, 0.1930, 0.4150, 0.2250, 0.5450, 0.5650],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679],
- [0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8029, -0.2294, 1.0461, -1.3274, -0.4808, -1.3214, 0.4222, 0.1729],
- [ 0.5322, -0.4257, 1.7850, -0.4723, -0.6244, -0.5832, 0.4501, 0.2225],
- [-1.7818, -1.9189, 1.1634, -1.3966, -0.2579, -1.3634, 0.2830, 0.2149],
- [ 0.7616, -0.3080, 1.6486, -0.0197, -0.4939, -0.2841, 0.5924, 0.1066],
- [ 0.7523, -0.2829, 1.6721, -0.1539, -0.2369, -0.1656, 0.1428, 0.1686],
- [ 0.8615, -0.2260, 1.7055, -0.0355, -0.3871, 0.1315, 0.7918, 0.1862],
- [ 0.6541, -0.3519, 1.6196, -0.1458, -0.3515, -0.2593, 0.2668, 0.1777],
- [ 0.7497, -0.3061, 1.7884, -0.3122, -0.4508, 0.1585, 0.5595, 0.2249]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.5790, -0.3940, 1.8076, -0.3152, -0.6520, -0.3844, 0.4970,
- 0.3238],
- [-2.2859, -2.2859, 0.9175, -1.3947, -0.3691, -1.2467, 0.2314,
- 0.3238],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.5359, -0.4370, 1.7095, -0.0303, -0.0804, -0.0380, 0.1044,
- 0.3392],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371],
- [ 0.5525, -0.4463, 1.7326, 0.0313, -0.2536, -0.0688, 0.1968,
- 0.3700],
- [ 0.5772, -0.3913, 1.8480, -0.2459, -0.4326, 0.1929, 0.5374,
- 0.4701]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0242, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0242, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.06501925364136696
- step: 2
- running loss: 0.03250962682068348
- Train Steps: 2/90 Loss: 0.0325 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4075, 0.8650, 0.5617, 0.4150, 0.4367, 0.5450, 0.5650],
- [0.6127, 0.4119, 0.8650, 0.5300, 0.4512, 0.5250, 0.5260, 0.5461],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6271, 0.4005, 0.8450, 0.6067, 0.3850, 0.4983, 0.6069, 0.4649],
- [0.6268, 0.4102, 0.8938, 0.3667, 0.4025, 0.2833, 0.6275, 0.5183],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6161, 0.4099, 0.8738, 0.4383, 0.3788, 0.5483, 0.5605, 0.5019],
- [0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2261, -0.6299, 1.5691, 0.0380, -0.4303, -0.4064, 0.3217, 0.2605],
- [ 0.8775, -0.2186, 1.6144, -0.0903, -0.2743, -0.0483, 0.2540, 0.2187],
- [ 0.4941, -0.4669, 1.6542, 0.0592, -0.4179, 0.0067, 0.4888, 0.2468],
- [ 0.6490, -0.3877, 1.5852, 0.0381, -0.5231, -0.2220, 0.4692, 0.1378],
- [ 0.6293, -0.3672, 1.8195, -0.9589, -0.4476, -1.1585, 0.6918, 0.1580],
- [-0.9213, -1.3341, 1.0464, -1.5667, -0.3857, -1.5674, 0.3233, 0.1719],
- [ 0.8097, -0.2308, 1.6492, -0.5242, -0.5474, 0.1092, 0.4527, 0.1488],
- [ 0.6409, -0.3545, 1.6522, -0.3213, -0.3163, -0.0979, 0.5912, 0.1635]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5770, -0.4036, 1.7095, 0.3084, -0.3691, -0.2690, 0.2314,
- 0.3238],
- [ 0.5443, -0.3831, 1.7095, 0.1621, -0.2016, 0.1390, 0.1437,
- 0.2364],
- [ 0.5742, -0.3792, 1.6460, 0.3084, -0.3460, 0.1467, 0.4162,
- 0.3161],
- [ 0.6105, -0.4360, 1.6171, 0.5162, -0.5076, 0.0159, 0.5171,
- -0.1385],
- [ 0.6092, -0.3913, 1.8423, -0.5923, -0.4268, -0.9772, 0.6125,
- 0.1082],
- [ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.5597, -0.3928, 1.7499, -0.2613, -0.5365, 0.2468, 0.3028,
- 0.0321],
- [ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0751, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0751, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.1400858722627163
- step: 3
- running loss: 0.04669529075423876
- Train Steps: 3/90 Loss: 0.0467 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6201, 0.4102, 0.7288, 0.2417, 0.4150, 0.2383, 0.6100, 0.5500],
- [0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183],
- [0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [0.6189, 0.4033, 0.8650, 0.5267, 0.4487, 0.5150, 0.5925, 0.5050],
- [0.6058, 0.3986, 0.8324, 0.4626, 0.3838, 0.4983, 0.5147, 0.5466],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567],
- [0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3854, -0.5699, 1.7729, -0.4065, -0.3826, -0.5799, 0.8162, 0.0487],
- [ 0.4473, -0.4987, 1.2553, -0.9025, -0.4371, -1.0451, 0.5099, 0.1875],
- [ 0.3850, -0.5415, 1.2425, -1.1285, -0.3311, -1.2141, 0.4639, 0.1034],
- [ 0.4943, -0.4585, 1.7603, -0.3231, -0.4710, -0.5697, 0.3823, 0.2806],
- [ 0.5609, -0.4599, 1.7936, 0.3726, -0.2832, 0.3955, 0.2943, 0.1781],
- [ 0.3167, -0.5832, 1.6743, 0.1032, -0.5132, 0.2972, 0.2819, 0.1813],
- [ 0.0717, -0.6920, 1.2379, -0.9312, -0.3837, -0.9721, 0.3222, 0.3046],
- [ 0.5122, -0.4441, 1.2345, -0.7984, -0.4852, -0.8042, 0.5456, 0.2060]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5784, -0.3913, 1.0801, -1.1697, -0.3691, -1.1851, 0.5316,
- 0.2545],
- [ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082],
- [ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928],
- [ 0.5726, -0.4230, 1.7095, 0.1467, -0.2132, 0.0928, 0.4508,
- 0.0467],
- [ 0.5124, -0.4446, 1.5587, -0.1493, -0.5134, 0.0159, 0.0912,
- 0.2386],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853],
- [ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0287, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0287, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.16878480091691017
- step: 4
- running loss: 0.04219620022922754
- Train Steps: 4/90 Loss: 0.0422 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4098, 0.8575, 0.3617, 0.3700, 0.3550, 0.5988, 0.5667],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967],
- [0.6271, 0.4020, 0.8375, 0.6083, 0.3925, 0.4867, 0.6037, 0.4626],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6260, 0.4153, 0.9000, 0.4533, 0.4025, 0.2633, 0.6223, 0.4967],
- [0.6150, 0.4097, 0.7468, 0.3194, 0.3825, 0.2633, 0.5463, 0.5800],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6402, -0.3300, 1.6367, -0.5436, -0.4823, -0.6285, 0.3887, 0.2062],
- [ 0.4304, -0.5247, 1.8311, -0.0857, -0.2697, 0.3819, 0.6370, 0.2235],
- [ 0.3012, -0.6414, 1.6228, 0.3746, -0.3542, -0.0797, 0.5618, 0.0932],
- [ 0.3471, -0.5312, 1.0608, -0.9703, -0.4720, -0.8061, 0.3146, 0.2213],
- [ 0.3248, -0.5650, 1.7027, -0.1101, -0.4417, -0.2336, 0.5059, 0.1623],
- [ 0.3386, -0.5307, 1.7406, -0.2570, -0.3301, -1.0203, 0.5861, 0.1280],
- [ 0.3174, -0.5268, 1.0960, -0.8999, -0.4709, -0.9923, 0.1387, 0.2535],
- [ 0.6184, -0.4022, 1.6616, -0.6118, -0.5818, -0.3922, 0.4269, 0.2213]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5783, -0.3933, 1.6748, -0.6154, -0.5769, -0.6462, 0.4797,
- 0.3315],
- [ 0.5772, -0.3913, 1.8480, -0.2459, -0.4326, 0.1929, 0.5374,
- 0.4701],
- [ 0.6105, -0.4293, 1.5824, 0.5239, -0.4730, -0.0380, 0.5025,
- -0.1492],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.6055, -0.3676, 1.8711, -0.1920, -0.4268, -1.0696, 0.5887,
- 0.0081],
- [ 0.5548, -0.3936, 1.1634, -0.8105, -0.5192, -1.0696, 0.2372,
- 0.3931],
- [ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0165, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0165, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.18524960055947304
- step: 5
- running loss: 0.037049920111894605
- Train Steps: 5/90 Loss: 0.0370 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6234, 0.4179, 0.7825, 0.3450, 0.3813, 0.2867, 0.5675, 0.5617],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6085, 0.4008, 0.8588, 0.5200, 0.4959, 0.4977, 0.5175, 0.5283],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986],
- [0.6164, 0.3956, 0.8757, 0.5088, 0.4300, 0.5320, 0.6022, 0.5202],
- [0.6200, 0.4071, 0.7338, 0.1917, 0.4200, 0.2450, 0.6150, 0.5517],
- [0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2791, -0.5424, 1.3593, -0.6483, -0.5082, -0.7606, 0.2373, 0.3004],
- [ 0.5529, -0.4208, 1.3162, -0.7741, -0.5420, -0.8749, 0.5742, 0.2522],
- [ 0.5527, -0.4374, 1.6764, 0.2120, -0.1778, 0.1175, 0.2001, 0.1639],
- [ 0.7011, -0.3902, 1.7922, 0.3820, -0.5591, 0.0326, 0.7921, 0.0431],
- [ 0.1883, -0.6151, 1.7248, -0.4770, -0.3421, -1.0000, 0.5109, 0.1547],
- [ 0.2386, -0.6325, 1.7365, 0.1798, -0.4045, 0.3961, 0.4558, 0.1295],
- [ 0.3358, -0.5460, 1.2014, -1.0744, -0.4078, -1.0060, 0.5095, 0.1908],
- [ 0.3464, -0.4803, 1.1684, -0.9478, -0.2323, -1.0542, 0.3087, 0.2845]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5935, -0.3558, 1.3284, -0.6924, -0.5249, -0.9618, 0.3353,
- 0.3084],
- [ 0.5915, -0.3682, 1.2187, -1.2313, -0.4326, -0.9541, 0.5778,
- 0.3777],
- [ 0.5245, -0.4347, 1.6806, 0.1159, 0.0046, 0.0129, 0.1044,
- 0.1544],
- [ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171],
- [ 0.5614, -0.4586, 1.7587, 0.0641, -0.2998, 0.1712, 0.4958,
- 0.1170],
- [ 0.5779, -0.4054, 1.1032, -1.4006, -0.3460, -1.1543, 0.5547,
- 0.2622],
- [ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0253, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0253, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.21053649485111237
- step: 6
- running loss: 0.03508941580851873
- Train Steps: 6/90 Loss: 0.0351 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960],
- [0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285],
- [0.6208, 0.4082, 0.8538, 0.3067, 0.3588, 0.3717, 0.6112, 0.5517],
- [0.6177, 0.4086, 0.8738, 0.3950, 0.3775, 0.5600, 0.6225, 0.5700],
- [0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3029, -0.6244, 1.8468, 0.1460, -0.4225, 0.1632, 0.9719, 0.1582],
- [ 0.3994, -0.5348, 1.6714, 0.0397, -0.2388, -0.1771, 0.2345, 0.1581],
- [ 0.2754, -0.6236, 1.7249, 0.0636, -0.3578, -0.0518, 0.3006, 0.1270],
- [ 0.6174, -0.3559, 1.3701, -0.3050, -0.6034, -0.3570, 0.1214, 0.1833],
- [ 0.5716, -0.4054, 1.5687, -0.6477, -0.6942, -0.6264, 0.4524, 0.2152],
- [ 0.2340, -0.6259, 1.7155, -0.2797, -0.4494, 0.2091, 0.7213, 0.2117],
- [ 0.6382, -0.3643, 1.3742, -0.9335, -0.3079, -1.3957, 0.6677, 0.2151],
- [ 0.5772, -0.3455, 1.0543, -0.8894, -0.1617, -1.3255, 0.2601, 0.3533]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051],
- [ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552],
- [ 0.5814, -0.4003, 1.6575, -0.8694, -0.6289, -0.5692, 0.5374,
- 0.2622],
- [ 0.5671, -0.3988, 1.7499, -0.4614, -0.5423, 0.3007, 0.5894,
- 0.3469],
- [ 0.6174, -0.4225, 1.4975, -1.1709, -0.3174, -1.1806, 0.6539,
- 0.1879],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0187, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0187, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.22919072955846786
- step: 7
- running loss: 0.03274153279406684
- Train Steps: 7/90 Loss: 0.0327 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.4103, 0.8950, 0.4400, 0.3912, 0.5650, 0.6050, 0.5133],
- [0.6122, 0.4006, 0.8850, 0.4217, 0.4088, 0.5517, 0.6063, 0.5517],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760],
- [ nan, nan, 0.6935, 0.1930, 0.4150, 0.2250, 0.5450, 0.5650],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000],
- [0.6147, 0.4081, 0.8538, 0.3400, 0.3663, 0.3150, 0.5142, 0.4875]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5962, -0.3807, 1.8008, -0.0904, -0.3684, 0.2387, 0.6538, 0.1876],
- [ 0.5492, -0.4183, 1.7119, -0.1476, -0.3619, 0.1762, 0.4529, 0.2305],
- [ 0.7731, -0.3102, 1.6519, 0.2326, -0.3488, -0.1404, 0.5892, 0.1673],
- [ 0.7477, -0.2326, 1.3266, -0.5177, -0.3959, -0.8787, 0.3715, 0.3245],
- [ 0.8838, -0.2180, 1.6062, 0.3130, -0.3376, -0.1118, 0.7455, 0.2682],
- [-1.4788, -1.7508, 1.1179, -1.3049, -0.2588, -1.3062, 0.2117, 0.2637],
- [ 0.8055, -0.2542, 1.6588, -0.3911, -0.6106, -0.3597, 0.4233, 0.1606],
- [ 0.9708, -0.1498, 1.6112, -0.6890, -0.6115, -0.9635, 0.1644, 0.1881]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6010, -0.3909, 1.8480, -0.2536, -0.4788, 0.3238, 0.5085,
- 0.0851],
- [ 0.5417, -0.4355, 1.8018, -0.3383, -0.3979, 0.2622, 0.5143,
- 0.2622],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469],
- [ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745],
- [-2.2859, -2.2859, 0.9175, -1.3947, -0.3691, -1.2467, 0.2314,
- 0.3238],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236],
- [ 0.5532, -0.4008, 1.6575, -0.7155, -0.5942, -0.8309, 0.0890,
- -0.0340]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0315, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0315, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2607172168791294
- step: 8
- running loss: 0.032589652109891176
- Train Steps: 8/90 Loss: 0.0326 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528],
- [0.6346, 0.4165, 0.9138, 0.3983, 0.3875, 0.4317, 0.7469, 0.5471],
- [ nan, nan, 0.8900, 0.3217, 0.5038, 0.2233, 0.6694, 0.5609],
- [0.6127, 0.4115, 0.7163, 0.2883, 0.3625, 0.2950, 0.5327, 0.5495],
- [0.6083, 0.3957, 0.8638, 0.4950, 0.4363, 0.5083, 0.5346, 0.4980],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6257, 0.4060, 0.8300, 0.2333, 0.4688, 0.2583, 0.7050, 0.5633],
- [ nan, nan, 0.7335, 0.2569, 0.3788, 0.2667, 0.5066, 0.5578]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9991, -0.1294, 1.8289, -0.3092, -0.4135, -0.4943, 0.7495, 0.1788],
- [ 0.6798, -0.3583, 1.8032, -0.0833, -0.4193, -0.0604, 0.7310, 0.2062],
- [-1.1939, -1.5435, 1.6261, -0.7515, -0.1293, -1.0238, 0.6422, 0.2504],
- [ 1.0001, -0.1213, 1.0754, -0.6870, -0.5014, -0.7288, 0.2028, 0.2405],
- [ 1.0468, -0.0862, 1.6703, 0.3435, -0.3764, 0.3130, 0.1611, 0.1331],
- [ 0.9204, -0.1412, 1.6262, 0.0114, -0.5259, -0.2258, 0.2021, 0.2598],
- [ 0.7783, -0.2752, 1.5301, -0.8552, -0.2124, -1.0131, 0.8405, 0.2358],
- [-0.8339, -1.2837, 1.0198, -0.9123, -0.5547, -0.8795, 0.0556, 0.2826]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.3883e-01, -3.6231e-01, 1.9173e+00, -7.3857e-01, -3.5173e-01,
- -5.8460e-01, 1.1495e+00, 2.6764e-01],
- [ 6.4542e-01, -3.6231e-01, 1.9346e+00, -4.4604e-01, -4.9607e-01,
- -2.9207e-01, 1.1642e+00, 2.4092e-01],
- [-2.2859e+00, -2.2859e+00, 1.8249e+00, -8.0015e-01, 4.0878e-02,
- -1.2543e+00, 8.0590e-01, 3.0505e-01],
- [ 5.4417e-01, -3.8545e-01, 1.0224e+00, -9.5412e-01, -6.1155e-01,
- -9.2333e-01, 1.7452e-01, 2.5215e-01],
- [ 5.2379e-01, -4.5797e-01, 1.7037e+00, 4.6189e-04, -2.7090e-01,
- 6.2048e-02, 1.8356e-01, 1.4106e-02],
- [ 6.0722e-01, -3.2394e-01, 1.8423e+00, -3.5366e-01, -4.9607e-01,
- -3.9215e-01, 2.0831e-01, 1.8522e-01],
- [ 6.0425e-01, -4.1045e-01, 1.5478e+00, -1.2082e+00, -1.2079e-01,
- -1.0927e+00, 9.7040e-01, 3.1574e-01],
- [-2.2859e+00, -2.2859e+00, 1.1020e+00, -1.0994e+00, -5.3649e-01,
- -1.0542e+00, 5.4227e-02, 2.9047e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1190, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1190, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3797225020825863
- step: 9
- running loss: 0.042191389120287366
- Train Steps: 9/90 Loss: 0.0422 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4157, 0.8337, 0.5800, 0.3763, 0.4200, 0.5547, 0.6125],
- [ nan, nan, 0.8900, 0.3217, 0.5038, 0.2233, 0.6694, 0.5609],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6250, 0.4103, 0.8950, 0.4400, 0.3912, 0.5650, 0.6050, 0.5133],
- [0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6196, 0.4090, 0.8450, 0.3900, 0.3575, 0.3933, 0.5975, 0.5583],
- [ nan, nan, 0.8938, 0.2850, 0.4662, 0.3117, 0.7406, 0.5528]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.2359, 0.0152, 1.4352, 0.4591, -0.4579, -0.1886, 0.3458, 0.3827],
- [-1.4108, -1.7165, 1.6278, -0.9198, -0.0999, -1.1243, 0.6107, 0.2600],
- [ 1.0880, -0.0631, 1.4730, -0.7942, -0.2523, -1.2433, 0.1780, 0.2025],
- [ 0.9040, -0.1922, 1.7536, -0.0645, -0.4241, 0.4290, 0.6224, 0.1873],
- [ 0.9999, -0.1657, 1.5762, -0.5998, -0.5918, -0.5274, 0.6032, 0.1100],
- [ 0.6061, -0.3682, 1.4800, -0.3083, -0.6131, -0.1629, 0.2296, 0.2176],
- [ 0.9515, -0.1362, 1.5629, -0.2965, -0.5961, -0.2386, 0.4166, 0.2649],
- [-0.7224, -1.2803, 1.7950, -0.7486, -0.2483, -0.8924, 0.8542, 0.2345]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6124, -0.3658, 1.5651, 0.3931, -0.5480, -0.3460, 0.2761,
- 0.5431],
- [-2.2859, -2.2859, 1.8249, -0.8002, 0.0409, -1.2543, 0.8059,
- 0.3050],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.6010, -0.3909, 1.8480, -0.2536, -0.4788, 0.3238, 0.5085,
- 0.0851],
- [ 0.6031, -0.4307, 1.7268, -0.8002, -0.6058, -0.6462, 0.6442,
- -0.0220],
- [ 0.5367, -0.4294, 1.5709, -0.4999, -0.6693, -0.3075, 0.2455,
- 0.3559],
- [ 0.5761, -0.3966, 1.6171, -0.4845, -0.6346, -0.4691, 0.4739,
- 0.2930],
- [-2.2859, -2.2859, 1.8423, -0.9695, -0.1323, -0.8463, 1.1349,
- 0.2676]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1079, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1079, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.48758096620440483
- step: 10
- running loss: 0.048758096620440484
- Train Steps: 10/90 Loss: 0.0488 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6120, 0.4014, 0.6863, 0.2817, 0.3700, 0.2783, 0.5513, 0.5667],
- [ nan, nan, 0.6859, 0.2194, 0.4150, 0.1867, 0.5153, 0.5729],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6182, 0.3972, 0.8720, 0.5527, 0.3638, 0.3582, 0.5696, 0.5395],
- [0.6275, 0.4013, 0.8850, 0.2833, 0.4975, 0.2233, 0.7058, 0.5343],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7067, -0.3148, 1.0492, -1.0354, -0.4981, -0.9338, 0.3020, 0.3286],
- [-0.7523, -1.2924, 0.9029, -1.2760, -0.3634, -1.3894, 0.3071, 0.3262],
- [ 0.6640, -0.3744, 1.8604, -0.2007, -0.5467, 0.1417, 0.5132, 0.2404],
- [ 0.5437, -0.4182, 1.7214, 0.1975, -0.6007, -0.5034, 0.4236, 0.2390],
- [ 0.3511, -0.5864, 1.7093, -0.9693, 0.0304, -1.2827, 1.0219, 0.1822],
- [ 1.1674, -0.0858, 1.8699, -0.0512, -0.5583, -0.3091, 0.7732, 0.1525],
- [ 0.3252, -0.5765, 1.7152, 0.1841, -0.2517, 0.2405, 0.2954, 0.2331],
- [ 0.3638, -0.5548, 1.8978, -0.1514, -0.4938, -0.0809, 0.1914, 0.1650]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5410, -0.4321, 0.8838, -0.9849, -0.5769, -1.0003, 0.2603,
- 0.3315],
- [-2.2859, -2.2859, 0.8824, -1.2727, -0.3691, -1.4237, 0.0943,
- 0.3604],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.5697, -0.4514, 1.7420, 0.2672, -0.6055, -0.6312, 0.3449,
- 0.2058],
- [ 0.6125, -0.4321, 1.8018, -0.9772, 0.0120, -1.2543, 0.9741,
- 0.1821],
- [ 0.6372, -0.4129, 1.8942, -0.0765, -0.6173, -0.4768, 0.6999,
- 0.0325],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0692, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0692, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5567609034478664
- step: 11
- running loss: 0.05061462758616968
- Train Steps: 11/90 Loss: 0.0506 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [0.6200, 0.4086, 0.8414, 0.2974, 0.4117, 0.2274, 0.5869, 0.5391],
- [0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6058, 0.3986, 0.8324, 0.4626, 0.3838, 0.4983, 0.5147, 0.5466],
- [0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3899, -0.5611, 1.6083, -1.0398, -0.0411, -1.4380, 0.9611, 0.1850],
- [ 0.7063, -0.3622, 1.7582, -0.4214, -0.6344, -0.0607, 0.7025, 0.2027],
- [ 0.0398, -0.7886, 1.7258, -0.0711, -0.5843, 0.0780, 0.3114, 0.1939],
- [ 0.3534, -0.5398, 1.6284, -0.9779, -0.2579, -1.2172, 0.6238, 0.2286],
- [ 0.5881, -0.3905, 1.2062, -1.0493, -0.2944, -1.2115, 0.6174, 0.4106],
- [ 0.1641, -0.6547, 1.5900, -0.0401, -0.4739, 0.0842, 0.2363, 0.2308],
- [ 0.3576, -0.5340, 1.6793, -0.2877, -0.6649, -0.5644, 0.2157, 0.2440],
- [ 0.3927, -0.5486, 1.7002, 0.2642, -0.4248, -0.0980, 0.4854, 0.1575]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.5368, -0.4406, 1.7730, -0.1766, -0.5249, -0.0534, 0.2314,
- 0.3469],
- [ 0.5776, -0.3987, 1.6005, -0.9121, -0.3844, -1.2358, 0.4247,
- 0.2043],
- [ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084],
- [ 0.5124, -0.4446, 1.5587, -0.1493, -0.5134, 0.0159, 0.0912,
- 0.2386],
- [ 0.5697, -0.4113, 1.7499, -0.2767, -0.6404, -0.7155, 0.1576,
- 0.4032],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0200, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0200, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5767896324396133
- step: 12
- running loss: 0.04806580270330111
- Train Steps: 12/90 Loss: 0.0481 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6225, 0.4116, 0.8662, 0.3517, 0.3663, 0.3233, 0.5837, 0.5317],
- [0.6286, 0.4060, 0.9188, 0.4333, 0.3675, 0.4167, 0.7034, 0.5528],
- [0.6275, 0.4013, 0.8850, 0.2833, 0.4975, 0.2233, 0.7058, 0.5343],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422],
- [0.6267, 0.4080, 0.8438, 0.2633, 0.4763, 0.1800, 0.6259, 0.5240]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7066, -0.3359, 1.6617, 0.1113, -0.4845, 0.2730, 0.3180, 0.1751],
- [ 0.6655, -0.3258, 1.6267, 0.1583, -0.5994, -0.3720, 0.3459, 0.2149],
- [ 0.7973, -0.2172, 1.5151, -0.6836, -0.6012, -0.6649, 0.2786, 0.2913],
- [ 0.7946, -0.2888, 1.9036, -0.3246, -0.5414, -0.2549, 0.8325, 0.2533],
- [ 0.5353, -0.4337, 1.5933, -0.9831, -0.0078, -1.1072, 0.9309, 0.2004],
- [ 0.3392, -0.5292, 1.6934, -0.5276, -0.3595, -1.0432, 0.5401, 0.1994],
- [-2.1027, -2.1584, 1.0679, -0.8955, -0.4812, -0.9877, 0.2749, 0.2430],
- [ 0.5163, -0.4084, 1.4821, -1.0341, -0.1654, -1.1220, 0.6254, 0.2533]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5778, -0.4389, 1.7107, 0.1192, -0.3921, 0.0815, 0.4741,
- 0.0711],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698],
- [ 0.5893, -0.3847, 1.7152, -0.6616, -0.5942, -0.7925, 0.4104,
- 0.1698],
- [ 0.6174, -0.4105, 1.9577, -0.2844, -0.5885, -0.3614, 0.9631,
- 0.2676],
- [ 0.6125, -0.4321, 1.8018, -0.9772, 0.0120, -1.2543, 0.9741,
- 0.1821],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [-2.2859, -2.2859, 1.2469, -1.0288, -0.4557, -1.2774, 0.0511,
- 0.2183],
- [ 0.6088, -0.4015, 1.6113, -1.0696, -0.0861, -1.4545, 0.6051,
- 0.1343]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0180, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0180, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5947947613894939
- step: 13
- running loss: 0.045753443183807224
- Train Steps: 13/90 Loss: 0.0458 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6107, 0.4050, 0.8700, 0.4850, 0.4470, 0.4848, 0.5043, 0.5431],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6271, 0.4024, 0.9000, 0.3517, 0.3700, 0.4517, 0.6931, 0.5285],
- [0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901],
- [0.6182, 0.3972, 0.8720, 0.5527, 0.3638, 0.3582, 0.5696, 0.5395],
- [ nan, nan, 0.7553, 0.2722, 0.3875, 0.2550, 0.5125, 0.5633],
- [ nan, nan, 0.8888, 0.3100, 0.5262, 0.2817, 0.7145, 0.6003],
- [0.6150, 0.4097, 0.7468, 0.3194, 0.3825, 0.2633, 0.5463, 0.5800]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8568, -0.1986, 1.7606, -0.0271, -0.2534, -0.0415, 0.1967, 0.1760],
- [ 1.1717, -0.0319, 1.6136, -0.6131, -0.6550, -0.5300, 0.3238, 0.1442],
- [ 1.0539, -0.1441, 1.7893, -0.6783, -0.5686, -0.2087, 0.8835, 0.2074],
- [ 1.0515, -0.1461, 1.7092, -0.7597, -0.5884, -0.7283, 0.6856, 0.1205],
- [ 0.7875, -0.2442, 1.7666, 0.2316, -0.5501, -0.6627, 0.4504, 0.2163],
- [-2.0296, -2.1249, 1.2080, -0.9242, -0.4599, -1.0183, 0.2166, 0.2925],
- [-1.3036, -1.6394, 1.8677, -0.7513, 0.0723, -1.0658, 1.0924, 0.3734],
- [ 0.5397, -0.3751, 1.1715, -0.8596, -0.4805, -1.1048, 0.2473, 0.3443]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3508e-01, -4.1527e-01, 1.7326e+00, -4.5727e-02, -2.2139e-01,
- -4.6642e-02, 4.3431e-02, 2.2284e-01],
- [ 5.2061e-01, -4.6028e-01, 1.4670e+00, -4.7683e-01, -6.5774e-01,
- -3.9985e-01, 1.8356e-01, 2.0831e-03],
- [ 6.1083e-01, -4.2731e-01, 1.8711e+00, -6.6159e-01, -5.7691e-01,
- -1.9969e-01, 9.1557e-01, 1.5543e-01],
- [ 6.0306e-01, -4.3072e-01, 1.7268e+00, -8.0015e-01, -6.0577e-01,
- -6.4619e-01, 6.4417e-01, -2.1963e-02],
- [ 5.6966e-01, -4.5138e-01, 1.7420e+00, 2.6720e-01, -6.0553e-01,
- -6.3118e-01, 3.4489e-01, 2.0578e-01],
- [-2.2859e+00, -2.2859e+00, 1.2030e+00, -1.0288e+00, -4.9607e-01,
- -1.1081e+00, 8.1293e-02, 3.1609e-01],
- [-2.2859e+00, -2.2859e+00, 1.8192e+00, -8.5404e-01, 1.4480e-01,
- -9.8491e-01, 1.0143e+00, 4.8673e-01],
- [ 5.5484e-01, -3.9360e-01, 1.1634e+00, -8.1049e-01, -5.1917e-01,
- -1.0696e+00, 2.3718e-01, 3.9307e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0493, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0493, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.644109707325697
- step: 14
- running loss: 0.04600783623754978
- Train Steps: 14/90 Loss: 0.0460 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6142, 0.4127, 0.7575, 0.3067, 0.3438, 0.4383, 0.5778, 0.5207],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6193, 0.3930, 0.8949, 0.4437, 0.3852, 0.5435, 0.6263, 0.5263],
- [0.6182, 0.3972, 0.8720, 0.5527, 0.3638, 0.3582, 0.5696, 0.5395],
- [0.6264, 0.4067, 0.9050, 0.4183, 0.3775, 0.4600, 0.6308, 0.4862],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3045, -0.5674, 1.3769, -0.9429, -0.6118, -0.4503, 0.2990, 0.2750],
- [ 0.0977, -0.6868, 1.3143, -0.7793, 0.0222, -1.2691, 0.4035, 0.3906],
- [ 0.4517, -0.4892, 1.5790, -0.6596, -0.6443, -0.5839, 0.3201, 0.2341],
- [ 0.0941, -0.8263, 1.9781, -0.0808, -0.4988, 0.1383, 0.6655, 0.1129],
- [ 0.4164, -0.5374, 1.9136, 0.3085, -0.5462, -0.6897, 0.5370, 0.1564],
- [ 0.2482, -0.6505, 2.1673, -0.2775, -0.6107, -0.3451, 0.8151, 0.0446],
- [-0.0211, -0.7762, 1.3772, -0.9917, -0.2255, -1.2161, 0.5604, 0.3626],
- [ 0.3111, -0.6232, 1.2350, -1.1596, -0.4139, -1.2741, 0.4848, 0.2642]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5509, -0.3798, 1.2129, -0.8694, -0.6982, -0.2613, 0.3830,
- 0.1193],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.5746, -0.4706, 1.8476, -0.2365, -0.5068, 0.2245, 0.6069,
- 0.1449],
- [ 0.5697, -0.4514, 1.7420, 0.2672, -0.6055, -0.6312, 0.3449,
- 0.2058],
- [ 0.6072, -0.4075, 1.8942, -0.3537, -0.5423, -0.1612, 0.6277,
- -0.0400],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853],
- [ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0427, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0427, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6868270449340343
- step: 15
- running loss: 0.04578846966226895
- Train Steps: 15/90 Loss: 0.0458 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6204, 0.4049, 0.7975, 0.2700, 0.3937, 0.2567, 0.5700, 0.5183],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6203, 0.4056, 0.8942, 0.4086, 0.3643, 0.3617, 0.5917, 0.5482],
- [ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422],
- [0.6219, 0.3934, 0.8688, 0.5267, 0.4313, 0.4967, 0.5988, 0.4983],
- [0.6257, 0.4167, 0.8775, 0.3433, 0.3563, 0.4133, 0.6200, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4056, -0.5116, 1.7553, -0.5207, -0.1930, -1.2530, 0.6542, 0.2057],
- [ 0.0376, -0.7179, 1.3283, -0.9410, -0.3670, -1.1820, 0.3219, 0.2579],
- [ 0.7586, -0.2953, 1.6136, -0.7672, -0.5980, -0.7300, 0.5055, 0.2464],
- [ 1.1241, -0.0934, 1.7848, -0.3410, -0.5859, -0.2160, 0.5642, 0.0793],
- [ 0.6242, -0.3637, 1.8110, -0.4475, -0.5276, -0.6335, 0.5430, 0.2081],
- [-2.5332, -2.4693, 1.1160, -0.8603, -0.3118, -1.1134, 0.3019, 0.2916],
- [ 0.6375, -0.3858, 1.7093, 0.0144, -0.3216, -0.1430, 0.4885, 0.1177],
- [ 0.4408, -0.4880, 1.7590, -0.6851, -0.6015, -0.5339, 0.6534, 0.3028]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.5798, -0.4156, 1.3977, -1.0388, -0.4672, -1.1004, 0.3469,
- 0.1082],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.5791, -0.4127, 1.8442, -0.3985, -0.6031, -0.6154, 0.4473,
- 0.2464],
- [-2.2859, -2.2859, 1.2469, -1.0288, -0.4557, -1.2774, 0.0511,
- 0.2183],
- [ 0.5864, -0.4690, 1.7268, 0.1467, -0.2940, 0.0082, 0.4797,
- 0.0159],
- [ 0.6040, -0.3614, 1.7672, -0.7001, -0.6404, -0.3768, 0.5778,
- 0.3315]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0251, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0251, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7119743004441261
- step: 16
- running loss: 0.04449839377775788
- Train Steps: 16/90 Loss: 0.0445 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413],
- [0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [0.6271, 0.4040, 0.9000, 0.3250, 0.4938, 0.2300, 0.7192, 0.5413],
- [0.6222, 0.3937, 0.8350, 0.5617, 0.4138, 0.4600, 0.5800, 0.5233],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6224, 0.4097, 0.7438, 0.2267, 0.3850, 0.2850, 0.5988, 0.5250],
- [0.6222, 0.4072, 0.7164, 0.2166, 0.3738, 0.3167, 0.6100, 0.5533],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.5698, -1.1630, 1.7988, -1.0738, 0.0985, -1.2631, 0.9678, 0.2631],
- [ 0.6247, -0.3964, 1.8911, -0.4913, -0.7389, -0.3246, 0.3989, 0.1941],
- [-1.1609, -1.5773, 1.9380, -0.7570, -0.0841, -1.2592, 0.9371, 0.2391],
- [ 0.4757, -0.5144, 1.6757, 0.2291, -0.5092, -0.3942, 0.3250, 0.1567],
- [ 0.1884, -0.6917, 1.8490, -0.1794, -0.5994, 0.2266, 0.3432, 0.1332],
- [ 0.6206, -0.3816, 1.1967, -1.1353, -0.5213, -1.1738, 0.2903, 0.2247],
- [ 0.6793, -0.3524, 1.1073, -1.2023, -0.6062, -1.0704, 0.3309, 0.2422],
- [ 0.5047, -0.4610, 1.5853, 0.0454, -0.5763, -0.2399, 0.3652, 0.1877]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6240, -0.3768, 1.6575, -1.2851, 0.2949, -1.2467, 1.1276,
- 0.2142],
- [ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [ 0.6108, -0.4201, 1.8711, -0.7848, -0.0053, -1.2236, 1.0362,
- 0.2142],
- [ 0.5880, -0.4676, 1.5709, 0.3084, -0.3748, -0.1612, 0.3931,
- 0.1313],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.5891, -0.3937, 1.1494, -1.2390, -0.5076, -0.9695, 0.4797,
- 0.1390],
- [ 0.5878, -0.4052, 1.0229, -1.2855, -0.5596, -0.8232, 0.5316,
- 0.2699],
- [ 0.6191, -0.4297, 1.4612, 0.2391, -0.4961, 0.0313, 1.1166,
- 0.1768]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1294, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1294, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8413371220231056
- step: 17
- running loss: 0.04949041894253563
- Train Steps: 17/90 Loss: 0.0495 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6189, 0.4029, 0.8375, 0.5767, 0.4745, 0.4829, 0.5551, 0.5598],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960],
- [0.6176, 0.4030, 0.8850, 0.4850, 0.3688, 0.4050, 0.5312, 0.5783],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3496, -0.6161, 1.8231, -0.7200, -0.6343, -0.1043, 0.8363, 0.1154],
- [ 0.2936, -0.5610, 1.7201, -0.4867, -0.2092, -0.3321, 0.2514, 0.2062],
- [ 0.5884, -0.4009, 1.6049, 0.0735, -0.1333, -0.5680, 0.4170, 0.2590],
- [-0.0426, -0.8502, 1.6135, 0.1152, -0.2904, -0.5447, 0.2721, 0.1971],
- [-0.1033, -0.8852, 2.0310, -0.9600, -0.3148, -1.1868, 1.0985, 0.2178],
- [ 0.2483, -0.6758, 1.7849, -0.3754, -0.3378, -0.4320, 0.2930, 0.1095],
- [-0.0405, -0.8126, 1.6816, -0.3414, -0.7438, -0.7737, 0.2725, 0.1434],
- [ 0.3748, -0.5497, 1.3174, -1.2418, -0.7503, -0.8426, 0.5530, 0.2293]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5886, -0.4253, 1.8654, -0.3460, -0.5480, 0.3623, 0.6587,
- 0.1005],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853],
- [ 0.5726, -0.4249, 1.5824, 0.3777, -0.0942, -0.0556, 0.2782,
- 0.2997],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610],
- [ 0.6388, -0.3623, 1.9173, -0.7386, -0.3517, -0.5846, 1.1495,
- 0.2676],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051],
- [ 0.5667, -0.4246, 1.8018, -0.0457, -0.5827, -0.4152, 0.1679,
- 0.3854],
- [ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0813, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0813, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9225974231958389
- step: 18
- running loss: 0.05125541239976883
- Train Steps: 18/90 Loss: 0.0513 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6198, 0.4101, 0.8838, 0.5283, 0.3763, 0.5267, 0.5913, 0.5567],
- [ nan, nan, 0.7425, 0.2117, 0.3937, 0.2433, 0.5438, 0.5567],
- [0.6146, 0.4124, 0.8800, 0.4867, 0.3800, 0.4500, 0.5106, 0.5524],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6268, 0.4102, 0.8938, 0.3667, 0.4025, 0.2833, 0.6275, 0.5183],
- [0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4430, -0.4844, 1.7350, -0.4449, -0.5268, -0.4250, 0.4806, 0.1754],
- [ 0.7304, -0.3097, 1.7664, -0.6020, -0.5014, -0.2737, 0.5494, 0.1550],
- [ 0.8088, -0.2873, 1.7017, -0.1383, -0.4139, -0.1332, 0.5881, 0.2076],
- [-2.3069, -2.3295, 1.1612, -1.1341, -0.2896, -1.2715, 0.1326, 0.3356],
- [ 0.6132, -0.3375, 1.7022, -0.2673, -0.5030, -0.4217, 0.3474, 0.1730],
- [ 0.4882, -0.4704, 1.8124, -0.1721, -0.5237, -0.8142, 0.6059, 0.0388],
- [ 0.4792, -0.4727, 1.7930, -0.9632, -0.2783, -1.1860, 0.7636, 0.1998],
- [ 0.5194, -0.4366, 1.5938, -0.0664, -0.4184, -0.2661, 0.3575, 0.1813]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5865, -0.3973, 1.8423, -0.0688, -0.5192, -0.2305, 0.4162,
- 0.1159],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.5770, -0.3918, 1.7961, 0.1544, -0.5480, 0.1467, 0.4450,
- 0.2853],
- [-2.2859, -2.2859, 1.1436, -1.3082, -0.4672, -1.1620, 0.2256,
- 0.2853],
- [ 0.5529, -0.3811, 1.7788, -0.0380, -0.5307, -0.2074, 0.0727,
- 0.2657],
- [ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.6092, -0.3913, 1.8423, -0.5923, -0.4268, -0.9772, 0.6125,
- 0.1082],
- [ 0.5079, -0.4714, 1.6575, 0.2237, -0.4961, 0.0774, 0.1465,
- -0.1061]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0290, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0290, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9516376163810492
- step: 19
- running loss: 0.050086190335844695
- Train Steps: 19/90 Loss: 0.0501 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6272, 0.4120, 0.9038, 0.4117, 0.3725, 0.3200, 0.6175, 0.5250],
- [0.6135, 0.3994, 0.7913, 0.3050, 0.3625, 0.3050, 0.5837, 0.5050],
- [0.6206, 0.4001, 0.8900, 0.3933, 0.3588, 0.3567, 0.5837, 0.5083],
- [0.6167, 0.4048, 0.6831, 0.3639, 0.3763, 0.3017, 0.5700, 0.5883],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [0.6199, 0.4093, 0.7913, 0.2533, 0.4288, 0.2467, 0.5975, 0.5700],
- [0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1724, -0.7311, 1.9600, -0.4847, -0.5312, -0.7648, 0.7926, 0.1386],
- [ 0.4509, -0.5342, 1.4227, -1.0582, -0.5859, -0.7999, 0.5457, 0.1146],
- [ 0.0037, -0.8193, 1.9162, -0.4894, -0.6796, -0.5751, 0.5336, 0.0400],
- [-0.0795, -0.8154, 1.1245, -0.8385, -0.5245, -0.8974, 0.2899, 0.3562],
- [ 0.7510, -0.2744, 1.6003, -0.7833, -0.2182, -1.1405, 0.3211, 0.1225],
- [ 0.1794, -0.6694, 1.7174, 0.0391, -0.2750, 0.1796, 0.3027, 0.1166],
- [-0.3175, -1.0336, 1.3863, -1.1355, -0.3639, -1.0212, 0.5602, 0.2073],
- [ 0.6083, -0.3555, 1.6484, 0.3436, -0.0741, -0.2059, 0.3531, 0.2449]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6111, -0.3828, 1.8885, -0.3844, -0.5654, -0.8079, 0.5663,
- 0.1390],
- [ 0.5477, -0.4413, 1.3688, -0.8771, -0.6115, -0.8771, 0.4104,
- 0.0467],
- [ 0.5807, -0.4378, 1.8249, -0.4691, -0.6289, -0.6385, 0.4104,
- 0.0620],
- [ 0.5626, -0.4162, 0.8692, -0.6051, -0.5480, -0.8925, 0.3469,
- 0.4316],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386],
- [ 0.5771, -0.3955, 1.3688, -1.1158, -0.3055, -1.1466, 0.4739,
- 0.3469],
- [ 0.5959, -0.3579, 1.6055, 0.3623, -0.0573, -0.2074, 0.3122,
- 0.4547]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0521, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0521, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0037182997912169
- step: 20
- running loss: 0.05018591498956084
- Train Steps: 20/90 Loss: 0.0502 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309],
- [0.6275, 0.4050, 0.9038, 0.3767, 0.3838, 0.3533, 0.7074, 0.5575],
- [0.6125, 0.3983, 0.8750, 0.4867, 0.4275, 0.4783, 0.5413, 0.5717]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1581, -0.7393, 1.1670, -1.3299, -0.6123, -1.1580, 0.3761, 0.1508],
- [ 0.7792, -0.3083, 1.7652, 0.1693, -0.4503, -0.1500, 0.5473, 0.1806],
- [-0.0364, -0.8090, 1.1568, -1.1909, -0.6619, -0.9205, 0.3632, 0.2406],
- [ 0.1614, -0.6613, 1.7122, -0.0781, -0.0744, -0.1382, 0.0251, 0.1446],
- [ 0.1000, -0.7275, 1.7813, -0.1652, -0.1890, -0.0821, 0.1867, 0.1571],
- [ 0.4845, -0.4482, 1.8444, -0.0939, -0.4233, -0.6957, 0.6399, 0.1452],
- [ 0.5154, -0.4710, 1.9175, -0.6788, -0.4608, -0.8603, 0.9891, 0.1327],
- [ 0.1317, -0.7220, 1.7761, -0.2175, -0.3860, -0.1320, 0.3566, 0.1352]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532],
- [ 0.5359, -0.4370, 1.7095, -0.0303, -0.0804, -0.0380, 0.1044,
- 0.3392],
- [ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661],
- [ 0.6125, -0.4153, 1.8885, -0.5461, -0.5134, -0.6539, 0.9814,
- 0.2890],
- [ 0.5432, -0.4462, 1.7557, -0.0380, -0.3113, -0.0765, 0.2141,
- 0.3546]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0353, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0353, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0390332210808992
- step: 21
- running loss: 0.049477772432423774
- Train Steps: 21/90 Loss: 0.0495 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309],
- [0.6339, 0.4118, 0.7988, 0.5800, 0.3912, 0.4583, 0.7343, 0.5760],
- [0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760],
- [0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927],
- [0.6139, 0.4019, 0.7137, 0.2150, 0.4375, 0.1533, 0.5293, 0.5006],
- [0.6058, 0.3986, 0.8324, 0.4626, 0.3838, 0.4983, 0.5147, 0.5466],
- [0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528],
- [0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5319, -0.4074, 1.9047, -0.0513, -0.4148, -0.4593, 0.4412, 0.1348],
- [ 0.2248, -0.6662, 1.6434, -0.0576, -0.4394, -0.2607, 0.5983, 0.2211],
- [ 0.5160, -0.4834, 1.7566, 0.1108, -0.4688, 0.0316, 0.6468, 0.1698],
- [ 0.5514, -0.4455, 1.8779, 0.2109, -0.5295, -0.2038, 0.3353, -0.0306],
- [ 0.0569, -0.7394, 0.9787, -1.4228, -0.3317, -1.4684, 0.0992, 0.2083],
- [ 0.1960, -0.6464, 1.6755, -0.2134, -0.4800, 0.0778, 0.1021, 0.1230],
- [ 0.3866, -0.5533, 2.0356, -0.8076, -0.3010, -0.5609, 0.8130, 0.1686],
- [ 0.2819, -0.5713, 1.0122, -1.2399, -0.3901, -1.1646, 0.2829, 0.3324]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661],
- [ 0.6421, -0.3840, 1.4035, 0.3931, -0.4788, -0.1689, 1.1057,
- 0.3745],
- [ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745],
- [ 0.6335, -0.4162, 1.7499, 0.3084, -0.4961, -0.2459, 0.6524,
- -0.0102],
- [ 0.5496, -0.4295, 1.0109, -1.2928, -0.2651, -1.5777, 0.1589,
- 0.0261],
- [ 0.5124, -0.4446, 1.5587, -0.1493, -0.5134, 0.0159, 0.0912,
- 0.2386],
- [ 0.6388, -0.3623, 1.9173, -0.7386, -0.3517, -0.5846, 1.1495,
- 0.2676],
- [ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0374, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0374, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0764061901718378
- step: 22
- running loss: 0.0489275540987199
- Train Steps: 22/90 Loss: 0.0489 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6108, 0.4011, 0.8037, 0.3400, 0.3700, 0.2933, 0.5658, 0.5617],
- [0.6127, 0.4066, 0.8550, 0.5567, 0.4662, 0.5141, 0.5070, 0.5412],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413],
- [0.6176, 0.3911, 0.8738, 0.4217, 0.3488, 0.4033, 0.6025, 0.4817],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6293, 0.3982, 0.8700, 0.5300, 0.3763, 0.4717, 0.7050, 0.5297],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4221, -0.4916, 1.3856, -0.8910, -0.5794, -0.8885, 0.3294, 0.2227],
- [ 0.3831, -0.5018, 1.5959, 0.2377, -0.1456, -0.1022, 0.0904, 0.2351],
- [ 0.6868, -0.3415, 1.8932, -0.6819, -0.1232, -1.1595, 0.8876, 0.1838],
- [ 0.5910, -0.4053, 1.6661, -0.5671, -0.6732, -0.4688, 0.3958, 0.1051],
- [ 0.2762, -0.5875, 1.6663, 0.0127, -0.2104, -0.0351, 0.2161, 0.1858],
- [ 0.2888, -0.6276, 1.7013, 0.0176, -0.4367, -0.1943, 0.6267, 0.1648],
- [ 0.4352, -0.4806, 1.5642, -0.6004, -0.6763, -0.3811, 0.2263, 0.1577],
- [ 0.2547, -0.6514, 1.8138, -0.4008, -0.4382, 0.1134, 0.4997, 0.0756]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5355, -0.4335, 1.4266, -0.7155, -0.5769, -0.9310, 0.3275,
- 0.3087],
- [ 0.5443, -0.4079, 1.6633, 0.2853, -0.1325, 0.0888, 0.0558,
- 0.2138],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142],
- [ 0.5669, -0.4794, 1.7499, -0.3383, -0.6751, -0.4229, 0.4970,
- -0.0611],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.6207, -0.4466, 1.7326, 0.1621, -0.5480, -0.1073, 0.9704,
- 0.1608],
- [ 0.5367, -0.4294, 1.5709, -0.4999, -0.6693, -0.3075, 0.2455,
- 0.3559],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0177, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0177, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0940845366567373
- step: 23
- running loss: 0.04756889289811901
- Train Steps: 23/90 Loss: 0.0476 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6117, 0.4018, 0.6562, 0.1967, 0.3738, 0.2550, 0.5280, 0.5103],
- [0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783],
- [ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194],
- [0.6203, 0.4076, 0.8611, 0.2878, 0.4050, 0.2554, 0.5907, 0.5496],
- [0.6236, 0.4084, 0.7738, 0.2133, 0.3663, 0.3233, 0.5813, 0.5567],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167],
- [0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [0.6125, 0.3983, 0.8750, 0.4867, 0.4275, 0.4783, 0.5413, 0.5717]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4754, -0.4291, 0.9876, -0.9167, -0.4275, -0.9420, 0.2557, 0.2084],
- [ 0.5949, -0.3673, 0.9835, -0.8483, -0.4139, -1.0030, 0.3359, 0.2510],
- [-1.5239, -1.7801, 1.4724, -0.5498, -0.4969, -0.6206, 0.1061, 0.2190],
- [ 1.0447, -0.0863, 1.6853, -0.6476, -0.2820, -0.6336, 0.6414, 0.1602],
- [ 0.7545, -0.2793, 1.3460, -0.7836, -0.4708, -0.6146, 0.4870, 0.2678],
- [ 0.8902, -0.1837, 1.6729, -0.4024, -0.5099, -0.6107, 0.3860, 0.1088],
- [ 0.6083, -0.3687, 1.9450, 0.3126, -0.4421, 0.1083, 0.5850, 0.1294],
- [ 0.5531, -0.4151, 1.8876, 0.2921, -0.2572, 0.2739, 0.4322, 0.0999]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5393, -0.4299, 0.7453, -1.3775, -0.5596, -1.1081, 0.1527,
- 0.0712],
- [ 0.5483, -0.3991, 0.8030, -1.2159, -0.5018, -1.1928, 0.2624,
- 0.3852],
- [-2.2859, -2.2859, 1.5478, -0.8309, -0.6289, -0.7232, 0.1198,
- 0.1133],
- [ 0.5790, -0.4031, 1.6915, -0.9564, -0.4152, -1.1063, 0.4425,
- 0.2528],
- [ 0.5946, -0.3995, 1.2880, -1.3005, -0.5942, -0.7925, 0.3988,
- 0.2853],
- [ 0.5483, -0.4249, 1.5305, -0.7386, -0.6115, -0.8694, 0.3353,
- 0.1005],
- [ 0.5865, -0.3973, 1.8423, -0.0688, -0.5192, -0.2305, 0.4162,
- 0.1159],
- [ 0.5432, -0.4462, 1.7557, -0.0380, -0.3113, -0.0765, 0.2141,
- 0.3546]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0574, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0574, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1515279468148947
- step: 24
- running loss: 0.04798033111728728
- Train Steps: 24/90 Loss: 0.0480 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6263, 0.4029, 0.8900, 0.4933, 0.4075, 0.5183, 0.6406, 0.4758],
- [0.6289, 0.4081, 0.8720, 0.3487, 0.3900, 0.3183, 0.6703, 0.5376],
- [ nan, nan, 0.7725, 0.2611, 0.3675, 0.2733, 0.5413, 0.5167],
- [0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672],
- [0.6075, 0.4000, 0.8513, 0.5183, 0.4510, 0.5329, 0.5180, 0.5280],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6179, 0.4008, 0.8600, 0.4015, 0.3932, 0.2515, 0.5711, 0.5438],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5418, -0.4124, 1.8235, -0.1119, -0.3812, 0.1074, 0.7229, 0.1254],
- [ 0.6203, -0.3487, 1.6785, -0.5720, -0.4225, -0.8041, 0.6948, 0.1912],
- [-1.7598, -1.9331, 1.1522, -1.0600, -0.4270, -0.9675, -0.0287, 0.3160],
- [ 1.1561, -0.0339, 1.7192, 0.2305, -0.5978, -0.1364, 0.6343, 0.0543],
- [ 0.7348, -0.2362, 1.6043, 0.1218, -0.2008, 0.1591, 0.0928, 0.1539],
- [ 0.6645, -0.3278, 1.6605, 0.1640, -0.3562, 0.2098, 0.6699, 0.1587],
- [ 0.8523, -0.1380, 1.3972, -0.5103, -0.4060, -0.9285, 0.1484, 0.2288],
- [ 0.9301, -0.1348, 1.4233, -0.9516, -0.4670, -0.7360, 0.4224, 0.2452]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6072, -0.4250, 1.8249, -0.0072, -0.4037, 0.1082, 0.6730,
- -0.0881],
- [ 0.6191, -0.4008, 1.7420, -0.6753, -0.4845, -0.8156, 0.8101,
- 0.1974],
- [-2.2859, -2.2859, 1.2820, -1.0801, -0.5885, -1.0234, 0.2141,
- 0.1005],
- [ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278],
- [ 0.5202, -0.4382, 1.6460, 0.1082, -0.2029, 0.1754, 0.1067,
- 0.1530],
- [ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.5680, -0.4345, 1.6864, -0.4315, -0.4698, -1.1241, 0.3518,
- 0.2261],
- [ 0.5778, -0.3930, 1.5189, -0.9387, -0.4326, -0.9156, 0.4855,
- 0.3392]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0297, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0297, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1811874378472567
- step: 25
- running loss: 0.04724749751389026
- Train Steps: 25/90 Loss: 0.0472 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6129, 0.4114, 0.6950, 0.2467, 0.4050, 0.2133, 0.5363, 0.5550],
- [0.6162, 0.4014, 0.8800, 0.5333, 0.3750, 0.4817, 0.5988, 0.5283],
- [0.6357, 0.4097, 0.9038, 0.3883, 0.4213, 0.2950, 0.6686, 0.5390],
- [0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.1092, -0.0598, 1.7175, -0.5010, -0.7258, -0.0769, 0.5840, 0.1207],
- [ 0.7990, -0.2687, 1.0544, -0.9522, -0.4777, -1.0258, 0.1616, 0.2518],
- [ 0.6795, -0.3569, 1.8876, 0.3645, -0.4024, 0.2626, 0.5803, 0.1490],
- [ 0.8305, -0.2397, 1.7730, -0.4483, -0.3952, -0.6633, 0.7930, 0.1416],
- [ 1.0673, -0.0639, 1.7255, 0.5401, -0.5086, -0.2005, 0.5255, 0.2547],
- [ 0.7767, -0.2754, 1.1634, -1.0008, -0.5500, -0.8639, 0.2313, 0.1580],
- [-1.7676, -1.9638, 1.1525, -1.1372, -0.3661, -1.0775, 0.0339, 0.3052],
- [ 0.5077, -0.4275, 1.8660, 0.0588, -0.0673, 0.2542, 0.3526, 0.2022]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.5448, -0.3859, 0.9242, -1.1466, -0.4152, -1.3005, 0.1910,
- 0.2776],
- [ 0.5603, -0.4319, 1.7788, 0.1775, -0.5538, -0.0611, 0.4797,
- 0.1544],
- [ 0.6504, -0.3936, 1.8885, -0.4922, -0.3402, -0.9233, 0.8022,
- 0.2035],
- [ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373],
- [ 0.5505, -0.4207, 1.7095, -0.0534, -0.0509, 0.1050, 0.3873,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0428, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0428, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2239569071680307
- step: 26
- running loss: 0.047075265660308875
- Train Steps: 26/90 Loss: 0.0471 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6138, 0.4101, 0.8800, 0.5083, 0.4637, 0.5950, 0.5587, 0.5077],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320],
- [0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167],
- [0.6277, 0.4036, 0.8688, 0.3617, 0.3925, 0.2600, 0.6132, 0.4742],
- [0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5252, -0.4151, 1.6701, 0.0175, -0.1096, 0.1072, 0.4594, 0.2154],
- [ 0.4531, -0.4956, 1.5928, 0.0686, -0.4088, 0.0361, 0.6365, 0.2085],
- [ 0.2903, -0.5712, 1.5617, -0.4637, -0.6604, -0.6051, 0.1524, 0.2101],
- [ 0.3504, -0.5716, 1.7172, -0.3043, -0.4345, 0.1715, 0.5451, 0.1113],
- [ 0.3246, -0.5665, 1.6040, -0.0296, -0.3346, 0.0444, 0.3115, 0.2818],
- [ 0.7752, -0.2857, 1.4962, -0.7700, -0.5900, -0.9204, 0.3944, 0.1760],
- [ 1.0919, -0.0991, 1.5440, -0.8091, -0.4919, -1.0663, 0.7723, 0.1144],
- [ 0.3260, -0.5237, 1.5657, -0.0825, -0.6330, -0.5299, 0.1789, 0.2688]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5491, -0.3918, 1.7788, 0.0620, -0.1439, 0.4624, 0.2946,
- 0.0592],
- [ 0.6273, -0.4393, 1.6402, 0.1313, -0.5076, 0.0467, 1.1532,
- 0.1715],
- [ 0.5433, -0.4032, 1.6344, -0.4922, -0.5769, -0.5846, 0.0357,
- 0.2567],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.5539, -0.4031, 1.7168, -0.0594, -0.3748, 0.0543, 0.1390,
- 0.3777],
- [ 0.5483, -0.4249, 1.5305, -0.7386, -0.6115, -0.8694, 0.3353,
- 0.1005],
- [ 0.6134, -0.4218, 1.7268, -0.6154, -0.4730, -1.0850, 0.5463,
- -0.0957],
- [ 0.5454, -0.4053, 1.6633, -0.1766, -0.6058, -0.5923, 0.1577,
- 0.4357]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0250, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0250, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2489608246833086
- step: 27
- running loss: 0.046257808321604026
- Train Steps: 27/90 Loss: 0.0463 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300],
- [0.6127, 0.4084, 0.8700, 0.4467, 0.3987, 0.4317, 0.5013, 0.5471],
- [0.6200, 0.4055, 0.8011, 0.2988, 0.3842, 0.2798, 0.5949, 0.5183],
- [0.6262, 0.4052, 0.8888, 0.4700, 0.3675, 0.5117, 0.6350, 0.5233],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6180, 0.4064, 0.7200, 0.2467, 0.4275, 0.2367, 0.5963, 0.6217],
- [0.6206, 0.4001, 0.8900, 0.3933, 0.3588, 0.3567, 0.5837, 0.5083]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4644, -0.4701, 1.7021, 0.0982, -0.1020, 0.1515, 0.2594, 0.2304],
- [ 0.6701, -0.3498, 1.1743, -1.0372, -0.5572, -0.7721, 0.4599, 0.2256],
- [ 0.5408, -0.4026, 1.7313, 0.0126, -0.4380, -0.1173, 0.1420, 0.1929],
- [ 0.6182, -0.3839, 1.3790, -0.8283, -0.5608, -0.8546, 0.4777, 0.1537],
- [ 0.7149, -0.3733, 1.9399, 0.0853, -0.4579, 0.1658, 0.8671, 0.1258],
- [ 0.4842, -0.4774, 1.7424, 0.0258, -0.5271, -0.1801, 0.4708, 0.1866],
- [ 0.0581, -0.7235, 1.0430, -0.9465, -0.4204, -1.1660, 0.2980, 0.3445],
- [ 0.5249, -0.4811, 1.8443, -0.2301, -0.6742, -0.5042, 0.4780, 0.0843]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5505, -0.4207, 1.7095, -0.0534, -0.0509, 0.1050, 0.3873,
- 0.3007],
- [ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621],
- [ 0.5441, -0.3997, 1.7326, -0.2228, -0.4441, -0.2921, 0.0296,
- 0.2409],
- [ 0.5781, -0.4129, 1.4142, -0.9057, -0.5115, -0.9937, 0.4621,
- 0.1080],
- [ 0.6063, -0.4142, 1.8192, -0.1150, -0.5885, 0.0774, 0.6471,
- 0.1313],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.5687, -0.4088, 1.0397, -1.1466, -0.3113, -1.1928, 0.4681,
- 0.5855],
- [ 0.5807, -0.4378, 1.8249, -0.4691, -0.6289, -0.6385, 0.4104,
- 0.0620]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0180, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0180, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2669219318777323
- step: 28
- running loss: 0.04524721185277615
- Train Steps: 28/90 Loss: 0.0452 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4049, 0.8638, 0.5617, 0.4125, 0.5100, 0.6013, 0.5317],
- [0.6126, 0.4073, 0.8750, 0.5133, 0.3800, 0.4333, 0.4986, 0.5378],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6200, 0.3993, 0.8639, 0.3687, 0.3658, 0.3139, 0.6002, 0.5374],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3292, -0.6127, 1.6667, 0.2913, -0.3799, 0.2480, 0.4851, 0.1940],
- [ 0.5045, -0.4484, 1.7734, 0.2048, -0.5201, -0.0983, 0.3444, 0.1669],
- [ 0.9764, -0.1649, 1.7343, -0.0197, -0.3502, 0.1187, 0.3336, 0.2008],
- [ 0.6445, -0.4002, 1.4565, -1.2108, -0.2055, -1.2694, 0.7895, 0.1524],
- [ 0.4371, -0.5265, 1.5985, -0.6078, -0.6801, -0.6091, 0.4320, 0.1052],
- [ 0.9756, -0.1525, 1.5874, 0.0874, -0.4362, -0.7796, 0.5513, 0.2914],
- [-0.1907, -0.9122, 1.2446, -1.0707, -0.4485, -0.9089, 0.2790, 0.2335],
- [ 0.3977, -0.5413, 1.5528, -0.5059, -0.7190, -0.1582, 0.2814, 0.1526]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5776, -0.4159, 1.7037, 0.3084, -0.3806, 0.0697, 0.4912,
- 0.1698],
- [ 0.5435, -0.4045, 1.7557, 0.0851, -0.5307, -0.2844, 0.0172,
- 0.1980],
- [ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057],
- [ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.5777, -0.4416, 1.7044, -0.5827, -0.5962, -0.8361, 0.4862,
- 0.1963],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.5367, -0.4294, 1.5709, -0.4999, -0.6693, -0.3075, 0.2455,
- 0.3559]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1221, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1221, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3889758083969355
- step: 29
- running loss: 0.04789571753092881
- Train Steps: 29/90 Loss: 0.0479 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6270, 0.4267, 0.7150, 0.3317, 0.4900, 0.1817, 0.5560, 0.6183],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [0.6200, 0.3978, 0.8900, 0.4550, 0.3775, 0.5200, 0.6150, 0.5367],
- [0.6086, 0.3981, 0.8700, 0.4750, 0.4512, 0.5283, 0.5324, 0.5038],
- [0.6203, 0.4072, 0.8892, 0.3523, 0.3783, 0.3017, 0.5898, 0.5478],
- [ nan, nan, 0.6992, 0.2791, 0.3950, 0.2383, 0.5483, 0.5819],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6179, 0.3961, 0.8347, 0.6020, 0.3887, 0.4624, 0.5714, 0.5373]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6447, -0.3391, 1.0203, -1.0295, -0.2438, -1.3132, 0.3005, 0.3568],
- [ 0.7181, -0.3352, 1.7661, -0.0676, -0.5545, 0.0418, 0.3674, 0.1823],
- [ 0.3253, -0.6027, 1.8249, -0.0198, -0.5174, 0.2183, 0.4973, 0.1816],
- [ 1.0088, -0.1655, 1.8082, -0.0407, -0.3024, 0.0897, 0.3959, 0.1048],
- [ 0.6614, -0.4317, 1.8519, -0.5815, -0.5667, -0.8422, 0.6848, 0.1938],
- [-0.8125, -1.3321, 1.0198, -1.1514, -0.5100, -1.2320, 0.2646, 0.2864],
- [ 0.6633, -0.3855, 1.5774, -0.5996, -0.7283, -0.3927, 0.4222, 0.1267],
- [ 0.8054, -0.3088, 1.6503, 0.4464, -0.5051, -0.2037, 0.5452, 0.1913]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.1010e-01, -3.1524e-01, 1.0166e+00, -7.5396e-01, -2.2633e-02,
- -1.4468e+00, 2.8226e-01, 5.7018e-01],
- [ 5.3684e-01, -4.4057e-01, 1.7730e+00, -1.7660e-01, -5.2494e-01,
- -5.3426e-02, 2.3141e-01, 3.4688e-01],
- [ 5.7760e-01, -4.4842e-01, 1.8249e+00, -1.8430e-01, -5.4226e-01,
- 1.1594e-01, 5.5473e-01, 1.9292e-01],
- [ 5.2494e-01, -4.4734e-01, 1.7326e+00, -9.1917e-02, -2.0162e-01,
- 1.5443e-01, 1.7328e-01, 4.1158e-02],
- [ 5.7921e-01, -4.0523e-01, 1.8214e+00, -6.5874e-01, -5.3842e-01,
- -8.9239e-01, 4.3812e-01, 2.4425e-01],
- [-2.2859e+00, -2.2859e+00, 9.4385e-01, -9.9666e-01, -4.6143e-01,
- -1.1851e+00, 2.4679e-01, 4.0188e-01],
- [ 5.2061e-01, -4.6028e-01, 1.4670e+00, -4.7683e-01, -6.5774e-01,
- -3.9985e-01, 1.8356e-01, 2.0831e-03],
- [ 5.6801e-01, -4.5619e-01, 1.5697e+00, 4.9469e-01, -4.9038e-01,
- -1.5026e-01, 3.5357e-01, 1.9563e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0670, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0670, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4559749905019999
- step: 30
- running loss: 0.048532499683399996
- Train Steps: 30/90 Loss: 0.0485 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6339, 0.4159, 0.8400, 0.5617, 0.3825, 0.4150, 0.7343, 0.5748],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117],
- [0.6150, 0.4097, 0.7468, 0.3194, 0.3825, 0.2633, 0.5463, 0.5800],
- [0.6125, 0.3999, 0.8750, 0.4883, 0.4750, 0.4700, 0.5533, 0.5617],
- [0.6179, 0.4040, 0.7412, 0.1850, 0.3825, 0.2783, 0.5837, 0.5600],
- [0.6160, 0.4086, 0.8775, 0.3983, 0.3750, 0.3783, 0.5128, 0.4882],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3969, -0.5640, 1.6768, 0.0966, -0.4565, -0.4329, 0.6561, 0.2496],
- [ 0.2968, -0.6093, 1.6604, -0.1043, -0.5758, -0.1632, 0.3509, 0.3169],
- [ 0.7539, -0.3650, 1.8065, -0.1882, -0.5726, -0.0869, 0.6342, 0.1433],
- [ 0.2691, -0.5931, 1.1348, -1.0432, -0.6014, -1.0747, 0.2123, 0.2751],
- [ 0.8217, -0.2588, 1.6736, -0.1274, -0.1709, -0.0524, 0.2523, 0.2500],
- [ 0.5199, -0.5038, 1.1993, -1.2389, -0.5359, -1.1787, 0.5091, 0.2795],
- [ 0.3096, -0.5846, 1.7719, -0.4621, -0.6384, -0.5422, 0.2241, 0.1382],
- [ 0.4792, -0.4978, 1.7307, 0.2366, -0.4688, -0.1269, 0.5286, 0.1288]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6421, -0.3647, 1.5940, 0.3084, -0.5192, -0.3691, 1.1057,
- 0.3692],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774],
- [ 0.5548, -0.3936, 1.1634, -0.8105, -0.5192, -1.0696, 0.2372,
- 0.3931],
- [ 0.5432, -0.4388, 1.7557, -0.0303, -0.0919, -0.1150, 0.2699,
- 0.3087],
- [ 0.5680, -0.4201, 1.1379, -1.4314, -0.5192, -1.0003, 0.4104,
- 0.3007],
- [ 0.5595, -0.3988, 1.7672, -0.4460, -0.5538, -0.5384, 0.0828,
- -0.0310],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4763062540441751
- step: 31
- running loss: 0.04762278238852178
- Train Steps: 31/90 Loss: 0.0476 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6109, 0.4041, 0.6975, 0.3167, 0.3513, 0.3383, 0.5153, 0.5319],
- [0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6139, 0.4019, 0.7137, 0.2150, 0.4375, 0.1533, 0.5293, 0.5006],
- [0.6224, 0.4061, 0.8988, 0.4300, 0.3838, 0.4750, 0.6112, 0.5483],
- [0.6357, 0.4159, 0.8788, 0.5583, 0.3638, 0.4433, 0.6488, 0.5297],
- [0.6250, 0.4103, 0.8950, 0.4400, 0.3912, 0.5650, 0.6050, 0.5133],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2273, -0.6596, 1.1092, -0.9489, -0.6768, -0.8663, 0.0910, 0.2431],
- [ 0.7486, -0.3217, 1.6883, 0.0425, -0.3411, -0.1704, 0.2981, 0.2276],
- [ 0.5208, -0.4361, 1.6414, 0.4044, -0.4322, -0.2129, 0.3308, 0.3269],
- [-0.0311, -0.8324, 1.0396, -1.3393, -0.3552, -1.6871, 0.3319, 0.1816],
- [ 0.7902, -0.3247, 1.8514, -0.2893, -0.5331, -0.0267, 0.5042, 0.1592],
- [ 0.3829, -0.5580, 1.7809, 0.2276, -0.5510, -0.3927, 0.5745, 0.2348],
- [ 0.6664, -0.3745, 1.8520, -0.2423, -0.4796, 0.2054, 0.6264, 0.1599],
- [ 0.4054, -0.5725, 1.5891, -0.7661, -0.6209, -0.6981, 0.5075, 0.3368]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5359, -0.4193, 0.9358, -0.8232, -0.6635, -0.7232, 0.0943,
- 0.1710],
- [ 0.5525, -0.4463, 1.7326, 0.0313, -0.2536, -0.0688, 0.1968,
- 0.3700],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007],
- [ 0.5496, -0.4295, 1.0109, -1.2928, -0.2651, -1.5777, 0.1589,
- 0.0261],
- [ 0.5888, -0.4101, 1.8654, -0.2998, -0.5134, -0.0919, 0.5374,
- 0.2468],
- [ 0.6504, -0.3647, 1.7730, 0.2930, -0.6058, -0.2382, 0.7109,
- 0.1608],
- [ 0.6010, -0.3909, 1.8480, -0.2536, -0.4788, 0.3238, 0.5085,
- 0.0851],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0216, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0216, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4978971872478724
- step: 32
- running loss: 0.04680928710149601
- Train Steps: 32/90 Loss: 0.0468 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6202, 0.4066, 0.8746, 0.3376, 0.3717, 0.3090, 0.5842, 0.5165],
- [ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194],
- [0.6179, 0.4008, 0.8600, 0.4015, 0.3932, 0.2515, 0.5711, 0.5438],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417],
- [0.6279, 0.4008, 0.8600, 0.4883, 0.4325, 0.5283, 0.7010, 0.5378],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6265, 0.4251, 0.7113, 0.3550, 0.4375, 0.2117, 0.5587, 0.6118]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6682, -0.3635, 1.1771, -0.8218, -0.3968, -0.9152, 0.4136, 0.3601],
- [ 0.6833, -0.3600, 1.7315, -0.4808, -0.6057, -0.7120, 0.4325, 0.1391],
- [-1.7206, -1.9316, 1.4490, -0.7641, -0.6125, -0.7112, 0.2333, 0.2175],
- [ 0.4376, -0.4658, 1.5066, -0.4000, -0.4101, -0.9178, 0.2045, 0.2454],
- [ 0.2897, -0.5951, 1.3823, -0.8484, -0.4510, -0.7861, 0.5288, 0.2703],
- [ 1.0572, -0.1334, 1.7365, 0.1202, -0.3583, 0.3940, 0.6201, 0.1901],
- [ 1.1275, -0.1140, 1.7753, 0.0943, -0.6044, -0.1970, 0.5039, 0.1713],
- [ 0.6546, -0.3253, 1.1122, -0.6033, -0.3702, -0.9839, 0.1899, 0.3367]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084],
- [ 0.5787, -0.4077, 1.7537, -0.7263, -0.5692, -0.8586, 0.4126,
- 0.1000],
- [-2.2859, -2.2859, 1.5478, -0.8309, -0.6289, -0.7232, 0.1198,
- 0.1133],
- [ 0.5680, -0.4345, 1.6864, -0.4315, -0.4698, -1.1241, 0.3518,
- 0.2261],
- [ 0.5977, -0.3792, 1.2995, -1.0311, -0.5192, -0.8386, 0.5836,
- 0.2160],
- [ 0.6141, -0.4345, 1.6864, -0.0303, -0.2882, 0.1544, 0.9521,
- 0.1982],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [ 0.6077, -0.3226, 0.9993, -0.6462, -0.2651, -1.3082, 0.2946,
- 0.5401]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0360, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0360, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.533901834860444
- step: 33
- running loss: 0.04648187378364982
- Train Steps: 33/90 Loss: 0.0465 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6147, 0.4107, 0.8137, 0.3333, 0.3750, 0.2683, 0.5006, 0.5412],
- [0.6205, 0.4062, 0.8337, 0.2683, 0.3675, 0.4283, 0.6338, 0.5250],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150],
- [0.6364, 0.4165, 0.9088, 0.4367, 0.4075, 0.3150, 0.6448, 0.5297],
- [0.6201, 0.4036, 0.8596, 0.3850, 0.3492, 0.3785, 0.5978, 0.5131],
- [0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1480, -0.7081, 1.4007, -0.5825, -0.5700, -1.0408, 0.2384, 0.2237],
- [ 0.5896, -0.4433, 1.4925, -0.8253, -0.5779, -0.3485, 0.6271, 0.2747],
- [ 0.3425, -0.5901, 1.0766, -0.9527, -0.5053, -1.1356, 0.2093, 0.2348],
- [ 0.8501, -0.2678, 1.8594, 0.1508, -0.2696, 0.3967, 0.5798, 0.2531],
- [ 0.2669, -0.6123, 1.8149, -0.1087, -0.3577, -0.8990, 0.6010, 0.2930],
- [ 0.7211, -0.3709, 1.6834, -0.3010, -0.6311, -0.5038, 0.3394, 0.2056],
- [ 0.0754, -0.7388, 1.3125, -0.8194, -0.4243, -1.1414, 0.2379, 0.2833],
- [ 0.3136, -0.6088, 1.6698, -0.2275, -0.5166, -0.0057, 0.3620, 0.2574]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5532, -0.3888, 1.4727, -0.7463, -0.5538, -1.0465, 0.0265,
- 0.2138],
- [ 0.5799, -0.4099, 1.5651, -1.0465, -0.5885, -0.3075, 0.6413,
- 0.1390],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928],
- [ 0.6537, -0.3623, 1.9115, -0.2690, -0.4037, -0.8309, 0.6926,
- 0.1608],
- [ 0.5783, -0.4216, 1.6847, -0.5078, -0.6732, -0.5377, 0.4752,
- 0.0839],
- [ 0.5695, -0.3927, 1.3226, -0.9002, -0.4672, -1.1928, 0.1737,
- 0.3700],
- [ 0.5546, -0.4620, 1.7788, -0.4229, -0.5192, -0.0226, 0.4277,
- 0.2468]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0253, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0253, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5591852515935898
- step: 34
- running loss: 0.04585838975275264
- Train Steps: 34/90 Loss: 0.0459 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6142, 0.3982, 0.8650, 0.4883, 0.3912, 0.4317, 0.5315, 0.5350],
- [0.6272, 0.4120, 0.9038, 0.4117, 0.3725, 0.3200, 0.6175, 0.5250],
- [0.6171, 0.4127, 0.8900, 0.4800, 0.4325, 0.5783, 0.5769, 0.5090],
- [0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301],
- [0.6198, 0.4164, 0.8700, 0.5067, 0.4625, 0.5650, 0.5464, 0.5197],
- [0.6153, 0.4117, 0.8688, 0.5167, 0.4895, 0.5647, 0.5524, 0.5136]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3723, -0.5713, 1.5412, -1.1410, -0.4123, -1.2833, 0.7458, 0.2572],
- [-0.0439, -0.7983, 1.3700, -0.6209, -0.6206, -1.1788, 0.1694, 0.3199],
- [ 0.4568, -0.5009, 1.5959, -0.0868, -0.5835, -0.4344, 0.1355, 0.2190],
- [ 0.3854, -0.5706, 1.7432, -0.5431, -0.6401, -0.9886, 0.6589, 0.2581],
- [ 0.6753, -0.3385, 1.6879, -0.1744, -0.3901, 0.1000, 0.3988, 0.2561],
- [ 0.6335, -0.3926, 1.5830, -0.1774, -0.4380, -0.2220, 0.5967, 0.2536],
- [ 0.2948, -0.5686, 1.6185, -0.0778, -0.3593, -0.0700, 0.2592, 0.2595],
- [ 0.4754, -0.4777, 1.6022, -0.0768, -0.2555, -0.0874, 0.2974, 0.2777]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.5513, -0.4467, 1.7095, -0.0303, -0.4788, -0.2921, 0.1692,
- 0.1854],
- [ 0.6111, -0.3828, 1.8885, -0.3844, -0.5654, -0.8079, 0.5663,
- 0.1390],
- [ 0.5645, -0.3797, 1.8249, -0.0688, -0.2882, 0.3854, 0.3789,
- 0.0652],
- [ 0.6010, -0.4562, 1.7198, -0.0090, -0.3464, 0.0108, 0.6294,
- 0.1627],
- [ 0.5770, -0.3624, 1.7326, 0.0543, -0.1497, 0.3238, 0.2378,
- 0.1146],
- [ 0.5564, -0.3842, 1.7268, 0.1005, -0.0250, 0.3225, 0.2658,
- 0.0862]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0343, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0343, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.593443851917982
- step: 35
- running loss: 0.04552696719765663
- Train Steps: 35/90 Loss: 0.0455 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4106, 0.8025, 0.2583, 0.4550, 0.1867, 0.6281, 0.4869],
- [0.6264, 0.4035, 0.8888, 0.4883, 0.4050, 0.5217, 0.6361, 0.4791],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150],
- [0.6135, 0.4115, 0.8838, 0.4667, 0.4288, 0.6050, 0.5778, 0.5097],
- [0.6196, 0.4088, 0.8888, 0.4583, 0.4500, 0.5683, 0.6138, 0.5883],
- [0.6286, 0.4040, 0.8696, 0.3047, 0.3924, 0.2887, 0.6300, 0.5367],
- [0.6132, 0.3930, 0.8672, 0.4754, 0.3712, 0.5222, 0.5974, 0.5098],
- [0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1774, -0.6739, 1.3619, -1.0813, -0.2807, -1.6227, 0.5180, 0.2143],
- [ 0.3338, -0.5876, 1.7390, -0.0251, -0.5067, -0.1704, 0.4971, 0.2085],
- [ 0.4705, -0.4790, 1.7364, -0.0638, -0.3783, 0.0956, 0.4293, 0.2739],
- [ 0.6925, -0.3200, 1.7505, -0.1045, -0.3952, 0.1059, 0.3062, 0.2407],
- [ 0.6138, -0.3833, 1.7019, -0.2001, -0.2819, 0.0471, 0.4653, 0.3456],
- [ 0.4023, -0.5554, 1.5965, -0.7829, -0.4790, -1.2861, 0.6305, 0.2734],
- [ 0.3821, -0.5534, 1.6308, -0.1025, -0.5651, -0.0953, 0.3352, 0.2354],
- [ 0.2864, -0.6015, 1.1239, -0.9904, -0.6295, -1.1959, 0.0964, 0.2094]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6058, -0.3892, 1.4208, -1.0927, -0.1843, -1.4237, 0.6154,
- -0.0370],
- [ 0.6074, -0.4223, 1.8192, -0.0303, -0.4152, 0.1236, 0.6524,
- -0.0731],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928],
- [ 0.5477, -0.3851, 1.7961, -0.1304, -0.3055, 0.5085, 0.3830,
- 0.0682],
- [ 0.5759, -0.3978, 1.8192, -0.1689, -0.2074, 0.3392, 0.5490,
- 0.4316],
- [ 0.6174, -0.4201, 1.7309, -0.8784, -0.4735, -0.9524, 0.6242,
- 0.1931],
- [ 0.5466, -0.4706, 1.7198, -0.0903, -0.5712, 0.1261, 0.4733,
- 0.0688],
- [ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0277, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0277, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6211165934801102
- step: 36
- running loss: 0.045031016485558614
- Train Steps: 36/90 Loss: 0.0450 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6185, 0.4042, 0.7700, 0.2250, 0.4062, 0.2117, 0.5763, 0.5150],
- [0.6161, 0.4076, 0.8900, 0.4667, 0.4125, 0.5917, 0.6262, 0.5367],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6286, 0.4086, 0.8408, 0.2801, 0.4163, 0.2800, 0.6725, 0.5393],
- [0.6280, 0.4055, 0.8600, 0.5317, 0.3800, 0.4700, 0.6275, 0.5133],
- [0.6241, 0.4143, 0.8938, 0.4650, 0.4075, 0.5350, 0.6250, 0.5550],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3947, -0.4847, 1.5870, -0.3033, -0.5490, -0.3614, 0.0847, 0.3221],
- [ 0.1355, -0.6723, 1.1828, -1.0753, -0.4606, -1.4371, 0.2556, 0.1990],
- [ 0.5287, -0.4141, 1.7355, -0.2123, -0.3593, 0.2886, 0.3807, 0.2076],
- [ 0.3074, -0.5518, 1.6790, -0.8450, -0.1968, -1.2325, 0.5182, 0.1985],
- [ 0.5691, -0.4298, 1.5528, -0.9136, -0.3392, -1.1796, 0.6582, 0.2453],
- [ 0.5131, -0.4907, 1.6979, 0.1381, -0.5536, -0.2265, 0.5239, 0.1741],
- [ 0.5242, -0.4441, 1.7725, -0.1746, -0.4777, 0.0534, 0.4184, 0.2524],
- [ 0.4037, -0.5380, 1.6122, -0.0100, -0.4977, -0.0729, 0.4697, 0.2294]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145],
- [ 0.5707, -0.4189, 1.2707, -1.2467, -0.4095, -1.3082, 0.3758,
- 0.0928],
- [ 0.5598, -0.4032, 1.8249, -0.1304, -0.3806, 0.4470, 0.6067,
- 0.1929],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.6174, -0.3984, 1.5975, -0.9921, -0.3633, -0.9926, 0.8205,
- 0.2050],
- [ 0.6148, -0.4130, 1.6864, 0.1698, -0.5307, -0.1150, 0.6125,
- 0.0851],
- [ 0.5968, -0.3725, 1.8423, -0.1381, -0.4037, 0.1852, 0.6009,
- 0.2776],
- [ 0.6273, -0.4393, 1.6402, 0.1313, -0.5076, 0.0467, 1.1532,
- 0.1715]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0234, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0234, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6445499509572983
- step: 37
- running loss: 0.04444729597181887
- Train Steps: 37/90 Loss: 0.0444 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6222, 0.3957, 0.8838, 0.5017, 0.3937, 0.4600, 0.5900, 0.5017],
- [0.6276, 0.4095, 0.8237, 0.2250, 0.4662, 0.1783, 0.6171, 0.4869],
- [0.6100, 0.4071, 0.7601, 0.3444, 0.3400, 0.4117, 0.5625, 0.5617],
- [0.6030, 0.3969, 0.7988, 0.3917, 0.3450, 0.3667, 0.5266, 0.4700],
- [0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927],
- [0.6200, 0.3993, 0.8639, 0.3687, 0.3658, 0.3139, 0.6002, 0.5374],
- [0.6196, 0.4068, 0.7645, 0.2234, 0.4575, 0.1737, 0.5926, 0.5284],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1872, -0.6722, 1.8496, -0.0055, -0.4651, -0.0681, 0.4674, 0.1633],
- [ 0.4701, -0.4734, 1.5983, -1.1720, -0.0912, -1.0880, 0.6450, 0.1887],
- [ 0.2988, -0.5591, 1.3431, -0.8010, -0.6583, -0.3453, 0.2976, 0.2991],
- [ 0.4385, -0.4926, 1.5840, -0.5594, -0.6529, -0.5741, 0.2479, 0.1788],
- [ 0.5675, -0.4378, 1.8293, 0.2816, -0.5209, -0.2343, 0.4806, 0.1298],
- [ 0.4914, -0.4685, 1.6776, -0.5615, -0.5379, -0.7593, 0.4647, 0.1668],
- [ 0.6671, -0.3534, 1.2841, -1.0860, -0.1890, -1.3536, 0.4186, 0.2550],
- [ 0.5687, -0.3997, 1.7216, 0.1144, -0.1886, 0.1014, 0.2823, 0.2538]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5879, -0.4583, 1.7961, 0.0313, -0.4672, -0.1612, 0.4393,
- 0.0313],
- [ 0.6127, -0.3944, 1.5189, -1.2467, -0.1323, -1.4622, 0.5646,
- -0.0369],
- [ 0.5318, -0.4056, 1.2249, -0.6949, -0.7155, -0.3844, 0.3122,
- 0.3084],
- [ 0.4992, -0.4525, 1.4035, -0.4768, -0.6924, -0.5923, 0.1465,
- -0.1151],
- [ 0.6335, -0.4162, 1.7499, 0.3084, -0.4961, -0.2459, 0.6524,
- -0.0102],
- [ 0.5777, -0.4416, 1.7044, -0.5827, -0.5962, -0.8361, 0.4862,
- 0.1963],
- [ 0.5761, -0.4070, 1.2452, -1.2541, -0.1725, -1.4835, 0.4511,
- 0.1545],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0146, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0146, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6591525804251432
- step: 38
- running loss: 0.04366191001118798
- Train Steps: 38/90 Loss: 0.0437 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6095, 0.4002, 0.8533, 0.5168, 0.5031, 0.5094, 0.5125, 0.5433],
- [0.6135, 0.4115, 0.8838, 0.4667, 0.4288, 0.6050, 0.5778, 0.5097],
- [0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [ nan, nan, 0.6488, 0.1817, 0.4325, 0.1867, 0.5475, 0.5733],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [0.6201, 0.4151, 0.8588, 0.5467, 0.3700, 0.3950, 0.5637, 0.5933]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.9073e-01, -3.3128e-01, 1.5764e+00, -1.0210e+00, -4.4448e-01,
- -1.1284e+00, 5.1971e-01, 9.0985e-02],
- [ 7.7653e-01, -2.6876e-01, 1.7526e+00, 9.8295e-04, -1.8860e-01,
- -1.1193e-01, 3.4547e-01, 1.6992e-01],
- [ 8.5491e-01, -2.1427e-01, 1.9309e+00, -2.1968e-01, -3.7515e-01,
- 3.5782e-01, 5.1594e-01, 1.0342e-01],
- [ 5.4454e-01, -4.1243e-01, 1.8518e+00, -1.7703e-01, -1.7207e-01,
- -6.9722e-02, 5.0051e-01, 1.7933e-01],
- [ 8.4427e-01, -2.5028e-01, 1.1858e+00, -1.1193e+00, -4.6734e-01,
- -1.2457e+00, 5.1291e-01, 1.8033e-01],
- [-1.3002e+00, -1.5918e+00, 1.1560e+00, -1.2955e+00, -2.7973e-01,
- -1.4764e+00, 3.8677e-01, 2.7004e-01],
- [ 6.1068e-01, -3.7035e-01, 1.7309e+00, -1.0349e-02, -3.6074e-01,
- 1.1573e-01, 3.6811e-01, 1.5947e-01],
- [ 6.7004e-01, -2.9821e-01, 1.7256e+00, 2.0917e-01, -6.2269e-01,
- -4.3491e-01, 3.2126e-01, 2.6025e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807],
- [ 0.5295, -0.4373, 1.6553, 0.1011, 0.0380, 0.0671, 0.0813,
- 0.2237],
- [ 0.5477, -0.3851, 1.7961, -0.1304, -0.3055, 0.5085, 0.3830,
- 0.0682],
- [ 0.5371, -0.4252, 1.7037, -0.0688, -0.0342, 0.0620, 0.3758,
- 0.2853],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [-2.2859, -2.2859, 0.7106, -1.4468, -0.2882, -1.4237, 0.2430,
- 0.3623],
- [ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386],
- [ 0.5785, -0.3687, 1.6806, 0.2391, -0.5769, -0.4614, 0.3180,
- 0.4547]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0425, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0425, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7016998585313559
- step: 39
- running loss: 0.0436333297059322
- Train Steps: 39/90 Loss: 0.0436 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6263, 0.4030, 0.9000, 0.4767, 0.3800, 0.5167, 0.6415, 0.4771],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6199, 0.4015, 0.8716, 0.5228, 0.3833, 0.4772, 0.5883, 0.5148],
- [0.6086, 0.3998, 0.8788, 0.4450, 0.4025, 0.4650, 0.5306, 0.5103],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6261, 0.4045, 0.8865, 0.5369, 0.3895, 0.4859, 0.6683, 0.5249],
- [0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4242, -0.5076, 1.9139, -0.3800, -0.3886, -0.0722, 0.5852, 0.0703],
- [ 0.6892, -0.3367, 1.8315, -0.3075, -0.3468, -0.2121, 0.3945, 0.1449],
- [ 0.5346, -0.4161, 1.7029, -0.1029, -0.4170, -0.3621, 0.4635, 0.3057],
- [ 0.5330, -0.4363, 1.8144, -0.1320, -0.4021, -0.2648, 0.4028, 0.0935],
- [ 0.4335, -0.4818, 1.7706, -0.5227, -0.3796, -0.3209, 0.2563, 0.1315],
- [ 0.5602, -0.4312, 1.8075, -0.1298, -0.2862, -0.0801, 0.4577, 0.1479],
- [ 0.6446, -0.3702, 1.8362, -0.1281, -0.4253, -0.1565, 0.6927, 0.0867],
- [ 0.4386, -0.4931, 1.0271, -1.2705, -0.3864, -1.4639, 0.2277, 0.2080]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6072, -0.4247, 1.8711, -0.0842, -0.5307, 0.1005, 0.6771,
- -0.0821],
- [ 0.5250, -0.4661, 1.7383, -0.0765, -0.4268, -0.0226, 0.2535,
- 0.2035],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.5773, -0.4316, 1.7399, 0.1287, -0.5153, -0.0817, 0.4313,
- 0.0919],
- [ 0.5253, -0.4392, 1.7730, -0.2305, -0.4268, -0.1381, 0.1651,
- 0.0712],
- [ 0.5778, -0.4389, 1.7107, 0.1192, -0.3921, 0.0815, 0.4741,
- 0.0711],
- [ 0.6059, -0.4177, 1.8087, 0.1941, -0.4868, -0.0414, 0.8010,
- 0.1385],
- [ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0207, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0207, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7223987337201834
- step: 40
- running loss: 0.04305996834300459
- Train Steps: 40/90 Loss: 0.0431 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6219, 0.3934, 0.8688, 0.5267, 0.4313, 0.4967, 0.5988, 0.4983],
- [0.6200, 0.4039, 0.8880, 0.4799, 0.3625, 0.4285, 0.5866, 0.5148],
- [0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719],
- [ nan, nan, 0.7725, 0.2611, 0.3675, 0.2733, 0.5413, 0.5167],
- [0.6203, 0.4096, 0.8862, 0.4267, 0.3538, 0.4117, 0.6025, 0.5650],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 9.7662e-01, -1.1745e-01, 1.6946e+00, -1.8736e-02, -2.7298e-01,
- 1.1961e-01, 6.4769e-01, 1.2871e-01],
- [ 8.3737e-01, -2.2624e-01, 1.7305e+00, -1.1073e-03, -3.4214e-01,
- -1.4048e-01, 4.8221e-01, 1.4542e-01],
- [ 6.4155e-01, -3.4631e-01, 1.7510e+00, -1.0764e-01, -2.6191e-01,
- -6.1281e-02, 3.9667e-01, 7.9823e-02],
- [ 7.0425e-01, -2.9291e-01, 1.8040e+00, -2.7007e-01, -4.8139e-01,
- -3.5236e-01, 3.2093e-01, 1.1178e-01],
- [ 8.2886e-01, -2.0781e-01, 1.6514e+00, 6.9484e-02, -3.7936e-01,
- -2.4129e-02, 2.7950e-01, 1.7206e-01],
- [-1.6621e+00, -1.8554e+00, 1.2965e+00, -1.3455e+00, -3.9351e-01,
- -1.0603e+00, 1.3691e-01, 1.4077e-01],
- [ 7.7488e-01, -2.3785e-01, 1.8051e+00, -5.3855e-01, -4.5368e-01,
- -4.5824e-01, 4.3108e-01, 2.1553e-01],
- [ 8.9303e-01, -1.7966e-01, 1.6875e+00, -8.4745e-01, -2.5337e-01,
- -1.0233e+00, 7.5601e-01, 7.6916e-02]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.5864, -0.4690, 1.7268, 0.1467, -0.2940, 0.0082, 0.4797,
- 0.0159],
- [ 0.5781, -0.4205, 1.8159, -0.0691, -0.6116, -0.3069, 0.4236,
- 0.0919],
- [ 0.5079, -0.4714, 1.6575, 0.2237, -0.4961, 0.0774, 0.1465,
- -0.1061],
- [-2.2859, -2.2859, 1.2820, -1.0801, -0.5885, -1.0234, 0.2141,
- 0.1005],
- [ 0.5790, -0.3940, 1.8076, -0.3152, -0.6520, -0.3844, 0.4970,
- 0.3238],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0354, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0354, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.757778873667121
- step: 41
- running loss: 0.04287265545529563
- Train Steps: 41/90 Loss: 0.0429 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505],
- [0.6115, 0.3998, 0.7063, 0.2383, 0.4037, 0.1950, 0.5320, 0.4993],
- [0.6200, 0.4059, 0.8700, 0.4900, 0.4163, 0.5000, 0.6162, 0.5467],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6168, 0.4029, 0.8523, 0.3417, 0.3588, 0.5000, 0.6125, 0.5400],
- [0.6157, 0.3956, 0.8323, 0.4138, 0.3479, 0.4431, 0.5914, 0.5131]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5364, -0.4022, 1.7602, -0.2904, -0.5344, -0.4302, 0.1283, 0.1212],
- [ 0.6020, -0.3860, 1.1146, -1.0672, -0.2285, -1.3674, 0.2108, 0.0569],
- [ 0.5184, -0.4487, 1.8782, 0.1548, -0.1674, 0.3226, 0.6550, 0.1418],
- [ 0.6975, -0.3259, 1.4950, -0.8409, -0.2680, -0.8396, 0.5728, 0.1666],
- [ 0.5019, -0.4184, 1.5995, -0.2894, -0.3780, -0.9116, 0.2311, 0.1745],
- [ 0.4203, -0.4804, 1.6434, -0.4923, -0.5403, -0.3176, 0.3912, 0.0179],
- [ 0.0798, -0.6898, 1.6994, -0.5779, -0.3896, 0.1759, 0.8041, 0.1626],
- [ 0.5689, -0.4070, 1.7062, -0.3296, -0.4752, -0.0741, 0.4950, 0.0697]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5433, -0.4032, 1.6344, -0.4922, -0.5769, -0.5846, 0.0357,
- 0.2567],
- [ 0.5384, -0.4393, 0.9762, -1.1851, -0.4210, -1.3852, 0.1712,
- 0.0201],
- [ 0.5776, -0.4109, 1.7326, -0.0226, -0.3633, 0.0236, 0.5605,
- 0.2391],
- [ 0.5977, -0.3792, 1.2995, -1.0311, -0.5192, -0.8386, 0.5836,
- 0.2160],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.5206, -0.4603, 1.4670, -0.4768, -0.6577, -0.3998, 0.1836,
- 0.0021],
- [ 0.5631, -0.4249, 1.6509, -0.7078, -0.6289, 0.0236, 0.5432,
- 0.2083],
- [ 0.5581, -0.4586, 1.5586, -0.3747, -0.6792, -0.2391, 0.4455,
- 0.0840]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0208, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0208, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.778610149398446
- step: 42
- running loss: 0.042347860699963005
- Train Steps: 42/90 Loss: 0.0423 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6161, 0.4099, 0.8738, 0.4383, 0.3788, 0.5483, 0.5605, 0.5019],
- [0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285],
- [0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578],
- [0.6223, 0.3990, 0.8037, 0.2800, 0.4000, 0.2283, 0.5864, 0.5208],
- [0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6264, 0.3972, 0.8853, 0.4771, 0.3853, 0.4511, 0.6293, 0.5334],
- [0.6179, 0.4008, 0.8600, 0.4015, 0.3932, 0.2515, 0.5711, 0.5438]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3502, -0.5350, 1.8028, -0.1968, -0.4146, 0.5732, 0.5760, 0.0674],
- [ 0.7001, -0.2969, 1.4979, -0.3223, -0.5520, -0.0171, 0.1787, 0.0619],
- [ 0.5623, -0.3918, 1.8224, -0.2495, -0.3759, -0.4400, 0.4877, 0.2254],
- [ 0.4395, -0.4990, 1.0913, -1.0361, -0.3294, -1.1480, 0.3250, 0.1471],
- [ 0.3808, -0.5393, 1.5022, -0.8524, -0.3207, -0.9561, 0.5060, 0.0491],
- [ 0.5369, -0.4363, 1.2601, -0.9757, -0.3010, -1.1186, 0.2567, 0.0580],
- [ 0.4902, -0.4967, 1.9258, 0.1589, -0.4215, 0.2360, 0.8296, 0.0872],
- [ 0.4893, -0.4287, 1.6650, -0.4043, -0.3079, -0.8211, 0.2554, 0.1332]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5597, -0.3928, 1.7499, -0.2613, -0.5365, 0.2468, 0.3028,
- 0.0321],
- [ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552],
- [ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928],
- [ 0.5232, -0.4189, 0.9358, -1.4006, -0.3921, -1.3698, 0.2555,
- 0.2906],
- [ 0.5885, -0.4429, 1.4266, -0.9926, -0.4383, -1.2313, 0.4228,
- 0.1195],
- [ 0.5598, -0.4201, 1.1898, -1.3005, -0.3806, -1.3313, 0.3873,
- 0.0774],
- [ 0.6075, -0.4514, 1.8032, -0.0822, -0.5061, -0.2023, 0.6208,
- 0.1779],
- [ 0.5680, -0.4345, 1.6864, -0.4315, -0.4698, -1.1241, 0.3518,
- 0.2261]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0289, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0289, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8074938002973795
- step: 43
- running loss: 0.04203473954179952
- Train Steps: 43/90 Loss: 0.0420 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6038, 0.3946, 0.8413, 0.4883, 0.3563, 0.4550, 0.5266, 0.4693],
- [0.6260, 0.4106, 0.8025, 0.2583, 0.4550, 0.1867, 0.6281, 0.4869],
- [ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6289, 0.4032, 0.8419, 0.5446, 0.4075, 0.5017, 0.6312, 0.5117],
- [0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833],
- [0.6034, 0.4011, 0.7350, 0.2533, 0.3438, 0.3367, 0.5516, 0.5084],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7905, -0.2252, 1.6335, -0.0263, -0.6232, -0.1224, 0.1979, 0.1167],
- [ 0.7494, -0.2658, 1.4417, -1.1422, -0.2417, -1.3671, 0.5485, 0.0467],
- [-1.5169, -1.7478, 1.5878, -1.2802, -0.0359, -1.0986, 0.6142, 0.1493],
- [ 0.7136, -0.3303, 1.8095, 0.1998, -0.4240, 0.2751, 0.4201, 0.1101],
- [ 0.9599, -0.1700, 1.7080, 0.1720, -0.4273, 0.1289, 0.6565, 0.1152],
- [ 0.6922, -0.3478, 1.7639, -0.0507, -0.5437, 0.0132, 0.4204, 0.0642],
- [ 0.7462, -0.2894, 1.2072, -1.1087, -0.6579, -0.7699, 0.3299, 0.0590],
- [ 0.7810, -0.2467, 1.7174, 0.1712, -0.2060, 0.1815, 0.2094, 0.1444]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5030, -0.4631, 1.5998, -0.0303, -0.6404, -0.1843, 0.1465,
- -0.1181],
- [ 0.6058, -0.3892, 1.4208, -1.0927, -0.1843, -1.4237, 0.6154,
- -0.0370],
- [-2.2859, -2.2859, 1.7210, -0.9772, 0.1852, -1.3698, 0.7986,
- 0.3104],
- [ 0.5778, -0.4389, 1.7107, 0.1192, -0.3921, 0.0815, 0.4741,
- 0.0711],
- [ 0.6189, -0.4238, 1.6026, 0.2295, -0.4037, 0.0313, 0.6298,
- 0.0774],
- [ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534],
- [ 0.5009, -0.4333, 1.1090, -1.1158, -0.6982, -0.7309, 0.2617,
- 0.0622],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0319, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0319, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8393607195466757
- step: 44
- running loss: 0.041803652716969904
- Train Steps: 44/90 Loss: 0.0418 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [0.6038, 0.3946, 0.8413, 0.4883, 0.3563, 0.4550, 0.5266, 0.4693],
- [0.6211, 0.4069, 0.8750, 0.5117, 0.4150, 0.4900, 0.5875, 0.5167],
- [0.6250, 0.4131, 0.8688, 0.2983, 0.4275, 0.2367, 0.6162, 0.5367],
- [0.6199, 0.4015, 0.8716, 0.5228, 0.3833, 0.4772, 0.5883, 0.5148],
- [0.6246, 0.4008, 0.8757, 0.5088, 0.4101, 0.5392, 0.6644, 0.5133],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4716, -0.4719, 1.7441, -0.2225, -0.5760, -0.4219, 0.7062, 0.1232],
- [ 0.5111, -0.4207, 1.4883, -0.2494, -0.6212, -0.3269, 0.2111, 0.1063],
- [ 0.5906, -0.4036, 1.6814, -0.0946, -0.4266, -0.0143, 0.4353, 0.0676],
- [ 0.7047, -0.3238, 1.6344, -1.1320, -0.3182, -1.2865, 0.5688, 0.0724],
- [ 0.4475, -0.5090, 1.6610, -0.0321, -0.5360, -0.1445, 0.3971, 0.0563],
- [ 0.3423, -0.5918, 1.6331, -0.1741, -0.3868, 0.2066, 0.6619, 0.1233],
- [ 0.3358, -0.5321, 1.5918, -0.2288, -0.3002, -0.1307, 0.2582, 0.1861],
- [ 0.6290, -0.3624, 1.6425, -0.1294, -0.1318, -0.0388, 0.1791, 0.0794]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499],
- [ 0.5030, -0.4631, 1.5998, -0.0303, -0.6404, -0.1843, 0.1465,
- -0.1181],
- [ 0.5828, -0.4066, 1.7557, 0.0774, -0.3691, -0.0226, 0.4277,
- 0.1005],
- [ 0.6011, -0.3778, 1.7268, -0.9079, -0.3113, -1.1928, 0.5605,
- 0.1929],
- [ 0.5773, -0.4316, 1.7399, 0.1287, -0.5153, -0.0817, 0.4313,
- 0.0919],
- [ 0.5993, -0.4345, 1.7587, 0.0641, -0.3917, 0.2048, 0.7827,
- 0.0852],
- [ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057],
- [ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0137, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0137, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8530531292781234
- step: 45
- running loss: 0.04117895842840274
- Train Steps: 45/90 Loss: 0.0412 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7625, 0.2433, 0.3713, 0.2867, 0.5235, 0.5220],
- [0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [0.6193, 0.3930, 0.8949, 0.4437, 0.3852, 0.5435, 0.6263, 0.5263],
- [0.6248, 0.4032, 0.7738, 0.1900, 0.4813, 0.1400, 0.5941, 0.4904],
- [0.6200, 0.4049, 0.8638, 0.5617, 0.4125, 0.5100, 0.6013, 0.5317],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6095, 0.4002, 0.8533, 0.5168, 0.5031, 0.5094, 0.5125, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.0062, -2.1088, 1.2378, -1.1293, -0.6208, -0.9271, 0.1880, 0.1063],
- [ 0.6630, -0.3410, 1.7287, 0.0418, -0.1805, 0.1816, 0.3918, 0.1792],
- [ 0.6831, -0.3443, 1.8144, -0.0936, -0.5714, 0.2910, 0.6572, 0.0492],
- [ 0.7003, -0.3176, 1.2593, -1.1093, -0.1448, -1.5150, 0.5180, 0.0846],
- [ 0.9637, -0.1827, 1.7292, 0.3907, -0.4397, 0.2822, 0.5706, 0.0913],
- [ 1.0779, -0.0850, 1.2507, -0.9365, -0.5781, -0.9499, 0.3741, 0.0290],
- [ 0.6943, -0.2916, 1.5082, -0.5144, -0.6949, -0.6832, 0.2075, 0.1789],
- [ 0.9264, -0.1761, 1.7016, 0.2059, -0.1610, 0.0985, 0.3311, 0.1391]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.2360, -1.1620, -0.5711, -0.9618, 0.1322,
- 0.1253],
- [ 0.5505, -0.4207, 1.7095, -0.0534, -0.0509, 0.1050, 0.3873,
- 0.3007],
- [ 0.5746, -0.4706, 1.8476, -0.2365, -0.5068, 0.2245, 0.6069,
- 0.1449],
- [ 0.5999, -0.4236, 1.2880, -1.4083, -0.0630, -1.6393, 0.4584,
- -0.0208],
- [ 0.5776, -0.4159, 1.7037, 0.3084, -0.3806, 0.0697, 0.4912,
- 0.1698],
- [ 0.4997, -0.4446, 1.1610, -0.9772, -0.6058, -1.0311, 0.1404,
- -0.1031],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.5295, -0.4373, 1.6553, 0.1011, 0.0380, 0.0671, 0.0813,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0267, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0267, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8797959005460143
- step: 46
- running loss: 0.04086512827273944
- Train Steps: 46/90 Loss: 0.0409 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6131, 0.4064, 0.8638, 0.5200, 0.4788, 0.4783, 0.5258, 0.5867],
- [0.6199, 0.4071, 0.7600, 0.2117, 0.4037, 0.2767, 0.6138, 0.5550],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6026, 0.3979, 0.8550, 0.4233, 0.3613, 0.5233, 0.5582, 0.4967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4136, -0.5211, 1.6518, -0.5711, -0.4877, -1.1473, 0.1868, 0.0327],
- [ 0.0622, -0.7807, 1.6220, 0.0317, -0.5489, -0.1672, 0.4488, 0.2454],
- [ 0.1679, -0.6337, 1.3389, -0.7607, -0.6656, -0.5568, 0.2576, 0.1309],
- [ 0.6829, -0.3497, 1.8155, -0.0984, -0.4948, 0.0420, 0.4061, 0.0627],
- [ 0.4903, -0.4681, 1.6231, 0.1573, -0.1295, -0.0120, 0.1925, 0.1658],
- [ 0.7352, -0.3283, 1.3336, -1.2855, -0.3736, -1.1986, 0.5552, 0.1144],
- [ 0.6767, -0.3760, 1.6842, 0.2048, -0.3348, 0.2051, 0.7426, 0.0626],
- [ 0.5668, -0.4108, 1.6528, -0.3251, -0.5824, 0.0828, 0.4796, 0.0241]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5485, -0.4209, 1.6691, -0.4152, -0.5249, -1.1081, 0.0725,
- 0.0021],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.5462, -0.4090, 1.7037, 0.1159, -0.0746, -0.0765, 0.1427,
- 0.4239],
- [ 0.5775, -0.4054, 1.2245, -1.3082, -0.4210, -1.0080, 0.5490,
- 0.2776],
- [ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.4974, -0.4482, 1.6633, -0.3306, -0.6173, 0.1313, 0.2925,
- 0.0081]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0180, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0180, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8977786405012012
- step: 47
- running loss: 0.040378268946834064
- Train Steps: 47/90 Loss: 0.0404 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6267, 0.4094, 0.8712, 0.3083, 0.4400, 0.2267, 0.6250, 0.5200],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [0.6128, 0.4084, 0.8738, 0.4683, 0.3613, 0.3700, 0.4960, 0.5397],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6192, 0.3980, 0.7078, 0.2750, 0.4250, 0.2100, 0.5450, 0.5783],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6218, 0.4137, 0.7263, 0.2233, 0.4075, 0.2650, 0.6212, 0.5783]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6224, -0.3776, 1.7414, -0.8322, -0.2218, -0.8424, 0.5022, 0.0806],
- [ 0.4926, -0.5226, 1.6715, 0.2126, -0.5110, 0.1479, 0.4177, 0.2301],
- [ 0.5360, -0.4621, 1.0516, -1.1743, -0.4601, -0.9034, 0.2978, 0.1392],
- [ 0.8273, -0.2386, 1.7271, 0.1335, -0.6301, -0.1857, 0.1177, 0.0842],
- [ 1.0371, -0.1818, 1.8480, 0.4197, -0.5808, -0.2250, 0.4951, 0.0019],
- [-1.5094, -1.7850, 1.0574, -1.0092, -0.3379, -0.9945, 0.2675, 0.1895],
- [ 0.6967, -0.3592, 1.9175, -0.3306, -0.5247, -0.1906, 0.6084, 0.0473],
- [ 0.7549, -0.3261, 1.1601, -0.8692, -0.4138, -0.8388, 0.4756, 0.2019]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6089, -0.3948, 1.7383, -0.8617, -0.2536, -1.2390, 0.6009,
- 0.1159],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853],
- [ 0.5443, -0.3994, 1.7499, -0.1227, -0.6173, -0.5769, 0.0049,
- 0.2071],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.5742, -0.4474, 0.9834, -1.0159, -0.3229, -1.3159, 0.2314,
- 0.3854],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.5863, -0.3749, 1.0686, -1.2543, -0.4037, -1.0619, 0.5836,
- 0.3854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1265, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1265, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.0243153674528003
- step: 48
- running loss: 0.04217323682193334
- Train Steps: 48/90 Loss: 0.0422 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4079, 0.8025, 0.2500, 0.3763, 0.3217, 0.6125, 0.5533],
- [0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364],
- [0.6202, 0.4066, 0.8746, 0.3376, 0.3717, 0.3090, 0.5842, 0.5165],
- [0.6172, 0.4055, 0.8175, 0.2650, 0.3550, 0.3683, 0.5787, 0.5550],
- [0.6339, 0.4112, 0.8838, 0.5067, 0.4000, 0.5433, 0.7549, 0.5378],
- [0.6200, 0.4024, 0.8390, 0.3139, 0.3525, 0.3833, 0.6162, 0.5383],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550],
- [0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5666, -0.4526, 1.4781, -0.7345, -0.4664, -0.7452, 0.4017, 0.1356],
- [ 0.3569, -0.5902, 1.4038, -0.7881, -0.1524, -1.2387, 0.5315, 0.1132],
- [ 0.8075, -0.3212, 1.7756, -0.3874, -0.5380, -0.8027, 0.3158, 0.0359],
- [ 0.4889, -0.5159, 1.4719, -0.5841, -0.6540, -0.5052, 0.1248, 0.1664],
- [ 0.3145, -0.6566, 1.7905, 0.4144, -0.3301, 0.5480, 0.6483, 0.1989],
- [ 0.2731, -0.6563, 1.5606, -0.4530, -0.5923, -0.3571, 0.4636, 0.1154],
- [ 0.4306, -0.5213, 1.4185, -0.5322, -0.6252, -0.2094, 0.3687, 0.1786],
- [ 0.3540, -0.5800, 1.2383, -0.7893, -0.4970, -0.7637, 0.4088, 0.1234]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5788, -0.4020, 1.4208, -1.1312, -0.5480, -0.8002, 0.5432,
- 0.2699],
- [ 0.6158, -0.3960, 1.4092, -1.2774, -0.2074, -1.1851, 0.8491,
- 0.1917],
- [ 0.5787, -0.4077, 1.7537, -0.7263, -0.5692, -0.8586, 0.4126,
- 0.1000],
- [ 0.5647, -0.4129, 1.4901, -1.0619, -0.6462, -0.5846, 0.3873,
- 0.2776],
- [ 0.6421, -0.3864, 1.7961, 0.0543, -0.4383, 0.2237, 1.2007,
- 0.1982],
- [ 0.5779, -0.4275, 1.5894, -0.8362, -0.6577, -0.5153, 0.5605,
- 0.2006],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776],
- [ 0.5730, -0.4562, 1.2195, -1.2440, -0.5497, -0.7711, 0.5704,
- 0.1779]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0430, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0430, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.067306305281818
- step: 49
- running loss: 0.04218992459758812
- Train Steps: 49/90 Loss: 0.0422 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6188, 0.4099, 0.7400, 0.2433, 0.3962, 0.2750, 0.6162, 0.5467],
- [0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [0.6263, 0.4057, 0.8800, 0.3833, 0.3650, 0.3717, 0.6375, 0.4804],
- [0.6084, 0.3981, 0.8588, 0.5233, 0.4600, 0.5367, 0.5680, 0.5006],
- [0.6172, 0.4055, 0.8175, 0.2650, 0.3550, 0.3683, 0.5787, 0.5550],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [ nan, nan, 0.6469, 0.1943, 0.4025, 0.2000, 0.5125, 0.5533]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5385, -0.4836, 1.2245, -0.9226, -0.4233, -1.0781, 0.2606, 0.1285],
- [ 0.6310, -0.4203, 1.2385, -0.8954, -0.5206, -0.7738, 0.6003, 0.1909],
- [ 0.7104, -0.3214, 1.5186, -0.3326, -0.2686, -0.7922, 0.3363, 0.3189],
- [ 0.8336, -0.3251, 1.8312, -0.2580, -0.6616, -0.3110, 0.6699, 0.0044],
- [ 0.6117, -0.4478, 1.7861, 0.4758, -0.2638, 0.4468, 0.2286, 0.1295],
- [ 0.6076, -0.4409, 1.5538, -0.6097, -0.7504, -0.3698, 0.2756, 0.2015],
- [ 0.5570, -0.4618, 1.5436, -0.8213, -0.4644, -0.8386, 0.4805, 0.1029],
- [-1.1730, -1.5919, 0.9783, -1.0332, -0.4394, -1.1426, 0.3340, 0.2623]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5598, -0.4201, 1.1898, -1.3005, -0.3806, -1.3313, 0.3873,
- 0.0774],
- [ 0.5725, -0.3926, 1.1321, -1.1620, -0.4557, -1.0157, 0.5605,
- 0.2391],
- [ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882],
- [ 0.6071, -0.4119, 1.7788, -0.5153, -0.6000, -0.5692, 0.6586,
- -0.0670],
- [ 0.5241, -0.4470, 1.6806, 0.1313, -0.1612, 0.1929, 0.3378,
- 0.0261],
- [ 0.5647, -0.4129, 1.4901, -1.0619, -0.6462, -0.5846, 0.3873,
- 0.2776],
- [ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807],
- [-2.2859, -2.2859, 0.7023, -1.3883, -0.4268, -1.3621, 0.0813,
- 0.2699]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0568, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0568, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.1241164905950427
- step: 50
- running loss: 0.04248232981190085
- Train Steps: 50/90 Loss: 0.0425 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4270, 0.8938, 0.4967, 0.3550, 0.4283, 0.5700, 0.5733],
- [0.6189, 0.4049, 0.8888, 0.4417, 0.4213, 0.5200, 0.5988, 0.5633],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679],
- [ nan, nan, 0.7515, 0.2708, 0.3987, 0.2267, 0.5162, 0.5567],
- [0.6206, 0.4001, 0.8900, 0.3933, 0.3588, 0.3567, 0.5837, 0.5083],
- [0.6126, 0.4067, 0.8638, 0.5383, 0.4188, 0.4850, 0.5016, 0.5392],
- [0.6198, 0.4115, 0.7762, 0.2717, 0.3713, 0.3200, 0.5837, 0.5683],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7524, -0.3048, 1.7285, 0.0559, -0.5789, -0.3614, 0.4482, 0.2964],
- [ 0.6012, -0.4074, 1.7178, -0.2578, -0.3618, 0.1045, 0.5429, 0.2140],
- [ 0.7497, -0.3409, 1.7492, 0.1629, -0.3651, 0.2797, 0.8453, 0.2304],
- [-1.1396, -1.5549, 1.0833, -1.2028, -0.4997, -1.1879, 0.1421, 0.2040],
- [ 0.7176, -0.3714, 1.7701, -0.4835, -0.6730, -0.7595, 0.3634, 0.0367],
- [ 0.5878, -0.4171, 1.6346, 0.3001, -0.3926, -0.0520, 0.1840, 0.1566],
- [ 0.6174, -0.4194, 1.3560, -1.0567, -0.6214, -1.0043, 0.3839, 0.2182],
- [ 0.4703, -0.5073, 1.3387, -1.1426, -0.4838, -1.0649, 0.5185, 0.2052]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6121, -0.3138, 1.8423, 0.0082, -0.6462, -0.3075, 0.3469,
- 0.3623],
- [ 0.5726, -0.4159, 1.8192, -0.2459, -0.3402, 0.1159, 0.4797,
- 0.3161],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371],
- [-2.2859, -2.2859, 1.1854, -1.0352, -0.4441, -1.2390, 0.0986,
- 0.2853],
- [ 0.5807, -0.4378, 1.8249, -0.4691, -0.6289, -0.6385, 0.4104,
- 0.0620],
- [ 0.5436, -0.4076, 1.7037, 0.2006, -0.3517, -0.0457, 0.0311,
- 0.2048],
- [ 0.5768, -0.3852, 1.2995, -1.0311, -0.5711, -0.8079, 0.4104,
- 0.3392],
- [ 0.5977, -0.3792, 1.2995, -1.0311, -0.5192, -0.8386, 0.5836,
- 0.2160]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0363, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0363, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.1603687768802047
- step: 51
- running loss: 0.04236017209569029
- Train Steps: 51/90 Loss: 0.0424 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6164, 0.3972, 0.7818, 0.3381, 0.3599, 0.3387, 0.5880, 0.5153],
- [0.6075, 0.4000, 0.8513, 0.5183, 0.4510, 0.5329, 0.5180, 0.5280],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6258, 0.4038, 0.8750, 0.4883, 0.3900, 0.5500, 0.6375, 0.5217],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6274, 0.4270, 0.8938, 0.4967, 0.3550, 0.4283, 0.5700, 0.5733],
- [0.6136, 0.4060, 0.8025, 0.3217, 0.3650, 0.3000, 0.5060, 0.5646],
- [0.6271, 0.4005, 0.8450, 0.6067, 0.3850, 0.4983, 0.6069, 0.4649]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7951, -0.3181, 1.3667, -0.9975, -0.6434, -0.7699, 0.4930, 0.1455],
- [ 0.6817, -0.3643, 1.6849, -0.0330, -0.1792, -0.0081, 0.2371, 0.2157],
- [ 0.5805, -0.4193, 1.4511, -0.7298, -0.5682, -1.1159, 0.2997, 0.2842],
- [ 0.7572, -0.3580, 1.8490, -0.1689, -0.4425, 0.1329, 0.8386, 0.1823],
- [-1.5188, -1.8176, 1.2768, -1.1645, -0.4267, -1.1418, 0.3316, 0.2596],
- [ 0.8045, -0.2769, 1.7545, -0.1248, -0.5806, -0.3896, 0.5359, 0.3604],
- [ 0.3195, -0.5944, 1.3143, -1.0067, -0.6936, -0.9331, 0.1250, 0.2017],
- [ 0.8338, -0.3000, 1.6501, 0.2392, -0.5147, -0.1556, 0.5634, 0.1801]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5614, -0.4514, 1.3252, -0.7240, -0.6237, -0.7215, 0.4299,
- 0.0942],
- [ 0.5202, -0.4382, 1.6460, 0.1082, -0.2029, 0.1754, 0.1067,
- 0.1530],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.6047, -0.4209, 1.7557, -0.0303, -0.4845, 0.2545, 0.6587,
- 0.1236],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.6121, -0.3138, 1.8423, 0.0082, -0.6462, -0.3075, 0.3469,
- 0.3623],
- [ 0.5483, -0.4105, 1.4208, -0.8002, -0.6000, -0.9002, 0.0511,
- 0.3220],
- [ 0.6105, -0.4360, 1.6171, 0.5162, -0.5076, 0.0159, 0.5171,
- -0.1385]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0298, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0298, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.1901937695220113
- step: 52
- running loss: 0.04211911095234637
- Train Steps: 52/90 Loss: 0.0421 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783],
- [0.6263, 0.4029, 0.8900, 0.4933, 0.4075, 0.5183, 0.6406, 0.4758],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6180, 0.4064, 0.7200, 0.2467, 0.4275, 0.2367, 0.5963, 0.6217],
- [0.6203, 0.4072, 0.8892, 0.3523, 0.3783, 0.3017, 0.5898, 0.5478],
- [0.6205, 0.4004, 0.8938, 0.4883, 0.3663, 0.5000, 0.6357, 0.4947],
- [0.6230, 0.4113, 0.7213, 0.1983, 0.4325, 0.2367, 0.6262, 0.5400],
- [0.6339, 0.4123, 0.8638, 0.5367, 0.4075, 0.5467, 0.7517, 0.5436]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2910, -0.6319, 0.8737, -1.2362, -0.5089, -1.1692, 0.2117, 0.3007],
- [ 0.5419, -0.4641, 1.7910, -0.0319, -0.4816, 0.1344, 0.4855, 0.1718],
- [ 0.4724, -0.5371, 1.8747, -0.3285, -0.5541, -0.7206, 0.4187, 0.2375],
- [ 0.4847, -0.4866, 1.0764, -1.1323, -0.4219, -1.1624, 0.3798, 0.4126],
- [ 0.3838, -0.5823, 1.7736, -0.6931, -0.6253, -0.8531, 0.3493, 0.2185],
- [ 0.4651, -0.5428, 1.7617, 0.0150, -0.6195, 0.0082, 0.4829, 0.1659],
- [-0.0310, -0.8469, 1.2318, -1.2809, -0.3572, -1.3031, 0.5550, 0.2932],
- [ 0.6677, -0.3943, 1.7676, 0.1537, -0.4608, 0.2785, 0.6763, 0.2784]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.4827e-01, -3.9908e-01, 8.0300e-01, -1.2159e+00, -5.0185e-01,
- -1.1928e+00, 2.6243e-01, 3.8516e-01],
- [ 6.0716e-01, -4.2502e-01, 1.8249e+00, -7.2363e-03, -4.0370e-01,
- 1.0824e-01, 6.7296e-01, -8.8090e-02],
- [ 6.0935e-01, -3.9469e-01, 1.8885e+00, -2.9977e-01, -5.7691e-01,
- -6.7698e-01, 6.0670e-01, 1.0054e-01],
- [ 5.6871e-01, -4.0878e-01, 1.0397e+00, -1.1466e+00, -3.1132e-01,
- -1.1928e+00, 4.6813e-01, 5.8553e-01],
- [ 5.7921e-01, -4.0523e-01, 1.8214e+00, -6.5874e-01, -5.3842e-01,
- -8.9239e-01, 4.3812e-01, 2.4425e-01],
- [ 5.8031e-01, -4.3672e-01, 1.8423e+00, -3.0331e-02, -5.9423e-01,
- 2.3557e-02, 6.5034e-01, -9.2270e-04],
- [ 5.9169e-01, -3.8607e-01, 1.0455e+00, -1.3698e+00, -2.8822e-01,
- -1.1928e+00, 6.0670e-01, 2.0831e-01],
- [ 6.4212e-01, -3.8157e-01, 1.7037e+00, 1.9292e-01, -4.0370e-01,
- 2.3911e-01, 1.1861e+00, 2.2489e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0241, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0241, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.2142448211088777
- step: 53
- running loss: 0.04177820417186562
- Train Steps: 53/90 Loss: 0.0418 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433],
- [0.6198, 0.4114, 0.8263, 0.3283, 0.3550, 0.3583, 0.5813, 0.5667],
- [0.6164, 0.3972, 0.7818, 0.3381, 0.3599, 0.3387, 0.5880, 0.5153],
- [0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6122, 0.4048, 0.7506, 0.3014, 0.3475, 0.3333, 0.5487, 0.5749]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 2.5260e-02, -7.6672e-01, 1.0277e+00, -1.2165e+00, -3.4002e-01,
- -1.3102e+00, 4.8023e-01, 3.9226e-01],
- [ 6.2153e-01, -4.3315e-01, 1.9337e+00, 1.7966e-03, -5.1253e-01,
- 9.0811e-02, 5.9042e-01, 1.6422e-01],
- [ 4.9724e-01, -5.0812e-01, 1.6399e+00, -7.6851e-01, -6.8466e-01,
- -7.5338e-01, 3.2359e-01, 3.3033e-01],
- [ 6.1532e-01, -4.3510e-01, 1.4187e+00, -8.1107e-01, -6.2200e-01,
- -7.1508e-01, 4.0486e-01, 1.7140e-01],
- [ 3.6046e-01, -5.7041e-01, 1.3250e+00, -1.0281e+00, -6.1943e-01,
- -6.3158e-01, 4.3742e-01, 2.4319e-01],
- [ 3.0706e-01, -6.2405e-01, 1.9129e+00, -7.9856e-01, -4.4438e-01,
- -9.1659e-01, 7.7821e-01, 1.9522e-01],
- [ 5.9681e-01, -4.0429e-01, 1.6356e+00, 4.3390e-01, -3.5430e-01,
- -3.2069e-01, 4.2337e-01, 4.7371e-01],
- [ 2.7878e-01, -6.1629e-01, 1.1768e+00, -9.2799e-01, -6.1763e-01,
- -7.4626e-01, 2.4131e-01, 3.0193e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237],
- [ 0.5768, -0.3857, 1.5305, -0.7694, -0.6462, -0.6308, 0.3988,
- 0.3315],
- [ 0.5614, -0.4514, 1.3252, -0.7240, -0.6237, -0.7215, 0.4299,
- 0.0942],
- [ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390],
- [ 0.5836, -0.3649, 1.7210, 0.3854, -0.3979, -0.2921, 0.3065,
- 0.4470],
- [ 0.5419, -0.4160, 1.1810, -0.8939, -0.6808, -0.7463, 0.2485,
- 0.3694]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0175, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0175, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.2317135045304894
- step: 54
- running loss: 0.04132802786167573
- Train Steps: 54/90 Loss: 0.0413 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6307, 0.4029, 0.8988, 0.4817, 0.3937, 0.3500, 0.7311, 0.5378],
- [0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783],
- [0.6223, 0.4130, 0.8100, 0.2983, 0.3525, 0.3900, 0.5694, 0.5103],
- [0.6234, 0.4179, 0.7825, 0.3450, 0.3813, 0.2867, 0.5675, 0.5617],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6261, 0.4045, 0.8865, 0.5369, 0.3895, 0.4859, 0.6683, 0.5249],
- [0.6198, 0.4164, 0.8700, 0.5067, 0.4625, 0.5650, 0.5464, 0.5197]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3991, -0.5190, 1.6326, -0.1913, -0.3595, -0.1455, 0.4408, 0.2888],
- [ 0.2619, -0.6540, 1.8745, -0.3648, -0.5207, -0.9587, 0.7523, 0.2107],
- [ 0.3949, -0.5408, 0.8073, -1.4307, -0.5081, -1.3098, 0.3159, 0.3495],
- [ 0.3794, -0.5318, 1.4255, -1.1229, -0.7340, -0.7321, 0.3473, 0.2941],
- [ 0.3057, -0.5531, 1.3458, -1.0888, -0.5595, -1.1431, 0.3626, 0.4253],
- [ 0.6391, -0.3797, 1.6804, 0.1259, -0.4331, -0.0430, 0.4921, 0.3337],
- [ 0.5814, -0.4455, 1.7452, -0.0697, -0.6352, -0.1981, 0.7558, 0.1840],
- [ 0.3373, -0.5463, 1.6790, -0.1625, -0.2885, -0.1123, 0.2628, 0.2724]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 6.2730e-01, -4.2490e-01, 1.8654e+00, -6.1124e-02, -4.6721e-01,
- -6.6928e-01, 1.0910e+00, 1.9818e-01],
- [ 5.4827e-01, -3.9908e-01, 8.0300e-01, -1.2159e+00, -5.0185e-01,
- -1.1928e+00, 2.6243e-01, 3.8516e-01],
- [ 5.8863e-01, -3.7837e-01, 1.4554e+00, -9.0793e-01, -6.5774e-01,
- -4.8453e-01, 3.4395e-01, 7.1216e-02],
- [ 5.9348e-01, -3.5581e-01, 1.3284e+00, -6.9238e-01, -5.2494e-01,
- -9.6182e-01, 3.3533e-01, 3.0839e-01],
- [ 5.7419e-01, -3.7921e-01, 1.6460e+00, 3.0839e-01, -3.4596e-01,
- 1.4673e-01, 4.1617e-01, 3.1609e-01],
- [ 6.0589e-01, -4.1768e-01, 1.8087e+00, 1.9408e-01, -4.8680e-01,
- -4.1391e-02, 8.0095e-01, 1.3848e-01],
- [ 5.7696e-01, -3.6243e-01, 1.7326e+00, 5.4350e-02, -1.4965e-01,
- 3.2379e-01, 2.3775e-01, 1.1464e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0297, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0297, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.261423231102526
- step: 55
- running loss: 0.04111678602004593
- Train Steps: 55/90 Loss: 0.0411 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6118, 0.4052, 0.8463, 0.3917, 0.3538, 0.3450, 0.5053, 0.5593],
- [0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [0.6211, 0.4069, 0.8750, 0.5117, 0.4150, 0.4900, 0.5875, 0.5167],
- [0.6274, 0.4003, 0.8638, 0.5967, 0.3688, 0.4900, 0.6108, 0.4661],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [0.6263, 0.4057, 0.8800, 0.3833, 0.3650, 0.3717, 0.6375, 0.4804]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0752, -0.7276, 1.4581, -0.7417, -0.6950, -0.7660, 0.0415, 0.3244],
- [ 0.3550, -0.5610, 1.6460, -0.5112, -0.6129, -0.5533, 0.4340, 0.2236],
- [ 0.4733, -0.4840, 1.6904, -0.1513, -0.3921, -0.0886, 0.5491, 0.2488],
- [ 0.5271, -0.4809, 1.5573, 0.0957, -0.5033, -0.2430, 0.6557, 0.2935],
- [ 0.3911, -0.4719, 1.0264, -1.1537, -0.1195, -1.2667, 0.3460, 0.4831],
- [ 0.4838, -0.4367, 1.6707, -0.3050, -0.5068, -0.9674, 0.4554, 0.3163],
- [ 0.3380, -0.5800, 1.6796, -0.6661, -0.6049, -0.2165, 0.6289, 0.3577],
- [ 0.7290, -0.3204, 1.6073, -0.8694, -0.6387, -0.6692, 0.6848, 0.1262]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5399, -0.4142, 1.6229, -0.4768, -0.6520, -0.6924, 0.0481,
- 0.2972],
- [ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [ 0.5828, -0.4066, 1.7557, 0.0774, -0.3691, -0.0226, 0.4277,
- 0.1005],
- [ 0.6120, -0.4371, 1.7037, 0.4701, -0.5827, -0.0226, 0.5354,
- -0.1331],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [ 0.5901, -0.4000, 1.8423, -0.0688, -0.5307, -0.9233, 0.3642,
- 0.1852],
- [ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [ 0.6071, -0.4119, 1.7788, -0.5153, -0.6000, -0.5692, 0.6586,
- -0.0670]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0350, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0350, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.296431795693934
- step: 56
- running loss: 0.04100771063739168
- Train Steps: 56/90 Loss: 0.0410 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6208, 0.4082, 0.8538, 0.3067, 0.3588, 0.3717, 0.6112, 0.5517],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [ nan, nan, 0.7612, 0.3250, 0.4037, 0.2533, 0.5438, 0.5767],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917],
- [0.6146, 0.4124, 0.8800, 0.4867, 0.3800, 0.4500, 0.5106, 0.5524]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.5338, -1.0908, 1.0218, -1.2956, -0.2762, -1.3676, 0.3314, 0.2925],
- [ 0.7514, -0.3161, 1.7225, -0.0812, -0.4777, -0.1617, 0.6445, 0.1176],
- [ 0.6451, -0.3701, 1.8502, -0.3280, -0.4483, -0.7106, 0.7212, 0.2513],
- [ 0.7114, -0.2943, 1.5651, -0.8324, -0.6431, -0.5288, 0.6222, 0.2602],
- [ 0.7963, -0.2482, 1.6338, -0.7183, -0.6423, -0.4676, 0.5556, 0.3234],
- [-0.1757, -0.8331, 1.1903, -0.9809, -0.4011, -1.0549, 0.2312, 0.3846],
- [ 0.4617, -0.4524, 1.3170, -0.5441, -0.5445, -0.7966, 0.2700, 0.4307],
- [ 0.5571, -0.3778, 1.7054, 0.1239, -0.4832, -0.2008, 0.3696, 0.2623]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534],
- [ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.5814, -0.4003, 1.6575, -0.8694, -0.6289, -0.5692, 0.5374,
- 0.2622],
- [ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [-2.2859, -2.2859, 1.2303, -0.7848, -0.4210, -1.1158, 0.2256,
- 0.3777],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470],
- [ 0.5529, -0.3811, 1.7788, -0.0380, -0.5307, -0.2074, 0.0727,
- 0.2657]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1399, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1399, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.436326772905886
- step: 57
- running loss: 0.042742574963261164
- Train Steps: 57/90 Loss: 0.0427 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6257, 0.4060, 0.8300, 0.2333, 0.4688, 0.2583, 0.7050, 0.5633],
- [0.6332, 0.4118, 0.9238, 0.4267, 0.4012, 0.4733, 0.7525, 0.5436],
- [0.6282, 0.4029, 0.8988, 0.3000, 0.4250, 0.4183, 0.7042, 0.5575],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967],
- [0.6186, 0.3967, 0.7337, 0.1992, 0.4120, 0.2508, 0.6105, 0.5395],
- [0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285],
- [0.6196, 0.4094, 0.7562, 0.2817, 0.3937, 0.3183, 0.6013, 0.6183]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 3.8910e-01, -4.9305e-01, 1.6736e+00, -1.0089e+00, -2.0297e-01,
- -1.2898e+00, 8.2985e-01, 3.0091e-01],
- [ 4.2471e-01, -4.7600e-01, 1.7696e+00, -1.4306e-01, -4.9965e-01,
- -9.5795e-02, 7.4937e-01, 2.1200e-01],
- [ 5.2550e-01, -4.1549e-01, 1.6856e+00, -5.4736e-01, -4.8786e-01,
- -3.7032e-01, 7.5352e-01, 2.1056e-01],
- [ 4.8281e-01, -4.3335e-01, 1.8100e+00, 1.3539e-03, -3.4686e-01,
- 2.0120e-01, 5.2785e-01, 3.4740e-01],
- [ 4.8238e-01, -4.4421e-01, 1.1127e+00, -1.1223e+00, -4.4218e-01,
- -1.2524e+00, 3.8058e-01, 2.7940e-01],
- [ 2.9010e-01, -5.9548e-01, 1.1687e+00, -1.0469e+00, -6.2078e-01,
- -1.0666e+00, 3.2697e-01, 1.9013e-01],
- [ 3.8660e-01, -4.7169e-01, 1.3892e+00, -2.7819e-01, -6.6921e-01,
- -3.4179e-01, -8.3418e-02, 1.6513e-01],
- [ 4.1583e-01, -4.8280e-01, 1.3151e+00, -8.3128e-01, -5.0556e-01,
- -9.7859e-01, 4.4635e-01, 5.0129e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6042, -0.4105, 1.5478, -1.2082, -0.1208, -1.0927, 0.9704,
- 0.3157],
- [ 0.6388, -0.3840, 1.9808, -0.3152, -0.4326, -0.0996, 1.1897,
- 0.2249],
- [ 0.6158, -0.4249, 1.8654, -0.9002, -0.3229, -0.3537, 0.9667,
- 0.2890],
- [ 0.5772, -0.3913, 1.8480, -0.2459, -0.4326, 0.1929, 0.5374,
- 0.4701],
- [ 0.5713, -0.4538, 1.1028, -1.3659, -0.3831, -1.1273, 0.5340,
- 0.2058],
- [ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552],
- [ 0.5759, -0.3948, 1.2072, -0.9849, -0.4672, -0.8156, 0.4912,
- 0.5701]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0216, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0216, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.4578881757333875
- step: 58
- running loss: 0.04237738234023082
- Train Steps: 58/90 Loss: 0.0424 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6245, 0.4100, 0.7762, 0.2583, 0.4963, 0.1517, 0.5875, 0.5417],
- [0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [0.6272, 0.4045, 0.8538, 0.5900, 0.3750, 0.4417, 0.5989, 0.4649],
- [0.6275, 0.4050, 0.9038, 0.3767, 0.3838, 0.3533, 0.7074, 0.5575],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679],
- [0.6140, 0.4034, 0.8850, 0.4317, 0.4288, 0.5067, 0.5825, 0.5533],
- [0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4276, -0.4216, 1.0481, -1.0719, -0.1652, -1.3034, 0.2338, 0.3338],
- [ 0.6741, -0.2671, 1.6596, 0.0128, -0.5760, -0.7280, 0.2549, 0.2385],
- [ 0.5957, -0.3851, 1.6734, 0.3216, -0.6498, -0.2558, 0.4546, 0.1138],
- [ 0.6606, -0.3073, 1.7693, -0.5237, -0.6310, -0.6105, 0.7902, 0.2340],
- [-0.8477, -1.3076, 1.6011, -1.1105, 0.0148, -1.0858, 0.9775, 0.3887],
- [ 0.6939, -0.2546, 1.6675, -0.2473, -0.4186, 0.2012, 0.3346, 0.2056],
- [ 0.5684, -0.3351, 1.5003, -1.0045, -0.4395, -0.9511, 0.5698, 0.2595],
- [ 0.5738, -0.3424, 1.1729, -1.0006, -0.6760, -0.5028, 0.3477, 0.2576]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5985, -0.3921, 1.2995, -1.0927, 0.0062, -1.5854, 0.4277,
- 0.2160],
- [ 0.5901, -0.4000, 1.8423, -0.0688, -0.5307, -0.9233, 0.3642,
- 0.1852],
- [ 0.6109, -0.4177, 1.6575, 0.4393, -0.5538, -0.2459, 0.4805,
- -0.1385],
- [ 0.6125, -0.4153, 1.8885, -0.5461, -0.5134, -0.6539, 0.9814,
- 0.2890],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371],
- [ 0.5499, -0.4225, 1.8018, -0.2921, -0.3055, 0.0543, 0.4046,
- 0.2699],
- [ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0630, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0630, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.520900216884911
- step: 59
- running loss: 0.04272712232008324
- Train Steps: 59/90 Loss: 0.0427 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4048, 0.8488, 0.2883, 0.4463, 0.2033, 0.6321, 0.5155],
- [0.6274, 0.4087, 0.8375, 0.5700, 0.4025, 0.4800, 0.5700, 0.6117],
- [0.6314, 0.4107, 0.8750, 0.5100, 0.3788, 0.4900, 0.7121, 0.5864],
- [0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6325, 0.4165, 0.9000, 0.4617, 0.3813, 0.4900, 0.7485, 0.5447],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [0.6239, 0.4107, 0.8162, 0.2763, 0.3625, 0.3600, 0.5988, 0.5700],
- [0.6250, 0.4008, 0.8950, 0.4183, 0.3550, 0.4383, 0.6361, 0.4927]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9893, -0.0856, 1.4816, -1.0489, -0.1731, -1.3726, 0.6884, 0.1718],
- [ 0.5411, -0.3862, 1.4689, 0.1822, -0.3924, -0.1881, 0.3981, 0.4081],
- [ 0.5717, -0.3800, 1.8185, 0.0153, -0.4474, -0.1558, 0.8028, 0.2387],
- [ 0.0362, -0.6552, 1.6265, -0.2109, -0.3773, -0.2945, 0.1722, 0.2111],
- [ 0.5384, -0.3899, 1.8012, -0.2197, -0.4780, -0.1419, 0.7585, 0.1865],
- [ 0.3051, -0.5194, 1.7123, -0.9239, -0.4580, -0.9475, 0.5561, 0.2275],
- [-0.0521, -0.7613, 1.2508, -1.1285, -0.5612, -0.7800, 0.2824, 0.3768],
- [ 0.6793, -0.2890, 1.6110, -0.4560, -0.6494, -0.4286, 0.3983, 0.0995]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6126, -0.4161, 1.6344, -0.9541, -0.2247, -1.3467, 0.6339,
- 0.0953],
- [ 0.6118, -0.3983, 1.5824, 0.3469, -0.4268, -0.0688, 0.3469,
- 0.5393],
- [ 0.6306, -0.3888, 1.7557, 0.0697, -0.5365, -0.0226, 1.0033,
- 0.4226],
- [ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.6355, -0.3623, 1.8711, -0.1535, -0.5249, -0.0226, 1.1715,
- 0.2302],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390],
- [ 0.5960, -0.3888, 1.4840, -1.0095, -0.6115, -0.6231, 0.4797,
- 0.3469],
- [ 0.6010, -0.4345, 1.8480, -0.3537, -0.6462, -0.2613, 0.6524,
- -0.0099]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0357, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0357, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5565678672865033
- step: 60
- running loss: 0.04260946445477506
- Train Steps: 60/90 Loss: 0.0426 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6160, 0.4086, 0.8775, 0.3983, 0.3750, 0.3783, 0.5128, 0.4882],
- [0.6361, 0.4102, 0.8650, 0.2983, 0.4888, 0.2000, 0.6702, 0.5459],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168],
- [ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6296, 0.4060, 0.9100, 0.3267, 0.4726, 0.3367, 0.7446, 0.5355],
- [0.6110, 0.4047, 0.8700, 0.4483, 0.3713, 0.3967, 0.5088, 0.5517]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7788, -0.2101, 1.7978, -0.4085, -0.6473, -0.3535, 0.1756, 0.1227],
- [ 0.8946, -0.1416, 1.6702, -0.9497, 0.0064, -1.1562, 0.9449, 0.2182],
- [ 0.9714, -0.1470, 1.8020, 0.1940, -0.4284, 0.0314, 0.6996, 0.1681],
- [-0.5611, -1.1197, 0.9617, -1.1811, -0.3930, -1.2175, 0.3303, 0.3160],
- [-1.0809, -1.4155, 0.9003, -1.1566, -0.3876, -1.1537, 0.2993, 0.4127],
- [ 0.6877, -0.3139, 1.9421, 0.1481, -0.5822, -0.3625, 0.5538, 0.1134],
- [ 0.7454, -0.2486, 1.7348, -0.7554, -0.2176, -0.6656, 0.9340, 0.1684],
- [ 0.8294, -0.1889, 1.6893, -0.1422, -0.6164, -0.2008, 0.1228, 0.2367]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5595, -0.3988, 1.7672, -0.4460, -0.5538, -0.5384, 0.0828,
- -0.0310],
- [ 0.6520, -0.3912, 1.7095, -0.9079, -0.0284, -1.3621, 0.8096,
- 0.2356],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013],
- [-2.2859, -2.2859, 0.8033, -1.1250, -0.3864, -1.3082, 0.1126,
- 0.4543],
- [ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.6224, -0.4105, 1.9173, -0.7771, -0.1030, -0.7308, 1.1532,
- 0.1875],
- [ 0.5363, -0.4168, 1.7326, -0.2151, -0.5711, -0.4537, 0.0640,
- 0.2622]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0806, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0806, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.6371513130143285
- step: 61
- running loss: 0.04323198873793981
- Train Steps: 61/90 Loss: 0.0432 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517],
- [0.6193, 0.4108, 0.7438, 0.2700, 0.3650, 0.3683, 0.6238, 0.5717],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6095, 0.4002, 0.8533, 0.5168, 0.5031, 0.5094, 0.5125, 0.5433],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433],
- [0.6226, 0.4125, 0.8800, 0.4900, 0.4512, 0.5600, 0.6275, 0.5517]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5904, -0.3481, 1.3853, -0.9300, -0.7038, -0.5478, 0.6628, 0.1901],
- [ 0.7472, -0.2219, 1.4910, -0.8652, -0.2222, -1.3944, 0.4443, 0.2165],
- [ 0.5629, -0.3763, 1.8600, -0.0448, -0.2762, -0.1915, 0.5234, 0.1410],
- [ 0.7760, -0.2245, 1.2375, -0.9950, -0.6542, -0.6808, 0.6083, 0.2506],
- [-2.2670, -2.2596, 1.1679, -1.1750, -0.4814, -1.3681, 0.2612, 0.2891],
- [ 0.9036, -0.1748, 1.7474, 0.1289, -0.1123, -0.3393, 0.3888, 0.2046],
- [ 0.9780, -0.1009, 1.8675, -0.0857, -0.2561, -0.0475, 0.7317, 0.1477],
- [ 0.9501, -0.1388, 1.8309, -0.0459, -0.2355, 0.0528, 0.6644, 0.2409]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622],
- [ 0.5747, -0.3886, 1.1494, -1.0388, -0.6000, -0.5846, 0.5952,
- 0.3546],
- [-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373],
- [ 0.5295, -0.4373, 1.6553, 0.1011, 0.0380, 0.0671, 0.0813,
- 0.2237],
- [ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237],
- [ 0.5897, -0.3804, 1.7788, -0.0226, -0.2016, 0.3007, 0.6125,
- 0.2622]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0268, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0268, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.6639770111069083
- step: 62
- running loss: 0.04296737114688562
- Train Steps: 62/90 Loss: 0.0430 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585],
- [0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136],
- [0.6162, 0.4014, 0.8800, 0.5333, 0.3750, 0.4817, 0.5988, 0.5283],
- [0.6353, 0.4128, 0.8488, 0.2600, 0.5525, 0.1616, 0.6694, 0.5540],
- [0.6336, 0.4086, 0.8900, 0.3950, 0.3900, 0.2950, 0.6504, 0.5066],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6336, 0.4191, 0.8938, 0.5167, 0.3937, 0.3517, 0.7343, 0.5748]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3569, -0.4949, 1.6938, -0.3559, -0.2054, 0.1943, 0.5201, 0.1686],
- [ 0.4867, -0.4273, 1.7035, -0.3954, -0.6700, -0.4678, 0.2266, 0.1097],
- [ 0.6877, -0.3373, 1.6928, 0.1891, -0.4250, -0.0477, 0.4994, 0.1601],
- [-0.9558, -1.3822, 1.5410, -1.2279, 0.1464, -1.2743, 0.9212, 0.3460],
- [ 0.9958, -0.1204, 1.7140, -0.5015, -0.3964, -0.9068, 0.7189, 0.0435],
- [ 0.8597, -0.1777, 1.4339, -1.0562, -0.4009, -0.8427, 0.4997, 0.2891],
- [ 0.3033, -0.5074, 1.3019, -0.7391, -0.6111, -0.5300, 0.2686, 0.2457],
- [ 0.5942, -0.3914, 1.8203, 0.0827, -0.3343, -0.6801, 0.7941, 0.2058]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936],
- [ 0.5235, -0.4273, 1.7499, -0.4306, -0.5827, -0.4614, 0.1651,
- 0.0862],
- [ 0.5603, -0.4319, 1.7788, 0.1775, -0.5538, -0.0611, 0.4797,
- 0.1544],
- [ 0.6487, -0.3792, 1.6344, -1.0850, 0.2659, -1.5397, 0.8059,
- 0.2730],
- [ 0.6405, -0.3984, 1.8249, -0.4614, -0.4845, -0.9233, 0.7182,
- 0.0539],
- [ 0.5778, -0.3930, 1.5189, -0.9387, -0.4326, -0.9156, 0.4855,
- 0.3392],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.6405, -0.3503, 1.8423, 0.1005, -0.4672, -0.6616, 1.1057,
- 0.3692]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0702, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0702, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.7341925194486976
- step: 63
- running loss: 0.043399881261090435
- Train Steps: 63/90 Loss: 0.0434 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6203, 0.4096, 0.8862, 0.4267, 0.3538, 0.4117, 0.6025, 0.5650],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6277, 0.4118, 0.8988, 0.3833, 0.3950, 0.2650, 0.6290, 0.4938],
- [0.6135, 0.4115, 0.8838, 0.4667, 0.4288, 0.6050, 0.5778, 0.5097]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8489, -0.2227, 1.7997, -0.5051, -0.4486, -0.5622, 0.6693, 0.2327],
- [ 0.2771, -0.5652, 1.6160, -0.3772, -0.4482, -0.3172, 0.3491, 0.1680],
- [ 0.1536, -0.6882, 1.8718, -0.3745, -0.4070, 0.1483, 0.8937, 0.1137],
- [ 0.5407, -0.3957, 1.7259, -0.1100, -0.5389, -0.6844, 0.4568, 0.2921],
- [ 0.3525, -0.5436, 0.8690, -1.2150, -0.4159, -1.3178, 0.2979, 0.2886],
- [ 0.5503, -0.4333, 1.7132, 0.0185, -0.0879, -0.1173, 0.5199, 0.1360],
- [ 0.4545, -0.4887, 1.8710, -0.6144, -0.2999, -1.2758, 0.7149, 0.0996],
- [ 0.3100, -0.5452, 1.8769, -0.2250, -0.1633, 0.1682, 0.5864, 0.1355]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5790, -0.3940, 1.8076, -0.3152, -0.6520, -0.3844, 0.4970,
- 0.3238],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.5886, -0.4253, 1.8654, -0.3460, -0.5480, 0.3623, 0.6587,
- 0.1005],
- [ 0.5902, -0.3493, 1.7961, -0.0072, -0.5942, -0.5615, 0.3180,
- 0.3161],
- [ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.6135, -0.3841, 1.8654, -0.5153, -0.4614, -1.0619, 0.6195,
- -0.0049],
- [ 0.5477, -0.3851, 1.7961, -0.1304, -0.3055, 0.5085, 0.3830,
- 0.0682]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0240, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0240, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.7581892935559154
- step: 64
- running loss: 0.04309670771181118
- Train Steps: 64/90 Loss: 0.0431 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3993, 0.8639, 0.3687, 0.3658, 0.3139, 0.6002, 0.5374],
- [0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767],
- [0.6239, 0.4206, 0.8750, 0.5400, 0.3688, 0.4850, 0.5737, 0.5700],
- [0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6289, 0.4032, 0.8419, 0.5446, 0.4075, 0.5017, 0.6312, 0.5117],
- [0.6293, 0.4024, 0.8750, 0.5000, 0.4012, 0.5733, 0.7121, 0.5633],
- [0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5196, -0.4510, 1.7051, -0.8963, -0.5621, -1.1083, 0.6279, 0.0341],
- [ 0.4282, -0.4812, 1.6406, -0.0781, 0.0335, -0.6546, 0.3637, 0.3195],
- [ 0.3719, -0.5328, 1.7015, -0.3336, -0.2812, -0.1746, 0.2990, 0.2221],
- [ 0.5406, -0.4317, 1.7712, -0.1215, -0.4843, -0.2928, 0.5235, 0.1871],
- [ 0.6402, -0.3560, 1.6619, 0.0413, -0.3322, -0.5363, 0.4270, 0.3390],
- [ 0.6243, -0.4074, 1.6774, -0.2420, -0.3619, -0.3324, 0.7050, 0.0932],
- [ 0.3252, -0.5740, 1.8606, -0.2420, -0.3334, 0.1080, 0.7974, 0.2020],
- [ 0.1856, -0.6645, 1.8283, -0.6080, -0.4700, 0.0561, 0.7195, 0.0907]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5777, -0.4416, 1.7044, -0.5827, -0.5962, -0.8361, 0.4862,
- 0.1963],
- [ 0.5959, -0.3579, 1.6055, 0.3623, -0.0573, -0.2074, 0.3122,
- 0.4547],
- [ 0.5539, -0.4031, 1.7168, -0.0594, -0.3748, 0.0543, 0.1390,
- 0.3777],
- [ 0.5960, -0.3430, 1.7557, 0.2083, -0.5827, -0.0457, 0.3642,
- 0.3469],
- [ 0.6038, -0.3464, 1.7037, 0.3931, -0.4441, -0.2613, 0.3007,
- 0.4624],
- [ 0.6189, -0.4238, 1.6026, 0.2295, -0.4037, 0.0313, 0.6298,
- 0.0774],
- [ 0.6207, -0.4273, 1.7557, 0.0236, -0.4326, 0.3623, 1.0033,
- 0.3157],
- [ 0.5886, -0.4253, 1.8654, -0.3460, -0.5480, 0.3623, 0.6587,
- 0.1005]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0390, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0390, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.797200477682054
- step: 65
- running loss: 0.04303385350280083
- Train Steps: 65/90 Loss: 0.0430 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6203, 0.4078, 0.8800, 0.5083, 0.3900, 0.5000, 0.6100, 0.5583],
- [0.6125, 0.3999, 0.8750, 0.4883, 0.4750, 0.4700, 0.5533, 0.5617],
- [0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6201, 0.4055, 0.7717, 0.1919, 0.4250, 0.2079, 0.5871, 0.5044]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6687, -0.3593, 1.8971, 0.1707, -0.4330, 0.0961, 0.6451, 0.1299],
- [ 0.5644, -0.4138, 1.7877, 0.0097, -0.0366, -0.0669, 0.4079, 0.2327],
- [ 0.7685, -0.2672, 1.7709, -0.5620, -0.6552, -0.2216, 0.4247, 0.1788],
- [-1.0692, -1.4496, 1.1144, -1.0959, -0.2064, -1.3422, 0.3366, 0.3403],
- [ 0.3732, -0.5458, 1.8620, 0.0651, -0.0702, -0.0228, 0.4075, 0.1777],
- [ 0.4668, -0.4828, 1.4026, -0.9949, -0.5788, -0.5876, 0.6636, 0.2020],
- [ 0.5879, -0.4394, 1.9501, 0.2348, -0.5701, -0.4968, 0.7669, 0.0505],
- [ 0.9447, -0.1949, 1.2377, -1.2274, -0.2411, -1.2837, 0.5303, 0.2160]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5794, -0.4023, 1.7788, 0.0620, -0.4845, 0.0236, 0.5316,
- 0.2930],
- [ 0.5432, -0.4388, 1.7557, -0.0303, -0.0919, -0.1150, 0.2699,
- 0.3087],
- [ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084],
- [ 0.5359, -0.4370, 1.7095, -0.0303, -0.0804, -0.0380, 0.1044,
- 0.3392],
- [ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.5785, -0.4129, 1.2785, -1.3996, -0.3227, -1.3259, 0.4258,
- 0.0438]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0485, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0485, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.845730713568628
- step: 66
- running loss: 0.04311713202376709
- Train Steps: 66/90 Loss: 0.0431 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6284, 0.4127, 0.8538, 0.5867, 0.4363, 0.5083, 0.6038, 0.5433],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6127, 0.4119, 0.8650, 0.5300, 0.4512, 0.5250, 0.5260, 0.5461],
- [0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3285, -0.5892, 1.8296, -0.5351, -0.5713, -0.4515, 0.6056, 0.1702],
- [ 0.1763, -0.6597, 1.6175, 0.2585, -0.3035, 0.0659, 0.3303, 0.1897],
- [ 0.4622, -0.5033, 1.6857, -0.1942, -0.5921, -0.4273, 0.6031, 0.1828],
- [ 0.2868, -0.5815, 1.7712, -0.0843, -0.2788, 0.4183, 0.5205, 0.2098],
- [ 0.4892, -0.4744, 1.8808, -0.3870, -0.2168, -0.9448, 0.8276, 0.1716],
- [ 0.4710, -0.4649, 1.6061, 0.1596, -0.1887, 0.1203, 0.1593, 0.2353],
- [ 0.7560, -0.3035, 1.0503, -1.2743, -0.3153, -1.2866, 0.1925, 0.1969],
- [ 0.5216, -0.4476, 1.9730, -0.6449, -0.3307, -0.5987, 0.9147, 0.1548]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.6166, -0.3795, 1.6575, 0.4239, -0.2709, 0.0620, 0.5028,
- 0.2237],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928],
- [ 0.6224, -0.4345, 1.9404, -0.2921, -0.3171, -0.8771, 1.0655,
- 0.2142],
- [ 0.5443, -0.3831, 1.7095, 0.1621, -0.2016, 0.1390, 0.1437,
- 0.2364],
- [ 0.5598, -0.4201, 1.1898, -1.3005, -0.3806, -1.3313, 0.3873,
- 0.0774],
- [ 0.6388, -0.3623, 1.9173, -0.7386, -0.3517, -0.5846, 1.1495,
- 0.2676]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0175, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0175, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.86319727730006
- step: 67
- running loss: 0.042734287720896415
- Train Steps: 67/90 Loss: 0.0427 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433],
- [0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901],
- [0.6117, 0.4019, 0.8538, 0.4067, 0.3513, 0.3583, 0.5663, 0.5133],
- [0.6268, 0.4029, 0.8500, 0.2683, 0.3937, 0.3500, 0.6860, 0.5297],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4266, -0.5112, 1.7698, 0.0365, -0.0220, 0.1191, 0.4562, 0.2976],
- [ 0.4087, -0.5572, 1.9169, 0.0073, -0.5026, 0.2506, 0.6863, 0.1113],
- [ 0.1075, -0.7343, 1.3653, -0.8740, -0.4800, -0.9601, 0.2076, 0.2042],
- [ 0.3999, -0.5319, 1.7445, -0.3835, -0.6190, -0.5022, 0.2338, 0.1659],
- [ 0.5842, -0.4576, 1.6458, -0.9018, -0.4912, -0.6226, 1.0400, 0.1461],
- [ 0.7194, -0.3208, 1.6277, 0.1032, -0.2578, -0.7793, 0.5120, 0.4292],
- [ 0.5912, -0.4189, 1.2441, -1.0201, -0.4948, -0.9039, 0.3610, 0.1170],
- [ 0.3899, -0.5879, 1.9004, 0.0208, -0.1882, 0.1316, 0.4283, 0.0949]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5505, -0.4207, 1.7095, -0.0534, -0.0509, 0.1050, 0.3873,
- 0.3007],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237],
- [ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220],
- [ 0.5393, -0.4294, 1.6575, -0.4075, -0.6635, -0.6308, 0.3296,
- 0.0851],
- [ 0.6092, -0.4249, 1.6402, -1.0465, -0.4672, -0.6693, 0.8827,
- 0.1608],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762],
- [ 0.4997, -0.4446, 1.1610, -0.9772, -0.6058, -1.0311, 0.1404,
- -0.1031],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0202, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0202, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.883371683768928
- step: 68
- running loss: 0.042402524761307764
- Train Steps: 68/90 Loss: 0.0424 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [ nan, nan, 0.6935, 0.1930, 0.4150, 0.2250, 0.5450, 0.5650],
- [0.6201, 0.4050, 0.7757, 0.2234, 0.4459, 0.1798, 0.5975, 0.5426],
- [0.6198, 0.4114, 0.8263, 0.3283, 0.3550, 0.3583, 0.5813, 0.5667],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533],
- [0.6200, 0.3993, 0.8639, 0.3687, 0.3658, 0.3139, 0.6002, 0.5374],
- [0.6198, 0.3997, 0.8582, 0.5361, 0.4117, 0.5016, 0.5942, 0.5134],
- [0.6222, 0.4169, 0.8638, 0.5650, 0.4313, 0.4783, 0.5637, 0.5633]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0256, -0.1258, 1.8074, -0.7311, -0.2773, -0.7034, 0.7876, 0.1885],
- [-1.4408, -1.7286, 1.1651, -1.0770, -0.1554, -0.9629, 0.2680, 0.3037],
- [ 0.9199, -0.2048, 1.3228, -1.0172, -0.2033, -1.1164, 0.5515, 0.1754],
- [ 1.0787, -0.1060, 1.6983, -0.5107, -0.6539, -0.3158, 0.4087, 0.2556],
- [-1.2559, -1.5952, 1.3378, -0.7338, -0.5142, -0.7789, 0.1824, 0.2635],
- [ 0.8443, -0.2714, 1.8309, -0.3378, -0.5479, -0.5533, 0.6412, 0.0626],
- [ 0.9600, -0.2078, 1.8367, 0.3667, -0.4265, 0.3660, 0.4879, 0.1222],
- [ 0.9529, -0.1759, 1.7623, 0.5614, -0.3391, 0.2584, 0.3591, 0.2860]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [-2.2859, -2.2859, 0.9175, -1.3947, -0.3691, -1.2467, 0.2314,
- 0.3238],
- [ 0.5784, -0.4153, 1.2972, -1.2541, -0.2265, -1.4553, 0.4741,
- 0.2203],
- [ 0.5768, -0.3857, 1.5305, -0.7694, -0.6462, -0.6308, 0.3988,
- 0.3315],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699],
- [ 0.5777, -0.4416, 1.7044, -0.5827, -0.5962, -0.8361, 0.4862,
- 0.1963],
- [ 0.5767, -0.4396, 1.6782, 0.1905, -0.3844, 0.0308, 0.4588,
- 0.0855],
- [ 0.5880, -0.3605, 1.7037, 0.3238, -0.2940, -0.0765, 0.3180,
- 0.3161]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0832, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0832, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.9666087506338954
- step: 69
- running loss: 0.042994329719331814
- Train Steps: 69/90 Loss: 0.0430 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.4050, 0.7313, 0.2433, 0.4075, 0.2117, 0.5649, 0.5656],
- [0.6339, 0.4102, 0.8588, 0.3133, 0.4425, 0.2117, 0.6417, 0.5089],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6275, 0.4024, 0.8600, 0.2283, 0.5350, 0.1800, 0.7074, 0.5413],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6115, 0.4005, 0.8838, 0.3867, 0.3763, 0.4700, 0.5800, 0.5550],
- [0.6186, 0.3967, 0.7337, 0.1992, 0.4120, 0.2508, 0.6105, 0.5395]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0945, -0.7125, 1.0857, -0.9566, -0.3616, -0.9317, 0.1844, 0.2622],
- [ 0.6179, -0.4146, 1.6800, -0.5893, -0.2319, -0.8998, 0.7288, 0.1170],
- [ 0.4128, -0.5567, 1.8628, 0.3670, -0.6036, -0.1763, 0.3295, 0.0783],
- [-0.0069, -0.8548, 1.7534, -0.8855, 0.1549, -1.0048, 0.9400, 0.2685],
- [ 0.4008, -0.5571, 1.9227, -0.1961, -0.5610, -0.1070, 0.5655, 0.1825],
- [ 0.7813, -0.2972, 1.7793, -0.0681, -0.6172, -0.1469, 0.3549, 0.2691],
- [ 0.6439, -0.3781, 1.7482, -0.2119, -0.6423, 0.3156, 0.2430, 0.1745],
- [ 0.5604, -0.4501, 1.1170, -1.0097, -0.3723, -0.8632, 0.3318, 0.2643]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5746, -0.4153, 1.0917, -1.1620, -0.4037, -1.3082, 0.3234,
- 0.3267],
- [ 0.6421, -0.3912, 1.6806, -0.8386, -0.2420, -1.3082, 0.6780,
- 0.0646],
- [ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.6125, -0.4273, 1.6864, -1.2313, 0.1852, -1.4545, 0.9814,
- 0.2142],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.5880, -0.3887, 1.8423, -0.3306, -0.6231, -0.5230, 0.4046,
- 0.1544],
- [ 0.5384, -0.4361, 1.7961, -0.4999, -0.5480, -0.1150, 0.3931,
- 0.2776],
- [ 0.5713, -0.4538, 1.1028, -1.3659, -0.3831, -1.1273, 0.5340,
- 0.2058]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0494, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0494, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.0159969767555594
- step: 70
- running loss: 0.043085671096507995
- Train Steps: 70/90 Loss: 0.0431 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6361, 0.4076, 0.8862, 0.5350, 0.3713, 0.4650, 0.6654, 0.5297],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679],
- [0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6276, 0.4235, 0.8888, 0.5333, 0.3800, 0.3117, 0.5427, 0.6164],
- [0.6270, 0.4267, 0.7150, 0.3317, 0.4900, 0.1817, 0.5560, 0.6183],
- [ nan, nan, 0.7525, 0.2291, 0.3838, 0.3017, 0.6050, 0.5667],
- [0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6908, -0.4021, 1.8993, 0.1808, -0.5602, -0.0866, 0.6491, 0.1600],
- [ 0.3828, -0.5673, 1.8360, -0.3507, -0.4966, 0.0851, 0.3386, 0.1186],
- [ 0.7606, -0.3255, 1.8966, 0.1732, -0.3291, 0.5183, 0.7372, 0.2568],
- [ 0.5708, -0.4434, 1.7218, -0.5815, -0.7062, -0.3660, 0.2640, 0.0845],
- [ 0.6328, -0.3785, 1.7117, 0.1811, -0.4657, -0.6712, 0.3862, 0.3782],
- [ 0.5071, -0.4653, 1.1100, -1.0328, -0.0600, -1.3080, 0.1529, 0.3689],
- [-0.3958, -1.1060, 1.3111, -1.2376, -0.3739, -0.9371, 0.3930, 0.2738],
- [ 0.4604, -0.5685, 1.9232, -0.0375, -0.5582, -0.0750, 0.5697, 0.0881]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6520, -0.4032, 1.8076, 0.1852, -0.5711, -0.1381, 0.7876,
- 0.1608],
- [ 0.5546, -0.4620, 1.7788, -0.4229, -0.5192, -0.0226, 0.4277,
- 0.2468],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371],
- [ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.6131, -0.3299, 1.8192, 0.1775, -0.5307, -0.8463, 0.2206,
- 0.5612],
- [ 0.6101, -0.3152, 1.0166, -0.7540, -0.0226, -1.4468, 0.2823,
- 0.5702],
- [-2.2859, -2.2859, 1.1898, -1.2278, -0.5134, -0.8925, 0.5085,
- 0.3315],
- [ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0906, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0906, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.1066278582438827
- step: 71
- running loss: 0.043755321947096935
- Train Steps: 71/90 Loss: 0.0438 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517],
- [0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986],
- [0.6317, 0.4038, 0.8287, 0.5900, 0.3800, 0.4717, 0.6295, 0.4986],
- [0.6176, 0.3911, 0.8738, 0.4217, 0.3488, 0.4033, 0.6025, 0.4817],
- [ nan, nan, 0.8850, 0.3000, 0.5363, 0.2250, 0.7343, 0.5771],
- [0.6125, 0.4035, 0.7825, 0.3100, 0.3463, 0.4900, 0.5832, 0.5637],
- [0.6138, 0.4101, 0.8800, 0.5083, 0.4637, 0.5950, 0.5587, 0.5077],
- [0.6224, 0.4097, 0.7438, 0.2267, 0.3850, 0.2850, 0.5988, 0.5250]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5412, -0.4559, 1.7843, 0.0259, -0.2841, 0.1660, 0.2377, 0.1969],
- [ 0.6741, -0.3356, 1.8233, -0.4519, -0.2874, -1.1560, 0.4323, 0.2217],
- [ 0.7896, -0.3125, 1.7426, 0.3888, -0.6745, -0.0785, 0.5314, 0.1593],
- [ 0.6071, -0.4343, 1.7290, -0.3111, -0.7544, -0.4238, 0.3074, 0.0894],
- [-1.1140, -1.5608, 1.7046, -0.9713, 0.0438, -1.1867, 0.8088, 0.3351],
- [ 0.4090, -0.5324, 1.3724, -0.7669, -0.7136, -0.0075, 0.4359, 0.2871],
- [ 0.6645, -0.3611, 1.7667, 0.1716, -0.2005, 0.3721, 0.2576, 0.2559],
- [ 0.7018, -0.3551, 1.2018, -1.1815, -0.5421, -0.9914, 0.5265, 0.1972]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622],
- [ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171],
- [ 0.6320, -0.4206, 1.5420, 0.4393, -0.5307, -0.1073, 0.6216,
- 0.0171],
- [ 0.5669, -0.4794, 1.7499, -0.3383, -0.6751, -0.4229, 0.4970,
- -0.0611],
- [-2.2859, -2.2859, 1.8018, -0.9002, 0.1910, -1.2467, 1.1057,
- 0.3799],
- [ 0.5432, -0.4224, 1.3284, -0.8540, -0.6866, -0.0226, 0.4077,
- 0.3177],
- [ 0.5491, -0.3918, 1.7788, 0.0620, -0.1439, 0.4624, 0.2946,
- 0.0592],
- [ 0.5891, -0.3937, 1.1494, -1.2390, -0.5076, -0.9695, 0.4797,
- 0.1390]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0398, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0398, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.146448607556522
- step: 72
- running loss: 0.043700675104951694
- Train Steps: 72/90 Loss: 0.0437 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [0.6265, 0.4091, 0.8950, 0.3533, 0.3600, 0.3967, 0.6295, 0.4901],
- [0.6115, 0.4005, 0.8838, 0.3867, 0.3763, 0.4700, 0.5800, 0.5550],
- [0.6098, 0.3991, 0.8638, 0.4717, 0.4263, 0.4967, 0.5212, 0.5650],
- [0.6201, 0.4116, 0.8725, 0.4733, 0.3700, 0.3217, 0.5386, 0.5767],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6273, 0.4100, 0.7137, 0.2133, 0.4000, 0.2650, 0.6075, 0.5633]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5118, -0.4524, 1.7195, -0.3490, -0.4138, -0.5709, 0.4172, 0.3501],
- [ 1.0943, -0.0776, 1.6530, -0.0977, -0.2364, -0.7991, 0.5785, 0.3261],
- [ 0.6776, -0.4029, 1.9237, -0.5634, -0.6072, -0.3619, 0.7648, 0.0746],
- [ 1.0923, -0.1342, 1.7725, -0.4052, -0.6281, 0.1162, 0.4874, 0.1301],
- [ 0.6887, -0.3659, 1.7554, 0.0926, -0.3180, 0.2316, 0.3344, 0.1946],
- [ 0.6969, -0.3097, 1.6428, 0.0207, -0.5718, -0.5985, 0.2854, 0.2422],
- [-2.1434, -2.2292, 1.0703, -1.2591, -0.3749, -1.2681, 0.2250, 0.1873],
- [ 0.3197, -0.6014, 1.0105, -1.1921, -0.3945, -1.0479, 0.4393, 0.2994]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822],
- [ 0.6077, -0.3965, 1.8480, -0.6539, -0.6231, -0.4537, 0.6216,
- -0.0220],
- [ 0.5384, -0.4361, 1.7961, -0.4999, -0.5480, -0.1150, 0.3931,
- 0.2776],
- [ 0.5308, -0.4425, 1.7037, -0.1073, -0.3171, 0.0082, 0.1217,
- 0.3238],
- [ 0.5781, -0.3848, 1.7441, -0.0996, -0.5769, -0.8002, 0.2021,
- 0.3778],
- [-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373],
- [ 0.6115, -0.3924, 1.0109, -1.3005, -0.4383, -1.0619, 0.5201,
- 0.3161]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0283, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0283, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.174727371893823
- step: 73
- running loss: 0.043489416053340044
- Train Steps: 73/90 Loss: 0.0435 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [0.6260, 0.4133, 0.8800, 0.5117, 0.3713, 0.3283, 0.6223, 0.4967],
- [0.6101, 0.3977, 0.8550, 0.5667, 0.3862, 0.4417, 0.5128, 0.4869],
- [0.6124, 0.4069, 0.8314, 0.5001, 0.3738, 0.4650, 0.5167, 0.5402],
- [0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5926, -0.4584, 1.1286, -1.4791, -0.3460, -1.2189, 0.5719, 0.2300],
- [ 0.2274, -0.6594, 1.7152, -0.5702, -0.6758, -0.4601, 0.1921, 0.1128],
- [ 0.6375, -0.3545, 1.6312, -0.4032, -0.2160, -0.9396, 0.4620, 0.3717],
- [ 0.2176, -0.6772, 1.8095, -0.1802, -0.4826, -0.6983, 0.5700, 0.1636],
- [ 0.0604, -0.7763, 1.6917, 0.1112, -0.4883, -0.2882, 0.1321, 0.1681],
- [ 0.4266, -0.5131, 1.5818, -0.1841, -0.4556, -0.1109, 0.2273, 0.2884],
- [ 0.5517, -0.4476, 1.8382, -0.2390, -0.5477, -0.2693, 0.7168, 0.2239],
- [ 0.4387, -0.5354, 1.6025, -0.0815, -0.5076, 0.0810, 0.6287, 0.2390]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.5235, -0.4273, 1.7499, -0.4306, -0.5827, -0.4614, 0.1651,
- 0.0862],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822],
- [ 0.6054, -0.3767, 1.7788, 0.0774, -0.5711, -0.7694, 0.5887,
- 0.0081],
- [ 0.5320, -0.4488, 1.6633, 0.3315, -0.5018, -0.2459, 0.0828,
- -0.0370],
- [ 0.5425, -0.4067, 1.5543, 0.0241, -0.5596, -0.1381, 0.1005,
- 0.2093],
- [ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499],
- [ 0.6191, -0.4297, 1.4612, 0.2391, -0.4961, 0.0313, 1.1166,
- 0.1768]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0268, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0268, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.2015545638278127
- step: 74
- running loss: 0.04326425086253801
- Train Steps: 74/90 Loss: 0.0433 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6153, 0.4117, 0.8688, 0.5167, 0.4895, 0.5647, 0.5524, 0.5136],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6264, 0.4069, 0.7900, 0.1650, 0.4275, 0.2267, 0.6290, 0.4947],
- [0.6140, 0.4034, 0.8850, 0.4317, 0.4288, 0.5067, 0.5825, 0.5533],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6250, 0.4106, 0.8700, 0.3717, 0.3588, 0.4967, 0.6038, 0.5167],
- [0.6364, 0.4144, 0.8625, 0.3083, 0.4913, 0.2000, 0.6448, 0.5274]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4025, -0.5363, 1.6971, 0.1246, -0.1701, 0.1078, 0.2141, 0.2565],
- [ 0.4167, -0.5550, 1.7186, -0.4311, -0.7491, -0.2282, 0.5160, 0.2378],
- [ 0.3892, -0.5351, 1.5867, 0.4916, -0.4973, -0.3103, 0.2211, 0.4570],
- [-0.0709, -0.8842, 1.3462, -1.3077, -0.3917, -1.4341, 0.6078, 0.1435],
- [ 0.5810, -0.4226, 1.7171, -0.1685, -0.4232, 0.1927, 0.2545, 0.2298],
- [ 0.3430, -0.6123, 1.4879, -1.0594, -0.5487, -1.2002, 0.5690, 0.0772],
- [ 0.7413, -0.3250, 1.6692, -0.4937, -0.6821, 0.0267, 0.6439, 0.2146],
- [ 0.1623, -0.6903, 1.6709, -0.8004, -0.1233, -1.2627, 0.6041, 0.2084]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.5635e-01, -3.8422e-01, 1.7268e+00, 1.0054e-01, -2.4997e-02,
- 3.2255e-01, 2.6581e-01, 8.6245e-02],
- [ 5.7783e-01, -4.3934e-01, 1.8018e+00, -4.6143e-01, -6.6928e-01,
- -1.3811e-01, 5.4896e-01, 2.0831e-01],
- [ 6.0381e-01, -3.4642e-01, 1.7037e+00, 3.9307e-01, -4.4411e-01,
- -2.6128e-01, 3.0069e-01, 4.6236e-01],
- [ 6.0739e-01, -4.0631e-01, 1.3630e+00, -1.5238e+00, -3.1132e-01,
- -1.2390e+00, 6.1950e-01, -9.2270e-04],
- [ 5.4990e-01, -4.2249e-01, 1.8018e+00, -2.9207e-01, -3.0554e-01,
- 5.4350e-02, 4.0462e-01, 2.6990e-01],
- [ 6.0421e-01, -4.2248e-01, 1.5420e+00, -1.2082e+00, -4.7298e-01,
- -1.0311e+00, 6.3800e-01, -2.1963e-02],
- [ 6.0098e-01, -3.8961e-01, 1.7326e+00, -5.6921e-01, -6.2887e-01,
- 8.1601e-03, 5.0277e-01, 1.0054e-01],
- [ 6.5365e-01, -3.7194e-01, 1.6979e+00, -8.6174e-01, -1.6859e-02,
- -1.3621e+00, 6.9257e-01, 1.5008e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0287, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0287, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.2302527902647853
- step: 75
- running loss: 0.04307003720353047
- Train Steps: 75/90 Loss: 0.0431 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726],
- [0.6261, 0.3987, 0.8688, 0.4917, 0.4300, 0.5333, 0.7010, 0.5309],
- [0.6279, 0.4050, 0.8025, 0.2200, 0.4313, 0.2733, 0.6820, 0.5320],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301],
- [0.6182, 0.3967, 0.8263, 0.3065, 0.3526, 0.4161, 0.6192, 0.5290]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.8661, -2.0538, 1.2785, -0.8249, -0.6488, -0.8578, 0.1658, 0.2023],
- [ 0.6903, -0.3068, 1.8189, 0.1420, -0.4976, -0.8075, 0.3031, 0.2389],
- [ 0.7957, -0.2790, 1.3134, -0.9248, -0.5435, -0.9453, 0.2031, 0.1206],
- [ 0.9262, -0.2276, 1.8351, 0.1683, -0.3136, 0.3725, 0.7447, 0.2883],
- [ 0.6961, -0.3637, 1.4982, -1.0795, -0.2947, -1.1262, 0.6684, 0.2113],
- [-0.1521, -0.8515, 1.1088, -1.1953, -0.3039, -1.4054, 0.2218, 0.2412],
- [ 0.8814, -0.2470, 1.7744, 0.1652, -0.3566, 0.0847, 0.4990, 0.2598],
- [ 0.7957, -0.3254, 1.6967, -0.7133, -0.7268, -0.3437, 0.6567, 0.1718]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837],
- [ 0.5901, -0.4000, 1.8423, -0.0688, -0.5307, -0.9233, 0.3642,
- 0.1852],
- [ 0.4997, -0.4446, 1.1610, -0.9772, -0.6058, -1.0311, 0.1404,
- -0.1031],
- [ 0.6059, -0.4442, 1.7268, -0.0149, -0.2998, 0.1775, 0.9521,
- 0.1661],
- [ 0.6141, -0.4153, 1.4208, -1.2697, -0.2940, -1.0234, 0.8644,
- 0.1715],
- [ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.6010, -0.4562, 1.7198, -0.0090, -0.3464, 0.0108, 0.6294,
- 0.1627],
- [ 0.5697, -0.4538, 1.5308, -0.8703, -0.6572, -0.3639, 0.5739,
- 0.1576]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0314, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0314, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.2616537315770984
- step: 76
- running loss: 0.042916496468119715
- Train Steps: 76/90 Loss: 0.0429 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6228, 0.4004, 0.8750, 0.5250, 0.3825, 0.5233, 0.6362, 0.5000],
- [0.6264, 0.4069, 0.7900, 0.1650, 0.4275, 0.2267, 0.6290, 0.4947],
- [0.6279, 0.4050, 0.8025, 0.2200, 0.4313, 0.2733, 0.6820, 0.5320],
- [0.6133, 0.4066, 0.6787, 0.2617, 0.3800, 0.2433, 0.5147, 0.5358],
- [0.6081, 0.3950, 0.8538, 0.4667, 0.3850, 0.4917, 0.5342, 0.4954],
- [0.6296, 0.4076, 0.8400, 0.5583, 0.3700, 0.4367, 0.6876, 0.5494],
- [0.6199, 0.4112, 0.8475, 0.3717, 0.3550, 0.4350, 0.6063, 0.6083]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5106, -0.4525, 1.8837, 0.1418, -0.5735, -0.5365, 0.4087, 0.0735],
- [ 0.7139, -0.3620, 1.7725, 0.1793, -0.4467, 0.1089, 0.4679, 0.1903],
- [ 0.2135, -0.6826, 1.3965, -1.3342, -0.2621, -1.3666, 0.6790, 0.1654],
- [ 0.5085, -0.4804, 1.4542, -1.1903, -0.2808, -1.1662, 0.6581, 0.2043],
- [ 0.0263, -0.7549, 0.9504, -1.1005, -0.4628, -1.2502, 0.0552, 0.2227],
- [ 0.5397, -0.4609, 1.7287, -0.2117, -0.5355, -0.0586, 0.2339, 0.0983],
- [ 0.2517, -0.6470, 1.7428, 0.1414, -0.4972, -0.3696, 0.7320, 0.2673],
- [ 0.2752, -0.5864, 1.6242, -0.6104, -0.6357, -0.2963, 0.3731, 0.3826]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.2730e-01, -4.1045e-01, 1.8480e+00, 1.0824e-01, -5.5381e-01,
- -5.0762e-01, 6.4140e-01, -4.8817e-03],
- [ 5.9082e-01, -4.3664e-01, 1.7557e+00, 1.3903e-01, -5.1917e-01,
- 1.3133e-01, 6.5289e-01, 2.3557e-02],
- [ 6.0739e-01, -4.0631e-01, 1.3630e+00, -1.5238e+00, -3.1132e-01,
- -1.2390e+00, 6.1950e-01, -9.2270e-04],
- [ 6.1413e-01, -4.1527e-01, 1.4208e+00, -1.2697e+00, -2.9400e-01,
- -1.0234e+00, 8.6439e-01, 1.7146e-01],
- [ 5.4700e-01, -4.0808e-01, 8.4919e-01, -1.0773e+00, -5.3072e-01,
- -1.1620e+00, 9.1240e-02, 1.8903e-01],
- [ 5.2269e-01, -4.6151e-01, 1.6575e+00, -1.3041e-01, -5.0762e-01,
- -1.4935e-02, 1.8150e-01, 2.0831e-03],
- [ 6.2236e-01, -4.0323e-01, 1.5940e+00, 2.9299e-01, -5.7691e-01,
- -2.6898e-01, 8.8998e-01, 2.5161e-01],
- [ 5.7742e-01, -3.8684e-01, 1.6286e+00, -5.6921e-01, -6.4619e-01,
- -2.7667e-01, 5.1432e-01, 5.2394e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0231, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0231, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.2847120529040694
- step: 77
- running loss: 0.04265859808966324
- Train Steps: 77/90 Loss: 0.0427 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064],
- [0.6122, 0.4006, 0.8850, 0.4217, 0.4088, 0.5517, 0.6063, 0.5517],
- [0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517],
- [0.6185, 0.4079, 0.8838, 0.4617, 0.4838, 0.5650, 0.6175, 0.5850]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3915, -0.5025, 1.7193, -0.3797, -0.6087, -1.2569, 0.5107, 0.0821],
- [ 0.4523, -0.4895, 1.5831, -0.5257, -0.6798, -0.7597, 0.3999, 0.1821],
- [ 0.3838, -0.5686, 1.6145, -0.2962, -0.6730, -0.5379, 0.5283, 0.3127],
- [ 0.1110, -0.6749, 1.5092, -0.0243, -0.5125, -0.6090, 0.4088, 0.3631],
- [ 0.3467, -0.5665, 1.6822, -0.2644, -0.0797, -0.4045, 0.2067, 0.1177],
- [ 0.7551, -0.3455, 1.6826, -0.6358, -0.4583, 0.0169, 0.7260, 0.1225],
- [ 0.3825, -0.5697, 1.6859, -0.4049, -0.2606, -0.2820, 0.3845, 0.1126],
- [ 0.4185, -0.5269, 1.6859, -0.5249, -0.1563, -0.1265, 0.5731, 0.2411]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5901, -0.4000, 1.8423, -0.0688, -0.5307, -0.9233, 0.3642,
- 0.1852],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5836, -0.3649, 1.7210, 0.3854, -0.3979, -0.2921, 0.3065,
- 0.4470],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532],
- [ 0.5417, -0.4355, 1.8018, -0.3383, -0.3979, 0.2622, 0.5143,
- 0.2622],
- [ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622],
- [ 0.5707, -0.4017, 1.7961, -0.1535, -0.0515, 0.3238, 0.5663,
- 0.4162]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0481, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0481, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.33284895028919
- step: 78
- running loss: 0.042728832696015254
- Train Steps: 78/90 Loss: 0.0427 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6176, 0.3911, 0.8738, 0.4217, 0.3488, 0.4033, 0.6025, 0.4817],
- [0.6127, 0.4118, 0.8650, 0.5083, 0.4088, 0.5367, 0.5300, 0.5456],
- [ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [0.6147, 0.4081, 0.8538, 0.3400, 0.3663, 0.3150, 0.5142, 0.4875],
- [0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097],
- [ nan, nan, 0.7097, 0.2346, 0.4250, 0.1850, 0.5175, 0.5583],
- [ nan, nan, 0.7553, 0.2722, 0.3875, 0.2550, 0.5125, 0.5633],
- [0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.4889, 0.1334, 1.8088, -0.1667, -0.6862, -0.3736, 0.5403, 0.0498],
- [ 1.3320, 0.0668, 1.7552, 0.3750, -0.3791, 0.2351, 0.4607, 0.2350],
- [-1.3916, -1.6578, 0.9473, -1.2229, -0.4628, -1.2002, 0.2467, 0.2536],
- [ 0.9997, -0.1110, 1.7093, -0.5094, -0.6704, -0.7536, 0.3662, 0.0959],
- [ 0.9938, -0.1120, 1.7601, 0.2227, -0.2643, 0.1579, 0.2913, 0.1372],
- [-0.9061, -1.3098, 0.9455, -1.1612, -0.3852, -1.3221, 0.2039, 0.2837],
- [-1.6012, -1.8199, 1.1570, -1.0037, -0.5747, -1.0591, 0.2236, 0.2229],
- [ 1.1578, -0.0632, 1.7333, -1.0920, 0.0493, -1.0529, 1.2628, 0.2168]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5669, -0.4794, 1.7499, -0.3383, -0.6751, -0.4229, 0.4970,
- -0.0611],
- [ 0.5443, -0.3840, 1.7095, 0.0620, -0.3979, 0.1929, 0.1622,
- 0.2341],
- [-2.2859, -2.2859, 0.8516, -1.3112, -0.4326, -1.2851, 0.0755,
- 0.2930],
- [ 0.5532, -0.4008, 1.6575, -0.7155, -0.5942, -0.8309, 0.0890,
- -0.0340],
- [ 0.5515, -0.4225, 1.7788, -0.0534, -0.2016, 0.1929, 0.1568,
- 0.0682],
- [-2.2859, -2.2859, 0.9922, -1.2021, -0.3229, -1.4314, 0.1044,
- 0.2930],
- [-2.2859, -2.2859, 1.2030, -1.0288, -0.4961, -1.1081, 0.0813,
- 0.3161],
- [ 0.6240, -0.3768, 1.6575, -1.2851, 0.2949, -1.2467, 1.1276,
- 0.2142]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1332, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1332, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.4660702189430594
- step: 79
- running loss: 0.043874306568899485
- Train Steps: 79/90 Loss: 0.0439 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6271, 0.4020, 0.8375, 0.6083, 0.3925, 0.4867, 0.6037, 0.4626],
- [0.6087, 0.3976, 0.8337, 0.3867, 0.3713, 0.3117, 0.5938, 0.5300],
- [0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892],
- [0.6299, 0.4008, 0.8450, 0.5350, 0.4213, 0.5000, 0.6350, 0.5100],
- [0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528],
- [0.6142, 0.3982, 0.8650, 0.4883, 0.3912, 0.4317, 0.5315, 0.5350],
- [0.6222, 0.3957, 0.8838, 0.5017, 0.3937, 0.4600, 0.5900, 0.5017],
- [0.6177, 0.4086, 0.8738, 0.3950, 0.3775, 0.5600, 0.6225, 0.5700]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4458, -0.5032, 1.5592, 0.0993, -0.4399, -0.3817, 0.4723, 0.1310],
- [ 0.7403, -0.2708, 1.5286, -0.8189, -0.5177, -0.9784, 0.4248, 0.1831],
- [ 0.1945, -0.5920, 1.5142, -0.3926, -0.6283, -0.8031, 0.0536, 0.2479],
- [ 0.6446, -0.3842, 1.5927, -0.0636, -0.3261, -0.2536, 0.5394, 0.1778],
- [ 0.1360, -0.7198, 1.8964, -0.9149, -0.2398, -0.8776, 1.0256, 0.1518],
- [ 0.2770, -0.6113, 1.6559, -0.1666, -0.4320, -0.4876, 0.1736, 0.1383],
- [ 0.2104, -0.6595, 1.6697, -0.1950, -0.4642, -0.4355, 0.3429, 0.0977],
- [ 0.4589, -0.4922, 1.5949, -0.6927, -0.3965, 0.1450, 0.6767, 0.2705]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6105, -0.4293, 1.5824, 0.5239, -0.4730, -0.0380, 0.5025,
- -0.1492],
- [ 0.5255, -0.4495, 1.5651, -0.4999, -0.5711, -0.8463, 0.4566,
- 0.1621],
- [ 0.5454, -0.4053, 1.6633, -0.1766, -0.6058, -0.5923, 0.1577,
- 0.4357],
- [ 0.6236, -0.4344, 1.6171, 0.1852, -0.3402, 0.0236, 0.6471,
- 0.0697],
- [ 0.6388, -0.3623, 1.9173, -0.7386, -0.3517, -0.5846, 1.1495,
- 0.2676],
- [ 0.5513, -0.4467, 1.7095, -0.0303, -0.4788, -0.2921, 0.1692,
- 0.1854],
- [ 0.5879, -0.4583, 1.7961, 0.0313, -0.4672, -0.1612, 0.4393,
- 0.0313],
- [ 0.5671, -0.3988, 1.7499, -0.4614, -0.5423, 0.3007, 0.5894,
- 0.3469]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0359, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0359, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.502012967132032
- step: 80
- running loss: 0.0437751620891504
- Train Steps: 80/90 Loss: 0.0438 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6186, 0.4013, 0.8191, 0.3188, 0.4279, 0.2060, 0.5767, 0.5411],
- [0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6350, 0.4144, 0.9250, 0.4100, 0.4125, 0.3750, 0.7129, 0.5945],
- [0.6314, 0.4050, 0.8227, 0.5431, 0.4150, 0.5517, 0.7121, 0.5690],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6143, 0.4040, 0.8237, 0.3333, 0.4075, 0.2117, 0.5137, 0.4973]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4675, -0.4482, 1.5720, -0.3319, -0.5601, -0.4890, 0.3313, 0.2283],
- [ 0.4698, -0.4146, 1.3785, -1.0504, -0.3257, -1.3054, 0.3977, 0.1078],
- [ 0.1245, -0.6607, 1.5465, -0.6004, -0.4580, -1.0717, 0.1884, 0.1108],
- [ 0.6066, -0.3798, 1.5851, -0.5191, -0.5383, -0.1436, 0.3088, 0.0781],
- [ 0.0988, -0.7262, 1.8452, -0.4678, -0.2128, -0.6663, 1.0411, 0.2591],
- [ 0.5097, -0.4873, 1.5568, -0.0783, -0.3597, 0.2482, 0.9797, 0.3026],
- [ 0.5244, -0.4431, 1.5430, 0.2526, -0.2678, -0.0672, 0.1750, 0.1631],
- [-0.1690, -0.8363, 1.3229, -0.9650, -0.4343, -1.2110, 0.2810, 0.0549]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.5713, -0.4321, 1.4975, -0.8134, -0.3094, -1.3345, 0.3779,
- 0.2134],
- [ 0.5485, -0.4209, 1.6691, -0.4152, -0.5249, -1.1081, 0.0725,
- 0.0021],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.6471, -0.3719, 1.9866, -0.3921, -0.3806, -0.5538, 1.0070,
- 0.4600],
- [ 0.6306, -0.4153, 1.5141, 0.2224, -0.3691, 0.2622, 1.0033,
- 0.3425],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610],
- [ 0.5515, -0.4201, 1.5189, -0.7463, -0.4037, -1.3082, 0.0869,
- 0.0111]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0330, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0330, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.53500113543123
- step: 81
- running loss: 0.043641989326311484
- Train Steps: 81/90 Loss: 0.0436 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6197, 0.4090, 0.7825, 0.2500, 0.4200, 0.2483, 0.5988, 0.5667],
- [0.6267, 0.4065, 0.8313, 0.2467, 0.4788, 0.1733, 0.6312, 0.5133],
- [0.6196, 0.4068, 0.7645, 0.2234, 0.4575, 0.1737, 0.5926, 0.5284],
- [0.6162, 0.4014, 0.8800, 0.5333, 0.3750, 0.4817, 0.5988, 0.5283],
- [0.6308, 0.3990, 0.8688, 0.5183, 0.3950, 0.4983, 0.6388, 0.5133],
- [0.6111, 0.4033, 0.8300, 0.3267, 0.3588, 0.3333, 0.5444, 0.5637],
- [0.6085, 0.4008, 0.8588, 0.5200, 0.4959, 0.4977, 0.5175, 0.5283],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2931, -0.5850, 1.3665, -1.0628, -0.4258, -1.0431, 0.5518, 0.2436],
- [ 0.7559, -0.2991, 1.5757, -1.0323, -0.1496, -1.3116, 0.6911, 0.1442],
- [ 0.7068, -0.3175, 1.2592, -1.1584, -0.2464, -1.3062, 0.4130, 0.1583],
- [ 0.2576, -0.6200, 1.7675, 0.3444, -0.5498, 0.0901, 0.4331, 0.1720],
- [ 0.3448, -0.5905, 1.7996, 0.2079, -0.4959, 0.1073, 0.7317, 0.1350],
- [-0.2655, -0.9400, 1.5480, -0.6480, -0.6969, -0.6634, 0.2014, 0.1471],
- [ 0.2986, -0.5727, 1.7204, 0.2381, -0.0618, 0.0382, 0.2197, 0.1804],
- [ 0.2452, -0.6121, 1.0176, -1.1555, -0.5223, -1.0575, 0.2831, 0.1055]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5764, -0.3969, 1.3284, -1.1312, -0.3460, -1.1389, 0.4797,
- 0.3315],
- [ 0.6085, -0.4084, 1.5536, -1.1466, -0.0746, -1.4853, 0.6298,
- 0.0851],
- [ 0.5761, -0.4070, 1.2452, -1.2541, -0.1725, -1.4835, 0.4511,
- 0.1545],
- [ 0.5603, -0.4319, 1.7788, 0.1775, -0.5538, -0.0611, 0.4797,
- 0.1544],
- [ 0.6275, -0.4430, 1.7268, 0.1082, -0.4614, 0.0159, 0.6644,
- 0.0851],
- [ 0.5366, -0.4232, 1.5478, -0.7771, -0.6289, -0.7463, 0.2288,
- 0.3177],
- [ 0.5245, -0.4347, 1.6806, 0.1159, 0.0046, 0.0129, 0.1044,
- 0.1544],
- [ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0293, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0293, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.564255858771503
- step: 82
- running loss: 0.04346653486306711
- Train Steps: 82/90 Loss: 0.0435 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6228, 0.4119, 0.7938, 0.2233, 0.4674, 0.1773, 0.6188, 0.5433],
- [ nan, nan, 0.7515, 0.2708, 0.3987, 0.2267, 0.5162, 0.5567],
- [0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [ nan, nan, 0.6469, 0.1943, 0.4025, 0.2000, 0.5125, 0.5533],
- [0.6128, 0.4116, 0.8450, 0.4583, 0.3675, 0.4867, 0.5337, 0.5446],
- [0.6153, 0.4119, 0.8463, 0.3833, 0.3600, 0.3200, 0.5106, 0.5563],
- [0.6180, 0.4064, 0.7200, 0.2467, 0.4275, 0.2367, 0.5963, 0.6217]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9934, -0.1945, 1.8464, -0.3701, -0.3761, -0.5561, 0.9081, 0.0323],
- [ 1.0571, -0.0771, 1.3978, -0.8396, -0.1539, -1.1052, 0.5071, 0.1309],
- [-1.6352, -1.8696, 1.1451, -0.8900, -0.4603, -0.8711, 0.1760, 0.1800],
- [ 0.6482, -0.3183, 1.6749, -0.8900, -0.1365, -0.8788, 0.6645, 0.0612],
- [-1.4340, -1.7223, 0.8990, -1.0310, -0.4178, -1.1867, 0.1727, 0.2790],
- [ 0.7065, -0.3046, 1.7416, 0.1067, -0.4849, 0.2435, 0.4102, 0.1512],
- [ 0.6215, -0.3323, 1.6901, -0.1141, -0.5998, -0.5355, 0.1440, 0.1141],
- [ 0.9429, -0.1613, 1.1300, -0.8821, -0.3557, -0.9696, 0.4113, 0.3535]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5908, -0.3832, 1.3804, -1.2543, -0.1270, -1.4671, 0.5721,
- 0.2237],
- [-2.2859, -2.2859, 1.1854, -1.0352, -0.4441, -1.2390, 0.0986,
- 0.2853],
- [ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [-2.2859, -2.2859, 0.7023, -1.3883, -0.4268, -1.3621, 0.0813,
- 0.2699],
- [ 0.5444, -0.3846, 1.6171, -0.1689, -0.5885, -0.0380, 0.1791,
- 0.2296],
- [ 0.5561, -0.3834, 1.6229, -0.5153, -0.6231, -0.8079, 0.0727,
- 0.2837],
- [ 0.5687, -0.4088, 1.0397, -1.1466, -0.3113, -1.1928, 0.4681,
- 0.5855]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0665, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0665, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.630719925276935
- step: 83
- running loss: 0.043743613557553435
- Train Steps: 83/90 Loss: 0.0437 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433],
- [0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679],
- [ nan, nan, 0.9050, 0.3500, 0.5138, 0.2300, 0.7359, 0.5702],
- [ nan, nan, 0.7192, 0.2346, 0.4037, 0.2050, 0.5138, 0.5650],
- [0.6202, 0.4064, 0.7879, 0.2179, 0.4567, 0.1725, 0.5955, 0.5478],
- [ nan, nan, 0.7525, 0.2291, 0.3838, 0.3017, 0.6050, 0.5667],
- [0.6199, 0.4065, 0.7598, 0.2385, 0.4317, 0.1981, 0.5933, 0.5221]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.1672, -0.0103, 1.4356, -0.7056, -0.6808, -0.2443, 0.4589, 0.0789],
- [ 1.2498, 0.1052, 1.4687, -0.1174, -0.2292, -0.6798, 0.1431, 0.2818],
- [-1.0567, -1.4895, 1.6280, -0.8708, 0.0069, -0.7950, 0.9648, 0.2630],
- [-0.4714, -1.0502, 1.8932, -0.3346, -0.0877, -0.7876, 0.8404, 0.2231],
- [-1.2858, -1.5904, 1.0354, -0.9126, -0.5178, -1.0090, 0.1041, 0.1625],
- [ 1.2956, 0.0862, 1.2792, -0.7547, -0.2526, -1.0581, 0.2798, 0.0854],
- [-1.0856, -1.4630, 1.2056, -0.8829, -0.5616, -0.7047, 0.3875, 0.2448],
- [ 1.5989, 0.2509, 1.3222, -0.8339, -0.3640, -0.9206, 0.3607, 0.0468]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237],
- [ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371],
- [-2.2859, -2.2859, 1.8942, -0.6693, 0.0871, -1.2236, 1.1130,
- 0.3478],
- [-2.2859, -2.2859, 1.0361, -1.2021, -0.4210, -1.3390, 0.0871,
- 0.3238],
- [ 0.5787, -0.4086, 1.3535, -1.2794, -0.1764, -1.4891, 0.4645,
- 0.2442],
- [-2.2859, -2.2859, 1.1898, -1.2278, -0.5134, -0.8925, 0.5085,
- 0.3315],
- [ 0.5774, -0.4082, 1.2235, -1.1844, -0.2919, -1.3709, 0.4544,
- 0.1256]]], device='cuda:0')
- loss_train_step before backward: tensor(0.2647, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.2647, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.8954066587612033
- step: 84
- running loss: 0.04637388879477623
- Train Steps: 84/90 Loss: 0.0464 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550],
- [0.6200, 0.3978, 0.8900, 0.4550, 0.3775, 0.5200, 0.6150, 0.5367],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6097, 0.4000, 0.7325, 0.2667, 0.3450, 0.3517, 0.5284, 0.5045],
- [0.6339, 0.4149, 0.8800, 0.5000, 0.3900, 0.5283, 0.7541, 0.5424],
- [0.6202, 0.4066, 0.8746, 0.3376, 0.3717, 0.3090, 0.5842, 0.5165],
- [0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933],
- [0.6277, 0.4029, 0.8250, 0.2433, 0.4325, 0.2100, 0.6366, 0.5207]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0092, -0.7831, 1.4251, -0.8901, -0.6195, -0.3991, 0.4450, 0.2552],
- [ 0.1866, -0.6944, 1.7594, -0.0529, -0.4564, 0.0751, 0.3812, 0.1549],
- [ 0.4072, -0.4985, 1.0225, -1.0642, -0.3926, -1.3225, 0.2822, 0.2517],
- [-0.3008, -0.9750, 1.1834, -1.0713, -0.5787, -0.8064, 0.1357, 0.1497],
- [ 0.4684, -0.5414, 1.8716, 0.1877, -0.3996, 0.2120, 0.9094, 0.1500],
- [ 0.7999, -0.2541, 1.8295, -0.6501, -0.4471, -1.0200, 0.4277, 0.0413],
- [ 0.4781, -0.4347, 1.6580, 0.4612, 0.0516, -0.3173, 0.2600, 0.3793],
- [ 0.3030, -0.6041, 1.5122, -1.0825, -0.1982, -1.4243, 0.6538, 0.1128]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776],
- [ 0.5776, -0.4484, 1.8249, -0.1843, -0.5423, 0.1159, 0.5547,
- 0.1929],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391],
- [ 0.5303, -0.4384, 1.0975, -1.0542, -0.6924, -0.6616, 0.1548,
- 0.0442],
- [ 0.6421, -0.3695, 1.7788, 0.0236, -0.4845, 0.1544, 1.1971,
- 0.2196],
- [ 0.5787, -0.4077, 1.7537, -0.7263, -0.5692, -0.8586, 0.4126,
- 0.1000],
- [ 0.5959, -0.3579, 1.6055, 0.3623, -0.0573, -0.2074, 0.3122,
- 0.4547],
- [ 0.6132, -0.4248, 1.5247, -1.1620, -0.2882, -1.3159, 0.6545,
- 0.1193]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0374, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0374, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.9328551990911365
- step: 85
- running loss: 0.04626888469518984
- Train Steps: 85/90 Loss: 0.0463 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6138, 0.4101, 0.8800, 0.5083, 0.4637, 0.5950, 0.5587, 0.5077],
- [0.6069, 0.3975, 0.8625, 0.5083, 0.4388, 0.5483, 0.5650, 0.4967],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467],
- [ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3836, -0.5184, 1.6498, 0.0154, -0.1134, 0.1239, 0.3044, 0.1965],
- [ 0.4918, -0.4554, 1.6638, 0.0633, -0.2440, 0.0597, 0.2894, 0.1129],
- [ 0.7150, -0.2920, 1.2913, -1.2055, -0.1567, -1.4490, 0.4141, 0.1894],
- [ 0.7872, -0.2240, 1.7332, -0.0770, -0.4979, -1.0182, 0.3480, 0.1206],
- [ 0.5916, -0.3943, 1.0506, -1.1736, -0.5250, -1.0944, 0.4367, 0.2910],
- [ 0.5973, -0.3884, 1.6319, 0.2809, -0.4084, 0.0409, 0.4515, 0.2362],
- [ 0.1417, -0.6834, 1.4807, -0.8859, -0.7096, -0.4316, 0.4162, 0.2001],
- [-2.0467, -2.1798, 1.6085, -1.1863, -0.0272, -1.3415, 0.9383, 0.2650]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5491, -0.3918, 1.7788, 0.0620, -0.1439, 0.4624, 0.2946,
- 0.0592],
- [ 0.5174, -0.4497, 1.6979, 0.0620, -0.2594, 0.2468, 0.3238,
- 0.0082],
- [ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850],
- [ 0.5901, -0.4000, 1.8423, -0.0688, -0.5307, -0.9233, 0.3642,
- 0.1852],
- [ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700],
- [ 0.5742, -0.3792, 1.6460, 0.3084, -0.3460, 0.1467, 0.4162,
- 0.3161],
- [ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391],
- [-2.2859, -2.2859, 1.7557, -1.1466, 0.0871, -1.0773, 1.1239,
- 0.2783]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0146, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0146, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.9474326334893703
- step: 86
- running loss: 0.04590037945917873
- Train Steps: 86/90 Loss: 0.0459 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6239, 0.4123, 0.8313, 0.2550, 0.4500, 0.2050, 0.6175, 0.5400],
- [0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505],
- [0.6084, 0.3981, 0.8588, 0.5233, 0.4600, 0.5367, 0.5680, 0.5006],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6248, 0.4185, 0.8500, 0.5767, 0.4463, 0.4550, 0.5613, 0.5917],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3863, -0.5068, 1.3909, -1.0064, -0.0798, -1.3737, 0.4426, 0.1917],
- [ 0.0985, -0.7115, 1.3916, -1.2851, -0.0720, -1.3754, 0.7241, 0.2478],
- [ 0.3931, -0.5064, 1.5499, -0.4864, -0.6317, -0.7154, 0.0686, 0.1628],
- [ 0.2412, -0.6454, 1.5829, 0.1082, -0.1753, 0.0653, 0.2417, 0.1627],
- [ 0.0780, -0.7639, 1.7483, -0.3355, -0.5922, -0.1877, 0.5523, 0.0529],
- [ 0.2777, -0.6107, 1.5568, 0.3643, -0.1859, -0.3246, 0.4243, 0.4047],
- [ 0.1649, -0.6641, 1.2672, -1.1711, -0.3359, -1.0829, 0.6421, 0.2771],
- [-0.1123, -0.8771, 1.6413, -0.5845, -0.6554, -0.2488, 0.6509, 0.2595]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.5958, -0.3818, 1.5536, -1.1081, -0.2074, -1.3390, 0.5663,
- 0.2083],
- [ 0.5433, -0.4032, 1.6344, -0.4922, -0.5769, -0.5846, 0.0357,
- 0.2567],
- [ 0.5241, -0.4470, 1.6806, 0.1313, -0.1612, 0.1929, 0.3378,
- 0.0261],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.5999, -0.3530, 1.6402, 0.3777, -0.2247, -0.1843, 0.3065,
- 0.4470],
- [ 0.5977, -0.3792, 1.2995, -1.0311, -0.5192, -0.8386, 0.5836,
- 0.2160],
- [ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0403, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0403, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.9877162650227547
- step: 87
- running loss: 0.04583581913819258
- Train Steps: 87/90 Loss: 0.0458 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6277, 0.4057, 0.8300, 0.2650, 0.4363, 0.1850, 0.6140, 0.4823],
- [0.6129, 0.3930, 0.8769, 0.5105, 0.4146, 0.5642, 0.6000, 0.5082],
- [0.6264, 0.4055, 0.8425, 0.2767, 0.4425, 0.2767, 0.7050, 0.5586],
- [0.6357, 0.4097, 0.9038, 0.3883, 0.4213, 0.2950, 0.6686, 0.5390],
- [0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285],
- [0.6193, 0.4108, 0.7438, 0.2700, 0.3650, 0.3683, 0.6238, 0.5717],
- [0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1744, -0.6968, 1.5299, -1.1521, -0.1250, -1.4734, 0.6400, 0.1067],
- [ 0.4351, -0.5575, 1.7017, 0.2760, -0.1643, 0.3751, 0.4155, 0.1889],
- [-0.1450, -0.9232, 1.6934, -1.1407, -0.0424, -1.2464, 1.1251, 0.2949],
- [ 0.1820, -0.6939, 1.7591, -0.4505, -0.2610, -1.0295, 0.6851, 0.1865],
- [ 0.4787, -0.4771, 1.3901, -0.4078, -0.5066, -0.3232, 0.0269, 0.1471],
- [ 0.1439, -0.7145, 1.1538, -1.0634, -0.5412, -0.6070, 0.4496, 0.3232],
- [ 0.1047, -0.7455, 1.5241, -0.7351, -0.6339, -0.3523, 0.4122, 0.2333],
- [ 0.3210, -0.5923, 1.4436, -0.4391, -0.5093, -0.9542, 0.0538, 0.3497]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6132, -0.4122, 1.5478, -1.0619, -0.2709, -1.4314, 0.5500,
- -0.0583],
- [ 0.5450, -0.4706, 1.7643, 0.0722, -0.3708, 0.3200, 0.4854,
- 0.0612],
- [ 0.6075, -0.4129, 1.6055, -1.0080, -0.2420, -1.0080, 0.9704,
- 0.2944],
- [ 0.6504, -0.3936, 1.8885, -0.4922, -0.3402, -0.9233, 0.8022,
- 0.2035],
- [ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552],
- [ 0.5747, -0.3886, 1.1494, -1.0388, -0.6000, -0.5846, 0.5952,
- 0.3546],
- [ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0404, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0404, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.0281651839613914
- step: 88
- running loss: 0.04577460436319763
- Train Steps: 88/90 Loss: 0.0458 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6267, 0.4080, 0.8438, 0.2633, 0.4763, 0.1800, 0.6259, 0.5240],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6286, 0.3977, 0.9038, 0.4733, 0.3900, 0.4150, 0.7074, 0.5320],
- [0.6259, 0.4133, 0.8200, 0.2317, 0.5025, 0.1533, 0.6250, 0.4947],
- [0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413],
- [0.6299, 0.4008, 0.8450, 0.5350, 0.4213, 0.5000, 0.6350, 0.5100]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1915, -0.6440, 1.4687, -0.5500, -0.6943, -0.3780, 0.1095, 0.2380],
- [ 0.3804, -0.5327, 1.4882, -1.2403, -0.1517, -1.4743, 0.5692, 0.2154],
- [-0.0196, -0.8108, 1.5399, -0.0779, -0.3252, 0.0614, 0.3196, 0.2752],
- [ 0.3610, -0.5834, 1.6524, -0.2101, -0.6242, -0.3816, 0.5197, 0.1405],
- [ 0.4290, -0.5124, 1.3662, -1.4068, -0.0729, -1.5972, 0.5732, 0.2222],
- [ 0.1196, -0.7140, 1.5856, -0.2194, -0.1171, -0.0294, 0.3316, 0.3566],
- [ 0.1048, -0.7306, 1.9049, -0.7238, -0.1563, -1.2254, 0.9249, 0.1957],
- [ 0.2424, -0.6750, 1.5581, 0.0550, -0.4404, -0.0346, 0.5450, 0.2040]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3672e-01, -4.2941e-01, 1.5709e+00, -4.9992e-01, -6.6928e-01,
- -3.0747e-01, 2.4546e-01, 3.5585e-01],
- [ 6.0878e-01, -4.0146e-01, 1.6113e+00, -1.0696e+00, -8.6143e-02,
- -1.4545e+00, 6.0510e-01, 1.3434e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 6.1742e-01, -4.4897e-01, 1.8885e+00, -9.9615e-02, -4.8453e-01,
- -3.6905e-01, 9.8137e-01, 1.7146e-01],
- [ 6.0514e-01, -3.7714e-01, 1.5016e+00, -1.2159e+00, 3.5104e-02,
- -1.5777e+00, 6.0099e-01, -9.2270e-04],
- [ 5.5052e-01, -4.2071e-01, 1.7095e+00, -5.3426e-02, -5.0936e-02,
- 1.0502e-01, 3.8730e-01, 3.0069e-01],
- [ 6.1083e-01, -4.2008e-01, 1.9346e+00, -5.5381e-01, -1.4965e-01,
- -1.0773e+00, 1.0545e+00, 2.1421e-01],
- [ 6.2361e-01, -4.3441e-01, 1.6171e+00, 1.8522e-01, -3.4018e-01,
- 2.3557e-02, 6.4711e-01, 6.9746e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0387, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0387, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.066847592592239
- step: 89
- running loss: 0.04569491677069932
- Train Steps: 89/90 Loss: 0.0457 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6249, 0.4142, 0.8350, 0.3283, 0.3613, 0.3700, 0.6188, 0.5400],
- [0.6086, 0.3981, 0.8700, 0.4750, 0.4512, 0.5283, 0.5324, 0.5038],
- [0.6236, 0.3977, 0.8985, 0.4806, 0.3835, 0.5216, 0.6613, 0.5166],
- [0.6163, 0.4001, 0.8788, 0.5033, 0.4012, 0.4633, 0.5338, 0.5767],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2890, -0.5716, 1.3113, -1.2958, -0.1987, -1.5838, 0.4590, 0.2383],
- [ 0.2683, -0.5812, 1.4407, -1.0742, -0.1550, -1.5367, 0.4240, 0.2070],
- [ 0.4971, -0.4523, 1.5860, -0.8335, -0.6447, -0.9323, 0.5781, 0.2279],
- [ 0.2467, -0.6203, 1.7023, -0.1982, -0.2799, -0.1041, 0.2939, 0.1046],
- [ 0.2707, -0.6597, 1.7397, -0.2222, -0.5196, 0.0379, 0.8742, 0.2198],
- [ 0.2934, -0.6259, 1.6778, -0.0822, -0.4994, -0.3037, 0.4800, 0.2324],
- [ 0.1104, -0.7315, 1.6631, -0.0197, -0.1773, -0.1624, 0.3443, 0.3367],
- [ 0.1810, -0.6591, 1.6120, -0.3214, -0.1508, -0.0314, 0.4300, 0.3403]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.6006, -0.3728, 1.5709, -0.7694, -0.6173, -0.5769, 0.5721,
- 0.2083],
- [ 0.5249, -0.4473, 1.7326, -0.0919, -0.2016, 0.1544, 0.1733,
- 0.0412],
- [ 0.5944, -0.4490, 1.8643, -0.0659, -0.5147, 0.1235, 0.7684,
- 0.1004],
- [ 0.5610, -0.4381, 1.7730, 0.0390, -0.4326, -0.1458, 0.1794,
- 0.3777],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0306, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0306, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 4.097452521324158
- step: 90
- running loss: 0.04552725023693509
- Valid Steps: 10/10 Loss: nan 5.8477
- --------------------------------------------------
- Epoch: 4 Train Loss: 0.0455 Valid Loss: nan
- --------------------------------------------------
- size of train loader is: 90
- torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6081, 0.3950, 0.8538, 0.4667, 0.3850, 0.4917, 0.5342, 0.4954],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [0.6263, 0.4233, 0.7924, 0.4626, 0.3788, 0.2883, 0.5573, 0.6047],
- [0.6250, 0.4013, 0.8525, 0.5417, 0.4037, 0.5117, 0.6325, 0.5017],
- [0.6216, 0.4167, 0.8588, 0.5583, 0.3975, 0.5167, 0.5775, 0.5667],
- [0.6124, 0.4030, 0.8650, 0.4867, 0.4999, 0.5106, 0.5137, 0.5773],
- [0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [0.6068, 0.3963, 0.8650, 0.4317, 0.4037, 0.5083, 0.5253, 0.4999]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 3.4651e-01, -5.8417e-01, 1.7318e+00, -5.1866e-01, -5.5468e-01,
- -4.1584e-01, 3.5147e-01, 5.9733e-02],
- [ 2.0160e-01, -6.7345e-01, 1.7936e+00, -4.8521e-01, -6.1855e-02,
- -4.0095e-01, 5.5372e-01, 2.8475e-01],
- [ 4.4827e-01, -4.2845e-01, 1.4146e+00, -5.0683e-01, -5.0848e-01,
- -1.2103e+00, 2.3973e-01, 5.2721e-01],
- [ 5.1592e-01, -4.7822e-01, 1.7209e+00, -1.5612e-01, -4.5384e-01,
- -2.8481e-01, 7.0564e-01, 1.2522e-01],
- [ 7.6344e-01, -2.9085e-01, 1.7067e+00, 8.7751e-04, -4.5148e-01,
- -2.4599e-01, 6.7884e-01, 2.8083e-01],
- [ 2.3650e-01, -6.3350e-01, 1.7368e+00, -3.9993e-01, -3.5953e-02,
- -4.3623e-01, 3.3012e-01, 2.6307e-01],
- [ 3.1048e-01, -5.8417e-01, 1.7569e+00, -4.4972e-01, -6.0495e-02,
- -4.0298e-01, 5.7915e-01, 3.2222e-01],
- [ 1.8989e-01, -6.7958e-01, 1.7583e+00, -5.6773e-01, -4.8253e-01,
- -2.4003e-01, 4.2579e-01, 8.0821e-02]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5227, -0.4615, 1.6575, -0.1304, -0.5076, -0.0149, 0.1815,
- 0.0021],
- [ 0.5598, -0.4273, 1.7152, -0.1227, -0.0065, 0.1917, 0.5143,
- 0.2853],
- [ 0.6069, -0.3309, 1.3742, -0.1493, -0.5365, -0.9541, 0.2884,
- 0.5071],
- [ 0.6010, -0.4321, 1.6517, 0.2160, -0.4210, 0.0774, 0.6356,
- 0.0313],
- [ 0.5853, -0.3614, 1.6806, 0.2930, -0.4499, 0.1005, 0.3815,
- 0.3315],
- [ 0.5428, -0.4244, 1.7095, -0.0380, 0.0232, 0.0725, 0.0866,
- 0.3806],
- [ 0.5505, -0.4207, 1.7095, -0.0534, -0.0509, 0.1050, 0.3873,
- 0.3007],
- [ 0.5168, -0.4556, 1.7095, -0.2921, -0.4210, 0.0620, 0.1404,
- 0.0231]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0548, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0548, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.054758865386247635
- step: 1
- running loss: 0.054758865386247635
- Train Steps: 1/90 Loss: 0.0548 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6197, 0.4090, 0.7825, 0.2500, 0.4200, 0.2483, 0.5988, 0.5667],
- [0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6239, 0.4107, 0.8162, 0.2763, 0.3625, 0.3600, 0.5988, 0.5700],
- [0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6201, 0.4027, 0.8029, 0.2728, 0.4042, 0.2310, 0.5980, 0.5391],
- [0.6236, 0.3967, 0.8675, 0.5400, 0.3862, 0.4517, 0.5825, 0.5200]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3067, -0.5911, 1.3730, -1.0648, -0.3434, -0.9991, 0.5182, 0.3448],
- [-0.3291, -0.9670, 1.0663, -1.0819, -0.3201, -1.1403, 0.2702, 0.3937],
- [ 0.5365, -0.4417, 1.6636, -1.0241, -0.0103, -1.3002, 0.7841, 0.2095],
- [ 0.7065, -0.3506, 1.7970, -0.1609, -0.5044, 0.1665, 0.2508, 0.1114],
- [-0.1420, -0.8803, 1.4987, -0.7562, -0.4674, -0.4524, 0.4285, 0.3405],
- [ 0.6437, -0.3572, 1.7996, -0.9246, -0.2193, -0.9633, 0.7583, 0.2072],
- [ 0.3890, -0.5329, 1.5026, -0.9721, -0.3509, -1.0259, 0.4017, 0.1881],
- [ 0.6239, -0.4407, 1.8843, 0.4963, -0.4543, 0.0053, 0.5245, 0.1646]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5764, -0.3969, 1.3284, -1.1312, -0.3460, -1.1389, 0.4797,
- 0.3315],
- [ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.5960, -0.3888, 1.4840, -1.0095, -0.6115, -0.6231, 0.4797,
- 0.3469],
- [ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [ 0.5785, -0.4259, 1.4228, -1.0261, -0.4190, -1.2189, 0.4763,
- 0.2043],
- [ 0.5944, -0.4538, 1.7210, 0.2083, -0.5018, -0.1997, 0.4046,
- 0.1159]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0447, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0447, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.09948705509305
- step: 2
- running loss: 0.049743527546525
- Train Steps: 2/90 Loss: 0.0497 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6296, 0.4060, 0.9100, 0.3267, 0.4726, 0.3367, 0.7446, 0.5355],
- [0.6226, 0.4098, 0.8912, 0.4100, 0.4025, 0.2383, 0.5763, 0.5367],
- [0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6126, 0.4073, 0.8750, 0.5133, 0.3800, 0.4333, 0.4986, 0.5378],
- [0.6209, 0.3920, 0.8650, 0.5367, 0.4400, 0.5067, 0.6025, 0.4950]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5210, -0.4563, 1.7610, -0.8858, -0.2437, -0.7401, 0.9069, 0.1881],
- [ 0.2435, -0.5746, 1.7808, -0.4829, -0.3414, -1.1378, 0.3184, 0.3190],
- [-0.1094, -0.8018, 0.9690, -1.3126, -0.3771, -1.2953, 0.2717, 0.4078],
- [ 0.4078, -0.5230, 1.8003, -0.1228, -0.0185, 0.1290, 0.4568, 0.3150],
- [ 0.7459, -0.3426, 1.8601, 0.1211, -0.5755, -0.1134, 0.6215, 0.1065],
- [ 0.4384, -0.4892, 1.4912, -1.1436, -0.3577, -1.0383, 0.4101, 0.1810],
- [ 0.6986, -0.3227, 1.7810, 0.1030, -0.4969, -0.2094, 0.2139, 0.2080],
- [ 0.7046, -0.3568, 1.7441, 0.0391, -0.2582, 0.1621, 0.4744, 0.1544]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.2236e-01, -4.1045e-01, 1.9173e+00, -7.7706e-01, -1.0299e-01,
- -7.3084e-01, 1.1532e+00, 1.8749e-01],
- [ 5.8995e-01, -3.9323e-01, 1.8307e+00, -3.9215e-01, -4.2679e-01,
- -1.1851e+00, 3.7575e-01, 1.9292e-01],
- [ 5.7131e-01, -3.6712e-01, 8.6651e-01, -1.0696e+00, -3.6905e-01,
- -1.2236e+00, 3.5266e-01, 2.6220e-01],
- [ 5.3712e-01, -4.2517e-01, 1.7037e+00, -6.8822e-02, -3.4180e-02,
- 6.2048e-02, 3.7575e-01, 2.8530e-01],
- [ 6.2401e-01, -4.3212e-01, 1.8423e+00, 1.8522e-01, -5.8845e-01,
- -1.6120e-01, 6.9623e-01, 1.1149e-02],
- [ 5.7898e-01, -4.0793e-01, 1.5929e+00, -1.0630e+00, -4.7294e-01,
- -1.0725e+00, 4.1374e-01, 8.0707e-02],
- [ 5.4353e-01, -4.0454e-01, 1.7557e+00, 8.5142e-02, -5.3072e-01,
- -2.8437e-01, 1.7213e-02, 1.9805e-01],
- [ 5.8199e-01, -4.7544e-01, 1.7095e+00, 1.9292e-01, -2.5358e-01,
- 5.4350e-02, 4.9700e-01, 4.6189e-04]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0220, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0220, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.12145851366221905
- step: 3
- running loss: 0.040486171220739685
- Train Steps: 3/90 Loss: 0.0405 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6276, 0.4120, 0.8738, 0.3133, 0.4225, 0.2217, 0.6203, 0.4892],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6201, 0.4151, 0.8588, 0.5467, 0.3700, 0.3950, 0.5637, 0.5933],
- [0.6107, 0.4050, 0.8700, 0.4850, 0.4470, 0.4848, 0.5043, 0.5431],
- [0.6147, 0.4112, 0.7988, 0.3200, 0.3775, 0.2767, 0.5150, 0.5550],
- [0.6229, 0.4066, 0.7612, 0.2967, 0.4250, 0.2167, 0.5925, 0.5350],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5027, -0.4413, 1.8971, -0.8446, -0.2785, -1.2732, 0.7965, 0.1499],
- [ 0.3700, -0.5806, 1.7208, 0.2440, -0.2295, 0.1421, 0.3970, 0.1733],
- [ 0.4950, -0.4589, 1.1742, -1.2359, -0.5166, -1.0659, 0.2792, 0.1711],
- [ 0.6150, -0.3365, 1.7022, 0.1509, -0.4928, -0.4111, 0.5337, 0.3872],
- [ 0.6815, -0.3452, 1.8120, -0.0561, -0.2128, 0.1955, 0.3699, 0.1365],
- [ 0.2446, -0.5919, 1.4378, -0.8585, -0.5198, -0.8801, 0.1190, 0.2465],
- [ 0.7320, -0.2839, 1.4079, -1.1618, -0.3278, -1.2204, 0.5434, 0.2279],
- [ 0.4952, -0.4617, 1.7817, 0.0618, -0.1303, 0.2405, 0.4049, 0.2265]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6128, -0.3828, 1.7499, -0.8386, -0.3344, -1.2620, 0.5792,
- -0.0263],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.5785, -0.3687, 1.6806, 0.2391, -0.5769, -0.4614, 0.3180,
- 0.4547],
- [ 0.5351, -0.4153, 1.7326, -0.0457, -0.2214, -0.0466, 0.0434,
- 0.2228],
- [ 0.5532, -0.3864, 1.4035, -0.8079, -0.5423, -1.0080, 0.0928,
- 0.2776],
- [ 0.5911, -0.4080, 1.2303, -0.9156, -0.3229, -1.2851, 0.4508,
- 0.1852],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0177, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0177, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.13915947452187538
- step: 4
- running loss: 0.034789868630468845
- Train Steps: 4/90 Loss: 0.0348 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6059, 0.4002, 0.7562, 0.2767, 0.3538, 0.3033, 0.5529, 0.5455],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [ nan, nan, 0.7425, 0.2117, 0.3937, 0.2433, 0.5438, 0.5567],
- [0.6157, 0.3956, 0.8323, 0.4138, 0.3479, 0.4431, 0.5914, 0.5131],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6200, 0.4086, 0.8414, 0.2974, 0.4117, 0.2274, 0.5869, 0.5391],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8880, -0.1930, 1.3037, -0.9538, -0.5272, -0.8709, 0.3894, 0.2118],
- [ 0.7731, -0.2506, 1.6970, 0.1314, -0.1712, 0.3505, 0.3391, 0.2241],
- [-1.9436, -2.0312, 1.3338, -0.8958, -0.3679, -1.0223, 0.3111, 0.2546],
- [ 0.9518, -0.1705, 1.7189, -0.3662, -0.5749, -0.1627, 0.4393, 0.1420],
- [ 0.7302, -0.2546, 1.4106, -1.0633, -0.1283, -1.3372, 0.4802, 0.2443],
- [ 0.9238, -0.1728, 1.6986, -0.2053, -0.4794, -0.0917, 0.3199, 0.2095],
- [ 0.9332, -0.1299, 1.6650, -0.8772, -0.2057, -1.0746, 0.5534, 0.1711],
- [ 0.8415, -0.2139, 1.7384, 0.2925, -0.3925, -0.0505, 0.5245, 0.2025]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5129, -0.4374, 1.2072, -1.0080, -0.6520, -0.8848, 0.2679,
- 0.2335],
- [ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386],
- [-2.2859, -2.2859, 1.1436, -1.3082, -0.4672, -1.1620, 0.2256,
- 0.2853],
- [ 0.5581, -0.4586, 1.5586, -0.3747, -0.6792, -0.2391, 0.4455,
- 0.0840],
- [ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.5776, -0.3987, 1.6005, -0.9121, -0.3844, -1.2358, 0.4247,
- 0.2043],
- [ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0299, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0299, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.16904453001916409
- step: 5
- running loss: 0.033808906003832816
- Train Steps: 5/90 Loss: 0.0338 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6189, 0.4033, 0.8650, 0.5267, 0.4487, 0.5150, 0.5925, 0.5050],
- [0.6193, 0.4108, 0.7438, 0.2700, 0.3650, 0.3683, 0.6238, 0.5717],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [0.6125, 0.3974, 0.7725, 0.2517, 0.3538, 0.3317, 0.5887, 0.5500],
- [0.6064, 0.4019, 0.8650, 0.4517, 0.4037, 0.5367, 0.5703, 0.5609],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482],
- [0.6282, 0.4029, 0.8988, 0.3000, 0.4250, 0.4183, 0.7042, 0.5575]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8357, -0.2498, 1.7605, 0.2656, -0.2423, 0.1694, 0.2467, 0.1434],
- [ 0.9518, -0.1559, 1.2529, -0.8951, -0.6224, -0.6567, 0.3689, 0.2523],
- [ 0.6504, -0.3560, 1.8047, -0.0042, -0.0985, 0.1004, 0.2837, 0.2233],
- [ 0.8765, -0.1998, 1.3278, -0.8894, -0.6295, -0.8862, 0.1980, 0.1767],
- [ 0.8432, -0.2417, 1.7446, -0.0057, -0.4000, 0.3157, 0.2729, 0.1645],
- [ 0.6089, -0.3450, 1.5589, -0.3284, -0.5271, -1.1253, 0.0505, 0.2763],
- [-0.9919, -1.4238, 1.7263, -1.0578, 0.1417, -1.2524, 1.0073, 0.2014],
- [ 1.0442, -0.1350, 1.7510, -0.6142, -0.4799, -0.3495, 0.8260, 0.1358]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5726, -0.4230, 1.7095, 0.1467, -0.2132, 0.0928, 0.4508,
- 0.0467],
- [ 0.5747, -0.3886, 1.1494, -1.0388, -0.6000, -0.5846, 0.5952,
- 0.3546],
- [ 0.5598, -0.4273, 1.7152, -0.1227, -0.0065, 0.1917, 0.5143,
- 0.2853],
- [ 0.5430, -0.4503, 1.2822, -1.1235, -0.6520, -0.7540, 0.4335,
- 0.2545],
- [ 0.5151, -0.4296, 1.7095, -0.1997, -0.4210, 0.1929, 0.3484,
- 0.3047],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [-2.2859, -2.2859, 1.6517, -1.2620, 0.2141, -1.1928, 1.1166,
- 0.2463],
- [ 0.6158, -0.4249, 1.8654, -0.9002, -0.3229, -0.3537, 0.9667,
- 0.2890]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0641, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0641, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2331299614161253
- step: 6
- running loss: 0.038854993569354214
- Train Steps: 6/90 Loss: 0.0389 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6261, 0.4029, 0.8720, 0.3364, 0.3665, 0.3753, 0.6531, 0.5183],
- [0.6218, 0.4098, 0.7238, 0.1850, 0.4238, 0.2517, 0.6288, 0.5350],
- [0.6108, 0.4011, 0.8037, 0.3400, 0.3700, 0.2933, 0.5658, 0.5617],
- [0.6364, 0.4144, 0.8625, 0.3083, 0.4913, 0.2000, 0.6448, 0.5274],
- [0.6203, 0.4072, 0.8892, 0.3523, 0.3783, 0.3017, 0.5898, 0.5478],
- [ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [0.6115, 0.4005, 0.8838, 0.3867, 0.3763, 0.4700, 0.5800, 0.5550],
- [0.6151, 0.4058, 0.7068, 0.2680, 0.3400, 0.4083, 0.5775, 0.5733]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9756, -0.1992, 1.7893, -0.4266, -0.4919, -0.2887, 0.6990, 0.1151],
- [ 0.9571, -0.1918, 1.1712, -1.0054, -0.2589, -1.1467, 0.5271, 0.2481],
- [ 0.8672, -0.2019, 1.5381, -0.4543, -0.4556, -0.7202, 0.2660, 0.1861],
- [ 0.8332, -0.2177, 1.7749, -0.5389, 0.0245, -1.0307, 0.5606, 0.1816],
- [ 0.7884, -0.2923, 1.8857, -0.3090, -0.4546, -0.6597, 0.4157, 0.1693],
- [-2.0707, -2.1505, 1.0834, -0.9208, -0.3323, -0.9233, 0.0729, 0.2109],
- [ 1.1957, -0.0154, 1.8045, -0.1536, -0.4955, 0.1855, 0.2426, 0.1583],
- [ 0.8495, -0.2346, 1.1987, -0.7389, -0.6078, -0.2832, 0.3070, 0.2877]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6059, -0.4249, 1.7420, -0.7321, -0.5931, -0.5523, 0.7307,
- 0.1080],
- [ 0.5861, -0.3929, 1.0570, -1.4314, -0.3286, -1.1235, 0.6182,
- 0.1852],
- [ 0.5355, -0.4335, 1.4266, -0.7155, -0.5769, -0.9310, 0.3275,
- 0.3087],
- [ 0.6537, -0.3719, 1.6979, -0.8617, -0.0169, -1.3621, 0.6926,
- 0.1501],
- [ 0.5792, -0.4052, 1.8214, -0.6587, -0.5384, -0.8924, 0.4381,
- 0.2442],
- [-2.2859, -2.2859, 0.8516, -1.3112, -0.4326, -1.2851, 0.0755,
- 0.2930],
- [ 0.5384, -0.4361, 1.7961, -0.4999, -0.5480, -0.1150, 0.3931,
- 0.2776],
- [ 0.5552, -0.4113, 0.9790, -1.0480, -0.7155, -0.3998, 0.3815,
- 0.3623]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0500, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0500, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.28310741670429707
- step: 7
- running loss: 0.04044391667204244
- Train Steps: 7/90 Loss: 0.0404 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6200, 0.4071, 0.7338, 0.1917, 0.4200, 0.2450, 0.6150, 0.5517],
- [0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683],
- [0.6292, 0.4010, 0.8988, 0.4800, 0.3638, 0.3817, 0.6357, 0.5051],
- [0.6176, 0.4017, 0.8788, 0.5100, 0.4075, 0.5250, 0.5913, 0.5033],
- [0.6102, 0.4020, 0.8638, 0.3717, 0.3625, 0.5017, 0.6038, 0.5500],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000],
- [0.6127, 0.4119, 0.8650, 0.5300, 0.4512, 0.5250, 0.5260, 0.5461]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7177, -0.2844, 1.2895, -0.7119, -0.6197, -0.5738, 0.1653, 0.3376],
- [ 0.6297, -0.4088, 1.2134, -1.3051, -0.3212, -1.3260, 0.5060, 0.2098],
- [ 0.5482, -0.4027, 1.6650, 0.0851, -0.4617, -0.6359, 0.1926, 0.2705],
- [ 0.6680, -0.3811, 1.8350, -0.1524, -0.4657, -0.6388, 0.6130, 0.1218],
- [ 0.6451, -0.3880, 1.7837, 0.0488, -0.2782, 0.2239, 0.3377, 0.1277],
- [ 0.6347, -0.3732, 1.6429, -0.5697, -0.4933, 0.0440, 0.5640, 0.2210],
- [ 0.7459, -0.3111, 1.7528, -0.4887, -0.6403, -0.4226, 0.3598, 0.0651],
- [ 0.6221, -0.3853, 1.7191, 0.1670, -0.1029, 0.1177, 0.2200, 0.1834]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.5779, -0.4054, 1.1032, -1.4006, -0.3460, -1.1543, 0.5547,
- 0.2622],
- [ 0.5777, -0.3915, 1.7961, 0.1698, -0.5192, -0.5307, 0.2141,
- 0.3392],
- [ 0.6204, -0.4336, 1.8654, -0.0688, -0.6058, -0.5230, 0.6503,
- 0.0472],
- [ 0.5668, -0.4306, 1.7730, 0.0697, -0.4037, 0.1390, 0.4450,
- 0.0390],
- [ 0.5324, -0.4293, 1.7037, -0.5692, -0.6115, 0.0313, 0.5028,
- 0.2545],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236],
- [ 0.5443, -0.3831, 1.7095, 0.1621, -0.2016, 0.1390, 0.1437,
- 0.2364]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0066, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0066, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.289715682156384
- step: 8
- running loss: 0.036214460269548
- Train Steps: 8/90 Loss: 0.0362 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4066, 0.8746, 0.3376, 0.3717, 0.3090, 0.5842, 0.5165],
- [0.6026, 0.3979, 0.8550, 0.4233, 0.3613, 0.5233, 0.5582, 0.4967],
- [ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767],
- [0.6276, 0.4095, 0.8237, 0.2250, 0.4662, 0.1783, 0.6171, 0.4869],
- [0.6120, 0.4014, 0.6863, 0.2817, 0.3700, 0.2783, 0.5513, 0.5667],
- [ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.1728, -0.0307, 1.7745, -0.4279, -0.6014, -0.7678, 0.3550, 0.1150],
- [ 1.1965, -0.0361, 1.7375, -0.0554, -0.5987, 0.2345, 0.5089, 0.1112],
- [-1.2754, -1.6184, 1.6297, -0.9376, 0.1132, -0.9360, 0.9832, 0.2434],
- [ 0.9482, -0.2055, 1.7096, 0.2033, -0.3678, 0.2868, 0.2874, 0.2983],
- [ 0.8853, -0.1925, 1.5021, -1.0313, -0.1642, -1.0260, 0.5182, 0.1084],
- [ 0.8353, -0.2145, 1.0419, -0.7815, -0.5458, -0.8351, 0.1290, 0.3244],
- [-0.7671, -1.2577, 1.3239, -0.5202, -0.6450, -0.6157, 0.0111, 0.2329],
- [ 0.9063, -0.2047, 0.9747, -0.9788, -0.5142, -1.0113, 0.2779, 0.1766]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5787, -0.4077, 1.7537, -0.7263, -0.5692, -0.8586, 0.4126,
- 0.1000],
- [ 0.4974, -0.4482, 1.6633, -0.3306, -0.6173, 0.1313, 0.2925,
- 0.0081],
- [-2.2859, -2.2859, 1.6517, -1.2620, 0.2141, -1.1928, 1.1166,
- 0.2463],
- [ 0.5539, -0.4031, 1.7168, -0.0594, -0.3748, 0.0543, 0.1390,
- 0.3777],
- [ 0.6127, -0.3944, 1.5189, -1.2467, -0.1323, -1.4622, 0.5646,
- -0.0369],
- [ 0.5410, -0.4321, 0.8838, -0.9849, -0.5769, -1.0003, 0.2603,
- 0.3315],
- [-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837],
- [ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1240, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1240, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.41375276912003756
- step: 9
- running loss: 0.045972529902226396
- Train Steps: 9/90 Loss: 0.0460 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4008, 0.8047, 0.2484, 0.4037, 0.2777, 0.6506, 0.5212],
- [ nan, nan, 0.7225, 0.2167, 0.3987, 0.2283, 0.5427, 0.5181],
- [0.6109, 0.4009, 0.8725, 0.4700, 0.3550, 0.4167, 0.5650, 0.5117],
- [0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183],
- [0.6097, 0.3988, 0.8650, 0.5250, 0.4213, 0.5200, 0.5675, 0.5050],
- [0.6098, 0.3991, 0.8638, 0.4717, 0.4263, 0.4967, 0.5212, 0.5650],
- [0.6267, 0.4065, 0.8313, 0.2467, 0.4788, 0.1733, 0.6312, 0.5133],
- [ nan, nan, 0.7515, 0.2708, 0.3987, 0.2267, 0.5162, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9313, -0.2026, 1.4965, -0.8860, -0.4646, -0.8344, 0.7200, 0.1272],
- [-1.7722, -1.9497, 1.0401, -1.0044, -0.4755, -0.9832, 0.1582, 0.2319],
- [ 1.1622, -0.0263, 1.8426, -0.0205, -0.6599, -0.2158, 0.2817, 0.1618],
- [ 1.0712, -0.1175, 1.1899, -1.1806, -0.3783, -1.2676, 0.5125, 0.1718],
- [ 1.0987, -0.0886, 1.7554, 0.2702, -0.3929, 0.3518, 0.2888, 0.1255],
- [ 0.8446, -0.2262, 1.7078, 0.0515, -0.3860, 0.2727, 0.2133, 0.2586],
- [ 1.0712, -0.1029, 1.5134, -0.9648, -0.2017, -1.2557, 0.7378, 0.2042],
- [-1.1009, -1.4880, 1.1267, -0.8914, -0.5174, -0.8465, 0.0194, 0.3053]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4345, 1.4308, -1.1384, -0.4213, -1.0031, 0.7190,
- 0.1214],
- [-2.2859, -2.2859, 1.0513, -1.2851, -0.4441, -1.2313, 0.2206,
- 0.1073],
- [ 0.5356, -0.4343, 1.7441, -0.1150, -0.6462, -0.3614, 0.3238,
- 0.0774],
- [ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082],
- [ 0.5303, -0.4440, 1.7095, 0.1390, -0.3402, 0.1159, 0.3353,
- 0.0467],
- [ 0.5308, -0.4425, 1.7037, -0.1073, -0.3171, 0.0082, 0.1217,
- 0.3238],
- [ 0.6085, -0.4084, 1.5536, -1.1466, -0.0746, -1.4853, 0.6298,
- 0.0851],
- [-2.2859, -2.2859, 1.1854, -1.0352, -0.4441, -1.2390, 0.0986,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0825, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0825, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4962860615924001
- step: 10
- running loss: 0.04962860615924001
- Train Steps: 10/90 Loss: 0.0496 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6364, 0.4092, 0.8525, 0.5633, 0.3875, 0.4950, 0.6599, 0.5285],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6269, 0.4073, 0.8900, 0.4933, 0.3625, 0.4183, 0.6288, 0.5150],
- [0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136],
- [0.6128, 0.4115, 0.7934, 0.3778, 0.3450, 0.4033, 0.5337, 0.5456],
- [0.6060, 0.3924, 0.8450, 0.5717, 0.4200, 0.5217, 0.5253, 0.4752],
- [ nan, nan, 0.7225, 0.2167, 0.3987, 0.2283, 0.5427, 0.5181],
- [0.6126, 0.4073, 0.8750, 0.5133, 0.3800, 0.4333, 0.4986, 0.5378]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0878, -0.1095, 1.6107, -0.0532, -0.4727, -0.1721, 0.7642, 0.1889],
- [ 0.8507, -0.2066, 1.5986, -0.0028, -0.3621, 0.0362, 0.4501, 0.2864],
- [ 0.8927, -0.2078, 1.7943, -0.2751, -0.5363, -0.4577, 0.5687, 0.1468],
- [ 0.6973, -0.3176, 1.6548, -0.6774, -0.6204, -0.6440, 0.2402, 0.1518],
- [ 0.7442, -0.2523, 1.3724, -0.8019, -0.6210, -0.5154, 0.2633, 0.2536],
- [ 0.6057, -0.3823, 1.5197, 0.0231, -0.3128, -0.0456, 0.1714, 0.1913],
- [-2.1630, -2.2389, 1.0234, -1.2700, -0.4265, -1.2075, 0.2050, 0.1823],
- [ 0.8390, -0.2155, 1.6746, -0.1264, -0.5022, -0.4144, 0.1339, 0.1871]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.5365e-01, -3.9601e-01, 1.6517e+00, 3.1609e-01, -4.9607e-01,
- 4.6189e-04, 7.6203e-01, 1.5543e-01],
- [ 5.7419e-01, -3.7921e-01, 1.6460e+00, 3.0839e-01, -3.4596e-01,
- 1.4673e-01, 4.1617e-01, 3.1609e-01],
- [ 6.0964e-01, -4.0462e-01, 1.8249e+00, -7.2363e-03, -6.1155e-01,
- -3.5366e-01, 6.1824e-01, 9.2841e-02],
- [ 5.2355e-01, -4.2731e-01, 1.7499e+00, -4.3064e-01, -5.8268e-01,
- -4.6143e-01, 1.6505e-01, 8.6245e-02],
- [ 5.4440e-01, -3.8522e-01, 1.3786e+00, -5.4087e-01, -6.9238e-01,
- -4.2294e-01, 1.7915e-01, 2.3412e-01],
- [ 5.1316e-01, -4.7360e-01, 1.6171e+00, 3.5458e-01, -3.4596e-01,
- 1.2363e-01, 1.4038e-01, -9.1096e-02],
- [-2.2859e+00, -2.2859e+00, 1.0513e+00, -1.2851e+00, -4.4411e-01,
- -1.2313e+00, 2.2057e-01, 1.0729e-01],
- [ 5.4353e-01, -4.0454e-01, 1.7557e+00, 8.5142e-02, -5.3072e-01,
- -2.8437e-01, 1.7213e-02, 1.9805e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0259, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0259, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5221390677616
- step: 11
- running loss: 0.047467187978327274
- Train Steps: 11/90 Loss: 0.0475 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6264, 0.4069, 0.7900, 0.1650, 0.4275, 0.2267, 0.6290, 0.4947],
- [0.6224, 0.3964, 0.8225, 0.5717, 0.4150, 0.4617, 0.5775, 0.5267],
- [0.6125, 0.4035, 0.7825, 0.3100, 0.3463, 0.4900, 0.5832, 0.5637],
- [0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [0.6216, 0.4167, 0.8588, 0.5583, 0.3975, 0.5167, 0.5775, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3029, -0.5813, 1.6058, 0.2992, -0.4347, -0.2948, 0.2373, 0.4066],
- [ 0.1432, -0.7466, 1.3805, -1.3673, -0.3420, -1.3187, 0.7347, 0.0626],
- [ 0.1686, -0.6956, 1.5923, 0.0621, -0.4416, -0.2541, 0.3519, 0.1528],
- [ 0.2014, -0.6436, 1.2887, -0.8800, -0.6039, 0.0331, 0.3587, 0.2998],
- [ 0.5978, -0.4018, 1.1764, -1.0731, -0.6076, -0.8882, 0.0660, 0.0662],
- [ 0.5845, -0.4183, 1.5968, -0.8787, -0.6331, -0.9194, 0.3063, 0.0992],
- [ 0.5553, -0.4389, 1.4506, -1.0662, -0.4033, -1.0708, 0.5415, 0.1191],
- [ 0.3629, -0.5407, 1.6632, 0.1891, -0.4559, 0.0866, 0.4513, 0.2735]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0381e-01, -3.4642e-01, 1.7037e+00, 3.9307e-01, -4.4411e-01,
- -2.6128e-01, 3.0069e-01, 4.6236e-01],
- [ 6.0739e-01, -4.0631e-01, 1.3630e+00, -1.5238e+00, -3.1132e-01,
- -1.2390e+00, 6.1950e-01, -9.2270e-04],
- [ 5.8915e-01, -4.5504e-01, 1.5132e+00, 3.5458e-01, -3.6905e-01,
- -1.5350e-01, 3.8152e-01, 1.4673e-01],
- [ 5.4319e-01, -4.2240e-01, 1.3284e+00, -8.5404e-01, -6.8661e-01,
- -2.2633e-02, 4.0770e-01, 3.1769e-01],
- [ 5.2194e-01, -4.5504e-01, 1.1415e+00, -9.1962e-01, -6.4042e-01,
- -9.3872e-01, 1.8562e-01, 1.4106e-02],
- [ 5.4825e-01, -4.2490e-01, 1.5305e+00, -7.3857e-01, -6.1155e-01,
- -8.6944e-01, 3.3533e-01, 1.0054e-01],
- [ 5.7962e-01, -3.8776e-01, 1.3688e+00, -1.0542e+00, -4.0947e-01,
- -1.1312e+00, 5.8938e-01, 1.9292e-01],
- [ 5.8528e-01, -3.6135e-01, 1.6806e+00, 2.9299e-01, -4.4988e-01,
- 1.0054e-01, 3.8152e-01, 3.3149e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0200, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0200, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5421810066327453
- step: 12
- running loss: 0.04518175055272877
- Train Steps: 12/90 Loss: 0.0452 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6307, 0.4029, 0.8988, 0.4817, 0.3937, 0.3500, 0.7311, 0.5378],
- [0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [ nan, nan, 0.6859, 0.2194, 0.4150, 0.1867, 0.5153, 0.5729],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6206, 0.4001, 0.8900, 0.3933, 0.3588, 0.3567, 0.5837, 0.5083],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1219, -0.7227, 1.8549, -0.1560, -0.5318, -0.7085, 0.7696, 0.0714],
- [ 0.8323, -0.2219, 1.6331, -0.1667, -0.2122, 0.0865, 0.1752, 0.2409],
- [-2.4100, -2.4214, 0.8703, -1.1893, -0.4963, -1.1885, 0.1118, 0.2354],
- [ 0.7064, -0.3345, 1.2014, -1.1894, -0.3038, -1.3511, 0.4325, 0.1430],
- [ 0.6720, -0.3316, 1.6616, -0.2564, -0.6401, -0.3004, 0.3643, 0.1793],
- [ 0.5241, -0.4137, 1.6657, -0.1205, -0.2329, 0.3921, 0.3336, 0.2425],
- [ 0.4922, -0.4644, 1.7609, -0.4795, -0.7322, -0.6432, 0.3411, 0.0464],
- [ 0.9622, -0.1731, 0.8906, -1.0965, -0.5967, -1.1497, 0.3895, 0.2532]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6273, -0.4249, 1.8654, -0.0611, -0.4672, -0.6693, 1.0910,
- 0.1982],
- [ 0.5505, -0.4207, 1.7095, -0.0534, -0.0509, 0.1050, 0.3873,
- 0.3007],
- [-2.2859, -2.2859, 0.8824, -1.2727, -0.3691, -1.4237, 0.0943,
- 0.3604],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.5807, -0.4378, 1.8249, -0.4691, -0.6289, -0.6385, 0.4104,
- 0.0620],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0205, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0205, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5627054637297988
- step: 13
- running loss: 0.04328503567152298
- Train Steps: 13/90 Loss: 0.0433 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6222, 0.4169, 0.8638, 0.5650, 0.4313, 0.4783, 0.5637, 0.5633],
- [0.6190, 0.4135, 0.8000, 0.4883, 0.3566, 0.3647, 0.5613, 0.5900],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6264, 0.4055, 0.8425, 0.2767, 0.4425, 0.2767, 0.7050, 0.5586],
- [0.6339, 0.4102, 0.9088, 0.4767, 0.3925, 0.5283, 0.7509, 0.5390],
- [0.6127, 0.4118, 0.8650, 0.5083, 0.4088, 0.5367, 0.5300, 0.5456],
- [0.6075, 0.4007, 0.8275, 0.4917, 0.4050, 0.5100, 0.5167, 0.5280],
- [0.6286, 0.4060, 0.9188, 0.4333, 0.3675, 0.4167, 0.7034, 0.5528]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4958, -0.4315, 1.4636, 0.0741, -0.4305, -0.2701, 0.1757, 0.2284],
- [ 0.0142, -0.7257, 1.3866, -0.4004, -0.6804, -0.9269, 0.1312, 0.2729],
- [ 0.4388, -0.4977, 1.5452, -0.0589, -0.6108, -0.3443, 0.2965, 0.2244],
- [ 0.1248, -0.7289, 1.5169, -1.3572, -0.3329, -1.3349, 0.9413, 0.0911],
- [ 0.2870, -0.6169, 1.5969, -0.4151, -0.4717, 0.0487, 0.7505, 0.2085],
- [ 0.3186, -0.5650, 1.4995, -0.1835, -0.3716, 0.0049, 0.0432, 0.1541],
- [ 0.1244, -0.6726, 1.3800, -0.3604, -0.4168, -0.0753, -0.0819, 0.1340],
- [ 0.3934, -0.5537, 1.8068, -0.5702, -0.5608, -0.6095, 0.8026, 0.1045]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5880, -0.3605, 1.7037, 0.3238, -0.2940, -0.0765, 0.3180,
- 0.3161],
- [ 0.5732, -0.3761, 1.4092, -0.0303, -0.6388, -0.6012, 0.3065,
- 0.4393],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.6075, -0.4129, 1.6055, -1.0080, -0.2420, -1.0080, 0.9704,
- 0.2944],
- [ 0.6421, -0.3912, 1.9115, -0.0842, -0.4730, 0.1544, 1.1824,
- 0.2035],
- [ 0.5443, -0.3840, 1.7095, 0.0620, -0.3979, 0.1929, 0.1622,
- 0.2341],
- [ 0.5200, -0.4353, 1.5363, -0.0149, -0.4152, 0.0697, 0.1005,
- 0.1530],
- [ 0.6174, -0.4105, 1.9577, -0.2844, -0.5885, -0.3614, 0.9631,
- 0.2676]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0514, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0514, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6141552468761802
- step: 14
- running loss: 0.043868231919727156
- Train Steps: 14/90 Loss: 0.0439 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6350, 0.4144, 0.9250, 0.4100, 0.4125, 0.3750, 0.7129, 0.5945],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000],
- [0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1153, -0.7137, 1.8762, -0.4651, -0.3093, -0.7997, 0.9198, 0.2673],
- [ 0.2380, -0.6188, 1.6252, -0.5804, -0.7252, -0.4364, 0.3297, 0.1032],
- [ 0.4029, -0.5418, 1.0334, -1.3869, -0.4907, -1.4458, 0.2220, 0.0935],
- [ 0.4753, -0.5046, 1.0829, -1.3684, -0.6306, -1.0880, 0.3981, 0.0556],
- [ 0.0737, -0.7363, 1.5422, 0.0759, -0.5337, -0.1861, 0.7105, 0.2673],
- [ 0.2822, -0.5751, 1.5885, -0.0442, -0.2840, -0.0404, 0.1796, 0.1596],
- [ 0.2269, -0.6090, 1.6293, -0.0549, -0.2520, 0.0713, 0.2031, 0.1629],
- [-0.0072, -0.7856, 1.5862, -0.1222, -0.6186, -0.3006, 0.3431, 0.3637]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6471, -0.3719, 1.9866, -0.3921, -0.3806, -0.5538, 1.0070,
- 0.4600],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236],
- [ 0.5598, -0.4201, 1.1898, -1.3005, -0.3806, -1.3313, 0.3873,
- 0.0774],
- [ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0415, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0415, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6556800110265613
- step: 15
- running loss: 0.04371200073510408
- Train Steps: 15/90 Loss: 0.0437 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896],
- [0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136],
- [0.6274, 0.4117, 0.8100, 0.5801, 0.4000, 0.4583, 0.5582, 0.6118],
- [0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150],
- [0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6296, 0.4076, 0.8400, 0.5583, 0.3700, 0.4367, 0.6876, 0.5494],
- [0.6161, 0.4076, 0.8900, 0.4667, 0.4125, 0.5917, 0.6262, 0.5367],
- [0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1218, -0.6983, 1.2977, -0.7802, -0.6482, -0.9329, 0.1384, 0.3076],
- [ 0.1443, -0.6555, 1.6092, -0.6454, -0.6445, -0.6126, 0.2774, 0.1033],
- [ 0.3040, -0.5442, 1.3830, -0.0120, -0.4510, -0.2879, 0.3900, 0.3919],
- [ 0.0439, -0.7803, 1.1903, -1.3528, -0.3130, -1.4106, 0.5307, 0.1385],
- [ 0.4143, -0.5202, 1.7113, -0.1068, -0.4546, 0.0416, 0.4255, 0.0655],
- [ 0.3109, -0.5906, 1.6933, -0.0469, -0.5008, -0.4294, 0.8705, 0.1147],
- [ 0.1866, -0.6275, 1.6986, -0.3177, -0.2417, 0.3220, 0.5624, 0.1782],
- [ 0.2944, -0.5437, 1.5597, -0.0388, -0.5235, -0.6436, 0.3104, 0.2505]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374],
- [ 0.5235, -0.4273, 1.7499, -0.4306, -0.5827, -0.4614, 0.1651,
- 0.0862],
- [ 0.6121, -0.3844, 1.4556, 0.3936, -0.4383, -0.1689, 0.2925,
- 0.5401],
- [ 0.5796, -0.4326, 1.4439, -1.1774, -0.2940, -1.3390, 0.3931,
- 0.0928],
- [ 0.5783, -0.4363, 1.7724, 0.0432, -0.5153, 0.0871, 0.4840,
- 0.0663],
- [ 0.6224, -0.4032, 1.5940, 0.2930, -0.5769, -0.2690, 0.8900,
- 0.2516],
- [ 0.5598, -0.4032, 1.8249, -0.1304, -0.3806, 0.4470, 0.6067,
- 0.1929],
- [ 0.5777, -0.3915, 1.7961, 0.1698, -0.5192, -0.5307, 0.2141,
- 0.3392]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0385, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0385, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6941816275939345
- step: 16
- running loss: 0.04338635172462091
- Train Steps: 16/90 Loss: 0.0434 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6228, 0.4119, 0.7938, 0.2233, 0.4674, 0.1773, 0.6188, 0.5433],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6229, 0.4086, 0.7538, 0.2600, 0.4775, 0.1617, 0.5900, 0.5383],
- [0.6165, 0.4106, 0.7575, 0.1733, 0.3838, 0.2650, 0.5680, 0.5116],
- [0.6211, 0.4069, 0.8750, 0.5117, 0.4150, 0.4900, 0.5875, 0.5167],
- [0.6190, 0.4135, 0.8000, 0.4883, 0.3566, 0.3647, 0.5613, 0.5900],
- [0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [0.6250, 0.4146, 0.8838, 0.3933, 0.3588, 0.4283, 0.6162, 0.5367]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 2.7160e-01, -6.1288e-01, 1.3024e+00, -1.0790e+00, -2.4634e-01,
- -1.3686e+00, 5.0634e-01, 1.6069e-01],
- [ 2.6692e-01, -5.8733e-01, 1.6549e+00, 1.7750e-01, -2.6255e-01,
- 1.4141e-01, 3.5143e-01, 1.6915e-01],
- [ 3.3903e-01, -5.6775e-01, 1.0713e+00, -1.1558e+00, -2.5916e-01,
- -1.4428e+00, 3.9940e-01, 2.0511e-01],
- [ 4.3410e-01, -5.4499e-01, 1.1180e+00, -1.2465e+00, -5.6629e-01,
- -1.0447e+00, 5.5686e-01, 4.9847e-02],
- [ 2.5437e-01, -6.0803e-01, 1.7518e+00, 1.5235e-01, -4.2358e-01,
- 4.0426e-02, 5.6769e-01, 1.3766e-01],
- [ 6.4946e-04, -7.1879e-01, 1.5425e+00, 7.2002e-03, -6.0888e-01,
- -6.5312e-01, 2.5940e-01, 3.6687e-01],
- [ 1.4826e-01, -6.5294e-01, 1.8039e+00, -3.0606e-01, -5.9294e-01,
- -5.2833e-02, 6.3769e-01, 2.8626e-01],
- [ 8.7252e-02, -6.8236e-01, 1.6944e+00, -4.0593e-01, -6.6378e-01,
- -3.7071e-01, 5.3112e-01, 2.8553e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5908, -0.3832, 1.3804, -1.2543, -0.1270, -1.4671, 0.5721,
- 0.2237],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.5911, -0.3984, 1.1956, -1.0850, -0.0804, -1.5392, 0.4393,
- 0.2006],
- [ 0.5618, -0.3895, 1.2129, -1.4853, -0.5134, -1.0619, 0.3378,
- 0.0772],
- [ 0.5828, -0.4066, 1.7557, 0.0774, -0.3691, -0.0226, 0.4277,
- 0.1005],
- [ 0.5732, -0.3761, 1.4092, -0.0303, -0.6388, -0.6012, 0.3065,
- 0.4393],
- [ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [ 0.6009, -0.3710, 1.7961, -0.4691, -0.6289, -0.3075, 0.5605,
- 0.1929]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0320, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0320, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7261817371472716
- step: 17
- running loss: 0.04271657277336892
- Train Steps: 17/90 Loss: 0.0427 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6160, 0.4086, 0.8775, 0.3983, 0.3750, 0.3783, 0.5128, 0.4882],
- [0.6122, 0.4006, 0.8850, 0.4217, 0.4088, 0.5517, 0.6063, 0.5517],
- [0.6277, 0.4029, 0.8250, 0.2433, 0.4325, 0.2100, 0.6366, 0.5207],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6274, 0.4117, 0.8100, 0.5801, 0.4000, 0.4583, 0.5582, 0.6118],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9092, -0.1473, 1.7510, -0.4465, -0.6588, -0.6622, 0.2803, 0.0766],
- [ 0.6855, -0.2995, 1.7302, -0.2893, -0.4286, 0.1628, 0.6664, 0.2222],
- [ 0.5960, -0.4104, 1.4210, -1.1680, -0.3207, -1.3542, 0.9038, 0.1072],
- [ 0.5793, -0.3802, 1.6687, 0.1535, -0.4748, -0.2674, 0.5813, 0.1793],
- [-2.6504, -2.5917, 1.0145, -1.4165, -0.4417, -1.2874, 0.2440, 0.2193],
- [ 0.6755, -0.2863, 1.4774, 0.1994, -0.5063, -0.2944, 0.5160, 0.4400],
- [ 0.4113, -0.4544, 1.6717, 0.0710, -0.1949, -0.0334, 0.2397, 0.2295],
- [ 0.6982, -0.2907, 1.6843, -0.0253, -0.4022, 0.0244, 0.4109, 0.3028]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5595, -0.3988, 1.7672, -0.4460, -0.5538, -0.5384, 0.0828,
- -0.0310],
- [ 0.5417, -0.4355, 1.8018, -0.3383, -0.3979, 0.2622, 0.5143,
- 0.2622],
- [ 0.6132, -0.4248, 1.5247, -1.1620, -0.2882, -1.3159, 0.6545,
- 0.1193],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091],
- [-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373],
- [ 0.6121, -0.3844, 1.4556, 0.3936, -0.4383, -0.1689, 0.2925,
- 0.5401],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634],
- [ 0.5539, -0.4031, 1.7168, -0.0594, -0.3748, 0.0543, 0.1390,
- 0.3777]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0168, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0168, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7429619831964374
- step: 18
- running loss: 0.04127566573313541
- Train Steps: 18/90 Loss: 0.0413 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6138, 0.4101, 0.8800, 0.5083, 0.4637, 0.5950, 0.5587, 0.5077],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6175, 0.4013, 0.7734, 0.2783, 0.4475, 0.1786, 0.5790, 0.5351],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1537, -0.7204, 1.6356, -0.8018, -0.6451, -0.8134, 0.7579, 0.1320],
- [ 0.5381, -0.3980, 1.6382, -0.0354, -0.6471, -0.7040, 0.4174, 0.4160],
- [ 0.3666, -0.4972, 1.6796, -0.2601, -0.1986, 0.0435, 0.4511, 0.3033],
- [ 0.2557, -0.5845, 1.7455, 0.0321, -0.1672, 0.1011, 0.3273, 0.2243],
- [ 0.2479, -0.6265, 1.6954, 0.2720, -0.5356, -0.2905, 0.6293, 0.1359],
- [ 0.5597, -0.4278, 1.1547, -1.2702, -0.3355, -1.5681, 0.3867, 0.1769],
- [ 0.3058, -0.5402, 1.7025, -0.1812, -0.2044, 0.0503, 0.5050, 0.2742],
- [ 0.2479, -0.6194, 1.8035, -0.1138, -0.5522, -0.0488, 0.5325, 0.2004]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6031, -0.4307, 1.7268, -0.8002, -0.6058, -0.6462, 0.6442,
- -0.0220],
- [ 0.5902, -0.3493, 1.7961, -0.0072, -0.5942, -0.5615, 0.3180,
- 0.3161],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.5491, -0.3918, 1.7788, 0.0620, -0.1439, 0.4624, 0.2946,
- 0.0592],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.5664, -0.4321, 1.2862, -1.0003, -0.2189, -1.4608, 0.3883,
- 0.1855],
- [ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0247, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0247, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7676935633644462
- step: 19
- running loss: 0.04040492438760243
- Train Steps: 19/90 Loss: 0.0404 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6113, 0.4006, 0.8700, 0.5350, 0.3638, 0.3767, 0.5097, 0.4882],
- [ nan, nan, 0.8850, 0.2817, 0.5112, 0.2183, 0.7184, 0.5436],
- [0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [0.6200, 0.4071, 0.7338, 0.1917, 0.4200, 0.2450, 0.6150, 0.5517],
- [0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100],
- [0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334],
- [ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5391, -0.4111, 1.8399, 0.5539, -0.5385, -0.3105, 0.1539, 0.1573],
- [-0.4865, -1.1033, 1.7536, -0.9170, 0.0708, -1.0576, 1.1059, 0.2863],
- [ 0.7090, -0.2659, 1.6790, -0.7104, -0.3165, -0.8219, 0.6182, 0.2112],
- [ 1.0314, -0.0671, 1.9320, 0.0433, -0.5273, 0.1994, 0.5365, 0.3001],
- [ 0.7391, -0.2983, 1.1664, -1.0349, -0.3136, -0.9762, 0.4261, 0.2512],
- [ 0.9982, -0.1675, 1.8577, 0.0253, -0.5594, -0.2516, 0.6394, 0.1182],
- [ 0.6368, -0.3559, 1.2847, -0.8534, -0.4753, -0.6293, 0.4588, 0.2208],
- [-2.1450, -2.2038, 0.8288, -1.1550, -0.3983, -1.0886, 0.1174, 0.3643]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5379, -0.4358, 1.7326, 0.1852, -0.6058, -0.5461, 0.0684,
- -0.0310],
- [-2.2859, -2.2859, 1.8018, -0.9849, 0.0755, -1.2774, 1.0326,
- 0.2249],
- [ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [ 0.5779, -0.4054, 1.1032, -1.4006, -0.3460, -1.1543, 0.5547,
- 0.2622],
- [ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697],
- [ 0.5730, -0.4562, 1.2195, -1.2440, -0.5497, -0.7711, 0.5704,
- 0.1779],
- [-2.2859, -2.2859, 0.8033, -1.1250, -0.3864, -1.3082, 0.1126,
- 0.4543]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1029, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1029, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8706217845901847
- step: 20
- running loss: 0.04353108922950923
- Train Steps: 20/90 Loss: 0.0435 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6277, 0.4029, 0.8250, 0.2433, 0.4325, 0.2100, 0.6366, 0.5207],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [0.6274, 0.4003, 0.8638, 0.5967, 0.3688, 0.4900, 0.6108, 0.4661],
- [0.6147, 0.4112, 0.7988, 0.3200, 0.3775, 0.2767, 0.5150, 0.5550],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6353, 0.4128, 0.9138, 0.3533, 0.4688, 0.3250, 0.7145, 0.5991]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5395, -0.4382, 1.1486, -1.2259, -0.4905, -0.8644, 0.3337, 0.1198],
- [ 0.6198, -0.3472, 1.6669, -0.0291, -0.5057, -0.2221, 0.2427, 0.3126],
- [ 0.4126, -0.5339, 1.4331, -1.0436, -0.2162, -1.1185, 0.6102, 0.1433],
- [ 0.5501, -0.4162, 1.8176, -0.2285, -0.5117, 0.0819, 0.6617, 0.2014],
- [ 0.6478, -0.3610, 1.6992, 0.5170, -0.4537, 0.1087, 0.4591, 0.1850],
- [ 0.1851, -0.6295, 1.3142, -0.7649, -0.5219, -0.7357, -0.0488, 0.2572],
- [ 0.2418, -0.6364, 1.9933, 0.0325, -0.2490, -0.7881, 0.7903, 0.2832],
- [-0.4642, -1.0908, 1.9517, -0.5161, -0.0796, -0.6657, 1.0323, 0.3870]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.6132, -0.4248, 1.5247, -1.1620, -0.2882, -1.3159, 0.6545,
- 0.1193],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [ 0.6120, -0.4371, 1.7037, 0.4701, -0.5827, -0.0226, 0.5354,
- -0.1331],
- [ 0.5532, -0.3864, 1.4035, -0.8079, -0.5423, -1.0080, 0.0928,
- 0.2776],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.6487, -0.3792, 1.9346, -0.6539, -0.1208, -0.7848, 1.0143,
- 0.4814]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0475, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0475, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9181526629254222
- step: 21
- running loss: 0.04372155537740106
- Train Steps: 21/90 Loss: 0.0437 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6211, 0.3993, 0.7650, 0.1933, 0.4575, 0.1550, 0.5965, 0.4895],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [0.6142, 0.4127, 0.7575, 0.3067, 0.3438, 0.4383, 0.5778, 0.5207],
- [0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578],
- [0.6205, 0.4081, 0.8950, 0.4017, 0.3788, 0.4700, 0.5963, 0.5667],
- [0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6364, 0.4092, 0.8525, 0.5633, 0.3875, 0.4950, 0.6599, 0.5285],
- [0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0581, -0.7464, 1.3099, -1.2809, -0.1847, -1.5320, 0.3973, 0.1047],
- [ 0.4789, -0.4865, 2.0377, 0.2317, -0.4262, 0.3754, 0.9245, 0.2274],
- [ 0.2831, -0.5276, 1.3031, -0.8277, -0.5686, -0.3106, 0.3127, 0.3282],
- [-0.0833, -0.8177, 1.0154, -1.2084, -0.3860, -1.3190, 0.2106, 0.2604],
- [ 0.7950, -0.2507, 1.9152, -0.1057, -0.5803, 0.0395, 0.5121, 0.2700],
- [ 0.3878, -0.5124, 1.9157, 0.1641, -0.0578, 0.0620, 0.4092, 0.2649],
- [ 0.6601, -0.3920, 1.8787, 0.6277, -0.4979, 0.0237, 0.6823, 0.2055],
- [ 0.4764, -0.4623, 1.5466, -0.8331, -0.2431, -1.1635, 0.6208, 0.2329]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.8284e-01, -4.4175e-01, 1.2476e+00, -1.3929e+00, -1.7275e-01,
- -1.5700e+00, 4.6937e-01, -2.4798e-02],
- [ 6.0260e-01, -4.4175e-01, 1.8654e+00, -8.4219e-02, -4.4411e-01,
- 2.6220e-01, 9.2654e-01, 1.5543e-01],
- [ 5.5087e-01, -3.7983e-01, 1.2129e+00, -8.6944e-01, -6.9815e-01,
- -2.6128e-01, 3.8302e-01, 1.1931e-01],
- [ 5.2315e-01, -4.1886e-01, 9.3580e-01, -1.4006e+00, -3.9215e-01,
- -1.3698e+00, 2.5553e-01, 2.9064e-01],
- [ 5.7991e-01, -4.0115e-01, 1.8480e+00, -4.3064e-01, -5.3649e-01,
- -1.1501e-01, 4.6813e-01, 3.3149e-01],
- [ 5.3712e-01, -4.2517e-01, 1.7037e+00, -6.8822e-02, -3.4180e-02,
- 6.2048e-02, 3.7575e-01, 2.8530e-01],
- [ 6.5365e-01, -3.9601e-01, 1.6517e+00, 3.1609e-01, -4.9607e-01,
- 4.6189e-04, 7.6203e-01, 1.5543e-01],
- [ 6.1742e-01, -4.2249e-01, 1.4975e+00, -1.1709e+00, -3.1736e-01,
- -1.1806e+00, 6.5391e-01, 1.8793e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0307, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0307, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9488857751712203
- step: 22
- running loss: 0.043131171598691835
- Train Steps: 22/90 Loss: 0.0431 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6259, 0.4133, 0.8200, 0.2317, 0.5025, 0.1533, 0.6250, 0.4947],
- [0.6160, 0.4086, 0.8775, 0.3983, 0.3750, 0.3783, 0.5128, 0.4882],
- [0.6223, 0.3990, 0.8037, 0.2800, 0.4000, 0.2283, 0.5864, 0.5208],
- [0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097],
- [0.6205, 0.4012, 0.8675, 0.3283, 0.3713, 0.3050, 0.5813, 0.5117],
- [0.6221, 0.4107, 0.7788, 0.3033, 0.3950, 0.2817, 0.6075, 0.5517],
- [0.6127, 0.4084, 0.8700, 0.4467, 0.3987, 0.4317, 0.5013, 0.5471],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4515, -0.4720, 1.4325, -1.0735, -0.0188, -1.3452, 0.6986, 0.1841],
- [ 0.6504, -0.3483, 1.7911, -0.2205, -0.5954, -0.4333, 0.2837, 0.1217],
- [ 0.1686, -0.6797, 1.4327, -0.8897, -0.3231, -1.0884, 0.6743, 0.1728],
- [ 0.2799, -0.5739, 1.7917, 0.1795, -0.2080, 0.2609, 0.3030, 0.1324],
- [ 0.0126, -0.7755, 1.7338, -0.6765, -0.5620, -0.7782, 0.6809, 0.1863],
- [ 0.5867, -0.3887, 1.3940, -0.7872, -0.4405, -0.8869, 0.5255, 0.3176],
- [ 0.5458, -0.4143, 1.7265, -0.0424, -0.4915, -0.1369, 0.3132, 0.2854],
- [ 0.4597, -0.4834, 1.8259, 0.0604, -0.1184, 0.2290, 0.6608, 0.2708]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0514e-01, -3.7714e-01, 1.5016e+00, -1.2159e+00, 3.5104e-02,
- -1.5777e+00, 6.0099e-01, -9.2270e-04],
- [ 5.5953e-01, -3.9877e-01, 1.7672e+00, -4.4604e-01, -5.5381e-01,
- -5.3841e-01, 8.2802e-02, -3.0981e-02],
- [ 5.8851e-01, -4.4288e-01, 1.4266e+00, -9.9261e-01, -4.3834e-01,
- -1.2313e+00, 4.2276e-01, 1.1948e-01],
- [ 5.5155e-01, -4.2249e-01, 1.7788e+00, -5.3426e-02, -2.0162e-01,
- 1.9292e-01, 1.5683e-01, 6.8210e-02],
- [ 5.7991e-01, -4.3295e-01, 1.7210e+00, -7.6936e-01, -5.7113e-01,
- -8.7714e-01, 3.9885e-01, 7.7444e-02],
- [ 5.8747e-01, -3.8876e-01, 1.3111e+00, -8.8483e-01, -4.6143e-01,
- -9.8491e-01, 5.2009e-01, 2.6220e-01],
- [ 5.4405e-01, -3.9969e-01, 1.7326e+00, -2.2279e-01, -4.4411e-01,
- -2.9207e-01, 2.9551e-02, 2.4088e-01],
- [ 5.5978e-01, -4.2731e-01, 1.7152e+00, -1.2271e-01, -6.4698e-03,
- 1.9169e-01, 5.1432e-01, 2.8530e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0256, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0256, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.974448068998754
- step: 23
- running loss: 0.042367307347771915
- Train Steps: 23/90 Loss: 0.0424 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4079, 0.8025, 0.2500, 0.3763, 0.3217, 0.6125, 0.5533],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6154, 0.4112, 0.7037, 0.2317, 0.4238, 0.1833, 0.5350, 0.5600],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6234, 0.4179, 0.7825, 0.3450, 0.3813, 0.2867, 0.5675, 0.5617],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [0.6261, 0.4131, 0.8938, 0.3550, 0.4000, 0.2683, 0.6188, 0.5283],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5464, -0.4343, 1.5575, -0.8826, -0.4123, -0.6552, 0.5500, 0.2263],
- [ 0.8880, -0.2405, 1.9189, -0.1182, -0.5029, -0.3676, 0.4924, 0.2121],
- [ 0.0587, -0.7420, 0.9993, -1.1595, -0.2452, -1.3513, 0.2003, 0.2672],
- [ 0.3428, -0.5721, 1.6978, -0.4107, -0.5528, -0.2838, 0.3479, 0.0575],
- [ 0.1762, -0.6208, 1.4553, -0.6422, -0.3622, -0.7972, 0.3625, 0.3719],
- [ 0.4635, -0.4896, 1.5542, -0.8813, -0.2341, -1.0112, 0.6000, 0.1013],
- [ 0.6053, -0.4251, 1.9318, -0.4953, -0.3275, -0.9114, 0.6566, 0.1574],
- [ 0.3713, -0.5982, 1.8847, 0.3658, -0.2720, 0.4730, 0.9611, 0.2410]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5788, -0.4020, 1.4208, -1.1312, -0.5480, -0.8002, 0.5432,
- 0.2699],
- [ 0.5880, -0.3887, 1.8423, -0.3306, -0.6231, -0.5230, 0.4046,
- 0.1544],
- [ 0.5565, -0.3864, 0.9647, -1.2159, -0.3286, -1.4391, 0.1852,
- 0.3007],
- [ 0.5206, -0.4603, 1.4670, -0.4768, -0.6577, -0.3998, 0.1836,
- 0.0021],
- [ 0.5935, -0.3558, 1.3284, -0.6924, -0.5249, -0.9618, 0.3353,
- 0.3084],
- [ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929],
- [ 0.6062, -0.3778, 1.8423, -0.6462, -0.4383, -1.0465, 0.5721,
- 0.1544],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0266, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0266, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.001086718402803
- step: 24
- running loss: 0.04171194660011679
- Train Steps: 24/90 Loss: 0.0417 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6139, 0.4019, 0.7137, 0.2150, 0.4375, 0.1533, 0.5293, 0.5006],
- [0.6250, 0.4146, 0.8838, 0.3933, 0.3588, 0.4283, 0.6162, 0.5367],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433],
- [0.6068, 0.3963, 0.8650, 0.4317, 0.4037, 0.5083, 0.5253, 0.4999],
- [0.6268, 0.4052, 0.8175, 0.2250, 0.4688, 0.1917, 0.6375, 0.5267],
- [0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6267, 0.4094, 0.8712, 0.3083, 0.4400, 0.2267, 0.6250, 0.5200]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1870, -0.6556, 1.0506, -1.2502, -0.2470, -1.4751, 0.2263, 0.1088],
- [ 0.9500, -0.1776, 1.8741, -0.2319, -0.6673, -0.1799, 0.6115, 0.2558],
- [ 0.5001, -0.4606, 1.8854, 0.0563, -0.1951, 0.3606, 0.6289, 0.2175],
- [ 0.3553, -0.5747, 1.8311, -0.1599, -0.5016, 0.1822, 0.3915, 0.0840],
- [ 0.4626, -0.5170, 1.5021, -1.0981, -0.1401, -1.4030, 0.7077, 0.1556],
- [ 0.5643, -0.4305, 1.2420, -1.1086, -0.3508, -1.2393, 0.2733, 0.1198],
- [ 0.5204, -0.5165, 1.8178, 0.2130, -0.5849, -0.0536, 0.5791, 0.3442],
- [ 0.3039, -0.5933, 1.8124, -0.8292, -0.2101, -1.0178, 0.6856, 0.2138]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5496, -0.4295, 1.0109, -1.2928, -0.2651, -1.5777, 0.1589,
- 0.0261],
- [ 0.6009, -0.3710, 1.7961, -0.4691, -0.6289, -0.3075, 0.5605,
- 0.1929],
- [ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237],
- [ 0.5168, -0.4556, 1.7095, -0.2921, -0.4210, 0.0620, 0.1404,
- 0.0231],
- [ 0.6092, -0.4143, 1.4901, -1.2467, -0.1208, -1.4006, 0.6587,
- 0.1467],
- [ 0.5598, -0.4201, 1.1898, -1.3005, -0.3806, -1.3313, 0.3873,
- 0.0774],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.6089, -0.3948, 1.7383, -0.8617, -0.2536, -1.2390, 0.6009,
- 0.1159]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0186, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0186, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0197177669033408
- step: 25
- running loss: 0.040788710676133634
- Train Steps: 25/90 Loss: 0.0408 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6286, 0.4097, 0.8107, 0.2414, 0.4425, 0.2483, 0.6745, 0.5385],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6198, 0.4115, 0.7762, 0.2717, 0.3713, 0.3200, 0.5837, 0.5683],
- [0.6248, 0.4185, 0.8500, 0.5767, 0.4463, 0.4550, 0.5613, 0.5917],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383],
- [0.6099, 0.4030, 0.8638, 0.5117, 0.4983, 0.4965, 0.5086, 0.5388]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6903, -0.3480, 1.6033, -1.1603, -0.2271, -1.2476, 0.8479, 0.1206],
- [ 0.9546, -0.2341, 1.9506, -0.1557, -0.5827, -0.3543, 0.8376, 0.1278],
- [ 0.7325, -0.3103, 1.4645, -1.0865, -0.5483, -0.8621, 0.4646, 0.2128],
- [ 0.9658, -0.1793, 1.7586, 0.3576, -0.2545, -0.2151, 0.4456, 0.2952],
- [ 0.6139, -0.3888, 1.7524, 0.0334, -0.2684, 0.1579, 0.3302, 0.1551],
- [ 0.9975, -0.1532, 1.7208, -0.8041, -0.6586, -0.6190, 0.5044, 0.1213],
- [-1.4734, -1.7306, 1.0105, -1.3044, -0.3757, -1.4271, 0.1577, 0.2327],
- [ 0.5141, -0.4748, 1.8513, -0.0350, -0.0525, -0.0404, 0.2714, 0.1178]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6174, -0.3936, 1.4586, -1.1709, -0.2420, -1.1389, 0.8296,
- 0.2012],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [ 0.5768, -0.3852, 1.2995, -1.0311, -0.5711, -0.8079, 0.4104,
- 0.3392],
- [ 0.5999, -0.3530, 1.6402, 0.3777, -0.2247, -0.1843, 0.3065,
- 0.4470],
- [ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753],
- [-2.2859, -2.2859, 1.0712, -1.2085, -0.3806, -1.3929, 0.0755,
- 0.2006],
- [ 0.5309, -0.4246, 1.7037, 0.0774, 0.0158, 0.0075, 0.0635,
- 0.2026]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0308, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0308, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0505003025755286
- step: 26
- running loss: 0.04040385779136649
- Train Steps: 26/90 Loss: 0.0404 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4112, 0.8862, 0.4100, 0.3638, 0.4917, 0.6088, 0.6050],
- [0.6273, 0.4105, 0.8988, 0.4517, 0.3912, 0.2550, 0.5894, 0.4811],
- [0.6208, 0.4082, 0.8538, 0.3067, 0.3588, 0.3717, 0.6112, 0.5517],
- [0.6224, 0.4097, 0.7438, 0.2267, 0.3850, 0.2850, 0.5988, 0.5250],
- [0.6182, 0.3998, 0.8793, 0.4191, 0.3552, 0.4285, 0.6038, 0.5312],
- [0.6286, 0.4086, 0.8408, 0.2801, 0.4163, 0.2800, 0.6725, 0.5393],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3545, -0.5765, 1.8130, -0.4768, -0.5115, -0.0557, 0.6404, 0.3101],
- [ 0.8690, -0.2657, 1.8614, -0.3410, -0.3643, -1.1425, 0.4606, -0.0299],
- [ 0.5647, -0.4242, 1.6549, -0.8860, -0.5752, -0.6408, 0.4819, 0.1474],
- [ 0.5902, -0.3918, 1.2179, -1.2495, -0.3850, -1.0606, 0.3993, 0.0960],
- [ 0.7421, -0.3249, 1.7175, -0.5679, -0.5654, -0.3985, 0.4766, 0.1935],
- [ 0.3850, -0.5400, 1.6495, -1.0489, -0.2069, -1.1659, 0.7564, 0.1570],
- [ 0.6308, -0.3960, 1.5943, 0.2649, -0.3435, -0.1843, 0.4090, 0.3099],
- [ 0.4256, -0.5386, 1.7622, -0.0179, -0.1365, -0.0189, 0.2498, 0.2253]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5777, -0.3868, 1.8076, -0.3921, -0.6058, -0.0149, 0.5259,
- 0.5085],
- [ 0.6116, -0.3898, 1.8654, -0.1997, -0.4788, -1.1081, 0.4367,
- -0.0637],
- [ 0.5814, -0.4003, 1.6575, -0.8694, -0.6289, -0.5692, 0.5374,
- 0.2622],
- [ 0.5891, -0.3937, 1.1494, -1.2390, -0.5076, -0.9695, 0.4797,
- 0.1390],
- [ 0.5697, -0.4393, 1.7754, -0.3503, -0.6453, -0.3067, 0.5028,
- 0.1677],
- [ 0.6174, -0.3984, 1.5975, -0.9921, -0.3633, -0.9926, 0.8205,
- 0.2050],
- [ 0.6134, -0.3910, 1.4497, 0.3546, -0.3517, -0.0919, 0.3296,
- 0.5239],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0119, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0119, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0623777024447918
- step: 27
- running loss: 0.039347322312770064
- Train Steps: 27/90 Loss: 0.0393 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6177, 0.4086, 0.8738, 0.3950, 0.3775, 0.5600, 0.6225, 0.5700],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600],
- [0.6277, 0.4057, 0.8300, 0.2650, 0.4363, 0.1850, 0.6140, 0.4823]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0085, -0.1871, 1.8592, -0.1330, -0.5335, -0.2461, 0.5151, 0.1121],
- [ 0.3415, -0.6384, 1.7394, -0.0560, -0.5300, -0.2992, 0.4617, 0.3580],
- [ 0.3285, -0.5683, 1.3489, -1.0629, -0.3567, -1.1883, 0.1364, 0.1996],
- [ 0.9330, -0.2222, 1.7962, -0.3420, -0.4949, -0.8077, 0.4360, 0.1853],
- [ 0.1978, -0.6507, 1.7339, -0.5420, -0.4382, 0.2163, 0.6497, 0.2670],
- [ 0.5605, -0.4510, 1.8137, -0.1079, -0.2602, 0.0344, 0.3814, 0.2185],
- [ 0.6950, -0.3404, 1.2603, -1.1607, -0.5204, -0.9337, 0.4292, 0.1559],
- [ 0.6403, -0.3981, 1.6608, -1.2186, -0.1359, -1.5327, 0.6665, -0.0350]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5865, -0.3973, 1.8423, -0.0688, -0.5192, -0.2305, 0.4162,
- 0.1159],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5695, -0.3927, 1.3226, -0.9002, -0.4672, -1.1928, 0.1737,
- 0.3700],
- [ 0.6016, -0.3633, 1.7152, -0.2228, -0.6115, -0.6385, 0.5028,
- 0.2699],
- [ 0.5671, -0.3988, 1.7499, -0.4614, -0.5423, 0.3007, 0.5894,
- 0.3469],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007],
- [ 0.6132, -0.4122, 1.5478, -1.0619, -0.2709, -1.4314, 0.5500,
- -0.0583]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0192, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0192, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.081544168293476
- step: 28
- running loss: 0.03862657743905272
- Train Steps: 28/90 Loss: 0.0386 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6257, 0.4024, 0.8612, 0.5352, 0.4361, 0.5253, 0.6680, 0.5166],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [0.6265, 0.4088, 0.8025, 0.1850, 0.4163, 0.2500, 0.6290, 0.4947],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6273, 0.4105, 0.8988, 0.4517, 0.3912, 0.2550, 0.5894, 0.4811],
- [0.6236, 0.3977, 0.8985, 0.4806, 0.3835, 0.5216, 0.6613, 0.5166],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483],
- [0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 7.0282e-01, -3.5619e-01, 1.7135e+00, -4.1727e-02, -3.3237e-01,
- 1.0901e-02, 6.6772e-01, 2.1084e-01],
- [ 3.8480e-01, -5.4974e-01, 1.7183e+00, -4.0325e-01, -5.3389e-01,
- -2.2765e-01, 2.6194e-01, 2.6075e-01],
- [ 7.6077e-01, -3.0297e-01, 1.5522e+00, -1.4613e+00, -3.3689e-01,
- -1.3400e+00, 7.1667e-01, 7.7529e-02],
- [ 7.0844e-01, -3.7311e-01, 1.6807e+00, 2.1612e-01, -2.8488e-01,
- -1.6373e-01, 1.3775e-01, 1.9161e-01],
- [ 9.8953e-01, -1.7994e-01, 1.8835e+00, -4.4454e-01, -4.4358e-01,
- -1.2113e+00, 4.9894e-01, 5.9100e-02],
- [ 2.3141e-01, -6.8316e-01, 1.7980e+00, -3.3082e-01, -5.0640e-01,
- 9.5241e-05, 7.1420e-01, 2.7764e-01],
- [ 4.2223e-01, -5.0048e-01, 1.6832e+00, -6.2590e-01, -5.5156e-01,
- -1.7219e-01, 3.4742e-01, 2.4207e-01],
- [ 7.0103e-01, -3.0725e-01, 9.7545e-01, -1.3389e+00, -4.3456e-01,
- -1.3520e+00, 1.6919e-01, 2.4624e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0425e-01, -4.2731e-01, 1.6920e+00, 1.8595e-01, -2.7171e-01,
- 1.4059e-01, 7.9965e-01, 1.0043e-01],
- [ 5.3684e-01, -4.4057e-01, 1.7730e+00, -1.7660e-01, -5.2494e-01,
- -5.3426e-02, 2.3141e-01, 3.4688e-01],
- [ 6.0785e-01, -3.9761e-01, 1.4208e+00, -1.4314e+00, -3.6328e-01,
- -1.1312e+00, 6.1950e-01, -9.2270e-04],
- [ 5.1928e-01, -4.6990e-01, 1.5767e+00, 4.0077e-01, -2.4203e-01,
- 7.7444e-02, 1.1776e-01, -6.1038e-02],
- [ 6.1161e-01, -3.8976e-01, 1.8654e+00, -1.9969e-01, -4.7875e-01,
- -1.1081e+00, 4.3668e-01, -6.3661e-02],
- [ 5.9436e-01, -4.4897e-01, 1.8643e+00, -6.5918e-02, -5.1472e-01,
- 1.2348e-01, 7.6842e-01, 1.0043e-01],
- [ 5.5456e-01, -4.6205e-01, 1.7788e+00, -4.2294e-01, -5.1917e-01,
- -2.2633e-02, 4.2771e-01, 2.4681e-01],
- [ 5.4827e-01, -3.9908e-01, 8.0300e-01, -1.2159e+00, -5.0185e-01,
- -1.1928e+00, 2.6243e-01, 3.8516e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0218, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0218, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1033018305897713
- step: 29
- running loss: 0.03804489070999211
- Train Steps: 29/90 Loss: 0.0380 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6264, 0.4069, 0.7900, 0.1650, 0.4275, 0.2267, 0.6290, 0.4947],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383],
- [0.6204, 0.4049, 0.7975, 0.2700, 0.3937, 0.2567, 0.5700, 0.5183],
- [0.6198, 0.3997, 0.8582, 0.5361, 0.4117, 0.5016, 0.5942, 0.5134],
- [0.6157, 0.4102, 0.8513, 0.3817, 0.3613, 0.3667, 0.5096, 0.5890],
- [0.6177, 0.4086, 0.8738, 0.3950, 0.3775, 0.5600, 0.6225, 0.5700],
- [0.6339, 0.4102, 0.8588, 0.3133, 0.4425, 0.2117, 0.6417, 0.5089]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9041, -0.2328, 1.4413, -1.2928, -0.3322, -1.1886, 0.6267, 0.0711],
- [ 0.7592, -0.2912, 1.6034, -0.9775, -0.1320, -1.3031, 0.6796, 0.1741],
- [-1.2913, -1.6136, 0.9918, -1.1419, -0.4308, -1.3234, 0.0654, 0.2949],
- [ 0.8360, -0.2591, 1.3810, -0.9939, -0.5174, -1.0080, 0.2762, 0.1503],
- [ 0.7807, -0.3532, 1.7547, 0.2277, -0.4817, 0.1521, 0.4084, 0.1783],
- [ 0.7861, -0.2807, 1.6459, -0.4515, -0.6758, -0.4393, 0.1187, 0.3166],
- [ 0.4825, -0.4640, 1.7166, -0.4412, -0.5613, 0.3670, 0.6141, 0.3111],
- [ 0.8659, -0.2588, 1.6902, -0.7670, -0.2796, -1.1250, 0.6830, 0.1375]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0739e-01, -4.0631e-01, 1.3630e+00, -1.5238e+00, -3.1132e-01,
- -1.2390e+00, 6.1950e-01, -9.2270e-04],
- [ 6.4707e-01, -3.8397e-01, 1.5767e+00, -1.0311e+00, -4.5727e-02,
- -1.5007e+00, 6.8892e-01, 1.0199e-01],
- [-2.2859e+00, -2.2859e+00, 1.0712e+00, -1.2085e+00, -3.8060e-01,
- -1.3929e+00, 7.5520e-02, 2.0062e-01],
- [ 5.7985e-01, -4.1555e-01, 1.3977e+00, -1.0388e+00, -4.6721e-01,
- -1.1004e+00, 3.4688e-01, 1.0824e-01],
- [ 5.7673e-01, -4.3957e-01, 1.6782e+00, 1.9046e-01, -3.8437e-01,
- 3.0841e-02, 4.5876e-01, 8.5521e-02],
- [ 5.5813e-01, -3.9120e-01, 1.6460e+00, -5.2302e-01, -6.1732e-01,
- -5.9230e-01, 6.8107e-02, 4.3475e-01],
- [ 5.6715e-01, -3.9885e-01, 1.7499e+00, -4.6143e-01, -5.4226e-01,
- 3.0069e-01, 5.8938e-01, 3.4688e-01],
- [ 6.4212e-01, -3.9120e-01, 1.6806e+00, -8.3865e-01, -2.4203e-01,
- -1.3082e+00, 6.7795e-01, 6.4585e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0340, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0340, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1373316459357738
- step: 30
- running loss: 0.0379110548645258
- Train Steps: 30/90 Loss: 0.0379 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6093, 0.3990, 0.8400, 0.4333, 0.3688, 0.4633, 0.5560, 0.5656],
- [0.6192, 0.3980, 0.7078, 0.2750, 0.4250, 0.2100, 0.5450, 0.5783],
- [ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [ nan, nan, 0.6469, 0.1943, 0.4025, 0.2000, 0.5125, 0.5533],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6193, 0.4034, 0.7757, 0.2347, 0.3733, 0.2919, 0.5930, 0.4926],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679],
- [0.6145, 0.4008, 0.8750, 0.5383, 0.3975, 0.4650, 0.5563, 0.5533]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9908, -0.1592, 1.7502, -0.3743, -0.6669, -0.1327, 0.2648, 0.2209],
- [-0.1769, -0.8809, 1.1025, -1.1944, -0.3271, -1.3661, 0.1683, 0.3038],
- [-0.9808, -1.4234, 1.6078, -1.3676, 0.0597, -1.3673, 0.7059, 0.2835],
- [-1.0429, -1.4503, 0.9365, -1.3842, -0.4058, -1.4962, 0.0476, 0.3043],
- [ 1.3095, 0.0057, 1.8528, 0.1808, -0.6550, -0.1029, 0.3944, 0.2550],
- [ 1.3513, 0.0706, 1.4051, -1.2822, -0.5891, -1.0075, 0.4432, -0.0362],
- [ 1.3579, 0.0215, 1.8816, 0.1004, -0.4905, 0.3744, 0.8456, 0.2072],
- [ 1.0344, -0.1677, 1.8004, 0.2043, -0.4927, -0.0642, 0.3500, 0.2197]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5283, -0.4429, 1.5940, -0.2844, -0.5827, -0.1458, 0.2823,
- 0.3267],
- [ 0.5742, -0.4474, 0.9834, -1.0159, -0.3229, -1.3159, 0.2314,
- 0.3854],
- [-2.2859, -2.2859, 1.7210, -0.9772, 0.1852, -1.3698, 0.7986,
- 0.3104],
- [-2.2859, -2.2859, 0.7023, -1.3883, -0.4268, -1.3621, 0.0813,
- 0.2699],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.5746, -0.4225, 1.2968, -1.2019, -0.5615, -0.9374, 0.4533,
- -0.0108],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371],
- [ 0.5526, -0.4347, 1.7557, 0.2006, -0.4499, -0.1381, 0.2834,
- 0.2699]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1383, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1383, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2756343595683575
- step: 31
- running loss: 0.04114949546994701
- Train Steps: 31/90 Loss: 0.0411 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4079, 0.8025, 0.2500, 0.3763, 0.3217, 0.6125, 0.5533],
- [0.6117, 0.4018, 0.6562, 0.1967, 0.3738, 0.2550, 0.5280, 0.5103],
- [0.6357, 0.4097, 0.9038, 0.3883, 0.4213, 0.2950, 0.6686, 0.5390],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083],
- [0.6211, 0.4069, 0.8750, 0.5117, 0.4150, 0.4900, 0.5875, 0.5167],
- [0.6168, 0.4055, 0.7575, 0.2950, 0.4050, 0.2717, 0.5938, 0.6167],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [0.6236, 0.3977, 0.8985, 0.4806, 0.3835, 0.5216, 0.6613, 0.5166]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7756, -0.2943, 1.5082, -1.1577, -0.5133, -0.9230, 0.4684, 0.1807],
- [ 0.3804, -0.5261, 0.9433, -1.4339, -0.4707, -1.3062, 0.1094, 0.1210],
- [ 0.6888, -0.3697, 1.7758, -0.6519, -0.3946, -1.1142, 0.5529, 0.1363],
- [ 0.6840, -0.3646, 1.5596, 0.2019, -0.4157, -0.2509, 0.2369, 0.3986],
- [ 0.8092, -0.3029, 1.7801, -0.0520, -0.4425, -0.0876, 0.3524, 0.1252],
- [ 0.6672, -0.3370, 1.2757, -1.0965, -0.4721, -1.1377, 0.3404, 0.3186],
- [ 0.3779, -0.5995, 1.8432, -0.2514, -0.4791, 0.1413, 0.7156, 0.2372],
- [ 0.3615, -0.6130, 1.7762, -0.2651, -0.5145, 0.0278, 0.5792, 0.2315]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5788, -0.4020, 1.4208, -1.1312, -0.5480, -0.8002, 0.5432,
- 0.2699],
- [ 0.5393, -0.4299, 0.7453, -1.3775, -0.5596, -1.1081, 0.1527,
- 0.0712],
- [ 0.6504, -0.3936, 1.8885, -0.4922, -0.3402, -0.9233, 0.8022,
- 0.2035],
- [ 0.6134, -0.3910, 1.4497, 0.3546, -0.3517, -0.0919, 0.3296,
- 0.5239],
- [ 0.5828, -0.4066, 1.7557, 0.0774, -0.3691, -0.0226, 0.4277,
- 0.1005],
- [ 0.5631, -0.4129, 1.2129, -0.9233, -0.4152, -1.0311, 0.4566,
- 0.5624],
- [ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554],
- [ 0.5944, -0.4490, 1.8643, -0.0659, -0.5147, 0.1235, 0.7684,
- 0.1004]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0165, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0165, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2920843735337257
- step: 32
- running loss: 0.04037763667292893
- Train Steps: 32/90 Loss: 0.0404 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6205, 0.4004, 0.8938, 0.4883, 0.3663, 0.5000, 0.6357, 0.4947],
- [0.6177, 0.4086, 0.8738, 0.3950, 0.3775, 0.5600, 0.6225, 0.5700],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7272, -0.3183, 1.7046, -0.5656, -0.4559, -1.0896, 0.1794, 0.1725],
- [ 0.4934, -0.5156, 1.8159, -0.1265, -0.5439, -0.0894, 0.4923, 0.2124],
- [ 0.6612, -0.3344, 1.7433, -0.5302, -0.4852, 0.2510, 0.5717, 0.3081],
- [ 0.7566, -0.2804, 1.0319, -1.3667, -0.4885, -1.1770, 0.3090, 0.1345],
- [ 0.6595, -0.3936, 1.5598, 0.0052, -0.4803, -0.1097, 0.5725, 0.2791],
- [-0.9094, -1.3789, 1.0018, -1.3468, -0.2830, -1.5701, 0.1808, 0.3358],
- [ 0.7135, -0.3099, 1.2355, -1.1843, -0.5799, -0.8957, 0.4245, 0.2362],
- [ 0.8254, -0.3237, 1.8059, 0.2547, -0.4740, -0.2230, 0.5220, 0.1259]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.4850e-01, -4.2094e-01, 1.6691e+00, -4.1524e-01, -5.2494e-01,
- -1.1081e+00, 7.2521e-02, 2.0831e-03],
- [ 5.8031e-01, -4.3672e-01, 1.8423e+00, -3.0331e-02, -5.9423e-01,
- 2.3557e-02, 6.5034e-01, -9.2270e-04],
- [ 5.6715e-01, -3.9885e-01, 1.7499e+00, -4.6143e-01, -5.4226e-01,
- 3.0069e-01, 5.8938e-01, 3.4688e-01],
- [ 5.0531e-01, -4.2810e-01, 8.9538e-01, -1.3698e+00, -5.4226e-01,
- -1.1389e+00, 2.4525e-01, 8.6245e-02],
- [ 6.2895e-01, -4.3453e-01, 1.3794e+00, 3.6792e-01, -4.8453e-01,
- 3.8953e-02, 9.2654e-01, 1.9283e-01],
- [-2.2859e+00, -2.2859e+00, 6.7598e-01, -1.4083e+00, -3.2864e-01,
- -1.4160e+00, 2.4873e-01, 3.4688e-01],
- [ 5.4660e-01, -4.0805e-01, 1.0668e+00, -1.1764e+00, -6.2887e-01,
- -7.6166e-01, 4.8545e-01, 3.0069e-01],
- [ 6.3949e-01, -4.2132e-01, 1.7037e+00, 3.6228e-01, -4.3256e-01,
- -1.0731e-01, 6.5602e-01, -4.8817e-03]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0586, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0586, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3506991527974606
- step: 33
- running loss: 0.0409302773574988
- Train Steps: 33/90 Loss: 0.0409 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6101, 0.3977, 0.8550, 0.5667, 0.3862, 0.4417, 0.5128, 0.4869],
- [0.6179, 0.4040, 0.7412, 0.1850, 0.3825, 0.2783, 0.5837, 0.5600],
- [0.6202, 0.4054, 0.8892, 0.4428, 0.3642, 0.3249, 0.5840, 0.5426],
- [0.6190, 0.4135, 0.8000, 0.4883, 0.3566, 0.3647, 0.5613, 0.5900],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6176, 0.4017, 0.8788, 0.5100, 0.4075, 0.5250, 0.5913, 0.5033],
- [0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3825, -0.5192, 1.5531, -0.5483, -0.5774, -0.2306, 0.2690, 0.1452],
- [ 0.3526, -0.5745, 1.5906, 0.1417, -0.4948, -0.4045, 0.2288, 0.1579],
- [ 0.5171, -0.4502, 1.0703, -1.4119, -0.5305, -1.0881, 0.4877, 0.2627],
- [ 0.4865, -0.4936, 1.6517, -0.4755, -0.5666, -0.7995, 0.4503, 0.1849],
- [ 0.8655, -0.1874, 1.4872, -0.2307, -0.6180, -0.7052, 0.3166, 0.3255],
- [ 0.4165, -0.5100, 1.6322, -0.2517, -0.3476, -0.0160, 0.4465, 0.2920],
- [ 0.4606, -0.5201, 1.6801, -0.0939, -0.3939, 0.0481, 0.4526, 0.2095],
- [ 0.4394, -0.4942, 1.3631, -1.2998, -0.2485, -1.3518, 0.7349, 0.1318]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.5320, -0.4488, 1.6633, 0.3315, -0.5018, -0.2459, 0.0828,
- -0.0370],
- [ 0.5680, -0.4201, 1.1379, -1.4314, -0.5192, -1.0003, 0.4104,
- 0.3007],
- [ 0.5788, -0.4135, 1.8214, -0.2408, -0.6039, -0.7854, 0.4115,
- 0.2203],
- [ 0.5732, -0.3761, 1.4092, -0.0303, -0.6388, -0.6012, 0.3065,
- 0.4393],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.5668, -0.4306, 1.7730, 0.0697, -0.4037, 0.1390, 0.4450,
- 0.0390],
- [ 0.6158, -0.3960, 1.4092, -1.2774, -0.2074, -1.1851, 0.8491,
- 0.1917]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3644524635747075
- step: 34
- running loss: 0.04013095481102081
- Train Steps: 34/90 Loss: 0.0401 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6109, 0.4009, 0.8725, 0.4700, 0.3550, 0.4167, 0.5650, 0.5117],
- [0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285],
- [0.6110, 0.4047, 0.8700, 0.4483, 0.3713, 0.3967, 0.5088, 0.5517],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6145, 0.4007, 0.8775, 0.4533, 0.4562, 0.5533, 0.6088, 0.5533],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083],
- [0.6115, 0.4081, 0.6725, 0.2433, 0.4088, 0.1933, 0.5167, 0.5544]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5411, -0.4536, 1.7278, -0.3899, -0.5399, -0.1135, 0.6217, 0.1842],
- [ 0.3986, -0.5291, 1.6807, -0.3776, -0.6092, -0.5501, 0.3713, 0.1663],
- [ 0.6661, -0.3267, 1.3198, -0.6602, -0.6227, -0.4715, 0.1822, 0.1441],
- [ 0.3818, -0.5150, 1.5559, -0.4041, -0.6201, -0.5724, 0.1614, 0.2175],
- [ 0.3520, -0.6014, 1.6483, 0.0400, -0.4569, -0.2901, 0.5195, 0.1823],
- [ 0.7070, -0.3190, 1.6861, -0.3544, -0.1871, 0.0213, 0.5974, 0.2302],
- [ 0.5853, -0.4072, 1.4730, 0.1342, -0.3969, -0.3106, 0.5202, 0.4160],
- [ 0.1553, -0.6436, 0.8552, -1.3981, -0.4279, -1.4607, 0.2782, 0.2042]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.5356, -0.4343, 1.7441, -0.1150, -0.6462, -0.3614, 0.3238,
- 0.0774],
- [ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552],
- [ 0.5363, -0.4168, 1.7326, -0.2151, -0.5711, -0.4537, 0.0640,
- 0.2622],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.5526, -0.4351, 1.7672, -0.1920, -0.1785, 0.2699, 0.5259,
- 0.2699],
- [ 0.6134, -0.3910, 1.4497, 0.3546, -0.3517, -0.0919, 0.3296,
- 0.5239],
- [ 0.5385, -0.4010, 0.8203, -1.1620, -0.3979, -1.3929, 0.1005,
- 0.2747]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0201, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0201, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3845558362081647
- step: 35
- running loss: 0.03955873817737613
- Train Steps: 35/90 Loss: 0.0396 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6293, 0.3982, 0.8700, 0.5300, 0.3763, 0.4717, 0.7050, 0.5297],
- [0.6136, 0.4117, 0.8700, 0.5167, 0.4188, 0.5083, 0.5147, 0.5495],
- [0.6280, 0.4101, 0.9050, 0.4533, 0.3775, 0.3217, 0.6338, 0.4915],
- [0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6114, 0.4018, 0.7213, 0.1967, 0.3763, 0.2700, 0.5875, 0.5533],
- [0.6250, 0.4106, 0.8700, 0.3717, 0.3588, 0.4967, 0.6038, 0.5167],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2897, -0.6276, 1.5647, -0.0474, -0.4891, -0.2550, 0.5759, 0.1923],
- [ 0.5408, -0.4012, 1.5358, 0.0289, -0.3157, -0.0208, 0.1720, 0.2009],
- [ 0.5524, -0.4431, 1.6840, -0.4082, -0.5403, -0.9415, 0.4238, 0.0553],
- [ 0.4974, -0.4879, 1.6445, -0.0998, -0.4748, -0.0273, 0.3334, 0.1735],
- [ 0.5754, -0.4577, 1.6794, -0.2655, -0.5683, -0.5576, 0.6392, 0.2151],
- [ 0.2067, -0.6470, 0.9056, -1.5566, -0.5469, -1.1697, 0.3692, 0.1897],
- [ 0.6249, -0.3607, 1.5125, -0.7481, -0.5763, -0.1067, 0.5937, 0.2508],
- [ 0.3513, -0.5097, 1.4105, 0.1475, -0.3911, -0.3318, 0.2325, 0.4419]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6207, -0.4466, 1.7326, 0.1621, -0.5480, -0.1073, 0.9704,
- 0.1608],
- [ 0.5482, -0.3841, 1.7326, 0.1005, -0.3517, 0.0620, 0.0912,
- 0.2522],
- [ 0.6148, -0.3918, 1.8942, -0.1920, -0.5423, -0.8002, 0.6414,
- -0.0156],
- [ 0.5783, -0.4363, 1.7724, 0.0432, -0.5153, 0.0871, 0.4840,
- 0.0663],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [ 0.5380, -0.4302, 1.0455, -1.3775, -0.5480, -1.0388, 0.4277,
- 0.2699],
- [ 0.6010, -0.3896, 1.7326, -0.5692, -0.6289, 0.0082, 0.5028,
- 0.1005],
- [ 0.5836, -0.3649, 1.7210, 0.3854, -0.3979, -0.2921, 0.3065,
- 0.4470]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0228, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0228, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4073562482371926
- step: 36
- running loss: 0.039093229117699795
- Train Steps: 36/90 Loss: 0.0391 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6179, 0.4008, 0.7505, 0.2678, 0.4368, 0.1891, 0.5831, 0.5263],
- [0.6124, 0.4075, 0.7696, 0.4153, 0.3475, 0.3767, 0.5157, 0.5427],
- [0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [ nan, nan, 0.7097, 0.2346, 0.4250, 0.1850, 0.5175, 0.5583],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6293, 0.3982, 0.8700, 0.5300, 0.3763, 0.4717, 0.7050, 0.5297]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6089, -0.3586, 0.9983, -1.0574, -0.4327, -1.0262, 0.4522, 0.3104],
- [ 0.7212, -0.3187, 1.2293, -1.0060, -0.4093, -1.1530, 0.3823, 0.1102],
- [ 0.9051, -0.1809, 1.4924, -0.3170, -0.7052, -0.3290, 0.1440, 0.1669],
- [ 0.1922, -0.6328, 0.9536, -0.8334, -0.5797, -0.8038, 0.2082, 0.3640],
- [ 0.9224, -0.2444, 2.0366, 0.1023, -0.4859, 0.4914, 0.9817, 0.1094],
- [-2.0891, -2.1434, 1.0060, -1.0435, -0.4197, -1.1671, 0.2153, 0.2539],
- [ 0.3065, -0.5146, 1.1554, -0.6968, -0.1279, -1.0569, 0.2675, 0.3804],
- [ 0.7788, -0.3376, 1.8395, 0.3493, -0.5287, 0.0480, 0.6786, 0.0816]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.5680, -0.4345, 1.1806, -1.0490, -0.2683, -1.4127, 0.4074,
- 0.1449],
- [ 0.5427, -0.4035, 1.2688, -0.3675, -0.6808, -0.5461, 0.0959,
- 0.2206],
- [ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085],
- [ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554],
- [-2.2859, -2.2859, 0.9922, -1.2021, -0.3229, -1.4314, 0.1044,
- 0.2930],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [ 0.6207, -0.4466, 1.7326, 0.1621, -0.5480, -0.1073, 0.9704,
- 0.1608]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0270, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0270, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4343694495037198
- step: 37
- running loss: 0.038766741878478915
- Train Steps: 37/90 Loss: 0.0388 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6113, 0.4088, 0.6859, 0.2208, 0.4363, 0.1700, 0.5188, 0.5533],
- [0.6161, 0.4055, 0.8675, 0.3867, 0.3713, 0.4033, 0.5195, 0.5162],
- [0.6117, 0.4018, 0.6562, 0.1967, 0.3738, 0.2550, 0.5280, 0.5103],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6272, 0.4120, 0.9038, 0.4117, 0.3725, 0.3200, 0.6175, 0.5250],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.0315, -1.4505, 0.8585, -1.1804, -0.3616, -1.3684, 0.2596, 0.2981],
- [ 0.3760, -0.4742, 1.6197, -0.2871, -0.5844, -0.3497, 0.1494, 0.1322],
- [ 0.2731, -0.5802, 0.7607, -1.3135, -0.4940, -1.0363, 0.2531, 0.2214],
- [ 0.6946, -0.3087, 1.6473, 0.2415, -0.1956, 0.1741, 0.4767, 0.1525],
- [ 0.7139, -0.3329, 1.4853, 0.1988, -0.4484, 0.0563, 0.7421, 0.2211],
- [ 0.5019, -0.4580, 1.7091, -0.0458, -0.5463, -0.2297, 0.5223, 0.2264],
- [ 0.7135, -0.3331, 1.8205, -0.2745, -0.5317, -0.6166, 0.6577, 0.1887],
- [ 0.4978, -0.4209, 1.3890, -0.5404, -0.6099, -0.6694, 0.2540, 0.2692]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5377, -0.3978, 0.8824, -1.2663, -0.2709, -1.5007, 0.1102,
- 0.2699],
- [ 0.5598, -0.4129, 1.7210, -0.4999, -0.5711, -0.4229, 0.1136,
- 0.0983],
- [ 0.5393, -0.4299, 0.7453, -1.3775, -0.5596, -1.1081, 0.1527,
- 0.0712],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.6289, -0.4345, 1.3794, 0.3679, -0.4845, 0.0390, 0.9265,
- 0.1928],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.6111, -0.3828, 1.8885, -0.3844, -0.5654, -0.8079, 0.5663,
- 0.1390],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0676, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0676, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.501950633712113
- step: 38
- running loss: 0.03952501667663455
- Train Steps: 38/90 Loss: 0.0395 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6203, 0.4056, 0.8942, 0.4086, 0.3643, 0.3617, 0.5917, 0.5482],
- [ nan, nan, 0.8938, 0.2850, 0.4662, 0.3117, 0.7406, 0.5528],
- [0.6159, 0.4085, 0.6900, 0.2283, 0.4088, 0.1950, 0.5123, 0.5397],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6200, 0.4059, 0.8700, 0.4900, 0.4163, 0.5000, 0.6162, 0.5467],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837],
- [0.6284, 0.4029, 0.8838, 0.3783, 0.3975, 0.2850, 0.6335, 0.5090]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6832, -0.3513, 1.7042, -0.1977, -0.6291, -0.4616, 0.5176, 0.1659],
- [-0.7401, -1.2939, 1.7845, -0.6514, -0.1847, -0.8358, 1.0248, 0.2050],
- [-0.5645, -1.1050, 0.7261, -1.0714, -0.4450, -1.3088, 0.0884, 0.2421],
- [ 0.6336, -0.3166, 1.6069, 0.0188, -0.1684, 0.2818, 0.4854, 0.2356],
- [ 0.6863, -0.3263, 1.6596, 0.1793, -0.3551, 0.1340, 0.4881, 0.2052],
- [ 0.6414, -0.3296, 1.4893, -0.1288, -0.5712, -0.1099, 0.2392, 0.1740],
- [ 0.4698, -0.4321, 0.7184, -0.8961, -0.6497, -0.8492, 0.1134, 0.3681],
- [ 0.7837, -0.2923, 1.5828, -0.4301, -0.5045, -0.8921, 0.5768, 0.0597]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5791, -0.4127, 1.8442, -0.3985, -0.6031, -0.6154, 0.4473,
- 0.2464],
- [-2.2859, -2.2859, 1.8423, -0.9695, -0.1323, -0.8463, 1.1349,
- 0.2676],
- [ 0.5591, -0.3990, 0.9012, -1.2313, -0.3979, -1.3852, 0.0804,
- 0.2071],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.5776, -0.4109, 1.7326, -0.0226, -0.3633, 0.0236, 0.5605,
- 0.2391],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103],
- [ 0.6165, -0.4249, 1.7961, -0.5384, -0.4499, -0.9695, 0.6401,
- 0.0652]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0914, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0914, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5933644147589803
- step: 39
- running loss: 0.04085549781433283
- Train Steps: 39/90 Loss: 0.0409 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4013, 0.8191, 0.3188, 0.4279, 0.2060, 0.5767, 0.5411],
- [0.6284, 0.4127, 0.8538, 0.5867, 0.4363, 0.5083, 0.6038, 0.5433],
- [0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683],
- [0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390],
- [0.6179, 0.4040, 0.7412, 0.1850, 0.3825, 0.2783, 0.5837, 0.5600],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6166, 0.4008, 0.8563, 0.5667, 0.4388, 0.4933, 0.5575, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5238, -0.3950, 1.2829, -0.8842, -0.3780, -1.2504, 0.3666, 0.1162],
- [ 0.2301, -0.5965, 1.5215, 0.3388, -0.3021, -0.0835, 0.4180, 0.1480],
- [ 0.3454, -0.4980, 1.4978, 0.1416, -0.5490, -0.5785, 0.3343, 0.2566],
- [ 0.5529, -0.4195, 1.7595, -0.3212, -0.4820, 0.2027, 0.8572, 0.1335],
- [ 0.3021, -0.5822, 1.6934, -0.0044, -0.4441, -0.0950, 0.3973, 0.1461],
- [ 0.3611, -0.5352, 0.9858, -1.2857, -0.5901, -1.0377, 0.4483, 0.2478],
- [ 0.2816, -0.5578, 1.3447, -0.7849, -0.6097, -0.5858, 0.5139, 0.3761],
- [ 0.3448, -0.5351, 1.4953, 0.3681, -0.2450, -0.1087, 0.2777, 0.2207]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.4321, 1.4975, -0.8134, -0.3094, -1.3345, 0.3779,
- 0.2134],
- [ 0.6166, -0.3795, 1.6575, 0.4239, -0.2709, 0.0620, 0.5028,
- 0.2237],
- [ 0.5777, -0.3915, 1.7961, 0.1698, -0.5192, -0.5307, 0.2141,
- 0.3392],
- [ 0.5886, -0.4253, 1.8654, -0.3460, -0.5480, 0.3623, 0.6587,
- 0.1005],
- [ 0.5250, -0.4661, 1.7383, -0.0765, -0.4268, -0.0226, 0.2535,
- 0.2035],
- [ 0.5680, -0.4201, 1.1379, -1.4314, -0.5192, -1.0003, 0.4104,
- 0.3007],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.5620, -0.4346, 1.6691, 0.3315, -0.2594, -0.0072, 0.2891,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0166, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0166, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.610002956353128
- step: 40
- running loss: 0.0402500739088282
- Train Steps: 40/90 Loss: 0.0403 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334],
- [0.6132, 0.4118, 0.8200, 0.3633, 0.3563, 0.5400, 0.5787, 0.5136],
- [0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168],
- [0.6277, 0.4029, 0.8250, 0.2433, 0.4325, 0.2100, 0.6366, 0.5207],
- [0.6176, 0.4030, 0.8850, 0.4850, 0.3688, 0.4050, 0.5312, 0.5783],
- [0.6229, 0.4066, 0.7612, 0.2967, 0.4250, 0.2167, 0.5925, 0.5350],
- [0.6274, 0.4003, 0.8638, 0.5967, 0.3688, 0.4900, 0.6108, 0.4661],
- [0.6223, 0.4130, 0.8100, 0.2983, 0.3525, 0.3900, 0.5694, 0.5103]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5868, -0.4193, 1.2456, -0.9690, -0.5729, -0.7326, 0.5721, 0.2117],
- [ 0.3966, -0.4664, 1.5337, -0.4057, -0.4873, 0.2182, 0.5452, 0.3014],
- [-0.0431, -0.8257, 0.8331, -1.1309, -0.4279, -1.1973, 0.2385, 0.2226],
- [ 0.3674, -0.5902, 1.4668, -0.8268, -0.2956, -1.1755, 0.6494, 0.1242],
- [ 0.3705, -0.5327, 1.7229, 0.2829, -0.4762, -0.2262, 0.2493, 0.2158],
- [ 0.4006, -0.4981, 1.2489, -0.7730, -0.3574, -1.0831, 0.3577, 0.2296],
- [ 0.3442, -0.6000, 1.7299, 0.7331, -0.4478, 0.0127, 0.5833, 0.1481],
- [ 0.3361, -0.5357, 1.5195, -0.5111, -0.5457, -0.3380, 0.4007, 0.2688]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5730, -0.4562, 1.2195, -1.2440, -0.5497, -0.7711, 0.5704,
- 0.1779],
- [ 0.5466, -0.3840, 1.5016, -0.6077, -0.6404, 0.2083, 0.3871,
- 0.0862],
- [ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013],
- [ 0.6132, -0.4248, 1.5247, -1.1620, -0.2882, -1.3159, 0.6545,
- 0.1193],
- [ 0.5667, -0.4246, 1.8018, -0.0457, -0.5827, -0.4152, 0.1679,
- 0.3854],
- [ 0.5911, -0.4080, 1.2303, -0.9156, -0.3229, -1.2851, 0.4508,
- 0.1852],
- [ 0.6120, -0.4371, 1.7037, 0.4701, -0.5827, -0.0226, 0.5354,
- -0.1331],
- [ 0.5886, -0.3784, 1.4554, -0.9079, -0.6577, -0.4845, 0.3440,
- 0.0712]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0313, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0313, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6413122108206153
- step: 41
- running loss: 0.040032005141966226
- Train Steps: 41/90 Loss: 0.0400 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6257, 0.4060, 0.8300, 0.2333, 0.4688, 0.2583, 0.7050, 0.5633],
- [0.6163, 0.4001, 0.8788, 0.5033, 0.4012, 0.4633, 0.5338, 0.5767],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6271, 0.4040, 0.9000, 0.3250, 0.4938, 0.2300, 0.7192, 0.5413],
- [ nan, nan, 0.8900, 0.3217, 0.5038, 0.2233, 0.6694, 0.5609],
- [0.6263, 0.4029, 0.8900, 0.4933, 0.4075, 0.5183, 0.6406, 0.4758]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7870, -0.2595, 1.2943, -1.1052, -0.3462, -1.0785, 0.7152, 0.2656],
- [ 0.4789, -0.4572, 1.4920, 0.2105, -0.5343, -0.1205, 0.1726, 0.2225],
- [ 0.6183, -0.3261, 1.4758, 0.0889, -0.4249, 0.0699, 0.1858, 0.2688],
- [ 0.7325, -0.2322, 1.4474, -0.0852, -0.2401, 0.0818, 0.2022, 0.2692],
- [ 0.6747, -0.3397, 1.6252, -0.1277, -0.6790, -0.1723, 0.4741, 0.0560],
- [ 0.2175, -0.6533, 1.6239, -0.7552, -0.1979, -1.1170, 0.7941, 0.1465],
- [-1.5191, -1.7626, 1.4842, -0.8116, -0.1347, -1.1538, 0.5951, 0.3030],
- [ 0.6647, -0.3226, 1.5790, 0.1086, -0.5153, 0.0848, 0.4939, 0.1292]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6042, -0.4105, 1.5478, -1.2082, -0.1208, -1.0927, 0.9704,
- 0.3157],
- [ 0.5610, -0.4381, 1.7730, 0.0390, -0.4326, -0.1458, 0.1794,
- 0.3777],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.6108, -0.4201, 1.8711, -0.7848, -0.0053, -1.2236, 1.0362,
- 0.2142],
- [-2.2859, -2.2859, 1.8249, -0.8002, 0.0409, -1.2543, 0.8059,
- 0.3050],
- [ 0.6072, -0.4250, 1.8249, -0.0072, -0.4037, 0.1082, 0.6730,
- -0.0881]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0408, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0408, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.682096759788692
- step: 42
- running loss: 0.040049922852111716
- Train Steps: 42/90 Loss: 0.0400 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585],
- [0.6228, 0.4004, 0.8750, 0.5250, 0.3825, 0.5233, 0.6362, 0.5000],
- [0.6151, 0.4058, 0.7068, 0.2680, 0.3400, 0.4083, 0.5775, 0.5733],
- [0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6226, 0.4001, 0.8438, 0.5733, 0.3862, 0.4250, 0.5750, 0.5283],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5004, -0.4306, 1.6711, -0.3141, -0.1863, 0.1056, 0.4462, 0.1948],
- [ 0.3833, -0.5706, 1.7636, 0.1744, -0.3820, -0.0903, 0.6075, 0.1104],
- [ 0.7137, -0.3340, 1.0047, -1.1597, -0.6519, -0.5842, 0.4302, 0.3509],
- [ 0.2005, -0.6191, 1.5591, -0.4497, -0.5971, -1.0111, 0.1772, 0.1835],
- [ 0.3202, -0.6021, 1.6128, 0.3261, -0.4215, -0.4935, 0.3956, 0.1628],
- [ 0.4754, -0.4941, 1.8339, -0.1144, -0.4388, -0.0946, 0.6142, 0.1738],
- [ 0.4219, -0.4805, 1.3762, -0.5846, -0.5242, -1.1056, 0.2978, 0.3035],
- [ 0.3386, -0.5356, 1.7691, -0.1034, -0.1531, 0.1973, 0.6431, 0.2470]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936],
- [ 0.5908, -0.4366, 1.7557, 0.1390, -0.5192, 0.1313, 0.6529,
- 0.0236],
- [ 0.5552, -0.4113, 0.9790, -1.0480, -0.7155, -0.3998, 0.3815,
- 0.3623],
- [ 0.5470, -0.3952, 1.6377, -0.4253, -0.6289, -0.8079, 0.0249,
- 0.2116],
- [ 0.5900, -0.4377, 1.6113, 0.3623, -0.5018, -0.3229, 0.3700,
- 0.1544],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0167, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0167, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6988404570147395
- step: 43
- running loss: 0.03950791760499394
- Train Steps: 43/90 Loss: 0.0395 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6277, 0.4029, 0.8250, 0.2433, 0.4325, 0.2100, 0.6366, 0.5207],
- [0.6263, 0.4029, 0.8900, 0.4933, 0.4075, 0.5183, 0.6406, 0.4758],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6128, 0.4118, 0.8638, 0.5333, 0.4625, 0.5267, 0.5193, 0.5475],
- [0.6163, 0.4001, 0.8788, 0.5033, 0.4012, 0.4633, 0.5338, 0.5767],
- [0.6314, 0.4050, 0.8227, 0.5431, 0.4150, 0.5517, 0.7121, 0.5690],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483],
- [0.6201, 0.4098, 0.8575, 0.3617, 0.3700, 0.3550, 0.5988, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7240, -0.3647, 1.4197, -1.1986, -0.3715, -1.4628, 0.6403, 0.0908],
- [ 0.3467, -0.5586, 1.7756, -0.1293, -0.4171, -0.0376, 0.5929, 0.1243],
- [ 0.5438, -0.4304, 1.6915, 0.2665, -0.4031, -0.1974, 0.4155, 0.2774],
- [ 0.4697, -0.4414, 1.6029, 0.1337, -0.1995, -0.1209, 0.1611, 0.2092],
- [ 0.1763, -0.6875, 1.6918, -0.0366, -0.4339, -0.2375, 0.3021, 0.2240],
- [ 0.3239, -0.5864, 1.5668, 0.0814, -0.3454, 0.1129, 0.7768, 0.3226],
- [ 0.5229, -0.4454, 1.6353, -0.4913, -0.5194, -0.0920, 0.3297, 0.1946],
- [ 0.4346, -0.4854, 1.6011, -0.7661, -0.5156, -0.7754, 0.4753, 0.2727]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6132, -0.4248, 1.5247, -1.1620, -0.2882, -1.3159, 0.6545,
- 0.1193],
- [ 0.6072, -0.4250, 1.8249, -0.0072, -0.4037, 0.1082, 0.6730,
- -0.0881],
- [ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.5447, -0.3838, 1.7037, 0.1775, -0.1497, 0.1467, 0.1128,
- 0.2431],
- [ 0.5610, -0.4381, 1.7730, 0.0390, -0.4326, -0.1458, 0.1794,
- 0.3777],
- [ 0.6306, -0.4153, 1.5141, 0.2224, -0.3691, 0.2622, 1.0033,
- 0.3425],
- [ 0.5546, -0.4620, 1.7788, -0.4229, -0.5192, -0.0226, 0.4277,
- 0.2468],
- [ 0.5783, -0.3933, 1.6748, -0.6154, -0.5769, -0.6462, 0.4797,
- 0.3315]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0154, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0154, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7142196567729115
- step: 44
- running loss: 0.03895953765392981
- Train Steps: 44/90 Loss: 0.0390 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482],
- [0.6138, 0.4054, 0.8750, 0.4750, 0.4363, 0.5017, 0.5086, 0.5822],
- [0.6164, 0.3972, 0.7818, 0.3381, 0.3599, 0.3387, 0.5880, 0.5153],
- [0.6185, 0.4079, 0.8838, 0.4617, 0.4838, 0.5650, 0.6175, 0.5850],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433],
- [0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [0.6271, 0.4005, 0.8450, 0.6067, 0.3850, 0.4983, 0.6069, 0.4649]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.8981, -2.0774, 1.7373, -1.0459, 0.1501, -1.2919, 0.9771, 0.2549],
- [ 0.6542, -0.3248, 1.6869, 0.0199, -0.3070, 0.0515, 0.1334, 0.2514],
- [ 0.7948, -0.2716, 1.4147, -0.7527, -0.6649, -0.6969, 0.3543, 0.1676],
- [ 0.5800, -0.3888, 1.8255, -0.1103, -0.1156, 0.1581, 0.5761, 0.3198],
- [ 0.7730, -0.2810, 1.3485, -0.9682, -0.6464, -0.4463, 0.5706, 0.2872],
- [ 0.5955, -0.4088, 1.9148, 0.0193, -0.5055, 0.0592, 0.5711, 0.1925],
- [ 0.5736, -0.3881, 0.9811, -1.1537, -0.4690, -1.4067, 0.1148, 0.1811],
- [ 0.9616, -0.1831, 1.7293, 0.5908, -0.5086, -0.0755, 0.4916, 0.0809]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.6517, -1.2620, 0.2141, -1.1928, 1.1166,
- 0.2463],
- [ 0.5491, -0.4132, 1.7557, -0.0919, -0.2709, 0.0313, 0.0635,
- 0.4032],
- [ 0.5614, -0.4514, 1.3252, -0.7240, -0.6237, -0.7215, 0.4299,
- 0.0942],
- [ 0.5707, -0.4017, 1.7961, -0.1535, -0.0515, 0.3238, 0.5663,
- 0.4162],
- [ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237],
- [ 0.5109, -0.4316, 1.1436, -1.3467, -0.3864, -1.4160, 0.1239,
- -0.0580],
- [ 0.6105, -0.4360, 1.6171, 0.5162, -0.5076, 0.0159, 0.5171,
- -0.1385]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0156, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0156, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7298358231782913
- step: 45
- running loss: 0.0384407960706287
- Train Steps: 45/90 Loss: 0.0384 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552],
- [0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250],
- [0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6248, 0.4032, 0.7738, 0.1900, 0.4813, 0.1400, 0.5941, 0.4904],
- [0.6168, 0.4029, 0.8523, 0.3417, 0.3588, 0.5000, 0.6125, 0.5400],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.2043, -2.2418, 1.7081, -0.8358, -0.0276, -1.0152, 0.8084, 0.2407],
- [ 0.5243, -0.4429, 1.9431, -0.0100, -0.6092, 0.0366, 0.5699, 0.1601],
- [ 0.6887, -0.3278, 1.3823, -0.8020, -0.3371, -1.0292, 0.6584, 0.2193],
- [ 0.6709, -0.3032, 1.7437, 0.0247, -0.1854, 0.2688, 0.3536, 0.2386],
- [ 0.9483, -0.1798, 1.2172, -0.9029, -0.1352, -1.4696, 0.4495, 0.1328],
- [ 0.6183, -0.3735, 1.6392, -0.4924, -0.5940, 0.1497, 0.6687, 0.2686],
- [ 0.6845, -0.2976, 1.3550, -0.4397, -0.6997, -0.2449, 0.1676, 0.2389],
- [ 0.7395, -0.2648, 1.2949, -0.8578, -0.2455, -1.1228, 0.3436, 0.2488]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.7557, -1.1466, 0.0871, -1.0773, 1.1239,
- 0.2783],
- [ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390],
- [ 0.6174, -0.4225, 1.4975, -1.1709, -0.3174, -1.1806, 0.6539,
- 0.1879],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853],
- [ 0.5999, -0.4236, 1.2880, -1.4083, -0.0630, -1.6393, 0.4584,
- -0.0208],
- [ 0.5631, -0.4249, 1.6509, -0.7078, -0.6289, 0.0236, 0.5432,
- 0.2083],
- [ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386],
- [ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0250, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0250, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7548013236373663
- step: 46
- running loss: 0.038147854861681874
- Train Steps: 46/90 Loss: 0.0381 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6127, 0.4119, 0.8650, 0.5300, 0.4512, 0.5250, 0.5260, 0.5461],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967],
- [ nan, nan, 0.7425, 0.2117, 0.3937, 0.2433, 0.5438, 0.5567],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6060, 0.3924, 0.8450, 0.5717, 0.4200, 0.5217, 0.5253, 0.4752],
- [0.6293, 0.3982, 0.8700, 0.5300, 0.3763, 0.4717, 0.7050, 0.5297],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5808, -0.4027, 1.7523, 0.1851, -0.2221, 0.0181, 0.2749, 0.2247],
- [ 0.1685, -0.6521, 1.8133, -0.9287, -0.1096, -1.2804, 0.6237, 0.1164],
- [ 0.5971, -0.3892, 1.8052, -0.2200, -0.3549, 0.2286, 0.5063, 0.3570],
- [-1.4883, -1.7690, 1.2798, -1.1738, -0.4054, -1.2219, 0.2785, 0.2763],
- [ 0.4992, -0.4773, 1.9323, -0.2267, -0.4326, 0.3214, 0.7804, 0.2136],
- [ 0.7130, -0.3289, 1.7113, 0.3647, -0.3429, 0.0021, 0.2070, 0.1561],
- [ 0.6170, -0.4312, 1.7823, 0.1004, -0.4980, -0.1354, 0.7443, 0.1665],
- [ 1.4584, 0.1012, 1.0517, -1.3167, -0.5159, -1.1998, 0.3566, 0.2365]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5443, -0.3831, 1.7095, 0.1621, -0.2016, 0.1390, 0.1437,
- 0.2364],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.5772, -0.3913, 1.8480, -0.2459, -0.4326, 0.1929, 0.5374,
- 0.4701],
- [-2.2859, -2.2859, 1.1436, -1.3082, -0.4672, -1.1620, 0.2256,
- 0.2853],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.5132, -0.4736, 1.6171, 0.3546, -0.3460, 0.1236, 0.1404,
- -0.0911],
- [ 0.6207, -0.4466, 1.7326, 0.1621, -0.5480, -0.1073, 0.9704,
- 0.1608],
- [ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0435, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0435, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7982591893523932
- step: 47
- running loss: 0.038260833816008366
- Train Steps: 47/90 Loss: 0.0383 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672],
- [ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6168, 0.4055, 0.7575, 0.2950, 0.4050, 0.2717, 0.5938, 0.6167],
- [0.6104, 0.4029, 0.8738, 0.4900, 0.4088, 0.4533, 0.5070, 0.5510],
- [0.6042, 0.3990, 0.6831, 0.2875, 0.3500, 0.3133, 0.5143, 0.5510],
- [0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5740, -0.4570, 1.2343, -1.2853, -0.3430, -1.3086, 0.2643, 0.1285],
- [ 0.7208, -0.4197, 1.9882, 0.2828, -0.5127, -0.1009, 0.7220, 0.0963],
- [-1.0776, -1.5501, 1.2808, -1.1677, -0.3018, -1.2895, 0.2042, 0.2265],
- [ 0.6298, -0.4848, 2.0928, -0.0428, -0.5081, -0.2292, 0.8535, 0.1820],
- [ 0.9159, -0.2347, 1.5106, -0.9483, -0.3872, -0.8938, 0.5857, 0.3830],
- [ 0.3516, -0.5812, 1.9792, 0.1283, -0.3523, 0.1517, 0.2751, 0.1903],
- [ 0.7296, -0.3684, 1.1634, -1.1615, -0.5584, -0.6658, 0.1552, 0.3173],
- [ 0.5119, -0.5072, 1.9833, -0.0225, -0.1524, 0.3139, 0.7481, 0.1937]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5109, -0.4316, 1.1436, -1.3467, -0.3864, -1.4160, 0.1239,
- -0.0580],
- [ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278],
- [-2.2859, -2.2859, 1.0712, -1.2085, -0.3806, -1.3929, 0.0755,
- 0.2006],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [ 0.5631, -0.4129, 1.2129, -0.9233, -0.4152, -1.0311, 0.4566,
- 0.5624],
- [ 0.5334, -0.4252, 1.7499, -0.0226, -0.3979, -0.1920, 0.0558,
- 0.2589],
- [ 0.5049, -0.4428, 0.8692, -0.9581, -0.6693, -0.8386, 0.0897,
- 0.2589],
- [ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0539, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0539, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.852201757952571
- step: 48
- running loss: 0.038587536624011896
- Train Steps: 48/90 Loss: 0.0386 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4270, 0.8938, 0.4967, 0.3550, 0.4283, 0.5700, 0.5733],
- [0.6196, 0.4088, 0.8888, 0.4583, 0.4500, 0.5683, 0.6138, 0.5883],
- [ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [0.6275, 0.4024, 0.7722, 0.2080, 0.4392, 0.2234, 0.6435, 0.5290],
- [0.6163, 0.4006, 0.8788, 0.4683, 0.3663, 0.4883, 0.5887, 0.5017],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6201, 0.4064, 0.8688, 0.5050, 0.4225, 0.5100, 0.6138, 0.5500],
- [0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3295, -0.5814, 1.8805, -0.0697, -0.5494, -0.1917, 0.4035, 0.2892],
- [ 0.7263, -0.3292, 1.8847, -0.2392, -0.1815, 0.3605, 0.5436, 0.2755],
- [-1.5498, -1.8469, 1.1289, -1.4847, -0.3832, -1.3001, 0.2906, 0.2223],
- [ 0.7428, -0.3750, 1.4495, -1.3152, -0.2427, -1.3279, 0.7417, 0.0372],
- [ 0.4533, -0.5365, 1.8424, -0.2498, -0.5483, 0.0561, 0.5313, 0.1250],
- [ 0.5942, -0.4407, 1.8397, 0.1174, -0.5398, -0.1064, 0.4411, 0.2406],
- [ 0.7884, -0.3214, 1.8269, 0.0239, -0.3524, 0.1700, 0.4697, 0.1303],
- [ 0.9849, -0.1455, 1.5928, -0.6527, -0.0722, -1.1593, 0.4756, 0.3225]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6121, -0.3138, 1.8423, 0.0082, -0.6462, -0.3075, 0.3469,
- 0.3623],
- [ 0.5759, -0.3978, 1.8192, -0.1689, -0.2074, 0.3392, 0.5490,
- 0.4316],
- [-2.2859, -2.2859, 0.8516, -1.3112, -0.4326, -1.2851, 0.0755,
- 0.2930],
- [ 0.6125, -0.4273, 1.2807, -1.3253, -0.2574, -1.2542, 0.6864,
- 0.1575],
- [ 0.5609, -0.4354, 1.7730, -0.1227, -0.5942, -0.0303, 0.4335,
- 0.0313],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.5784, -0.4088, 1.7268, 0.0467, -0.3344, 0.0697, 0.5490,
- 0.2545],
- [ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0276, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0276, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8798270765691996
- step: 49
- running loss: 0.03836381788916734
- Train Steps: 49/90 Loss: 0.0384 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6271, 0.4040, 0.9000, 0.3250, 0.4938, 0.2300, 0.7192, 0.5413],
- [0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413],
- [0.6203, 0.4073, 0.8189, 0.2398, 0.4400, 0.2054, 0.5929, 0.5501],
- [0.6193, 0.4108, 0.7425, 0.2350, 0.3887, 0.2750, 0.5900, 0.5717],
- [0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064],
- [ nan, nan, 0.8888, 0.3100, 0.5262, 0.2817, 0.7145, 0.6003],
- [0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0420, -0.7969, 1.9109, -0.8322, -0.1011, -0.8858, 0.9601, 0.1443],
- [ 0.6866, -0.3095, 1.7257, -0.0317, -0.5664, -0.5953, 0.2935, 0.1853],
- [ 0.2108, -0.6950, 1.9938, -0.5592, -0.2313, -0.8180, 0.9008, 0.1145],
- [ 0.5528, -0.4017, 1.3345, -1.0168, -0.2567, -1.0105, 0.4153, 0.2544],
- [ 0.6255, -0.3738, 1.1034, -1.1191, -0.5814, -0.7942, 0.3894, 0.3216],
- [ 0.7603, -0.3225, 1.7301, 0.1145, -0.2304, 0.4093, 0.1185, 0.0922],
- [-1.5151, -1.8184, 1.7782, -0.8214, -0.0171, -0.7889, 0.8844, 0.3772],
- [ 0.9260, -0.1868, 1.1814, -1.0478, -0.6314, -0.6874, 0.0614, 0.0822]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6108, -0.4201, 1.8711, -0.7848, -0.0053, -1.2236, 1.0362,
- 0.2142],
- [ 0.5901, -0.4000, 1.8423, -0.0688, -0.5307, -0.9233, 0.3642,
- 0.1852],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142],
- [ 0.5792, -0.4048, 1.4965, -1.1781, -0.2534, -1.3371, 0.4528,
- 0.2549],
- [ 0.5746, -0.3882, 1.1436, -1.2005, -0.4903, -1.0157, 0.4393,
- 0.3546],
- [ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532],
- [-2.2859, -2.2859, 1.8192, -0.8540, 0.1448, -0.9849, 1.0143,
- 0.4867],
- [ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0425, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0425, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.9223244320601225
- step: 50
- running loss: 0.03844648864120245
- Train Steps: 50/90 Loss: 0.0384 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986],
- [ nan, nan, 0.8850, 0.3000, 0.5363, 0.2250, 0.7343, 0.5771],
- [0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364],
- [0.6260, 0.4153, 0.9000, 0.4533, 0.4025, 0.2633, 0.6223, 0.4967],
- [0.6250, 0.4146, 0.8838, 0.3933, 0.3588, 0.4283, 0.6162, 0.5367],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167],
- [0.6113, 0.4104, 0.8650, 0.4250, 0.3925, 0.5967, 0.5787, 0.5116],
- [0.6203, 0.4076, 0.8611, 0.2878, 0.4050, 0.2554, 0.5907, 0.5496]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5079, -0.4253, 1.7334, -0.5518, -0.2087, -1.0789, 0.5205, 0.1830],
- [-2.1959, -2.2539, 1.6896, -0.8953, 0.1348, -0.9927, 0.8918, 0.3466],
- [ 0.7811, -0.3041, 1.3094, -1.1007, -0.1904, -1.1196, 0.6205, 0.1609],
- [ 0.6017, -0.3955, 1.7478, -0.2562, -0.3932, -0.8967, 0.5031, 0.0947],
- [ 0.5943, -0.3944, 1.5952, -0.4495, -0.6258, -0.1813, 0.4599, 0.2885],
- [ 0.5591, -0.3863, 1.4813, -0.8121, -0.5755, -0.7021, 0.1551, 0.1755],
- [ 0.7742, -0.2851, 1.6700, -0.2640, -0.4691, 0.5642, 0.4217, 0.1421],
- [ 0.5294, -0.4214, 1.5105, -0.9518, -0.3016, -0.8068, 0.4605, 0.2259]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171],
- [-2.2859, -2.2859, 1.8018, -0.9002, 0.1910, -1.2467, 1.1057,
- 0.3799],
- [ 0.6158, -0.3960, 1.4092, -1.2774, -0.2074, -1.1851, 0.8491,
- 0.1917],
- [ 0.6055, -0.3676, 1.8711, -0.1920, -0.4268, -1.0696, 0.5887,
- 0.0081],
- [ 0.6009, -0.3710, 1.7961, -0.4691, -0.6289, -0.3075, 0.5605,
- 0.1929],
- [ 0.5483, -0.4249, 1.5305, -0.7386, -0.6115, -0.8694, 0.3353,
- 0.1005],
- [ 0.5376, -0.3903, 1.7095, -0.3229, -0.4730, 0.4701, 0.3871,
- 0.0772],
- [ 0.5790, -0.4031, 1.6915, -0.9564, -0.4152, -1.1063, 0.4425,
- 0.2528]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0130, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0130, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.9353253524750471
- step: 51
- running loss: 0.037947555930883274
- Train Steps: 51/90 Loss: 0.0379 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6282, 0.4034, 0.7830, 0.2080, 0.4532, 0.2080, 0.6404, 0.5323],
- [ nan, nan, 0.6859, 0.2194, 0.4150, 0.1867, 0.5153, 0.5729],
- [0.6189, 0.4049, 0.8888, 0.4417, 0.4213, 0.5200, 0.5988, 0.5633],
- [0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672],
- [0.6117, 0.4019, 0.8538, 0.4067, 0.3513, 0.3583, 0.5663, 0.5133],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [0.6104, 0.4029, 0.8738, 0.4900, 0.4088, 0.4533, 0.5070, 0.5510]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7340, -0.3327, 1.4879, -1.1681, -0.0782, -1.3344, 0.7709, 0.1514],
- [-0.8746, -1.3776, 1.0971, -1.3145, -0.2678, -1.3062, 0.2473, 0.3377],
- [ 1.0725, -0.1333, 1.8916, -0.2227, -0.3438, 0.3868, 0.5975, 0.2453],
- [ 1.0992, -0.1554, 1.8354, 0.2487, -0.5583, -0.2261, 0.6530, 0.0657],
- [ 0.5148, -0.4407, 1.8467, -0.5045, -0.5949, -0.5906, 0.3270, 0.1523],
- [ 0.8868, -0.2235, 1.5971, -1.0558, -0.2848, -1.0580, 0.6227, 0.1622],
- [-2.1556, -2.2283, 1.1792, -1.2546, -0.2982, -1.1205, 0.3752, 0.3032],
- [ 0.7901, -0.2864, 1.8163, 0.0887, -0.4164, 0.0258, 0.2030, 0.1948]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6158, -0.4225, 1.3307, -1.3253, -0.1924, -1.3252, 0.6721,
- 0.1727],
- [-2.2859, -2.2859, 0.8824, -1.2727, -0.3691, -1.4237, 0.0943,
- 0.3604],
- [ 0.5726, -0.4159, 1.8192, -0.2459, -0.3402, 0.1159, 0.4797,
- 0.3161],
- [ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278],
- [ 0.5393, -0.4294, 1.6575, -0.4075, -0.6635, -0.6308, 0.3296,
- 0.0851],
- [ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929],
- [-2.2859, -2.2859, 0.8516, -1.3112, -0.4326, -1.2851, 0.0755,
- 0.2930],
- [ 0.5334, -0.4252, 1.7499, -0.0226, -0.3979, -0.1920, 0.0558,
- 0.2589]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0704, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0704, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.0057187657803297
- step: 52
- running loss: 0.038571514726544805
- Train Steps: 52/90 Loss: 0.0386 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [0.6196, 0.4068, 0.7645, 0.2234, 0.4575, 0.1737, 0.5926, 0.5284],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402],
- [0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.0896, -0.8350, 1.8853, -0.9877, -0.0115, -1.3491, 0.7882, 0.0851],
- [ 0.2664, -0.6331, 1.5328, -0.9256, -0.5753, -0.5936, 0.5557, 0.4303],
- [ 0.2831, -0.6234, 1.7790, -0.2026, -0.5764, -0.2057, 0.4492, 0.1349],
- [ 0.2364, -0.6552, 1.3299, -1.3268, -0.1236, -1.4516, 0.5103, 0.1620],
- [ 0.4781, -0.5272, 1.7003, 0.0702, -0.4299, -0.1375, 0.4306, 0.1112],
- [ 0.4198, -0.5174, 1.7888, -0.1580, -0.2072, 0.1132, 0.0995, 0.0633],
- [ 0.4876, -0.4862, 1.3988, -1.0940, -0.6574, -0.4401, 0.6009, 0.2054],
- [ 0.3655, -0.5576, 1.7252, -0.1082, -0.3145, -0.9504, 0.4736, 0.3978]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.5865, -0.3973, 1.8423, -0.0688, -0.5192, -0.2305, 0.4162,
- 0.1159],
- [ 0.5761, -0.4070, 1.2452, -1.2541, -0.1725, -1.4835, 0.4511,
- 0.1545],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091],
- [ 0.5515, -0.4225, 1.7788, -0.0534, -0.2016, 0.1929, 0.1568,
- 0.0682],
- [ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0279, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0279, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.0335935931652784
- step: 53
- running loss: 0.038369690437080724
- Train Steps: 53/90 Loss: 0.0384 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6107, 0.4050, 0.8700, 0.4850, 0.4470, 0.4848, 0.5043, 0.5431],
- [0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986],
- [0.6179, 0.4008, 0.8600, 0.4015, 0.3932, 0.2515, 0.5711, 0.5438],
- [0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183],
- [0.6219, 0.4114, 0.8175, 0.2817, 0.3925, 0.2783, 0.5900, 0.5350],
- [0.6207, 0.4110, 0.8738, 0.5000, 0.4800, 0.5633, 0.6300, 0.5433],
- [0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064],
- [0.6197, 0.3930, 0.8793, 0.4736, 0.4152, 0.5464, 0.6308, 0.5235]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4297, -0.4967, 1.6639, -0.1452, -0.3522, -0.0779, 0.1092, 0.1016],
- [-0.0545, -0.8022, 1.8791, -0.6957, -0.2669, -1.4643, 0.7409, 0.1956],
- [ 0.1286, -0.6560, 1.5928, -0.6839, -0.3915, -1.2792, 0.3740, 0.2566],
- [ 0.2547, -0.6250, 1.2035, -1.4607, -0.3059, -1.5024, 0.5547, 0.1377],
- [-0.0957, -0.8061, 1.3868, -1.1668, -0.4368, -1.0446, 0.5488, 0.2840],
- [ 0.9410, -0.2207, 1.7451, -0.2033, -0.2315, 0.1644, 0.6318, 0.2716],
- [ 0.5294, -0.4676, 1.7483, -0.0365, -0.1930, -0.0027, 0.2473, 0.0986],
- [ 0.0932, -0.7754, 1.6980, -0.1735, -0.4596, 0.0964, 0.6184, 0.1822]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5351, -0.4153, 1.7326, -0.0457, -0.2214, -0.0466, 0.0434,
- 0.2228],
- [ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171],
- [ 0.5680, -0.4345, 1.6864, -0.4315, -0.4698, -1.1241, 0.3518,
- 0.2261],
- [ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082],
- [ 0.5868, -0.3858, 1.4901, -0.9849, -0.4730, -1.0003, 0.4393,
- 0.1852],
- [ 0.5811, -0.3878, 1.7499, 0.0236, -0.0688, 0.3161, 0.6240,
- 0.2237],
- [ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532],
- [ 0.5762, -0.4706, 1.7754, -0.0984, -0.3680, 0.2380, 0.6277,
- 0.1322]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0433, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0433, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.076877860352397
- step: 54
- running loss: 0.03846070111763698
- Train Steps: 54/90 Loss: 0.0385 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6307, 0.4045, 0.8025, 0.5833, 0.3775, 0.4867, 0.6892, 0.5459],
- [0.6159, 0.4085, 0.6900, 0.2283, 0.4088, 0.1950, 0.5123, 0.5397],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019],
- [0.6201, 0.4027, 0.8029, 0.2728, 0.4042, 0.2310, 0.5980, 0.5391],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364],
- [0.6277, 0.4029, 0.8250, 0.2433, 0.4325, 0.2100, 0.6366, 0.5207],
- [0.6179, 0.3993, 0.8925, 0.4789, 0.3879, 0.4900, 0.6041, 0.5279]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4562, -0.5427, 1.6782, 0.0694, -0.4969, -0.1446, 0.6702, 0.2226],
- [-0.1811, -0.8887, 1.0253, -1.2883, -0.3301, -1.3747, 0.0681, 0.2783],
- [ 0.8465, -0.3035, 1.8925, -0.1580, -0.5932, -0.4709, 0.5199, 0.1389],
- [ 0.0712, -0.7317, 1.5243, -1.0640, -0.3472, -1.1446, 0.3364, 0.2090],
- [ 0.4380, -0.5539, 1.8446, 0.0722, -0.3768, 0.1330, 0.3437, 0.1530],
- [-0.0180, -0.8051, 1.5171, -1.2229, -0.1305, -1.3399, 0.7107, 0.2390],
- [-0.0261, -0.8366, 1.5963, -1.1512, -0.2253, -1.3229, 0.6682, 0.1688],
- [ 0.6177, -0.4220, 1.8000, -0.2515, -0.5198, -0.0278, 0.3949, 0.1956]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6273, -0.4177, 1.4208, 0.4085, -0.5423, -0.0380, 0.8973,
- 0.2356],
- [ 0.5591, -0.3990, 0.9012, -1.2313, -0.3979, -1.3852, 0.0804,
- 0.2071],
- [ 0.6372, -0.4129, 1.8942, -0.0765, -0.6173, -0.4768, 0.6999,
- 0.0325],
- [ 0.5785, -0.4259, 1.4228, -1.0261, -0.4190, -1.2189, 0.4763,
- 0.2043],
- [ 0.5778, -0.4389, 1.7107, 0.1192, -0.3921, 0.0815, 0.4741,
- 0.0711],
- [ 0.6158, -0.3960, 1.4092, -1.2774, -0.2074, -1.1851, 0.8491,
- 0.1917],
- [ 0.6132, -0.4248, 1.5247, -1.1620, -0.2882, -1.3159, 0.6545,
- 0.1193],
- [ 0.5680, -0.4417, 1.8365, -0.0740, -0.4941, -0.0227, 0.5045,
- 0.1525]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0463, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0463, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.1231579910963774
- step: 55
- running loss: 0.03860287256538868
- Train Steps: 55/90 Loss: 0.0386 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4117, 0.8100, 0.5801, 0.4000, 0.4583, 0.5582, 0.6118],
- [0.6193, 0.4108, 0.7425, 0.2350, 0.3887, 0.2750, 0.5900, 0.5717],
- [0.6241, 0.4143, 0.8938, 0.4650, 0.4075, 0.5350, 0.6250, 0.5550],
- [0.6193, 0.4108, 0.7438, 0.2700, 0.3650, 0.3683, 0.6238, 0.5717],
- [0.6228, 0.4004, 0.8750, 0.5250, 0.3825, 0.5233, 0.6362, 0.5000],
- [0.6097, 0.4024, 0.8488, 0.3717, 0.3875, 0.5517, 0.5836, 0.5591],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726],
- [0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3630, -0.5458, 1.6351, 0.1872, -0.3983, -0.4754, 0.3092, 0.3913],
- [ 0.2263, -0.6256, 1.3120, -1.3146, -0.4408, -1.4280, 0.5027, 0.2511],
- [ 0.2667, -0.6203, 1.8972, -0.3692, -0.3622, -0.1678, 0.5342, 0.2368],
- [ 0.2488, -0.6262, 1.3356, -1.1459, -0.5657, -0.8258, 0.5429, 0.2466],
- [ 0.3914, -0.5923, 1.8955, -0.0133, -0.4285, -0.2241, 0.6273, 0.0103],
- [ 0.0806, -0.7299, 1.6935, -0.6597, -0.4001, 0.0085, 0.5942, 0.2190],
- [ 0.4726, -0.4565, 1.3884, -1.1786, -0.5101, -1.2969, 0.1848, -0.0156],
- [ 0.4982, -0.4523, 1.7932, 0.2545, 0.0606, -0.6184, 0.3437, 0.3104]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6121, -0.3844, 1.4556, 0.3936, -0.4383, -0.1689, 0.2925,
- 0.5401],
- [ 0.5746, -0.3882, 1.1436, -1.2005, -0.4903, -1.0157, 0.4393,
- 0.3546],
- [ 0.5968, -0.3725, 1.8423, -0.1381, -0.4037, 0.1852, 0.6009,
- 0.2776],
- [ 0.5747, -0.3886, 1.1494, -1.0388, -0.6000, -0.5846, 0.5952,
- 0.3546],
- [ 0.5908, -0.4366, 1.7557, 0.1390, -0.5192, 0.1313, 0.6529,
- 0.0236],
- [ 0.5301, -0.4273, 1.6344, -0.5692, -0.4961, 0.2622, 0.4098,
- 0.2966],
- [ 0.4997, -0.4446, 1.1610, -0.9772, -0.6058, -1.0311, 0.1404,
- -0.1031],
- [ 0.5959, -0.3579, 1.6055, 0.3623, -0.0573, -0.2074, 0.3122,
- 0.4547]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0382, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0382, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.161322994157672
- step: 56
- running loss: 0.038595053467101285
- Train Steps: 56/90 Loss: 0.0386 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250],
- [0.6189, 0.4033, 0.8650, 0.5267, 0.4487, 0.5150, 0.5925, 0.5050],
- [0.6274, 0.4117, 0.8100, 0.5801, 0.4000, 0.4583, 0.5582, 0.6118],
- [0.6264, 0.3972, 0.8853, 0.4771, 0.3853, 0.4511, 0.6293, 0.5334],
- [0.6197, 0.4090, 0.7825, 0.2500, 0.4200, 0.2483, 0.5988, 0.5667],
- [0.6308, 0.3990, 0.8688, 0.5183, 0.3950, 0.4983, 0.6388, 0.5133],
- [0.6201, 0.4050, 0.7757, 0.2234, 0.4459, 0.1798, 0.5975, 0.5426],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4092, -0.5138, 1.8144, -0.1999, -0.3258, -0.0545, 0.4307, 0.1656],
- [ 0.4702, -0.4808, 1.7870, -0.0377, -0.1724, -0.1545, 0.3535, 0.1485],
- [ 0.3143, -0.5592, 1.5140, 0.1310, -0.4379, -0.4050, 0.2350, 0.4357],
- [ 0.6361, -0.4048, 1.8149, -0.2947, -0.5310, -0.4568, 0.6891, 0.1461],
- [ 0.0101, -0.7609, 1.3474, -1.3365, -0.3493, -1.3944, 0.4397, 0.2876],
- [ 0.4153, -0.5429, 1.7749, -0.1072, -0.4279, -0.2432, 0.6392, 0.1070],
- [ 0.1730, -0.6428, 1.2906, -1.3643, -0.2571, -1.6480, 0.3724, 0.1490],
- [ 0.5979, -0.4057, 1.8060, -0.3230, -0.5050, -0.1868, 0.4154, 0.1172]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5991, -0.3803, 1.8018, -0.0534, -0.3460, 0.1852, 0.5374,
- 0.1390],
- [ 0.5726, -0.4230, 1.7095, 0.1467, -0.2132, 0.0928, 0.4508,
- 0.0467],
- [ 0.6121, -0.3844, 1.4556, 0.3936, -0.4383, -0.1689, 0.2925,
- 0.5401],
- [ 0.6075, -0.4514, 1.8032, -0.0822, -0.5061, -0.2023, 0.6208,
- 0.1779],
- [ 0.5764, -0.3969, 1.3284, -1.1312, -0.3460, -1.1389, 0.4797,
- 0.3315],
- [ 0.6275, -0.4430, 1.7268, 0.1082, -0.4614, 0.0159, 0.6644,
- 0.0851],
- [ 0.5784, -0.4153, 1.2972, -1.2541, -0.2265, -1.4553, 0.4741,
- 0.2203],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0283, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0283, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.189621262252331
- step: 57
- running loss: 0.03841440810969001
- Train Steps: 57/90 Loss: 0.0384 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6207, 0.4110, 0.8738, 0.5000, 0.4800, 0.5633, 0.6300, 0.5433],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390],
- [0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783],
- [0.6189, 0.4049, 0.8888, 0.4417, 0.4213, 0.5200, 0.5988, 0.5633],
- [0.6129, 0.4114, 0.6950, 0.2467, 0.4050, 0.2133, 0.5363, 0.5550],
- [0.6193, 0.4108, 0.7425, 0.2350, 0.3887, 0.2750, 0.5900, 0.5717],
- [0.6339, 0.4123, 0.8638, 0.5367, 0.4075, 0.5467, 0.7517, 0.5436],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6948, -0.3456, 1.8998, 0.0423, -0.1258, 0.0501, 0.6125, 0.2276],
- [ 0.3303, -0.5843, 1.8655, 0.0207, -0.4595, -0.1592, 0.3264, 0.0907],
- [ 0.1220, -0.6186, 1.3515, -0.9253, -0.1465, -1.4670, 0.3551, 0.3518],
- [ 0.5260, -0.4394, 1.9115, -0.1421, -0.3737, 0.0497, 0.5564, 0.1762],
- [ 0.0102, -0.7211, 1.0825, -1.0281, -0.4573, -1.3510, 0.0147, 0.2470],
- [ 0.3063, -0.5432, 1.2584, -1.0544, -0.5468, -1.2192, 0.4297, 0.2727],
- [ 0.4030, -0.5388, 1.8117, 0.1530, -0.4069, 0.0941, 0.9434, 0.1687],
- [ 0.5400, -0.4138, 1.0428, -1.2295, -0.5807, -1.2244, 0.2741, 0.0920]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5811, -0.3878, 1.7499, 0.0236, -0.0688, 0.3161, 0.6240,
- 0.2237],
- [ 0.5250, -0.4661, 1.7383, -0.0765, -0.4268, -0.0226, 0.2535,
- 0.2035],
- [ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854],
- [ 0.5726, -0.4159, 1.8192, -0.2459, -0.3402, 0.1159, 0.4797,
- 0.3161],
- [ 0.5448, -0.3859, 0.9242, -1.1466, -0.4152, -1.3005, 0.1910,
- 0.2776],
- [ 0.5746, -0.3882, 1.1436, -1.2005, -0.4903, -1.0157, 0.4393,
- 0.3546],
- [ 0.6421, -0.3816, 1.7037, 0.1929, -0.4037, 0.2391, 1.1861,
- 0.2249],
- [ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0236, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0236, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.213260428979993
- step: 58
- running loss: 0.038159662568620564
- Train Steps: 58/90 Loss: 0.0382 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383],
- [0.6113, 0.4104, 0.8650, 0.4250, 0.3925, 0.5967, 0.5787, 0.5116],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519],
- [0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450],
- [0.6286, 0.4040, 0.8696, 0.3047, 0.3924, 0.2887, 0.6300, 0.5367],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6250, 0.4106, 0.8700, 0.3717, 0.3588, 0.4967, 0.6038, 0.5167],
- [0.6084, 0.3981, 0.8588, 0.5233, 0.4600, 0.5367, 0.5680, 0.5006]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7516, -0.2493, 1.6080, -0.1194, -0.3354, -0.4171, 0.2348, 0.2148],
- [ 0.1629, -0.6173, 1.6239, -0.3986, -0.4561, 0.0161, 0.4742, 0.1740],
- [ 0.4031, -0.4658, 1.5838, 0.0052, -0.1537, -0.3175, 0.1387, 0.2543],
- [ 0.5602, -0.4197, 1.6715, -0.3033, -0.3595, -0.1238, 0.5330, 0.2328],
- [ 0.4136, -0.4696, 1.6398, -1.0198, -0.4677, -1.4931, 0.7789, 0.1734],
- [ 0.2517, -0.6238, 1.5922, 0.0767, -0.4577, -0.4736, 0.5105, 0.1944],
- [ 0.7147, -0.2776, 1.5206, -0.6886, -0.5991, -0.2994, 0.6749, 0.2496],
- [ 0.4535, -0.4406, 1.5970, 0.0072, -0.1969, -0.2337, 0.1553, 0.1555]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01],
- [ 5.3759e-01, -3.9030e-01, 1.7095e+00, -3.2286e-01, -4.7298e-01,
- 4.7005e-01, 3.8714e-01, 7.7228e-02],
- [ 5.3181e-01, -4.3102e-01, 1.6864e+00, 5.4350e-02, -1.5543e-01,
- 1.3133e-01, 6.3480e-02, 2.6342e-01],
- [ 5.9602e-01, -4.1016e-01, 1.8018e+00, -1.6120e-01, -3.3441e-01,
- 1.1594e-01, 5.4896e-01, 2.3141e-01],
- [ 6.1742e-01, -4.2008e-01, 1.7309e+00, -8.7840e-01, -4.7351e-01,
- -9.5238e-01, 6.2423e-01, 1.9310e-01],
- [ 5.7748e-01, -4.6066e-01, 1.6741e+00, 1.9623e-01, -4.0362e-01,
- -1.2115e-01, 4.5876e-01, 1.9786e-01],
- [ 6.0098e-01, -3.8961e-01, 1.7326e+00, -5.6921e-01, -6.2887e-01,
- 8.1601e-03, 5.0277e-01, 1.0054e-01],
- [ 5.2408e-01, -4.4696e-01, 1.6806e+00, 1.3133e-01, -1.6120e-01,
- 1.9292e-01, 3.3778e-01, 2.6129e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0338, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0338, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.2470400538295507
- step: 59
- running loss: 0.038085424641178825
- Train Steps: 59/90 Loss: 0.0381 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6277, 0.4013, 0.8888, 0.4767, 0.3600, 0.3567, 0.6148, 0.4672],
- [0.6186, 0.4013, 0.8191, 0.3188, 0.4279, 0.2060, 0.5767, 0.5411],
- [0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300],
- [ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.4318, 0.1613, 1.6839, -0.1669, -0.6021, -0.5390, 0.4439, 0.0423],
- [ 1.2904, 0.1413, 1.4333, -0.7264, -0.3609, -1.2067, 0.1633, 0.1794],
- [ 1.0495, -0.0377, 1.2722, -0.9776, -0.3320, -1.2180, 0.2905, 0.1976],
- [ 1.0321, -0.0924, 1.4443, 0.2232, -0.4871, 0.0177, 0.6999, 0.2629],
- [ 0.9761, -0.0933, 1.1736, -1.0616, -0.5285, -0.7053, 0.4697, 0.2521],
- [-1.8286, -1.9014, 1.0395, -0.8848, -0.4785, -1.0044, 0.0752, 0.2423],
- [ 0.9323, -0.1344, 1.6433, 0.1998, -0.2636, 0.3281, 0.3945, 0.1279],
- [-2.3898, -2.3125, 1.5499, -0.9304, 0.1178, -1.0389, 0.9971, 0.3760]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6132, -0.4324, 1.8192, -0.0842, -0.6231, -0.6385, 0.5537,
- -0.1278],
- [ 0.5713, -0.4321, 1.4975, -0.8134, -0.3094, -1.3345, 0.3779,
- 0.2134],
- [ 0.5796, -0.4326, 1.4439, -1.1774, -0.2940, -1.3390, 0.3931,
- 0.0928],
- [ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478],
- [ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621],
- [-2.2859, -2.2859, 1.2469, -1.0288, -0.4557, -1.2774, 0.0511,
- 0.2183],
- [ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0677, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0677, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.3147859629243612
- step: 60
- running loss: 0.038579766048739354
- Train Steps: 60/90 Loss: 0.0386 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6157, 0.3956, 0.8323, 0.4138, 0.3479, 0.4431, 0.5914, 0.5131],
- [0.6214, 0.4040, 0.8838, 0.3500, 0.3600, 0.5183, 0.6362, 0.5200],
- [0.6142, 0.3982, 0.8650, 0.4883, 0.3912, 0.4317, 0.5315, 0.5350],
- [0.6225, 0.4116, 0.8662, 0.3517, 0.3663, 0.3233, 0.5837, 0.5317],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6984, -0.3234, 1.5211, -0.3551, -0.5158, -0.1597, 0.2934, 0.1677],
- [ 0.3330, -0.5850, 1.6220, -0.5140, -0.4023, 0.2297, 0.7771, 0.2031],
- [ 0.2629, -0.6181, 1.7049, 0.1271, -0.3293, -0.2368, 0.1755, 0.1922],
- [ 0.8531, -0.1939, 1.6120, -0.5787, -0.4479, -0.9063, 0.3136, 0.2825],
- [ 0.6014, -0.4231, 1.7773, 0.0603, -0.4681, -0.4661, 0.7059, 0.1404],
- [ 0.3605, -0.4942, 1.2824, -0.5865, -0.5712, -0.4829, 0.1422, 0.3245],
- [ 0.4777, -0.4718, 1.7059, -0.5215, -0.2062, -0.9239, 0.8412, 0.1175],
- [ 0.3245, -0.5201, 1.0536, -1.0747, -0.4880, -0.8538, 0.3860, 0.3297]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5581, -0.4586, 1.5586, -0.3747, -0.6792, -0.2391, 0.4455,
- 0.0840],
- [ 0.5841, -0.4199, 1.7961, -0.6693, -0.6231, 0.1082, 0.6529,
- 0.1159],
- [ 0.5513, -0.4467, 1.7095, -0.0303, -0.4788, -0.2921, 0.1692,
- 0.1854],
- [ 0.5893, -0.3847, 1.7152, -0.6616, -0.5942, -0.7925, 0.4104,
- 0.1698],
- [ 0.6372, -0.4129, 1.8942, -0.0765, -0.6173, -0.4768, 0.6999,
- 0.0325],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0159, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0159, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.330700110644102
- step: 61
- running loss: 0.038208198535149215
- Train Steps: 61/90 Loss: 0.0382 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4087, 0.8375, 0.5700, 0.4025, 0.4800, 0.5700, 0.6117],
- [0.6275, 0.4048, 0.8488, 0.2883, 0.4463, 0.2033, 0.6321, 0.5155],
- [0.6190, 0.4135, 0.8000, 0.4883, 0.3566, 0.3647, 0.5613, 0.5900],
- [ nan, nan, 0.7240, 0.2722, 0.3900, 0.2567, 0.5168, 0.5933],
- [0.6100, 0.4071, 0.7601, 0.3444, 0.3400, 0.4117, 0.5625, 0.5617],
- [0.6304, 0.4024, 0.8925, 0.4800, 0.3937, 0.4817, 0.7485, 0.5297],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5166, -0.4363, 1.5673, 0.4389, -0.3340, 0.1185, 0.6154, 0.3582],
- [ 0.7376, -0.2867, 1.6665, -0.9273, -0.1881, -1.2132, 0.7366, 0.0424],
- [ 1.0151, -0.0670, 1.6360, 0.0200, -0.5590, -0.5761, 0.2286, 0.3074],
- [-1.9647, -2.0599, 1.0725, -0.9805, -0.3861, -0.9972, 0.1053, 0.3411],
- [ 0.7916, -0.2394, 1.3005, -0.7080, -0.6687, -0.2677, 0.3271, 0.2352],
- [ 0.7570, -0.3201, 1.7928, -0.0304, -0.3660, 0.1177, 0.9711, 0.0881],
- [ 0.8833, -0.1852, 1.0046, -1.0671, -0.4564, -1.1528, 0.3791, 0.2964],
- [ 0.7265, -0.2935, 1.6835, -0.2477, -0.4694, -0.0076, 0.2798, 0.0503]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6118, -0.3983, 1.5824, 0.3469, -0.4268, -0.0688, 0.3469,
- 0.5393],
- [ 0.6126, -0.4161, 1.6344, -0.9541, -0.2247, -1.3467, 0.6339,
- 0.0953],
- [ 0.5732, -0.3761, 1.4092, -0.0303, -0.6388, -0.6012, 0.3065,
- 0.4393],
- [-2.2859, -2.2859, 1.0580, -1.0288, -0.4845, -1.1004, 0.1011,
- 0.4543],
- [ 0.5318, -0.4056, 1.2249, -0.6949, -0.7155, -0.3844, 0.3122,
- 0.3084],
- [ 0.6257, -0.4273, 1.8365, -0.0688, -0.4672, -0.0611, 1.1715,
- 0.1608],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0206, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0206, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.351294633001089
- step: 62
- running loss: 0.03792410698388853
- Train Steps: 62/90 Loss: 0.0379 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [0.6173, 0.4114, 0.7325, 0.2500, 0.4213, 0.1917, 0.5338, 0.5700],
- [0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822],
- [0.6314, 0.4050, 0.8227, 0.5431, 0.4150, 0.5517, 0.7121, 0.5690],
- [0.6135, 0.4115, 0.8838, 0.4667, 0.4288, 0.6050, 0.5778, 0.5097],
- [ nan, nan, 0.7625, 0.2433, 0.3713, 0.2867, 0.5235, 0.5220],
- [0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9962, -0.0934, 1.1704, -0.8884, -0.5775, -0.9059, 0.1283, 0.0411],
- [ 0.5343, -0.3708, 0.9757, -1.0411, -0.3740, -1.2404, 0.1549, 0.3203],
- [ 0.6687, -0.3089, 1.6206, -0.1213, -0.5958, -0.6372, 0.2967, 0.3001],
- [ 0.9464, -0.1766, 1.5838, 0.2979, -0.3140, 0.3399, 1.1289, 0.2496],
- [ 0.8749, -0.1664, 1.7403, -0.0485, -0.2417, 0.4094, 0.6488, 0.1518],
- [-2.2750, -2.2735, 1.1797, -0.9250, -0.4382, -1.0054, 0.2959, 0.2726],
- [ 0.6862, -0.2847, 1.3516, -0.7464, -0.5798, -1.0829, 0.1398, 0.1386],
- [ 0.9209, -0.1630, 1.7334, -0.0049, -0.1730, 0.2321, 0.8091, 0.1891]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141],
- [ 0.5652, -0.3858, 1.0975, -1.1312, -0.3402, -1.4006, 0.1794,
- 0.3469],
- [ 0.5697, -0.4113, 1.7499, -0.2767, -0.6404, -0.7155, 0.1576,
- 0.4032],
- [ 0.6306, -0.4153, 1.5141, 0.2224, -0.3691, 0.2622, 1.0033,
- 0.3425],
- [ 0.5477, -0.3851, 1.7961, -0.1304, -0.3055, 0.5085, 0.3830,
- 0.0682],
- [-2.2859, -2.2859, 1.2360, -1.1620, -0.5711, -0.9618, 0.1322,
- 0.1253],
- [ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220],
- [ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0234, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0234, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.3746544364839792
- step: 63
- running loss: 0.03769292756323776
- Train Steps: 63/90 Loss: 0.0377 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4098, 0.8575, 0.3617, 0.3700, 0.3550, 0.5988, 0.5667],
- [0.6233, 0.4091, 0.8100, 0.2950, 0.3563, 0.3883, 0.6013, 0.5200],
- [0.6132, 0.3930, 0.8672, 0.4754, 0.3712, 0.5222, 0.5974, 0.5098],
- [0.6257, 0.4024, 0.8612, 0.5352, 0.4361, 0.5253, 0.6680, 0.5166],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6144, 0.4032, 0.8563, 0.3283, 0.3525, 0.4200, 0.5775, 0.5583]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7226, -0.3009, 1.7418, -0.4842, -0.5559, -0.6611, 0.4734, 0.2265],
- [ 0.4656, -0.4734, 1.4132, -0.7718, -0.6248, -0.4430, 0.5061, 0.1726],
- [ 0.5246, -0.4583, 1.6505, 0.0021, -0.4564, 0.2503, 0.5228, 0.1319],
- [ 0.3909, -0.5617, 1.6733, 0.2320, -0.2253, 0.2285, 0.9119, 0.1082],
- [ 0.7132, -0.3086, 0.9763, -0.9335, -0.6716, -0.7552, 0.1523, 0.2934],
- [ 0.4919, -0.4394, 1.6127, 0.4938, -0.3679, -0.0914, 0.3918, 0.3192],
- [ 0.6757, -0.3431, 1.2730, -1.1612, -0.1780, -1.3853, 0.4469, 0.1643],
- [ 0.1408, -0.6819, 1.6216, -0.6210, -0.6415, -0.3399, 0.3708, 0.2362]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5783, -0.3933, 1.6748, -0.6154, -0.5769, -0.6462, 0.4797,
- 0.3315],
- [ 0.5932, -0.3962, 1.4554, -0.9233, -0.6404, -0.4922, 0.4912,
- 0.1159],
- [ 0.5466, -0.4706, 1.7198, -0.0903, -0.5712, 0.1261, 0.4733,
- 0.0688],
- [ 0.6042, -0.4273, 1.6920, 0.1860, -0.2717, 0.1406, 0.7997,
- 0.1004],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.5520, -0.4237, 1.6691, -0.7694, -0.6577, -0.3460, 0.3815,
- 0.2930]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.3852399922907352
- step: 64
- running loss: 0.03726937487954274
- Train Steps: 64/90 Loss: 0.0373 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6125, 0.3974, 0.7725, 0.2517, 0.3538, 0.3317, 0.5887, 0.5500],
- [0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006],
- [0.6087, 0.3976, 0.8337, 0.3867, 0.3713, 0.3117, 0.5938, 0.5300]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.2325, -0.9602, 1.2720, -1.0271, -0.3637, -0.9876, 0.4289, 0.2526],
- [ 0.4505, -0.5013, 1.8136, 0.0927, -0.1843, 0.4526, 0.6604, 0.1773],
- [ 0.8687, -0.2430, 1.0114, -1.1226, -0.5207, -0.9863, 0.3914, 0.2113],
- [ 0.6572, -0.3616, 1.5121, -0.5709, -0.6501, -0.3371, 0.4752, 0.4255],
- [ 0.7217, -0.3336, 1.3004, -1.0443, -0.2111, -1.2127, 0.4349, 0.1557],
- [ 0.3431, -0.5483, 1.2653, -0.8805, -0.6536, -0.6157, 0.3194, 0.1887],
- [ 0.3079, -0.6517, 1.7307, 0.5960, -0.5675, 0.1268, 0.7210, 0.0536],
- [ 0.6070, -0.3925, 1.6147, -0.2691, -0.6091, -0.5846, 0.4420, 0.1639]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698],
- [ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.5430, -0.4503, 1.2822, -1.1235, -0.6520, -0.7540, 0.4335,
- 0.2545],
- [ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261],
- [ 0.5255, -0.4495, 1.5651, -0.4999, -0.5711, -0.8463, 0.4566,
- 0.1621]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0348, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0348, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.420039724558592
- step: 65
- running loss: 0.03723138037782449
- Train Steps: 65/90 Loss: 0.0372 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6357, 0.4097, 0.9038, 0.3883, 0.4213, 0.2950, 0.6686, 0.5390],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [0.6203, 0.4096, 0.8862, 0.4267, 0.3538, 0.4117, 0.6025, 0.5650],
- [0.6117, 0.4019, 0.8538, 0.4067, 0.3513, 0.3583, 0.5663, 0.5133],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [0.6257, 0.4167, 0.8775, 0.3433, 0.3563, 0.4133, 0.6200, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5645, -0.4307, 1.5633, -0.5971, -0.4191, -0.9917, 0.6887, 0.1340],
- [ 0.7224, -0.3567, 1.5991, -0.2279, -0.4591, 0.3066, 0.8598, 0.1349],
- [ 0.2056, -0.6534, 1.6414, -0.3374, -0.5785, -0.4410, 0.4228, 0.3512],
- [ 0.9953, -0.1275, 1.5085, -0.5903, -0.7189, -0.6876, 0.1175, 0.0700],
- [ 0.3773, -0.5384, 1.5414, -0.1393, -0.3429, 0.0346, 0.3330, 0.2650],
- [ 0.5671, -0.4097, 1.5155, 0.2661, -0.4947, -0.1401, 0.4437, 0.1082],
- [ 0.5217, -0.4431, 1.5807, -0.2839, -0.1187, 0.0570, 0.4562, 0.2333],
- [ 0.3458, -0.5701, 1.5426, -0.6712, -0.6604, -0.4480, 0.5075, 0.3324]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6504, -0.3936, 1.8885, -0.4922, -0.3402, -0.9233, 0.8022,
- 0.2035],
- [ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554],
- [ 0.5790, -0.3940, 1.8076, -0.3152, -0.6520, -0.3844, 0.4970,
- 0.3238],
- [ 0.5393, -0.4294, 1.6575, -0.4075, -0.6635, -0.6308, 0.3296,
- 0.0851],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391],
- [ 0.5598, -0.4273, 1.7152, -0.1227, -0.0065, 0.1917, 0.5143,
- 0.2853],
- [ 0.6040, -0.3614, 1.7672, -0.7001, -0.6404, -0.3768, 0.5778,
- 0.3315]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0216, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0216, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.4416198935359716
- step: 66
- running loss: 0.03699424081115109
- Train Steps: 66/90 Loss: 0.0370 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833],
- [0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268],
- [ nan, nan, 0.7725, 0.2611, 0.3675, 0.2733, 0.5413, 0.5167],
- [0.6129, 0.4063, 0.8738, 0.5250, 0.4313, 0.4733, 0.5230, 0.5874],
- [0.6261, 0.4066, 0.8325, 0.2150, 0.4763, 0.2667, 0.7002, 0.5633]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5820, -0.4485, 1.6251, -0.0173, -0.6775, 0.0058, 0.4772, 0.1054],
- [ 0.8377, -0.2557, 1.0665, -1.3392, -0.4191, -1.2162, 0.4193, 0.1196],
- [ 0.7974, -0.2742, 1.7813, -0.2587, -0.6334, -0.5775, 0.6602, 0.2203],
- [ 0.5028, -0.4743, 1.6421, -0.0655, -0.2851, 0.0431, 0.2469, 0.1449],
- [ 0.6925, -0.3438, 1.5984, -0.0479, -0.4077, 0.1826, 0.6601, 0.2109],
- [-1.8189, -2.0004, 1.1048, -1.0873, -0.5757, -0.9374, 0.1628, 0.3239],
- [ 0.8944, -0.1868, 1.5583, 0.3516, -0.4822, 0.1158, 0.2916, 0.2641],
- [ 0.9293, -0.2237, 1.5071, -1.1302, -0.2633, -1.0934, 1.0246, 0.1812]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534],
- [ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082],
- [ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474],
- [-2.2859, -2.2859, 1.2820, -1.0801, -0.5885, -1.0234, 0.2141,
- 0.1005],
- [ 0.5453, -0.4091, 1.7499, 0.1390, -0.2940, -0.0996, 0.1300,
- 0.4272],
- [ 0.6059, -0.4080, 1.5594, -1.2928, -0.0861, -1.0542, 0.9485,
- 0.3157]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0246, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0246, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.466248968616128
- step: 67
- running loss: 0.03680968609874818
- Train Steps: 67/90 Loss: 0.0368 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6279, 0.4050, 0.8025, 0.2200, 0.4313, 0.2733, 0.6820, 0.5320],
- [0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6125, 0.3974, 0.7725, 0.2517, 0.3538, 0.3317, 0.5887, 0.5500],
- [0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183],
- [0.6307, 0.4045, 0.8025, 0.5833, 0.3775, 0.4867, 0.6892, 0.5459],
- [0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5870, -0.4610, 1.3650, -1.2479, -0.2693, -0.9599, 0.7566, 0.1759],
- [ 0.4017, -0.5527, 1.6589, -0.4986, -0.6020, -0.2526, 0.3103, 0.3728],
- [ 0.7367, -0.3825, 1.8921, 0.1706, -0.5680, -0.3303, 0.6146, -0.0270],
- [ 0.3524, -0.5784, 1.2265, -1.1055, -0.5940, -0.6406, 0.4431, 0.2381],
- [ 0.4678, -0.5461, 1.7167, -0.0406, -0.5576, -0.2412, 0.6385, 0.1505],
- [ 0.4932, -0.5308, 1.5870, 0.1907, -0.4714, 0.0821, 0.8611, 0.1655],
- [ 0.6406, -0.3934, 1.2931, -0.9672, -0.4253, -0.9065, 0.1885, 0.2612],
- [ 0.3304, -0.5758, 1.4735, -0.6033, -0.6111, -0.6446, 0.1847, 0.2778]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6141, -0.4153, 1.4208, -1.2697, -0.2940, -1.0234, 0.8644,
- 0.1715],
- [ 0.5757, -0.3917, 1.7095, -0.4768, -0.6346, -0.4229, 0.3931,
- 0.3238],
- [ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.5430, -0.4503, 1.2822, -1.1235, -0.6520, -0.7540, 0.4335,
- 0.2545],
- [ 0.5944, -0.4543, 1.8018, 0.0082, -0.6058, -0.4306, 0.4162,
- 0.1082],
- [ 0.6273, -0.4177, 1.4208, 0.4085, -0.5423, -0.0380, 0.8973,
- 0.2356],
- [ 0.5695, -0.3927, 1.3226, -0.9002, -0.4672, -1.1928, 0.1737,
- 0.3700],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0130, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0130, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.479281820356846
- step: 68
- running loss: 0.036460026769953614
- Train Steps: 68/90 Loss: 0.0365 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6168, 0.4081, 0.8438, 0.3367, 0.3500, 0.4667, 0.6212, 0.5400],
- [0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822],
- [0.6203, 0.4078, 0.8800, 0.5083, 0.3900, 0.5000, 0.6100, 0.5583],
- [0.6183, 0.4076, 0.8838, 0.4517, 0.3813, 0.4483, 0.5775, 0.5633],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6216, 0.4100, 0.7350, 0.2067, 0.4325, 0.2050, 0.5950, 0.5333],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4208, -0.5286, 1.5660, -0.6811, -0.6707, -0.0938, 0.6795, 0.2433],
- [ 0.4844, -0.5045, 1.7132, -0.2291, -0.6871, -0.6187, 0.3231, 0.2310],
- [ 0.5048, -0.5052, 1.8143, 0.1818, -0.5250, 0.1356, 0.5827, 0.1163],
- [ 0.4867, -0.5049, 1.7367, -0.1605, -0.5433, -0.1209, 0.4980, 0.3073],
- [ 0.8457, -0.3071, 1.1189, -1.3358, -0.4476, -1.1324, 0.6848, 0.1563],
- [ 0.5788, -0.4468, 1.1256, -1.3333, -0.3039, -1.3215, 0.4436, 0.2026],
- [ 0.4527, -0.5231, 1.6824, -0.2289, -0.6501, -0.3667, 0.5120, 0.2052],
- [ 0.2446, -0.6634, 1.8283, 0.1008, -0.0463, 0.0247, 0.2871, 0.1155]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5631, -0.4008, 1.6113, -0.7309, -0.6693, -0.1304, 0.5836,
- 0.2083],
- [ 0.5697, -0.4113, 1.7499, -0.2767, -0.6404, -0.7155, 0.1576,
- 0.4032],
- [ 0.5794, -0.4023, 1.7788, 0.0620, -0.4845, 0.0236, 0.5316,
- 0.2930],
- [ 0.5700, -0.4032, 1.7961, -0.1997, -0.5249, -0.2151, 0.3815,
- 0.3161],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.5853, -0.3920, 1.1090, -1.3313, -0.2882, -1.3390, 0.4624,
- 0.1775],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0102, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0102, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.4894718173891306
- step: 69
- running loss: 0.03607930170129175
- Train Steps: 69/90 Loss: 0.0361 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585],
- [0.6200, 0.4086, 0.8414, 0.2974, 0.4117, 0.2274, 0.5869, 0.5391],
- [0.6211, 0.4069, 0.8750, 0.5117, 0.4150, 0.4900, 0.5875, 0.5167],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136],
- [0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097],
- [0.6188, 0.4099, 0.7400, 0.2433, 0.3962, 0.2750, 0.6162, 0.5467],
- [0.6163, 0.4001, 0.8788, 0.5033, 0.4012, 0.4633, 0.5338, 0.5767],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4360, -0.4805, 1.6811, -0.2378, -0.3917, 0.2554, 0.4442, 0.2100],
- [ 0.7784, -0.3134, 1.6368, -0.8815, -0.3902, -1.1743, 0.6127, 0.2010],
- [ 0.4541, -0.5240, 1.7817, 0.1039, -0.5630, -0.0397, 0.6083, 0.1318],
- [ 0.6691, -0.3933, 0.9687, -1.3212, -0.6521, -1.1426, 0.4844, 0.1541],
- [ 0.1000, -0.7177, 1.7854, -0.0132, -0.3303, -0.0130, 0.2209, 0.1332],
- [ 0.6156, -0.4212, 1.1427, -1.2524, -0.5990, -1.0458, 0.6910, 0.2183],
- [ 0.3454, -0.6151, 1.7236, 0.0457, -0.5889, -0.2072, 0.4643, 0.2432],
- [ 0.4638, -0.4857, 1.7544, -0.1624, -0.1677, -0.1337, 0.4243, 0.2240]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936],
- [ 0.5776, -0.3987, 1.6005, -0.9121, -0.3844, -1.2358, 0.4247,
- 0.2043],
- [ 0.5828, -0.4066, 1.7557, 0.0774, -0.3691, -0.0226, 0.4277,
- 0.1005],
- [ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862],
- [ 0.5515, -0.4225, 1.7788, -0.0534, -0.2016, 0.1929, 0.1568,
- 0.0682],
- [ 0.5725, -0.3926, 1.1321, -1.1620, -0.4557, -1.0157, 0.5605,
- 0.2391],
- [ 0.5610, -0.4381, 1.7730, 0.0390, -0.4326, -0.1458, 0.1794,
- 0.3777],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0172, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0172, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.506653221324086
- step: 70
- running loss: 0.03580933173320123
- Train Steps: 70/90 Loss: 0.0358 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4153, 0.9000, 0.4533, 0.4025, 0.2633, 0.6223, 0.4967],
- [0.6198, 0.4115, 0.7762, 0.2717, 0.3713, 0.3200, 0.5837, 0.5683],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334],
- [0.6212, 0.4033, 0.8938, 0.4167, 0.3813, 0.4267, 0.5613, 0.5583],
- [0.6156, 0.4125, 0.8850, 0.4833, 0.4550, 0.5817, 0.5765, 0.5084],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8442, -0.2878, 1.8822, -0.1673, -0.3492, -1.0421, 0.6640, 0.0426],
- [ 0.7231, -0.3500, 1.3377, -0.9462, -0.5609, -0.9033, 0.3942, 0.3295],
- [ 0.1892, -0.6626, 1.2548, -0.9844, -0.6395, -0.4658, 0.4720, 0.2314],
- [ 0.4308, -0.5277, 1.1750, -1.1513, -0.5298, -0.9072, 0.5386, 0.2045],
- [ 0.3504, -0.5871, 1.8031, -0.1725, -0.5336, -0.3180, 0.3269, 0.2640],
- [ 0.2974, -0.5743, 1.7718, 0.0869, -0.1664, 0.3221, 0.2445, 0.2308],
- [ 0.6129, -0.4227, 1.6943, -0.1902, -0.5263, 0.1815, 0.7341, 0.1576],
- [ 0.4973, -0.4933, 1.8406, -0.6789, -0.4059, -0.7973, 0.6918, 0.1629]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6055, -0.3676, 1.8711, -0.1920, -0.4268, -1.0696, 0.5887,
- 0.0081],
- [ 0.5768, -0.3852, 1.2995, -1.0311, -0.5711, -0.8079, 0.4104,
- 0.3392],
- [ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545],
- [ 0.5730, -0.4562, 1.2195, -1.2440, -0.5497, -0.7711, 0.5704,
- 0.1779],
- [ 0.5832, -0.4231, 1.8423, -0.3614, -0.5249, -0.3152, 0.3065,
- 0.2930],
- [ 0.5573, -0.3808, 1.8018, -0.0534, -0.1843, 0.4008, 0.3769,
- 0.0622],
- [ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0133, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0133, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5199545985087752
- step: 71
- running loss: 0.03549231828885599
- Train Steps: 71/90 Loss: 0.0355 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [0.6117, 0.4019, 0.8538, 0.4067, 0.3513, 0.3583, 0.5663, 0.5133],
- [0.6222, 0.4072, 0.7164, 0.2166, 0.3738, 0.3167, 0.6100, 0.5533],
- [0.6151, 0.4125, 0.8738, 0.4417, 0.3575, 0.3783, 0.5138, 0.5483],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114],
- [0.6140, 0.4034, 0.8850, 0.4317, 0.4288, 0.5067, 0.5825, 0.5533],
- [0.6161, 0.4024, 0.8838, 0.4583, 0.3688, 0.3733, 0.5311, 0.5344],
- [0.6268, 0.4052, 0.8175, 0.2250, 0.4688, 0.1917, 0.6375, 0.5267]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5248, -0.4641, 1.7781, -0.0666, -0.2967, -0.0768, 0.3448, 0.2732],
- [ 0.6372, -0.3929, 1.7426, -0.5425, -0.6774, -0.6999, 0.3333, 0.1048],
- [ 0.3841, -0.5738, 1.0240, -1.3658, -0.5510, -0.8458, 0.6108, 0.2919],
- [ 0.4925, -0.4407, 1.6825, -0.1932, -0.6649, -0.5765, 0.1744, 0.2428],
- [ 0.3824, -0.5599, 1.7717, 0.1097, -0.2361, 0.2542, 0.5080, 0.1504],
- [ 0.6134, -0.4053, 1.7990, -0.2906, -0.3894, 0.1740, 0.5379, 0.2180],
- [ 0.6294, -0.4055, 1.8176, -0.1581, -0.6121, -0.5588, 0.3933, 0.0921],
- [ 0.5168, -0.5085, 1.4544, -1.3228, -0.1340, -1.4168, 0.8669, 0.1577]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057],
- [ 0.5393, -0.4294, 1.6575, -0.4075, -0.6635, -0.6308, 0.3296,
- 0.0851],
- [ 0.5878, -0.4052, 1.0229, -1.2855, -0.5596, -0.8232, 0.5316,
- 0.2699],
- [ 0.5551, -0.3807, 1.7499, -0.2459, -0.6346, -0.5384, 0.0871,
- 0.2468],
- [ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764],
- [ 0.5499, -0.4225, 1.8018, -0.2921, -0.3055, 0.0543, 0.4046,
- 0.2699],
- [ 0.5598, -0.4273, 1.7961, -0.1689, -0.5827, -0.5615, 0.1671,
- 0.1824],
- [ 0.6092, -0.4143, 1.4901, -1.2467, -0.1208, -1.4006, 0.6587,
- 0.1467]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0061, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0061, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5260266615077853
- step: 72
- running loss: 0.03508370363205257
- Train Steps: 72/90 Loss: 0.0351 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6268, 0.4061, 0.8350, 0.2433, 0.4575, 0.2283, 0.6350, 0.5300],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6218, 0.4137, 0.7263, 0.2233, 0.4075, 0.2650, 0.6212, 0.5783],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [0.6168, 0.4029, 0.8523, 0.3417, 0.3588, 0.5000, 0.6125, 0.5400],
- [0.6296, 0.4076, 0.8400, 0.5583, 0.3700, 0.4367, 0.6876, 0.5494],
- [0.6040, 0.4002, 0.7338, 0.2267, 0.3975, 0.2100, 0.5231, 0.4778]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6334, -0.4061, 1.6728, -1.1904, -0.2485, -1.1383, 0.6056, 0.1502],
- [ 0.3768, -0.5504, 1.8360, 0.0306, -0.2621, 0.2948, 0.3617, 0.1141],
- [ 0.7082, -0.3276, 1.7454, 0.2261, -0.4818, -0.0875, 0.3203, 0.2773],
- [ 0.4360, -0.5399, 1.2143, -1.1695, -0.4310, -1.0023, 0.5435, 0.3108],
- [ 0.7105, -0.3180, 1.7618, -0.0368, -0.3727, -0.7612, 0.3573, 0.3785],
- [ 0.3095, -0.5736, 1.6237, -0.7393, -0.6324, -0.0113, 0.5981, 0.2196],
- [ 0.5194, -0.4909, 1.8626, 0.1112, -0.5627, -0.3060, 0.6890, 0.1156],
- [ 0.3677, -0.5692, 1.1701, -1.4718, -0.4682, -1.2223, 0.2472, 0.0670]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6093, -0.4104, 1.5709, -1.1620, -0.1727, -1.2313, 0.6471,
- 0.1621],
- [ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007],
- [ 0.5863, -0.3749, 1.0686, -1.2543, -0.4037, -1.0619, 0.5836,
- 0.3854],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762],
- [ 0.5631, -0.4249, 1.6509, -0.7078, -0.6289, 0.0236, 0.5432,
- 0.2083],
- [ 0.6224, -0.4032, 1.5940, 0.2930, -0.5769, -0.2690, 0.8900,
- 0.2516],
- [ 0.5037, -0.4375, 1.1032, -1.2390, -0.4499, -1.3159, 0.1301,
- -0.0791]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0123, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0123, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5383365219458938
- step: 73
- running loss: 0.03477173317734101
- Train Steps: 73/90 Loss: 0.0348 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679],
- [0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250],
- [0.6200, 0.3993, 0.8519, 0.4923, 0.3962, 0.4717, 0.6013, 0.5433],
- [0.6080, 0.4010, 0.8750, 0.4500, 0.4825, 0.5617, 0.5837, 0.5583],
- [0.6144, 0.4032, 0.8563, 0.3283, 0.3525, 0.4200, 0.5775, 0.5583],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6142, 0.3982, 0.8650, 0.4883, 0.3912, 0.4317, 0.5315, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.4418e-01, -4.4780e-01, 1.1559e+00, -1.3561e+00, -5.1064e-01,
- -1.0292e+00, 4.7806e-01, 1.2951e-01],
- [-1.2765e+00, -1.6316e+00, 1.6362e+00, -1.2697e+00, 1.1559e-01,
- -9.7170e-01, 9.1181e-01, 3.6843e-01],
- [ 8.5290e-01, -2.6447e-01, 1.9745e+00, -1.9718e-01, -5.8324e-01,
- -2.4832e-01, 6.2102e-01, 1.3964e-01],
- [ 6.6774e-01, -3.8083e-01, 1.7173e+00, 6.7244e-04, -4.7820e-01,
- -1.5158e-01, 5.3323e-01, 1.8132e-01],
- [ 6.6050e-01, -3.5493e-01, 1.7779e+00, -1.7826e-01, -1.4851e-01,
- 1.2158e-01, 2.9447e-01, 1.5800e-01],
- [ 1.0210e+00, -1.2668e-01, 1.6734e+00, -6.9517e-01, -6.8150e-01,
- -4.4491e-01, 4.3289e-01, 2.1880e-01],
- [ 6.1925e-01, -3.6422e-01, 1.4968e+00, -5.0518e-01, -4.9594e-01,
- -1.0194e+00, 1.9179e-01, 3.0572e-01],
- [ 7.5316e-01, -3.0276e-01, 1.7571e+00, -1.9632e-03, -4.9168e-01,
- -3.3726e-01, 1.9005e-01, 1.0943e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371],
- [ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390],
- [ 0.5777, -0.4415, 1.6491, -0.0122, -0.4557, -0.1073, 0.4912,
- 0.2237],
- [ 0.5223, -0.4336, 1.7557, -0.2074, -0.0573, 0.3084, 0.4104,
- 0.2930],
- [ 0.5520, -0.4237, 1.6691, -0.7694, -0.6577, -0.3460, 0.3815,
- 0.2930],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.5513, -0.4467, 1.7095, -0.0303, -0.4788, -0.2921, 0.1692,
- 0.1854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0367, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0367, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5750327026471496
- step: 74
- running loss: 0.03479773922496148
- Train Steps: 74/90 Loss: 0.0348 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6127, 0.4084, 0.8700, 0.4467, 0.3987, 0.4317, 0.5013, 0.5471],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6164, 0.4076, 0.8838, 0.4117, 0.3713, 0.5550, 0.6238, 0.5350],
- [0.6064, 0.4019, 0.8650, 0.4517, 0.4037, 0.5367, 0.5703, 0.5609],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6286, 0.4040, 0.8696, 0.3047, 0.3924, 0.2887, 0.6300, 0.5367],
- [0.6346, 0.4092, 0.7712, 0.5917, 0.4037, 0.4767, 0.7343, 0.5725],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5096, -0.4283, 1.7634, -0.3190, -0.4290, -0.4513, 0.0348, 0.2677],
- [ 0.3356, -0.5704, 1.5252, -1.1793, -0.3212, -1.1210, 0.4800, 0.1549],
- [ 0.5342, -0.4191, 1.7516, -0.4826, -0.4391, 0.1963, 0.6230, 0.2563],
- [ 0.5286, -0.4179, 1.7052, -0.2810, -0.3513, 0.1736, 0.3214, 0.2292],
- [ 0.6839, -0.3655, 1.7865, 0.1565, -0.4345, -0.2359, 0.5560, 0.0668],
- [ 0.5197, -0.4576, 1.6927, -0.9587, -0.3475, -1.0610, 0.6220, 0.1999],
- [ 0.7338, -0.3127, 1.4894, -0.0715, -0.4081, -0.2553, 0.6845, 0.3432],
- [ 0.4452, -0.4472, 1.5220, -0.7479, -0.5569, -0.9219, 0.1381, 0.2604]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5441, -0.3997, 1.7326, -0.2228, -0.4441, -0.2921, 0.0296,
- 0.2409],
- [ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807],
- [ 0.5614, -0.4032, 1.7961, -0.3844, -0.5711, 0.2776, 0.5952,
- 0.1852],
- [ 0.5151, -0.4296, 1.7095, -0.1997, -0.4210, 0.1929, 0.3484,
- 0.3047],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.6174, -0.4201, 1.7309, -0.8784, -0.4735, -0.9524, 0.6242,
- 0.1931],
- [ 0.6454, -0.3960, 1.2764, 0.4470, -0.4210, -0.0842, 1.1057,
- 0.3585],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0165, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0165, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5915102371945977
- step: 75
- running loss: 0.0345534698292613
- Train Steps: 75/90 Loss: 0.0346 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6329, 0.4175, 0.8550, 0.2333, 0.5425, 0.2250, 0.7398, 0.5609],
- [0.6357, 0.4159, 0.8788, 0.5583, 0.3638, 0.4433, 0.6488, 0.5297],
- [0.6137, 0.4035, 0.8850, 0.4417, 0.3900, 0.4283, 0.5449, 0.5617],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6139, 0.4019, 0.7137, 0.2150, 0.4375, 0.1533, 0.5293, 0.5006],
- [0.6263, 0.4233, 0.7924, 0.4626, 0.3788, 0.2883, 0.5573, 0.6047],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2447, -0.6079, 1.0104, -1.2852, -0.3396, -1.1869, 0.1303, 0.1883],
- [-0.0586, -0.8399, 1.7326, -1.1093, 0.0978, -0.9643, 0.9543, 0.2626],
- [ 0.6468, -0.3746, 1.8626, 0.2272, -0.6019, -0.0139, 0.5560, 0.1825],
- [ 0.6586, -0.3544, 1.8994, -0.1186, -0.5412, -0.0036, 0.1801, 0.2295],
- [ 0.6833, -0.3836, 1.8911, -0.0879, -0.6091, -0.1609, 0.6603, 0.1590],
- [ 0.1767, -0.6590, 1.0074, -1.3677, -0.3177, -1.2696, 0.1919, 0.1461],
- [ 0.6589, -0.3171, 1.5171, -0.1376, -0.4783, -0.5075, 0.2700, 0.4525],
- [ 0.7460, -0.2777, 1.8169, -0.8115, -0.1521, -0.7355, 0.5067, 0.1502]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.6372, -0.3575, 1.6633, -1.2082, 0.2199, -1.2467, 1.1313,
- 0.3050],
- [ 0.6504, -0.3647, 1.7730, 0.2930, -0.6058, -0.2382, 0.7109,
- 0.1608],
- [ 0.5488, -0.4221, 1.8018, -0.2459, -0.4845, -0.3075, 0.2309,
- 0.3087],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [ 0.5496, -0.4295, 1.0109, -1.2928, -0.2651, -1.5777, 0.1589,
- 0.0261],
- [ 0.6069, -0.3309, 1.3742, -0.1493, -0.5365, -0.9541, 0.2884,
- 0.5071],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0396, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0396, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.6311407880857587
- step: 76
- running loss: 0.03462027352744419
- Train Steps: 76/90 Loss: 0.0346 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6022, 0.3994, 0.8025, 0.3350, 0.3350, 0.4400, 0.5565, 0.5025],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [ nan, nan, 0.7425, 0.2117, 0.3937, 0.2433, 0.5438, 0.5567],
- [0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6245, 0.4100, 0.7762, 0.2583, 0.4963, 0.1517, 0.5875, 0.5417],
- [0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5096, -0.4109, 1.4637, -0.8476, -0.5801, -0.3472, 0.3674, 0.1769],
- [ 0.8256, -0.1998, 1.7899, -0.3321, -0.4646, -0.4428, 0.3457, 0.2799],
- [-1.3500, -1.6118, 1.2457, -1.3102, -0.3704, -1.1211, 0.2283, 0.2938],
- [ 0.8079, -0.2665, 1.8021, 0.1917, -0.4374, -0.2552, 0.4940, 0.0330],
- [ 0.8357, -0.2062, 1.8751, -0.1593, -0.4265, 0.0671, 0.5050, 0.2274],
- [ 0.6870, -0.3474, 1.9166, -0.1231, -0.2887, -0.2234, 0.7814, 0.2500],
- [ 0.5397, -0.4013, 1.2299, -1.1997, 0.0241, -1.3590, 0.4714, 0.2773],
- [ 0.7879, -0.2330, 1.6780, -0.3961, -0.5048, -0.8428, 0.1642, 0.2339]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.4956, -0.4413, 1.4208, -0.7386, -0.7386, -0.2536, 0.2843,
- 0.0351],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [-2.2859, -2.2859, 1.1436, -1.3082, -0.4672, -1.1620, 0.2256,
- 0.2853],
- [ 0.6335, -0.4162, 1.7499, 0.3084, -0.4961, -0.2459, 0.6524,
- -0.0102],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.6454, -0.3719, 1.9115, -0.1304, -0.5076, -0.2844, 1.0033,
- 0.4386],
- [ 0.5985, -0.3921, 1.2995, -1.0927, 0.0062, -1.5854, 0.4277,
- 0.2160],
- [ 0.5470, -0.3952, 1.6377, -0.4253, -0.6289, -0.8079, 0.0249,
- 0.2116]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0323, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0323, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.6634310306981206
- step: 77
- running loss: 0.03459001338568988
- Train Steps: 77/90 Loss: 0.0346 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6180, 0.4064, 0.7200, 0.2467, 0.4275, 0.2367, 0.5963, 0.6217],
- [0.6185, 0.4079, 0.8838, 0.4617, 0.4838, 0.5650, 0.6175, 0.5850],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6175, 0.4013, 0.8900, 0.4500, 0.4375, 0.5850, 0.6175, 0.5383],
- [0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901],
- [0.6277, 0.4036, 0.8688, 0.3617, 0.3925, 0.2600, 0.6132, 0.4742],
- [0.6160, 0.4093, 0.8314, 0.4417, 0.3675, 0.4583, 0.5250, 0.5808],
- [0.6026, 0.3979, 0.8550, 0.4233, 0.3613, 0.5233, 0.5582, 0.4967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4709, -0.4453, 1.0841, -1.2571, -0.3121, -1.3903, 0.4219, 0.4682],
- [ 0.5359, -0.4140, 1.8498, -0.2117, -0.0671, 0.0072, 0.5131, 0.3493],
- [ 0.7026, -0.3289, 1.8133, -0.0557, -0.2773, -0.1168, 0.3260, 0.2713],
- [ 0.3245, -0.5751, 1.8032, -0.2144, -0.1927, 0.1364, 0.4431, 0.2329],
- [ 0.6127, -0.4122, 1.7185, -0.8684, -0.6133, -0.9153, 0.6240, 0.0393],
- [ 0.4521, -0.5100, 1.7841, -0.8017, -0.4028, -1.2877, 0.5973, 0.0130],
- [ 0.4815, -0.4352, 1.6452, -0.2379, -0.5157, -0.3379, 0.2251, 0.3758],
- [ 0.7428, -0.3001, 1.7455, -0.2779, -0.5834, -0.0526, 0.3788, 0.1368]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5687, -0.4088, 1.0397, -1.1466, -0.3113, -1.1928, 0.4681,
- 0.5855],
- [ 0.5707, -0.4017, 1.7961, -0.1535, -0.0515, 0.3238, 0.5663,
- 0.4162],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.5664, -0.4321, 1.8249, -0.2074, -0.2651, 0.4162, 0.5663,
- 0.2006],
- [ 0.6031, -0.4307, 1.7268, -0.8002, -0.6058, -0.6462, 0.6442,
- -0.0220],
- [ 0.6134, -0.4218, 1.7268, -0.6154, -0.4730, -1.0850, 0.5463,
- -0.0957],
- [ 0.5592, -0.3956, 1.5543, -0.2456, -0.5885, -0.1689, 0.1392,
- 0.3968],
- [ 0.4974, -0.4482, 1.6633, -0.3306, -0.6173, 0.1313, 0.2925,
- 0.0081]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0132, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0132, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.6766006434336305
- step: 78
- running loss: 0.03431539286453372
- Train Steps: 78/90 Loss: 0.0343 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4059, 0.8700, 0.4900, 0.4163, 0.5000, 0.6162, 0.5467],
- [0.6197, 0.4090, 0.7825, 0.2500, 0.4200, 0.2483, 0.5988, 0.5667],
- [0.6026, 0.3979, 0.8550, 0.4233, 0.3613, 0.5233, 0.5582, 0.4967],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019],
- [0.6339, 0.4123, 0.8638, 0.5367, 0.4075, 0.5467, 0.7517, 0.5436],
- [0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927],
- [0.6261, 0.4029, 0.8720, 0.3364, 0.3665, 0.3753, 0.6531, 0.5183],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5273, -0.4399, 1.8495, -0.1630, -0.2124, -0.1018, 0.4078, 0.2698],
- [ 0.5313, -0.4043, 1.3250, -1.3841, -0.2848, -1.3463, 0.4527, 0.3200],
- [ 0.6812, -0.3249, 1.7316, -0.3940, -0.4961, -0.0325, 0.3028, 0.1664],
- [ 0.5938, -0.4122, 1.8738, -0.2503, -0.4744, -0.6442, 0.5637, 0.1794],
- [ 0.7607, -0.2876, 1.7610, -0.0193, -0.2895, 0.1308, 0.7129, 0.3571],
- [ 0.5551, -0.4227, 1.8155, 0.0922, -0.4076, -0.4884, 0.3522, 0.0836],
- [ 0.4960, -0.4693, 1.6879, -0.8681, -0.4898, -0.7002, 0.5810, 0.1831],
- [ 0.3679, -0.5129, 1.5779, -0.5601, -0.5443, -0.5261, 0.1086, 0.2616]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5776, -0.4109, 1.7326, -0.0226, -0.3633, 0.0236, 0.5605,
- 0.2391],
- [ 0.5764, -0.3969, 1.3284, -1.1312, -0.3460, -1.1389, 0.4797,
- 0.3315],
- [ 0.4974, -0.4482, 1.6633, -0.3306, -0.6173, 0.1313, 0.2925,
- 0.0081],
- [ 0.6372, -0.4129, 1.8942, -0.0765, -0.6173, -0.4768, 0.6999,
- 0.0325],
- [ 0.6421, -0.3816, 1.7037, 0.1929, -0.4037, 0.2391, 1.1861,
- 0.2249],
- [ 0.6335, -0.4162, 1.7499, 0.3084, -0.4961, -0.2459, 0.6524,
- -0.0102],
- [ 0.6059, -0.4249, 1.7420, -0.7321, -0.5931, -0.5523, 0.7307,
- 0.1080],
- [ 0.5367, -0.4294, 1.5709, -0.4999, -0.6693, -0.3075, 0.2455,
- 0.3559]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0195, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0195, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.6960538187995553
- step: 79
- running loss: 0.0341272635291083
- Train Steps: 79/90 Loss: 0.0341 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6262, 0.4085, 0.8438, 0.3150, 0.4025, 0.2633, 0.6339, 0.4810],
- [0.6203, 0.4096, 0.8862, 0.4267, 0.3538, 0.4117, 0.6025, 0.5650],
- [0.6197, 0.3930, 0.8793, 0.4736, 0.4152, 0.5464, 0.6308, 0.5235],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [0.6364, 0.4154, 0.8938, 0.3717, 0.4500, 0.2583, 0.6448, 0.5285],
- [0.6361, 0.4102, 0.8650, 0.2983, 0.4888, 0.2000, 0.6702, 0.5459],
- [0.6113, 0.4006, 0.8700, 0.5350, 0.3638, 0.3767, 0.5097, 0.4882],
- [0.6198, 0.4164, 0.8700, 0.5067, 0.4625, 0.5650, 0.5464, 0.5197]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6406, -0.3394, 1.5624, -0.9658, -0.4175, -1.0238, 0.4895, 0.0445],
- [ 0.8191, -0.2232, 1.7964, -0.2992, -0.5615, -0.4212, 0.4417, 0.3493],
- [ 0.4210, -0.5028, 1.7112, -0.0776, -0.3531, 0.2189, 0.5235, 0.2024],
- [ 0.3600, -0.5230, 1.3145, -1.0232, -0.6596, -0.3986, 0.5122, 0.2381],
- [ 0.4282, -0.4474, 1.7761, -0.7220, -0.1502, -1.1927, 0.5608, 0.2853],
- [ 0.2045, -0.6083, 1.6021, -1.0291, -0.0299, -1.2599, 0.7801, 0.2161],
- [ 0.6250, -0.3410, 1.7121, 0.1525, -0.6350, -0.5269, 0.2029, 0.1414],
- [ 0.5669, -0.3527, 1.7078, 0.0535, -0.1782, 0.1812, 0.2248, 0.2197]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6064, -0.3990, 1.6113, -0.8309, -0.4268, -1.0696, 0.6421,
- -0.0640],
- [ 0.5790, -0.3940, 1.8076, -0.3152, -0.6520, -0.3844, 0.4970,
- 0.3238],
- [ 0.5762, -0.4706, 1.7754, -0.0984, -0.3680, 0.2380, 0.6277,
- 0.1322],
- [ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545],
- [ 0.6537, -0.3671, 1.8423, -0.5692, -0.2074, -1.0927, 0.6926,
- 0.1554],
- [ 0.6520, -0.3912, 1.7095, -0.9079, -0.0284, -1.3621, 0.8096,
- 0.2356],
- [ 0.5379, -0.4358, 1.7326, 0.1852, -0.6058, -0.5461, 0.0684,
- -0.0310],
- [ 0.5770, -0.3624, 1.7326, 0.0543, -0.1497, 0.3238, 0.2378,
- 0.1146]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0123, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0123, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.7083711810410023
- step: 80
- running loss: 0.03385463976301253
- Train Steps: 80/90 Loss: 0.0339 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6064, 0.4019, 0.8650, 0.4517, 0.4037, 0.5367, 0.5703, 0.5609],
- [0.6284, 0.4093, 0.8900, 0.4700, 0.3650, 0.3850, 0.6212, 0.5167],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6113, 0.4088, 0.6859, 0.2208, 0.4363, 0.1700, 0.5188, 0.5533],
- [ nan, nan, 0.9088, 0.3783, 0.4562, 0.2617, 0.6741, 0.5575],
- [0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750],
- [0.6124, 0.4075, 0.7696, 0.4153, 0.3475, 0.3767, 0.5157, 0.5427],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7237, -0.3149, 1.8418, -0.0973, -0.3929, 0.4974, 0.4936, 0.1480],
- [ 0.5533, -0.4386, 2.0288, -0.0327, -0.5607, -0.3373, 0.6171, 0.0117],
- [ 0.6416, -0.3521, 1.0816, -1.1016, -0.4769, -1.0356, 0.4465, 0.2484],
- [-0.0895, -0.8211, 1.0411, -1.2723, -0.3167, -1.3586, 0.1415, 0.1784],
- [ 0.0629, -0.7368, 2.1032, -0.5214, -0.1178, -1.0612, 0.8426, 0.1643],
- [ 0.6451, -0.3647, 1.2101, -1.0310, -0.4915, -0.8103, 0.5724, 0.2705],
- [ 0.6988, -0.3193, 1.5759, -0.4323, -0.6370, -0.3351, 0.2304, 0.1933],
- [ 0.6208, -0.3282, 1.7503, -0.0942, -0.2058, -0.9515, 0.4224, 0.3945]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5151, -0.4296, 1.7095, -0.1997, -0.4210, 0.1929, 0.3484,
- 0.3047],
- [ 0.6164, -0.3956, 1.8249, -0.1150, -0.6000, -0.5076, 0.5836,
- 0.1005],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391],
- [ 0.5377, -0.3978, 0.8824, -1.2663, -0.2709, -1.5007, 0.1102,
- 0.2699],
- [-2.2859, -2.2859, 1.9115, -0.5384, -0.1785, -1.0773, 0.8278,
- 0.2890],
- [ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700],
- [ 0.5427, -0.4035, 1.2688, -0.3675, -0.6808, -0.5461, 0.0959,
- 0.2206],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1471, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1471, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.855488333851099
- step: 81
- running loss: 0.03525294239322344
- Train Steps: 81/90 Loss: 0.0353 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933],
- [0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383],
- [0.6202, 0.4064, 0.7879, 0.2179, 0.4567, 0.1725, 0.5955, 0.5478],
- [0.6268, 0.4029, 0.8500, 0.2683, 0.3937, 0.3500, 0.6860, 0.5297],
- [0.6314, 0.4050, 0.8227, 0.5431, 0.4150, 0.5517, 0.7121, 0.5690],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6124, 0.4069, 0.8314, 0.5001, 0.3738, 0.4650, 0.5167, 0.5402],
- [0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.1345, -1.4592, 0.8842, -1.3519, -0.4274, -1.4106, 0.0831, 0.2845],
- [ 0.6571, -0.3376, 1.7894, 0.0479, -0.3040, -0.0971, 0.2572, 0.1928],
- [ 0.6914, -0.2991, 1.3371, -1.1903, -0.2251, -1.5271, 0.3750, 0.1489],
- [ 0.8150, -0.2614, 1.6584, -0.8945, -0.5536, -0.6788, 0.8310, 0.1009],
- [ 0.7828, -0.2719, 1.6696, 0.2097, -0.3416, 0.3041, 0.8104, 0.3228],
- [ 0.4134, -0.5005, 1.9845, -0.5028, -0.5451, -0.6079, 0.6033, 0.1341],
- [ 0.7405, -0.2666, 1.6761, 0.0836, -0.4790, -0.1078, 0.1919, 0.2615],
- [ 0.7103, -0.3291, 1.7885, 0.0517, -0.3564, -0.0208, 0.6138, 0.1718]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859e+00, -2.2859e+00, 8.0331e-01, -1.1250e+00, -3.8637e-01,
- -1.3082e+00, 1.1262e-01, 4.5430e-01],
- [ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01],
- [ 5.7870e-01, -4.0862e-01, 1.3535e+00, -1.2794e+00, -1.7640e-01,
- -1.4891e+00, 4.6447e-01, 2.4425e-01],
- [ 6.0919e-01, -4.2490e-01, 1.6402e+00, -1.0465e+00, -4.6721e-01,
- -6.6928e-01, 8.8267e-01, 1.6077e-01],
- [ 6.3060e-01, -4.1527e-01, 1.5141e+00, 2.2241e-01, -3.6905e-01,
- 2.6220e-01, 1.0033e+00, 3.4245e-01],
- [ 6.0733e-01, -4.0577e-01, 1.8885e+00, -4.9992e-01, -5.9423e-01,
- -4.7683e-01, 6.4134e-01, 1.5443e-01],
- [ 5.4249e-01, -4.0670e-01, 1.5543e+00, 2.4057e-02, -5.5958e-01,
- -1.3811e-01, 1.0049e-01, 2.0932e-01],
- [ 6.0095e-01, -4.5619e-01, 1.7198e+00, -9.0441e-03, -3.4644e-01,
- 1.0758e-02, 6.2944e-01, 1.6266e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0413, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0413, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.8967771902680397
- step: 82
- running loss: 0.03532655110082975
- Train Steps: 82/90 Loss: 0.0353 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6145, 0.4007, 0.8775, 0.4533, 0.4562, 0.5533, 0.6088, 0.5533],
- [0.6182, 0.3998, 0.8793, 0.4191, 0.3552, 0.4285, 0.6038, 0.5312],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6201, 0.4098, 0.8575, 0.3617, 0.3700, 0.3550, 0.5988, 0.5667],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [0.6350, 0.4043, 0.8738, 0.5650, 0.3850, 0.4750, 0.6401, 0.4950],
- [ nan, nan, 0.6469, 0.1943, 0.4025, 0.2000, 0.5125, 0.5533]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7146, -0.3538, 1.8599, 0.1712, -0.4480, 0.1083, 0.5213, 0.0950],
- [ 0.6914, -0.3237, 1.8138, -0.0676, -0.1364, 0.2315, 0.5159, 0.2180],
- [ 0.7692, -0.2724, 1.7680, -0.3090, -0.5663, -0.4063, 0.5210, 0.2294],
- [ 0.6687, -0.3124, 1.3652, -0.8803, -0.4324, -1.0893, 0.5859, 0.3320],
- [ 0.5855, -0.3619, 1.7856, -0.5942, -0.4860, -0.7473, 0.4492, 0.2532],
- [ 0.6554, -0.3291, 1.1327, -1.2360, -0.4624, -1.1226, 0.3796, 0.2043],
- [ 0.8600, -0.2718, 1.8370, 0.4075, -0.4948, -0.0732, 0.7471, 0.0575],
- [-1.5150, -1.7410, 0.9122, -1.3620, -0.3742, -1.4723, 0.1188, 0.2358]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7829e-01, -4.3634e-01, 1.7724e+00, 4.3211e-02, -5.1531e-01,
- 8.7136e-02, 4.8401e-01, 6.6263e-02],
- [ 5.5260e-01, -4.3510e-01, 1.7672e+00, -1.9199e-01, -1.7852e-01,
- 2.6990e-01, 5.2587e-01, 2.6990e-01],
- [ 5.6966e-01, -4.3934e-01, 1.7754e+00, -3.5028e-01, -6.4527e-01,
- -3.0670e-01, 5.0278e-01, 1.6774e-01],
- [ 5.9151e-01, -3.6821e-01, 1.2187e+00, -1.2313e+00, -4.3256e-01,
- -9.5412e-01, 5.7783e-01, 3.7768e-01],
- [ 5.7829e-01, -3.9330e-01, 1.6748e+00, -6.1540e-01, -5.7691e-01,
- -6.4619e-01, 4.7968e-01, 3.3149e-01],
- [ 5.3557e-01, -4.2171e-01, 1.0339e+00, -1.4776e+00, -5.0762e-01,
- -1.1081e+00, 4.2194e-01, 2.8530e-01],
- [ 6.4706e-01, -4.1832e-01, 1.7499e+00, 3.2379e-01, -5.0762e-01,
- -9.1917e-02, 6.7064e-01, 4.6189e-04],
- [-2.2859e+00, -2.2859e+00, 7.0230e-01, -1.3883e+00, -4.2679e-01,
- -1.3621e+00, 8.1293e-02, 2.6990e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0244, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0244, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.921171437948942
- step: 83
- running loss: 0.03519483660179448
- Train Steps: 83/90 Loss: 0.0352 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4120, 0.8013, 0.2350, 0.4888, 0.1533, 0.6281, 0.4895],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6336, 0.4154, 0.8900, 0.2767, 0.4988, 0.2867, 0.7422, 0.5540],
- [0.6219, 0.4114, 0.8175, 0.2817, 0.3925, 0.2783, 0.5900, 0.5350],
- [0.6185, 0.4042, 0.7700, 0.2250, 0.4062, 0.2117, 0.5763, 0.5150],
- [0.6245, 0.4115, 0.8700, 0.4883, 0.4625, 0.5517, 0.6100, 0.5217],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6224, 0.4061, 0.8988, 0.4300, 0.3838, 0.4750, 0.6112, 0.5483]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3414, -0.5336, 1.3901, -1.0815, -0.1304, -1.4158, 0.6134, 0.1596],
- [ 0.5834, -0.4411, 1.6709, 0.4052, -0.5984, 0.0808, 0.5935, 0.1417],
- [ 0.3512, -0.5651, 1.7264, -0.7843, -0.0953, -1.0059, 0.9068, 0.1935],
- [ 0.4498, -0.4207, 1.3572, -0.9040, -0.5169, -0.8613, 0.3319, 0.2744],
- [ 0.3137, -0.5197, 1.1709, -1.0104, -0.5328, -1.2113, 0.2474, 0.1146],
- [ 0.5019, -0.4251, 1.6912, 0.0891, -0.2694, 0.3256, 0.5324, 0.2705],
- [ 0.3252, -0.5454, 1.1839, -1.0608, -0.4705, -1.1051, 0.3983, 0.2607],
- [ 0.4924, -0.4734, 1.8682, -0.0788, -0.6548, 0.1146, 0.5234, 0.1737]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6053, -0.3830, 1.4150, -1.2005, -0.0284, -1.5777, 0.6154,
- -0.0250],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.6405, -0.3671, 1.8249, -1.0080, 0.0178, -0.9618, 1.1422,
- 0.2730],
- [ 0.5868, -0.3858, 1.4901, -0.9849, -0.4730, -1.0003, 0.4393,
- 0.1852],
- [ 0.5707, -0.4189, 1.2707, -1.2467, -0.4095, -1.3082, 0.3758,
- 0.0928],
- [ 0.5987, -0.3852, 1.7326, -0.0303, -0.1497, 0.2622, 0.5316,
- 0.1236],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698],
- [ 0.5888, -0.4101, 1.8654, -0.2998, -0.5134, -0.0919, 0.5374,
- 0.2468]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0179, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0179, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.9391067922115326
- step: 84
- running loss: 0.034989366573946815
- Train Steps: 84/90 Loss: 0.0350 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6212, 0.4033, 0.8938, 0.4167, 0.3813, 0.4267, 0.5613, 0.5583],
- [0.6196, 0.4090, 0.8450, 0.3900, 0.3575, 0.3933, 0.5975, 0.5583],
- [0.6357, 0.4097, 0.9038, 0.3883, 0.4213, 0.2950, 0.6686, 0.5390],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3572, -0.5497, 1.8215, -0.4146, -0.5355, -0.3716, 0.3348, 0.2300],
- [ 0.5134, -0.4433, 1.6347, -0.5314, -0.6411, -0.5109, 0.4675, 0.2653],
- [ 0.3472, -0.5754, 1.7767, -0.6031, -0.3520, -1.0399, 0.7266, 0.1543],
- [ 0.5444, -0.4306, 1.1022, -1.4533, -0.3904, -1.3121, 0.5611, 0.1790],
- [ 0.3579, -0.5079, 1.5207, 0.2874, -0.3740, -0.3332, 0.2718, 0.4807],
- [ 0.6658, -0.3873, 1.7824, -0.3300, -0.4989, 0.1572, 0.8910, 0.1273],
- [ 0.5774, -0.4470, 1.5978, 0.1259, -0.3104, 0.0424, 0.7856, 0.1582],
- [ 0.4544, -0.4687, 1.3953, -0.5208, -0.5736, -0.3778, 0.0729, 0.1174]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5832, -0.4231, 1.8423, -0.3614, -0.5249, -0.3152, 0.3065,
- 0.2930],
- [ 0.5761, -0.3966, 1.6171, -0.4845, -0.6346, -0.4691, 0.4739,
- 0.2930],
- [ 0.6504, -0.3936, 1.8885, -0.4922, -0.3402, -0.9233, 0.8022,
- 0.2035],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.5836, -0.3649, 1.7210, 0.3854, -0.3979, -0.2921, 0.3065,
- 0.4470],
- [ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715],
- [ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0091, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0091, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.948201633989811
- step: 85
- running loss: 0.03468472510576248
- Train Steps: 85/90 Loss: 0.0347 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6122, 0.4006, 0.8850, 0.4217, 0.4088, 0.5517, 0.6063, 0.5517],
- [0.6234, 0.4179, 0.7825, 0.3450, 0.3813, 0.2867, 0.5675, 0.5617],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750],
- [0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4660, -0.5156, 1.7767, -0.2449, -0.3343, 0.1230, 0.6427, 0.1392],
- [ 0.3637, -0.5195, 1.6345, -0.0874, -0.6553, -0.6107, 0.3418, 0.3022],
- [ 0.4526, -0.5204, 1.7592, -0.2958, -0.4045, 0.2497, 0.6422, 0.1193],
- [ 0.0573, -0.7052, 1.4025, -0.9575, -0.4882, -1.0913, 0.3891, 0.2999],
- [ 0.4061, -0.4735, 1.1699, -0.8911, -0.0988, -1.4151, 0.2780, 0.3841],
- [ 0.5552, -0.4399, 1.6502, 0.2949, -0.3972, 0.1041, 0.5352, 0.2250],
- [ 0.5542, -0.4346, 1.0822, -1.1684, -0.5267, -1.0174, 0.6169, 0.2570],
- [ 0.7242, -0.3797, 1.7948, -0.4427, -0.6509, -0.5013, 0.7786, 0.0073]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5960, -0.4102, 1.8018, -0.1612, -0.3344, 0.1159, 0.5490,
- 0.2314],
- [ 0.5902, -0.3493, 1.7961, -0.0072, -0.5942, -0.5615, 0.3180,
- 0.3161],
- [ 0.5417, -0.4355, 1.8018, -0.3383, -0.3979, 0.2622, 0.5143,
- 0.2622],
- [ 0.5935, -0.3558, 1.3284, -0.6924, -0.5249, -0.9618, 0.3353,
- 0.3084],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [ 0.5742, -0.3792, 1.6460, 0.3084, -0.3460, 0.1467, 0.4162,
- 0.3161],
- [ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700],
- [ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0161, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0161, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.964329108595848
- step: 86
- running loss: 0.034468943123207536
- Train Steps: 86/90 Loss: 0.0345 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6282, 0.4034, 0.7830, 0.2080, 0.4532, 0.2080, 0.6404, 0.5323],
- [0.6150, 0.3935, 0.8696, 0.5158, 0.4647, 0.5329, 0.6041, 0.5153],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6258, 0.4143, 0.8525, 0.2617, 0.4950, 0.1667, 0.6219, 0.4967],
- [0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585],
- [0.6272, 0.4120, 0.9038, 0.4117, 0.3725, 0.3200, 0.6175, 0.5250],
- [0.6193, 0.4108, 0.7425, 0.2350, 0.3887, 0.2750, 0.5900, 0.5717]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5998, -0.3945, 1.2492, -1.0363, -0.3124, -1.2346, 0.6505, 0.1615],
- [ 0.4399, -0.5304, 1.5987, 0.2052, -0.2732, 0.2001, 0.5628, 0.1825],
- [ 0.4002, -0.4918, 1.2865, -1.0020, -0.2786, -1.2601, 0.4286, 0.2335],
- [ 0.5183, -0.4353, 1.5123, -0.2794, -0.6870, -0.2431, 0.3356, 0.1993],
- [ 0.1278, -0.6566, 1.4739, -1.0337, -0.1406, -1.3200, 0.6354, 0.1578],
- [ 0.4852, -0.4609, 1.6576, -0.2034, -0.3438, 0.3765, 0.6157, 0.1554],
- [-0.0316, -0.7990, 1.7789, -0.2751, -0.6377, -0.7240, 0.6715, 0.2198],
- [ 0.5022, -0.4296, 1.0507, -1.0560, -0.6059, -0.9220, 0.4741, 0.3350]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.1577e-01, -4.2249e-01, 1.3307e+00, -1.3253e+00, -1.9244e-01,
- -1.3252e+00, 6.7213e-01, 1.7271e-01],
- [ 5.5484e-01, -4.6823e-01, 1.7309e+00, 9.6578e-02, -1.3942e-01,
- 1.7573e-01, 5.0451e-01, 9.4188e-02],
- [ 5.7679e-01, -4.0308e-01, 1.3838e+00, -1.1527e+00, -2.1876e-01,
- -1.4216e+00, 4.3790e-01, 1.8502e-01],
- [ 5.3672e-01, -4.2941e-01, 1.5709e+00, -4.9992e-01, -6.6928e-01,
- -3.0747e-01, 2.4546e-01, 3.5585e-01],
- [ 6.0479e-01, -3.7229e-01, 1.6517e+00, -1.0773e+00, 4.6189e-04,
- -1.5161e+00, 5.8660e-01, 8.0947e-03],
- [ 5.1155e-01, -4.3318e-01, 1.7557e+00, -3.1517e-01, -2.5358e-01,
- 3.9307e-01, 4.1387e-01, 2.9364e-01],
- [ 6.1114e-01, -3.8276e-01, 1.8885e+00, -3.8445e-01, -5.6536e-01,
- -8.0785e-01, 5.6628e-01, 1.3903e-01],
- [ 5.7460e-01, -3.8822e-01, 1.1436e+00, -1.2005e+00, -4.9030e-01,
- -1.0157e+00, 4.3926e-01, 3.5458e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0250, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0250, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.9893352556973696
- step: 87
- running loss: 0.03436017535284333
- Train Steps: 87/90 Loss: 0.0344 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6339, 0.4118, 0.7988, 0.5800, 0.3912, 0.4583, 0.7343, 0.5760],
- [0.6214, 0.4175, 0.7300, 0.2883, 0.4338, 0.2167, 0.5698, 0.5773],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000],
- [ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552],
- [0.6222, 0.3957, 0.8838, 0.5017, 0.3937, 0.4600, 0.5900, 0.5017],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6628, -0.3783, 1.3547, 0.0378, -0.5396, -0.2219, 0.7363, 0.3387],
- [ 0.9989, -0.0989, 1.0561, -1.0009, -0.3688, -1.1186, 0.2621, 0.3592],
- [ 0.8307, -0.2395, 1.6155, -0.5370, -0.7173, -0.2707, 0.4068, 0.0687],
- [-2.1859, -2.2229, 1.5529, -1.2666, 0.0458, -1.3234, 0.9324, 0.2525],
- [ 0.4735, -0.4862, 1.6272, -0.1025, -0.5379, -0.1717, 0.3618, 0.0620],
- [ 0.6799, -0.3520, 1.6324, -0.1560, -0.4966, 0.0338, 0.2962, 0.1715],
- [ 0.6859, -0.3553, 1.3792, 0.0354, -0.5202, -0.0513, 0.8590, 0.3345],
- [ 0.6793, -0.2859, 1.6432, -0.9845, -0.1492, -1.2886, 0.5025, 0.1337]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6421, -0.3840, 1.4035, 0.3931, -0.4788, -0.1689, 1.1057,
- 0.3745],
- [ 0.5841, -0.3574, 1.0859, -0.9541, -0.2824, -1.2851, 0.3460,
- 0.3808],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236],
- [-2.2859, -2.2859, 1.7557, -1.1466, 0.0871, -1.0773, 1.1239,
- 0.2783],
- [ 0.5879, -0.4583, 1.7961, 0.0313, -0.4672, -0.1612, 0.4393,
- 0.0313],
- [ 0.5250, -0.4661, 1.7383, -0.0765, -0.4268, -0.0226, 0.2535,
- 0.2035],
- [ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0192, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0192, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.0085563641041517
- step: 88
- running loss: 0.034188140501183545
- Train Steps: 88/90 Loss: 0.0342 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7240, 0.2722, 0.3900, 0.2567, 0.5168, 0.5933],
- [0.6269, 0.4073, 0.8900, 0.4933, 0.3625, 0.4183, 0.6288, 0.5150],
- [0.6364, 0.4165, 0.9088, 0.4367, 0.4075, 0.3150, 0.6448, 0.5297],
- [0.6286, 0.4060, 0.9188, 0.4333, 0.3675, 0.4167, 0.7034, 0.5528],
- [0.6075, 0.4007, 0.8275, 0.4917, 0.4050, 0.5100, 0.5167, 0.5280],
- [0.6208, 0.4082, 0.8538, 0.3067, 0.3588, 0.3717, 0.6112, 0.5517],
- [0.6107, 0.4050, 0.8700, 0.4850, 0.4470, 0.4848, 0.5043, 0.5431],
- [0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.8385, -1.9641, 0.9082, -1.2111, -0.4355, -1.1670, 0.1173, 0.2900],
- [ 0.6356, -0.3790, 1.6797, -0.1061, -0.5099, -0.3461, 0.6313, 0.1793],
- [ 0.5199, -0.3973, 1.6923, -0.4086, -0.3310, -0.9304, 0.6163, 0.2632],
- [ 0.7638, -0.3175, 1.7645, -0.4099, -0.4495, -0.4600, 0.9688, 0.2919],
- [ 0.7587, -0.2697, 1.4616, -0.1549, -0.3250, 0.0384, 0.2837, 0.1989],
- [ 0.7750, -0.2476, 1.4770, -0.9351, -0.5789, -0.6013, 0.5758, 0.2528],
- [ 0.6622, -0.3352, 1.5473, -0.1471, -0.1829, -0.0524, 0.2201, 0.1321],
- [ 0.6658, -0.3254, 1.6760, -0.0658, -0.3696, -0.5965, 0.7424, 0.2032]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.0580, -1.0288, -0.4845, -1.1004, 0.1011,
- 0.4543],
- [ 0.6096, -0.4046, 1.8249, -0.0072, -0.6115, -0.3537, 0.6182,
- 0.0928],
- [ 0.6537, -0.3623, 1.9115, -0.2690, -0.4037, -0.8309, 0.6926,
- 0.1608],
- [ 0.6174, -0.4105, 1.9577, -0.2844, -0.5885, -0.3614, 0.9631,
- 0.2676],
- [ 0.5200, -0.4353, 1.5363, -0.0149, -0.4152, 0.0697, 0.1005,
- 0.1530],
- [ 0.5814, -0.4003, 1.6575, -0.8694, -0.6289, -0.5692, 0.5374,
- 0.2622],
- [ 0.5351, -0.4153, 1.7326, -0.0457, -0.2214, -0.0466, 0.0434,
- 0.2228],
- [ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0178, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0178, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.0263278856873512
- step: 89
- running loss: 0.034003684108846646
- Train Steps: 89/90 Loss: 0.0340 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6264, 0.3972, 0.8853, 0.4771, 0.3853, 0.4511, 0.6293, 0.5334],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6245, 0.4100, 0.7762, 0.2583, 0.4963, 0.1517, 0.5875, 0.5417],
- [0.6228, 0.4119, 0.7938, 0.2233, 0.4674, 0.1773, 0.6188, 0.5433],
- [0.6095, 0.4002, 0.8533, 0.5168, 0.5031, 0.5094, 0.5125, 0.5433],
- [0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6361, 0.4076, 0.8862, 0.5350, 0.3713, 0.4650, 0.6654, 0.5297]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2155, -0.6617, 1.7206, -0.2581, -0.0849, -0.0335, 0.5475, 0.2886],
- [ 0.3063, -0.6256, 1.7132, -0.2126, -0.6088, -0.1912, 0.7480, 0.2040],
- [ 0.4653, -0.5070, 1.7394, 0.1501, -0.6849, -0.4636, 0.5853, 0.0483],
- [ 0.5087, -0.4337, 1.2280, -1.1637, -0.1142, -1.4992, 0.4559, 0.2600],
- [ 0.6276, -0.3673, 1.3516, -1.2932, -0.1980, -1.4377, 0.6106, 0.2283],
- [ 0.3026, -0.6008, 1.6122, -0.0452, -0.0811, -0.0767, 0.2343, 0.2728],
- [ 0.5610, -0.3835, 1.2338, -0.8067, -0.7232, -0.4564, 0.3050, 0.3639],
- [ 0.5379, -0.4936, 1.6835, 0.0435, -0.6293, -0.0777, 0.8585, 0.2135]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5371, -0.4252, 1.7037, -0.0688, -0.0342, 0.0620, 0.3758,
- 0.2853],
- [ 0.6075, -0.4514, 1.8032, -0.0822, -0.5061, -0.2023, 0.6208,
- 0.1779],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.5985, -0.3921, 1.2995, -1.0927, 0.0062, -1.5854, 0.4277,
- 0.2160],
- [ 0.5908, -0.3832, 1.3804, -1.2543, -0.1270, -1.4671, 0.5721,
- 0.2237],
- [ 0.5295, -0.4373, 1.6553, 0.1011, 0.0380, 0.0671, 0.0813,
- 0.2237],
- [ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.6520, -0.4032, 1.8076, 0.1852, -0.5711, -0.1381, 0.7876,
- 0.1608]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0141, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0141, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 3.0404702592641115
- step: 90
- running loss: 0.03378300288071235
- Valid Steps: 10/10 Loss: nan 7.2476
- --------------------------------------------------
- Epoch: 5 Train Loss: 0.0338 Valid Loss: nan
- --------------------------------------------------
- size of train loader is: 90
- torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6364, 0.4165, 0.9088, 0.4367, 0.4075, 0.3150, 0.6448, 0.5297],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [ nan, nan, 0.7192, 0.2346, 0.4037, 0.2050, 0.5138, 0.5650],
- [0.6115, 0.4081, 0.6725, 0.2433, 0.4088, 0.1933, 0.5167, 0.5544],
- [0.6293, 0.4024, 0.8750, 0.5000, 0.4012, 0.5733, 0.7121, 0.5633],
- [0.6257, 0.4024, 0.8612, 0.5352, 0.4361, 0.5253, 0.6680, 0.5166],
- [0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7542, -0.2940, 1.0693, -1.0823, -0.2846, -1.4095, 0.2587, 0.1636],
- [ 0.4289, -0.4749, 1.8449, -0.2291, -0.3635, -0.8582, 0.6296, 0.2635],
- [ 0.7719, -0.2729, 1.6264, -0.6635, -0.6348, -0.6002, 0.4818, 0.1923],
- [-2.1929, -2.2416, 1.0535, -1.1674, -0.3955, -1.2469, 0.2622, 0.2562],
- [ 0.8213, -0.2376, 0.9933, -1.0158, -0.3735, -1.2595, 0.0756, 0.2700],
- [ 0.7109, -0.3502, 1.7879, 0.0807, -0.3919, 0.4202, 0.9466, 0.2984],
- [ 0.7260, -0.3469, 1.6753, 0.1680, -0.3162, 0.1486, 0.9122, 0.1825],
- [ 0.6985, -0.3393, 1.7840, -0.1376, -0.3363, 0.1751, 0.5855, 0.2307]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.6537, -0.3623, 1.9115, -0.2690, -0.4037, -0.8309, 0.6926,
- 0.1608],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753],
- [-2.2859, -2.2859, 1.0361, -1.2021, -0.4210, -1.3390, 0.0871,
- 0.3238],
- [ 0.5385, -0.4010, 0.8203, -1.1620, -0.3979, -1.3929, 0.1005,
- 0.2747],
- [ 0.6207, -0.4273, 1.7557, 0.0236, -0.4326, 0.3623, 1.0033,
- 0.3157],
- [ 0.6042, -0.4273, 1.6920, 0.1860, -0.2717, 0.1406, 0.7997,
- 0.1004],
- [ 0.5960, -0.4102, 1.8018, -0.1612, -0.3344, 0.1159, 0.5490,
- 0.2314]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0097, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0097, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.009727518074214458
- step: 1
- running loss: 0.009727518074214458
- Train Steps: 1/90 Loss: 0.0097 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6265, 0.4251, 0.7113, 0.3550, 0.4375, 0.2117, 0.5587, 0.6118],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6199, 0.4093, 0.7913, 0.2533, 0.4288, 0.2467, 0.5975, 0.5700],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6289, 0.4032, 0.8419, 0.5446, 0.4075, 0.5017, 0.6312, 0.5117]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6004, -0.3672, 1.2156, -0.6897, -0.3006, -1.1231, 0.3223, 0.4997],
- [ 0.3149, -0.5709, 1.8053, -0.1810, -0.4348, -0.0823, 0.1140, 0.1113],
- [ 0.3440, -0.6193, 1.6411, 0.4132, -0.2512, 0.0696, 0.2662, 0.1777],
- [ 0.6088, -0.4192, 1.4295, -1.1601, -0.3286, -1.0138, 0.7049, 0.2644],
- [ 0.5523, -0.4556, 1.0715, -1.3078, -0.4542, -1.0982, 0.6409, 0.2777],
- [ 0.4007, -0.5953, 1.8373, -0.6258, -0.2968, -0.8168, 1.0265, 0.0947],
- [ 0.1804, -0.6489, 1.6171, -0.5607, -0.6342, -0.7571, 0.2685, 0.2146],
- [ 0.4799, -0.5429, 1.6780, 0.1912, -0.4269, 0.0378, 0.8408, 0.1385]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6077, -0.3226, 0.9993, -0.6462, -0.2651, -1.3082, 0.2946,
- 0.5401],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610],
- [ 0.5771, -0.3955, 1.3688, -1.1158, -0.3055, -1.1466, 0.4739,
- 0.3469],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.6189, -0.4238, 1.6026, 0.2295, -0.4037, 0.0313, 0.6298,
- 0.0774]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0148, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0148, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.024488049559295177
- step: 2
- running loss: 0.012244024779647589
- Train Steps: 2/90 Loss: 0.0122 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6201, 0.4064, 0.8688, 0.5050, 0.4225, 0.5100, 0.6138, 0.5500],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6200, 0.4039, 0.8880, 0.4799, 0.3625, 0.4285, 0.5866, 0.5148],
- [0.6260, 0.4133, 0.8800, 0.5117, 0.3713, 0.3283, 0.6223, 0.4967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7454, -0.3024, 1.7860, -0.3361, -0.0949, 0.0958, 0.5557, 0.2262],
- [ 0.5253, -0.4278, 1.4045, -0.8717, -0.5915, -0.6161, 0.2637, 0.3010],
- [ 0.6854, -0.3350, 1.7206, -0.0855, -0.2740, -0.0146, 0.3494, 0.2215],
- [ 0.7256, -0.3493, 1.4856, 0.0067, -0.3950, -0.1420, 0.8548, 0.2708],
- [ 0.8537, -0.2916, 1.6853, 0.2443, -0.5348, -0.2137, 0.6321, 0.1528],
- [-1.9638, -2.1107, 1.0300, -1.3369, -0.1977, -1.5700, 0.3303, 0.3528],
- [ 0.7093, -0.3309, 1.8327, -0.2651, -0.5227, -0.4226, 0.3786, 0.1303],
- [ 0.8808, -0.2086, 1.8253, -0.0877, -0.4148, -0.8260, 0.6492, 0.1294]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.5784, -0.4088, 1.7268, 0.0467, -0.3344, 0.0697, 0.5490,
- 0.2545],
- [ 0.6289, -0.4345, 1.3794, 0.3679, -0.4845, 0.0390, 0.9265,
- 0.1928],
- [ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261],
- [-2.2859, -2.2859, 0.6760, -1.4083, -0.3286, -1.4160, 0.2487,
- 0.3469],
- [ 0.5781, -0.4205, 1.8159, -0.0691, -0.6116, -0.3069, 0.4236,
- 0.0919],
- [ 0.6054, -0.3767, 1.7788, 0.0774, -0.5711, -0.7694, 0.5887,
- 0.0081]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0186, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0186, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.043093412183225155
- step: 3
- running loss: 0.014364470727741718
- Train Steps: 3/90 Loss: 0.0144 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6286, 0.4078, 0.8063, 0.2267, 0.4788, 0.1533, 0.5953, 0.4913],
- [0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150],
- [0.6221, 0.4107, 0.7788, 0.3033, 0.3950, 0.2817, 0.6075, 0.5517],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [0.6268, 0.4029, 0.8500, 0.2683, 0.3937, 0.3500, 0.6860, 0.5297]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0854, -0.7300, 1.3343, -1.0758, -0.0698, -1.3501, 0.5538, 0.1221],
- [ 0.4797, -0.4857, 1.3555, -1.0026, -0.2330, -1.1817, 0.5848, 0.1294],
- [ 0.4533, -0.4955, 1.4321, -0.8290, -0.3719, -0.8532, 0.5331, 0.2373],
- [ 0.3314, -0.5476, 1.5791, -0.4780, -0.6127, -0.5765, 0.2331, 0.1541],
- [ 0.3811, -0.5397, 1.6226, 0.3160, -0.5185, -0.2334, 0.2644, 0.2589],
- [ 0.2556, -0.6680, 1.6682, 0.1286, -0.5395, 0.0395, 0.4531, 0.3512],
- [ 0.4959, -0.4210, 1.4818, -0.3671, -0.0721, -0.9667, 0.3686, 0.3975],
- [ 0.6450, -0.4446, 1.6500, -0.9368, -0.5058, -0.4594, 1.1056, 0.0772]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6177, -0.4022, 1.4381, -1.2390, -0.0746, -1.5777, 0.4639,
- -0.0168],
- [ 0.5796, -0.4326, 1.4439, -1.1774, -0.2940, -1.3390, 0.3931,
- 0.0928],
- [ 0.5875, -0.3888, 1.3111, -0.8848, -0.4614, -0.9849, 0.5201,
- 0.2622],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.5777, -0.3915, 1.7961, 0.1698, -0.5192, -0.5307, 0.2141,
- 0.3392],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882],
- [ 0.6092, -0.4249, 1.6402, -1.0465, -0.4672, -0.6693, 0.8827,
- 0.1608]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0271, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0271, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.07015109900385141
- step: 4
- running loss: 0.017537774750962853
- Train Steps: 4/90 Loss: 0.0175 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6083, 0.3957, 0.8638, 0.4950, 0.4363, 0.5083, 0.5346, 0.4980],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [0.6189, 0.4029, 0.8375, 0.5767, 0.4745, 0.4829, 0.5551, 0.5598],
- [ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482],
- [0.6085, 0.4008, 0.8588, 0.5200, 0.4959, 0.4977, 0.5175, 0.5283],
- [0.6329, 0.4175, 0.8550, 0.2333, 0.5425, 0.2250, 0.7398, 0.5609],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6732, -0.3474, 1.6743, -0.0082, -0.4961, 0.0134, 0.2572, -0.0216],
- [ 0.8126, -0.2493, 1.6378, 0.0115, -0.5362, 0.0172, 0.2641, 0.2438],
- [ 0.7811, -0.2574, 1.5581, 0.0977, -0.4353, 0.0952, 0.1921, 0.1597],
- [ 0.7708, -0.2707, 1.4945, 0.3949, -0.2718, -0.1890, 0.3506, 0.2391],
- [-2.1231, -2.2121, 1.5529, -1.2340, 0.0463, -1.3679, 1.1119, 0.3009],
- [ 0.6323, -0.3641, 1.6140, 0.1118, -0.1846, -0.1604, 0.1566, 0.1369],
- [ 0.3799, -0.5494, 1.6646, -1.1212, 0.0340, -1.5158, 1.2984, 0.2480],
- [ 0.9152, -0.1758, 1.4520, -1.0302, -0.5784, -0.9599, 0.4555, 0.2445]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.2379e-01, -4.5797e-01, 1.7037e+00, 4.6189e-04, -2.7090e-01,
- 6.2048e-02, 1.8356e-01, 1.4106e-02],
- [ 5.5393e-01, -4.0308e-01, 1.7168e+00, -5.9398e-02, -3.7483e-01,
- 5.4350e-02, 1.3903e-01, 3.7768e-01],
- [ 5.1784e-01, -4.4796e-01, 1.6748e+00, 6.2048e-02, -2.7667e-01,
- 2.0831e-01, 1.0666e-01, 2.3862e-01],
- [ 5.7258e-01, -4.2487e-01, 1.5824e+00, 3.7768e-01, -9.4206e-02,
- -5.5582e-02, 2.7815e-01, 2.9966e-01],
- [-2.2859e+00, -2.2859e+00, 1.6517e+00, -1.2620e+00, 2.1409e-01,
- -1.1928e+00, 1.1166e+00, 2.4627e-01],
- [ 5.2448e-01, -4.3472e-01, 1.6806e+00, 1.1594e-01, 4.6468e-03,
- 1.2940e-02, 1.0439e-01, 1.5443e-01],
- [ 6.3718e-01, -3.5749e-01, 1.6633e+00, -1.2082e+00, 2.1986e-01,
- -1.2467e+00, 1.1313e+00, 3.0505e-01],
- [ 5.7783e-01, -3.9299e-01, 1.5189e+00, -9.3872e-01, -4.3256e-01,
- -9.1563e-01, 4.8545e-01, 3.3918e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0189, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0189, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.08904801961034536
- step: 5
- running loss: 0.017809603922069074
- Train Steps: 5/90 Loss: 0.0178 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6128, 0.4084, 0.8738, 0.4683, 0.3613, 0.3700, 0.4960, 0.5397],
- [0.6353, 0.4128, 0.9138, 0.3533, 0.4688, 0.3250, 0.7145, 0.5991],
- [0.6226, 0.4098, 0.8912, 0.4100, 0.4025, 0.2383, 0.5763, 0.5367],
- [0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767],
- [0.6157, 0.3956, 0.8323, 0.4138, 0.3479, 0.4431, 0.5914, 0.5131]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4679, -0.5249, 1.1432, -1.3886, -0.4626, -1.0470, 0.5615, 0.1004],
- [ 0.6455, -0.3653, 1.5256, -0.2370, -0.4582, -0.1594, 0.2167, 0.2687],
- [ 0.3983, -0.5051, 1.6557, 0.0228, -0.5898, -0.5597, 0.1420, 0.1631],
- [-0.1206, -0.9222, 1.9298, -0.4981, 0.0381, -0.9257, 1.4428, 0.4271],
- [ 0.5584, -0.3998, 1.7356, -0.2781, -0.2670, -1.1656, 0.4453, 0.2577],
- [ 0.4390, -0.5099, 1.8071, -0.1181, -0.3375, -0.1904, 0.1749, 0.0565],
- [ 0.6322, -0.3949, 1.6822, 0.0830, -0.2574, 0.1128, 0.3545, 0.3131],
- [ 0.5678, -0.4407, 1.5844, -0.4062, -0.5713, -0.2275, 0.3795, 0.1258]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.5443, -0.3994, 1.7499, -0.1227, -0.6173, -0.5769, 0.0049,
- 0.2071],
- [ 0.6487, -0.3792, 1.9346, -0.6539, -0.1208, -0.7848, 1.0143,
- 0.4814],
- [ 0.5900, -0.3932, 1.8307, -0.3921, -0.4268, -1.1851, 0.3758,
- 0.1929],
- [ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220],
- [ 0.5539, -0.4031, 1.7168, -0.0594, -0.3748, 0.0543, 0.1390,
- 0.3777],
- [ 0.5581, -0.4586, 1.5586, -0.3747, -0.6792, -0.2391, 0.4455,
- 0.0840]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0255, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0255, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.11450012680143118
- step: 6
- running loss: 0.019083354466905195
- Train Steps: 6/90 Loss: 0.0191 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4059, 0.8700, 0.4900, 0.4163, 0.5000, 0.6162, 0.5467],
- [0.6265, 0.4088, 0.8025, 0.1850, 0.4163, 0.2500, 0.6290, 0.4947],
- [0.6182, 0.3967, 0.8263, 0.3065, 0.3526, 0.4161, 0.6192, 0.5290],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422],
- [0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6223, 0.4130, 0.8100, 0.2983, 0.3525, 0.3900, 0.5694, 0.5103]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7570, -0.3084, 1.7813, 0.1175, -0.2729, 0.0054, 0.5814, 0.2380],
- [ 0.6667, -0.3722, 1.4920, -1.2591, -0.1936, -1.2800, 0.9240, 0.1477],
- [ 0.4875, -0.4911, 1.6052, -0.7537, -0.5829, -0.5058, 0.7371, 0.1907],
- [ 0.8956, -0.2000, 1.7388, 0.0653, -0.1816, -0.1037, 0.2166, 0.2962],
- [ 0.8230, -0.2782, 1.6046, 0.5730, -0.1689, -0.0237, 0.2071, 0.1582],
- [-2.3906, -2.3562, 1.1195, -0.9826, -0.3667, -1.1408, 0.1601, 0.2370],
- [ 0.6998, -0.2829, 1.6248, -0.1946, -0.5209, -0.8853, 0.0696, 0.2143],
- [ 0.8486, -0.2305, 1.5716, -0.7333, -0.5215, -0.5948, 0.5211, 0.1786]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7760e-01, -4.1093e-01, 1.7326e+00, -2.2633e-02, -3.6328e-01,
- 2.3557e-02, 5.6051e-01, 2.3911e-01],
- [ 6.0785e-01, -3.9761e-01, 1.4208e+00, -1.4314e+00, -3.6328e-01,
- -1.1312e+00, 6.1950e-01, -9.2270e-04],
- [ 5.6966e-01, -4.5379e-01, 1.5308e+00, -8.7027e-01, -6.5720e-01,
- -3.6388e-01, 5.7392e-01, 1.5759e-01],
- [ 5.4908e-01, -4.2902e-01, 1.7788e+00, -1.0731e-01, -2.6513e-01,
- -1.0731e-01, 2.5553e-01, 3.0567e-01],
- [ 5.1928e-01, -4.6990e-01, 1.5767e+00, 4.0077e-01, -2.4203e-01,
- 7.7444e-02, 1.1776e-01, -6.1038e-02],
- [-2.2859e+00, -2.2859e+00, 1.2469e+00, -1.0288e+00, -4.5566e-01,
- -1.2774e+00, 5.1142e-02, 2.1834e-01],
- [ 5.4700e-01, -3.9515e-01, 1.6377e+00, -4.2531e-01, -6.2887e-01,
- -8.0785e-01, 2.4925e-02, 2.1157e-01],
- [ 5.8863e-01, -3.7837e-01, 1.4554e+00, -9.0793e-01, -6.5774e-01,
- -4.8453e-01, 3.4395e-01, 7.1216e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0186, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0186, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.1331191835924983
- step: 7
- running loss: 0.019017026227499758
- Train Steps: 7/90 Loss: 0.0190 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6199, 0.4112, 0.8475, 0.3717, 0.3550, 0.4350, 0.6063, 0.6083],
- [0.6200, 0.4024, 0.8390, 0.3139, 0.3525, 0.3833, 0.6162, 0.5383],
- [0.6246, 0.4028, 0.8738, 0.4867, 0.4088, 0.5667, 0.6362, 0.5200],
- [0.6275, 0.4071, 0.8075, 0.2100, 0.4700, 0.1533, 0.6148, 0.4834],
- [0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717],
- [0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6204, 0.4055, 0.8438, 0.5733, 0.4574, 0.4801, 0.5487, 0.5617],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7288, -0.3058, 1.7726, -0.3509, -0.5335, -0.3225, 0.3383, 0.4083],
- [ 0.2176, -0.6818, 1.7063, -0.6477, -0.6027, -0.5390, 0.5127, 0.2334],
- [ 0.6948, -0.3953, 1.9897, 0.2174, -0.3004, 0.3106, 0.7040, 0.1262],
- [ 0.3446, -0.6356, 1.5774, -1.2607, 0.0750, -1.6404, 0.7996, 0.0959],
- [ 0.3937, -0.5598, 0.9217, -0.9847, -0.4205, -1.2133, 0.1011, 0.2899],
- [ 0.3524, -0.5586, 1.6655, -0.3946, -0.6244, -0.4767, 0.1380, 0.1607],
- [ 0.5475, -0.4451, 1.7190, 0.6524, -0.0622, -0.1217, 0.2448, 0.2306],
- [ 0.3456, -0.5822, 1.4855, -0.7606, -0.6151, -0.3893, 0.4202, 0.2709]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5774, -0.3868, 1.6286, -0.5692, -0.6462, -0.2767, 0.5143,
- 0.5239],
- [ 0.5779, -0.4275, 1.5894, -0.8362, -0.6577, -0.5153, 0.5605,
- 0.2006],
- [ 0.5990, -0.4256, 1.7499, -0.0380, -0.3979, 0.3315, 0.6529,
- 0.1159],
- [ 0.6126, -0.4057, 1.4439, -1.3159, -0.1150, -1.5777, 0.5537,
- -0.0530],
- [ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546],
- [ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.5795, -0.4129, 1.6113, 0.3623, -0.1733, -0.0684, 0.2487,
- 0.3084],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0201, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0201, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.1532347435131669
- step: 8
- running loss: 0.019154342939145863
- Train Steps: 8/90 Loss: 0.0192 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4024, 0.7722, 0.2080, 0.4392, 0.2234, 0.6435, 0.5290],
- [0.6222, 0.3937, 0.8350, 0.5617, 0.4138, 0.4600, 0.5800, 0.5233],
- [0.6101, 0.3977, 0.8550, 0.5667, 0.3862, 0.4417, 0.5128, 0.4869],
- [0.6274, 0.4270, 0.8938, 0.4967, 0.3550, 0.4283, 0.5700, 0.5733],
- [0.6212, 0.4033, 0.8938, 0.4167, 0.3813, 0.4267, 0.5613, 0.5583],
- [0.6274, 0.4087, 0.8375, 0.5700, 0.4025, 0.4800, 0.5700, 0.6117],
- [0.6226, 0.4125, 0.8800, 0.4900, 0.4512, 0.5600, 0.6275, 0.5517],
- [0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2550, -0.6382, 1.3415, -1.3801, -0.2225, -1.3262, 0.6423, 0.1599],
- [ 0.5471, -0.4761, 1.7444, 0.2648, -0.4119, -0.1947, 0.4265, 0.1811],
- [ 0.6230, -0.3890, 1.8253, 0.3196, -0.4646, -0.2568, 0.0844, 0.0392],
- [ 0.8241, -0.2153, 1.8669, -0.0251, -0.5363, -0.3031, 0.3125, 0.3552],
- [ 0.9951, -0.1611, 1.9537, -0.3856, -0.5628, -0.2982, 0.3243, 0.1922],
- [ 0.4725, -0.4909, 1.6059, 0.3220, -0.4420, -0.0804, 0.3932, 0.4512],
- [ 0.4065, -0.5371, 1.8867, -0.1379, -0.1220, 0.2303, 0.4275, 0.2397],
- [-0.3573, -1.0397, 0.9568, -1.4847, -0.3793, -1.4299, 0.2651, 0.1650]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4273, 1.2807, -1.3253, -0.2574, -1.2542, 0.6864,
- 0.1575],
- [ 0.5880, -0.4676, 1.5709, 0.3084, -0.3748, -0.1612, 0.3931,
- 0.1313],
- [ 0.5320, -0.4488, 1.6633, 0.3315, -0.5018, -0.2459, 0.0828,
- -0.0370],
- [ 0.6121, -0.3138, 1.8423, 0.0082, -0.6462, -0.3075, 0.3469,
- 0.3623],
- [ 0.5832, -0.4231, 1.8423, -0.3614, -0.5249, -0.3152, 0.3065,
- 0.2930],
- [ 0.6118, -0.3983, 1.5824, 0.3469, -0.4268, -0.0688, 0.3469,
- 0.5393],
- [ 0.5897, -0.3804, 1.7788, -0.0226, -0.2016, 0.3007, 0.6125,
- 0.2622],
- [ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0303, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0303, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.18358023930341005
- step: 9
- running loss: 0.020397804367045563
- Train Steps: 9/90 Loss: 0.0204 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6137, 0.4035, 0.8850, 0.4417, 0.3900, 0.4283, 0.5449, 0.5617],
- [0.6113, 0.4104, 0.8650, 0.4250, 0.3925, 0.5967, 0.5787, 0.5116],
- [0.6224, 0.4097, 0.7438, 0.2267, 0.3850, 0.2850, 0.5988, 0.5250],
- [0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917],
- [0.6164, 0.4119, 0.7913, 0.2650, 0.3538, 0.3500, 0.5614, 0.5038],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 4.3023e-01, -5.1482e-01, 1.7756e+00, 1.1360e-03, -3.6645e-01,
- -2.6895e-01, 8.9147e-02, 2.8362e-01],
- [ 2.5134e-01, -6.3133e-01, 1.8180e+00, -1.0324e-01, -3.5429e-01,
- 4.1739e-01, 4.8015e-01, 1.5867e-01],
- [ 3.5384e-01, -5.8044e-01, 1.1428e+00, -1.0631e+00, -3.7179e-01,
- -9.1526e-01, 4.6287e-01, 2.6820e-01],
- [ 4.9021e-01, -4.6988e-01, 1.6632e+00, -4.7789e-01, -3.7840e-01,
- -1.0013e+00, 1.3930e-01, 1.6179e-01],
- [ 7.3768e-01, -3.5570e-01, 1.7729e+00, -6.4928e-01, -2.7519e-01,
- -8.5562e-01, 8.7062e-01, 2.3698e-01],
- [ 6.4180e-01, -3.7256e-01, 1.3457e+00, -3.2032e-01, -4.9100e-01,
- -7.8489e-01, 2.0598e-01, 4.0648e-01],
- [ 4.5137e-02, -7.7108e-01, 1.3785e+00, -9.7295e-01, -5.5496e-01,
- -6.3843e-01, 3.7514e-01, 1.5874e-01],
- [ 5.1822e-01, -4.5746e-01, 1.6131e+00, -6.3268e-01, -4.9638e-01,
- -8.6872e-01, 2.3403e-01, 9.5013e-02]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5488, -0.4221, 1.8018, -0.2459, -0.4845, -0.3075, 0.2309,
- 0.3087],
- [ 0.5376, -0.3903, 1.7095, -0.3229, -0.4730, 0.4701, 0.3871,
- 0.0772],
- [ 0.5891, -0.3937, 1.1494, -1.2390, -0.5076, -0.9695, 0.4797,
- 0.1390],
- [ 0.5711, -0.4015, 1.6979, -0.6770, -0.5365, -1.0619, 0.1712,
- 0.1494],
- [ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470],
- [ 0.5614, -0.3834, 1.3688, -1.0619, -0.6520, -0.6693, 0.3069,
- 0.0412],
- [ 0.5483, -0.4249, 1.5305, -0.7386, -0.6115, -0.8694, 0.3353,
- 0.1005]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0199, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0199, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.20348771009594202
- step: 10
- running loss: 0.020348771009594202
- Train Steps: 10/90 Loss: 0.0203 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6124, 0.4030, 0.8650, 0.4867, 0.4999, 0.5106, 0.5137, 0.5773],
- [0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [0.6183, 0.4076, 0.8838, 0.4517, 0.3813, 0.4483, 0.5775, 0.5633],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6204, 0.4055, 0.8438, 0.5733, 0.4574, 0.4801, 0.5487, 0.5617],
- [0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3729, -0.5449, 1.6055, -0.3498, -0.5716, -0.1778, 0.1901, 0.2834],
- [ 0.4369, -0.5189, 1.7649, -0.1136, -0.0909, -0.0224, 0.1736, 0.2540],
- [ 0.2000, -0.6297, 1.3461, -1.0150, -0.4848, -1.0933, 0.1059, 0.2289],
- [ 0.4935, -0.4793, 1.7645, -0.2782, -0.5397, -0.1706, 0.3770, 0.3191],
- [ 0.6598, -0.3805, 0.9234, -1.1801, -0.5330, -1.1614, 0.4945, 0.2989],
- [ 0.4239, -0.5186, 1.6509, 0.3934, -0.2377, -0.0880, 0.3082, 0.2429],
- [ 0.3168, -0.5442, 1.8375, -0.1244, -0.4559, -0.0795, 0.3196, 0.0846],
- [ 0.8351, -0.2616, 1.8822, -0.5596, -0.4198, -1.2483, 0.5455, 0.0289]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.5428, -0.4244, 1.7095, -0.0380, 0.0232, 0.0725, 0.0866,
- 0.3806],
- [ 0.5695, -0.3927, 1.3226, -0.9002, -0.4672, -1.1928, 0.1737,
- 0.3700],
- [ 0.5700, -0.4032, 1.7961, -0.1997, -0.5249, -0.2151, 0.3815,
- 0.3161],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391],
- [ 0.5795, -0.4129, 1.6113, 0.3623, -0.1733, -0.0684, 0.2487,
- 0.3084],
- [ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0111, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0111, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2146181659772992
- step: 11
- running loss: 0.019510742361572655
- Train Steps: 11/90 Loss: 0.0195 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6282, 0.4029, 0.8988, 0.3000, 0.4250, 0.4183, 0.7042, 0.5575],
- [0.6142, 0.4127, 0.7575, 0.3067, 0.3438, 0.4383, 0.5778, 0.5207],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600],
- [0.6276, 0.4235, 0.8888, 0.5333, 0.3800, 0.3117, 0.5427, 0.6164],
- [0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064],
- [0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4516, -0.5583, 1.8792, 0.0522, -0.5844, -0.0748, 0.2217, 0.0709],
- [ 0.4405, -0.5722, 1.8521, -0.5826, -0.3625, -0.8696, 0.6606, 0.0918],
- [ 0.5997, -0.4428, 1.7636, -0.7638, -0.4621, -0.3558, 0.7902, 0.2362],
- [ 0.2645, -0.6072, 1.2612, -0.9835, -0.6613, -0.3469, 0.1612, 0.2814],
- [ 0.4704, -0.4822, 1.2027, -1.1104, -0.6415, -0.7891, 0.2351, 0.2756],
- [ 0.5423, -0.4085, 1.7679, 0.4169, -0.5631, -0.7001, 0.2094, 0.4311],
- [ 0.4537, -0.5225, 1.8894, 0.1367, -0.0578, 0.1081, 0.0480, 0.0833],
- [ 0.7690, -0.3127, 1.0238, -1.1468, -0.4148, -1.2598, 0.2532, 0.3526]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.6158, -0.4249, 1.8654, -0.9002, -0.3229, -0.3537, 0.9667,
- 0.2890],
- [ 0.5509, -0.3798, 1.2129, -0.8694, -0.6982, -0.2613, 0.3830,
- 0.1193],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007],
- [ 0.6131, -0.3299, 1.8192, 0.1775, -0.5307, -0.8463, 0.2206,
- 0.5612],
- [ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532],
- [ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0157, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0157, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.23029645066708326
- step: 12
- running loss: 0.019191370888923604
- Train Steps: 12/90 Loss: 0.0192 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6224, 0.3964, 0.8225, 0.5717, 0.4150, 0.4617, 0.5775, 0.5267],
- [0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6275, 0.4081, 0.8063, 0.2017, 0.4825, 0.1583, 0.6156, 0.4869],
- [0.6271, 0.4024, 0.9000, 0.3517, 0.3700, 0.4517, 0.6931, 0.5285],
- [0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4027, -0.4660, 1.7366, -0.1386, -0.4564, -0.1423, 0.1591, 0.1444],
- [ 0.6957, -0.3605, 1.5943, 0.3531, -0.5002, -0.2424, 0.2633, 0.1841],
- [ 0.5300, -0.4568, 1.6870, -0.1113, -0.3307, -0.0143, 0.4898, 0.2449],
- [ 0.6326, -0.3350, 1.5015, -0.6746, -0.6986, -0.8277, 0.0879, 0.2304],
- [ 0.4498, -0.4974, 1.7203, -0.1162, -0.3503, -0.0235, 0.2616, 0.2893],
- [ 0.7554, -0.3196, 1.3230, -1.4331, -0.1000, -1.5792, 0.5366, 0.1289],
- [ 0.3027, -0.6293, 1.6558, -0.7107, -0.6150, -0.2197, 0.6912, 0.2634],
- [ 0.4142, -0.4875, 1.4956, -0.6237, -0.7294, -0.4820, 0.0756, 0.2566]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.5891, -0.4550, 1.5132, 0.3546, -0.3691, -0.1535, 0.3815,
- 0.1467],
- [ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.6124, -0.4010, 1.4381, -1.3544, -0.0573, -1.5546, 0.5573,
- -0.0369],
- [ 0.6108, -0.4273, 1.8711, -0.6616, -0.5769, -0.1997, 0.9156,
- 0.1554],
- [ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0114, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0114, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.24174558836966753
- step: 13
- running loss: 0.018595814489974424
- Train Steps: 13/90 Loss: 0.0186 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6271, 0.4020, 0.8375, 0.6083, 0.3925, 0.4867, 0.6037, 0.4626],
- [0.6154, 0.4112, 0.7037, 0.2317, 0.4238, 0.1833, 0.5350, 0.5600],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6147, 0.4081, 0.8538, 0.3400, 0.3663, 0.3150, 0.5142, 0.4875],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6264, 0.3972, 0.8853, 0.4771, 0.3853, 0.4511, 0.6293, 0.5334],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5512, -0.4012, 1.5784, -0.9252, -0.4582, -0.8089, 0.3276, 0.1340],
- [ 0.4327, -0.5581, 1.7039, 0.5004, -0.5014, 0.0815, 0.4479, 0.0676],
- [ 0.4325, -0.4672, 0.9178, -1.0695, -0.3671, -1.2160, 0.0401, 0.3845],
- [ 0.4380, -0.4994, 1.1840, -1.0801, -0.3987, -0.9658, 0.3837, 0.2877],
- [ 0.4125, -0.4639, 1.6765, -0.6351, -0.6692, -0.6472, 0.0339, 0.1250],
- [ 0.5317, -0.4280, 1.4851, -0.9166, -0.4635, -0.7955, 0.3967, 0.1464],
- [ 0.5392, -0.4849, 1.8711, 0.0163, -0.5591, -0.0264, 0.6641, 0.2175],
- [ 0.4610, -0.4635, 1.2291, -1.0568, -0.2196, -1.1458, 0.3517, 0.3011]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.6105, -0.4293, 1.5824, 0.5239, -0.4730, -0.0380, 0.5025,
- -0.1492],
- [ 0.5565, -0.3864, 0.9647, -1.2159, -0.3286, -1.4391, 0.1852,
- 0.3007],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698],
- [ 0.5532, -0.4008, 1.6575, -0.7155, -0.5942, -0.8309, 0.0890,
- -0.0340],
- [ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807],
- [ 0.6075, -0.4514, 1.8032, -0.0822, -0.5061, -0.2023, 0.6208,
- 0.1779],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0152, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0152, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.25693511217832565
- step: 14
- running loss: 0.018352508012737547
- Train Steps: 14/90 Loss: 0.0184 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6250, 0.4013, 0.8525, 0.5417, 0.4037, 0.5117, 0.6325, 0.5017],
- [0.6284, 0.4093, 0.8900, 0.4700, 0.3650, 0.3850, 0.6212, 0.5167],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [0.6248, 0.4185, 0.8500, 0.5767, 0.4463, 0.4550, 0.5613, 0.5917],
- [0.6173, 0.4114, 0.7325, 0.2500, 0.4213, 0.1917, 0.5338, 0.5700],
- [0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5322, -0.4184, 1.0965, -1.2336, -0.5849, -1.0054, 0.0292, 0.1176],
- [ 0.6723, -0.3306, 0.9867, -1.1890, -0.4479, -1.1556, 0.2707, 0.3578],
- [ 0.4028, -0.5790, 1.8348, 0.1465, -0.5165, 0.0932, 0.5661, 0.0781],
- [ 0.6797, -0.3641, 1.9693, -0.1283, -0.6853, -0.4126, 0.5338, 0.0233],
- [ 0.6101, -0.3995, 1.8501, -0.2459, -0.1648, 0.1547, 0.4233, 0.2387],
- [ 0.5019, -0.4424, 1.6876, 0.3924, -0.2844, -0.1806, 0.2704, 0.4552],
- [ 0.2637, -0.5703, 1.0420, -1.2051, -0.4135, -1.2683, 0.0640, 0.3140],
- [ 0.7188, -0.3408, 1.7550, -0.8725, -0.6918, -0.6287, 0.5274, 0.0789]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.6010, -0.4321, 1.6517, 0.2160, -0.4210, 0.0774, 0.6356,
- 0.0313],
- [ 0.6164, -0.3956, 1.8249, -0.1150, -0.6000, -0.5076, 0.5836,
- 0.1005],
- [ 0.5598, -0.4273, 1.7152, -0.1227, -0.0065, 0.1917, 0.5143,
- 0.2853],
- [ 0.5999, -0.3530, 1.6402, 0.3777, -0.2247, -0.1843, 0.3065,
- 0.4470],
- [ 0.5652, -0.3858, 1.0975, -1.1312, -0.3402, -1.4006, 0.1794,
- 0.3469],
- [ 0.6031, -0.4307, 1.7268, -0.8002, -0.6058, -0.6462, 0.6442,
- -0.0220]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0100, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0100, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2669285275042057
- step: 15
- running loss: 0.017795235166947046
- Train Steps: 15/90 Loss: 0.0178 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6339, 0.4123, 0.8638, 0.5367, 0.4075, 0.5467, 0.7517, 0.5436],
- [0.6275, 0.4071, 0.8075, 0.2100, 0.4700, 0.1533, 0.6148, 0.4834],
- [0.6196, 0.4088, 0.8888, 0.4583, 0.4500, 0.5683, 0.6138, 0.5883],
- [0.6164, 0.4076, 0.8838, 0.4117, 0.3713, 0.5550, 0.6238, 0.5350],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390],
- [0.6198, 0.4075, 0.8650, 0.5617, 0.4150, 0.4367, 0.5450, 0.5650],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 7.4917e-01, -2.9123e-01, 1.6643e+00, -7.0021e-02, -5.4613e-01,
- 1.4726e-02, 8.5315e-01, 2.3208e-01],
- [ 8.6743e-01, -1.8532e-01, 1.3006e+00, -1.5328e+00, -2.5562e-01,
- -1.6725e+00, 4.7231e-01, 1.7271e-02],
- [ 5.4636e-01, -3.7996e-01, 1.6489e+00, -4.4702e-01, -2.6623e-01,
- 3.5819e-02, 3.5702e-01, 3.4274e-01],
- [ 4.5332e-01, -4.3862e-01, 1.6072e+00, -5.8392e-01, -5.8043e-01,
- 1.6829e-02, 4.6827e-01, 2.7635e-01],
- [ 5.6296e-01, -3.9525e-01, 1.6733e+00, -2.4120e-01, -5.7210e-01,
- -2.1659e-01, 1.4990e-01, 1.5060e-01],
- [ 5.0192e-01, -4.1994e-01, 1.5758e+00, 1.8359e-01, -5.1492e-01,
- -4.4539e-01, 2.1972e-01, 2.5103e-01],
- [ 5.4839e-01, -4.0392e-01, 1.6198e+00, -1.2432e-01, -5.0563e-01,
- -2.9601e-01, 7.4577e-02, 1.6226e-01],
- [ 5.4278e-01, -4.0608e-01, 1.6447e+00, -8.1759e-02, -3.0073e-01,
- -3.1521e-04, 3.1638e-01, 1.0237e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6421, -0.3816, 1.7037, 0.1929, -0.4037, 0.2391, 1.1861,
- 0.2249],
- [ 0.6126, -0.4057, 1.4439, -1.3159, -0.1150, -1.5777, 0.5537,
- -0.0530],
- [ 0.5759, -0.3978, 1.8192, -0.1689, -0.2074, 0.3392, 0.5490,
- 0.4316],
- [ 0.5614, -0.4032, 1.7961, -0.3844, -0.5711, 0.2776, 0.5952,
- 0.1852],
- [ 0.5250, -0.4661, 1.7383, -0.0765, -0.4268, -0.0226, 0.2535,
- 0.2035],
- [ 0.5770, -0.4036, 1.7095, 0.3084, -0.3691, -0.2690, 0.2314,
- 0.3238],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854],
- [ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0220, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0220, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2889367938041687
- step: 16
- running loss: 0.018058549612760544
- Train Steps: 16/90 Loss: 0.0181 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [0.6258, 0.4038, 0.8750, 0.4883, 0.3900, 0.5500, 0.6375, 0.5217],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6201, 0.4017, 0.8871, 0.4621, 0.3517, 0.4675, 0.5999, 0.5106],
- [0.6122, 0.4048, 0.7506, 0.3014, 0.3475, 0.3333, 0.5487, 0.5749],
- [0.6138, 0.4101, 0.8800, 0.5083, 0.4637, 0.5950, 0.5587, 0.5077],
- [0.6277, 0.4083, 0.8350, 0.2717, 0.4562, 0.1800, 0.5918, 0.4878]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.1427e-01, -4.1541e-01, 1.5883e+00, -6.4958e-02, -2.9016e-01,
- 1.5929e-03, 1.6787e-01, 2.4338e-01],
- [ 7.8892e-01, -2.8638e-01, 1.8092e+00, -2.2893e-01, -4.9548e-01,
- 9.6385e-02, 7.4152e-01, 1.4290e-01],
- [ 6.8751e-01, -3.0935e-01, 1.6456e+00, -3.5264e-01, -5.5861e-01,
- -2.3207e-01, 2.7769e-01, 2.3544e-01],
- [ 7.5018e-01, -3.1599e-01, 1.6878e+00, 2.2710e-01, -5.1088e-01,
- -2.6419e-01, 6.2945e-01, 1.2593e-02],
- [ 6.6079e-01, -3.3558e-01, 1.6973e+00, -3.2839e-01, -6.2279e-01,
- -3.0419e-01, 4.1372e-01, 1.6635e-01],
- [ 5.2827e-01, -3.7146e-01, 1.0872e+00, -1.0771e+00, -6.6342e-01,
- -8.6333e-01, 2.2034e-01, 3.3167e-01],
- [ 5.4088e-01, -4.0553e-01, 1.7217e+00, -8.6147e-02, -1.6320e-01,
- 1.2475e-01, 3.7833e-01, 2.1609e-01],
- [ 4.0423e-01, -4.5951e-01, 1.3544e+00, -1.2396e+00, -2.0415e-01,
- -1.3715e+00, 2.6294e-01, 7.7104e-02]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386],
- [ 0.6047, -0.4209, 1.7557, -0.0303, -0.4845, 0.2545, 0.6587,
- 0.1236],
- [ 0.5368, -0.4406, 1.7730, -0.1766, -0.5249, -0.0534, 0.2314,
- 0.3469],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.5783, -0.4306, 1.8114, -0.1515, -0.6617, -0.1268, 0.4851,
- 0.0727],
- [ 0.5419, -0.4160, 1.1810, -0.8939, -0.6808, -0.7463, 0.2485,
- 0.3694],
- [ 0.5491, -0.3918, 1.7788, 0.0620, -0.1439, 0.4624, 0.2946,
- 0.0592],
- [ 0.6132, -0.4002, 1.5709, -1.0311, -0.1785, -1.4545, 0.4474,
- -0.0328]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0146, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0146, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3035747576504946
- step: 17
- running loss: 0.017857338685323212
- Train Steps: 17/90 Loss: 0.0179 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726],
- [0.6200, 0.3978, 0.8900, 0.4550, 0.3775, 0.5200, 0.6150, 0.5367],
- [0.6097, 0.4000, 0.7325, 0.2667, 0.3450, 0.3517, 0.5284, 0.5045],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6203, 0.4096, 0.8862, 0.4267, 0.3538, 0.4117, 0.6025, 0.5650],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7755, -0.2566, 1.2124, -1.1970, -0.2807, -1.3949, 0.4493, 0.0860],
- [ 0.6724, -0.2987, 1.2744, -0.9307, -0.5428, -0.9535, 0.2306, 0.0451],
- [ 0.5606, -0.4360, 1.8056, -0.0158, -0.4819, 0.1948, 0.5600, 0.1729],
- [ 0.4362, -0.4670, 1.2045, -0.9879, -0.5975, -0.6874, 0.2234, 0.1330],
- [ 0.5636, -0.4414, 1.9031, -0.1640, -0.5636, -0.0504, 0.6355, 0.0259],
- [ 0.7105, -0.3035, 1.0117, -1.1499, -0.4594, -1.1200, 0.4641, 0.2386],
- [ 0.5666, -0.4205, 1.8395, -0.2656, -0.5567, -0.3416, 0.5523, 0.3514],
- [ 0.4156, -0.5127, 1.7689, 0.0910, 0.0355, 0.0478, 0.2672, 0.1672]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082],
- [ 0.4997, -0.4446, 1.1610, -0.9772, -0.6058, -1.0311, 0.1404,
- -0.1031],
- [ 0.5776, -0.4484, 1.8249, -0.1843, -0.5423, 0.1159, 0.5547,
- 0.1929],
- [ 0.5303, -0.4384, 1.0975, -1.0542, -0.6924, -0.6616, 0.1548,
- 0.0442],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.5790, -0.3940, 1.8076, -0.3152, -0.6520, -0.3844, 0.4970,
- 0.3238],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0073, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0073, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3108562519773841
- step: 18
- running loss: 0.01726979177652134
- Train Steps: 18/90 Loss: 0.0173 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378],
- [0.6120, 0.4014, 0.6863, 0.2817, 0.3700, 0.2783, 0.5513, 0.5667],
- [0.6236, 0.3977, 0.8985, 0.4806, 0.3835, 0.5216, 0.6613, 0.5166],
- [0.6228, 0.4119, 0.7938, 0.2233, 0.4674, 0.1773, 0.6188, 0.5433],
- [0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356],
- [0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6249, -0.3117, 1.7247, -0.4558, -0.2784, -1.0430, 0.3631, 0.0424],
- [ 0.5624, -0.4253, 1.5911, -1.0202, 0.0873, -1.0461, 0.9152, 0.1176],
- [ 0.4162, -0.4453, 0.9554, -0.9243, -0.5591, -0.8039, 0.1167, 0.2402],
- [ 0.5104, -0.4898, 1.7279, -0.0447, -0.4961, 0.3425, 0.7043, 0.1548],
- [ 0.5848, -0.3563, 1.2298, -1.0580, -0.2116, -1.2383, 0.4013, 0.1006],
- [ 0.5874, -0.3731, 1.3149, -0.9523, -0.3525, -1.0013, 0.5720, 0.1029],
- [ 0.3819, -0.4549, 1.5499, -0.3267, -0.6556, -0.6680, -0.0268, 0.1221],
- [ 0.7683, -0.3051, 1.7793, -0.1137, -0.4840, 0.5970, 0.6887, 0.1274]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171],
- [ 0.6207, -0.3936, 1.7788, -1.1235, 0.1448, -1.0850, 1.1459,
- 0.1982],
- [ 0.5410, -0.4321, 0.8838, -0.9849, -0.5769, -1.0003, 0.2603,
- 0.3315],
- [ 0.5944, -0.4490, 1.8643, -0.0659, -0.5147, 0.1235, 0.7684,
- 0.1004],
- [ 0.5908, -0.3832, 1.3804, -1.2543, -0.1270, -1.4671, 0.5721,
- 0.2237],
- [ 0.6174, -0.4225, 1.4975, -1.1709, -0.3174, -1.1806, 0.6539,
- 0.1879],
- [ 0.5470, -0.3952, 1.6377, -0.4253, -0.6289, -0.8079, 0.0249,
- 0.2116],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0143, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0143, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3251751000061631
- step: 19
- running loss: 0.017114478947692795
- Train Steps: 19/90 Loss: 0.0171 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412],
- [0.6266, 0.4101, 0.8350, 0.2333, 0.3950, 0.2950, 0.6264, 0.4921],
- [0.6097, 0.4000, 0.7325, 0.2667, 0.3450, 0.3517, 0.5284, 0.5045],
- [ nan, nan, 0.7553, 0.2722, 0.3875, 0.2550, 0.5125, 0.5633],
- [0.6371, 0.4092, 0.8337, 0.5850, 0.3950, 0.5117, 0.6559, 0.5262],
- [0.6286, 0.4055, 0.9000, 0.4717, 0.3763, 0.4683, 0.7018, 0.5494],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320],
- [0.6260, 0.4120, 0.8013, 0.2350, 0.4888, 0.1533, 0.6281, 0.4895]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.3753e-01, -3.0106e-01, 1.7238e+00, -5.3725e-01, -4.3524e-01,
- -8.7413e-01, 2.8037e-01, 1.2785e-01],
- [ 7.4946e-01, -2.6226e-01, 1.4602e+00, -1.1061e+00, -3.8495e-01,
- -8.8866e-01, 5.5216e-01, 3.8942e-02],
- [ 5.6102e-01, -3.5893e-01, 1.1527e+00, -1.0360e+00, -5.2439e-01,
- -6.7386e-01, 1.5924e-01, 1.1717e-01],
- [-1.0101e+00, -1.4053e+00, 1.1850e+00, -1.0470e+00, -4.7518e-01,
- -1.0037e+00, -4.8302e-02, 1.9874e-01],
- [ 8.3262e-01, -2.4918e-01, 1.6203e+00, 3.0693e-01, -3.8922e-01,
- 1.4029e-01, 6.9893e-01, 5.8639e-02],
- [ 9.2227e-01, -1.9825e-01, 1.8672e+00, -6.0081e-02, -4.7998e-01,
- 2.9148e-02, 8.5436e-01, 8.6164e-02],
- [ 7.5961e-01, -2.9459e-01, 1.6385e+00, 1.3106e-01, -3.7348e-01,
- 1.4454e-01, 6.9711e-01, 1.6616e-01],
- [ 7.5710e-01, -2.3514e-01, 1.3542e+00, -1.1528e+00, 1.2851e-03,
- -1.4269e+00, 5.6705e-01, 8.8829e-02]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5784, -0.4006, 1.7911, -0.5701, -0.5192, -1.0331, 0.4137,
- 0.2139],
- [ 0.6081, -0.3918, 1.5709, -1.2082, -0.4614, -0.9233, 0.6072,
- -0.0129],
- [ 0.5303, -0.4384, 1.0975, -1.0542, -0.6924, -0.6616, 0.1548,
- 0.0442],
- [-2.2859, -2.2859, 1.2030, -1.0288, -0.4961, -1.1081, 0.0813,
- 0.3161],
- [ 0.6569, -0.3960, 1.5651, 0.4162, -0.4614, 0.0774, 0.7438,
- 0.1447],
- [ 0.6174, -0.4129, 1.8711, -0.1073, -0.5480, -0.1227, 0.9558,
- 0.2516],
- [ 0.6273, -0.4393, 1.6402, 0.1313, -0.5076, 0.0467, 1.1532,
- 0.1715],
- [ 0.6053, -0.3830, 1.4150, -1.2005, -0.0284, -1.5777, 0.6154,
- -0.0250]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0515, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0515, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.37666080240160227
- step: 20
- running loss: 0.018833040120080115
- Train Steps: 20/90 Loss: 0.0188 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6336, 0.4191, 0.8938, 0.5167, 0.3937, 0.3517, 0.7343, 0.5748],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250],
- [0.6268, 0.4029, 0.8500, 0.2683, 0.3937, 0.3500, 0.6860, 0.5297],
- [0.6277, 0.4057, 0.8300, 0.2650, 0.4363, 0.1850, 0.6140, 0.4823],
- [0.6030, 0.3969, 0.7988, 0.3917, 0.3450, 0.3667, 0.5266, 0.4700],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4839, -0.5000, 1.6635, 0.1886, -0.3307, -0.3211, 0.7333, 0.2023],
- [ 0.6194, -0.3507, 1.4114, -0.9203, -0.3818, -0.8013, 0.4622, 0.0413],
- [ 0.6624, -0.3586, 1.8314, -0.1974, -0.5329, 0.0903, 0.6837, 0.1530],
- [ 0.8656, -0.2259, 1.5109, -0.8773, -0.4817, -0.4769, 0.8409, 0.1297],
- [ 0.8424, -0.2204, 1.4692, -1.0215, -0.1932, -1.1525, 0.6111, -0.0318],
- [ 0.6292, -0.3355, 1.4958, -0.4595, -0.6039, -0.3666, 0.1997, 0.0866],
- [-0.7059, -1.2208, 0.8391, -1.2020, -0.2385, -1.2981, 0.0876, 0.3023],
- [ 0.7580, -0.2258, 1.6574, -0.7458, -0.0913, -1.0447, 0.4986, 0.0383]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6405, -0.3503, 1.8423, 0.1005, -0.4672, -0.6616, 1.1057,
- 0.3692],
- [ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807],
- [ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390],
- [ 0.6092, -0.4249, 1.6402, -1.0465, -0.4672, -0.6693, 0.8827,
- 0.1608],
- [ 0.6132, -0.4122, 1.5478, -1.0619, -0.2709, -1.4314, 0.5500,
- -0.0583],
- [ 0.4992, -0.4525, 1.4035, -0.4768, -0.6924, -0.5923, 0.1465,
- -0.1151],
- [-2.2859, -2.2859, 0.6760, -1.4083, -0.3286, -1.4160, 0.2487,
- 0.3469],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0800, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0800, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4566794792190194
- step: 21
- running loss: 0.02174664186757235
- Train Steps: 21/90 Loss: 0.0217 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6211, 0.3993, 0.7650, 0.1933, 0.4575, 0.1550, 0.5965, 0.4895],
- [0.6357, 0.4159, 0.8788, 0.5583, 0.3638, 0.4433, 0.6488, 0.5297],
- [0.6257, 0.4167, 0.8775, 0.3433, 0.3563, 0.4133, 0.6200, 0.5667],
- [0.6262, 0.4163, 0.8850, 0.5183, 0.3763, 0.4150, 0.6025, 0.5500],
- [0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917],
- [0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467],
- [0.6200, 0.4086, 0.8414, 0.2974, 0.4117, 0.2274, 0.5869, 0.5391]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7002, -0.3247, 1.2085, -1.4291, -0.0743, -1.4957, 0.4974, -0.0724],
- [ 0.4774, -0.4847, 1.7084, 0.1631, -0.4217, -0.1387, 0.7403, 0.0630],
- [ 0.5838, -0.3842, 1.6920, -0.6905, -0.5661, -0.2903, 0.6077, 0.2382],
- [ 0.5264, -0.4329, 1.6813, -0.0333, -0.4499, -0.3091, 0.4960, 0.1460],
- [ 0.7125, -0.3116, 1.7807, -0.0740, -0.3466, -0.4352, 0.7564, 0.0296],
- [ 0.3954, -0.4781, 1.2758, -0.6132, -0.4806, -0.7404, 0.2370, 0.3153],
- [ 0.7172, -0.2866, 1.5352, -0.8002, -0.5477, -0.3290, 0.5524, 0.1585],
- [ 0.5864, -0.3569, 1.5866, -0.9917, -0.2150, -1.0676, 0.5041, 0.0485]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5828, -0.4417, 1.2476, -1.3929, -0.1727, -1.5700, 0.4694,
- -0.0248],
- [ 0.6504, -0.3647, 1.7730, 0.2930, -0.6058, -0.2382, 0.7109,
- 0.1608],
- [ 0.6040, -0.3614, 1.7672, -0.7001, -0.6404, -0.3768, 0.5778,
- 0.3315],
- [ 0.6066, -0.3632, 1.8018, 0.1082, -0.5480, -0.3691, 0.4970,
- 0.2545],
- [ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470],
- [ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391],
- [ 0.5776, -0.3987, 1.6005, -0.9121, -0.3844, -1.2358, 0.4247,
- 0.2043]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0122, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0122, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.46885613538324833
- step: 22
- running loss: 0.02131164251742038
- Train Steps: 22/90 Loss: 0.0213 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332],
- [0.6199, 0.4071, 0.7600, 0.2117, 0.4037, 0.2767, 0.6138, 0.5550],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6277, 0.4029, 0.8250, 0.2433, 0.4325, 0.2100, 0.6366, 0.5207],
- [0.6090, 0.4010, 0.7838, 0.3483, 0.3538, 0.3783, 0.5462, 0.5077],
- [0.6258, 0.4143, 0.8525, 0.2617, 0.4950, 0.1667, 0.6219, 0.4967],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6196, -0.3860, 1.7935, 0.0901, -0.3193, 0.3917, 0.6185, 0.0979],
- [ 0.4539, -0.5063, 1.5252, 0.2251, -0.4727, 0.1093, 0.7826, 0.1797],
- [ 0.6377, -0.3392, 1.3309, -1.1378, -0.4062, -1.0118, 0.6385, 0.1555],
- [ 0.4766, -0.4483, 1.3842, -1.0258, -0.1972, -1.3189, 0.4875, 0.1217],
- [ 0.6683, -0.3750, 1.4588, -0.9875, -0.2644, -1.2481, 0.7489, 0.0369],
- [ 0.4246, -0.4852, 1.4205, -0.7114, -0.6263, -0.5640, 0.2431, 0.1097],
- [ 0.5375, -0.3941, 1.6243, -1.0296, -0.0724, -1.3532, 0.6481, 0.0064],
- [ 0.5658, -0.3992, 1.6959, -0.5945, -0.7086, -0.3629, 0.4398, 0.2220]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.9913e-01, -3.8029e-01, 1.8018e+00, -5.3426e-02, -3.4596e-01,
- 1.8522e-01, 5.3741e-01, 1.3903e-01],
- [ 6.1907e-01, -4.2971e-01, 1.4612e+00, 2.3911e-01, -4.9607e-01,
- 3.1255e-02, 1.1166e+00, 1.7680e-01],
- [ 5.7754e-01, -4.0539e-01, 1.2245e+00, -1.3082e+00, -4.2102e-01,
- -1.0080e+00, 5.4896e-01, 2.7760e-01],
- [ 5.7679e-01, -4.0308e-01, 1.3838e+00, -1.1527e+00, -2.1876e-01,
- -1.4216e+00, 4.3790e-01, 1.8502e-01],
- [ 6.1322e-01, -4.2479e-01, 1.5247e+00, -1.1620e+00, -2.8822e-01,
- -1.3159e+00, 6.5445e-01, 1.1931e-01],
- [ 5.2702e-01, -4.3356e-01, 1.3342e+00, -6.7698e-01, -6.5196e-01,
- -5.3841e-01, 2.3702e-01, 5.9193e-02],
- [ 6.0479e-01, -3.7229e-01, 1.6517e+00, -1.0773e+00, 4.6189e-04,
- -1.5161e+00, 5.8660e-01, 8.0947e-03],
- [ 5.8672e-01, -3.9369e-01, 1.7499e+00, -7.1547e-01, -6.4042e-01,
- -3.8445e-01, 4.7390e-01, 3.3918e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0080, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0080, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4768888894468546
- step: 23
- running loss: 0.02073429954116759
- Train Steps: 23/90 Loss: 0.0207 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [0.6201, 0.4050, 0.7757, 0.2234, 0.4459, 0.1798, 0.5975, 0.5426],
- [ nan, nan, 0.8850, 0.2817, 0.5112, 0.2183, 0.7184, 0.5436],
- [0.6263, 0.4233, 0.7924, 0.4626, 0.3788, 0.2883, 0.5573, 0.6047],
- [0.6200, 0.4059, 0.8700, 0.4900, 0.4163, 0.5000, 0.6162, 0.5467],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.4015e-01, -3.2741e-01, 1.5286e+00, -5.8203e-01, -2.0736e-01,
- -1.0390e+00, 3.7237e-01, 3.1638e-01],
- [ 7.2093e-01, -2.9143e-01, 1.7759e+00, -1.1026e+00, -2.6972e-01,
- -1.1049e+00, 7.2239e-01, -1.2084e-01],
- [ 8.0535e-01, -2.6231e-01, 1.2904e+00, -1.1777e+00, -3.5370e-01,
- -1.2656e+00, 5.2912e-01, -1.9021e-02],
- [-5.5371e-01, -1.1874e+00, 1.8828e+00, -1.1686e+00, -5.8424e-03,
- -1.0682e+00, 1.1814e+00, 1.2185e-01],
- [ 5.2261e-01, -4.1431e-01, 1.4245e+00, -1.9491e-01, -5.6756e-01,
- -6.1366e-01, 3.0487e-01, 4.5116e-01],
- [ 5.1992e-01, -4.8953e-01, 1.7885e+00, -5.3513e-02, -4.4506e-01,
- 3.5400e-01, 6.2226e-01, 1.5005e-01],
- [ 8.1954e-01, -3.3110e-01, 1.7693e+00, 2.3936e-01, -6.8419e-01,
- -1.6167e-03, 7.2799e-01, 3.1313e-02],
- [ 6.6607e-01, -3.6078e-01, 9.4744e-01, -1.1733e+00, -5.2266e-01,
- -1.1170e+00, 3.4295e-01, 1.2331e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882],
- [ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [ 0.5784, -0.4153, 1.2972, -1.2541, -0.2265, -1.4553, 0.4741,
- 0.2203],
- [-2.2859, -2.2859, 1.8018, -0.9849, 0.0755, -1.2774, 1.0326,
- 0.2249],
- [ 0.6069, -0.3309, 1.3742, -0.1493, -0.5365, -0.9541, 0.2884,
- 0.5071],
- [ 0.5776, -0.4109, 1.7326, -0.0226, -0.3633, 0.0236, 0.5605,
- 0.2391],
- [ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [ 0.5483, -0.3991, 0.8030, -1.2159, -0.5018, -1.1928, 0.2624,
- 0.3852]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0837, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0837, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5606125611811876
- step: 24
- running loss: 0.02335885671588282
- Train Steps: 24/90 Loss: 0.0234 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578],
- [0.6225, 0.4191, 0.8500, 0.4167, 0.3688, 0.3233, 0.5650, 0.5650],
- [0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7548, -0.3042, 1.0104, -1.2783, -0.3081, -1.4168, 0.4733, 0.0882],
- [ 0.3597, -0.5199, 1.6652, -0.3600, -0.5313, -0.7199, 0.4997, 0.3743],
- [ 1.0061, -0.1646, 1.8598, -0.2678, -0.5175, 0.0687, 0.8785, 0.2038],
- [-1.9778, -2.1261, 1.0573, -1.3015, -0.3206, -1.1825, 0.1999, 0.2042],
- [ 0.6407, -0.3564, 1.7933, -0.4605, -0.3934, -0.7640, 0.5918, 0.2892],
- [ 0.9847, -0.1499, 1.5094, -1.0364, -0.2792, -1.1041, 0.7410, 0.0412],
- [ 0.8230, -0.2356, 1.6608, -0.5814, -0.5504, -0.7949, 0.4448, 0.0940],
- [ 0.9102, -0.2794, 1.8353, -0.3655, -0.5575, -0.4388, 0.9005, 0.0544]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5232, -0.4189, 0.9358, -1.4006, -0.3921, -1.3698, 0.2555,
- 0.2906],
- [ 0.5894, -0.3503, 1.6402, -0.3614, -0.5827, -0.7925, 0.3238,
- 0.3238],
- [ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [-2.2859, -2.2859, 0.8516, -1.3112, -0.4326, -1.2851, 0.0755,
- 0.2930],
- [ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928],
- [ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0251, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0251, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5856962967664003
- step: 25
- running loss: 0.023427851870656012
- Train Steps: 25/90 Loss: 0.0234 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.6935, 0.1930, 0.4150, 0.2250, 0.5450, 0.5650],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683],
- [0.6209, 0.3920, 0.8650, 0.5367, 0.4400, 0.5067, 0.6025, 0.4950],
- [0.6197, 0.3986, 0.8800, 0.4617, 0.4188, 0.4783, 0.5687, 0.5550],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6214, 0.4175, 0.7300, 0.2883, 0.4338, 0.2167, 0.5698, 0.5773],
- [0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.1749, -2.2414, 1.2203, -1.2753, -0.2672, -1.2905, 0.4293, 0.2614],
- [ 0.8052, -0.2676, 1.6369, -0.9690, -0.5289, -1.0819, 0.6849, 0.2336],
- [ 0.7062, -0.3927, 1.7734, 0.1701, -0.3351, 0.0147, 0.6362, 0.0818],
- [ 0.6538, -0.3901, 1.7930, -0.1344, -0.4454, -0.0750, 0.6346, 0.2179],
- [ 0.7714, -0.2682, 1.5192, -1.0016, -0.5511, -1.1028, 0.7895, 0.1417],
- [ 0.8999, -0.1971, 1.8160, -0.1320, -0.1471, 0.0496, 0.6020, 0.2215],
- [ 0.9350, -0.1469, 1.1757, -0.9296, -0.3252, -1.4202, 0.5431, 0.3288],
- [ 0.6527, -0.3576, 1.3160, -0.9865, -0.6031, -1.0946, 0.3418, -0.0273]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859e+00, -2.2859e+00, 9.1750e-01, -1.3947e+00, -3.6905e-01,
- -1.2467e+00, 2.3141e-01, 3.2379e-01],
- [ 5.7783e-01, -3.9299e-01, 1.5189e+00, -9.3872e-01, -4.3256e-01,
- -9.1563e-01, 4.8545e-01, 3.3918e-01],
- [ 5.8199e-01, -4.7544e-01, 1.7095e+00, 1.9292e-01, -2.5358e-01,
- 5.4350e-02, 4.9700e-01, 4.6189e-04],
- [ 5.7627e-01, -4.4503e-01, 1.7788e+00, -1.5350e-01, -3.5173e-01,
- -7.6520e-02, 3.4111e-01, 2.7760e-01],
- [ 5.9766e-01, -3.7916e-01, 1.2995e+00, -1.0311e+00, -5.1917e-01,
- -8.3865e-01, 5.8360e-01, 2.1601e-01],
- [ 5.5381e-01, -4.1386e-01, 1.7557e+00, -1.8430e-01, -4.5897e-02,
- 1.2417e-01, 4.2194e-01, 2.8530e-01],
- [ 5.8412e-01, -3.5743e-01, 1.0859e+00, -9.5412e-01, -2.8245e-01,
- -1.2851e+00, 3.4601e-01, 3.8081e-01],
- [ 5.2194e-01, -4.5504e-01, 1.1415e+00, -9.1962e-01, -6.4042e-01,
- -9.3872e-01, 1.8562e-01, 1.4106e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.606036888435483
- step: 26
- running loss: 0.02330911109367242
- Train Steps: 26/90 Loss: 0.0233 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6233, 0.4091, 0.8100, 0.2950, 0.3563, 0.3883, 0.6013, 0.5200],
- [0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [ nan, nan, 0.7335, 0.2569, 0.3788, 0.2667, 0.5066, 0.5578],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320],
- [0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 8.0656e-01, -2.6370e-01, 1.5694e+00, -7.0661e-01, -6.7750e-01,
- -6.1102e-01, 4.4837e-01, 1.7196e-01],
- [ 8.3816e-01, -2.6449e-01, 1.4815e+00, -9.9043e-01, -6.0364e-01,
- -6.2400e-01, 7.4873e-01, 1.6229e-01],
- [ 7.6048e-01, -3.0167e-01, 1.4651e+00, 1.3264e-01, -4.7946e-01,
- -7.3808e-01, 4.7905e-01, 5.3412e-01],
- [-2.4250e+00, -2.4390e+00, 1.1478e+00, -1.2399e+00, -4.5536e-01,
- -1.1712e+00, 1.5048e-01, 2.7632e-01],
- [ 9.2886e-01, -2.3300e-01, 1.6681e+00, 3.6170e-02, -4.2982e-01,
- -2.4203e-02, 1.0275e+00, 1.7328e-01],
- [ 7.3659e-01, -3.2591e-01, 1.6391e+00, -1.1892e+00, -4.0055e-01,
- -1.3278e+00, 5.5345e-01, 4.1518e-02],
- [ 7.4341e-01, -3.3253e-01, 1.7754e+00, -2.0403e-01, -2.2468e-03,
- -5.2091e-02, 6.5368e-01, 2.7859e-01],
- [ 7.0976e-01, -3.3546e-01, 1.8470e+00, -7.5960e-01, -4.6156e-01,
- -1.2449e+00, 5.1825e-01, 1.3950e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.5932, -0.3962, 1.4554, -0.9233, -0.6404, -0.4922, 0.4912,
- 0.1159],
- [ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778],
- [-2.2859, -2.2859, 1.1020, -1.0994, -0.5365, -1.0542, 0.0542,
- 0.2905],
- [ 0.6273, -0.4393, 1.6402, 0.1313, -0.5076, 0.0467, 1.1532,
- 0.1715],
- [ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.5371, -0.4252, 1.7037, -0.0688, -0.0342, 0.0620, 0.3758,
- 0.2853],
- [ 0.5784, -0.4006, 1.7911, -0.5701, -0.5192, -1.0331, 0.4137,
- 0.2139]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0194, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0194, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6254050750285387
- step: 27
- running loss: 0.023163150926982914
- Train Steps: 27/90 Loss: 0.0232 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433],
- [0.6084, 0.3981, 0.8588, 0.5233, 0.4600, 0.5367, 0.5680, 0.5006],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [0.6339, 0.4149, 0.8800, 0.5000, 0.3900, 0.5283, 0.7541, 0.5424],
- [0.6268, 0.4052, 0.8175, 0.2250, 0.4688, 0.1917, 0.6375, 0.5267],
- [0.6117, 0.4019, 0.8538, 0.4067, 0.3513, 0.3583, 0.5663, 0.5133]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.3912, -2.4006, 1.1279, -1.3163, -0.4300, -1.4060, 0.2300, 0.2982],
- [ 0.8388, -0.2334, 1.3732, -1.0759, -0.6614, -0.8011, 0.6057, 0.2207],
- [ 0.7985, -0.2911, 1.6881, 0.1210, -0.2120, -0.0144, 0.3557, 0.1805],
- [ 0.5698, -0.3962, 1.3641, -0.7297, -0.7064, -0.7499, 0.3289, 0.3488],
- [ 0.6580, -0.3809, 1.7488, -0.0879, -0.1178, -0.1244, 0.4801, 0.3834],
- [ 1.0398, -0.1747, 1.8704, 0.0921, -0.5262, 0.1024, 1.1594, 0.2341],
- [ 0.6474, -0.3854, 1.4655, -1.3019, -0.1727, -1.7691, 0.7337, 0.1894],
- [ 0.5760, -0.4059, 1.7574, -0.5579, -0.7147, -0.9041, 0.3207, 0.1653]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373],
- [ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237],
- [ 0.5241, -0.4470, 1.6806, 0.1313, -0.1612, 0.1929, 0.3378,
- 0.0261],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.5505, -0.4207, 1.7095, -0.0534, -0.0509, 0.1050, 0.3873,
- 0.3007],
- [ 0.6421, -0.3695, 1.7788, 0.0236, -0.4845, 0.1544, 1.1971,
- 0.2196],
- [ 0.6092, -0.4143, 1.4901, -1.2467, -0.1208, -1.4006, 0.6587,
- 0.1467],
- [ 0.5393, -0.4294, 1.6575, -0.4075, -0.6635, -0.6308, 0.3296,
- 0.0851]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0175, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0175, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6429185438901186
- step: 28
- running loss: 0.022961376567504237
- Train Steps: 28/90 Loss: 0.0230 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6228, 0.4119, 0.7938, 0.2233, 0.4674, 0.1773, 0.6188, 0.5433],
- [0.6113, 0.4104, 0.8650, 0.4250, 0.3925, 0.5967, 0.5787, 0.5116],
- [0.6239, 0.4123, 0.8313, 0.2550, 0.4500, 0.2050, 0.6175, 0.5400],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167],
- [0.6289, 0.4032, 0.8419, 0.5446, 0.4075, 0.5017, 0.6312, 0.5117],
- [0.6151, 0.4125, 0.8738, 0.4417, 0.3575, 0.3783, 0.5138, 0.5483]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3600, -0.5704, 1.3240, -1.3796, -0.1798, -1.6076, 0.5185, 0.2673],
- [ 0.5784, -0.3890, 1.7528, -0.3822, -0.4831, 0.4132, 0.5336, 0.2449],
- [ 0.4459, -0.5053, 1.4762, -1.3180, -0.2036, -1.4446, 0.5637, 0.2456],
- [ 0.2246, -0.7033, 1.5671, 0.0254, -0.4486, -0.2696, 0.4713, 0.3428],
- [ 0.5284, -0.4739, 1.8616, -0.5123, -0.5799, -0.8014, 0.6375, 0.2973],
- [ 0.3499, -0.5930, 1.6678, -0.6075, -0.6182, -0.9334, 0.5179, 0.2470],
- [ 0.6549, -0.4208, 1.5748, 0.0875, -0.4565, -0.0380, 0.7645, 0.2367],
- [ 0.0652, -0.7456, 1.5443, -0.4409, -0.7221, -0.7115, 0.0813, 0.4008]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5908, -0.3832, 1.3804, -1.2543, -0.1270, -1.4671, 0.5721,
- 0.2237],
- [ 0.5376, -0.3903, 1.7095, -0.3229, -0.4730, 0.4701, 0.3871,
- 0.0772],
- [ 0.5958, -0.3818, 1.5536, -1.1081, -0.2074, -1.3390, 0.5663,
- 0.2083],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091],
- [ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005],
- [ 0.6189, -0.4238, 1.6026, 0.2295, -0.4037, 0.0313, 0.6298,
- 0.0774],
- [ 0.5551, -0.3807, 1.7499, -0.2459, -0.6346, -0.5384, 0.0871,
- 0.2468]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6651229802519083
- step: 29
- running loss: 0.022935275181100286
- Train Steps: 29/90 Loss: 0.0229 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783],
- [0.6098, 0.3991, 0.8638, 0.4717, 0.4263, 0.4967, 0.5212, 0.5650],
- [0.6304, 0.4024, 0.8925, 0.4800, 0.3937, 0.4817, 0.7485, 0.5297],
- [0.6156, 0.4125, 0.8850, 0.4833, 0.4550, 0.5817, 0.5765, 0.5084],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726],
- [0.6364, 0.4154, 0.8938, 0.3717, 0.4500, 0.2583, 0.6448, 0.5285],
- [ nan, nan, 0.7525, 0.2291, 0.3838, 0.3017, 0.6050, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6743, -0.3775, 1.9492, -0.5144, -0.6510, -0.6040, 0.7542, 0.2366],
- [ 0.4084, -0.5484, 0.8144, -1.2229, -0.4932, -1.3891, 0.2318, 0.3599],
- [ 0.3393, -0.5796, 1.6478, -0.0665, -0.3251, -0.0515, 0.2467, 0.3376],
- [ 0.7025, -0.3827, 1.7598, -0.0448, -0.4856, -0.1389, 0.9078, 0.2428],
- [ 0.4087, -0.5155, 1.7943, -0.0300, -0.2215, 0.2901, 0.3834, 0.2715],
- [ 0.5483, -0.4365, 1.2142, -1.0301, -0.6042, -1.1357, 0.1894, 0.0886],
- [ 0.5886, -0.3989, 1.8939, -0.6754, -0.2331, -1.3781, 0.6624, 0.3241],
- [-1.2400, -1.6479, 1.1698, -1.2162, -0.5687, -1.1057, 0.4281, 0.4191]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.5483, -0.3991, 0.8030, -1.2159, -0.5018, -1.1928, 0.2624,
- 0.3852],
- [ 0.5308, -0.4425, 1.7037, -0.1073, -0.3171, 0.0082, 0.1217,
- 0.3238],
- [ 0.6257, -0.4273, 1.8365, -0.0688, -0.4672, -0.0611, 1.1715,
- 0.1608],
- [ 0.5573, -0.3808, 1.8018, -0.0534, -0.1843, 0.4008, 0.3769,
- 0.0622],
- [ 0.4997, -0.4446, 1.1610, -0.9772, -0.6058, -1.0311, 0.1404,
- -0.1031],
- [ 0.6537, -0.3671, 1.8423, -0.5692, -0.2074, -1.0927, 0.6926,
- 0.1554],
- [-2.2859, -2.2859, 1.1898, -1.2278, -0.5134, -0.8925, 0.5085,
- 0.3315]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0337, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0337, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6988462079316378
- step: 30
- running loss: 0.02329487359772126
- Train Steps: 30/90 Loss: 0.0233 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986],
- [0.6145, 0.4007, 0.8775, 0.4533, 0.4562, 0.5533, 0.6088, 0.5533],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6127, 0.4084, 0.8700, 0.4467, 0.3987, 0.4317, 0.5013, 0.5471],
- [0.6114, 0.4018, 0.7213, 0.1967, 0.3763, 0.2700, 0.5875, 0.5533],
- [0.6087, 0.3976, 0.8337, 0.3867, 0.3713, 0.3117, 0.5938, 0.5300],
- [0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6304, 0.4024, 0.8925, 0.4800, 0.3937, 0.4817, 0.7485, 0.5297]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6474, -0.3811, 1.9037, -0.6287, -0.2022, -1.5291, 0.5346, 0.2287],
- [ 0.5797, -0.4225, 1.7740, -0.1589, -0.1278, 0.2441, 0.5273, 0.3165],
- [ 0.3275, -0.6175, 1.7052, -0.4999, -0.6740, -0.2583, 0.5826, 0.3372],
- [ 0.0698, -0.7592, 1.6376, -0.3294, -0.4746, -0.4258, 0.0776, 0.3240],
- [ 0.0830, -0.7637, 0.9233, -1.3943, -0.5709, -1.1773, 0.2773, 0.2900],
- [ 0.2631, -0.6277, 1.5495, -0.5722, -0.5926, -0.8965, 0.4296, 0.3226],
- [ 0.3554, -0.5687, 1.5380, -0.6126, -0.7502, -0.5382, 0.2942, 0.2498],
- [ 0.5028, -0.5089, 1.7308, -0.0581, -0.4410, -0.0810, 0.8824, 0.2342]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171],
- [ 0.5526, -0.4351, 1.7672, -0.1920, -0.1785, 0.2699, 0.5259,
- 0.2699],
- [ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.5441, -0.3997, 1.7326, -0.2228, -0.4441, -0.2921, 0.0296,
- 0.2409],
- [ 0.5380, -0.4302, 1.0455, -1.3775, -0.5480, -1.0388, 0.4277,
- 0.2699],
- [ 0.5255, -0.4495, 1.5651, -0.4999, -0.5711, -0.8463, 0.4566,
- 0.1621],
- [ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.6257, -0.4273, 1.8365, -0.0688, -0.4672, -0.0611, 1.1715,
- 0.1608]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0212, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0212, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7200348284095526
- step: 31
- running loss: 0.023226929948695244
- Train Steps: 31/90 Loss: 0.0232 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4003, 0.8638, 0.5967, 0.3688, 0.4900, 0.6108, 0.4661],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6175, 0.3997, 0.6737, 0.2500, 0.4313, 0.1933, 0.5513, 0.5750],
- [0.6113, 0.4088, 0.6859, 0.2208, 0.4363, 0.1700, 0.5188, 0.5533],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6193, 0.4050, 0.7313, 0.2433, 0.4075, 0.2117, 0.5649, 0.5656],
- [0.6274, 0.4117, 0.8100, 0.5801, 0.4000, 0.4583, 0.5582, 0.6118],
- [0.6216, 0.4099, 0.7225, 0.2033, 0.4188, 0.2217, 0.5975, 0.5283]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6033, -0.4310, 1.8310, 0.4091, -0.5672, 0.0558, 0.7620, 0.1504],
- [ 0.8779, -0.2438, 2.1329, -0.3252, -0.5712, -0.5649, 0.7217, 0.2011],
- [-1.6352, -1.8747, 0.9210, -1.2440, -0.4025, -1.2805, 0.1456, 0.4208],
- [-0.6913, -1.2824, 1.0063, -1.2538, -0.3921, -1.3718, 0.0689, 0.3489],
- [ 0.7124, -0.3332, 1.8602, -0.3045, -0.5819, -0.0313, 0.3551, 0.0675],
- [ 0.4150, -0.5009, 1.2186, -1.2239, -0.4369, -1.1337, 0.3174, 0.2731],
- [ 0.6908, -0.3370, 1.6717, 0.3058, -0.4995, -0.0272, 0.5719, 0.5020],
- [ 0.5956, -0.4108, 1.1865, -1.2724, -0.4142, -1.2213, 0.2497, 0.2512]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6120, -0.4371, 1.7037, 0.4701, -0.5827, -0.0226, 0.5354,
- -0.1331],
- [ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.5663, -0.4396, 0.8261, -1.1312, -0.2940, -1.3929, 0.2603,
- 0.3700],
- [ 0.5377, -0.3978, 0.8824, -1.2663, -0.2709, -1.5007, 0.1102,
- 0.2699],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.5746, -0.4153, 1.0917, -1.1620, -0.4037, -1.3082, 0.3234,
- 0.3267],
- [ 0.6121, -0.3844, 1.4556, 0.3936, -0.4383, -0.1689, 0.2925,
- 0.5401],
- [ 0.5850, -0.3925, 1.0513, -1.3467, -0.3517, -1.2620, 0.4739,
- 0.1544]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1585, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1585, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8785099294036627
- step: 32
- running loss: 0.02745343529386446
- Train Steps: 32/90 Loss: 0.0275 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6257, 0.4024, 0.8612, 0.5352, 0.4361, 0.5253, 0.6680, 0.5166],
- [0.6200, 0.4024, 0.8390, 0.3139, 0.3525, 0.3833, 0.6162, 0.5383],
- [0.6068, 0.3963, 0.8650, 0.4317, 0.4037, 0.5083, 0.5253, 0.4999],
- [0.6142, 0.4127, 0.7575, 0.3067, 0.3438, 0.4383, 0.5778, 0.5207],
- [0.6151, 0.4125, 0.8738, 0.4417, 0.3575, 0.3783, 0.5138, 0.5483],
- [0.6127, 0.4084, 0.8700, 0.4467, 0.3987, 0.4317, 0.5013, 0.5471],
- [0.6286, 0.4055, 0.9000, 0.4717, 0.3763, 0.4683, 0.7018, 0.5494],
- [0.6332, 0.4118, 0.9238, 0.4267, 0.4012, 0.4733, 0.7525, 0.5436]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.2940e-01, -4.4512e-01, 1.6529e+00, 1.7090e-01, -2.1387e-01,
- 7.7426e-03, 6.9037e-01, 1.7068e-01],
- [ 3.4147e-01, -5.6399e-01, 1.5299e+00, -8.4593e-01, -6.4986e-01,
- -7.0577e-01, 4.4995e-01, 2.9445e-01],
- [-1.7507e-03, -7.9282e-01, 1.6644e+00, -4.0141e-01, -4.2820e-01,
- -1.7175e-01, 1.8599e-01, 1.2021e-01],
- [ 2.9183e-01, -5.5249e-01, 1.0889e+00, -9.9410e-01, -6.0846e-01,
- -4.8721e-01, 2.1527e-01, 3.2583e-01],
- [ 3.6955e-01, -5.1423e-01, 1.5982e+00, -2.9987e-01, -6.4598e-01,
- -8.5654e-01, -3.9592e-02, 2.8502e-01],
- [ 3.0083e-01, -5.7450e-01, 1.6468e+00, -3.0167e-01, -4.3349e-01,
- -5.5919e-01, -3.3376e-02, 2.7009e-01],
- [ 5.5085e-01, -4.4781e-01, 1.8877e+00, -1.4398e-01, -5.1621e-01,
- -3.5603e-01, 8.2913e-01, 1.9243e-01],
- [ 4.9237e-01, -4.7212e-01, 1.7585e+00, -3.4924e-01, -4.0652e-01,
- -2.1576e-01, 8.1910e-01, 3.0801e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6042, -0.4273, 1.6920, 0.1860, -0.2717, 0.1406, 0.7997,
- 0.1004],
- [ 0.5779, -0.4275, 1.5894, -0.8362, -0.6577, -0.5153, 0.5605,
- 0.2006],
- [ 0.5168, -0.4556, 1.7095, -0.2921, -0.4210, 0.0620, 0.1404,
- 0.0231],
- [ 0.5509, -0.3798, 1.2129, -0.8694, -0.6982, -0.2613, 0.3830,
- 0.1193],
- [ 0.5551, -0.3807, 1.7499, -0.2459, -0.6346, -0.5384, 0.0871,
- 0.2468],
- [ 0.5441, -0.3997, 1.7326, -0.2228, -0.4441, -0.2921, 0.0296,
- 0.2409],
- [ 0.6174, -0.4129, 1.8711, -0.1073, -0.5480, -0.1227, 0.9558,
- 0.2516],
- [ 0.6388, -0.3840, 1.9808, -0.3152, -0.4326, -0.0996, 1.1897,
- 0.2249]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0250, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0250, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9034913182258606
- step: 33
- running loss: 0.02737852479472305
- Train Steps: 33/90 Loss: 0.0274 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617],
- [0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450],
- [0.6286, 0.4040, 0.8696, 0.3047, 0.3924, 0.2887, 0.6300, 0.5367],
- [0.6127, 0.4066, 0.8550, 0.5567, 0.4662, 0.5141, 0.5070, 0.5412],
- [0.6115, 0.4005, 0.8838, 0.3867, 0.3763, 0.4700, 0.5800, 0.5550],
- [0.6263, 0.4233, 0.7924, 0.4626, 0.3788, 0.2883, 0.5573, 0.6047],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6201, 0.4116, 0.8725, 0.4733, 0.3700, 0.3217, 0.5386, 0.5767]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.9099, -2.0520, 0.9409, -1.3096, -0.3855, -1.2464, 0.1902, 0.2886],
- [ 0.5154, -0.4495, 1.7163, -0.2839, -0.3076, 0.1952, 0.4860, 0.1330],
- [ 0.5209, -0.4609, 1.6231, -1.0219, -0.5351, -1.0031, 0.5578, 0.1162],
- [ 0.5516, -0.4181, 1.5766, 0.2001, -0.1115, -0.0064, 0.2084, 0.1623],
- [ 0.4060, -0.5142, 1.6260, -0.6436, -0.6285, -0.1200, 0.2998, 0.1566],
- [ 0.7592, -0.2388, 1.3822, -0.2121, -0.4769, -0.7826, 0.2621, 0.4588],
- [ 0.6406, -0.4045, 1.7779, -0.2040, -0.5504, -0.4587, 0.7782, 0.1341],
- [ 0.6576, -0.3083, 1.6359, -0.1900, -0.5963, -0.8407, 0.2722, 0.2160]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084],
- [ 0.5960, -0.4102, 1.8018, -0.1612, -0.3344, 0.1159, 0.5490,
- 0.2314],
- [ 0.6174, -0.4201, 1.7309, -0.8784, -0.4735, -0.9524, 0.6242,
- 0.1931],
- [ 0.5443, -0.4079, 1.6633, 0.2853, -0.1325, 0.0888, 0.0558,
- 0.2138],
- [ 0.5384, -0.4361, 1.7961, -0.4999, -0.5480, -0.1150, 0.3931,
- 0.2776],
- [ 0.6069, -0.3309, 1.3742, -0.1493, -0.5365, -0.9541, 0.2884,
- 0.5071],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [ 0.5781, -0.3848, 1.7441, -0.0996, -0.5769, -0.8002, 0.2021,
- 0.3778]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0114, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0114, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9148482903838158
- step: 34
- running loss: 0.026907302658347523
- Train Steps: 34/90 Loss: 0.0269 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6286, 0.4097, 0.8107, 0.2414, 0.4425, 0.2483, 0.6745, 0.5385],
- [0.6128, 0.4084, 0.8738, 0.4683, 0.3613, 0.3700, 0.4960, 0.5397],
- [0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6201, 0.4055, 0.7717, 0.1919, 0.4250, 0.2079, 0.5871, 0.5044],
- [0.6332, 0.4118, 0.9238, 0.4267, 0.4012, 0.4733, 0.7525, 0.5436]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 3.4211e-01, -5.5436e-01, 1.4170e+00, 6.1026e-02, -4.8594e-01,
- -1.1325e-01, 7.7582e-01, 3.2171e-01],
- [ 1.4285e-01, -6.6580e-01, 1.4183e+00, -1.1495e+00, -3.2002e-01,
- -1.2133e+00, 6.1003e-01, 2.7405e-01],
- [ 3.6311e-01, -4.9852e-01, 1.6690e+00, -1.9997e-01, -6.7210e-01,
- -5.8863e-01, -4.3973e-02, 1.5990e-01],
- [ 4.6845e-01, -4.8369e-01, 1.6665e+00, -1.5795e-02, -4.8026e-01,
- -1.5836e-03, 2.7498e-01, 9.0587e-02],
- [ 6.5483e-01, -3.1392e-01, 1.6959e+00, 2.6545e-01, -4.1626e-01,
- -8.7995e-02, 4.2136e-01, 2.3797e-01],
- [ 1.4710e-01, -6.7734e-01, 1.7470e+00, -3.3103e-01, -4.3827e-01,
- -2.9471e-01, -9.0514e-03, 3.3535e-02],
- [ 3.4537e-01, -5.5135e-01, 1.1529e+00, -1.3529e+00, -3.9643e-01,
- -1.2332e+00, 2.8598e-01, 1.4165e-01],
- [ 3.7841e-01, -5.3315e-01, 1.7237e+00, -4.0560e-01, -4.5712e-01,
- -7.7054e-02, 7.7512e-01, 2.6182e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478],
- [ 0.6174, -0.3936, 1.4586, -1.1709, -0.2420, -1.1389, 0.8296,
- 0.2012],
- [ 0.5443, -0.3994, 1.7499, -0.1227, -0.6173, -0.5769, 0.0049,
- 0.2071],
- [ 0.5783, -0.4363, 1.7724, 0.0432, -0.5153, 0.0871, 0.4840,
- 0.0663],
- [ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220],
- [ 0.5785, -0.4129, 1.2785, -1.3996, -0.3227, -1.3259, 0.4258,
- 0.0438],
- [ 0.6388, -0.3840, 1.9808, -0.3152, -0.4326, -0.0996, 1.1897,
- 0.2249]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0243, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0243, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9391738977283239
- step: 35
- running loss: 0.02683353993509497
- Train Steps: 35/90 Loss: 0.0268 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [0.6268, 0.4061, 0.8350, 0.2433, 0.4575, 0.2283, 0.6350, 0.5300],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [0.6234, 0.4179, 0.7825, 0.3450, 0.3813, 0.2867, 0.5675, 0.5617],
- [ nan, nan, 0.7240, 0.2722, 0.3900, 0.2567, 0.5168, 0.5933],
- [ nan, nan, 0.7515, 0.2708, 0.3987, 0.2267, 0.5162, 0.5567],
- [0.6228, 0.4004, 0.8750, 0.5250, 0.3825, 0.5233, 0.6362, 0.5000],
- [0.6168, 0.4081, 0.8438, 0.3367, 0.3500, 0.4667, 0.6212, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9853, -0.0791, 1.4641, -0.3702, -0.1446, -1.0620, 0.2074, 0.3532],
- [ 0.7470, -0.2884, 1.5556, -0.9633, -0.2606, -1.1213, 0.5230, 0.1062],
- [ 0.7365, -0.3068, 1.7563, -0.0710, -0.4981, -0.0292, 0.3810, 0.1003],
- [ 0.8464, -0.1846, 1.4369, -0.5832, -0.4998, -0.7952, 0.3272, 0.2525],
- [-1.5989, -1.7973, 0.9184, -0.9933, -0.5291, -0.9933, 0.0321, 0.2860],
- [-1.6922, -1.8783, 1.0217, -1.0603, -0.4737, -1.0298, 0.0809, 0.2151],
- [ 0.7276, -0.3266, 1.7957, 0.2800, -0.4631, 0.1283, 0.7769, -0.0928],
- [ 0.7260, -0.2884, 1.6461, -0.5658, -0.5884, -0.0756, 0.7037, 0.1972]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882],
- [ 0.6093, -0.4104, 1.5709, -1.1620, -0.1727, -1.2313, 0.6471,
- 0.1621],
- [ 0.5368, -0.4406, 1.7730, -0.1766, -0.5249, -0.0534, 0.2314,
- 0.3469],
- [ 0.5935, -0.3558, 1.3284, -0.6924, -0.5249, -0.9618, 0.3353,
- 0.3084],
- [-2.2859, -2.2859, 1.0580, -1.0288, -0.4845, -1.1004, 0.1011,
- 0.4543],
- [-2.2859, -2.2859, 1.1854, -1.0352, -0.4441, -1.2390, 0.0986,
- 0.2853],
- [ 0.5908, -0.4366, 1.7557, 0.1390, -0.5192, 0.1313, 0.6529,
- 0.0236],
- [ 0.5631, -0.4008, 1.6113, -0.7309, -0.6693, -0.1304, 0.5836,
- 0.2083]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0352, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0352, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9743791241198778
- step: 36
- running loss: 0.027066086781107716
- Train Steps: 36/90 Loss: 0.0271 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6203, 0.4021, 0.8780, 0.5031, 0.3667, 0.3882, 0.5842, 0.5405],
- [0.6268, 0.4102, 0.8938, 0.3667, 0.4025, 0.2833, 0.6275, 0.5183],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833],
- [0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6122, 0.4048, 0.7506, 0.3014, 0.3475, 0.3333, 0.5487, 0.5749]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6996, -0.3346, 1.8437, 0.1672, -0.5292, -0.3474, 0.3822, -0.0497],
- [ 0.3493, -0.5383, 1.9132, -0.5951, -0.3706, -0.8605, 0.4897, 0.1431],
- [ 0.2542, -0.6049, 1.8101, -0.1032, -0.4832, -0.2194, 0.4467, 0.1163],
- [-0.0646, -0.8031, 0.9565, -0.8911, -0.5063, -0.8299, 0.2433, 0.3494],
- [ 0.7911, -0.2365, 1.7099, 0.5081, -0.3259, -0.1652, 0.2937, 0.3079],
- [ 0.5108, -0.4289, 1.9848, -0.2359, -0.5005, -0.0401, 0.5715, 0.0395],
- [ 0.3191, -0.5725, 1.0183, -1.2563, -0.4272, -0.9802, 0.4102, 0.2090],
- [ 0.2063, -0.6487, 1.2193, -0.8771, -0.5484, -0.6147, 0.2950, 0.2426]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5791, -0.4289, 1.7694, 0.0379, -0.5923, -0.4927, 0.4126,
- 0.2107],
- [ 0.6092, -0.3913, 1.8423, -0.5923, -0.4268, -0.9772, 0.6125,
- 0.1082],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085],
- [ 0.6038, -0.3464, 1.7037, 0.3931, -0.4441, -0.2613, 0.3007,
- 0.4624],
- [ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.5419, -0.4160, 1.1810, -0.8939, -0.6808, -0.7463, 0.2485,
- 0.3694]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0252, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0252, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9995399247854948
- step: 37
- running loss: 0.02701459256177013
- Train Steps: 37/90 Loss: 0.0270 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6198, 0.4164, 0.8700, 0.5067, 0.4625, 0.5650, 0.5464, 0.5197],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [0.6222, 0.3937, 0.8350, 0.5617, 0.4138, 0.4600, 0.5800, 0.5233],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4743, -0.4986, 1.7012, 0.2645, -0.5962, -0.0472, 0.6053, -0.0269],
- [ 0.4422, -0.4338, 1.6112, -0.2641, -0.3141, -0.9310, 0.3228, 0.4357],
- [ 0.4612, -0.4605, 1.7693, -0.4304, -0.6406, -0.4055, 0.3712, 0.1926],
- [ 0.3480, -0.5056, 1.7185, -0.0225, -0.2127, 0.2593, 0.2440, 0.0680],
- [ 0.2740, -0.6009, 1.6386, -0.3549, -0.6436, -0.3869, 0.3592, 0.2311],
- [ 0.6209, -0.3838, 1.4577, -1.1062, -0.4147, -0.9839, 0.5374, 0.0921],
- [ 0.1334, -0.7052, 1.5866, 0.1677, -0.4499, -0.1311, 0.4455, 0.1111],
- [ 0.3481, -0.5082, 1.0693, -1.1434, -0.2080, -1.2564, 0.3309, 0.3095]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822],
- [ 0.5880, -0.3887, 1.8423, -0.3306, -0.6231, -0.5230, 0.4046,
- 0.1544],
- [ 0.5770, -0.3624, 1.7326, 0.0543, -0.1497, 0.3238, 0.2378,
- 0.1146],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929],
- [ 0.5880, -0.4676, 1.5709, 0.3084, -0.3748, -0.1612, 0.3931,
- 0.1313],
- [ 0.5862, -0.3530, 1.1032, -1.0619, -0.1497, -1.3852, 0.3411,
- 0.3931]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0145, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0145, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0140107097104192
- step: 38
- running loss: 0.026684492360800505
- Train Steps: 38/90 Loss: 0.0267 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167],
- [0.6202, 0.4079, 0.8025, 0.2500, 0.3763, 0.3217, 0.6125, 0.5533],
- [ nan, nan, 0.8900, 0.3217, 0.5038, 0.2233, 0.6694, 0.5609],
- [0.6248, 0.4185, 0.8500, 0.5767, 0.4463, 0.4550, 0.5613, 0.5917],
- [0.6260, 0.4153, 0.9000, 0.4533, 0.4025, 0.2633, 0.6223, 0.4967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8678, -0.2009, 1.2378, -0.9185, -0.5161, -0.8553, 0.4915, 0.3090],
- [ 0.9830, -0.1437, 1.6857, 0.4170, -0.5662, -0.0471, 0.5264, -0.0337],
- [-1.3197, -1.6164, 0.8604, -1.1805, -0.3298, -1.2823, 0.1823, 0.3035],
- [ 0.6939, -0.3165, 1.7800, -0.2526, -0.5378, 0.3783, 0.5597, 0.0969],
- [ 0.5649, -0.3764, 1.3641, -0.9624, -0.5757, -0.6402, 0.3433, 0.2319],
- [-1.4869, -1.6870, 1.6115, -0.8906, -0.0766, -1.0723, 0.6136, 0.3026],
- [ 0.9856, -0.0815, 1.6037, 0.4419, -0.2505, -0.1491, 0.2663, 0.3167],
- [ 0.7317, -0.2633, 1.8326, -0.1786, -0.4594, -0.8693, 0.3603, 0.0575]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5915, -0.3682, 1.2187, -1.2313, -0.4326, -0.9541, 0.5778,
- 0.3777],
- [ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261],
- [-2.2859, -2.2859, 0.6760, -1.4083, -0.3286, -1.4160, 0.2487,
- 0.3469],
- [ 0.5886, -0.4253, 1.8654, -0.3460, -0.5480, 0.3623, 0.6587,
- 0.1005],
- [ 0.5788, -0.4020, 1.4208, -1.1312, -0.5480, -0.8002, 0.5432,
- 0.2699],
- [-2.2859, -2.2859, 1.8249, -0.8002, 0.0409, -1.2543, 0.8059,
- 0.3050],
- [ 0.5999, -0.3530, 1.6402, 0.3777, -0.2247, -0.1843, 0.3065,
- 0.4470],
- [ 0.6055, -0.3676, 1.8711, -0.1920, -0.4268, -1.0696, 0.5887,
- 0.0081]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0564, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0564, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.070364617742598
- step: 39
- running loss: 0.027445246608784564
- Train Steps: 39/90 Loss: 0.0274 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183],
- [0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [ nan, nan, 0.6512, 0.1717, 0.4100, 0.1983, 0.5253, 0.5240],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [ nan, nan, 0.6992, 0.2791, 0.3950, 0.2383, 0.5483, 0.5819],
- [0.6275, 0.4008, 0.8047, 0.2484, 0.4037, 0.2777, 0.6506, 0.5212],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083],
- [0.6246, 0.4028, 0.8738, 0.4867, 0.4088, 0.5667, 0.6362, 0.5200]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5133, -0.4663, 1.8136, 0.0464, -0.6048, -0.3059, 0.4851, 0.1031],
- [ 0.8636, -0.1945, 1.7741, -0.3626, -0.4596, -0.9356, 0.1393, 0.1064],
- [-1.3675, -1.6747, 0.9541, -1.3097, -0.3199, -1.3510, 0.3645, 0.2582],
- [ 0.8342, -0.1883, 1.7027, -0.1683, -0.4789, -0.1760, 0.1873, 0.3101],
- [-0.7556, -1.2731, 0.9561, -0.9565, -0.4540, -1.0619, 0.3168, 0.3183],
- [ 0.6719, -0.3621, 1.5153, -0.9662, -0.3847, -0.8820, 0.7084, 0.1236],
- [ 0.8573, -0.1882, 1.5670, 0.4527, -0.3737, 0.0536, 0.4860, 0.3803],
- [ 0.7862, -0.2566, 1.8451, 0.0945, -0.3737, 0.4714, 0.6448, 0.1194]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.9440e-01, -4.5427e-01, 1.8018e+00, 8.1601e-03, -6.0577e-01,
- -4.3064e-01, 4.1617e-01, 1.0824e-01],
- [ 5.4850e-01, -4.2094e-01, 1.6691e+00, -4.1524e-01, -5.2494e-01,
- -1.1081e+00, 7.2521e-02, 2.0831e-03],
- [-2.2859e+00, -2.2859e+00, 7.2217e-01, -1.4930e+00, -3.9215e-01,
- -1.3698e+00, 1.4038e-01, 1.3434e-01],
- [ 5.5525e-01, -3.9923e-01, 1.7557e+00, -2.6898e-01, -4.9030e-01,
- -2.6898e-01, 5.4227e-02, 4.1446e-01],
- [-2.2859e+00, -2.2859e+00, 9.4385e-01, -9.9666e-01, -4.6143e-01,
- -1.1851e+00, 2.4679e-01, 4.0188e-01],
- [ 6.1248e-01, -4.3453e-01, 1.4308e+00, -1.1384e+00, -4.2133e-01,
- -1.0031e+00, 7.1897e-01, 1.2136e-01],
- [ 6.1339e-01, -3.9099e-01, 1.4497e+00, 3.5458e-01, -3.5173e-01,
- -9.1917e-02, 3.2956e-01, 5.2394e-01],
- [ 5.9902e-01, -4.2556e-01, 1.7499e+00, -3.8029e-02, -3.9792e-01,
- 3.3149e-01, 6.5289e-01, 1.1594e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0867, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0867, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1570822978392243
- step: 40
- running loss: 0.02892705744598061
- Train Steps: 40/90 Loss: 0.0289 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6145, 0.4008, 0.8750, 0.5383, 0.3975, 0.4650, 0.5563, 0.5533],
- [0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6260, 0.4253, 0.8938, 0.4400, 0.3675, 0.3650, 0.5675, 0.5767],
- [0.6346, 0.4165, 0.9138, 0.3983, 0.3875, 0.4317, 0.7469, 0.5471],
- [0.6202, 0.3983, 0.8555, 0.5429, 0.3842, 0.4370, 0.5866, 0.5398]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6094, -0.3628, 1.5813, 0.1503, -0.3496, -0.1345, 0.4158, 0.2550],
- [ 0.6345, -0.3623, 1.7048, -0.3339, -0.5686, -0.4320, 0.3378, 0.0178],
- [-1.7347, -1.9036, 1.1396, -0.9692, -0.5907, -0.9591, 0.2255, 0.2204],
- [ 0.7350, -0.2342, 1.5293, -0.2934, -0.1893, -1.0755, 0.4044, 0.5002],
- [ 0.5159, -0.4508, 1.6632, -0.3091, -0.3144, 0.0313, 0.3703, 0.1639],
- [ 0.6471, -0.3102, 1.6732, -0.2953, -0.5726, -0.5657, 0.2926, 0.3481],
- [ 0.3940, -0.5209, 1.8598, -0.4337, -0.3804, -0.2356, 0.9161, 0.1679],
- [ 0.8231, -0.2564, 1.5874, 0.1697, -0.5144, -0.3017, 0.5055, 0.0932]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5526, -0.4347, 1.7557, 0.2006, -0.4499, -0.1381, 0.2834,
- 0.2699],
- [ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822],
- [ 0.5604, -0.4620, 1.7961, -0.1997, -0.3229, 0.1082, 0.4104,
- 0.2545],
- [ 0.6058, -0.3216, 1.8423, -0.2536, -0.5885, -0.6000, 0.3353,
- 0.3777],
- [ 0.6454, -0.3623, 1.9346, -0.4460, -0.4961, -0.2921, 1.1642,
- 0.2409],
- [ 0.5786, -0.4463, 1.6655, 0.2216, -0.5115, -0.2675, 0.4236,
- 0.2075]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0186, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0186, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.175681502558291
- step: 41
- running loss: 0.028675158598982706
- Train Steps: 41/90 Loss: 0.0287 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4050, 0.7757, 0.2234, 0.4459, 0.1798, 0.5975, 0.5426],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [ nan, nan, 0.8888, 0.3100, 0.5262, 0.2817, 0.7145, 0.6003],
- [0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672],
- [ nan, nan, 0.7335, 0.2569, 0.3788, 0.2667, 0.5066, 0.5578],
- [0.6353, 0.4128, 0.8488, 0.2600, 0.5525, 0.1616, 0.6694, 0.5540],
- [0.6233, 0.4091, 0.8100, 0.2950, 0.3563, 0.3883, 0.6013, 0.5200]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0867, -0.0981, 1.1628, -0.9375, -0.2822, -1.2437, 0.4180, 0.2015],
- [ 0.9307, -0.1722, 1.8503, -0.2443, -0.6467, -0.3693, 0.4760, 0.2033],
- [ 0.9050, -0.1645, 1.7764, 0.1137, -0.5736, 0.1382, 0.2548, 0.2371],
- [-1.1627, -1.4897, 1.6139, -0.6616, 0.0053, -0.9658, 0.8065, 0.4578],
- [ 1.1114, -0.1139, 1.6950, 0.5100, -0.6381, -0.2213, 0.4249, 0.0464],
- [-1.7602, -1.8987, 1.0080, -0.9613, -0.5353, -1.0438, 0.1584, 0.2675],
- [-0.5237, -1.0626, 1.4513, -1.0196, 0.0815, -1.1053, 0.7325, 0.3350],
- [ 0.8871, -0.2171, 1.4334, -0.6789, -0.6656, -0.4187, 0.4968, 0.2156]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5784, -0.4153, 1.2972, -1.2541, -0.2265, -1.4553, 0.4741,
- 0.2203],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [-2.2859, -2.2859, 1.8192, -0.8540, 0.1448, -0.9849, 1.0143,
- 0.4867],
- [ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278],
- [-2.2859, -2.2859, 1.1020, -1.0994, -0.5365, -1.0542, 0.0542,
- 0.2905],
- [ 0.6487, -0.3792, 1.6344, -1.0850, 0.2659, -1.5397, 0.8059,
- 0.2730],
- [ 0.5932, -0.3962, 1.4554, -0.9233, -0.6404, -0.4922, 0.4912,
- 0.1159]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1006, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1006, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2762999096885324
- step: 42
- running loss: 0.0303880930878222
- Train Steps: 42/90 Loss: 0.0304 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [0.6128, 0.4115, 0.7934, 0.3778, 0.3450, 0.4033, 0.5337, 0.5456],
- [0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578],
- [ nan, nan, 0.6992, 0.2791, 0.3950, 0.2383, 0.5483, 0.5819],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [ nan, nan, 0.7515, 0.2708, 0.3987, 0.2267, 0.5162, 0.5567],
- [0.6202, 0.4064, 0.7879, 0.2179, 0.4567, 0.1725, 0.5955, 0.5478]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5861, -0.4517, 1.3542, -0.8687, -0.6011, -0.7119, 0.5024, 0.1682],
- [ 0.7095, -0.3213, 1.5826, -0.2131, -0.7450, -0.2024, 0.2908, 0.2557],
- [ 0.6829, -0.3612, 1.0833, -1.0107, -0.4194, -1.0598, 0.4923, 0.3245],
- [-1.2285, -1.5793, 1.0233, -0.6672, -0.4874, -0.9407, 0.3913, 0.3521],
- [ 0.8831, -0.2063, 1.9609, -0.4306, -0.1119, -0.9870, 0.5996, 0.1698],
- [ 0.6656, -0.3915, 1.3575, -0.8236, -0.2261, -1.1903, 0.6117, 0.1950],
- [-1.9736, -2.0531, 1.1866, -0.7312, -0.4399, -0.9817, 0.2848, 0.2794],
- [ 0.5745, -0.4375, 1.3779, -0.8132, -0.2105, -1.1064, 0.5409, 0.2714]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [ 0.5444, -0.3852, 1.3786, -0.5409, -0.6924, -0.4229, 0.1791,
- 0.2341],
- [ 0.5232, -0.4189, 0.9358, -1.4006, -0.3921, -1.3698, 0.2555,
- 0.2906],
- [-2.2859, -2.2859, 0.9438, -0.9967, -0.4614, -1.1851, 0.2468,
- 0.4019],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [-2.2859, -2.2859, 1.1854, -1.0352, -0.4441, -1.2390, 0.0986,
- 0.2853],
- [ 0.5787, -0.4086, 1.3535, -1.2794, -0.1764, -1.4891, 0.4645,
- 0.2442]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0686, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0686, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3449399406090379
- step: 43
- running loss: 0.03127767303741948
- Train Steps: 43/90 Loss: 0.0313 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6198, 0.4164, 0.8700, 0.5067, 0.4625, 0.5650, 0.5464, 0.5197],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6299, 0.4008, 0.8450, 0.5350, 0.4213, 0.5000, 0.6350, 0.5100],
- [0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150],
- [0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901],
- [0.6132, 0.3930, 0.8672, 0.4754, 0.3712, 0.5222, 0.5974, 0.5098],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5280, -0.4528, 1.7985, -0.1869, -0.4356, -0.3924, 0.1520, 0.1324],
- [ 0.4553, -0.4516, 1.6858, 0.0901, -0.1598, 0.0426, 0.3004, 0.2708],
- [ 0.6611, -0.3818, 1.6391, 0.0695, -0.3394, -0.2443, 0.2703, 0.2813],
- [ 0.5227, -0.4709, 1.6064, 0.1950, -0.3658, -0.1808, 0.7545, 0.2764],
- [ 0.2876, -0.6182, 1.3079, -1.2030, -0.2005, -1.5260, 0.6327, 0.2942],
- [ 0.4826, -0.5128, 1.6825, -0.7884, -0.6276, -0.8875, 0.7476, 0.1812],
- [ 0.6377, -0.3845, 1.6858, -0.0881, -0.5576, -0.0028, 0.5573, 0.2380],
- [-1.4403, -1.7426, 1.4023, -0.8333, -0.6012, -1.0315, 0.3907, 0.2474]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220],
- [ 0.5770, -0.3624, 1.7326, 0.0543, -0.1497, 0.3238, 0.2378,
- 0.1146],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854],
- [ 0.6236, -0.4344, 1.6171, 0.1852, -0.3402, 0.0236, 0.6471,
- 0.0697],
- [ 0.5796, -0.4326, 1.4439, -1.1774, -0.2940, -1.3390, 0.3931,
- 0.0928],
- [ 0.6031, -0.4307, 1.7268, -0.8002, -0.6058, -0.6462, 0.6442,
- -0.0220],
- [ 0.5466, -0.4706, 1.7198, -0.0903, -0.5712, 0.1261, 0.4733,
- 0.0688],
- [-2.2859, -2.2859, 1.5767, -0.7540, -0.6404, -0.7309, 0.1753,
- 0.0893]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0320, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0320, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3768937001004815
- step: 44
- running loss: 0.031293038638647304
- Train Steps: 44/90 Loss: 0.0313 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8463, 0.2550, 0.5850, 0.2133, 0.7129, 0.6072],
- [0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6168, 0.4081, 0.8438, 0.3367, 0.3500, 0.4667, 0.6212, 0.5400],
- [0.6202, 0.4054, 0.8892, 0.4428, 0.3642, 0.3249, 0.5840, 0.5426],
- [0.6146, 0.4124, 0.8800, 0.4867, 0.3800, 0.4500, 0.5106, 0.5524],
- [0.6196, 0.4090, 0.8450, 0.3900, 0.3575, 0.3933, 0.5975, 0.5583]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.9036, -2.0651, 1.5527, -1.0373, 0.3358, -1.2065, 1.1035, 0.3857],
- [ 0.7392, -0.3105, 1.2319, -0.7023, -0.4938, -0.9192, 0.4006, 0.3703],
- [ 0.2073, -0.6865, 1.8889, -0.1462, -0.2306, -1.0921, 0.7502, 0.2691],
- [ 0.3774, -0.5846, 1.7424, -0.3205, -0.6016, -0.1874, 0.4186, -0.0711],
- [ 0.6282, -0.4141, 1.4711, -0.7965, -0.6304, -0.2250, 0.5941, 0.2709],
- [ 0.3742, -0.5652, 1.6887, -0.3577, -0.5015, -0.8006, 0.2380, 0.1651],
- [ 0.6018, -0.4007, 1.6369, 0.0416, -0.4545, -0.3304, 0.0900, 0.1834],
- [ 0.3541, -0.5593, 1.5228, -0.5119, -0.6283, -0.5439, 0.4191, 0.2913]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.6229, -1.1081, 0.4162, -1.3005, 1.0070,
- 0.5188],
- [ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.5631, -0.4008, 1.6113, -0.7309, -0.6693, -0.1304, 0.5836,
- 0.2083],
- [ 0.5788, -0.4135, 1.8214, -0.2408, -0.6039, -0.7854, 0.4115,
- 0.2203],
- [ 0.5529, -0.3811, 1.7788, -0.0380, -0.5307, -0.2074, 0.0727,
- 0.2657],
- [ 0.5761, -0.3966, 1.6171, -0.4845, -0.6346, -0.4691, 0.4739,
- 0.2930]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0201, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0201, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3970262268558145
- step: 45
- running loss: 0.031045027263462543
- Train Steps: 45/90 Loss: 0.0310 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6080, 0.4010, 0.8750, 0.4500, 0.4825, 0.5617, 0.5837, 0.5583],
- [0.6093, 0.3990, 0.8400, 0.4333, 0.3688, 0.4633, 0.5560, 0.5656],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567],
- [0.6236, 0.4084, 0.7738, 0.2133, 0.3663, 0.3233, 0.5813, 0.5567],
- [0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833],
- [ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482],
- [ nan, nan, 0.8625, 0.2550, 0.5487, 0.2200, 0.7335, 0.5737],
- [0.6346, 0.4165, 0.9138, 0.3983, 0.3875, 0.4317, 0.7469, 0.5471]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7793, -0.3316, 1.8120, -0.0791, -0.2213, -0.0297, 0.2753, 0.1217],
- [ 0.7511, -0.3460, 1.6510, -0.1584, -0.7081, -0.2885, 0.1719, 0.1961],
- [ 0.5894, -0.4178, 1.1553, -0.9393, -0.3990, -1.2565, 0.3335, 0.3389],
- [ 0.9679, -0.2385, 1.2764, -1.0876, -0.6151, -0.9484, 0.3991, 0.2344],
- [ 0.7129, -0.3717, 0.9215, -0.8002, -0.5720, -1.0232, 0.1904, 0.3818],
- [-2.0511, -2.0903, 1.6392, -1.0022, 0.1126, -1.3098, 1.0287, 0.2427],
- [-2.1435, -2.1390, 1.6120, -0.9052, 0.1158, -1.2869, 0.8389, 0.2812],
- [ 0.6420, -0.4414, 1.9973, -0.1654, -0.5391, -0.2443, 0.8296, 0.0792]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5223, -0.4336, 1.7557, -0.2074, -0.0573, 0.3084, 0.4104,
- 0.2930],
- [ 0.5283, -0.4429, 1.5940, -0.2844, -0.5827, -0.1458, 0.2823,
- 0.3267],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853],
- [ 0.5946, -0.3995, 1.2880, -1.3005, -0.5942, -0.7925, 0.3988,
- 0.2853],
- [ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085],
- [-2.2859, -2.2859, 1.6517, -1.2620, 0.2141, -1.1928, 1.1166,
- 0.2463],
- [-2.2859, -2.2859, 1.6979, -1.1081, 0.2487, -1.2697, 1.1020,
- 0.3638],
- [ 0.6454, -0.3623, 1.9346, -0.4460, -0.4961, -0.2921, 1.1642,
- 0.2409]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0219, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0219, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4189534233883023
- step: 46
- running loss: 0.030846813551919615
- Train Steps: 46/90 Loss: 0.0308 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6176, 0.3911, 0.8738, 0.4217, 0.3488, 0.4033, 0.6025, 0.4817],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833],
- [0.6179, 0.4082, 0.6688, 0.2667, 0.3588, 0.3317, 0.5750, 0.5783],
- [0.6262, 0.4085, 0.8438, 0.3150, 0.4025, 0.2633, 0.6339, 0.4810],
- [0.6246, 0.4028, 0.8738, 0.4867, 0.4088, 0.5667, 0.6362, 0.5200],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167],
- [0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.1889, -0.9780, 1.7402, -0.4658, -0.6240, -0.6522, 0.4429, 0.1077],
- [ 0.0686, -0.7909, 1.6863, -0.0267, -0.1338, -0.0739, 0.4993, 0.3025],
- [ 0.1904, -0.7449, 1.7578, -0.1275, -0.4826, -0.2689, 0.4755, 0.1531],
- [ 0.1659, -0.7163, 0.8696, -1.2486, -0.6219, -0.9096, 0.4073, 0.4252],
- [ 0.7232, -0.3879, 1.6732, -1.1300, -0.3453, -1.2824, 0.7050, 0.0514],
- [ 0.3838, -0.5893, 1.7987, -0.0436, -0.2872, 0.2436, 0.6770, 0.2404],
- [ 0.0072, -0.8378, 1.6215, -0.9220, -0.4324, -1.1284, 0.3920, 0.1993],
- [ 0.2877, -0.6304, 1.7888, -0.1657, -0.3686, -1.1141, 0.4204, 0.3036]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6692e-01, -4.7937e-01, 1.7499e+00, -3.3826e-01, -6.7506e-01,
- -4.2294e-01, 4.9700e-01, -6.1124e-02],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 5.7263e-01, -4.7952e-01, 1.7788e+00, -1.4935e-02, -5.7113e-01,
- -6.8822e-02, 5.0277e-01, -5.3426e-02],
- [ 5.6796e-01, -4.0046e-01, 8.0300e-01, -1.0542e+00, -6.2887e-01,
- -7.5396e-01, 3.6998e-01, 3.8537e-01],
- [ 6.0641e-01, -3.9900e-01, 1.6113e+00, -8.3095e-01, -4.2679e-01,
- -1.0696e+00, 6.4212e-01, -6.4044e-02],
- [ 5.9902e-01, -4.2556e-01, 1.7499e+00, -3.8029e-02, -3.9792e-01,
- 3.3149e-01, 6.5289e-01, 1.1594e-01],
- [ 5.4825e-01, -4.2490e-01, 1.5305e+00, -7.3857e-01, -6.1155e-01,
- -8.6944e-01, 3.3533e-01, 1.0054e-01],
- [ 5.9007e-01, -4.0000e-01, 1.8423e+00, -6.8822e-02, -5.3072e-01,
- -9.2333e-01, 3.6420e-01, 1.8522e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0493, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0493, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4682444604113698
- step: 47
- running loss: 0.031239243838539783
- Train Steps: 47/90 Loss: 0.0312 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [ nan, nan, 0.7525, 0.2291, 0.3838, 0.3017, 0.6050, 0.5667],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6128, 0.4116, 0.8450, 0.4583, 0.3675, 0.4867, 0.5337, 0.5446],
- [0.6211, 0.4069, 0.8750, 0.5117, 0.4150, 0.4900, 0.5875, 0.5167],
- [0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8121, -0.2857, 1.8553, -0.0534, -0.1376, 0.1054, 0.5305, 0.2714],
- [-2.7636, -2.6309, 1.3694, -0.9074, -0.3612, -1.1750, 0.5475, 0.2775],
- [ 0.6600, -0.4128, 1.1770, -1.2402, -0.3847, -1.2091, 0.5563, 0.2726],
- [ 0.4320, -0.4988, 1.7527, -0.0355, -0.5599, -0.0758, 0.2460, 0.2057],
- [ 0.6434, -0.4167, 1.8805, 0.2187, -0.4504, 0.0262, 0.4597, 0.1198],
- [ 0.0658, -0.7735, 0.9937, -1.2910, -0.3749, -1.4518, 0.4108, 0.1898],
- [ 0.5898, -0.4576, 1.5912, -1.0806, -0.4569, -1.1443, 0.6867, 0.0462],
- [-0.1678, -0.9171, 1.3631, -1.1337, -0.2762, -1.3283, 0.5493, 0.2750]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [-2.2859, -2.2859, 1.1898, -1.2278, -0.5134, -0.8925, 0.5085,
- 0.3315],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.5444, -0.3846, 1.6171, -0.1689, -0.5885, -0.0380, 0.1791,
- 0.2296],
- [ 0.5828, -0.4066, 1.7557, 0.0774, -0.3691, -0.0226, 0.4277,
- 0.1005],
- [ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013],
- [ 0.6042, -0.4225, 1.5420, -1.2082, -0.4730, -1.0311, 0.6380,
- -0.0220],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0356, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0356, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5038310894742608
- step: 48
- running loss: 0.0313298143640471
- Train Steps: 48/90 Loss: 0.0313 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097],
- [0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006],
- [0.6113, 0.4088, 0.6859, 0.2208, 0.4363, 0.1700, 0.5188, 0.5533],
- [0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750],
- [0.6101, 0.3977, 0.8550, 0.5667, 0.3862, 0.4417, 0.5128, 0.4869],
- [0.6274, 0.4003, 0.8638, 0.5967, 0.3688, 0.4900, 0.6108, 0.4661],
- [0.6115, 0.3998, 0.7063, 0.2383, 0.4037, 0.1950, 0.5320, 0.4993]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2730, -0.6282, 1.9380, -0.1759, -0.0889, 0.0257, 0.2360, 0.1262],
- [ 0.5148, -0.5094, 1.9247, -0.4342, -0.5837, -0.4938, 0.5429, 0.0917],
- [ 0.3095, -0.6562, 1.8017, 0.1408, -0.6035, -0.1369, 0.7252, 0.1669],
- [-0.0043, -0.8408, 1.0899, -1.4017, -0.2449, -1.5586, 0.4209, 0.3265],
- [ 0.3186, -0.6128, 1.1535, -1.3408, -0.5048, -1.0129, 0.7331, 0.4692],
- [ 0.0875, -0.7587, 1.8255, 0.1383, -0.4714, -0.2924, 0.2298, 0.1498],
- [-0.2003, -0.9530, 1.7477, 0.2234, -0.5687, -0.0849, 0.5988, 0.1860],
- [ 0.1707, -0.7150, 1.1711, -1.5067, -0.3215, -1.4711, 0.4139, 0.1151]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5515, -0.4225, 1.7788, -0.0534, -0.2016, 0.1929, 0.1568,
- 0.0682],
- [ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261],
- [ 0.5377, -0.3978, 0.8824, -1.2663, -0.2709, -1.5007, 0.1102,
- 0.2699],
- [ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700],
- [ 0.5320, -0.4488, 1.6633, 0.3315, -0.5018, -0.2459, 0.0828,
- -0.0370],
- [ 0.6120, -0.4371, 1.7037, 0.4701, -0.5827, -0.0226, 0.5354,
- -0.1331],
- [ 0.5384, -0.4393, 0.9762, -1.1851, -0.4210, -1.3852, 0.1712,
- 0.0201]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0528, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0528, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5566692175343633
- step: 49
- running loss: 0.031768759541517616
- Train Steps: 49/90 Loss: 0.0318 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6138, 0.4101, 0.8800, 0.5083, 0.4637, 0.5950, 0.5587, 0.5077],
- [0.6267, 0.4080, 0.8438, 0.2633, 0.4763, 0.1800, 0.6259, 0.5240],
- [0.6175, 0.4013, 0.8900, 0.4500, 0.4375, 0.5850, 0.6175, 0.5383],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [ nan, nan, 0.9088, 0.3783, 0.4562, 0.2617, 0.6741, 0.5575],
- [0.6343, 0.4097, 0.9287, 0.4367, 0.4313, 0.3600, 0.7248, 0.5841],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3308, -0.5791, 1.6990, -0.1509, -0.1670, 0.1825, 0.3153, 0.1267],
- [ 0.4098, -0.5453, 1.5226, -1.3711, -0.0952, -1.5752, 0.6124, 0.0944],
- [ 0.1309, -0.7259, 1.6941, -0.4025, -0.3071, 0.3146, 0.4373, 0.1410],
- [ 0.5440, -0.4765, 1.5739, -0.0572, -0.5781, -0.0772, 0.4984, 0.0258],
- [ 0.1205, -0.6980, 1.2362, -0.9301, -0.7810, -0.5665, 0.1186, 0.2302],
- [-1.3172, -1.6646, 1.9684, -0.6178, -0.2131, -1.3039, 0.8897, 0.1631],
- [ 0.5473, -0.4987, 1.7234, -0.6042, -0.4938, -0.6665, 0.9100, 0.1871],
- [ 0.4775, -0.4528, 1.5372, -0.2494, -0.4017, -0.9764, 0.2984, 0.4618]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5491, -0.3918, 1.7788, 0.0620, -0.1439, 0.4624, 0.2946,
- 0.0592],
- [ 0.6088, -0.4015, 1.6113, -1.0696, -0.0861, -1.4545, 0.6051,
- 0.1343],
- [ 0.5664, -0.4321, 1.8249, -0.2074, -0.2651, 0.4162, 0.5663,
- 0.2006],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [-2.2859, -2.2859, 1.9115, -0.5384, -0.1785, -1.0773, 0.8278,
- 0.2890],
- [ 0.6438, -0.3936, 2.0039, -0.2690, -0.2940, -0.6231, 1.0618,
- 0.4119],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0497, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0497, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6064054938033223
- step: 50
- running loss: 0.032128109876066444
- Train Steps: 50/90 Loss: 0.0321 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6277, 0.4013, 0.8888, 0.4767, 0.3600, 0.3567, 0.6148, 0.4672],
- [0.6099, 0.4030, 0.8638, 0.5117, 0.4983, 0.4965, 0.5086, 0.5388],
- [0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6275, 0.4024, 0.7722, 0.2080, 0.4392, 0.2234, 0.6435, 0.5290],
- [0.6307, 0.4029, 0.8988, 0.4817, 0.3937, 0.3500, 0.7311, 0.5378],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4499, -0.5199, 1.6845, -0.3348, -0.3485, 0.1269, 0.3157, 0.2084],
- [ 0.6747, -0.3542, 1.6891, -0.3486, -0.6206, -0.6618, 0.4907, 0.0787],
- [ 0.6204, -0.3971, 1.6820, -0.0836, -0.0091, -0.0460, 0.1695, 0.1800],
- [ 0.6887, -0.3090, 1.6003, -0.6434, -0.4770, -1.1085, 0.1139, 0.1487],
- [ 0.4262, -0.4737, 1.1590, -1.4543, -0.3002, -1.2801, 0.6216, 0.1746],
- [ 0.4292, -0.5376, 1.8991, -0.2834, -0.4674, -0.6994, 0.8851, 0.1040],
- [ 0.4682, -0.5118, 1.7642, -0.4536, -0.5753, 0.2361, 0.9281, 0.1673],
- [-2.8165, -2.6293, 1.1189, -1.1107, -0.3079, -1.3337, 0.3717, 0.2679]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6039e-01, -4.6197e-01, 1.7961e+00, -1.9969e-01, -3.2286e-01,
- 1.0824e-01, 4.1039e-01, 2.5450e-01],
- [ 6.1322e-01, -4.3241e-01, 1.8192e+00, -8.4219e-02, -6.2309e-01,
- -6.3849e-01, 5.5366e-01, -1.2778e-01],
- [ 5.3095e-01, -4.2456e-01, 1.7037e+00, 7.7444e-02, 1.5763e-02,
- 7.5237e-03, 6.3480e-02, 2.0256e-01],
- [ 5.4850e-01, -4.2094e-01, 1.6691e+00, -4.1524e-01, -5.2494e-01,
- -1.1081e+00, 7.2521e-02, 2.0831e-03],
- [ 6.1248e-01, -4.2731e-01, 1.2807e+00, -1.3253e+00, -2.5737e-01,
- -1.2542e+00, 6.8644e-01, 1.5750e-01],
- [ 6.2730e-01, -4.2490e-01, 1.8654e+00, -6.1124e-02, -4.6721e-01,
- -6.6928e-01, 1.0910e+00, 1.9818e-01],
- [ 6.0095e-01, -4.4175e-01, 1.9346e+00, -2.8437e-01, -5.4804e-01,
- 1.2363e-01, 9.4481e-01, 1.7146e-01],
- [-2.2859e+00, -2.2859e+00, 1.1841e+00, -1.3082e+00, -3.0554e-01,
- -1.3621e+00, 3.0069e-01, 3.0839e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0178, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0178, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6242366721853614
- step: 51
- running loss: 0.03184777788598748
- Train Steps: 51/90 Loss: 0.0318 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6144, 0.4032, 0.8563, 0.3283, 0.3525, 0.4200, 0.5775, 0.5583],
- [0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552],
- [0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505],
- [ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.2281, -1.5360, 1.2552, -0.9789, -0.3538, -0.9091, 0.2718, 0.2497],
- [ 1.1770, 0.0143, 1.8093, -0.5852, -0.1964, -1.1055, 0.6296, 0.0711],
- [ 1.0187, -0.1383, 1.4644, -1.1879, -0.4770, -0.8713, 0.7727, -0.0471],
- [ 1.4524, 0.1545, 1.5627, -0.7437, -0.6868, -0.2045, 0.5216, 0.1345],
- [ 1.1354, -0.0355, 1.5601, 0.3532, -0.4657, -0.0231, 0.3850, 0.3366],
- [-2.4087, -2.3484, 1.7172, -1.0239, 0.0941, -1.0496, 0.9831, 0.2392],
- [ 0.8638, -0.1748, 1.5705, -0.4505, -0.6372, -0.3944, 0.0331, 0.1580],
- [-1.9988, -2.0419, 0.9688, -1.1190, -0.3952, -1.2128, 0.0812, 0.2310]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171],
- [ 0.6042, -0.4225, 1.5420, -1.2082, -0.4730, -1.0311, 0.6380,
- -0.0220],
- [ 0.5520, -0.4237, 1.6691, -0.7694, -0.6577, -0.3460, 0.3815,
- 0.2930],
- [ 0.6038, -0.3464, 1.7037, 0.3931, -0.4441, -0.2613, 0.3007,
- 0.4624],
- [-2.2859, -2.2859, 1.7557, -1.1466, 0.0871, -1.0773, 1.1239,
- 0.2783],
- [ 0.5433, -0.4032, 1.6344, -0.4922, -0.5769, -0.5846, 0.0357,
- 0.2567],
- [-2.2859, -2.2859, 1.0712, -1.2085, -0.3806, -1.3929, 0.0755,
- 0.2006]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0748, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0748, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6990172257646918
- step: 52
- running loss: 0.032673408187782534
- Train Steps: 52/90 Loss: 0.0327 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505],
- [0.6110, 0.3984, 0.8750, 0.4933, 0.4625, 0.4950, 0.5578, 0.5676],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767],
- [0.6176, 0.4017, 0.8788, 0.5100, 0.4075, 0.5250, 0.5913, 0.5033],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6122, 0.4048, 0.7506, 0.3014, 0.3475, 0.3333, 0.5487, 0.5749],
- [0.6153, 0.4119, 0.8463, 0.3833, 0.3600, 0.3200, 0.5106, 0.5563]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.1634, -0.8578, 1.6840, -0.5581, -0.5848, -0.7154, 0.0890, 0.1144],
- [ 0.4708, -0.4999, 1.8414, -0.1520, -0.0150, -0.1076, 0.5225, 0.1580],
- [ 0.2425, -0.6167, 1.7604, -0.0780, -0.2799, 0.0465, 0.4614, 0.2296],
- [ 0.4320, -0.5397, 1.8275, -0.0124, -0.2925, 0.0979, 0.6848, 0.0197],
- [ 0.1199, -0.6968, 1.9107, -0.3066, -0.4233, -0.0754, 0.6837, 0.0897],
- [ 0.6303, -0.3232, 1.3665, -0.7849, -0.5192, -0.9144, 0.5360, 0.3355],
- [ 0.5060, -0.4484, 1.1837, -1.1058, -0.6574, -0.7888, 0.4394, 0.1935],
- [-0.2262, -0.9020, 1.6467, -0.6310, -0.5496, -0.9648, 0.1842, 0.1176]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.4331e-01, -4.0323e-01, 1.6344e+00, -4.9222e-01, -5.7691e-01,
- -5.8460e-01, 3.5720e-02, 2.5666e-01],
- [ 5.3637e-01, -4.4573e-01, 1.7557e+00, -7.2363e-03, -1.4965e-01,
- 4.6189e-04, 2.9049e-01, 3.3573e-01],
- [ 5.5393e-01, -4.0308e-01, 1.7168e+00, -5.9398e-02, -3.7483e-01,
- 5.4350e-02, 1.3903e-01, 3.7768e-01],
- [ 5.6680e-01, -4.3056e-01, 1.7730e+00, 6.9746e-02, -4.0370e-01,
- 1.3903e-01, 4.4503e-01, 3.8953e-02],
- [ 6.0035e-01, -3.7467e-01, 1.8885e+00, -1.9969e-01, -5.0185e-01,
- -1.4935e-02, 5.4896e-01, 1.7752e-01],
- [ 5.8320e-01, -3.5928e-01, 1.3515e+00, -6.0770e-01, -5.2494e-01,
- -9.3102e-01, 3.3533e-01, 3.4688e-01],
- [ 5.4186e-01, -4.1601e-01, 1.1810e+00, -8.9394e-01, -6.8083e-01,
- -7.4627e-01, 2.4855e-01, 3.6938e-01],
- [ 5.5606e-01, -3.8337e-01, 1.6229e+00, -5.1532e-01, -6.2309e-01,
- -8.0785e-01, 7.2734e-02, 2.8371e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0443, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0443, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7433188473805785
- step: 53
- running loss: 0.03289280844114299
- Train Steps: 53/90 Loss: 0.0329 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6296, 0.4076, 0.8400, 0.5583, 0.3700, 0.4367, 0.6876, 0.5494],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600],
- [0.6142, 0.3982, 0.8650, 0.4883, 0.3912, 0.4317, 0.5315, 0.5350],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4969, -0.4439, 1.6245, -0.3381, -0.5079, -0.0939, 0.2119, 0.2477],
- [-0.6186, -1.1474, 1.1579, -1.4484, -0.2859, -1.4703, 0.1573, 0.1548],
- [ 0.0448, -0.7621, 1.7929, 0.1244, -0.4749, -0.2750, 0.8188, 0.1702],
- [ 0.3577, -0.5050, 1.1513, -1.2380, -0.5836, -0.7809, 0.4390, 0.2825],
- [ 0.5176, -0.4453, 1.7902, -0.0143, -0.4640, -0.1327, 0.1576, 0.1083],
- [ 0.6601, -0.3388, 1.9378, -0.6587, -0.2862, -1.3708, 0.4818, 0.0354],
- [-0.0375, -0.7674, 1.7133, 0.1232, -0.3605, -0.0799, 0.3183, 0.2255],
- [ 0.5661, -0.4524, 1.9309, -0.3699, -0.4641, 0.2918, 1.0282, 0.1330]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.6224, -0.4032, 1.5940, 0.2930, -0.5769, -0.2690, 0.8900,
- 0.2516],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007],
- [ 0.5513, -0.4467, 1.7095, -0.0303, -0.4788, -0.2921, 0.1692,
- 0.1854],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391],
- [ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0540, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0540, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7973417667672038
- step: 54
- running loss: 0.033284106791985256
- Train Steps: 54/90 Loss: 0.0333 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300],
- [0.6289, 0.4024, 0.9088, 0.4567, 0.3937, 0.5633, 0.7058, 0.5609],
- [0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000],
- [0.6280, 0.4055, 0.8600, 0.5317, 0.3800, 0.4700, 0.6275, 0.5133],
- [0.6286, 0.4060, 0.9188, 0.4333, 0.3675, 0.4167, 0.7034, 0.5528],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6193, -0.2979, 1.2371, -1.2785, -0.4778, -0.9031, 0.4626, 0.2020],
- [ 0.6740, -0.3263, 1.8320, -0.1743, -0.3139, 0.2781, 0.8246, 0.2859],
- [ 0.3821, -0.4890, 1.8163, -0.2113, -0.3938, -0.2797, 0.0329, 0.0312],
- [ 0.8386, -0.1654, 1.7004, -0.5014, -0.6979, -0.4120, 0.3056, 0.0958],
- [ 0.6805, -0.3059, 1.7077, 0.1778, -0.4999, -0.0904, 0.5120, 0.1148],
- [ 0.6076, -0.3326, 1.9301, -0.3129, -0.4152, -0.5163, 0.7245, 0.2601],
- [ 0.6592, -0.3361, 1.6481, 0.1911, -0.3928, -0.1796, 0.3788, 0.1469],
- [-2.6177, -2.5506, 1.3313, -1.1060, -0.2942, -1.1176, 0.2407, 0.2369]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621],
- [ 0.6191, -0.4273, 1.9115, -0.1766, -0.4672, 0.3161, 0.9741,
- 0.3050],
- [ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236],
- [ 0.6148, -0.4130, 1.6864, 0.1698, -0.5307, -0.1150, 0.6125,
- 0.0851],
- [ 0.6174, -0.4105, 1.9577, -0.2844, -0.5885, -0.3614, 0.9631,
- 0.2676],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0118, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0118, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8091550571843982
- step: 55
- running loss: 0.0328937283124436
- Train Steps: 55/90 Loss: 0.0329 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4087, 0.8375, 0.5700, 0.4025, 0.4800, 0.5700, 0.6117],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6296, 0.4076, 0.8400, 0.5583, 0.3700, 0.4367, 0.6876, 0.5494],
- [0.6203, 0.4096, 0.8862, 0.4267, 0.3538, 0.4117, 0.6025, 0.5650],
- [0.6107, 0.4050, 0.8700, 0.4850, 0.4470, 0.4848, 0.5043, 0.5431],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1492, -0.6670, 1.5802, 0.2910, -0.4135, 0.0303, 0.4409, 0.4137],
- [ 0.0904, -0.6878, 1.2486, -1.1853, -0.5766, -0.7854, 0.4236, 0.2221],
- [ 0.7112, -0.3543, 1.6138, -1.0944, -0.4004, -1.0451, 0.4618, -0.0073],
- [ 0.6569, -0.3063, 1.7665, -0.0456, -0.6110, -0.5325, 0.3404, 0.3160],
- [ 0.0950, -0.7382, 1.8449, 0.1953, -0.5241, -0.2591, 0.8265, 0.0738],
- [ 0.1203, -0.6797, 1.9646, -0.3627, -0.5742, -0.3932, 0.5246, 0.2486],
- [ 0.3048, -0.5792, 1.8250, -0.0522, -0.2013, 0.0844, 0.1208, 0.0362],
- [ 0.4090, -0.4900, 1.2181, -1.1421, -0.3401, -1.0661, 0.3066, 0.2915]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6118, -0.3983, 1.5824, 0.3469, -0.4268, -0.0688, 0.3469,
- 0.5393],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007],
- [ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807],
- [ 0.5902, -0.3493, 1.7961, -0.0072, -0.5942, -0.5615, 0.3180,
- 0.3161],
- [ 0.6224, -0.4032, 1.5940, 0.2930, -0.5769, -0.2690, 0.8900,
- 0.2516],
- [ 0.5790, -0.3940, 1.8076, -0.3152, -0.6520, -0.3844, 0.4970,
- 0.3238],
- [ 0.5351, -0.4153, 1.7326, -0.0457, -0.2214, -0.0466, 0.0434,
- 0.2228],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0284, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0284, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.837601481936872
- step: 56
- running loss: 0.03281431217744414
- Train Steps: 56/90 Loss: 0.0328 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6197, 0.4091, 0.8800, 0.4783, 0.3538, 0.4767, 0.5950, 0.5550],
- [0.6137, 0.4035, 0.8850, 0.4417, 0.3900, 0.4283, 0.5449, 0.5617],
- [0.6357, 0.4118, 0.8400, 0.2500, 0.5413, 0.1633, 0.6725, 0.5586],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6159, 0.4085, 0.6900, 0.2283, 0.4088, 0.1950, 0.5123, 0.5397],
- [0.6286, 0.4078, 0.8063, 0.2267, 0.4788, 0.1533, 0.5953, 0.4913],
- [0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.2243e-01, -7.1268e-01, 1.8774e+00, 6.4187e-02, -6.6555e-01,
- 2.1508e-01, 6.6481e-01, 2.6001e-01],
- [ 1.7363e-01, -6.6750e-01, 1.9018e+00, -4.6164e-02, -6.1875e-01,
- 1.2968e-02, 3.6536e-01, 1.5884e-01],
- [ 9.2766e-02, -7.3770e-01, 1.7641e+00, -1.0756e+00, 6.6186e-02,
- -1.2018e+00, 9.7435e-01, 1.6731e-01],
- [ 8.2140e-01, -2.5627e-01, 1.3991e+00, -1.0600e+00, -3.0525e-01,
- -1.0604e+00, 4.5021e-01, 1.6918e-01],
- [ 1.3324e-01, -6.6895e-01, 9.5730e-01, -1.1570e+00, -5.2132e-01,
- -1.1929e+00, 1.6117e-03, 1.6140e-01],
- [ 2.9194e-01, -5.9492e-01, 1.3999e+00, -1.0445e+00, -2.3796e-01,
- -1.2431e+00, 3.2231e-01, 5.8498e-02],
- [ 3.9386e-01, -4.8484e-01, 1.6187e+00, 4.3420e-01, -6.6633e-01,
- -2.1455e-01, 3.6329e-01, 4.0566e-01],
- [ 4.8454e-01, -4.5625e-01, 1.7858e+00, -7.6451e-02, -6.4586e-01,
- 6.5811e-02, 2.6243e-01, 2.7170e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5763, -0.3963, 1.7788, -0.0765, -0.6520, -0.0842, 0.4624,
- 0.2776],
- [ 0.5488, -0.4221, 1.8018, -0.2459, -0.4845, -0.3075, 0.2309,
- 0.3087],
- [ 0.6504, -0.3840, 1.5940, -1.1312, 0.2141, -1.5315, 0.8205,
- 0.2944],
- [ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850],
- [ 0.5591, -0.3990, 0.9012, -1.2313, -0.3979, -1.3852, 0.0804,
- 0.2071],
- [ 0.6177, -0.4022, 1.4381, -1.2390, -0.0746, -1.5777, 0.4639,
- -0.0168],
- [ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778],
- [ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0451, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0451, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8827112754806876
- step: 57
- running loss: 0.03303002237685417
- Train Steps: 57/90 Loss: 0.0330 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6236, 0.4084, 0.7738, 0.2133, 0.3663, 0.3233, 0.5813, 0.5567],
- [0.6200, 0.3978, 0.8900, 0.4550, 0.3775, 0.5200, 0.6150, 0.5367],
- [0.6229, 0.4086, 0.7538, 0.2600, 0.4775, 0.1617, 0.5900, 0.5383],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6229, 0.4066, 0.7612, 0.2967, 0.4250, 0.2167, 0.5925, 0.5350],
- [0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250],
- [ nan, nan, 0.6512, 0.1717, 0.4100, 0.1983, 0.5253, 0.5240],
- [0.6200, 0.4039, 0.8880, 0.4799, 0.3625, 0.4285, 0.5866, 0.5148]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.0962, -0.0636, 1.3366, -0.8499, -0.5666, -0.6745, 0.4527, 0.2407],
- [ 0.5959, -0.3922, 1.8984, 0.1088, -0.5427, 0.2683, 0.5982, 0.1676],
- [ 1.0458, -0.0688, 1.2324, -0.9085, -0.1895, -1.3337, 0.2850, 0.2403],
- [-1.5538, -1.7963, 1.3983, -0.8904, -0.4080, -0.8523, 0.2596, 0.2820],
- [ 0.7834, -0.1845, 1.3360, -0.7645, -0.3736, -1.0244, 0.2668, 0.2252],
- [ 1.0495, -0.0705, 2.0186, 0.0522, -0.6195, -0.0223, 0.7219, 0.1308],
- [-2.0917, -2.1628, 1.0026, -1.2713, -0.3786, -1.3109, 0.1309, 0.2826],
- [ 0.8242, -0.2368, 1.9117, 0.2111, -0.6423, -0.1351, 0.4206, 0.0966]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5946, -0.3995, 1.2880, -1.3005, -0.5942, -0.7925, 0.3988,
- 0.2853],
- [ 0.5776, -0.4484, 1.8249, -0.1843, -0.5423, 0.1159, 0.5547,
- 0.1929],
- [ 0.5911, -0.3984, 1.1956, -1.0850, -0.0804, -1.5392, 0.4393,
- 0.2006],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.5911, -0.4080, 1.2303, -0.9156, -0.3229, -1.2851, 0.4508,
- 0.1852],
- [ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390],
- [-2.2859, -2.2859, 0.7222, -1.4930, -0.3921, -1.3698, 0.1404,
- 0.1343],
- [ 0.5781, -0.4205, 1.8159, -0.0691, -0.6116, -0.3069, 0.4236,
- 0.0919]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0496, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0496, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.932359985075891
- step: 58
- running loss: 0.03331655146682571
- Train Steps: 58/90 Loss: 0.0333 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6200, 0.3993, 0.8639, 0.3687, 0.3658, 0.3139, 0.6002, 0.5374],
- [0.6137, 0.4035, 0.8850, 0.4417, 0.3900, 0.4283, 0.5449, 0.5617],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150],
- [0.6095, 0.4002, 0.8533, 0.5168, 0.5031, 0.5094, 0.5125, 0.5433],
- [0.6275, 0.4048, 0.8488, 0.2883, 0.4463, 0.2033, 0.6321, 0.5155]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3482, -0.5847, 1.6769, -0.4344, -0.6912, -0.1510, 0.5420, 0.2736],
- [ 0.4134, -0.4870, 1.6019, -0.5267, -0.6652, -0.7825, 0.3372, 0.1593],
- [ 0.5419, -0.4151, 1.6395, -0.1511, -0.5665, -0.2487, 0.1434, 0.2999],
- [ 0.0570, -0.7853, 1.8228, -0.4909, -0.6013, -0.4838, 0.6780, 0.1995],
- [ 0.5814, -0.4066, 1.7251, -0.4970, -0.4215, -1.3153, 0.4057, 0.0920],
- [ 0.6046, -0.3934, 1.6968, -0.0545, -0.2929, 0.4666, 0.5360, 0.3136],
- [ 0.5320, -0.4514, 1.5537, 0.1591, -0.1326, 0.0026, 0.1167, 0.3526],
- [ 0.3389, -0.5410, 1.5506, -0.9298, -0.3028, -1.3098, 0.5715, 0.1343]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.5777, -0.4416, 1.7044, -0.5827, -0.5962, -0.8361, 0.4862,
- 0.1963],
- [ 0.5488, -0.4221, 1.8018, -0.2459, -0.4845, -0.3075, 0.2309,
- 0.3087],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928],
- [ 0.5295, -0.4373, 1.6553, 0.1011, 0.0380, 0.0671, 0.0813,
- 0.2237],
- [ 0.6126, -0.4161, 1.6344, -0.9541, -0.2247, -1.3467, 0.6339,
- 0.0953]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0151, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0151, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.9474248168990016
- step: 59
- running loss: 0.03300720028642375
- Train Steps: 59/90 Loss: 0.0330 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6196, 0.4094, 0.7562, 0.2817, 0.3937, 0.3183, 0.6013, 0.6183],
- [0.6219, 0.3934, 0.8688, 0.5267, 0.4313, 0.4967, 0.5988, 0.4983],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167],
- [0.6090, 0.4045, 0.7250, 0.2100, 0.4075, 0.2300, 0.5476, 0.5663]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3817, -0.5736, 1.9059, -0.1225, -0.4497, -0.2011, 0.2088, 0.1125],
- [ 0.9177, -0.2117, 1.3480, -0.8393, -0.3956, -0.8119, 0.5936, 0.5077],
- [ 0.4320, -0.5735, 1.8279, 0.1515, -0.3319, -0.0194, 0.5533, 0.1154],
- [ 0.4054, -0.5492, 1.9774, 0.1779, -0.5931, -0.5311, 0.6125, 0.0498],
- [ 0.6835, -0.3405, 0.9233, -0.8949, -0.5273, -0.9222, 0.1870, 0.4747],
- [ 0.4931, -0.4520, 1.6901, -0.4726, -0.6711, -0.3948, 0.3562, 0.0879],
- [ 0.4073, -0.5244, 1.8318, -0.3861, -0.5785, -0.7754, 0.4366, 0.1827],
- [-0.1445, -0.8772, 1.1068, -1.2608, -0.4086, -1.1984, 0.1743, 0.3005]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220],
- [ 0.5759, -0.3948, 1.2072, -0.9849, -0.4672, -0.8156, 0.4912,
- 0.5701],
- [ 0.5864, -0.4690, 1.7268, 0.1467, -0.2940, 0.0082, 0.4797,
- 0.0159],
- [ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085],
- [ 0.5206, -0.4603, 1.4670, -0.4768, -0.6577, -0.3998, 0.1836,
- 0.0021],
- [ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005],
- [ 0.5269, -0.4176, 1.0628, -1.3159, -0.4037, -1.2236, 0.2432,
- 0.3297]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0221, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0221, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.969504582695663
- step: 60
- running loss: 0.03282507637826105
- Train Steps: 60/90 Loss: 0.0328 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6129, 0.4069, 0.8750, 0.5067, 0.3875, 0.4233, 0.5235, 0.5881],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483],
- [ nan, nan, 0.7192, 0.2346, 0.4037, 0.2050, 0.5138, 0.5650],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6097, 0.4000, 0.7325, 0.2667, 0.3450, 0.3517, 0.5284, 0.5045],
- [0.6228, 0.4004, 0.8750, 0.5250, 0.3825, 0.5233, 0.6362, 0.5000]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7551, -0.2848, 1.7650, 0.1631, -0.5179, -0.3995, 0.1350, 0.3288],
- [ 0.6782, -0.3846, 1.5860, -1.0260, -0.3997, -1.1093, 0.5274, 0.0610],
- [ 0.6320, -0.3760, 1.7877, -0.3926, -0.4922, -0.0774, 0.3800, 0.2165],
- [-1.5177, -1.8227, 1.0843, -1.3910, -0.4651, -1.3811, 0.1120, 0.2572],
- [ 0.8237, -0.2159, 1.7595, 0.2419, -0.5153, -0.2390, 0.3561, 0.3817],
- [ 0.8449, -0.2290, 1.5374, 0.1570, -0.4488, -0.2010, 0.8125, 0.3838],
- [ 0.3734, -0.5396, 1.2457, -1.0386, -0.6445, -0.7832, 0.1265, 0.0847],
- [ 0.8764, -0.2458, 1.8436, 0.1623, -0.4635, -0.0212, 0.6347, 0.1237]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5452, -0.4067, 1.7557, 0.0543, -0.4961, -0.3306, 0.1323,
- 0.4306],
- [ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807],
- [ 0.5546, -0.4620, 1.7788, -0.4229, -0.5192, -0.0226, 0.4277,
- 0.2468],
- [-2.2859, -2.2859, 1.0361, -1.2021, -0.4210, -1.3390, 0.0871,
- 0.3238],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478],
- [ 0.5303, -0.4384, 1.0975, -1.0542, -0.6924, -0.6616, 0.1548,
- 0.0442],
- [ 0.5908, -0.4366, 1.7557, 0.1390, -0.5192, 0.1313, 0.6529,
- 0.0236]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0242, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0242, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.9937451435253024
- step: 61
- running loss: 0.03268434661516889
- Train Steps: 61/90 Loss: 0.0327 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6124, 0.4075, 0.7696, 0.4153, 0.3475, 0.3767, 0.5157, 0.5427],
- [0.6264, 0.4069, 0.7900, 0.1650, 0.4275, 0.2267, 0.6290, 0.4947],
- [0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378],
- [0.6216, 0.4099, 0.7225, 0.2033, 0.4188, 0.2217, 0.5975, 0.5283],
- [0.6128, 0.4084, 0.8738, 0.4683, 0.3613, 0.3700, 0.4960, 0.5397],
- [0.6075, 0.4000, 0.8513, 0.5183, 0.4510, 0.5329, 0.5180, 0.5280]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3201, -0.6410, 1.7896, -0.1694, -0.4898, 0.0609, 0.4582, 0.2323],
- [ 0.4722, -0.4604, 1.3808, -0.3858, -0.7707, -0.5247, 0.1395, 0.3130],
- [ 0.5414, -0.4888, 1.3641, -1.3285, -0.4477, -1.3795, 0.6508, 0.0505],
- [ 0.3127, -0.6277, 1.7601, -0.0538, -0.2350, -0.0423, 0.4265, 0.3210],
- [ 0.2437, -0.6771, 1.7701, -1.0139, -0.0564, -1.2747, 1.0509, 0.1679],
- [ 0.5388, -0.4299, 1.0035, -1.1597, -0.4715, -1.3582, 0.1529, 0.2767],
- [ 0.8609, -0.2031, 1.7233, 0.0268, -0.7773, -0.6240, 0.2071, 0.1623],
- [ 0.5464, -0.4439, 1.6349, 0.1478, -0.3280, 0.1001, 0.1455, 0.2697]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6039e-01, -4.6197e-01, 1.7961e+00, -1.9969e-01, -3.2286e-01,
- 1.0824e-01, 4.1039e-01, 2.5450e-01],
- [ 5.4267e-01, -4.0354e-01, 1.2688e+00, -3.6754e-01, -6.8083e-01,
- -5.4611e-01, 9.5867e-02, 2.2059e-01],
- [ 6.0739e-01, -4.0631e-01, 1.3630e+00, -1.5238e+00, -3.1132e-01,
- -1.2390e+00, 6.1950e-01, -9.2270e-04],
- [ 5.3712e-01, -4.2517e-01, 1.7037e+00, -6.8822e-02, -3.4180e-02,
- 6.2048e-02, 3.7575e-01, 2.8530e-01],
- [ 6.2072e-01, -3.9360e-01, 1.7788e+00, -1.1235e+00, 1.4480e-01,
- -1.0850e+00, 1.1459e+00, 1.9818e-01],
- [ 5.8505e-01, -3.9246e-01, 1.0513e+00, -1.3467e+00, -3.5173e-01,
- -1.2620e+00, 4.7390e-01, 1.5443e-01],
- [ 5.4434e-01, -3.9938e-01, 1.7499e+00, -1.2271e-01, -6.1732e-01,
- -5.7691e-01, 4.8756e-03, 2.0706e-01],
- [ 5.2021e-01, -4.3818e-01, 1.6460e+00, 1.0824e-01, -2.0286e-01,
- 1.7544e-01, 1.0666e-01, 1.5296e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0184, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0184, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.0121765499934554
- step: 62
- running loss: 0.03245446048376541
- Train Steps: 62/90 Loss: 0.0325 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6129, 0.4069, 0.8750, 0.5067, 0.3875, 0.4233, 0.5235, 0.5881],
- [0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6214, 0.4040, 0.8838, 0.3500, 0.3600, 0.5183, 0.6362, 0.5200],
- [0.6241, 0.4143, 0.8938, 0.4650, 0.4075, 0.5350, 0.6250, 0.5550],
- [0.6286, 0.4055, 0.9000, 0.4717, 0.3763, 0.4683, 0.7018, 0.5494],
- [0.6265, 0.4088, 0.8025, 0.1850, 0.4163, 0.2500, 0.6290, 0.4947]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5197, -0.4527, 1.5950, 0.1716, -0.5711, -0.4149, 0.0090, 0.3377],
- [ 0.3744, -0.5529, 1.5663, -1.0771, -0.2237, -1.2708, 0.4707, 0.0513],
- [ 0.5448, -0.4714, 1.7032, -0.2847, -0.6567, -0.5911, 0.5097, 0.0959],
- [ 0.6185, -0.3506, 1.0383, -0.7511, -0.1287, -1.3023, 0.0891, 0.4953],
- [ 0.3769, -0.5777, 1.6381, -0.5963, -0.5831, 0.0237, 0.5714, 0.2753],
- [ 0.5147, -0.4490, 1.7073, -0.1214, -0.4517, 0.0542, 0.3266, 0.3011],
- [ 0.5869, -0.4673, 1.8068, -0.0764, -0.5900, -0.2953, 0.7764, 0.1528],
- [ 0.4651, -0.5418, 1.3631, -1.2766, -0.3952, -1.2271, 0.5598, 0.0800]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.4515e-01, -4.0670e-01, 1.7557e+00, 5.4350e-02, -4.9607e-01,
- -3.3056e-01, 1.3228e-01, 4.3063e-01],
- [ 6.1264e-01, -3.8707e-01, 1.6229e+00, -1.0773e+00, -2.1316e-01,
- -1.3698e+00, 5.8291e-01, -2.0913e-02],
- [ 6.2200e-01, -4.4357e-01, 1.8711e+00, -3.6905e-01, -6.1732e-01,
- -4.9992e-01, 6.7021e-01, 6.9746e-02],
- [ 6.1611e-01, -3.0754e-01, 1.1678e+00, -6.5000e-01, 8.1293e-02,
- -1.4006e+00, 2.5450e-01, 5.6243e-01],
- [ 5.8412e-01, -4.1986e-01, 1.7961e+00, -6.6928e-01, -6.2309e-01,
- 1.0824e-01, 6.5289e-01, 1.1594e-01],
- [ 5.9677e-01, -3.7252e-01, 1.8423e+00, -1.3811e-01, -4.0370e-01,
- 1.8522e-01, 6.0092e-01, 2.7760e-01],
- [ 6.1742e-01, -4.1286e-01, 1.8711e+00, -1.0731e-01, -5.4804e-01,
- -1.2271e-01, 9.5578e-01, 2.5161e-01],
- [ 6.0785e-01, -3.9761e-01, 1.4208e+00, -1.4314e+00, -3.6328e-01,
- -1.1312e+00, 6.1950e-01, -9.2270e-04]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0129, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0129, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.0250426745042205
- step: 63
- running loss: 0.03214353451594001
- Train Steps: 63/90 Loss: 0.0321 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667],
- [0.6113, 0.4088, 0.6859, 0.2208, 0.4363, 0.1700, 0.5188, 0.5533],
- [0.6292, 0.4010, 0.8988, 0.4800, 0.3638, 0.3817, 0.6357, 0.5051],
- [0.6275, 0.4024, 0.7722, 0.2080, 0.4392, 0.2234, 0.6435, 0.5290],
- [0.6279, 0.4008, 0.8600, 0.4883, 0.4325, 0.5283, 0.7010, 0.5378],
- [0.6250, 0.4131, 0.8688, 0.2983, 0.4275, 0.2367, 0.6162, 0.5367]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5808, -0.4370, 1.2576, -1.1996, -0.3212, -1.3849, 0.3267, 0.0878],
- [ 0.4906, -0.5313, 1.7542, 0.1598, -0.3029, 0.2531, 0.3300, 0.1995],
- [ 0.3006, -0.5941, 1.0523, -0.8893, -0.6749, -0.6995, 0.1450, 0.3257],
- [ 0.6383, -0.3974, 0.9691, -1.1039, -0.3556, -1.3894, 0.1058, 0.2528],
- [ 0.5203, -0.4946, 1.8813, -0.0241, -0.6237, -0.5090, 0.5620, 0.1526],
- [ 0.4117, -0.5442, 1.3097, -1.0924, -0.3429, -1.2326, 0.5587, 0.1343],
- [ 0.6043, -0.4339, 1.7556, 0.0348, -0.4017, 0.2142, 0.6594, 0.2807],
- [ 0.4688, -0.4926, 1.7983, -0.7584, -0.3287, -1.0427, 0.5270, 0.1824]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082],
- [ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315],
- [ 0.5377, -0.3978, 0.8824, -1.2663, -0.2709, -1.5007, 0.1102,
- 0.2699],
- [ 0.6204, -0.4336, 1.8654, -0.0688, -0.6058, -0.5230, 0.6503,
- 0.0472],
- [ 0.6125, -0.4273, 1.2807, -1.3253, -0.2574, -1.2542, 0.6864,
- 0.1575],
- [ 0.6141, -0.4345, 1.6864, -0.0303, -0.2882, 0.1544, 0.9521,
- 0.1982],
- [ 0.6011, -0.3778, 1.7268, -0.9079, -0.3113, -1.1928, 0.5605,
- 0.1929]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.035658926703036
- step: 64
- running loss: 0.031807170729734935
- Train Steps: 64/90 Loss: 0.0318 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [ nan, nan, 0.7240, 0.2722, 0.3900, 0.2567, 0.5168, 0.5933],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064],
- [0.6192, 0.3980, 0.7078, 0.2750, 0.4250, 0.2100, 0.5450, 0.5783],
- [0.6249, 0.4142, 0.8350, 0.3283, 0.3613, 0.3700, 0.6188, 0.5400],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.1619, -0.0507, 1.9458, 0.1117, -0.3936, 0.2390, 0.6420, 0.0908],
- [-0.6746, -1.2440, 0.9450, -1.3181, -0.3175, -1.5303, 0.2282, 0.3076],
- [-0.8266, -1.3301, 1.1158, -1.1625, -0.5268, -1.1397, 0.0964, 0.2674],
- [ 0.9588, -0.1822, 1.7905, 0.1449, -0.5481, -0.2363, 0.5219, 0.3334],
- [ 0.9507, -0.1960, 1.8109, 0.1169, -0.0843, 0.0336, 0.1701, 0.1119],
- [-0.1099, -0.8441, 1.0848, -1.0951, -0.3097, -1.2919, 0.2902, 0.3028],
- [ 1.0059, -0.1478, 1.7145, -0.5904, -0.6488, -0.6710, 0.6253, 0.1184],
- [ 0.9850, -0.1714, 1.1831, -1.1515, -0.3543, -1.2117, 0.5893, 0.1447]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5991, -0.3803, 1.8018, -0.0534, -0.3460, 0.1852, 0.5374,
- 0.1390],
- [-2.2859, -2.2859, 0.6760, -1.4083, -0.3286, -1.4160, 0.2487,
- 0.3469],
- [-2.2859, -2.2859, 1.0580, -1.0288, -0.4845, -1.1004, 0.1011,
- 0.4543],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532],
- [ 0.5742, -0.4474, 0.9834, -1.0159, -0.3229, -1.3159, 0.2314,
- 0.3854],
- [ 0.6006, -0.3728, 1.5709, -0.7694, -0.6173, -0.5769, 0.5721,
- 0.2083],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1430, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1430, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.1786455223336816
- step: 65
- running loss: 0.03351762342051818
- Train Steps: 65/90 Loss: 0.0335 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6286, 0.4040, 0.8696, 0.3047, 0.3924, 0.2887, 0.6300, 0.5367],
- [0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [0.6200, 0.4059, 0.8700, 0.4900, 0.4163, 0.5000, 0.6162, 0.5467],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390],
- [0.6263, 0.4029, 0.8900, 0.4933, 0.4075, 0.5183, 0.6406, 0.4758]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4607, -0.5076, 1.5616, -0.9819, -0.3880, -1.0047, 0.6830, 0.2139],
- [ 0.2895, -0.5994, 1.3120, -1.0558, -0.5012, -1.1000, 0.1815, 0.0953],
- [ 0.7617, -0.2370, 1.0651, -0.8918, -0.0222, -1.3057, 0.3144, 0.5303],
- [ 0.2737, -0.6040, 1.1433, -1.1580, -0.5218, -0.9390, 0.1412, 0.1010],
- [ 0.6965, -0.3564, 1.6710, -0.2018, -0.2981, -0.0155, 0.4957, 0.2971],
- [ 0.6458, -0.3963, 1.7657, 0.1448, -0.5660, -0.6313, 0.6119, 0.0351],
- [ 0.5802, -0.4325, 1.6830, -0.1799, -0.3756, -0.1435, 0.3684, 0.2465],
- [ 0.6696, -0.3913, 1.7502, -0.2301, -0.3841, -0.0126, 0.6902, 0.1319]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6174, -0.4201, 1.7309, -0.8784, -0.4735, -0.9524, 0.6242,
- 0.1931],
- [ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141],
- [ 0.5776, -0.4109, 1.7326, -0.0226, -0.3633, 0.0236, 0.5605,
- 0.2391],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.5250, -0.4661, 1.7383, -0.0765, -0.4268, -0.0226, 0.2535,
- 0.2035],
- [ 0.6072, -0.4250, 1.8249, -0.0072, -0.4037, 0.1082, 0.6730,
- -0.0881]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0131, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0131, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.1917926585301757
- step: 66
- running loss: 0.03320897967469963
- Train Steps: 66/90 Loss: 0.0332 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6176, 0.4017, 0.8788, 0.5100, 0.4075, 0.5250, 0.5913, 0.5033],
- [0.6202, 0.3983, 0.8555, 0.5429, 0.3842, 0.4370, 0.5866, 0.5398],
- [0.6171, 0.4127, 0.8900, 0.4800, 0.4325, 0.5783, 0.5769, 0.5090],
- [0.6162, 0.4014, 0.8800, 0.5333, 0.3750, 0.4817, 0.5988, 0.5283],
- [0.6262, 0.4163, 0.8850, 0.5183, 0.3763, 0.4150, 0.6025, 0.5500],
- [0.6168, 0.4081, 0.8438, 0.3367, 0.3500, 0.4667, 0.6212, 0.5400],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5727, -0.3898, 1.5611, -0.2527, -0.5054, -0.5357, 0.4417, 0.4269],
- [ 0.4821, -0.4974, 1.6563, -0.2330, -0.2927, -0.1848, 0.4000, 0.1484],
- [ 0.5810, -0.4360, 1.5775, -0.0716, -0.4948, -0.5690, 0.5614, 0.1391],
- [ 1.0774, -0.0504, 1.6881, -0.3434, -0.2318, 0.0752, 0.3415, 0.1202],
- [ 0.6469, -0.3567, 1.6222, -0.0748, -0.4106, -0.3050, 0.4462, 0.2349],
- [ 0.7940, -0.2222, 1.6271, -0.2506, -0.5119, -0.7262, 0.5196, 0.1868],
- [ 0.4953, -0.4453, 1.4948, -1.0337, -0.5386, -0.4044, 0.5632, 0.2785],
- [ 0.4612, -0.5075, 1.6961, -0.4348, -0.0683, -0.3957, 0.2832, 0.0984]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5668, -0.4306, 1.7730, 0.0697, -0.4037, 0.1390, 0.4450,
- 0.0390],
- [ 0.5786, -0.4463, 1.6655, 0.2216, -0.5115, -0.2675, 0.4236,
- 0.2075],
- [ 0.5645, -0.3797, 1.8249, -0.0688, -0.2882, 0.3854, 0.3789,
- 0.0652],
- [ 0.5603, -0.4319, 1.7788, 0.1775, -0.5538, -0.0611, 0.4797,
- 0.1544],
- [ 0.6066, -0.3632, 1.8018, 0.1082, -0.5480, -0.3691, 0.4970,
- 0.2545],
- [ 0.5631, -0.4008, 1.6113, -0.7309, -0.6693, -0.1304, 0.5836,
- 0.2083],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0361, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0361, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.227865911088884
- step: 67
- running loss: 0.033251730016252
- Train Steps: 67/90 Loss: 0.0333 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4087, 0.8375, 0.5700, 0.4025, 0.4800, 0.5700, 0.6117],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6275, 0.4071, 0.8075, 0.2100, 0.4700, 0.1533, 0.6148, 0.4834],
- [0.6124, 0.4069, 0.8314, 0.5001, 0.3738, 0.4650, 0.5167, 0.5402],
- [ nan, nan, 0.6935, 0.1930, 0.4150, 0.2250, 0.5450, 0.5650],
- [0.6222, 0.4169, 0.8638, 0.5650, 0.4313, 0.4783, 0.5637, 0.5633],
- [0.6101, 0.3977, 0.8550, 0.5667, 0.3862, 0.4417, 0.5128, 0.4869],
- [0.6208, 0.4082, 0.8538, 0.3067, 0.3588, 0.3717, 0.6112, 0.5517]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8848, -0.1729, 1.4798, 0.2416, -0.3977, -0.0930, 0.4602, 0.4335],
- [ 0.7835, -0.2613, 1.4584, -1.2296, -0.3859, -1.1383, 0.6709, -0.0248],
- [ 0.8811, -0.2185, 1.4146, -1.4647, -0.0089, -1.5862, 0.7598, 0.0176],
- [ 0.7126, -0.2833, 1.5513, -0.0976, -0.4387, -0.1353, 0.2362, 0.2505],
- [-2.0188, -2.1723, 1.0514, -1.4950, -0.3005, -1.1029, 0.2591, 0.3280],
- [ 0.7695, -0.2286, 1.6170, 0.1916, -0.2849, -0.1186, 0.4334, 0.3344],
- [ 0.7143, -0.2892, 1.6661, 0.1946, -0.4831, -0.2583, 0.2466, 0.0479],
- [ 0.8132, -0.2083, 1.5800, -0.8766, -0.5991, -0.5577, 0.4929, 0.1575]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6118, -0.3983, 1.5824, 0.3469, -0.4268, -0.0688, 0.3469,
- 0.5393],
- [ 0.6042, -0.4225, 1.5420, -1.2082, -0.4730, -1.0311, 0.6380,
- -0.0220],
- [ 0.6126, -0.4057, 1.4439, -1.3159, -0.1150, -1.5777, 0.5537,
- -0.0530],
- [ 0.5425, -0.4067, 1.5543, 0.0241, -0.5596, -0.1381, 0.1005,
- 0.2093],
- [-2.2859, -2.2859, 0.9175, -1.3947, -0.3691, -1.2467, 0.2314,
- 0.3238],
- [ 0.5880, -0.3605, 1.7037, 0.3238, -0.2940, -0.0765, 0.3180,
- 0.3161],
- [ 0.5320, -0.4488, 1.6633, 0.3315, -0.5018, -0.2459, 0.0828,
- -0.0370],
- [ 0.5814, -0.4003, 1.6575, -0.8694, -0.6289, -0.5692, 0.5374,
- 0.2622]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0155, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0155, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.243375484831631
- step: 68
- running loss: 0.03299081595340634
- Train Steps: 68/90 Loss: 0.0330 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783],
- [0.6222, 0.3957, 0.8838, 0.5017, 0.3937, 0.4600, 0.5900, 0.5017],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6343, 0.4097, 0.9287, 0.4367, 0.4313, 0.3600, 0.7248, 0.5841],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6201, 0.4017, 0.8871, 0.4621, 0.3517, 0.4675, 0.5999, 0.5106],
- [0.6142, 0.3982, 0.8650, 0.4883, 0.3912, 0.4317, 0.5315, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5492, -0.3537, 1.0510, -1.1307, -0.1063, -1.2669, 0.3406, 0.4151],
- [ 0.5106, -0.4782, 1.6313, -0.0935, -0.4695, -0.1447, 0.2669, 0.0259],
- [ 0.5940, -0.3389, 1.5285, -0.8920, -0.1874, -1.3273, 0.2889, 0.0553],
- [ 0.6428, -0.3886, 1.6949, -0.4102, -0.3667, -0.6118, 0.9077, 0.2542],
- [ 0.7256, -0.3228, 1.7196, -0.2445, -0.4457, 0.0972, 0.4459, 0.1771],
- [ 0.4534, -0.4983, 1.6209, -0.9948, -0.3314, -0.8475, 0.8452, 0.2427],
- [ 0.5449, -0.4638, 1.6773, -0.2547, -0.5393, -0.0724, 0.4429, 0.1515],
- [ 0.4045, -0.5464, 1.6156, -0.0932, -0.4692, -0.2328, 0.0977, 0.1593]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854],
- [ 0.5879, -0.4583, 1.7961, 0.0313, -0.4672, -0.1612, 0.4393,
- 0.0313],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.6438, -0.3936, 2.0039, -0.2690, -0.2940, -0.6231, 1.0618,
- 0.4119],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237],
- [ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.5783, -0.4306, 1.8114, -0.1515, -0.6617, -0.1268, 0.4851,
- 0.0727],
- [ 0.5513, -0.4467, 1.7095, -0.0303, -0.4788, -0.2921, 0.1692,
- 0.1854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.2540173502638936
- step: 69
- running loss: 0.032666918119766575
- Train Steps: 69/90 Loss: 0.0327 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6147, 0.4026, 0.6600, 0.2467, 0.4088, 0.2150, 0.5489, 0.5773],
- [0.6265, 0.4088, 0.8025, 0.1850, 0.4163, 0.2500, 0.6290, 0.4947],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6250, 0.4008, 0.8950, 0.4183, 0.3550, 0.4383, 0.6361, 0.4927],
- [0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [0.6286, 0.4086, 0.8408, 0.2801, 0.4163, 0.2800, 0.6725, 0.5393],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2508, -0.5467, 0.7966, -1.1588, -0.2903, -1.3747, 0.0383, 0.3953],
- [ 0.5413, -0.4319, 1.3898, -1.3634, -0.2613, -1.1370, 0.5943, 0.0810],
- [ 0.6342, -0.3566, 1.4557, 0.1665, -0.3822, -0.0786, 0.7992, 0.3457],
- [ 0.5728, -0.4382, 1.8137, -0.3345, -0.5790, -0.2428, 0.4343, 0.0768],
- [ 0.9392, -0.1153, 1.6216, -0.5804, -0.5492, -0.3375, 0.1921, 0.1121],
- [ 0.1582, -0.6907, 1.7376, -0.2148, -0.5145, -0.3778, 0.1450, 0.0500],
- [ 0.3844, -0.5329, 1.5189, -0.9580, -0.1783, -1.0164, 0.7031, 0.2159],
- [ 0.4851, -0.4767, 1.8267, -0.0919, -0.3050, -0.2376, 0.7236, 0.2378]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.5318e-01, -4.2640e-01, 7.6259e-01, -1.1466e+00, -3.9792e-01,
- -1.2928e+00, 2.4936e-01, 3.8081e-01],
- [ 6.0785e-01, -3.9761e-01, 1.4208e+00, -1.4314e+00, -3.6328e-01,
- -1.1312e+00, 6.1950e-01, -9.2270e-04],
- [ 6.4542e-01, -3.9842e-01, 1.3804e+00, 2.5450e-01, -4.5566e-01,
- -3.8029e-02, 1.1057e+00, 3.4780e-01],
- [ 6.0095e-01, -4.3453e-01, 1.8480e+00, -3.5366e-01, -6.4619e-01,
- -2.6128e-01, 6.5240e-01, -9.9401e-03],
- [ 5.8406e-01, -3.7783e-01, 1.6113e+00, -6.4619e-01, -6.6351e-01,
- -2.5358e-01, 3.5423e-01, 8.0233e-02],
- [ 5.8435e-01, -4.4657e-01, 1.8423e+00, -1.9969e-01, -5.9423e-01,
- -3.9985e-01, 4.2194e-01, 4.6651e-02],
- [ 6.1742e-01, -3.9842e-01, 1.5975e+00, -9.9214e-01, -3.6328e-01,
- -9.9261e-01, 8.2047e-01, 2.0505e-01],
- [ 6.4542e-01, -3.7194e-01, 1.9115e+00, -1.3041e-01, -5.0762e-01,
- -2.8437e-01, 1.0033e+00, 4.3864e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.2742685051634908
- step: 70
- running loss: 0.032489550073764153
- Train Steps: 70/90 Loss: 0.0325 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.4008, 0.8950, 0.4183, 0.3550, 0.4383, 0.6361, 0.4927],
- [0.6179, 0.4008, 0.7505, 0.2678, 0.4368, 0.1891, 0.5831, 0.5263],
- [0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6135, 0.3994, 0.7913, 0.3050, 0.3625, 0.3050, 0.5837, 0.5050],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5122, -0.4982, 1.8205, -0.3142, -0.5797, -0.0591, 0.6661, 0.1336],
- [ 0.7927, -0.2415, 1.0857, -1.1362, -0.2013, -1.2924, 0.4452, 0.1942],
- [ 0.3741, -0.4858, 1.5175, -0.3482, -0.5324, -0.6665, 0.0663, 0.2776],
- [ 0.2747, -0.5748, 1.5718, -0.4627, -0.4041, -0.8664, 0.1605, 0.1826],
- [ 0.2877, -0.5945, 1.3762, -0.9632, -0.5021, -0.6231, 0.5349, 0.1469],
- [ 0.4169, -0.4920, 1.3689, -1.1018, -0.2625, -0.9907, 0.6163, 0.2166],
- [ 0.5410, -0.4205, 1.5504, -0.5371, -0.5861, -0.2351, 0.3069, 0.0715],
- [ 0.4781, -0.5026, 1.9161, -0.2707, -0.1580, -0.6887, 0.9722, 0.1813]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6010, -0.4345, 1.8480, -0.3537, -0.6462, -0.2613, 0.6524,
- -0.0099],
- [ 0.5680, -0.4345, 1.1806, -1.0490, -0.2683, -1.4127, 0.4074,
- 0.1449],
- [ 0.5470, -0.3952, 1.6377, -0.4253, -0.6289, -0.8079, 0.0249,
- 0.2116],
- [ 0.5485, -0.4209, 1.6691, -0.4152, -0.5249, -1.1081, 0.0725,
- 0.0021],
- [ 0.5477, -0.4413, 1.3688, -0.8771, -0.6115, -0.8771, 0.4104,
- 0.0467],
- [ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929],
- [ 0.5206, -0.4603, 1.4670, -0.4768, -0.6577, -0.3998, 0.1836,
- 0.0021],
- [ 0.6224, -0.4345, 1.9404, -0.2921, -0.3171, -0.8771, 1.0655,
- 0.2142]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0152, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0152, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.289423213340342
- step: 71
- running loss: 0.03224539737099073
- Train Steps: 71/90 Loss: 0.0322 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6199, 0.4112, 0.8475, 0.3717, 0.3550, 0.4350, 0.6063, 0.6083],
- [0.6125, 0.4035, 0.7825, 0.3100, 0.3463, 0.4900, 0.5832, 0.5637],
- [0.6271, 0.4020, 0.8375, 0.6083, 0.3925, 0.4867, 0.6037, 0.4626],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4479, -0.4506, 1.6853, -0.1913, -0.3362, -0.1246, 0.2974, 0.2190],
- [ 0.4831, -0.4276, 1.6233, 0.2123, -0.4058, -0.0915, 0.4055, 0.2812],
- [ 0.6317, -0.3702, 1.9464, -0.4200, -0.3204, -1.1019, 0.9239, 0.0278],
- [ 0.7079, -0.2363, 1.5972, -0.7340, -0.6275, -0.5334, 0.4059, 0.3556],
- [ 0.3797, -0.4968, 1.3329, -0.9653, -0.6206, -0.1962, 0.4695, 0.2560],
- [ 0.6649, -0.3512, 1.5966, 0.2881, -0.5217, -0.2935, 0.5200, 0.0120],
- [ 0.5794, -0.3805, 1.7459, -0.2830, -0.0878, -0.1662, 0.4798, 0.1850],
- [ 0.6714, -0.3518, 1.7962, -0.2419, -0.2278, -0.1438, 0.3156, 0.0287]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.5742, -0.3792, 1.6460, 0.3084, -0.3460, 0.1467, 0.4162,
- 0.3161],
- [ 0.6224, -0.4345, 1.9404, -0.2921, -0.3171, -0.8771, 1.0655,
- 0.2142],
- [ 0.5774, -0.3868, 1.6286, -0.5692, -0.6462, -0.2767, 0.5143,
- 0.5239],
- [ 0.5432, -0.4224, 1.3284, -0.8540, -0.6866, -0.0226, 0.4077,
- 0.3177],
- [ 0.6105, -0.4293, 1.5824, 0.5239, -0.4730, -0.0380, 0.5025,
- -0.1492],
- [ 0.5598, -0.4273, 1.7152, -0.1227, -0.0065, 0.1917, 0.5143,
- 0.2853],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0154, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0154, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.304807144217193
- step: 72
- running loss: 0.032011210336349905
- Train Steps: 72/90 Loss: 0.0320 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300],
- [0.6284, 0.4093, 0.8900, 0.4700, 0.3650, 0.3850, 0.6212, 0.5167],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896],
- [ nan, nan, 0.7240, 0.2722, 0.3900, 0.2567, 0.5168, 0.5933],
- [0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5122, -0.4145, 1.0718, -0.8660, -0.4378, -0.9175, 0.5208, 0.2975],
- [ 0.6879, -0.2902, 1.8188, -0.6282, -0.4061, -0.6229, 0.5794, 0.0858],
- [ 0.7078, -0.2872, 1.2850, -1.0183, -0.4095, -0.7490, 0.5825, 0.1415],
- [ 0.7138, -0.3128, 1.8883, 0.1094, -0.5699, -0.3284, 0.4986, 0.0147],
- [ 0.7631, -0.2945, 1.7644, -0.9269, 0.1532, -1.1395, 1.0988, 0.0832],
- [ 0.6894, -0.2747, 1.4439, -0.3181, -0.5894, -0.7208, 0.0107, 0.2779],
- [-2.3025, -2.3297, 1.0130, -1.0680, -0.5892, -0.9831, 0.0336, 0.3642],
- [ 0.8791, -0.1854, 1.9073, -0.1095, -0.5426, -0.0348, 0.5527, 0.0630]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390],
- [ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621],
- [ 0.6164, -0.3956, 1.8249, -0.1150, -0.6000, -0.5076, 0.5836,
- 0.1005],
- [ 0.6207, -0.3936, 1.7788, -1.1235, 0.1448, -1.0850, 1.1459,
- 0.1982],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374],
- [-2.2859, -2.2859, 1.0580, -1.0288, -0.4845, -1.1004, 0.1011,
- 0.4543],
- [ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0139, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0139, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.318676040507853
- step: 73
- running loss: 0.03176268548640895
- Train Steps: 73/90 Loss: 0.0318 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402],
- [0.6265, 0.4251, 0.7113, 0.3550, 0.4375, 0.2117, 0.5587, 0.6118],
- [0.6164, 0.4102, 0.8850, 0.4867, 0.4213, 0.5983, 0.5609, 0.5038],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 4.7113e-01, -4.8745e-01, 1.8539e+00, 3.2894e-01, -4.1775e-01,
- -7.5153e-03, 5.0597e-01, 2.3463e-01],
- [ 5.6107e-01, -4.3712e-01, 1.7635e+00, 2.1942e-01, -4.4146e-01,
- -9.3449e-02, 4.3632e-01, 6.4832e-02],
- [ 5.3550e-01, -3.7773e-01, 1.2046e+00, -7.6963e-01, -3.0734e-01,
- -1.2585e+00, 3.2494e-01, 4.1616e-01],
- [ 7.6130e-01, -2.6534e-01, 1.8936e+00, 1.0186e-03, -2.6857e-01,
- 4.8667e-01, 3.9008e-01, 1.2859e-01],
- [ 3.2546e-01, -5.6136e-01, 1.1409e+00, -1.3242e+00, -5.3331e-01,
- -1.1989e+00, 3.7836e-01, 1.0678e-01],
- [ 3.6444e-01, -5.2857e-01, 1.3846e+00, -1.0845e+00, -5.1766e-01,
- -1.0753e+00, 6.7351e-01, 2.3173e-01],
- [ 5.3250e-01, -4.3660e-01, 1.9654e+00, 2.5421e-03, -4.6362e-01,
- -5.2328e-01, 7.3296e-01, 3.7710e-02],
- [ 5.8066e-01, -4.1115e-01, 1.5945e+00, -8.2236e-01, -6.5597e-01,
- -3.0854e-01, 5.7481e-01, 1.6315e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091],
- [ 0.6077, -0.3226, 0.9993, -0.6462, -0.2651, -1.3082, 0.2946,
- 0.5401],
- [ 0.5614, -0.3912, 1.8018, -0.0380, -0.3402, 0.4778, 0.3049,
- 0.0412],
- [ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853],
- [ 0.5915, -0.3682, 1.2187, -1.2313, -0.4326, -0.9541, 0.5778,
- 0.3777],
- [ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0116, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0116, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.3302313135936856
- step: 74
- running loss: 0.03148961234586062
- Train Steps: 74/90 Loss: 0.0315 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6129, 0.4063, 0.8738, 0.5250, 0.4313, 0.4733, 0.5230, 0.5874],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837],
- [0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183],
- [0.6098, 0.3991, 0.8638, 0.4717, 0.4263, 0.4967, 0.5212, 0.5650],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7608, -0.2662, 1.7774, 0.1798, -0.3807, -0.0089, 0.3839, 0.2495],
- [ 0.3894, -0.5370, 1.4442, -0.7026, -0.7186, -0.3379, 0.3447, 0.1872],
- [ 0.4860, -0.4275, 1.6876, 0.2732, -0.4168, -0.2713, 0.4909, 0.4333],
- [ 0.5600, -0.3997, 1.0086, -1.0654, -0.6472, -1.0534, 0.2950, 0.2712],
- [ 0.7111, -0.3574, 1.9038, -0.0752, -0.6639, -0.4627, 0.6405, 0.0408],
- [ 0.5216, -0.4450, 1.8388, -0.1305, -0.3204, 0.0560, 0.3731, 0.1311],
- [ 0.4902, -0.5181, 1.9518, -0.7413, -0.3985, -0.9655, 1.0865, 0.0022],
- [ 0.5865, -0.3897, 1.7734, 0.0111, -0.2692, 0.1866, 0.3871, 0.1497]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5453, -0.4091, 1.7499, 0.1390, -0.2940, -0.0996, 0.1300,
- 0.4272],
- [ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386],
- [ 0.5836, -0.3649, 1.7210, 0.3854, -0.3979, -0.2921, 0.3065,
- 0.4470],
- [ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103],
- [ 0.5944, -0.4543, 1.8018, 0.0082, -0.6058, -0.4306, 0.4162,
- 0.1082],
- [ 0.5308, -0.4425, 1.7037, -0.1073, -0.3171, 0.0082, 0.1217,
- 0.3238],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0163, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0163, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.34652538318187
- step: 75
- running loss: 0.0312870051090916
- Train Steps: 75/90 Loss: 0.0313 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4050, 0.7757, 0.2234, 0.4459, 0.1798, 0.5975, 0.5426],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402],
- [0.6353, 0.4128, 0.8488, 0.2600, 0.5525, 0.1616, 0.6694, 0.5540],
- [0.6198, 0.4130, 0.8762, 0.4117, 0.3650, 0.4900, 0.5707, 0.5103],
- [0.6202, 0.4054, 0.8892, 0.4428, 0.3642, 0.3249, 0.5840, 0.5426],
- [0.6364, 0.4154, 0.8938, 0.3717, 0.4500, 0.2583, 0.6448, 0.5285],
- [0.6150, 0.3935, 0.8696, 0.5158, 0.4647, 0.5329, 0.6041, 0.5153],
- [0.6122, 0.3993, 0.8738, 0.4667, 0.4517, 0.4879, 0.5155, 0.4927]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8955, -0.1829, 1.1701, -1.0772, -0.3697, -1.3048, 0.5593, 0.1958],
- [ 0.7101, -0.3358, 1.6189, 0.2893, -0.5472, -0.0433, 0.4882, 0.1745],
- [-0.0980, -0.8081, 1.4499, -1.2712, -0.0374, -1.2084, 0.8850, 0.3341],
- [ 0.5643, -0.3828, 1.7756, -0.2794, -0.7242, -0.0340, 0.4257, 0.1475],
- [ 0.3468, -0.5400, 1.7338, -0.1671, -0.7472, -0.5945, 0.3265, 0.2328],
- [ 0.3623, -0.5074, 1.7292, -0.6107, -0.3487, -1.0112, 0.6881, 0.2868],
- [ 0.6102, -0.4003, 1.6497, 0.1532, -0.3328, 0.2402, 0.4762, 0.1845],
- [ 0.4357, -0.5049, 1.7483, -0.0460, -0.4219, 0.1342, 0.1869, 0.0424]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5784, -0.4153, 1.2972, -1.2541, -0.2265, -1.4553, 0.4741,
- 0.2203],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091],
- [ 0.6487, -0.3792, 1.6344, -1.0850, 0.2659, -1.5397, 0.8059,
- 0.2730],
- [ 0.5771, -0.3784, 1.7614, -0.3844, -0.6000, -0.0226, 0.3501,
- 0.0712],
- [ 0.5788, -0.4135, 1.8214, -0.2408, -0.6039, -0.7854, 0.4115,
- 0.2203],
- [ 0.6537, -0.3671, 1.8423, -0.5692, -0.2074, -1.0927, 0.6926,
- 0.1554],
- [ 0.5548, -0.4682, 1.7309, 0.0966, -0.1394, 0.1757, 0.5045,
- 0.0942],
- [ 0.5417, -0.4417, 1.7499, -0.1304, -0.1994, -0.0324, 0.0951,
- -0.0099]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0295, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0295, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.376007336191833
- step: 76
- running loss: 0.03126325442357675
- Train Steps: 76/90 Loss: 0.0313 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6164, 0.3972, 0.7818, 0.3381, 0.3599, 0.3387, 0.5880, 0.5153],
- [0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719],
- [0.6098, 0.3991, 0.8638, 0.4717, 0.4263, 0.4967, 0.5212, 0.5650],
- [0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6156, 0.4125, 0.8850, 0.4833, 0.4550, 0.5817, 0.5765, 0.5084],
- [0.6339, 0.4159, 0.8400, 0.5617, 0.3825, 0.4150, 0.7343, 0.5748],
- [0.6292, 0.4010, 0.8988, 0.4800, 0.3638, 0.3817, 0.6357, 0.5051]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.2599e-01, -3.7787e-01, 1.7324e+00, -1.4953e-03, -5.6675e-01,
- -2.6756e-01, 4.9518e-01, 4.3112e-01],
- [ 6.0591e-01, -4.0288e-01, 1.4897e+00, -9.1640e-01, -6.3901e-01,
- -7.7692e-01, 4.3591e-01, 9.2140e-02],
- [ 3.4257e-01, -5.5844e-01, 1.6558e+00, 1.9338e-01, -4.5376e-01,
- -9.7340e-03, 3.2920e-01, 2.5710e-01],
- [ 4.5453e-01, -4.8078e-01, 1.7472e+00, -1.1293e-01, -2.6900e-01,
- 2.1217e-03, 2.5967e-01, 2.2156e-01],
- [ 6.1328e-01, -3.6578e-01, 1.3096e+00, -7.1024e-01, -6.7487e-01,
- -5.3066e-01, 2.5872e-01, 3.9249e-01],
- [ 5.6314e-01, -4.0142e-01, 1.8560e+00, -1.5741e-01, -1.9763e-01,
- 2.6406e-01, 4.0827e-01, 1.1360e-01],
- [ 5.6209e-01, -4.2365e-01, 1.7095e+00, 1.2593e-01, -4.6443e-01,
- -4.5895e-01, 9.4878e-01, 2.4540e-01],
- [ 7.4072e-01, -3.3453e-01, 1.8828e+00, -1.8974e-01, -5.4737e-01,
- -6.2025e-01, 7.1610e-01, 1.2509e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5614, -0.4514, 1.3252, -0.7240, -0.6237, -0.7215, 0.4299,
- 0.0942],
- [ 0.5079, -0.4714, 1.6575, 0.2237, -0.4961, 0.0774, 0.1465,
- -0.1061],
- [ 0.5308, -0.4425, 1.7037, -0.1073, -0.3171, 0.0082, 0.1217,
- 0.3238],
- [ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.5573, -0.3808, 1.8018, -0.0534, -0.1843, 0.4008, 0.3769,
- 0.0622],
- [ 0.6421, -0.3647, 1.5940, 0.3084, -0.5192, -0.3691, 1.1057,
- 0.3692],
- [ 0.6204, -0.4336, 1.8654, -0.0688, -0.6058, -0.5230, 0.6503,
- 0.0472]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0097, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0097, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.3856922583654523
- step: 77
- running loss: 0.030983016342408472
- Train Steps: 77/90 Loss: 0.0310 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6329, 0.4175, 0.8550, 0.2333, 0.5425, 0.2250, 0.7398, 0.5609],
- [0.6275, 0.4008, 0.8047, 0.2484, 0.4037, 0.2777, 0.6506, 0.5212],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550],
- [0.6133, 0.4066, 0.6787, 0.2617, 0.3800, 0.2433, 0.5147, 0.5358],
- [0.6064, 0.3953, 0.8738, 0.4417, 0.3663, 0.4683, 0.5511, 0.5416]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4816, -0.4621, 1.4665, -0.6807, -0.7312, -0.5035, 0.4855, 0.4826],
- [ 0.3986, -0.5600, 1.7154, 0.1935, -0.3803, 0.1976, 0.4821, 0.1684],
- [ 0.6346, -0.3935, 1.7428, -0.9900, 0.1062, -1.3640, 1.2291, 0.2949],
- [ 0.6019, -0.4224, 1.4777, -0.9274, -0.5007, -0.9511, 0.6558, 0.1184],
- [ 0.4079, -0.5341, 1.7268, -0.0303, -0.6630, -0.3353, 0.3375, 0.2916],
- [ 0.5138, -0.4476, 1.8194, -0.0117, -0.0904, 0.0841, 0.4128, 0.2011],
- [ 0.3823, -0.5219, 0.9403, -0.9047, -0.5712, -1.1431, 0.1388, 0.1978],
- [ 0.5692, -0.4489, 1.8115, -0.0553, -0.6830, -0.0256, 0.2668, 0.1434]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7685e-01, -3.8992e-01, 1.3861e+00, -7.7706e-01, -5.8845e-01,
- -5.4611e-01, 5.0277e-01, 5.6243e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 6.3718e-01, -3.5749e-01, 1.6633e+00, -1.2082e+00, 2.1986e-01,
- -1.2467e+00, 1.1313e+00, 3.0505e-01],
- [ 6.1248e-01, -4.3453e-01, 1.4308e+00, -1.1384e+00, -4.2133e-01,
- -1.0031e+00, 7.1897e-01, 1.2136e-01],
- [ 5.4324e-01, -4.3364e-01, 1.7095e+00, -1.7660e-01, -5.9423e-01,
- -4.8453e-01, 3.0069e-01, 2.8530e-01],
- [ 5.3508e-01, -4.3212e-01, 1.7326e+00, -1.3811e-01, 4.6329e-02,
- 1.0491e-01, 4.1617e-01, 2.7760e-01],
- [ 5.4700e-01, -4.0808e-01, 8.4919e-01, -1.0773e+00, -5.3072e-01,
- -1.1620e+00, 9.1240e-02, 1.8903e-01],
- [ 5.1490e-01, -4.6028e-01, 1.7499e+00, -2.4588e-01, -5.9423e-01,
- -1.2271e-01, 2.5964e-01, 2.1549e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.3952217008918524
- step: 78
- running loss: 0.03070797052425452
- Train Steps: 78/90 Loss: 0.0307 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6127, 0.4115, 0.7163, 0.2883, 0.3625, 0.2950, 0.5327, 0.5495],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917],
- [0.6339, 0.4112, 0.8838, 0.5067, 0.4000, 0.5433, 0.7549, 0.5378],
- [0.6339, 0.4123, 0.8638, 0.5367, 0.4075, 0.5467, 0.7517, 0.5436],
- [0.6086, 0.3998, 0.8788, 0.4450, 0.4025, 0.4650, 0.5306, 0.5103],
- [0.6314, 0.4107, 0.8750, 0.5100, 0.3788, 0.4900, 0.7121, 0.5864],
- [0.6115, 0.4005, 0.8838, 0.3867, 0.3763, 0.4700, 0.5800, 0.5550],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5093, -0.4510, 1.1529, -0.9447, -0.5343, -0.9558, 0.1538, 0.2929],
- [ 0.1427, -0.6651, 1.3357, -0.4493, -0.5945, -0.9460, 0.1704, 0.4914],
- [ 0.6095, -0.4259, 1.8378, 0.1008, -0.3755, 0.2174, 0.8572, 0.2546],
- [ 0.6198, -0.4002, 1.7901, 0.1931, -0.3497, 0.1815, 0.8804, 0.2716],
- [ 0.6056, -0.3894, 1.8245, -0.1414, -0.4505, -0.2102, 0.1380, 0.1016],
- [ 0.6638, -0.3807, 1.8774, 0.2831, -0.4401, -0.0791, 0.8332, 0.2436],
- [ 0.5925, -0.3957, 1.8014, -0.3808, -0.5423, -0.1721, 0.1716, 0.2022],
- [ 0.5354, -0.4287, 1.1695, -1.0155, -0.5159, -0.9060, 0.4118, 0.2849]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5442, -0.3855, 1.0224, -0.9541, -0.6115, -0.9233, 0.1745,
- 0.2522],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470],
- [ 0.6421, -0.3864, 1.7961, 0.0543, -0.4383, 0.2237, 1.2007,
- 0.1982],
- [ 0.6421, -0.3816, 1.7037, 0.1929, -0.4037, 0.2391, 1.1861,
- 0.2249],
- [ 0.5253, -0.4392, 1.7730, -0.2305, -0.4268, -0.1381, 0.1651,
- 0.0712],
- [ 0.6306, -0.3888, 1.7557, 0.0697, -0.5365, -0.0226, 1.0033,
- 0.4226],
- [ 0.5384, -0.4361, 1.7961, -0.4999, -0.5480, -0.1150, 0.3931,
- 0.2776],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0158, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0158, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.411067308858037
- step: 79
- running loss: 0.03051983935263338
- Train Steps: 79/90 Loss: 0.0305 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6147, 0.4026, 0.6600, 0.2467, 0.4088, 0.2150, 0.5489, 0.5773],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [ nan, nan, 0.7097, 0.2346, 0.4250, 0.1850, 0.5175, 0.5583],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6265, 0.4091, 0.8950, 0.3533, 0.3600, 0.3967, 0.6295, 0.4901],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483],
- [0.6262, 0.4085, 0.8438, 0.3150, 0.4025, 0.2633, 0.6339, 0.4810]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4540, -0.4703, 0.9067, -0.9209, -0.3107, -1.2274, 0.2563, 0.5059],
- [ 0.5799, -0.4351, 1.6240, -0.3646, -0.6393, -0.2595, 0.2720, 0.1308],
- [-1.5526, -1.8142, 0.9907, -1.0273, -0.3385, -1.2593, 0.1713, 0.4376],
- [ 0.6030, -0.4002, 1.0383, -1.0469, -0.4132, -1.0222, 0.5741, 0.3941],
- [ 0.8599, -0.2390, 1.9401, -0.4291, -0.5709, -0.3774, 0.7098, 0.1473],
- [ 0.8415, -0.3189, 1.8656, 0.4869, -0.5526, -0.0591, 0.6731, 0.1598],
- [ 0.6112, -0.4141, 1.7905, -0.2452, -0.4861, 0.1768, 0.4030, 0.2684],
- [ 0.8313, -0.2715, 1.6857, -0.6763, -0.3634, -0.9058, 0.6089, 0.0587]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.5318e-01, -4.2640e-01, 7.6259e-01, -1.1466e+00, -3.9792e-01,
- -1.2928e+00, 2.4936e-01, 3.8081e-01],
- [ 5.2061e-01, -4.6028e-01, 1.4670e+00, -4.7683e-01, -6.5774e-01,
- -3.9985e-01, 1.8356e-01, 2.0831e-03],
- [-2.2859e+00, -2.2859e+00, 9.9216e-01, -1.2021e+00, -3.2286e-01,
- -1.4314e+00, 1.0439e-01, 2.9299e-01],
- [ 5.9919e-01, -3.9684e-01, 9.3067e-01, -1.3497e+00, -4.7298e-01,
- -1.0465e+00, 5.2587e-01, 2.9299e-01],
- [ 6.0774e-01, -3.9646e-01, 1.8480e+00, -6.5389e-01, -6.2309e-01,
- -4.5373e-01, 6.2155e-01, -2.1963e-02],
- [ 6.2488e-01, -4.3518e-01, 1.8018e+00, 2.5450e-01, -6.1732e-01,
- -1.9969e-01, 6.4006e-01, 2.9135e-02],
- [ 5.5456e-01, -4.6205e-01, 1.7788e+00, -4.2294e-01, -5.1917e-01,
- -2.2633e-02, 4.2771e-01, 2.4681e-01],
- [ 6.0641e-01, -3.9900e-01, 1.6113e+00, -8.3095e-01, -4.2679e-01,
- -1.0696e+00, 6.4212e-01, -6.4044e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0272, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0272, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.4382315184921026
- step: 80
- running loss: 0.030477893981151284
- Train Steps: 80/90 Loss: 0.0305 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6249, 0.4142, 0.8350, 0.3283, 0.3613, 0.3700, 0.6188, 0.5400],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6202, 0.4079, 0.8025, 0.2500, 0.3763, 0.3217, 0.6125, 0.5533],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6080, 0.4010, 0.8750, 0.4500, 0.4825, 0.5617, 0.5837, 0.5583],
- [0.6192, 0.3980, 0.7078, 0.2750, 0.4250, 0.2100, 0.5450, 0.5783]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6583, -0.3531, 1.5451, -0.6303, -0.7401, -0.6090, 0.5511, 0.2601],
- [ 0.5577, -0.4344, 1.7469, 0.0396, -0.2320, 0.3542, 0.4915, 0.2582],
- [ 0.5700, -0.4197, 1.3435, -1.0377, -0.5919, -0.7790, 0.5803, 0.3046],
- [ 0.4306, -0.4598, 1.5850, 0.0190, -0.3076, -1.0861, 0.4304, 0.5001],
- [ 0.6816, -0.3647, 1.1919, -1.0613, -0.2055, -1.3733, 0.5535, 0.2396],
- [ 0.5564, -0.4769, 1.8245, -0.1644, -0.5580, 0.2945, 0.5149, 0.0401],
- [ 0.4822, -0.5046, 1.7714, -0.1248, -0.1793, 0.1259, 0.3947, 0.1858],
- [-0.4371, -1.0689, 0.9644, -0.8736, -0.3876, -1.3416, 0.3498, 0.4060]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6006, -0.3728, 1.5709, -0.7694, -0.6173, -0.5769, 0.5721,
- 0.2083],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.5788, -0.4020, 1.4208, -1.1312, -0.5480, -0.8002, 0.5432,
- 0.2699],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.5223, -0.4336, 1.7557, -0.2074, -0.0573, 0.3084, 0.4104,
- 0.2930],
- [ 0.5742, -0.4474, 0.9834, -1.0159, -0.3229, -1.3159, 0.2314,
- 0.3854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0289, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0289, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.4671557769179344
- step: 81
- running loss: 0.03045871329528314
- Train Steps: 81/90 Loss: 0.0305 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6102, 0.4020, 0.8638, 0.3717, 0.3625, 0.5017, 0.6038, 0.5500],
- [0.6339, 0.4118, 0.7988, 0.5800, 0.3912, 0.4583, 0.7343, 0.5760],
- [0.6190, 0.4135, 0.8000, 0.4883, 0.3566, 0.3647, 0.5613, 0.5900],
- [0.6246, 0.4028, 0.8738, 0.4867, 0.4088, 0.5667, 0.6362, 0.5200],
- [0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417],
- [0.6329, 0.4175, 0.8550, 0.2333, 0.5425, 0.2250, 0.7398, 0.5609],
- [0.6260, 0.4106, 0.8025, 0.2583, 0.4550, 0.1867, 0.6281, 0.4869]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3561, -0.5817, 1.6280, -0.5200, -0.6140, 0.0832, 0.4891, 0.2815],
- [ 0.6177, -0.4275, 1.4497, 0.2524, -0.5313, -0.1602, 0.7929, 0.3334],
- [ 0.3869, -0.4878, 1.4759, 0.0193, -0.6202, -0.5727, 0.1075, 0.3970],
- [ 0.6651, -0.3817, 1.7857, -0.0360, -0.4305, 0.4036, 0.5908, 0.1152],
- [ 0.1623, -0.6708, 1.5985, -0.0590, -0.7143, -0.5345, 0.0244, 0.3532],
- [ 0.5097, -0.4573, 1.2580, -1.0156, -0.4474, -0.7448, 0.4199, 0.3221],
- [ 0.6839, -0.3833, 1.6283, -1.0961, 0.1755, -1.2620, 1.0173, 0.3348],
- [ 0.6074, -0.4254, 1.3506, -1.1489, -0.2045, -1.2801, 0.4970, 0.0799]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5324, -0.4293, 1.7037, -0.5692, -0.6115, 0.0313, 0.5028,
- 0.2545],
- [ 0.6421, -0.3840, 1.4035, 0.3931, -0.4788, -0.1689, 1.1057,
- 0.3745],
- [ 0.5732, -0.3761, 1.4092, -0.0303, -0.6388, -0.6012, 0.3065,
- 0.4393],
- [ 0.5990, -0.4256, 1.7499, -0.0380, -0.3979, 0.3315, 0.6529,
- 0.1159],
- [ 0.5454, -0.4053, 1.6633, -0.1766, -0.6058, -0.5923, 0.1577,
- 0.4357],
- [ 0.5977, -0.3792, 1.2995, -1.0311, -0.5192, -0.8386, 0.5836,
- 0.2160],
- [ 0.6372, -0.3575, 1.6633, -1.2082, 0.2199, -1.2467, 1.1313,
- 0.3050],
- [ 0.6058, -0.3892, 1.4208, -1.0927, -0.1843, -1.4237, 0.6154,
- -0.0370]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0116, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0116, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.478730901144445
- step: 82
- running loss: 0.030228425623712744
- Train Steps: 82/90 Loss: 0.0302 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6173, 0.4114, 0.7325, 0.2500, 0.4213, 0.1917, 0.5338, 0.5700],
- [0.6364, 0.4165, 0.9088, 0.4367, 0.4075, 0.3150, 0.6448, 0.5297],
- [0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6274, 0.4003, 0.8638, 0.5967, 0.3688, 0.4900, 0.6108, 0.4661],
- [0.6201, 0.4055, 0.7717, 0.1919, 0.4250, 0.2079, 0.5871, 0.5044]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1982, -0.6441, 1.4626, -1.0844, -0.4183, -0.8629, 0.2960, 0.2098],
- [ 0.1744, -0.6380, 0.9460, -1.1304, -0.3395, -1.1559, 0.1845, 0.3836],
- [ 0.4829, -0.4581, 1.7923, -0.2788, -0.4446, -0.5581, 0.5883, 0.2422],
- [ 0.5963, -0.3333, 1.1338, -1.0038, -0.1605, -1.0222, 0.3700, 0.4745],
- [ 0.5174, -0.4872, 1.6344, 0.1660, -0.4626, 0.0136, 0.4201, 0.1727],
- [ 0.4432, -0.5566, 1.8790, -0.1467, -0.3513, -0.7157, 0.9092, 0.3290],
- [ 0.6365, -0.4482, 1.6620, 0.3447, -0.5882, 0.0983, 0.6642, 0.0769],
- [ 0.6277, -0.3859, 1.1277, -1.2750, -0.3528, -1.0574, 0.4644, 0.2117]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.5652, -0.3858, 1.0975, -1.1312, -0.3402, -1.4006, 0.1794,
- 0.3469],
- [ 0.6537, -0.3623, 1.9115, -0.2690, -0.4037, -0.8309, 0.6926,
- 0.1608],
- [ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.6120, -0.4371, 1.7037, 0.4701, -0.5827, -0.0226, 0.5354,
- -0.1331],
- [ 0.5785, -0.4129, 1.2785, -1.3996, -0.3227, -1.3259, 0.4258,
- 0.0438]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0205, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0205, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.499280705116689
- step: 83
- running loss: 0.03011181572429746
- Train Steps: 83/90 Loss: 0.0301 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533],
- [0.6087, 0.3976, 0.8337, 0.3867, 0.3713, 0.3117, 0.5938, 0.5300],
- [0.6239, 0.4206, 0.8750, 0.5400, 0.3688, 0.4850, 0.5737, 0.5700]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6278, -0.3582, 1.4195, -0.5272, -0.5106, -0.8733, 0.2839, 0.3572],
- [ 0.6381, -0.4075, 1.6691, -0.0616, -0.2797, 0.1486, 0.6430, 0.1670],
- [ 0.5569, -0.3845, 1.3425, -0.9565, -0.0864, -1.1992, 0.4094, 0.2892],
- [ 0.6495, -0.4110, 1.7332, 0.0540, -0.4006, 0.1699, 0.6011, 0.0674],
- [ 0.7700, -0.2943, 0.8941, -1.3155, -0.2464, -1.4069, 0.3876, 0.2335],
- [-1.6801, -1.9175, 1.2121, -0.9034, -0.5852, -0.9538, 0.2522, 0.3485],
- [ 0.8054, -0.2963, 1.5600, -0.6453, -0.5434, -0.6607, 0.5905, 0.2499],
- [ 0.9652, -0.1676, 1.7356, 0.2161, -0.5124, 0.1078, 0.6526, 0.2850]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7790e-01, -3.8397e-01, 1.5420e+00, -4.3064e-01, -5.4226e-01,
- -9.7721e-01, 2.0412e-01, 3.9283e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 5.9107e-01, -3.8879e-01, 1.4727e+00, -9.5412e-01, -9.1917e-02,
- -1.4930e+00, 3.9885e-01, 2.0831e-01],
- [ 5.7777e-01, -4.3888e-01, 1.7107e+00, 1.1921e-01, -3.9207e-01,
- 8.1507e-02, 4.7413e-01, 7.1077e-02],
- [ 5.5664e-01, -4.1601e-01, 9.9353e-01, -1.3313e+00, -2.8245e-01,
- -1.5161e+00, 2.1441e-01, 1.2532e-01],
- [-2.2859e+00, -2.2859e+00, 1.5074e+00, -1.0388e+00, -5.4226e-01,
- -9.8491e-01, 2.1986e-01, 2.6990e-01],
- [ 5.2546e-01, -4.4950e-01, 1.5651e+00, -4.9992e-01, -5.7113e-01,
- -8.4634e-01, 4.5658e-01, 1.6212e-01],
- [ 5.9601e-01, -3.4305e-01, 1.7557e+00, 2.0831e-01, -5.8268e-01,
- -4.5727e-02, 3.6420e-01, 3.4688e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0224, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0224, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5217115683481097
- step: 84
- running loss: 0.03002037581366797
- Train Steps: 84/90 Loss: 0.0300 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6109, 0.4041, 0.6975, 0.3167, 0.3513, 0.3383, 0.5153, 0.5319],
- [0.6275, 0.4048, 0.8488, 0.2883, 0.4463, 0.2033, 0.6321, 0.5155],
- [0.6260, 0.4106, 0.8025, 0.2583, 0.4550, 0.1867, 0.6281, 0.4869],
- [0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6115, 0.4081, 0.6725, 0.2433, 0.4088, 0.1933, 0.5167, 0.5544],
- [0.6267, 0.4065, 0.8313, 0.2467, 0.4788, 0.1733, 0.6312, 0.5133],
- [0.6325, 0.4066, 0.8438, 0.5350, 0.3925, 0.5267, 0.7113, 0.5795],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3755, -0.5651, 1.0948, -0.7817, -0.6757, -0.6152, 0.0656, 0.2687],
- [ 0.5311, -0.4885, 1.6368, -0.8579, -0.2738, -1.1269, 0.7426, 0.1522],
- [ 0.5372, -0.4697, 1.4447, -1.0901, -0.2341, -1.2558, 0.6001, 0.1076],
- [ 0.4377, -0.5105, 1.8264, 0.0483, -0.0860, 0.1494, 0.4169, 0.2315],
- [ 0.3859, -0.5386, 0.9109, -1.0271, -0.4228, -1.2534, 0.1069, 0.3340],
- [ 0.7784, -0.3214, 1.5264, -1.0374, -0.1043, -1.2875, 0.6998, 0.2039],
- [ 0.4173, -0.5761, 1.7450, 0.3063, -0.5354, 0.3130, 0.9737, 0.3100],
- [ 0.4244, -0.5139, 1.1205, -0.9749, -0.6412, -0.6767, 0.4314, 0.2827]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5359, -0.4193, 0.9358, -0.8232, -0.6635, -0.7232, 0.0943,
- 0.1710],
- [ 0.6126, -0.4161, 1.6344, -0.9541, -0.2247, -1.3467, 0.6339,
- 0.0953],
- [ 0.6058, -0.3892, 1.4208, -1.0927, -0.1843, -1.4237, 0.6154,
- -0.0370],
- [ 0.5371, -0.4252, 1.7037, -0.0688, -0.0342, 0.0620, 0.3758,
- 0.2853],
- [ 0.5385, -0.4010, 0.8203, -1.1620, -0.3979, -1.3929, 0.1005,
- 0.2747],
- [ 0.6085, -0.4084, 1.5536, -1.1466, -0.0746, -1.4853, 0.6298,
- 0.0851],
- [ 0.6355, -0.4080, 1.6113, 0.1852, -0.4730, 0.1467, 0.9996,
- 0.3905],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0115, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0115, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5331995971500874
- step: 85
- running loss: 0.029802348201765732
- Train Steps: 85/90 Loss: 0.0298 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6203, 0.4056, 0.8942, 0.4086, 0.3643, 0.3617, 0.5917, 0.5482],
- [0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167],
- [0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [0.6122, 0.4006, 0.8850, 0.4217, 0.4088, 0.5517, 0.6063, 0.5517],
- [0.6150, 0.4097, 0.7468, 0.3194, 0.3825, 0.2633, 0.5463, 0.5800]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3192, -0.5970, 1.7006, 0.2154, -0.0810, 0.0501, 0.5673, 0.2228],
- [ 0.5665, -0.4718, 1.7598, -0.4422, -0.5859, -0.2406, 0.6804, 0.2694],
- [ 0.7055, -0.3615, 1.8326, -0.3187, -0.5280, -0.6149, 0.5665, 0.2291],
- [ 0.2587, -0.6218, 0.9886, -1.2755, -0.3026, -1.5091, 0.3010, 0.1494],
- [ 0.6178, -0.4333, 1.6893, -0.4250, -0.4827, -0.8889, 0.5407, 0.1794],
- [ 0.7328, -0.3638, 1.1332, -1.3629, -0.4475, -1.1089, 0.5656, 0.1385],
- [ 0.4559, -0.5146, 1.7819, -0.2563, -0.3102, 0.2478, 0.6159, 0.2551],
- [ 0.6827, -0.3224, 1.0977, -0.8458, -0.4696, -1.0328, 0.1970, 0.4110]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.5791, -0.4127, 1.8442, -0.3985, -0.6031, -0.6154, 0.4473,
- 0.2464],
- [ 0.5109, -0.4316, 1.1436, -1.3467, -0.3864, -1.4160, 0.1239,
- -0.0580],
- [ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005],
- [ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [ 0.5417, -0.4355, 1.8018, -0.3383, -0.3979, 0.2622, 0.5143,
- 0.2622],
- [ 0.5548, -0.3936, 1.1634, -0.8105, -0.5192, -1.0696, 0.2372,
- 0.3931]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0113, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0113, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5444738110527396
- step: 86
- running loss: 0.02958690477968302
- Train Steps: 86/90 Loss: 0.0296 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168],
- [0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6270, 0.4267, 0.7150, 0.3317, 0.4900, 0.1817, 0.5560, 0.6183],
- [0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6234, 0.4179, 0.7825, 0.3450, 0.3813, 0.2867, 0.5675, 0.5617],
- [0.6122, 0.3993, 0.8738, 0.4667, 0.4517, 0.4879, 0.5155, 0.4927],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3100, -0.6210, 0.9047, -1.3336, -0.3877, -1.2771, 0.5174, 0.1538],
- [ 0.5911, -0.4111, 1.6927, -0.4872, -0.4639, -0.9790, 0.3204, 0.0526],
- [ 0.6158, -0.4260, 1.3063, -1.2585, -0.1382, -1.3125, 0.6621, 0.1757],
- [ 0.7599, -0.2690, 1.0580, -1.0242, -0.0868, -1.2896, 0.4650, 0.4659],
- [ 0.4308, -0.5882, 1.8699, 0.0753, -0.5445, 0.1465, 0.6465, -0.0091],
- [ 0.4604, -0.4707, 1.4312, -0.7649, -0.4340, -0.8856, 0.4767, 0.3702],
- [ 0.1774, -0.7147, 1.8432, -0.1140, -0.2642, 0.0558, 0.2860, -0.0067],
- [ 0.5459, -0.4278, 1.3398, -0.5322, -0.5929, -0.7123, 0.4224, 0.4162]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013],
- [ 0.5485, -0.4209, 1.6691, -0.4152, -0.5249, -1.1081, 0.0725,
- 0.0021],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.6101, -0.3152, 1.0166, -0.7540, -0.0226, -1.4468, 0.2823,
- 0.5702],
- [ 0.5783, -0.4363, 1.7724, 0.0432, -0.5153, 0.0871, 0.4840,
- 0.0663],
- [ 0.5935, -0.3558, 1.3284, -0.6924, -0.5249, -0.9618, 0.3353,
- 0.3084],
- [ 0.5417, -0.4417, 1.7499, -0.1304, -0.1994, -0.0324, 0.0951,
- -0.0099],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0161, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0161, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.56061180960387
- step: 87
- running loss: 0.029432319650619196
- Train Steps: 87/90 Loss: 0.0294 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [0.6200, 0.4071, 0.7338, 0.1917, 0.4200, 0.2450, 0.6150, 0.5517],
- [0.6257, 0.4060, 0.8300, 0.2333, 0.4688, 0.2583, 0.7050, 0.5633],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6101, 0.4042, 0.7775, 0.2617, 0.3713, 0.2817, 0.5440, 0.5650],
- [0.6128, 0.4115, 0.7934, 0.3778, 0.3450, 0.4033, 0.5337, 0.5456],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 4.6039e-01, -4.2506e-01, 1.4384e+00, -4.5731e-01, -4.3452e-02,
- -1.2264e+00, 3.2788e-01, 3.5845e-01],
- [ 6.0257e-01, -4.3626e-01, 1.1690e+00, -1.2788e+00, -3.3650e-01,
- -1.1651e+00, 6.8741e-01, 1.0520e-01],
- [ 5.2720e-01, -4.8766e-01, 1.6576e+00, -1.2388e+00, -1.2994e-01,
- -1.1635e+00, 1.0622e+00, 1.5645e-01],
- [ 4.8725e-01, -4.7966e-01, 1.4731e+00, -7.5758e-01, -6.2636e-01,
- -6.2725e-01, 5.6555e-01, 3.4666e-01],
- [ 4.4267e-01, -4.8748e-01, 1.3048e+00, -9.8736e-01, -4.8932e-01,
- -9.7766e-01, 2.2468e-01, 9.5480e-02],
- [ 3.4075e-01, -5.4505e-01, 1.4408e+00, -4.8045e-01, -6.5704e-01,
- -4.3574e-01, 2.1275e-01, 1.5534e-01],
- [ 3.3860e-01, -5.9572e-01, 1.7547e+00, -3.4798e-01, -5.4307e-01,
- -8.2032e-02, 4.6923e-01, 4.7698e-02],
- [ 5.7668e-01, -3.5282e-01, 1.1982e+00, -6.6692e-01, -1.3449e-03,
- -1.3082e+00, 3.4176e-01, 3.4211e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882],
- [ 0.5779, -0.4054, 1.1032, -1.4006, -0.3460, -1.1543, 0.5547,
- 0.2622],
- [ 0.6042, -0.4105, 1.5478, -1.2082, -0.1208, -1.0927, 0.9704,
- 0.3157],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.5320, -0.4189, 1.3053, -1.0773, -0.5711, -0.9849, 0.2267,
- 0.3237],
- [ 0.5444, -0.3852, 1.3786, -0.5409, -0.6924, -0.4229, 0.1791,
- 0.2341],
- [ 0.5546, -0.4620, 1.7788, -0.4229, -0.5192, -0.0226, 0.4277,
- 0.2468],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0105, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0105, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.5710743190720677
- step: 88
- running loss: 0.02921675362581895
- Train Steps: 88/90 Loss: 0.0292 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [0.6146, 0.4124, 0.8800, 0.4867, 0.3800, 0.4500, 0.5106, 0.5524],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6229, 0.4066, 0.8513, 0.5717, 0.4500, 0.5050, 0.6025, 0.5350],
- [0.6110, 0.3984, 0.8750, 0.4933, 0.4625, 0.4950, 0.5578, 0.5676],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6268, 0.4052, 0.8175, 0.2250, 0.4688, 0.1917, 0.6375, 0.5267]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7000, -0.3330, 1.2081, -1.3917, -0.5066, -1.2099, 0.5612, 0.2131],
- [ 0.5759, -0.4134, 1.6561, -0.3017, -0.5498, -0.2371, 0.2067, 0.2265],
- [ 0.5207, -0.4113, 1.6869, -0.0436, -0.5771, -0.3776, 0.2774, 0.1871],
- [ 0.5346, -0.4634, 1.6791, 0.0160, -0.4440, -0.0452, 0.5119, 0.0538],
- [ 0.2992, -0.6279, 1.5847, 0.2346, -0.2637, -0.1236, 0.4955, 0.2122],
- [ 0.4735, -0.4733, 1.6959, -0.1844, -0.1443, -0.1494, 0.2397, 0.2195],
- [ 0.4997, -0.4006, 1.0715, -1.2295, -0.1961, -1.5172, 0.3869, 0.3413],
- [ 0.9420, -0.1883, 1.4086, -1.4380, -0.1689, -1.5866, 0.7342, 0.0748]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6966e-01, -4.4656e-01, 1.1973e+00, -1.1871e+00, -4.5712e-01,
- -9.9653e-01, 5.2186e-01, 2.0324e-01],
- [ 5.3684e-01, -4.4057e-01, 1.7730e+00, -1.7660e-01, -5.2494e-01,
- -5.3426e-02, 2.3141e-01, 3.4688e-01],
- [ 5.5289e-01, -3.8106e-01, 1.7788e+00, -3.8029e-02, -5.3072e-01,
- -2.0739e-01, 7.2734e-02, 2.6568e-01],
- [ 5.7777e-01, -4.3888e-01, 1.7107e+00, 1.1921e-01, -3.9207e-01,
- 8.1507e-02, 4.7413e-01, 7.1077e-02],
- [ 5.9107e-01, -4.0805e-01, 1.6460e+00, 3.5458e-01, -2.0739e-01,
- 4.6651e-02, 4.9700e-01, 1.8522e-01],
- [ 5.3637e-01, -4.4573e-01, 1.7557e+00, -7.2363e-03, -1.4965e-01,
- 4.6189e-04, 2.9049e-01, 3.3573e-01],
- [ 5.8620e-01, -3.5296e-01, 1.1032e+00, -1.0619e+00, -1.4965e-01,
- -1.3852e+00, 3.4111e-01, 3.9307e-01],
- [ 6.0918e-01, -4.1432e-01, 1.4901e+00, -1.2467e+00, -1.2079e-01,
- -1.4006e+00, 6.5866e-01, 1.4673e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0142, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0142, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.58522439096123
- step: 89
- running loss: 0.02904746506698011
- Train Steps: 89/90 Loss: 0.0290 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6271, 0.4024, 0.9000, 0.3517, 0.3700, 0.4517, 0.6931, 0.5285],
- [0.6296, 0.4076, 0.8400, 0.5583, 0.3700, 0.4367, 0.6876, 0.5494],
- [0.6248, 0.4185, 0.8500, 0.5767, 0.4463, 0.4550, 0.5613, 0.5917],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6364, 0.4165, 0.9088, 0.4367, 0.4075, 0.3150, 0.6448, 0.5297],
- [0.6286, 0.4097, 0.8107, 0.2414, 0.4425, 0.2483, 0.6745, 0.5385]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5832, -0.3993, 1.1275, -1.2637, -0.5853, -1.0007, -0.0508, -0.0505],
- [ 0.3885, -0.4997, 1.5609, 0.1179, -0.3980, -0.2911, 0.2709, 0.3850],
- [ 0.3156, -0.6031, 1.7021, -0.8399, -0.6414, -0.2453, 0.6654, 0.1443],
- [ 0.7260, -0.3384, 1.6161, 0.0791, -0.5394, -0.3537, 0.7659, 0.1245],
- [ 0.4992, -0.4325, 1.5218, 0.1549, -0.1555, -0.3437, 0.2934, 0.4448],
- [ 0.6640, -0.2715, 1.1294, -0.9919, -0.0219, -1.3958, 0.1330, 0.4381],
- [ 0.6496, -0.3338, 1.8106, -0.5189, -0.3958, -0.9181, 0.5184, 0.1384],
- [ 0.6956, -0.3518, 1.4355, -1.4073, -0.2439, -1.3482, 0.7373, 0.1280]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007],
- [ 0.6108, -0.4273, 1.8711, -0.6616, -0.5769, -0.1997, 0.9156,
- 0.1554],
- [ 0.6224, -0.4032, 1.5940, 0.2930, -0.5769, -0.2690, 0.8900,
- 0.2516],
- [ 0.5999, -0.3530, 1.6402, 0.3777, -0.2247, -0.1843, 0.3065,
- 0.4470],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [ 0.6537, -0.3623, 1.9115, -0.2690, -0.4037, -0.8309, 0.6926,
- 0.1608],
- [ 0.6174, -0.3936, 1.4586, -1.1709, -0.2420, -1.1389, 0.8296,
- 0.2012]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0192, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0192, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.604389033280313
- step: 90
- running loss: 0.02893765592533681
- Valid Steps: 10/10 Loss: nan 7.8799
- --------------------------------------------------
- Epoch: 6 Train Loss: 0.0289 Valid Loss: nan
- --------------------------------------------------
- size of train loader is: 90
- torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717],
- [0.6218, 0.4098, 0.7238, 0.1850, 0.4238, 0.2517, 0.6288, 0.5350],
- [0.6222, 0.4072, 0.7164, 0.2166, 0.3738, 0.3167, 0.6100, 0.5533],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [ nan, nan, 0.7612, 0.3250, 0.4037, 0.2533, 0.5438, 0.5767],
- [0.6206, 0.4001, 0.8900, 0.3933, 0.3588, 0.3567, 0.5837, 0.5083],
- [0.6165, 0.4106, 0.7575, 0.1733, 0.3838, 0.2650, 0.5680, 0.5116],
- [0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6477, -0.3526, 0.9691, -0.9025, -0.4389, -1.1085, 0.2247, 0.2755],
- [ 0.8920, -0.2164, 1.2343, -1.0870, -0.2121, -1.2321, 0.6169, 0.2335],
- [ 0.5558, -0.4183, 1.1495, -1.0581, -0.4915, -0.8478, 0.4433, 0.2620],
- [ 0.5084, -0.4509, 1.4207, -1.0557, -0.0454, -1.4207, 0.5016, 0.1969],
- [-0.0192, -0.7355, 1.4270, -0.7559, -0.2804, -1.0867, 0.2119, 0.3873],
- [ 0.6241, -0.4014, 1.9214, -0.2800, -0.5671, -0.6757, 0.3070, 0.0805],
- [ 0.5517, -0.4502, 1.3219, -1.2073, -0.4492, -1.1858, 0.5526, 0.0171],
- [ 0.4252, -0.5143, 1.6961, -0.5656, -0.6468, -0.3361, 0.3515, 0.2301]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546],
- [ 0.5861, -0.3929, 1.0570, -1.4314, -0.3286, -1.1235, 0.6182,
- 0.1852],
- [ 0.5878, -0.4052, 1.0229, -1.2855, -0.5596, -0.8232, 0.5316,
- 0.2699],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [-2.2859, -2.2859, 1.2303, -0.7848, -0.4210, -1.1158, 0.2256,
- 0.3777],
- [ 0.5807, -0.4378, 1.8249, -0.4691, -0.6289, -0.6385, 0.4104,
- 0.0620],
- [ 0.5618, -0.3895, 1.2129, -1.4853, -0.5134, -1.0619, 0.3378,
- 0.0772],
- [ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1317, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1317, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.13170002400875092
- step: 1
- running loss: 0.13170002400875092
- Train Steps: 1/90 Loss: 0.1317 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567],
- [0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6265, 0.4251, 0.7113, 0.3550, 0.4375, 0.2117, 0.5587, 0.6118],
- [0.6361, 0.4076, 0.8862, 0.5350, 0.3713, 0.4650, 0.6654, 0.5297],
- [0.6241, 0.4143, 0.8938, 0.4650, 0.4075, 0.5350, 0.6250, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6647, -0.3062, 1.0944, -1.2538, -0.3591, -1.2520, 0.4543, 0.4699],
- [ 0.3959, -0.5123, 1.6735, -0.1118, -0.0870, -0.0970, 0.0966, 0.1922],
- [ 0.3720, -0.4999, 1.1677, -1.1540, -0.4044, -1.2134, 0.3068, 0.2836],
- [ 0.6326, -0.3810, 1.6411, -0.0633, -0.3244, -0.1243, 0.2366, 0.2300],
- [ 0.6179, -0.3312, 1.6827, -0.9184, -0.2375, -1.5012, 0.2988, -0.0049],
- [ 0.7444, -0.2400, 1.0723, -0.8516, -0.3273, -1.3134, 0.1802, 0.4339],
- [ 0.7338, -0.3491, 1.6987, 0.1139, -0.6360, -0.2006, 0.7904, 0.1141],
- [ 0.5614, -0.3962, 1.7932, -0.2720, -0.4493, 0.1143, 0.6197, 0.2201]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084],
- [ 0.5359, -0.4370, 1.7095, -0.0303, -0.0804, -0.0380, 0.1044,
- 0.3392],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853],
- [ 0.5525, -0.4463, 1.7326, 0.0313, -0.2536, -0.0688, 0.1968,
- 0.3700],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.6077, -0.3226, 0.9993, -0.6462, -0.2651, -1.3082, 0.2946,
- 0.5401],
- [ 0.6520, -0.4032, 1.8076, 0.1852, -0.5711, -0.1381, 0.7876,
- 0.1608],
- [ 0.5968, -0.3725, 1.8423, -0.1381, -0.4037, 0.1852, 0.6009,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0086, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0086, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.1403241939842701
- step: 2
- running loss: 0.07016209699213505
- Train Steps: 2/90 Loss: 0.0702 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6128, 0.4116, 0.8450, 0.4583, 0.3675, 0.4867, 0.5337, 0.5446],
- [0.6197, 0.3930, 0.8793, 0.4736, 0.4152, 0.5464, 0.6308, 0.5235],
- [ nan, nan, 0.7335, 0.2569, 0.3788, 0.2667, 0.5066, 0.5578],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578],
- [0.6038, 0.3946, 0.8413, 0.4883, 0.3563, 0.4550, 0.5266, 0.4693],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7319, -0.2171, 1.5618, -0.3020, -0.4572, -0.2303, 0.1329, 0.3098],
- [ 0.8232, -0.2165, 1.7220, -0.2264, -0.2888, 0.0441, 0.4587, 0.1933],
- [-1.7871, -1.9312, 1.1732, -1.2439, -0.4154, -1.3333, 0.0692, 0.2836],
- [ 0.7511, -0.2776, 1.8293, -0.2960, -0.3076, -0.5175, 0.9069, 0.3537],
- [ 0.9328, -0.1419, 1.4058, 0.0273, -0.4443, -0.1959, 0.7334, 0.2985],
- [ 0.9208, -0.1094, 0.9170, -1.5732, -0.3551, -1.5826, 0.2315, 0.1841],
- [ 0.7178, -0.2332, 1.5154, -0.1506, -0.5277, -0.4161, -0.0780, 0.2460],
- [ 0.8859, -0.2005, 1.6186, 0.0542, -0.3882, -0.3113, 0.3717, 0.1910]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5444, -0.3846, 1.6171, -0.1689, -0.5885, -0.0380, 0.1791,
- 0.2296],
- [ 0.5762, -0.4706, 1.7754, -0.0984, -0.3680, 0.2380, 0.6277,
- 0.1322],
- [-2.2859, -2.2859, 1.1020, -1.0994, -0.5365, -1.0542, 0.0542,
- 0.2905],
- [ 0.6454, -0.3719, 1.9115, -0.1304, -0.5076, -0.2844, 1.0033,
- 0.4386],
- [ 0.6289, -0.4345, 1.3794, 0.3679, -0.4845, 0.0390, 0.9265,
- 0.1928],
- [ 0.5232, -0.4189, 0.9358, -1.4006, -0.3921, -1.3698, 0.2555,
- 0.2906],
- [ 0.5030, -0.4631, 1.5998, -0.0303, -0.6404, -0.1843, 0.1465,
- -0.1181],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0370, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0370, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.1773393712937832
- step: 3
- running loss: 0.0591131237645944
- Train Steps: 3/90 Loss: 0.0591 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917],
- [0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356],
- [0.6102, 0.3999, 0.8750, 0.5133, 0.3825, 0.4750, 0.5637, 0.5083],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6250, 0.4106, 0.8700, 0.3717, 0.3588, 0.4967, 0.6038, 0.5167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 8.9299e-01, -1.9483e-01, 1.3833e+00, 1.2987e-01, -4.8383e-01,
- -5.3583e-03, 7.4699e-01, 2.4503e-01],
- [-1.8940e+00, -2.0092e+00, 1.3015e+00, -1.1059e+00, -3.3122e-01,
- -1.2012e+00, 2.4633e-01, 2.7320e-01],
- [ 6.9292e-01, -2.6543e-01, 1.1853e+00, -5.9782e-01, -6.0230e-01,
- -8.2772e-01, 1.5278e-01, 4.5867e-01],
- [ 6.5121e-01, -2.5269e-01, 1.3511e+00, -6.1461e-01, -7.2152e-02,
- -1.2816e+00, 1.5690e-01, 5.1567e-01],
- [ 7.7100e-01, -2.4929e-01, 1.3652e+00, -1.1704e+00, -3.3382e-01,
- -1.2479e+00, 6.5034e-01, 1.7962e-01],
- [ 8.6061e-01, -1.8263e-01, 1.6473e+00, -7.0001e-04, -5.0347e-01,
- -9.7541e-02, 1.6001e-01, 6.7358e-02],
- [ 5.2133e-01, -3.6087e-01, 1.4178e+00, -9.9686e-01, -1.1791e-01,
- -1.4057e+00, 3.2734e-01, 2.0587e-01],
- [ 8.5589e-01, -1.9249e-01, 1.6359e+00, -5.9213e-01, -6.1109e-01,
- 4.0864e-02, 5.4792e-01, 2.4353e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6289, -0.4345, 1.3794, 0.3679, -0.4845, 0.0390, 0.9265,
- 0.1928],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470],
- [ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882],
- [ 0.6174, -0.4225, 1.4975, -1.1709, -0.3174, -1.1806, 0.6539,
- 0.1879],
- [ 0.5326, -0.4390, 1.7557, 0.0851, -0.5192, -0.0919, 0.3180,
- 0.0620],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.6010, -0.3896, 1.7326, -0.5692, -0.6289, 0.0082, 0.5028,
- 0.1005]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0179, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0179, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.19527801871299744
- step: 4
- running loss: 0.04881950467824936
- Train Steps: 4/90 Loss: 0.0488 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167],
- [0.6124, 0.4075, 0.7696, 0.4153, 0.3475, 0.3767, 0.5157, 0.5427],
- [ nan, nan, 0.7850, 0.2700, 0.4288, 0.1717, 0.5199, 0.4999],
- [0.6216, 0.4100, 0.7350, 0.2067, 0.4325, 0.2050, 0.5950, 0.5333],
- [0.6034, 0.4011, 0.7350, 0.2533, 0.3438, 0.3367, 0.5516, 0.5084],
- [0.6201, 0.4098, 0.8575, 0.3617, 0.3700, 0.3550, 0.5988, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6983, -0.2865, 1.5583, 0.4364, 0.0205, -0.2002, 0.3845, 0.5493],
- [ 0.6020, -0.3588, 1.7794, -0.0123, -0.0761, 0.4327, 0.5758, 0.3702],
- [ 0.8159, -0.2481, 1.7206, -0.3957, -0.6126, -0.8451, 0.4303, 0.1379],
- [ 0.7543, -0.2461, 1.3220, -0.4106, -0.6687, -0.5468, 0.0829, 0.3590],
- [-0.8455, -1.2990, 1.2776, -1.2102, -0.3380, -1.4762, 0.2634, 0.1421],
- [ 0.4908, -0.4188, 1.0813, -1.3411, -0.3358, -1.4621, 0.4294, 0.2803],
- [ 0.8225, -0.2386, 1.1020, -1.1353, -0.6921, -0.7803, 0.3172, 0.0501],
- [ 0.5905, -0.3740, 1.6765, -0.6473, -0.5600, -0.7175, 0.5019, 0.3938]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5959, -0.3579, 1.6055, 0.3623, -0.0573, -0.2074, 0.3122,
- 0.4547],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005],
- [ 0.5427, -0.4035, 1.2688, -0.3675, -0.6808, -0.5461, 0.0959,
- 0.2206],
- [-2.2859, -2.2859, 1.3400, -1.0388, -0.3055, -1.4930, 0.1157,
- 0.0231],
- [ 0.5853, -0.3920, 1.1090, -1.3313, -0.2882, -1.3390, 0.4624,
- 0.1775],
- [ 0.5009, -0.4333, 1.1090, -1.1158, -0.6982, -0.7309, 0.2617,
- 0.0622],
- [ 0.5783, -0.3933, 1.6748, -0.6154, -0.5769, -0.6462, 0.4797,
- 0.3315]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0562, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0562, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.25149907171726227
- step: 5
- running loss: 0.05029981434345245
- Train Steps: 5/90 Loss: 0.0503 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6179, 0.4040, 0.7412, 0.1850, 0.3825, 0.2783, 0.5837, 0.5600],
- [0.6270, 0.4267, 0.7150, 0.3317, 0.4900, 0.1817, 0.5560, 0.6183],
- [0.6127, 0.4119, 0.8650, 0.5300, 0.4512, 0.5250, 0.5260, 0.5461],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417],
- [0.6241, 0.4143, 0.8938, 0.4650, 0.4075, 0.5350, 0.6250, 0.5550],
- [0.6143, 0.4040, 0.8237, 0.3333, 0.4075, 0.2117, 0.5137, 0.4973],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [0.6127, 0.4118, 0.8650, 0.5083, 0.4088, 0.5367, 0.5300, 0.5456]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7097, -0.2963, 1.1037, -1.2898, -0.6142, -1.1763, 0.4470, 0.2839],
- [ 0.6372, -0.2858, 0.9849, -0.8955, -0.1584, -1.5070, 0.1930, 0.5510],
- [ 0.6837, -0.3201, 1.6716, 0.2550, -0.1896, 0.0621, 0.3190, 0.2978],
- [ 0.2536, -0.5944, 1.3756, -1.0492, -0.5398, -0.9605, 0.6340, 0.2841],
- [ 0.4375, -0.4863, 1.8205, -0.1297, -0.4469, 0.1487, 0.6756, 0.3328],
- [ 0.0804, -0.6800, 1.4471, -0.7608, -0.4763, -1.2891, 0.1751, 0.0604],
- [ 0.7134, -0.3114, 1.6887, -0.1343, -0.5369, -0.1299, 0.2081, 0.2759],
- [ 0.6029, -0.3714, 1.6532, 0.1362, -0.3379, 0.1308, 0.3406, 0.2828]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5680, -0.4201, 1.1379, -1.4314, -0.5192, -1.0003, 0.4104,
- 0.3007],
- [ 0.6101, -0.3152, 1.0166, -0.7540, -0.0226, -1.4468, 0.2823,
- 0.5702],
- [ 0.5443, -0.3831, 1.7095, 0.1621, -0.2016, 0.1390, 0.1437,
- 0.2364],
- [ 0.5977, -0.3792, 1.2995, -1.0311, -0.5192, -0.8386, 0.5836,
- 0.2160],
- [ 0.5968, -0.3725, 1.8423, -0.1381, -0.4037, 0.1852, 0.6009,
- 0.2776],
- [ 0.5515, -0.4201, 1.5189, -0.7463, -0.4037, -1.3082, 0.0869,
- 0.0111],
- [ 0.5368, -0.4406, 1.7730, -0.1766, -0.5249, -0.0534, 0.2314,
- 0.3469],
- [ 0.5443, -0.3840, 1.7095, 0.0620, -0.3979, 0.1929, 0.1622,
- 0.2341]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0141, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0141, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2655968349426985
- step: 6
- running loss: 0.04426613915711641
- Train Steps: 6/90 Loss: 0.0443 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100],
- [0.6124, 0.4083, 0.6954, 0.3069, 0.3650, 0.2750, 0.5163, 0.5485],
- [0.6133, 0.4066, 0.6787, 0.2617, 0.3800, 0.2433, 0.5147, 0.5358],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285],
- [ nan, nan, 0.9050, 0.3500, 0.5138, 0.2300, 0.7359, 0.5702],
- [0.6264, 0.4067, 0.9050, 0.4183, 0.3775, 0.4600, 0.6308, 0.4862],
- [0.6343, 0.4097, 0.9287, 0.4367, 0.4313, 0.3600, 0.7248, 0.5841]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9006, -0.2117, 1.6638, -0.3467, -0.6132, -0.4659, 0.5123, 0.1801],
- [ 0.5317, -0.3793, 0.8797, -0.9900, -0.5496, -0.9261, 0.0243, 0.3312],
- [ 0.6930, -0.3017, 0.7817, -1.0732, -0.4704, -1.2299, -0.0311, 0.2055],
- [ 0.6957, -0.3161, 1.7998, -0.4552, -0.5499, -0.4094, 0.6199, 0.2619],
- [ 0.6030, -0.3544, 1.2631, -0.3271, -0.5427, -0.2744, -0.1266, 0.2739],
- [-1.6762, -1.8580, 1.8621, -0.7469, 0.1769, -1.1852, 0.8146, 0.5468],
- [ 0.6555, -0.3252, 1.7625, -0.2648, -0.5922, -0.1266, 0.5597, 0.1009],
- [ 0.7671, -0.2935, 1.6871, -0.2076, -0.3535, -0.5524, 0.7757, 0.3876]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697],
- [ 0.5425, -0.3998, 0.9263, -0.8683, -0.6000, -1.0157, 0.0990,
- 0.2476],
- [ 0.5470, -0.4081, 0.8492, -1.0773, -0.5307, -1.1620, 0.0912,
- 0.1890],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552],
- [-2.2859, -2.2859, 1.8942, -0.6693, 0.0871, -1.2236, 1.1130,
- 0.3478],
- [ 0.6072, -0.4075, 1.8942, -0.3537, -0.5423, -0.1612, 0.6277,
- -0.0400],
- [ 0.6438, -0.3936, 2.0039, -0.2690, -0.2940, -0.6231, 1.0618,
- 0.4119]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2878271918743849
- step: 7
- running loss: 0.04111817026776927
- Train Steps: 7/90 Loss: 0.0411 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6214, 0.4175, 0.7300, 0.2883, 0.4338, 0.2167, 0.5698, 0.5773],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6179, 0.4008, 0.7505, 0.2678, 0.4368, 0.1891, 0.5831, 0.5263],
- [0.6201, 0.4036, 0.8596, 0.3850, 0.3492, 0.3785, 0.5978, 0.5131],
- [0.6271, 0.4005, 0.8450, 0.6067, 0.3850, 0.4983, 0.6069, 0.4649],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4279, -0.4700, 1.1087, -0.9343, -0.3377, -1.1035, 0.2972, 0.4786],
- [-0.1724, -0.8663, 1.5414, -0.3400, -0.5710, -0.8034, 0.1809, 0.4591],
- [ 0.6113, -0.3749, 1.2179, -1.1382, -0.3679, -1.2327, 0.2952, 0.1828],
- [ 0.8218, -0.2730, 1.8150, -0.3814, -0.7125, -0.3080, 0.3443, 0.1518],
- [ 0.6928, -0.3667, 1.6759, 0.5193, -0.4929, 0.2824, 0.4658, 0.1233],
- [ 0.4151, -0.4984, 0.9299, -1.0047, -0.5191, -1.0346, 0.3536, 0.3316],
- [ 0.2671, -0.6461, 2.0895, -0.5594, -0.3277, -0.3334, 1.0386, 0.3234],
- [ 0.2336, -0.6060, 1.0417, -1.1955, -0.3274, -1.4027, 0.1155, 0.1706]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5841, -0.3574, 1.0859, -0.9541, -0.2824, -1.2851, 0.3460,
- 0.3808],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.5680, -0.4345, 1.1806, -1.0490, -0.2683, -1.4127, 0.4074,
- 0.1449],
- [ 0.5783, -0.4216, 1.6847, -0.5078, -0.6732, -0.5377, 0.4752,
- 0.0839],
- [ 0.6105, -0.4360, 1.6171, 0.5162, -0.5076, 0.0159, 0.5171,
- -0.1385],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391],
- [ 0.6388, -0.3623, 1.9173, -0.7386, -0.3517, -0.5846, 1.1495,
- 0.2676],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0310, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0310, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3187991585582495
- step: 8
- running loss: 0.039849894819781184
- Train Steps: 8/90 Loss: 0.0398 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6236, 0.3967, 0.8675, 0.5400, 0.3862, 0.4517, 0.5825, 0.5200],
- [0.6264, 0.4067, 0.9050, 0.4183, 0.3775, 0.4600, 0.6308, 0.4862],
- [0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006],
- [0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433],
- [0.6275, 0.4008, 0.8047, 0.2484, 0.4037, 0.2777, 0.6506, 0.5212],
- [ nan, nan, 0.8463, 0.2550, 0.5850, 0.2133, 0.7129, 0.6072],
- [0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6302, -0.3617, 1.5214, 0.2184, -0.5714, -0.2713, 0.1169, 0.2587],
- [ 0.6184, -0.3406, 1.6832, -0.3059, -0.7060, -0.2061, 0.3594, 0.0877],
- [ 0.6739, -0.3559, 1.5060, 0.3060, -0.6391, -0.1109, 0.3257, 0.1549],
- [ 0.6845, -0.3340, 1.4835, 0.2149, -0.5401, -0.0175, 0.7316, 0.3698],
- [ 0.7103, -0.3030, 1.6602, -0.1140, -0.5570, 0.0523, 0.2161, 0.2177],
- [ 0.6586, -0.3380, 1.3056, -1.1049, -0.5090, -0.9922, 0.3005, 0.1256],
- [-1.3580, -1.6826, 1.5312, -1.0974, 0.1189, -1.1105, 0.7331, 0.5404],
- [ 0.3991, -0.5250, 1.5950, -1.2209, 0.0730, -1.0968, 0.8225, 0.2854]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5944, -0.4538, 1.7210, 0.2083, -0.5018, -0.1997, 0.4046,
- 0.1159],
- [ 0.6072, -0.4075, 1.8942, -0.3537, -0.5423, -0.1612, 0.6277,
- -0.0400],
- [ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261],
- [ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237],
- [ 0.6125, -0.4345, 1.4308, -1.1384, -0.4213, -1.0031, 0.7190,
- 0.1214],
- [-2.2859, -2.2859, 1.6229, -1.1081, 0.4162, -1.3005, 1.0070,
- 0.5188],
- [ 0.6240, -0.3768, 1.6575, -1.2851, 0.2949, -1.2467, 1.1276,
- 0.2142]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0426, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0426, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3614186178892851
- step: 9
- running loss: 0.04015762420992056
- Train Steps: 9/90 Loss: 0.0402 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6197, 0.4090, 0.7825, 0.2500, 0.4200, 0.2483, 0.5988, 0.5667],
- [0.6200, 0.4070, 0.8938, 0.4183, 0.3538, 0.4567, 0.6175, 0.5400],
- [0.6147, 0.4081, 0.8538, 0.3400, 0.3663, 0.3150, 0.5142, 0.4875],
- [0.6219, 0.3934, 0.8688, 0.5267, 0.4313, 0.4967, 0.5988, 0.4983],
- [0.6113, 0.4104, 0.8650, 0.4250, 0.3925, 0.5967, 0.5787, 0.5116],
- [0.6197, 0.4091, 0.8800, 0.4783, 0.3538, 0.4767, 0.5950, 0.5550],
- [0.6075, 0.4000, 0.8513, 0.5183, 0.4510, 0.5329, 0.5180, 0.5280]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6971, -0.3688, 1.7350, -0.1732, -0.4029, 0.2110, 0.7087, 0.2211],
- [ 0.4011, -0.5162, 1.1709, -1.1587, -0.3880, -1.3280, 0.4589, 0.3671],
- [ 0.4933, -0.4978, 1.7170, -0.3445, -0.6054, -0.3812, 0.6053, 0.2377],
- [ 0.3137, -0.5505, 1.5822, -0.6377, -0.6560, -1.0371, 0.2575, 0.1115],
- [ 0.5390, -0.4642, 1.6548, 0.1634, -0.3035, -0.1852, 0.5739, 0.1073],
- [ 0.2994, -0.5938, 1.6538, -0.3325, -0.4045, 0.1992, 0.3695, 0.1887],
- [ 0.5082, -0.4512, 1.6077, -0.1040, -0.5408, -0.2585, 0.5207, 0.4175],
- [ 0.4719, -0.4648, 1.5714, 0.0954, -0.1144, -0.0728, 0.1835, 0.2303]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.5764, -0.3969, 1.3284, -1.1312, -0.3460, -1.1389, 0.4797,
- 0.3315],
- [ 0.5778, -0.4061, 1.8423, -0.3537, -0.6520, -0.1766, 0.5663,
- 0.2083],
- [ 0.5532, -0.4008, 1.6575, -0.7155, -0.5942, -0.8309, 0.0890,
- -0.0340],
- [ 0.5864, -0.4690, 1.7268, 0.1467, -0.2940, 0.0082, 0.4797,
- 0.0159],
- [ 0.5376, -0.3903, 1.7095, -0.3229, -0.4730, 0.4701, 0.3871,
- 0.0772],
- [ 0.5763, -0.3963, 1.7788, -0.0765, -0.6520, -0.0842, 0.4624,
- 0.2776],
- [ 0.5202, -0.4382, 1.6460, 0.1082, -0.2029, 0.1754, 0.1067,
- 0.1530]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0139, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0139, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3753113318234682
- step: 10
- running loss: 0.03753113318234682
- Train Steps: 10/90 Loss: 0.0375 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6204, 0.4091, 0.8950, 0.4783, 0.3613, 0.4617, 0.6063, 0.5617],
- [0.6176, 0.4030, 0.8850, 0.4850, 0.3688, 0.4050, 0.5312, 0.5783],
- [0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150],
- [0.6203, 0.4076, 0.8611, 0.2878, 0.4050, 0.2554, 0.5907, 0.5496],
- [0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334],
- [0.6087, 0.3976, 0.8337, 0.3867, 0.3713, 0.3117, 0.5938, 0.5300],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6200, 0.4049, 0.8638, 0.5617, 0.4125, 0.5100, 0.6013, 0.5317]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1967, -0.6591, 1.7363, -0.0536, -0.5547, -0.1339, 0.6452, 0.2842],
- [ 0.5217, -0.4646, 1.6759, -0.0579, -0.5454, -0.3497, 0.2499, 0.1922],
- [ 0.2723, -0.6024, 1.3251, -1.0565, -0.2461, -1.3451, 0.3772, 0.1343],
- [ 0.2139, -0.6353, 1.6743, -0.9571, -0.2936, -0.9478, 0.5853, 0.1640],
- [ 0.7073, -0.3514, 1.2029, -1.1984, -0.5418, -0.8277, 0.5706, 0.1203],
- [ 0.6006, -0.4165, 1.5692, -0.5432, -0.5794, -0.7226, 0.4796, 0.1666],
- [ 0.5078, -0.4796, 1.7146, 0.1441, -0.4945, -0.1384, 0.4881, 0.3228],
- [ 0.2629, -0.6667, 1.6207, 0.2619, -0.2709, 0.1349, 0.5074, 0.1552]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5797, -0.3965, 1.8480, -0.0765, -0.6173, -0.1535, 0.5143,
- 0.3084],
- [ 0.5667, -0.4246, 1.8018, -0.0457, -0.5827, -0.4152, 0.1679,
- 0.3854],
- [ 0.5796, -0.4326, 1.4439, -1.1774, -0.2940, -1.3390, 0.3931,
- 0.0928],
- [ 0.5790, -0.4031, 1.6915, -0.9564, -0.4152, -1.1063, 0.4425,
- 0.2528],
- [ 0.5730, -0.4562, 1.2195, -1.2440, -0.5497, -0.7711, 0.5704,
- 0.1779],
- [ 0.5255, -0.4495, 1.5651, -0.4999, -0.5711, -0.8463, 0.4566,
- 0.1621],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.5776, -0.4159, 1.7037, 0.3084, -0.3806, 0.0697, 0.4912,
- 0.1698]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0162, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0162, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3914999272674322
- step: 11
- running loss: 0.035590902478857475
- Train Steps: 11/90 Loss: 0.0356 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6034, 0.4011, 0.7350, 0.2533, 0.3438, 0.3367, 0.5516, 0.5084],
- [ nan, nan, 0.7850, 0.2700, 0.4288, 0.1717, 0.5199, 0.4999],
- [0.6250, 0.4116, 0.8700, 0.4850, 0.4650, 0.5567, 0.6088, 0.5183],
- [0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194],
- [0.6136, 0.4029, 0.8263, 0.3350, 0.3625, 0.3067, 0.5675, 0.5167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9551, -0.1943, 1.7838, 0.1884, -0.5463, -0.5159, 0.7997, 0.0161],
- [ 0.8863, -0.2218, 1.0800, -1.0480, -0.5751, -0.7298, 0.4776, 0.0688],
- [-2.0307, -2.0780, 1.1745, -1.1032, -0.3354, -1.3458, 0.2500, 0.2283],
- [ 0.9031, -0.1703, 1.7089, -0.1087, -0.1458, 0.2899, 0.7898, 0.2564],
- [ 0.8976, -0.1821, 1.7356, -0.1796, -0.4199, -0.2579, 0.3009, 0.0515],
- [ 0.9155, -0.2161, 1.6589, 0.0400, -0.2482, -0.0904, 0.4978, 0.2829],
- [-1.8523, -1.9757, 1.3322, -0.7963, -0.5985, -0.8162, 0.2899, 0.2414],
- [ 0.8009, -0.2398, 1.5121, -0.6899, -0.5752, -0.8632, 0.4632, 0.1816]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.5009, -0.4333, 1.1090, -1.1158, -0.6982, -0.7309, 0.2617,
- 0.0622],
- [-2.2859, -2.2859, 1.3400, -1.0388, -0.3055, -1.4930, 0.1157,
- 0.0231],
- [ 0.6012, -0.3846, 1.7326, -0.0457, -0.1381, 0.2853, 0.5259,
- 0.1082],
- [ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220],
- [ 0.5525, -0.4463, 1.7326, 0.0313, -0.2536, -0.0688, 0.1968,
- 0.3700],
- [-2.2859, -2.2859, 1.5478, -0.8309, -0.6289, -0.7232, 0.1198,
- 0.1133],
- [ 0.5483, -0.4249, 1.5305, -0.7386, -0.6115, -0.8694, 0.3353,
- 0.1005]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0300, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0300, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.42152343317866325
- step: 12
- running loss: 0.03512695276488861
- Train Steps: 12/90 Loss: 0.0351 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6226, 0.4098, 0.8912, 0.4100, 0.4025, 0.2383, 0.5763, 0.5367],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6277, 0.4029, 0.8250, 0.2433, 0.4325, 0.2100, 0.6366, 0.5207],
- [0.6147, 0.4112, 0.7988, 0.3200, 0.3775, 0.2767, 0.5150, 0.5550],
- [0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [0.6125, 0.3974, 0.7725, 0.2517, 0.3538, 0.3317, 0.5887, 0.5500],
- [0.6286, 0.3977, 0.9038, 0.4733, 0.3900, 0.4150, 0.7074, 0.5320]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.0789, -0.8634, 1.9075, -0.1872, -0.4406, -0.8748, 0.5152, 0.2647],
- [ 0.1872, -0.6352, 1.2463, -0.5061, 0.0230, -1.0992, 0.3518, 0.4947],
- [ 0.6524, -0.4729, 1.6001, -1.0348, -0.2872, -1.0288, 0.8581, -0.0065],
- [-0.0280, -0.7967, 1.5074, -0.6335, -0.5794, -0.7054, 0.1318, 0.1660],
- [-0.4311, -1.0920, 1.1659, -1.0859, -0.4291, -1.2241, 0.2571, -0.0169],
- [ 0.3784, -0.5783, 1.1265, -1.1726, -0.5293, -0.8971, 0.4713, 0.1641],
- [ 0.4242, -0.5747, 1.4232, -0.9487, -0.6321, -0.5319, 0.5436, 0.0818],
- [ 0.7843, -0.3753, 1.9808, -0.0349, -0.5629, -0.0903, 0.9297, -0.0148]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5900, -0.3932, 1.8307, -0.3921, -0.4268, -1.1851, 0.3758,
- 0.1929],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [ 0.6132, -0.4248, 1.5247, -1.1620, -0.2882, -1.3159, 0.6545,
- 0.1193],
- [ 0.5532, -0.3864, 1.4035, -0.8079, -0.5423, -1.0080, 0.0928,
- 0.2776],
- [ 0.5109, -0.4316, 1.1436, -1.3467, -0.3864, -1.4160, 0.1239,
- -0.0580],
- [ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853],
- [ 0.5430, -0.4503, 1.2822, -1.1235, -0.6520, -0.7540, 0.4335,
- 0.2545],
- [ 0.6174, -0.4490, 1.8885, -0.0996, -0.4845, -0.3691, 0.9814,
- 0.1715]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0638, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0638, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4853547476232052
- step: 13
- running loss: 0.0373349805864004
- Train Steps: 13/90 Loss: 0.0373 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6339, 0.4118, 0.7988, 0.5800, 0.3912, 0.4583, 0.7343, 0.5760],
- [0.6261, 0.3987, 0.8688, 0.4917, 0.4300, 0.5333, 0.7010, 0.5309],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [0.6153, 0.4119, 0.8463, 0.3833, 0.3600, 0.3200, 0.5106, 0.5563],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6357, 0.4118, 0.8400, 0.2500, 0.5413, 0.1633, 0.6725, 0.5586]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3884, -0.5688, 1.7313, -0.1300, -0.3147, -0.0134, 0.2209, 0.1419],
- [ 0.3916, -0.5727, 1.6822, -0.3438, -0.6272, -0.4326, 0.3543, 0.1778],
- [ 0.5572, -0.4856, 1.4594, 0.1052, -0.5221, -0.1518, 0.9008, 0.2143],
- [ 0.1300, -0.7581, 1.7953, -0.2225, -0.3607, 0.2561, 0.8635, 0.0762],
- [ 0.4244, -0.4891, 1.6533, -0.0386, -0.3964, -0.9195, 0.3851, 0.3819],
- [ 0.3393, -0.5632, 1.5823, -0.5397, -0.6619, -0.7676, 0.0628, 0.0785],
- [ 0.5327, -0.5028, 1.9065, -0.4265, -0.6527, -0.1407, 0.5415, -0.1797],
- [ 0.2941, -0.6158, 1.6339, -1.2622, 0.2046, -1.5005, 0.8122, 0.0909]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5525, -0.4463, 1.7326, 0.0313, -0.2536, -0.0688, 0.1968,
- 0.3700],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.6421, -0.3840, 1.4035, 0.3931, -0.4788, -0.1689, 1.1057,
- 0.3745],
- [ 0.6059, -0.4442, 1.7268, -0.0149, -0.2998, 0.1775, 0.9521,
- 0.1661],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762],
- [ 0.5561, -0.3834, 1.6229, -0.5153, -0.6231, -0.8079, 0.0727,
- 0.2837],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.6504, -0.3840, 1.5940, -1.1312, 0.2141, -1.5315, 0.8205,
- 0.2944]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0220, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0220, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.507318003103137
- step: 14
- running loss: 0.036237000221652646
- Train Steps: 14/90 Loss: 0.0362 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6267, 0.4065, 0.8313, 0.2467, 0.4788, 0.1733, 0.6312, 0.5133],
- [0.6286, 0.4078, 0.8063, 0.2267, 0.4788, 0.1533, 0.5953, 0.4913],
- [0.6314, 0.4050, 0.8227, 0.5431, 0.4150, 0.5517, 0.7121, 0.5690],
- [0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6204, 0.4091, 0.8950, 0.4783, 0.3613, 0.4617, 0.6063, 0.5617],
- [0.6357, 0.4159, 0.8788, 0.5583, 0.3638, 0.4433, 0.6488, 0.5297],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5010, -0.5033, 1.5258, -1.1736, -0.0400, -1.5058, 0.5740, 0.0958],
- [-0.6502, -1.2076, 1.3271, -1.1346, -0.1043, -1.5780, 0.2231, 0.0820],
- [ 0.1329, -0.7390, 1.6516, 0.0745, -0.3668, 0.2067, 0.9566, 0.2391],
- [ 0.5369, -0.4364, 1.6081, -0.6326, -0.6196, -0.3833, 0.2194, 0.1052],
- [ 0.6469, -0.4278, 1.8764, -0.1909, -0.5821, -0.4823, 0.7569, 0.0944],
- [ 0.5482, -0.4222, 1.8058, -0.0620, -0.5755, -0.1781, 0.4873, 0.2051],
- [ 0.6252, -0.3851, 1.7494, 0.2343, -0.5574, -0.2445, 0.6326, 0.0882],
- [ 0.5591, -0.4175, 1.8093, -0.2406, -0.5084, -0.0182, 0.4032, 0.1271]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6085, -0.4084, 1.5536, -1.1466, -0.0746, -1.4853, 0.6298,
- 0.0851],
- [ 0.6177, -0.4022, 1.4381, -1.2390, -0.0746, -1.5777, 0.4639,
- -0.0168],
- [ 0.6306, -0.4153, 1.5141, 0.2224, -0.3691, 0.2622, 1.0033,
- 0.3425],
- [ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [ 0.5797, -0.3965, 1.8480, -0.0765, -0.6173, -0.1535, 0.5143,
- 0.3084],
- [ 0.6504, -0.3647, 1.7730, 0.2930, -0.6058, -0.2382, 0.7109,
- 0.1608],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0457, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0457, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5530113819986582
- step: 15
- running loss: 0.03686742546657721
- Train Steps: 15/90 Loss: 0.0369 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550],
- [0.6339, 0.4112, 0.8838, 0.5067, 0.4000, 0.5433, 0.7549, 0.5378],
- [0.6133, 0.4066, 0.6787, 0.2617, 0.3800, 0.2433, 0.5147, 0.5358],
- [0.6069, 0.3975, 0.8625, 0.5083, 0.4388, 0.5483, 0.5650, 0.4967],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6364, 0.4165, 0.9088, 0.4367, 0.4075, 0.3150, 0.6448, 0.5297],
- [0.6265, 0.4091, 0.8950, 0.3533, 0.3600, 0.3967, 0.6295, 0.4901]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.0703e-01, -4.5975e-01, 1.6656e+00, 3.0593e-01, -4.7732e-01,
- -6.5627e-01, 3.7422e-01, 3.8503e-01],
- [ 2.5524e-01, -6.5057e-01, 1.5289e+00, -9.4455e-01, -6.3493e-01,
- -4.8177e-01, 5.8337e-01, 1.9329e-01],
- [ 2.5623e-01, -6.6214e-01, 1.9205e+00, -1.1357e-01, -4.2664e-01,
- 1.1833e-01, 1.0859e+00, 1.5132e-01],
- [ 5.9606e-01, -4.3202e-01, 9.1692e-01, -1.0829e+00, -4.4209e-01,
- -1.2715e+00, 4.4589e-02, 1.2495e-01],
- [ 2.9305e-01, -6.0549e-01, 1.8639e+00, -2.6410e-02, -2.2537e-01,
- 6.4895e-02, 2.5880e-01, -2.0055e-02],
- [ 2.7239e-01, -6.2210e-01, 1.7858e+00, 4.2429e-02, -1.7373e-01,
- -4.4527e-02, 4.1536e-01, 8.2200e-02],
- [ 4.3774e-01, -5.2086e-01, 1.9156e+00, -2.9562e-01, -3.8331e-01,
- -9.2735e-01, 6.8998e-01, 1.6443e-01],
- [ 6.5677e-01, -3.7615e-01, 1.9344e+00, -6.7801e-01, -5.9268e-01,
- -6.3210e-01, 6.6573e-01, -4.8861e-04]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776],
- [ 0.6421, -0.3864, 1.7961, 0.0543, -0.4383, 0.2237, 1.2007,
- 0.1982],
- [ 0.5470, -0.4081, 0.8492, -1.0773, -0.5307, -1.1620, 0.0912,
- 0.1890],
- [ 0.5174, -0.4497, 1.6979, 0.0620, -0.2594, 0.2468, 0.3238,
- 0.0082],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.6537, -0.3623, 1.9115, -0.2690, -0.4037, -0.8309, 0.6926,
- 0.1608],
- [ 0.6077, -0.3965, 1.8480, -0.6539, -0.6231, -0.4537, 0.6216,
- -0.0220]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0160, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0160, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5689659360796213
- step: 16
- running loss: 0.03556037100497633
- Train Steps: 16/90 Loss: 0.0356 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6230, 0.4113, 0.7213, 0.1983, 0.4325, 0.2367, 0.6262, 0.5400],
- [0.6286, 0.3977, 0.9038, 0.4733, 0.3900, 0.4150, 0.7074, 0.5320],
- [0.6202, 0.3983, 0.8555, 0.5429, 0.3842, 0.4370, 0.5866, 0.5398],
- [0.6102, 0.3999, 0.8750, 0.5133, 0.3825, 0.4750, 0.5637, 0.5083],
- [0.6250, 0.4110, 0.7238, 0.2067, 0.4263, 0.1883, 0.5625, 0.5633],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6248, 0.4185, 0.8500, 0.5767, 0.4463, 0.4550, 0.5613, 0.5917],
- [0.6115, 0.4081, 0.6725, 0.2433, 0.4088, 0.1933, 0.5167, 0.5544]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.0935, -0.8722, 1.3125, -1.3468, -0.2899, -1.2047, 0.7003, 0.2224],
- [ 0.2963, -0.6498, 2.0224, -0.2893, -0.5738, -0.3291, 0.8886, 0.0073],
- [ 0.6682, -0.4188, 1.9193, 0.1636, -0.6020, -0.1303, 0.5732, 0.0660],
- [ 0.6721, -0.3713, 1.9908, -0.0065, -0.5589, 0.0488, 0.3812, -0.0499],
- [ 0.1340, -0.7117, 1.1896, -1.2837, -0.2576, -1.3935, 0.3966, 0.1664],
- [ 0.5357, -0.4752, 1.9282, 0.1077, -0.5723, -0.4885, 0.5420, 0.1317],
- [ 0.7458, -0.3102, 1.8443, 0.3719, -0.2171, -0.0943, 0.5615, 0.3431],
- [ 0.4249, -0.5251, 1.0434, -1.1636, -0.3758, -1.2958, 0.1229, 0.1956]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5917, -0.3861, 1.0455, -1.3698, -0.2882, -1.1928, 0.6067,
- 0.2083],
- [ 0.6174, -0.4490, 1.8885, -0.0996, -0.4845, -0.3691, 0.9814,
- 0.1715],
- [ 0.5786, -0.4463, 1.6655, 0.2216, -0.5115, -0.2675, 0.4236,
- 0.2075],
- [ 0.5326, -0.4390, 1.7557, 0.0851, -0.5192, -0.0919, 0.3180,
- 0.0620],
- [ 0.6010, -0.3875, 1.0570, -1.3313, -0.3171, -1.4160, 0.3122,
- 0.3161],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698],
- [ 0.5999, -0.3530, 1.6402, 0.3777, -0.2247, -0.1843, 0.3065,
- 0.4470],
- [ 0.5385, -0.4010, 0.8203, -1.1620, -0.3979, -1.3929, 0.1005,
- 0.2747]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0314, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0314, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6003932412713766
- step: 17
- running loss: 0.035317249486551565
- Train Steps: 17/90 Loss: 0.0353 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6069, 0.3975, 0.8625, 0.5083, 0.4388, 0.5483, 0.5650, 0.4967],
- [0.6179, 0.4008, 0.8600, 0.4015, 0.3932, 0.2515, 0.5711, 0.5438],
- [0.6197, 0.3986, 0.8800, 0.4617, 0.4188, 0.4783, 0.5687, 0.5550],
- [0.6304, 0.4024, 0.8925, 0.4800, 0.3937, 0.4817, 0.7485, 0.5297],
- [0.6187, 0.4104, 0.7126, 0.2069, 0.3950, 0.2517, 0.5913, 0.5683],
- [0.6090, 0.4010, 0.7838, 0.3483, 0.3538, 0.3783, 0.5462, 0.5077],
- [0.6175, 0.4013, 0.7734, 0.2783, 0.4475, 0.1786, 0.5790, 0.5351],
- [0.6147, 0.4081, 0.8538, 0.3400, 0.3663, 0.3150, 0.5142, 0.4875]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3610, -0.5678, 1.9080, 0.1425, -0.2585, 0.2075, 0.4586, 0.0328],
- [ 0.5887, -0.4187, 1.6380, -0.3694, -0.3123, -1.1082, 0.3524, 0.3063],
- [ 0.3034, -0.5894, 1.8729, -0.0846, -0.3672, 0.0066, 0.5765, 0.1857],
- [ 0.2782, -0.6576, 1.9356, -0.0492, -0.4911, -0.0651, 1.0794, 0.1156],
- [ 0.3067, -0.6011, 1.0080, -1.2048, -0.4256, -1.1716, 0.5215, 0.3087],
- [ 0.1235, -0.7251, 1.5411, -0.6893, -0.6735, -0.5725, 0.3647, 0.0733],
- [ 0.6070, -0.4165, 1.3449, -0.9162, -0.1758, -1.4343, 0.4269, 0.1630],
- [ 0.7717, -0.3102, 1.7616, -0.6037, -0.6002, -0.8472, 0.3638, 0.0585]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5174, -0.4497, 1.6979, 0.0620, -0.2594, 0.2468, 0.3238,
- 0.0082],
- [ 0.5680, -0.4345, 1.6864, -0.4315, -0.4698, -1.1241, 0.3518,
- 0.2261],
- [ 0.5763, -0.4450, 1.7788, -0.1535, -0.3517, -0.0765, 0.3411,
- 0.2776],
- [ 0.6257, -0.4273, 1.8365, -0.0688, -0.4672, -0.0611, 1.1715,
- 0.1608],
- [ 0.5718, -0.3905, 1.0053, -1.3305, -0.4614, -1.1235, 0.4450,
- 0.3392],
- [ 0.5270, -0.4336, 1.3342, -0.6770, -0.6520, -0.5384, 0.2370,
- 0.0592],
- [ 0.5664, -0.4321, 1.2862, -1.0003, -0.2189, -1.4608, 0.3883,
- 0.1855],
- [ 0.5532, -0.4008, 1.6575, -0.7155, -0.5942, -0.8309, 0.0890,
- -0.0340]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0180, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0180, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.618410861119628
- step: 18
- running loss: 0.03435615895109044
- Train Steps: 18/90 Loss: 0.0344 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4024, 0.8600, 0.2283, 0.5350, 0.1800, 0.7074, 0.5413],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6265, 0.4251, 0.7113, 0.3550, 0.4375, 0.2117, 0.5587, 0.6118],
- [0.6350, 0.4144, 0.9250, 0.4100, 0.4125, 0.3750, 0.7129, 0.5945],
- [0.6325, 0.4066, 0.8438, 0.5350, 0.3925, 0.5267, 0.7113, 0.5795],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6092, 0.4001, 0.8638, 0.4867, 0.4288, 0.5367, 0.5484, 0.5064]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1828, -0.6863, 1.7455, -1.3498, 0.0840, -1.6401, 0.8802, 0.0811],
- [ 0.5804, -0.3763, 1.8545, -0.1124, -0.2649, 0.2308, 0.4292, 0.1976],
- [ 0.4637, -0.4524, 1.1475, -0.7110, -0.3402, -1.3154, 0.0892, 0.4226],
- [ 0.5057, -0.4969, 1.9738, -0.3320, -0.3745, -0.6609, 0.8750, 0.2639],
- [ 0.7743, -0.3068, 1.7448, 0.1914, -0.5778, 0.1589, 0.8097, 0.1874],
- [ 0.4554, -0.5175, 1.7897, 0.2008, -0.5382, -0.1848, 0.2942, 0.0439],
- [ 0.6566, -0.3793, 1.5341, 0.1815, -0.5751, -0.1516, 0.8652, 0.2552],
- [ 0.3685, -0.5335, 1.7676, -0.0991, -0.3964, 0.0852, 0.0582, 0.0316]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4273, 1.6864, -1.2313, 0.1852, -1.4545, 0.9814,
- 0.2142],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.6077, -0.3226, 0.9993, -0.6462, -0.2651, -1.3082, 0.2946,
- 0.5401],
- [ 0.6471, -0.3719, 1.9866, -0.3921, -0.3806, -0.5538, 1.0070,
- 0.4600],
- [ 0.6355, -0.4080, 1.6113, 0.1852, -0.4730, 0.1467, 0.9996,
- 0.3905],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478],
- [ 0.5278, -0.4377, 1.7037, -0.0380, -0.3055, 0.1929, 0.2473,
- 0.0532]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0174, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0174, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6358400098979473
- step: 19
- running loss: 0.033465263678839334
- Train Steps: 19/90 Loss: 0.0335 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6100, 0.4071, 0.7601, 0.3444, 0.3400, 0.4117, 0.5625, 0.5617],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6265, 0.4091, 0.8950, 0.3533, 0.3600, 0.3967, 0.6295, 0.4901],
- [0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [0.6122, 0.4006, 0.8850, 0.4217, 0.4088, 0.5517, 0.6063, 0.5517],
- [0.6339, 0.4149, 0.8800, 0.5000, 0.3900, 0.5283, 0.7541, 0.5424],
- [ nan, nan, 0.7192, 0.2346, 0.4037, 0.2050, 0.5138, 0.5650],
- [0.6339, 0.4112, 0.8838, 0.5067, 0.4000, 0.5433, 0.7549, 0.5378]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7286, -0.3021, 1.2757, -0.6294, -0.6770, -0.5504, 0.3182, 0.2894],
- [ 0.6831, -0.3413, 1.7177, 0.1549, -0.2115, -0.1291, 0.4543, 0.2399],
- [ 1.0485, -0.1073, 1.8392, -0.5684, -0.5598, -0.7251, 0.6200, 0.0672],
- [ 0.6044, -0.4001, 1.7955, 0.1472, -0.2306, -0.2585, 0.1774, 0.2846],
- [ 0.6464, -0.3412, 1.8233, -0.2097, -0.3579, 0.0401, 0.5192, 0.2039],
- [ 0.8227, -0.2677, 1.8455, 0.1839, -0.4548, 0.0273, 0.8990, 0.1851],
- [-1.6205, -1.8727, 1.1033, -1.1597, -0.3460, -1.5958, 0.1061, 0.0953],
- [ 0.7472, -0.3128, 1.8387, 0.1208, -0.4073, 0.0710, 1.0212, 0.2521]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3178e-01, -4.0564e-01, 1.2249e+00, -6.9494e-01, -7.1547e-01,
- -3.8445e-01, 3.1224e-01, 3.0839e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 6.0774e-01, -3.9646e-01, 1.8480e+00, -6.5389e-01, -6.2309e-01,
- -4.5373e-01, 6.2155e-01, -2.1963e-02],
- [ 5.5254e-01, -4.4627e-01, 1.7326e+00, 3.1255e-02, -2.5358e-01,
- -6.8822e-02, 1.9677e-01, 3.6998e-01],
- [ 5.4169e-01, -4.3549e-01, 1.8018e+00, -3.3826e-01, -3.9792e-01,
- 2.6220e-01, 5.1432e-01, 2.6220e-01],
- [ 6.4212e-01, -3.6953e-01, 1.7788e+00, 2.3557e-02, -4.8453e-01,
- 1.5443e-01, 1.1971e+00, 2.1955e-01],
- [-2.2859e+00, -2.2859e+00, 1.0361e+00, -1.2021e+00, -4.2102e-01,
- -1.3390e+00, 8.7067e-02, 3.2379e-01],
- [ 6.4212e-01, -3.8638e-01, 1.7961e+00, 5.4350e-02, -4.3834e-01,
- 2.2371e-01, 1.2007e+00, 1.9818e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0273, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0273, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6631092224270105
- step: 20
- running loss: 0.033155461121350524
- Train Steps: 20/90 Loss: 0.0332 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4101, 0.8838, 0.5283, 0.3763, 0.5267, 0.5913, 0.5567],
- [0.6339, 0.4102, 0.9088, 0.4767, 0.3925, 0.5283, 0.7509, 0.5390],
- [0.6265, 0.4088, 0.8025, 0.1850, 0.4163, 0.2500, 0.6290, 0.4947],
- [0.6371, 0.4092, 0.8337, 0.5850, 0.3950, 0.5117, 0.6559, 0.5262],
- [0.6129, 0.3930, 0.8769, 0.5105, 0.4146, 0.5642, 0.6000, 0.5082],
- [0.6129, 0.4069, 0.8750, 0.5067, 0.3875, 0.4233, 0.5235, 0.5881],
- [0.6132, 0.3930, 0.8672, 0.4754, 0.3712, 0.5222, 0.5974, 0.5098],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5992, -0.3980, 1.7403, 0.2467, -0.4589, -0.0764, 0.5850, 0.3452],
- [ 0.3023, -0.6008, 1.8628, -0.1265, -0.4278, -0.0498, 1.0072, 0.2887],
- [ 0.8948, -0.2468, 1.3591, -1.4355, -0.3035, -1.3032, 0.6622, 0.1346],
- [ 0.8589, -0.2448, 1.5960, 0.3670, -0.4593, -0.1402, 0.7276, 0.1638],
- [ 0.5828, -0.4151, 1.7459, 0.1039, -0.2371, 0.0888, 0.3880, 0.1641],
- [ 0.5667, -0.4005, 1.7422, 0.1350, -0.4795, -0.4804, 0.1186, 0.3531],
- [ 0.5436, -0.4430, 1.7085, -0.0783, -0.4442, -0.0420, 0.4933, 0.1895],
- [ 0.4270, -0.5031, 1.7202, -0.4630, -0.4792, -0.2533, 0.3859, 0.2487]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7696e-01, -3.9176e-01, 1.7961e+00, 1.5443e-01, -5.4804e-01,
- 1.4673e-01, 4.4503e-01, 2.8530e-01],
- [ 6.4212e-01, -3.9120e-01, 1.9115e+00, -8.4219e-02, -4.7298e-01,
- 1.5443e-01, 1.1824e+00, 2.0352e-01],
- [ 6.0785e-01, -3.9761e-01, 1.4208e+00, -1.4314e+00, -3.6328e-01,
- -1.1312e+00, 6.1950e-01, -9.2270e-04],
- [ 6.5695e-01, -3.9601e-01, 1.5651e+00, 4.1617e-01, -4.6143e-01,
- 7.7444e-02, 7.4375e-01, 1.4474e-01],
- [ 5.4496e-01, -4.7064e-01, 1.7643e+00, 7.2204e-02, -3.7076e-01,
- 3.2001e-01, 4.8543e-01, 6.1219e-02],
- [ 5.4515e-01, -4.0670e-01, 1.7557e+00, 5.4350e-02, -4.9607e-01,
- -3.3056e-01, 1.3228e-01, 4.3063e-01],
- [ 5.4660e-01, -4.7064e-01, 1.7198e+00, -9.0292e-02, -5.7125e-01,
- 1.2613e-01, 4.7328e-01, 6.8827e-02],
- [ 5.5456e-01, -4.6205e-01, 1.7788e+00, -4.2294e-01, -5.1917e-01,
- -2.2633e-02, 4.2771e-01, 2.4681e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0139, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0139, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6769989067688584
- step: 21
- running loss: 0.03223804317946945
- Train Steps: 21/90 Loss: 0.0322 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6211, 0.3993, 0.7650, 0.1933, 0.4575, 0.1550, 0.5965, 0.4895],
- [0.6152, 0.4131, 0.6863, 0.2567, 0.3625, 0.3300, 0.5765, 0.5305],
- [0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6271, 0.4020, 0.8375, 0.6083, 0.3925, 0.4867, 0.6037, 0.4626],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6113, 0.4104, 0.8650, 0.4250, 0.3925, 0.5967, 0.5787, 0.5116],
- [0.6165, 0.4106, 0.7575, 0.1733, 0.3838, 0.2650, 0.5680, 0.5116],
- [0.6168, 0.4081, 0.8438, 0.3367, 0.3500, 0.4667, 0.6212, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6423, -0.4058, 1.3525, -1.2299, -0.0814, -1.5329, 0.4613, 0.1552],
- [ 0.3405, -0.5563, 1.0578, -0.8936, -0.5718, -0.7640, 0.4300, 0.3576],
- [ 0.5428, -0.4354, 1.0600, -0.8942, -0.3129, -1.1778, 0.3834, 0.4182],
- [ 0.7124, -0.3692, 1.7740, 0.6887, -0.4695, -0.0495, 0.6066, 0.1195],
- [ 0.4637, -0.4929, 1.9336, 0.4006, -0.5610, -0.1530, 0.5893, 0.3991],
- [ 0.6304, -0.3523, 1.9097, -0.1257, -0.4233, 0.4380, 0.6059, 0.1707],
- [ 0.7436, -0.3395, 1.3049, -1.1675, -0.4783, -1.1101, 0.5469, 0.1452],
- [ 0.1071, -0.7279, 1.7459, -0.5140, -0.5872, -0.1503, 0.7996, 0.2985]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5828, -0.4417, 1.2476, -1.3929, -0.1727, -1.5700, 0.4694,
- -0.0248],
- [ 0.5557, -0.3779, 0.8838, -1.1004, -0.6115, -0.7617, 0.3769,
- 0.1644],
- [ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.6105, -0.4293, 1.5824, 0.5239, -0.4730, -0.0380, 0.5025,
- -0.1492],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.5376, -0.3903, 1.7095, -0.3229, -0.4730, 0.4701, 0.3871,
- 0.0772],
- [ 0.5618, -0.3895, 1.2129, -1.4853, -0.5134, -1.0619, 0.3378,
- 0.0772],
- [ 0.5631, -0.4008, 1.6113, -0.7309, -0.6693, -0.1304, 0.5836,
- 0.2083]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0238, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0238, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7008060766384006
- step: 22
- running loss: 0.031854821665381845
- Train Steps: 22/90 Loss: 0.0319 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6097, 0.4024, 0.8488, 0.3717, 0.3875, 0.5517, 0.5836, 0.5591],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [ nan, nan, 0.7225, 0.2167, 0.3987, 0.2283, 0.5427, 0.5181],
- [0.6266, 0.4070, 0.8712, 0.5600, 0.3713, 0.4783, 0.5775, 0.6100],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5192, -0.4303, 1.5533, -0.5186, -0.4992, 0.2291, 0.8167, 0.2161],
- [ 0.8398, -0.2164, 1.5734, -0.2260, -0.6431, -0.7362, 0.4513, 0.3491],
- [ 0.7983, -0.2492, 1.6775, -0.1223, -0.1407, 0.0391, 0.6358, 0.2704],
- [-0.8730, -1.3414, 0.9229, -1.2942, -0.4417, -1.3333, 0.1050, 0.1558],
- [ 0.7856, -0.2575, 1.5907, 0.4904, -0.6020, -0.0994, 0.5406, 0.3689],
- [ 0.7232, -0.2932, 1.6110, 0.1631, -0.2535, 0.0306, 0.2218, 0.2326],
- [ 0.7808, -0.2383, 1.7002, 0.0022, -0.2485, 0.3157, 0.6546, 0.3287],
- [ 0.6408, -0.3928, 1.9235, -0.6045, -0.2477, -1.1302, 0.9770, 0.1677]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5301, -0.4273, 1.6344, -0.5692, -0.4961, 0.2622, 0.4098,
- 0.2966],
- [ 0.6016, -0.3633, 1.7152, -0.2228, -0.6115, -0.6385, 0.5028,
- 0.2699],
- [ 0.5598, -0.4273, 1.7152, -0.1227, -0.0065, 0.1917, 0.5143,
- 0.2853],
- [-2.2859, -2.2859, 1.0513, -1.2851, -0.4441, -1.2313, 0.2206,
- 0.1073],
- [ 0.6081, -0.4059, 1.7383, 0.3007, -0.5711, -0.0765, 0.3815,
- 0.5316],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0592, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0592, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7600116161629558
- step: 23
- running loss: 0.03304398331143286
- Train Steps: 23/90 Loss: 0.0330 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6147, 0.4107, 0.8137, 0.3333, 0.3750, 0.2683, 0.5006, 0.5412],
- [0.6107, 0.4050, 0.8700, 0.4850, 0.4470, 0.4848, 0.5043, 0.5431],
- [0.6276, 0.4095, 0.8237, 0.2250, 0.4662, 0.1783, 0.6171, 0.4869],
- [0.6209, 0.3920, 0.8650, 0.5367, 0.4400, 0.5067, 0.6025, 0.4950],
- [0.6250, 0.4110, 0.7238, 0.2067, 0.4263, 0.1883, 0.5625, 0.5633],
- [0.6193, 0.3930, 0.8949, 0.4437, 0.3852, 0.5435, 0.6263, 0.5263],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6224, 0.3964, 0.8225, 0.5717, 0.4150, 0.4617, 0.5775, 0.5267]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6867, -0.2904, 1.3013, -0.6740, -0.6040, -0.8209, 0.2258, 0.2238],
- [ 0.7138, -0.2789, 1.6832, 0.0185, -0.3824, 0.1739, 0.3402, 0.2216],
- [ 0.8082, -0.2157, 1.4416, -1.2298, -0.1667, -1.0268, 0.7925, 0.1555],
- [ 0.6891, -0.3364, 1.7066, 0.2175, -0.3908, 0.2481, 0.6137, 0.1848],
- [ 0.6694, -0.3064, 1.0297, -1.0955, -0.3065, -1.2666, 0.5156, 0.3473],
- [ 0.6522, -0.3592, 1.7807, -0.1508, -0.5552, 0.2680, 0.8259, 0.2385],
- [-1.8048, -1.9724, 0.9865, -1.1424, -0.2959, -1.4394, 0.2511, 0.3223],
- [ 0.8061, -0.2563, 1.6035, 0.4277, -0.5191, -0.0406, 0.5202, 0.2413]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.5319e-01, -3.8879e-01, 1.4727e+00, -7.4627e-01, -5.5381e-01,
- -1.0465e+00, 2.6467e-02, 2.1383e-01],
- [ 5.3508e-01, -4.1527e-01, 1.7326e+00, -4.5727e-02, -2.2139e-01,
- -4.6642e-02, 4.3431e-02, 2.2284e-01],
- [ 6.1270e-01, -3.9438e-01, 1.5189e+00, -1.2467e+00, -1.3233e-01,
- -1.4622e+00, 5.6463e-01, -3.6943e-02],
- [ 5.8199e-01, -4.7544e-01, 1.7095e+00, 1.9292e-01, -2.5358e-01,
- 5.4350e-02, 4.9700e-01, 4.6189e-04],
- [ 6.0098e-01, -3.8745e-01, 1.0570e+00, -1.3313e+00, -3.1709e-01,
- -1.4160e+00, 3.1224e-01, 3.1609e-01],
- [ 5.7460e-01, -4.7064e-01, 1.8476e+00, -2.3654e-01, -5.0683e-01,
- 2.2450e-01, 6.0688e-01, 1.4491e-01],
- [-2.2859e+00, -2.2859e+00, 6.7598e-01, -1.4083e+00, -3.2864e-01,
- -1.4160e+00, 2.4873e-01, 3.4688e-01],
- [ 5.8915e-01, -4.5504e-01, 1.5132e+00, 3.5458e-01, -3.6905e-01,
- -1.5350e-01, 3.8152e-01, 1.4673e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0274, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0274, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7874013772234321
- step: 24
- running loss: 0.032808390717643
- Train Steps: 24/90 Loss: 0.0328 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760],
- [0.6277, 0.4057, 0.8300, 0.2650, 0.4363, 0.1850, 0.6140, 0.4823],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320],
- [0.6276, 0.4120, 0.8738, 0.3133, 0.4225, 0.2217, 0.6203, 0.4892],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [0.6186, 0.3967, 0.7337, 0.1992, 0.4120, 0.2508, 0.6105, 0.5395],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5090, -0.4777, 1.5581, 0.3141, -0.4932, 0.0368, 0.8771, 0.3633],
- [ 0.2972, -0.5898, 1.5307, -1.0487, -0.2588, -1.2857, 0.4801, 0.0661],
- [ 0.3602, -0.5758, 1.5941, 0.1848, -0.5014, 0.1306, 0.7942, 0.2363],
- [ 0.6912, -0.3386, 1.7118, -0.7506, -0.3744, -1.1410, 0.4392, 0.1324],
- [ 0.4615, -0.4464, 1.5727, 0.1339, -0.2828, 0.2632, 0.0648, 0.2474],
- [ 0.6099, -0.3904, 0.9968, -1.2696, -0.4457, -0.9995, 0.4334, 0.2862],
- [ 0.7335, -0.3144, 1.1439, -1.0194, -0.5125, -0.8689, 0.5599, 0.4064],
- [ 0.5387, -0.4068, 1.7121, -0.0485, -0.3344, 0.5094, 0.5716, 0.2331]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745],
- [ 0.6132, -0.4122, 1.5478, -1.0619, -0.2709, -1.4314, 0.5500,
- -0.0583],
- [ 0.6273, -0.4393, 1.6402, 0.1313, -0.5076, 0.0467, 1.1532,
- 0.1715],
- [ 0.6128, -0.3828, 1.7499, -0.8386, -0.3344, -1.2620, 0.5792,
- -0.0263],
- [ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386],
- [ 0.5713, -0.4538, 1.1028, -1.3659, -0.3831, -1.1273, 0.5340,
- 0.2058],
- [ 0.5915, -0.3682, 1.2187, -1.2313, -0.4326, -0.9541, 0.5778,
- 0.3777],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0127, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0127, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8001020578667521
- step: 25
- running loss: 0.032004082314670085
- Train Steps: 25/90 Loss: 0.0320 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019],
- [0.6196, 0.4094, 0.7562, 0.2817, 0.3937, 0.3183, 0.6013, 0.6183],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064],
- [0.6125, 0.3983, 0.8750, 0.4867, 0.4275, 0.4783, 0.5413, 0.5717],
- [ nan, nan, 0.6935, 0.1930, 0.4150, 0.2250, 0.5450, 0.5650],
- [0.6128, 0.4084, 0.8738, 0.4683, 0.3613, 0.3700, 0.4960, 0.5397]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7484, -0.2701, 0.9943, -1.1130, -0.4974, -0.8206, 0.6677, 0.3461],
- [ 0.5576, -0.4452, 1.8740, -0.1048, -0.5938, -0.3413, 0.9014, 0.0819],
- [ 0.8929, -0.1953, 1.1599, -0.9577, -0.4125, -0.6735, 0.6857, 0.4512],
- [ 0.9197, -0.1350, 1.6207, -0.0053, -0.5782, -0.4007, 0.4676, 0.3432],
- [ 0.7223, -0.2809, 1.6813, 0.0196, -0.0342, 0.1632, 0.1919, 0.1328],
- [ 0.5366, -0.4327, 1.7118, -0.0477, -0.3475, 0.1408, 0.4257, 0.2003],
- [-1.7881, -1.9381, 1.0119, -1.3121, -0.1923, -1.0895, 0.3048, 0.1346],
- [ 0.8429, -0.1873, 1.6756, -0.1547, -0.6246, -0.4101, 0.2499, 0.1570]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700],
- [ 0.6372, -0.4129, 1.8942, -0.0765, -0.6173, -0.4768, 0.6999,
- 0.0325],
- [ 0.5759, -0.3948, 1.2072, -0.9849, -0.4672, -0.8156, 0.4912,
- 0.5701],
- [ 0.5902, -0.3493, 1.7961, -0.0072, -0.5942, -0.5615, 0.3180,
- 0.3161],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532],
- [ 0.5432, -0.4462, 1.7557, -0.0380, -0.3113, -0.0765, 0.2141,
- 0.3546],
- [-2.2859, -2.2859, 0.9175, -1.3947, -0.3691, -1.2467, 0.2314,
- 0.3238],
- [ 0.5443, -0.3994, 1.7499, -0.1227, -0.6173, -0.5769, 0.0049,
- 0.2071]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0233, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0233, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.823372888378799
- step: 26
- running loss: 0.03166818801456919
- Train Steps: 26/90 Loss: 0.0317 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6263, 0.4057, 0.8800, 0.3833, 0.3650, 0.3717, 0.6375, 0.4804],
- [0.6259, 0.4133, 0.8200, 0.2317, 0.5025, 0.1533, 0.6250, 0.4947],
- [0.6127, 0.4118, 0.8650, 0.5083, 0.4088, 0.5367, 0.5300, 0.5456],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334],
- [0.6118, 0.4052, 0.8463, 0.3917, 0.3538, 0.3450, 0.5053, 0.5593]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 7.5652e-01, -2.7539e-01, 1.5621e+00, 1.8410e-01, -5.1502e-01,
- -3.3857e-01, 3.6783e-01, 2.5592e-01],
- [ 7.1403e-01, -3.2540e-01, 1.6967e+00, -5.6145e-01, -5.9954e-01,
- -3.0913e-01, 7.2090e-01, 7.3376e-02],
- [ 8.6360e-01, -2.0367e-01, 1.3451e+00, -1.1514e+00, -1.1073e-02,
- -1.2515e+00, 5.7336e-01, 1.6534e-01],
- [ 7.4604e-01, -2.7205e-01, 1.5418e+00, 1.2414e-01, -3.3964e-01,
- 4.4680e-01, 3.2297e-01, 3.0321e-01],
- [ 5.8184e-01, -4.4765e-01, 1.8961e+00, -3.1615e-01, -2.8393e-01,
- -6.9362e-01, 1.0925e+00, 1.8052e-01],
- [-1.9204e+00, -2.0319e+00, 8.3474e-01, -1.2677e+00, -4.0795e-01,
- -1.1218e+00, 1.0736e-01, 1.9626e-01],
- [ 5.2281e-01, -4.1657e-01, 1.1166e+00, -1.1776e+00, -5.2418e-01,
- -6.1399e-01, 5.6810e-01, 2.8002e-01],
- [ 7.1040e-01, -2.8060e-01, 1.4571e+00, -4.6101e-01, -5.9290e-01,
- -4.6358e-01, -8.6606e-04, 2.7863e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.9013e-01, -4.1570e-01, 1.7557e+00, 1.9292e-01, -5.4226e-01,
- -5.9230e-01, 3.5843e-01, 1.6982e-01],
- [ 6.0710e-01, -4.1186e-01, 1.7788e+00, -5.1532e-01, -6.0000e-01,
- -5.6921e-01, 6.5857e-01, -6.7050e-02],
- [ 6.0514e-01, -3.7714e-01, 1.5016e+00, -1.2159e+00, 3.5104e-02,
- -1.5777e+00, 6.0099e-01, -9.2270e-04],
- [ 5.4428e-01, -3.8399e-01, 1.7095e+00, 6.2048e-02, -3.9792e-01,
- 1.9292e-01, 1.6218e-01, 2.3412e-01],
- [ 6.2236e-01, -4.3453e-01, 1.9404e+00, -2.9207e-01, -3.1709e-01,
- -8.7714e-01, 1.0655e+00, 2.1421e-01],
- [-2.2859e+00, -2.2859e+00, 8.5162e-01, -1.3112e+00, -4.3256e-01,
- -1.2851e+00, 7.5520e-02, 2.9299e-01],
- [ 5.7296e-01, -4.5619e-01, 1.2195e+00, -1.2440e+00, -5.4966e-01,
- -7.7109e-01, 5.7045e-01, 1.7788e-01],
- [ 5.3990e-01, -4.1424e-01, 1.6229e+00, -4.7683e-01, -6.5196e-01,
- -6.9238e-01, 4.8058e-02, 2.9724e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0186, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0186, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8419636627659202
- step: 27
- running loss: 0.031183839361700747
- Train Steps: 27/90 Loss: 0.0312 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6097, 0.3988, 0.8650, 0.5250, 0.4213, 0.5200, 0.5675, 0.5050],
- [0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167],
- [0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6284, 0.4127, 0.8538, 0.5867, 0.4363, 0.5083, 0.6038, 0.5433],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [0.6137, 0.4035, 0.8850, 0.4417, 0.3900, 0.4283, 0.5449, 0.5617],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6225, -0.3881, 1.5662, -0.0781, -0.2772, 0.1189, 0.2225, 0.0775],
- [ 0.9844, -0.1379, 1.7062, -0.6037, -0.4731, 0.3303, 0.7703, 0.1002],
- [ 0.7208, -0.3073, 1.5178, 0.1953, -0.3616, -0.4154, 0.4075, 0.4878],
- [ 0.6547, -0.3556, 1.4913, 0.1435, -0.2581, -0.0428, 0.4009, 0.1827],
- [ 0.3554, -0.5601, 1.6440, -0.4274, -0.5485, -0.4182, 0.5138, 0.2277],
- [ 0.4400, -0.5040, 1.6787, -0.3374, -0.5218, -0.4229, 0.8214, 0.1786],
- [ 0.3061, -0.5993, 1.7038, -0.4087, -0.4780, -0.3626, 0.3291, 0.2741],
- [ 0.5325, -0.4226, 1.4472, -0.5124, -0.4933, -0.2613, 0.2442, 0.2672]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5303, -0.4440, 1.7095, 0.1390, -0.3402, 0.1159, 0.3353,
- 0.0467],
- [ 0.5886, -0.4253, 1.8654, -0.3460, -0.5480, 0.3623, 0.6587,
- 0.1005],
- [ 0.6038, -0.3464, 1.7037, 0.3931, -0.4441, -0.2613, 0.3007,
- 0.4624],
- [ 0.6166, -0.3795, 1.6575, 0.4239, -0.2709, 0.0620, 0.5028,
- 0.2237],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499],
- [ 0.5488, -0.4221, 1.8018, -0.2459, -0.4845, -0.3075, 0.2309,
- 0.3087],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0205, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0205, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8624861231073737
- step: 28
- running loss: 0.030803075825263346
- Train Steps: 28/90 Loss: 0.0308 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6173, 0.4114, 0.7325, 0.2500, 0.4213, 0.1917, 0.5338, 0.5700],
- [0.6280, 0.4055, 0.8600, 0.5317, 0.3800, 0.4700, 0.6275, 0.5133],
- [0.6265, 0.4088, 0.8025, 0.1850, 0.4163, 0.2500, 0.6290, 0.4947],
- [0.6140, 0.4034, 0.8850, 0.4317, 0.4288, 0.5067, 0.5825, 0.5533],
- [0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6183, 0.4076, 0.8838, 0.4517, 0.3813, 0.4483, 0.5775, 0.5633],
- [0.6180, 0.4064, 0.7200, 0.2467, 0.4275, 0.2367, 0.5963, 0.6217]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1548, -0.6666, 1.0475, -1.1813, -0.3556, -1.3011, 0.2236, 0.2432],
- [ 0.7615, -0.3281, 1.7192, 0.2027, -0.5713, -0.0331, 0.7117, 0.0804],
- [ 0.5163, -0.4591, 1.4358, -1.4274, -0.3775, -1.1807, 0.6925, 0.0060],
- [ 0.6276, -0.3860, 1.7572, -0.2639, -0.3414, 0.2167, 0.4277, 0.1755],
- [ 0.3829, -0.5351, 1.7428, -0.1622, -0.5477, -0.1566, 0.4544, 0.1677],
- [ 0.5205, -0.4734, 1.5797, 0.3457, -0.2694, 0.0125, 0.0688, 0.0827],
- [ 0.3644, -0.5406, 1.6827, -0.2990, -0.5216, -0.1021, 0.4127, 0.3489],
- [ 0.4920, -0.4392, 1.0327, -1.2483, -0.3211, -1.2385, 0.4642, 0.4751]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6518e-01, -3.8584e-01, 1.0975e+00, -1.1312e+00, -3.4018e-01,
- -1.4006e+00, 1.7945e-01, 3.4688e-01],
- [ 6.1484e-01, -4.1301e-01, 1.6864e+00, 1.6982e-01, -5.3072e-01,
- -1.1501e-01, 6.1247e-01, 8.5142e-02],
- [ 6.0785e-01, -3.9761e-01, 1.4208e+00, -1.4314e+00, -3.6328e-01,
- -1.1312e+00, 6.1950e-01, -9.2270e-04],
- [ 5.4990e-01, -4.2249e-01, 1.8018e+00, -2.9207e-01, -3.0554e-01,
- 5.4350e-02, 4.0462e-01, 2.6990e-01],
- [ 5.8655e-01, -3.9731e-01, 1.8423e+00, -6.8822e-02, -5.1917e-01,
- -2.3048e-01, 4.1617e-01, 1.1594e-01],
- [ 5.1928e-01, -4.6990e-01, 1.5767e+00, 4.0077e-01, -2.4203e-01,
- 7.7444e-02, 1.1776e-01, -6.1038e-02],
- [ 5.7003e-01, -4.0316e-01, 1.7961e+00, -1.9969e-01, -5.2494e-01,
- -2.1509e-01, 3.8152e-01, 3.1609e-01],
- [ 5.6871e-01, -4.0878e-01, 1.0397e+00, -1.1466e+00, -3.1132e-01,
- -1.1928e+00, 4.6813e-01, 5.8553e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0100, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0100, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.872529580257833
- step: 29
- running loss: 0.030087226905442518
- Train Steps: 29/90 Loss: 0.0301 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6101, 0.4042, 0.7775, 0.2617, 0.3713, 0.2817, 0.5440, 0.5650],
- [0.6267, 0.4065, 0.8313, 0.2467, 0.4788, 0.1733, 0.6312, 0.5133],
- [0.6271, 0.4005, 0.8450, 0.6067, 0.3850, 0.4983, 0.6069, 0.4649],
- [0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413],
- [0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [0.6115, 0.4005, 0.8838, 0.3867, 0.3763, 0.4700, 0.5800, 0.5550],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679],
- [0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5629, -0.4008, 1.1536, -0.9999, -0.5741, -0.8104, 0.1382, 0.2250],
- [ 0.6017, -0.3874, 1.4440, -1.0393, -0.1726, -1.3285, 0.5830, 0.1753],
- [ 0.9206, -0.2273, 1.5587, 0.5413, -0.6123, 0.1463, 0.4333, 0.0461],
- [ 0.4372, -0.4923, 1.6003, -1.1577, 0.0245, -1.0948, 1.0326, 0.1909],
- [ 0.4308, -0.4589, 1.1894, -0.9453, -0.4832, -0.9390, 0.0674, 0.2499],
- [ 0.9319, -0.1536, 1.6204, -0.4210, -0.6550, 0.0903, 0.3071, 0.1912],
- [-1.7346, -1.9065, 1.4397, -1.1808, 0.0347, -1.2153, 0.9088, 0.3061],
- [ 0.4637, -0.4995, 1.7508, -0.1255, -0.5554, -0.0794, 0.1084, 0.0721]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5320, -0.4189, 1.3053, -1.0773, -0.5711, -0.9849, 0.2267,
- 0.3237],
- [ 0.6085, -0.4084, 1.5536, -1.1466, -0.0746, -1.4853, 0.6298,
- 0.0851],
- [ 0.6105, -0.4360, 1.6171, 0.5162, -0.5076, 0.0159, 0.5171,
- -0.1385],
- [ 0.6240, -0.3768, 1.6575, -1.2851, 0.2949, -1.2467, 1.1276,
- 0.2142],
- [ 0.5695, -0.3927, 1.3226, -0.9002, -0.4672, -1.1928, 0.1737,
- 0.3700],
- [ 0.5384, -0.4361, 1.7961, -0.4999, -0.5480, -0.1150, 0.3931,
- 0.2776],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371],
- [ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0269, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0269, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8993881745263934
- step: 30
- running loss: 0.029979605817546447
- Train Steps: 30/90 Loss: 0.0300 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6135, 0.4115, 0.8838, 0.4667, 0.4288, 0.6050, 0.5778, 0.5097],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6279, 0.4008, 0.8600, 0.4883, 0.4325, 0.5283, 0.7010, 0.5378],
- [0.6075, 0.4007, 0.8275, 0.4917, 0.4050, 0.5100, 0.5167, 0.5280],
- [0.6132, 0.4118, 0.8200, 0.3633, 0.3563, 0.5400, 0.5787, 0.5136]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5029, -0.4627, 1.8340, -1.0026, -0.1339, -1.6149, 0.6156, 0.0087],
- [ 0.5740, -0.4009, 1.7582, -0.1699, -0.1234, -0.0320, 0.4125, 0.2843],
- [ 0.6356, -0.3398, 1.7359, -0.1383, -0.2639, 0.2198, 0.3052, 0.1666],
- [ 0.3308, -0.5629, 0.8794, -1.2483, -0.5392, -1.3448, 0.4178, 0.3191],
- [ 0.4581, -0.5044, 1.5836, -0.5473, -0.7216, -0.5668, 0.2383, 0.0167],
- [ 0.6568, -0.3957, 1.7078, 0.0030, -0.3137, 0.0374, 0.8280, 0.2264],
- [ 0.3297, -0.5640, 1.5501, -0.0372, -0.3852, -0.0481, 0.0236, 0.2139],
- [ 0.3091, -0.5670, 1.4527, -0.6382, -0.6001, -0.0085, 0.4038, 0.2477]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.5477, -0.3851, 1.7961, -0.1304, -0.3055, 0.5085, 0.3830,
- 0.0682],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391],
- [ 0.5206, -0.4603, 1.4670, -0.4768, -0.6577, -0.3998, 0.1836,
- 0.0021],
- [ 0.6141, -0.4345, 1.6864, -0.0303, -0.2882, 0.1544, 0.9521,
- 0.1982],
- [ 0.5200, -0.4353, 1.5363, -0.0149, -0.4152, 0.0697, 0.1005,
- 0.1530],
- [ 0.5466, -0.3840, 1.5016, -0.6077, -0.6404, 0.2083, 0.3871,
- 0.0862]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9131455309689045
- step: 31
- running loss: 0.02945630745060982
- Train Steps: 31/90 Loss: 0.0295 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892],
- [0.6239, 0.4107, 0.8162, 0.2763, 0.3625, 0.3600, 0.5988, 0.5700],
- [0.6233, 0.4091, 0.8100, 0.2950, 0.3563, 0.3883, 0.6013, 0.5200],
- [0.6357, 0.4139, 0.8450, 0.5883, 0.3775, 0.4950, 0.6488, 0.5297],
- [ nan, nan, 0.7612, 0.3250, 0.4037, 0.2533, 0.5438, 0.5767],
- [0.6260, 0.4106, 0.8025, 0.2583, 0.4550, 0.1867, 0.6281, 0.4869],
- [0.6185, 0.4042, 0.7700, 0.2250, 0.4062, 0.2117, 0.5763, 0.5150],
- [0.6263, 0.4030, 0.9000, 0.4767, 0.3800, 0.5167, 0.6415, 0.4771]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.5724e-01, -3.3551e-01, 1.7339e+00, -1.7214e-01, -5.4087e-01,
- -5.7657e-01, -7.2407e-04, 3.0961e-01],
- [ 6.4453e-01, -3.8119e-01, 1.4042e+00, -9.6262e-01, -4.7842e-01,
- -6.2890e-01, 3.6548e-01, 2.1259e-01],
- [ 5.3622e-01, -4.3840e-01, 1.4795e+00, -8.7511e-01, -5.5252e-01,
- -4.6950e-01, 5.1470e-01, 2.1833e-01],
- [ 7.7063e-01, -3.1686e-01, 1.7194e+00, 3.5740e-01, -4.2742e-01,
- -1.5771e-02, 7.1987e-01, 9.3308e-02],
- [-9.5819e-01, -1.3890e+00, 1.2376e+00, -9.8477e-01, -3.6917e-01,
- -1.1233e+00, 1.0399e-01, 3.1658e-01],
- [ 2.9942e-01, -5.8920e-01, 1.5002e+00, -1.1685e+00, -1.3334e-01,
- -1.4075e+00, 5.3227e-01, 5.6465e-02],
- [ 5.8947e-01, -4.2299e-01, 1.2521e+00, -1.2410e+00, -3.8749e-01,
- -1.2859e+00, 3.7489e-01, 5.9306e-02],
- [ 6.0614e-01, -4.2630e-01, 1.8852e+00, -1.1434e-01, -4.4070e-01,
- 1.6359e-01, 5.1914e-01, 5.1127e-02]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.4544e-01, -4.0531e-01, 1.6633e+00, -1.7660e-01, -6.0577e-01,
- -5.9230e-01, 1.5773e-01, 4.3570e-01],
- [ 5.9601e-01, -3.8879e-01, 1.4840e+00, -1.0095e+00, -6.1155e-01,
- -6.2309e-01, 4.7968e-01, 3.4688e-01],
- [ 5.9319e-01, -3.9615e-01, 1.4554e+00, -9.2333e-01, -6.4042e-01,
- -4.9222e-01, 4.9122e-01, 1.1594e-01],
- [ 6.5036e-01, -3.7434e-01, 1.6171e+00, 4.3156e-01, -5.4226e-01,
- 4.6189e-04, 7.1085e-01, 1.6077e-01],
- [-2.2859e+00, -2.2859e+00, 1.2303e+00, -7.8476e-01, -4.2102e-01,
- -1.1158e+00, 2.2564e-01, 3.7768e-01],
- [ 6.0577e-01, -3.8922e-01, 1.4208e+00, -1.0927e+00, -1.8430e-01,
- -1.4237e+00, 6.1538e-01, -3.6992e-02],
- [ 5.7067e-01, -4.1886e-01, 1.2707e+00, -1.2467e+00, -4.0947e-01,
- -1.3082e+00, 3.7575e-01, 9.2841e-02],
- [ 6.0716e-01, -4.2471e-01, 1.8711e+00, -8.4219e-02, -5.3072e-01,
- 1.0054e-01, 6.7707e-01, -8.2079e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0478, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0478, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9609342403709888
- step: 32
- running loss: 0.0300291950115934
- Train Steps: 32/90 Loss: 0.0300 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6263, 0.4030, 0.9000, 0.4767, 0.3800, 0.5167, 0.6415, 0.4771],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6257, 0.4024, 0.8612, 0.5352, 0.4361, 0.5253, 0.6680, 0.5166],
- [0.6124, 0.4030, 0.8650, 0.4867, 0.4999, 0.5106, 0.5137, 0.5773],
- [0.6286, 0.4055, 0.9000, 0.4717, 0.3763, 0.4683, 0.7018, 0.5494]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5655, -0.4276, 1.7582, -0.6235, -0.6276, -0.6743, 0.5222, 0.1137],
- [ 0.6222, -0.3659, 1.6961, 0.1163, -0.4128, -0.2092, 0.3120, 0.3234],
- [ 0.3312, -0.5525, 1.7061, -0.5412, -0.6122, -0.6128, 0.1507, 0.2349],
- [ 0.4998, -0.4658, 1.7805, -0.3142, -0.5496, -0.0170, 0.4519, 0.0428],
- [ 0.2381, -0.6250, 1.6805, -0.2034, -0.1010, -0.1950, -0.0042, 0.2027],
- [ 0.6927, -0.3407, 1.6134, -0.0583, -0.2813, 0.0023, 0.7085, 0.1154],
- [ 0.3413, -0.5539, 1.7100, -0.2173, -0.0598, -0.2415, -0.0140, 0.1867],
- [ 0.9364, -0.1751, 1.8455, -0.3379, -0.5334, -0.3309, 0.9051, 0.1270]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697],
- [ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [ 0.6072, -0.4247, 1.8711, -0.0842, -0.5307, 0.1005, 0.6771,
- -0.0821],
- [ 0.5359, -0.4370, 1.7095, -0.0303, -0.0804, -0.0380, 0.1044,
- 0.3392],
- [ 0.6042, -0.4273, 1.6920, 0.1860, -0.2717, 0.1406, 0.7997,
- 0.1004],
- [ 0.5428, -0.4244, 1.7095, -0.0380, 0.0232, 0.0725, 0.0866,
- 0.3806],
- [ 0.6174, -0.4129, 1.8711, -0.1073, -0.5480, -0.1227, 0.9558,
- 0.2516]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9831647351384163
- step: 33
- running loss: 0.02979287076177019
- Train Steps: 33/90 Loss: 0.0298 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4112, 0.8862, 0.4100, 0.3638, 0.4917, 0.6088, 0.6050],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719],
- [0.6268, 0.4102, 0.8938, 0.3667, 0.4025, 0.2833, 0.6275, 0.5183],
- [0.6246, 0.4028, 0.8738, 0.4867, 0.4088, 0.5667, 0.6362, 0.5200],
- [0.6129, 0.4114, 0.6950, 0.2467, 0.4050, 0.2133, 0.5363, 0.5550],
- [0.6075, 0.4000, 0.8513, 0.5183, 0.4510, 0.5329, 0.5180, 0.5280]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5627, -0.4001, 1.8025, -0.3814, -0.5554, -0.0953, 0.4415, 0.3466],
- [ 0.5360, -0.4458, 1.7127, 0.1136, -0.3603, 0.2607, 0.8678, 0.2304],
- [ 0.6083, -0.3793, 1.3611, -1.2816, -0.2590, -1.5712, 0.4258, 0.0313],
- [ 0.1714, -0.6622, 1.6124, 0.1800, -0.4743, -0.0086, 0.1030, 0.1615],
- [ 0.3095, -0.5768, 1.9215, -0.7524, -0.3930, -1.1578, 0.5276, 0.1364],
- [ 0.6881, -0.3396, 1.8548, -0.0897, -0.3718, 0.2349, 0.6835, 0.0806],
- [ 0.4803, -0.4402, 1.0330, -1.1984, -0.4795, -1.2689, 0.0921, 0.2523],
- [ 0.4115, -0.4979, 1.7126, 0.0244, -0.1545, 0.0139, -0.0112, 0.0896]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5777, -0.3868, 1.8076, -0.3921, -0.6058, -0.0149, 0.5259,
- 0.5085],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [ 0.5079, -0.4714, 1.6575, 0.2237, -0.4961, 0.0774, 0.1465,
- -0.1061],
- [ 0.6092, -0.3913, 1.8423, -0.5923, -0.4268, -0.9772, 0.6125,
- 0.1082],
- [ 0.5990, -0.4256, 1.7499, -0.0380, -0.3979, 0.3315, 0.6529,
- 0.1159],
- [ 0.5448, -0.3859, 0.9242, -1.1466, -0.4152, -1.3005, 0.1910,
- 0.2776],
- [ 0.5202, -0.4382, 1.6460, 0.1082, -0.2029, 0.1754, 0.1067,
- 0.1530]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0109, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0109, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.994059644639492
- step: 34
- running loss: 0.029237048371749765
- Train Steps: 34/90 Loss: 0.0292 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6332, 0.4118, 0.9238, 0.4267, 0.4012, 0.4733, 0.7525, 0.5436],
- [0.6361, 0.4102, 0.8650, 0.2983, 0.4888, 0.2000, 0.6702, 0.5459],
- [0.6201, 0.4102, 0.7288, 0.2417, 0.4150, 0.2383, 0.6100, 0.5500],
- [0.6361, 0.4076, 0.8862, 0.5350, 0.3713, 0.4650, 0.6654, 0.5297],
- [0.6197, 0.4090, 0.7825, 0.2500, 0.4200, 0.2483, 0.5988, 0.5667],
- [0.6293, 0.4024, 0.8750, 0.5000, 0.4012, 0.5733, 0.7121, 0.5633],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [0.6325, 0.4165, 0.9000, 0.4617, 0.3813, 0.4900, 0.7485, 0.5447]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5234, -0.4664, 1.8526, -0.3534, -0.4182, -0.0822, 0.6591, 0.2142],
- [ 0.4352, -0.4940, 1.8282, -0.9907, -0.0305, -1.4398, 0.5311, 0.0966],
- [ 0.8282, -0.2346, 1.2444, -1.2653, -0.4695, -1.2162, 0.1952, 0.1267],
- [ 0.4458, -0.5179, 1.8325, 0.1580, -0.5393, -0.1276, 0.4210, 0.1183],
- [ 0.3377, -0.5294, 1.3695, -1.2153, -0.4092, -1.1784, 0.1472, 0.2982],
- [ 0.3720, -0.5473, 1.8003, 0.0639, -0.3505, 0.3625, 0.4583, 0.2098],
- [ 0.4163, -0.5423, 1.5022, 0.1460, -0.4588, -0.0288, 0.5502, 0.1593],
- [ 0.4728, -0.4942, 1.9267, -0.1118, -0.4895, -0.0021, 0.5690, 0.1152]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6388, -0.3840, 1.9808, -0.3152, -0.4326, -0.0996, 1.1897,
- 0.2249],
- [ 0.6520, -0.3912, 1.7095, -0.9079, -0.0284, -1.3621, 0.8096,
- 0.2356],
- [ 0.5784, -0.3913, 1.0801, -1.1697, -0.3691, -1.1851, 0.5316,
- 0.2545],
- [ 0.6520, -0.4032, 1.8076, 0.1852, -0.5711, -0.1381, 0.7876,
- 0.1608],
- [ 0.5764, -0.3969, 1.3284, -1.1312, -0.3460, -1.1389, 0.4797,
- 0.3315],
- [ 0.6207, -0.4273, 1.7557, 0.0236, -0.4326, 0.3623, 1.0033,
- 0.3157],
- [ 0.6289, -0.4345, 1.3794, 0.3679, -0.4845, 0.0390, 0.9265,
- 0.1928],
- [ 0.6355, -0.3623, 1.8711, -0.1535, -0.5249, -0.0226, 1.1715,
- 0.2302]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0352, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0352, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0292295143008232
- step: 35
- running loss: 0.029406557551452092
- Train Steps: 35/90 Loss: 0.0294 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6286, 0.4055, 0.9000, 0.4717, 0.3763, 0.4683, 0.7018, 0.5494],
- [0.6161, 0.4024, 0.8838, 0.4583, 0.3688, 0.3733, 0.5311, 0.5344],
- [0.6193, 0.4034, 0.7757, 0.2347, 0.3733, 0.2919, 0.5930, 0.4926],
- [0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309],
- [0.6260, 0.4253, 0.8938, 0.4400, 0.3675, 0.3650, 0.5675, 0.5767],
- [0.6274, 0.4003, 0.8638, 0.5967, 0.3688, 0.4900, 0.6108, 0.4661]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6034, -0.4226, 1.7620, 0.0535, -0.4684, -0.2611, 0.4431, 0.1771],
- [ 0.3709, -0.5737, 1.6665, 0.0294, -0.3556, -0.0046, 0.5314, 0.1333],
- [ 0.6214, -0.4068, 1.9044, -0.2927, -0.4045, -0.1387, 0.9449, 0.1900],
- [ 0.4604, -0.4612, 1.8500, -0.3535, -0.5022, -0.5301, 0.0546, 0.1342],
- [ 0.7181, -0.2917, 1.3510, -1.4534, -0.4692, -0.9523, 0.3789, 0.0521],
- [ 0.6420, -0.3510, 1.8920, -0.1968, -0.3205, -0.5772, 0.5890, 0.1643],
- [ 0.5143, -0.4038, 1.8072, -0.4124, -0.4635, -0.6016, 0.2431, 0.3779],
- [ 0.3334, -0.5918, 1.6142, 0.2640, -0.4179, -0.0050, 0.4461, 0.1309]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.6174, -0.4129, 1.8711, -0.1073, -0.5480, -0.1227, 0.9558,
- 0.2516],
- [ 0.5598, -0.4273, 1.7961, -0.1689, -0.5827, -0.5615, 0.1671,
- 0.1824],
- [ 0.5746, -0.4225, 1.2968, -1.2019, -0.5615, -0.9374, 0.4533,
- -0.0108],
- [ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661],
- [ 0.6058, -0.3216, 1.8423, -0.2536, -0.5885, -0.6000, 0.3353,
- 0.3777],
- [ 0.6120, -0.4371, 1.7037, 0.4701, -0.5827, -0.0226, 0.5354,
- -0.1331]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0150, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0150, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.044245726428926
- step: 36
- running loss: 0.029006825734136835
- Train Steps: 36/90 Loss: 0.0290 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6325, 0.4165, 0.9000, 0.4617, 0.3813, 0.4900, 0.7485, 0.5447],
- [0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167],
- [0.6202, 0.4054, 0.8892, 0.4428, 0.3642, 0.3249, 0.5840, 0.5426],
- [0.6151, 0.4125, 0.8738, 0.4417, 0.3575, 0.3783, 0.5138, 0.5483],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6150, 0.4097, 0.7468, 0.3194, 0.3825, 0.2633, 0.5463, 0.5800],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6145, 0.4007, 0.8775, 0.4533, 0.4562, 0.5533, 0.6088, 0.5533]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6509, -0.3897, 1.9019, -0.1008, -0.3962, -0.0111, 1.1013, 0.1596],
- [ 0.8432, -0.2382, 1.8771, -0.3779, -0.3931, 0.3015, 0.7711, 0.0823],
- [ 0.4317, -0.4885, 1.8653, -0.3814, -0.5239, -0.7898, 0.4372, 0.1703],
- [ 0.5510, -0.3753, 1.7102, -0.2932, -0.5679, -0.6029, 0.1613, 0.2940],
- [ 0.6017, -0.4021, 1.8899, 0.1966, -0.5129, -0.6484, 0.6649, -0.0689],
- [ 0.0397, -0.6923, 1.1594, -0.9537, -0.5354, -1.0902, 0.1489, 0.3543],
- [ 0.4427, -0.4832, 1.5935, -0.3385, -0.4276, -0.2279, 0.1961, 0.2921],
- [ 0.6207, -0.3919, 1.8145, -0.1962, -0.0353, 0.1292, 0.5811, 0.2452]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6355, -0.3623, 1.8711, -0.1535, -0.5249, -0.0226, 1.1715,
- 0.2302],
- [ 0.5886, -0.4253, 1.8654, -0.3460, -0.5480, 0.3623, 0.6587,
- 0.1005],
- [ 0.5788, -0.4135, 1.8214, -0.2408, -0.6039, -0.7854, 0.4115,
- 0.2203],
- [ 0.5551, -0.3807, 1.7499, -0.2459, -0.6346, -0.5384, 0.0871,
- 0.2468],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.5548, -0.3936, 1.1634, -0.8105, -0.5192, -1.0696, 0.2372,
- 0.3931],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.5526, -0.4351, 1.7672, -0.1920, -0.1785, 0.2699, 0.5259,
- 0.2699]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0128, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0128, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0570316752418876
- step: 37
- running loss: 0.02856842365518615
- Train Steps: 37/90 Loss: 0.0286 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6257, 0.4060, 0.8300, 0.2333, 0.4688, 0.2583, 0.7050, 0.5633],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6279, 0.4008, 0.8600, 0.4883, 0.4325, 0.5283, 0.7010, 0.5378],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6248, 0.4185, 0.8500, 0.5767, 0.4463, 0.4550, 0.5613, 0.5917],
- [0.6336, 0.4154, 0.8900, 0.2767, 0.4988, 0.2867, 0.7422, 0.5540],
- [0.6208, 0.4082, 0.8538, 0.3067, 0.3588, 0.3717, 0.6112, 0.5517]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4441, -0.4830, 1.6680, -1.0800, -0.2465, -1.0681, 0.7556, 0.2798],
- [ 0.1950, -0.6115, 1.0745, -1.2398, -0.6409, -0.9222, 0.2341, 0.2288],
- [ 0.6605, -0.4018, 2.1028, -0.1091, -0.3462, -0.7841, 0.9333, 0.0228],
- [ 0.7723, -0.3271, 1.7699, 0.2168, -0.3638, 0.3681, 0.7759, 0.0680],
- [ 0.7062, -0.3003, 1.1744, -1.1470, -0.4890, -0.9774, 0.3316, 0.2041],
- [ 0.5624, -0.4178, 1.6652, 0.5979, -0.2604, -0.0412, 0.2334, 0.3466],
- [ 0.0958, -0.7478, 1.8266, -0.7130, -0.0486, -0.9561, 0.8954, 0.2009],
- [ 0.1288, -0.6920, 1.6375, -0.5932, -0.7722, -0.3912, 0.4008, 0.1899]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6042, -0.4105, 1.5478, -1.2082, -0.1208, -1.0927, 0.9704,
- 0.3157],
- [ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853],
- [ 0.6224, -0.4345, 1.9404, -0.2921, -0.3171, -0.8771, 1.0655,
- 0.2142],
- [ 0.6141, -0.4345, 1.6864, -0.0303, -0.2882, 0.1544, 0.9521,
- 0.1982],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.5999, -0.3530, 1.6402, 0.3777, -0.2247, -0.1843, 0.3065,
- 0.4470],
- [ 0.6405, -0.3671, 1.8249, -1.0080, 0.0178, -0.9618, 1.1422,
- 0.2730],
- [ 0.5814, -0.4003, 1.6575, -0.8694, -0.6289, -0.5692, 0.5374,
- 0.2622]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0323, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0323, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.089333820156753
- step: 38
- running loss: 0.028666679477809293
- Train Steps: 38/90 Loss: 0.0287 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6102, 0.3999, 0.8750, 0.5133, 0.3825, 0.4750, 0.5637, 0.5083],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6274, 0.4117, 0.8100, 0.5801, 0.4000, 0.4583, 0.5582, 0.6118],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6206, 0.4001, 0.8900, 0.3933, 0.3588, 0.3567, 0.5837, 0.5083],
- [0.6087, 0.3951, 0.8387, 0.5833, 0.4188, 0.4933, 0.5146, 0.4830],
- [ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194],
- [0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 8.8443e-01, -1.9540e-01, 1.8931e+00, 8.7475e-02, -4.4813e-01,
- 1.6257e-03, 6.1814e-01, -9.2434e-03],
- [ 5.0552e-01, -3.8126e-01, 1.2175e+00, -1.0401e+00, -1.1707e-01,
- -1.3590e+00, 5.7183e-01, 4.3613e-01],
- [ 8.3590e-01, -2.1532e-01, 1.6410e+00, 2.7475e-01, -4.1388e-01,
- -1.0416e-01, 7.4442e-01, 4.3882e-01],
- [ 7.6310e-01, -2.6186e-01, 1.6897e+00, -2.8506e-01, -4.8278e-01,
- -1.4364e-01, 4.4101e-01, 2.6590e-01],
- [ 8.4191e-01, -2.4302e-01, 1.9611e+00, -4.4580e-01, -6.0415e-01,
- -6.3684e-01, 6.8879e-01, 9.6068e-03],
- [ 8.2173e-01, -2.3409e-01, 1.7656e+00, 4.0293e-01, -3.2369e-01,
- 5.4397e-02, 5.1337e-01, 2.0862e-02],
- [-2.3548e+00, -2.3337e+00, 1.3308e+00, -7.7752e-01, -6.2998e-01,
- -7.0770e-01, 5.5572e-01, 2.3119e-01],
- [ 8.9218e-01, -1.8210e-01, 1.3559e+00, -9.9058e-01, -5.4404e-01,
- -8.9048e-01, 3.4451e-01, 9.2592e-03]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5326, -0.4390, 1.7557, 0.0851, -0.5192, -0.0919, 0.3180,
- 0.0620],
- [ 0.5862, -0.3530, 1.1032, -1.0619, -0.1497, -1.3852, 0.3411,
- 0.3931],
- [ 0.6121, -0.3844, 1.4556, 0.3936, -0.4383, -0.1689, 0.2925,
- 0.5401],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.5807, -0.4378, 1.8249, -0.4691, -0.6289, -0.6385, 0.4104,
- 0.0620],
- [ 0.5258, -0.4610, 1.5882, 0.4085, -0.3517, -0.0072, 0.0910,
- -0.0550],
- [-2.2859, -2.2859, 1.5478, -0.8309, -0.6289, -0.7232, 0.1198,
- 0.1133],
- [ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0324, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0324, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1217780141159892
- step: 39
- running loss: 0.028763538823486902
- Train Steps: 39/90 Loss: 0.0288 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6138, 0.4054, 0.8750, 0.4750, 0.4363, 0.5017, 0.5086, 0.5822],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [0.6126, 0.4039, 0.8237, 0.3967, 0.3625, 0.3600, 0.5894, 0.6138],
- [0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6199, 0.4071, 0.7600, 0.2117, 0.4037, 0.2767, 0.6138, 0.5550],
- [0.6350, 0.4144, 0.9250, 0.4100, 0.4125, 0.3750, 0.7129, 0.5945],
- [0.6336, 0.4191, 0.8938, 0.5167, 0.3937, 0.3517, 0.7343, 0.5748]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6440, -0.3332, 1.7672, -0.0498, -0.3202, 0.0893, 0.1455, 0.0761],
- [ 0.5725, -0.4177, 1.4867, 0.1828, -0.4769, -0.0204, 1.0719, 0.2339],
- [ 0.5976, -0.4119, 1.8482, -0.3967, -0.5806, -0.0978, 0.7626, 0.1046],
- [ 0.4039, -0.4856, 1.5727, -0.4861, -0.6485, -0.5054, 0.4126, 0.3674],
- [ 0.8189, -0.2302, 1.6996, 0.4369, -0.4249, -0.1943, 0.2815, 0.3349],
- [ 0.6293, -0.3363, 1.3477, -1.3669, -0.4102, -1.0134, 0.5384, 0.2066],
- [ 0.5771, -0.4255, 1.9855, -0.3066, -0.2413, -0.5252, 1.0077, 0.2510],
- [ 0.0591, -0.7700, 1.8177, 0.0866, -0.3968, -0.5449, 0.8709, 0.1763]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5491, -0.4132, 1.7557, -0.0919, -0.2709, 0.0313, 0.0635,
- 0.4032],
- [ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [ 0.5438, -0.4206, 1.5189, -0.4537, -0.6115, -0.6231, 0.4365,
- 0.5491],
- [ 0.6038, -0.3464, 1.7037, 0.3931, -0.4441, -0.2613, 0.3007,
- 0.4624],
- [ 0.5775, -0.4054, 1.2245, -1.3082, -0.4210, -1.0080, 0.5490,
- 0.2776],
- [ 0.6471, -0.3719, 1.9866, -0.3921, -0.3806, -0.5538, 1.0070,
- 0.4600],
- [ 0.6405, -0.3503, 1.8423, 0.1005, -0.4672, -0.6616, 1.1057,
- 0.3692]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0169, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0169, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1386554995551705
- step: 40
- running loss: 0.028466387488879263
- Train Steps: 40/90 Loss: 0.0285 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6361, 0.4076, 0.8862, 0.5350, 0.3713, 0.4650, 0.6654, 0.5297],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6068, 0.3963, 0.8650, 0.4317, 0.4037, 0.5083, 0.5253, 0.4999],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6140, 0.4034, 0.8850, 0.4317, 0.4288, 0.5067, 0.5825, 0.5533],
- [0.6246, 0.4008, 0.8757, 0.5088, 0.4101, 0.5392, 0.6644, 0.5133]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6078, -0.3606, 1.5451, -0.5208, -0.7137, -0.4924, 0.4499, 0.2869],
- [ 0.7464, -0.3133, 1.7544, 0.2531, -0.5698, -0.1807, 1.0396, 0.2625],
- [ 0.3502, -0.5323, 1.8783, -0.4356, -0.3516, -1.3380, 0.7168, 0.1584],
- [ 0.4409, -0.4870, 1.5813, -0.2868, -0.5962, -0.1480, 0.2479, 0.2000],
- [ 0.5728, -0.4096, 1.6914, -0.2556, -0.5003, 0.0082, 0.4210, 0.1230],
- [ 0.3514, -0.5419, 1.7136, 0.0634, -0.1271, -0.0333, 0.4373, 0.2821],
- [ 0.8080, -0.2471, 1.7488, -0.1996, -0.3355, 0.0799, 0.5897, 0.2356],
- [ 0.6162, -0.4141, 1.7027, 0.1096, -0.3821, 0.1523, 1.0530, 0.2057]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.6520, -0.4032, 1.8076, 0.1852, -0.5711, -0.1381, 0.7876,
- 0.1608],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.5168, -0.4556, 1.7095, -0.2921, -0.4210, 0.0620, 0.1404,
- 0.0231],
- [ 0.5359, -0.4370, 1.7095, -0.0303, -0.0804, -0.0380, 0.1044,
- 0.3392],
- [ 0.5499, -0.4225, 1.8018, -0.2921, -0.3055, 0.0543, 0.4046,
- 0.2699],
- [ 0.5993, -0.4345, 1.7587, 0.0641, -0.3917, 0.2048, 0.7827,
- 0.0852]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0150, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0150, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1536407247185707
- step: 41
- running loss: 0.028137578651672455
- Train Steps: 41/90 Loss: 0.0281 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6110, 0.3984, 0.8750, 0.4933, 0.4625, 0.4950, 0.5578, 0.5676],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6034, 0.4011, 0.7350, 0.2533, 0.3438, 0.3367, 0.5516, 0.5084],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6182, 0.3972, 0.8720, 0.5527, 0.3638, 0.3582, 0.5696, 0.5395],
- [0.6293, 0.4024, 0.8750, 0.5000, 0.4012, 0.5733, 0.7121, 0.5633],
- [0.6282, 0.4034, 0.7830, 0.2080, 0.4532, 0.2080, 0.6404, 0.5323],
- [0.6198, 0.4101, 0.8838, 0.5283, 0.3763, 0.5267, 0.5913, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8460, -0.2558, 1.8253, 0.0117, -0.2213, -0.0403, 0.4005, 0.2357],
- [ 0.7772, -0.2915, 1.8138, 0.0177, -0.2384, 0.2574, 0.7008, 0.2388],
- [ 0.4894, -0.4668, 1.1931, -1.0988, -0.6886, -0.7637, 0.4019, 0.1059],
- [ 0.4321, -0.5275, 1.7577, 0.3191, -0.4827, -0.1046, 0.5689, 0.3247],
- [ 0.0895, -0.7132, 1.6919, 0.1463, -0.7607, -0.6208, 0.4622, 0.2670],
- [ 0.5809, -0.4362, 1.8019, 0.0882, -0.4585, 0.2636, 1.0704, 0.2819],
- [ 0.4095, -0.5098, 1.3689, -1.2173, -0.2903, -1.4032, 0.7033, 0.1959],
- [ 0.7698, -0.3002, 1.7510, 0.2490, -0.5625, 0.0499, 0.6418, 0.3161]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3637e-01, -4.4573e-01, 1.7557e+00, -7.2363e-03, -1.4965e-01,
- 4.6189e-04, 2.9049e-01, 3.3573e-01],
- [ 5.7090e-01, -3.9330e-01, 1.7961e+00, -2.2633e-02, -1.9007e-01,
- 3.9307e-01, 6.1824e-01, 2.0831e-01],
- [ 5.0092e-01, -4.3333e-01, 1.1090e+00, -1.1158e+00, -6.9815e-01,
- -7.3087e-01, 2.6170e-01, 6.2199e-02],
- [ 5.8834e-01, -3.5935e-01, 1.7557e+00, 2.5450e-01, -4.1524e-01,
- -6.1124e-02, 3.3533e-01, 3.0069e-01],
- [ 5.6966e-01, -4.5138e-01, 1.7420e+00, 2.6720e-01, -6.0553e-01,
- -6.3118e-01, 3.4489e-01, 2.0578e-01],
- [ 6.2072e-01, -4.2731e-01, 1.7557e+00, 2.3557e-02, -4.3256e-01,
- 3.6228e-01, 1.0033e+00, 3.1574e-01],
- [ 6.1577e-01, -4.2249e-01, 1.3307e+00, -1.3253e+00, -1.9244e-01,
- -1.3252e+00, 6.7213e-01, 1.7271e-01],
- [ 5.7696e-01, -3.9176e-01, 1.7961e+00, 1.5443e-01, -5.4804e-01,
- 1.4673e-01, 4.4503e-01, 2.8530e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0151, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0151, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.168755921535194
- step: 42
- running loss: 0.027827521941314142
- Train Steps: 42/90 Loss: 0.0278 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [0.6203, 0.4076, 0.8611, 0.2878, 0.4050, 0.2554, 0.5907, 0.5496],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332],
- [0.6259, 0.4133, 0.8200, 0.2317, 0.5025, 0.1533, 0.6250, 0.4947],
- [0.6113, 0.4006, 0.8700, 0.5350, 0.3638, 0.3767, 0.5097, 0.4882],
- [0.6182, 0.3967, 0.8263, 0.3065, 0.3526, 0.4161, 0.6192, 0.5290],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.3789, -2.3153, 1.4070, -1.0507, -0.0678, -1.0513, 0.8482, 0.3953],
- [ 0.8099, -0.2315, 1.6563, -0.7828, -0.4231, -0.8245, 0.5477, 0.2148],
- [ 0.8783, -0.2303, 1.5719, 0.3158, -0.5748, 0.1391, 0.9184, 0.1658],
- [ 0.7176, -0.2993, 1.4880, -0.9552, -0.0990, -1.3317, 0.5871, 0.2044],
- [ 0.6668, -0.3495, 1.7761, 0.3125, -0.7050, -0.3645, 0.2513, 0.0879],
- [ 0.7174, -0.3138, 1.5611, -0.6533, -0.6790, -0.3239, 0.7629, 0.2343],
- [ 0.6578, -0.3283, 1.6729, 0.4010, -0.5496, 0.0428, 0.5500, 0.2054],
- [ 0.7935, -0.2433, 0.9686, -1.1348, -0.4325, -1.1478, 0.3664, 0.3375]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859e+00, -2.2859e+00, 1.7210e+00, -9.7721e-01, 1.8522e-01,
- -1.3698e+00, 7.9859e-01, 3.1039e-01],
- [ 5.7904e-01, -4.0308e-01, 1.6915e+00, -9.5640e-01, -4.1518e-01,
- -1.1063e+00, 4.4251e-01, 2.5281e-01],
- [ 6.1907e-01, -4.2971e-01, 1.4612e+00, 2.3911e-01, -4.9607e-01,
- 3.1255e-02, 1.1166e+00, 1.7680e-01],
- [ 6.0514e-01, -3.7714e-01, 1.5016e+00, -1.2159e+00, 3.5104e-02,
- -1.5777e+00, 6.0099e-01, -9.2270e-04],
- [ 5.3788e-01, -4.3580e-01, 1.7326e+00, 1.8522e-01, -6.0577e-01,
- -5.4611e-01, 6.8408e-02, -3.0981e-02],
- [ 5.6966e-01, -4.5379e-01, 1.5308e+00, -8.7027e-01, -6.5720e-01,
- -3.6388e-01, 5.7392e-01, 1.5759e-01],
- [ 6.1149e-01, -3.7244e-01, 1.7557e+00, 3.4688e-01, -4.4411e-01,
- -1.0731e-01, 4.9122e-01, 2.3911e-01],
- [ 5.2315e-01, -4.1886e-01, 9.3580e-01, -1.4006e+00, -3.9215e-01,
- -1.3698e+00, 2.5553e-01, 2.9064e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0226, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0226, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1913132583722472
- step: 43
- running loss: 0.027704959497029006
- Train Steps: 43/90 Loss: 0.0277 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378],
- [0.6263, 0.4233, 0.7924, 0.4626, 0.3788, 0.2883, 0.5573, 0.6047],
- [0.6193, 0.4050, 0.7313, 0.2433, 0.4075, 0.2117, 0.5649, 0.5656],
- [0.6200, 0.4024, 0.8390, 0.3139, 0.3525, 0.3833, 0.6162, 0.5383],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083],
- [0.6264, 0.4069, 0.7900, 0.1650, 0.4275, 0.2267, 0.6290, 0.4947],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019],
- [ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7687, -0.3415, 1.8001, -0.9061, 0.0913, -1.0675, 1.0970, 0.1657],
- [ 0.6264, -0.3643, 1.4945, 0.0356, -0.5496, -0.5861, 0.2921, 0.4837],
- [ 0.4643, -0.4759, 1.1842, -1.0252, -0.4246, -1.0295, 0.3400, 0.2287],
- [ 0.8361, -0.2656, 1.6420, -0.5933, -0.7136, -0.2909, 0.6242, 0.2097],
- [ 0.8740, -0.2414, 1.6095, 0.5303, -0.4926, 0.1541, 0.4730, 0.3609],
- [ 0.6245, -0.4205, 1.4561, -1.2001, -0.3846, -1.1205, 0.6975, 0.0619],
- [ 1.0365, -0.1871, 1.9594, 0.1347, -0.6793, -0.2144, 0.7839, 0.0429],
- [-2.2615, -2.2909, 1.0155, -0.9692, -0.4837, -1.0926, 0.3438, 0.3451]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.2072e-01, -3.9360e-01, 1.7788e+00, -1.1235e+00, 1.4480e-01,
- -1.0850e+00, 1.1459e+00, 1.9818e-01],
- [ 6.0687e-01, -3.3095e-01, 1.3742e+00, -1.4927e-01, -5.3649e-01,
- -9.5412e-01, 2.8843e-01, 5.0705e-01],
- [ 5.7460e-01, -4.1527e-01, 1.0917e+00, -1.1620e+00, -4.0370e-01,
- -1.3082e+00, 3.2339e-01, 3.2671e-01],
- [ 5.7794e-01, -4.2748e-01, 1.5894e+00, -8.3617e-01, -6.5774e-01,
- -5.1532e-01, 5.6051e-01, 2.0062e-01],
- [ 6.1339e-01, -3.9099e-01, 1.4497e+00, 3.5458e-01, -3.5173e-01,
- -9.1917e-02, 3.2956e-01, 5.2394e-01],
- [ 6.0739e-01, -4.0631e-01, 1.3630e+00, -1.5238e+00, -3.1132e-01,
- -1.2390e+00, 6.1950e-01, -9.2270e-04],
- [ 6.3718e-01, -4.1286e-01, 1.8942e+00, -7.6520e-02, -6.1732e-01,
- -4.7683e-01, 6.9989e-01, 3.2524e-02],
- [-2.2859e+00, -2.2859e+00, 1.0712e+00, -1.2085e+00, -3.8060e-01,
- -1.3929e+00, 7.5520e-02, 2.0062e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0253, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0253, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2166265537962317
- step: 44
- running loss: 0.027650603495368905
- Train Steps: 44/90 Loss: 0.0277 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6204, 0.4091, 0.8950, 0.4783, 0.3613, 0.4617, 0.6063, 0.5617],
- [0.6151, 0.4125, 0.8738, 0.4417, 0.3575, 0.3783, 0.5138, 0.5483],
- [0.6262, 0.4163, 0.8850, 0.5183, 0.3763, 0.4150, 0.6025, 0.5500],
- [0.6286, 0.4078, 0.8063, 0.2267, 0.4788, 0.1533, 0.5953, 0.4913],
- [0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967],
- [0.6149, 0.4054, 0.6713, 0.2333, 0.4025, 0.2017, 0.5213, 0.5695]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3303, -0.6281, 1.9273, -0.1490, -0.3484, -0.8306, 1.1438, 0.2584],
- [ 0.4734, -0.4978, 1.7926, -0.0348, -0.6413, -0.0802, 0.6553, 0.2107],
- [ 0.5969, -0.3857, 1.6875, -0.2029, -0.6595, -0.4201, 0.2257, 0.2553],
- [ 0.5582, -0.4341, 1.7493, 0.0950, -0.6047, -0.2364, 0.6304, 0.2153],
- [ 0.9523, -0.1903, 1.3590, -1.0891, -0.0964, -1.3481, 0.5336, 0.0817],
- [ 0.5434, -0.4403, 1.5534, 0.2962, -0.5464, -0.3564, 0.3453, 0.4208],
- [ 0.6677, -0.3801, 1.7239, -0.2702, -0.4015, 0.3340, 0.7229, 0.3309],
- [ 0.0454, -0.7430, 0.8856, -1.2511, -0.3786, -1.2249, 0.1690, 0.3114]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.5797, -0.3965, 1.8480, -0.0765, -0.6173, -0.1535, 0.5143,
- 0.3084],
- [ 0.5551, -0.3807, 1.7499, -0.2459, -0.6346, -0.5384, 0.0871,
- 0.2468],
- [ 0.6066, -0.3632, 1.8018, 0.1082, -0.5480, -0.3691, 0.4970,
- 0.2545],
- [ 0.6177, -0.4022, 1.4381, -1.2390, -0.0746, -1.5777, 0.4639,
- -0.0168],
- [ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778],
- [ 0.5772, -0.3913, 1.8480, -0.2459, -0.4326, 0.1929, 0.5374,
- 0.4701],
- [ 0.5544, -0.4133, 0.8145, -1.2082, -0.4268, -1.3544, 0.1221,
- 0.3446]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0183, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0183, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2349761677905917
- step: 45
- running loss: 0.027443914839790928
- Train Steps: 45/90 Loss: 0.0274 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6264, 0.3972, 0.8853, 0.4771, 0.3853, 0.4511, 0.6293, 0.5334],
- [0.6357, 0.4139, 0.8450, 0.5883, 0.3775, 0.4950, 0.6488, 0.5297],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167],
- [0.6229, 0.4066, 0.7612, 0.2967, 0.4250, 0.2167, 0.5925, 0.5350],
- [0.6128, 0.4084, 0.8738, 0.4683, 0.3613, 0.3700, 0.4960, 0.5397],
- [ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9098, -0.2486, 1.7183, 0.3586, -0.4905, -0.0874, 0.6515, 0.0615],
- [ 0.8520, -0.2772, 1.7976, -0.0532, -0.5135, -0.2319, 0.7554, 0.2187],
- [ 0.7001, -0.3704, 1.6785, 0.3245, -0.5234, -0.0619, 0.8263, 0.1866],
- [ 0.9059, -0.2046, 1.4341, -1.0454, -0.3405, -1.1364, 0.4722, 0.2305],
- [ 0.4765, -0.4984, 1.7903, -0.3978, -0.5214, 0.2342, 0.6698, 0.2583],
- [ 0.7447, -0.3002, 1.2680, -1.0002, -0.2925, -1.2363, 0.4049, 0.2539],
- [ 0.6188, -0.3739, 1.7510, -0.1212, -0.6343, -0.6131, 0.1172, 0.2812],
- [-1.8595, -2.0222, 1.0496, -1.0688, -0.3478, -1.2907, 0.3034, 0.3780]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.3949e-01, -4.2132e-01, 1.7037e+00, 3.6228e-01, -4.3256e-01,
- -1.0731e-01, 6.5602e-01, -4.8817e-03],
- [ 6.0754e-01, -4.5138e-01, 1.8032e+00, -8.2167e-02, -5.0606e-01,
- -2.0228e-01, 6.2076e-01, 1.7788e-01],
- [ 6.5036e-01, -3.7434e-01, 1.6171e+00, 4.3156e-01, -5.4226e-01,
- 4.6189e-04, 7.1085e-01, 1.6077e-01],
- [ 5.7962e-01, -3.8776e-01, 1.3688e+00, -1.0542e+00, -4.0947e-01,
- -1.1312e+00, 5.8938e-01, 1.9292e-01],
- [ 5.8857e-01, -4.2525e-01, 1.8654e+00, -3.4596e-01, -5.4804e-01,
- 3.6228e-01, 6.5866e-01, 1.0054e-01],
- [ 5.9107e-01, -4.0805e-01, 1.2303e+00, -9.1563e-01, -3.2286e-01,
- -1.2851e+00, 4.5081e-01, 1.8522e-01],
- [ 5.4434e-01, -3.9938e-01, 1.7499e+00, -1.2271e-01, -6.1732e-01,
- -5.7691e-01, 4.8756e-03, 2.0706e-01],
- [-2.2859e+00, -2.2859e+00, 1.0712e+00, -1.2085e+00, -3.8060e-01,
- -1.3929e+00, 7.5520e-02, 2.0062e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0147, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0147, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2496287617832422
- step: 46
- running loss: 0.027165842647461788
- Train Steps: 46/90 Loss: 0.0272 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [0.6272, 0.4120, 0.9038, 0.4117, 0.3725, 0.3200, 0.6175, 0.5250],
- [0.6357, 0.4097, 0.9038, 0.3883, 0.4213, 0.2950, 0.6686, 0.5390],
- [0.6276, 0.4235, 0.8888, 0.5333, 0.3800, 0.3117, 0.5427, 0.6164],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6078, -0.4097, 1.7259, -0.0724, -0.5862, -0.7533, 0.3447, 0.1366],
- [ 0.5774, -0.4651, 1.8405, -0.4214, -0.6620, -0.7016, 0.6259, 0.1238],
- [ 0.5436, -0.4845, 1.7181, -0.5656, -0.4144, -0.8562, 0.7794, 0.1202],
- [ 0.5757, -0.4130, 1.6117, 0.2724, -0.5682, -0.5975, 0.3891, 0.3914],
- [ 0.5848, -0.4938, 1.6619, -1.2172, 0.1144, -1.1763, 1.1502, 0.1497],
- [ 0.5044, -0.4762, 1.2517, -0.6019, -0.6974, -0.6827, 0.2724, 0.4284],
- [ 0.3666, -0.5354, 1.0874, -0.8050, -0.0549, -1.2401, 0.2733, 0.4696],
- [-0.0309, -0.8409, 1.6236, -0.3864, -0.3763, 0.3611, 0.4533, 0.2188]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5901, -0.4000, 1.8423, -0.0688, -0.5307, -0.9233, 0.3642,
- 0.1852],
- [ 0.6111, -0.3828, 1.8885, -0.3844, -0.5654, -0.8079, 0.5663,
- 0.1390],
- [ 0.6504, -0.3936, 1.8885, -0.4922, -0.3402, -0.9233, 0.8022,
- 0.2035],
- [ 0.6131, -0.3299, 1.8192, 0.1775, -0.5307, -0.8463, 0.2206,
- 0.5612],
- [ 0.6207, -0.3936, 1.7788, -1.1235, 0.1448, -1.0850, 1.1459,
- 0.1982],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0190, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0190, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2686103787273169
- step: 47
- running loss: 0.026991710185687592
- Train Steps: 47/90 Loss: 0.0270 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6075, 0.4007, 0.8275, 0.4917, 0.4050, 0.5100, 0.5167, 0.5280],
- [0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6241, 0.4143, 0.8938, 0.4650, 0.4075, 0.5350, 0.6250, 0.5550],
- [ nan, nan, 0.8463, 0.2550, 0.5850, 0.2133, 0.7129, 0.6072],
- [0.6164, 0.4119, 0.7913, 0.2650, 0.3538, 0.3500, 0.5614, 0.5038],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6150, 0.3935, 0.8696, 0.5158, 0.4647, 0.5329, 0.6041, 0.5153],
- [ nan, nan, 0.7425, 0.2117, 0.3937, 0.2433, 0.5438, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7934, -0.2449, 1.5154, 0.1089, -0.4596, -0.0577, 0.1022, 0.1616],
- [ 0.7675, -0.2803, 1.3185, -0.8539, -0.6104, -0.9508, 0.4776, 0.2612],
- [ 0.7659, -0.2658, 1.7689, -0.0246, -0.5172, -0.0341, 0.4878, 0.2810],
- [-1.6360, -1.8705, 1.5813, -0.9753, 0.1505, -1.2824, 1.0845, 0.4963],
- [ 0.7661, -0.2863, 1.3170, -0.9580, -0.6673, -0.7572, 0.3504, 0.1004],
- [ 1.1099, -0.0842, 1.4959, -0.8644, -0.0398, -1.5079, 0.7408, 0.0879],
- [ 0.8245, -0.2819, 1.6245, 0.2629, -0.2925, 0.0168, 0.4868, 0.1323],
- [-1.3622, -1.6601, 1.1851, -0.9392, -0.4514, -1.1801, 0.2310, 0.3086]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5200, -0.4353, 1.5363, -0.0149, -0.4152, 0.0697, 0.1005,
- 0.1530],
- [ 0.5697, -0.4442, 1.3529, -0.9515, -0.5774, -0.7801, 0.5253,
- 0.1931],
- [ 0.5968, -0.3725, 1.8423, -0.1381, -0.4037, 0.1852, 0.6009,
- 0.2776],
- [-2.2859, -2.2859, 1.6229, -1.1081, 0.4162, -1.3005, 1.0070,
- 0.5188],
- [ 0.5614, -0.3834, 1.3688, -1.0619, -0.6520, -0.6693, 0.3069,
- 0.0412],
- [ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.5548, -0.4682, 1.7309, 0.0966, -0.1394, 0.1757, 0.5045,
- 0.0942],
- [-2.2859, -2.2859, 1.1436, -1.3082, -0.4672, -1.1620, 0.2256,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0482, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0482, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3167862426489592
- step: 48
- running loss: 0.027433046721853316
- Train Steps: 48/90 Loss: 0.0274 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6357, 0.4139, 0.8450, 0.5883, 0.3775, 0.4950, 0.6488, 0.5297],
- [0.6097, 0.4000, 0.7325, 0.2667, 0.3450, 0.3517, 0.5284, 0.5045],
- [0.6293, 0.3982, 0.8700, 0.5300, 0.3763, 0.4717, 0.7050, 0.5297],
- [0.6361, 0.4076, 0.8862, 0.5350, 0.3713, 0.4650, 0.6654, 0.5297]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6029, -0.4274, 1.6532, -0.8771, -0.5784, -0.8858, 0.4872, 0.2820],
- [ 0.3579, -0.5499, 1.8201, -0.1189, -0.1082, -0.1930, 0.1005, 0.1570],
- [ 0.2582, -0.6223, 1.5448, -0.6216, -0.5208, -0.6415, 0.1641, 0.4178],
- [ 0.4707, -0.5316, 1.7071, -0.0668, -0.2033, -0.1684, 0.5052, 0.3034],
- [ 0.5893, -0.4368, 1.6961, 0.2884, -0.4750, -0.2678, 0.7490, 0.2008],
- [ 0.4832, -0.4854, 1.1430, -1.1649, -0.5456, -0.9242, 0.1531, 0.1965],
- [ 0.5110, -0.4910, 1.6933, 0.1101, -0.4684, -0.2975, 0.7455, 0.2333],
- [ 0.6602, -0.4004, 1.7785, 0.1148, -0.5072, -0.3808, 0.7389, 0.2826]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6801e-01, -4.3934e-01, 1.5920e+00, -6.6715e-01, -6.4527e-01,
- -5.4566e-01, 5.1492e-01, 1.7534e-01],
- [ 5.5155e-01, -4.2249e-01, 1.7788e+00, -5.3426e-02, -2.0162e-01,
- 1.9292e-01, 1.5683e-01, 6.8210e-02],
- [ 5.3672e-01, -4.2941e-01, 1.5709e+00, -4.9992e-01, -6.6928e-01,
- -3.0747e-01, 2.4546e-01, 3.5585e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 6.5036e-01, -3.7434e-01, 1.6171e+00, 4.3156e-01, -5.4226e-01,
- 4.6189e-04, 7.1085e-01, 1.6077e-01],
- [ 5.3031e-01, -4.3841e-01, 1.0975e+00, -1.0542e+00, -6.9238e-01,
- -6.6159e-01, 1.5477e-01, 4.4164e-02],
- [ 6.2072e-01, -4.4656e-01, 1.7326e+00, 1.6212e-01, -5.4804e-01,
- -1.0731e-01, 9.7040e-01, 1.6077e-01],
- [ 6.5201e-01, -4.0323e-01, 1.8076e+00, 1.8522e-01, -5.7113e-01,
- -1.3811e-01, 7.8762e-01, 1.6077e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0196, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0196, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.336402228102088
- step: 49
- running loss: 0.027273514859226286
- Train Steps: 49/90 Loss: 0.0273 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183],
- [0.6179, 0.4008, 0.7505, 0.2678, 0.4368, 0.1891, 0.5831, 0.5263],
- [0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6030, 0.3969, 0.7988, 0.3917, 0.3450, 0.3667, 0.5266, 0.4700]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4697, -0.4949, 1.2264, -1.1615, -0.3115, -1.3537, 0.3968, 0.1538],
- [ 0.4581, -0.5103, 1.2444, -1.2177, -0.2020, -1.4147, 0.4720, 0.1634],
- [ 0.7304, -0.3405, 1.2307, -0.9687, -0.2543, -1.3712, 0.4215, 0.2137],
- [ 0.5282, -0.5000, 1.8005, 0.1556, -0.2945, -0.0716, 0.3245, 0.3234],
- [-0.5072, -1.1353, 0.9631, -1.1802, -0.3185, -1.3331, 0.3396, 0.2513],
- [ 0.3824, -0.5971, 1.8023, 0.2948, -0.4119, 0.2892, 1.0153, 0.3397],
- [ 0.5125, -0.4644, 1.8052, -0.4985, -0.6533, -0.4799, 0.4674, 0.3349],
- [ 0.4898, -0.4892, 1.6309, -0.3511, -0.6462, -0.5951, 0.1274, 0.1258]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5598, -0.4201, 1.1898, -1.3005, -0.3806, -1.3313, 0.3873,
- 0.0774],
- [ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082],
- [ 0.5680, -0.4345, 1.1806, -1.0490, -0.2683, -1.4127, 0.4074,
- 0.1449],
- [ 0.5525, -0.4463, 1.7326, 0.0313, -0.2536, -0.0688, 0.1968,
- 0.3700],
- [ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371],
- [ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.4992, -0.4525, 1.4035, -0.4768, -0.6924, -0.5923, 0.1465,
- -0.1151]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0342, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0342, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.370650378987193
- step: 50
- running loss: 0.027413007579743864
- Train Steps: 50/90 Loss: 0.0274 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6064, 0.4019, 0.8650, 0.4517, 0.4037, 0.5367, 0.5703, 0.5609],
- [0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528],
- [0.6199, 0.4112, 0.8475, 0.3717, 0.3550, 0.4350, 0.6063, 0.6083],
- [0.6199, 0.4015, 0.8716, 0.5228, 0.3833, 0.4772, 0.5883, 0.5148],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483],
- [0.6122, 0.3993, 0.8738, 0.4667, 0.4517, 0.4879, 0.5155, 0.4927],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [ nan, nan, 0.8463, 0.2550, 0.5850, 0.2133, 0.7129, 0.6072]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5260, -0.4512, 1.5853, -0.1327, -0.3905, 0.0258, 0.3438, 0.2133],
- [ 0.7047, -0.3586, 1.7669, -0.6288, -0.3302, -0.8932, 0.9293, 0.2138],
- [ 0.6988, -0.2873, 1.4754, -0.5759, -0.5988, -0.5795, 0.3050, 0.4355],
- [ 0.7808, -0.3050, 1.6218, 0.2099, -0.4843, -0.3894, 0.3224, 0.0286],
- [ 0.6624, -0.3391, 1.5512, -0.4366, -0.5093, -0.2878, 0.2257, 0.2136],
- [ 0.5930, -0.4043, 1.6772, -0.0725, -0.2312, -0.2132, 0.0442, -0.0188],
- [ 0.5222, -0.4644, 1.6615, -0.3672, -0.5627, -0.3673, 0.6173, 0.2334],
- [-1.8953, -2.0281, 1.5608, -1.1411, 0.3558, -1.4730, 1.0002, 0.4625]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5151, -0.4296, 1.7095, -0.1997, -0.4210, 0.1929, 0.3484,
- 0.3047],
- [ 0.6388, -0.3623, 1.9173, -0.7386, -0.3517, -0.5846, 1.1495,
- 0.2676],
- [ 0.5774, -0.3868, 1.6286, -0.5692, -0.6462, -0.2767, 0.5143,
- 0.5239],
- [ 0.5773, -0.4316, 1.7399, 0.1287, -0.5153, -0.0817, 0.4313,
- 0.0919],
- [ 0.5546, -0.4620, 1.7788, -0.4229, -0.5192, -0.0226, 0.4277,
- 0.2468],
- [ 0.5417, -0.4417, 1.7499, -0.1304, -0.1994, -0.0324, 0.0951,
- -0.0099],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [-2.2859, -2.2859, 1.6229, -1.1081, 0.4162, -1.3005, 1.0070,
- 0.5188]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0200, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0200, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3906042091548443
- step: 51
- running loss: 0.027266749199114593
- Train Steps: 51/90 Loss: 0.0273 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6026, 0.3979, 0.8550, 0.4233, 0.3613, 0.5233, 0.5582, 0.4967],
- [0.6216, 0.4099, 0.7225, 0.2033, 0.4188, 0.2217, 0.5975, 0.5283],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6224, 0.4097, 0.7438, 0.2267, 0.3850, 0.2850, 0.5988, 0.5250],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726],
- [0.6110, 0.3984, 0.8750, 0.4933, 0.4625, 0.4950, 0.5578, 0.5676]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4308, -0.5393, 1.9275, -0.0703, -0.3942, -0.2634, 0.1682, 0.1026],
- [ 0.3524, -0.5766, 1.8549, -0.2104, -0.5534, -0.6176, 0.5192, 0.3368],
- [ 0.3679, -0.5808, 1.7765, -0.1534, -0.5801, 0.0536, 0.4781, 0.1789],
- [ 0.4266, -0.5153, 1.0804, -1.1756, -0.2470, -1.4130, 0.3684, 0.2618],
- [ 0.5300, -0.4728, 1.1145, -1.2576, -0.2983, -1.2850, 0.6322, 0.2698],
- [ 0.2888, -0.6171, 1.1986, -1.1513, -0.3956, -1.0440, 0.6077, 0.2181],
- [ 0.6086, -0.4195, 1.2452, -0.9810, -0.4951, -1.0781, 0.2751, 0.0469],
- [ 0.2659, -0.6734, 1.8575, 0.0737, -0.0717, -0.0615, 0.3565, 0.3574]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6634e-01, -3.9546e-01, 1.7788e+00, -2.3818e-01, -4.0370e-01,
- -2.6898e-01, 8.2802e-02, -2.1963e-02],
- [ 5.8799e-01, -3.8868e-01, 1.8423e+00, -3.3056e-01, -6.2309e-01,
- -5.2302e-01, 4.0462e-01, 1.5443e-01],
- [ 4.9740e-01, -4.4819e-01, 1.6633e+00, -3.3056e-01, -6.1732e-01,
- 1.3133e-01, 2.9255e-01, 8.0947e-03],
- [ 5.8505e-01, -3.9246e-01, 1.0513e+00, -1.3467e+00, -3.5173e-01,
- -1.2620e+00, 4.7390e-01, 1.5443e-01],
- [ 5.7841e-01, -4.0847e-01, 1.0859e+00, -1.3929e+00, -4.0370e-01,
- -1.1158e+00, 5.6051e-01, 2.4681e-01],
- [ 5.8909e-01, -3.9369e-01, 1.1494e+00, -1.2390e+00, -5.0762e-01,
- -9.6952e-01, 4.7968e-01, 1.3903e-01],
- [ 4.9971e-01, -4.4465e-01, 1.1610e+00, -9.7721e-01, -6.0577e-01,
- -1.0311e+00, 1.4038e-01, -1.0312e-01],
- [ 5.3637e-01, -4.4573e-01, 1.7557e+00, -7.2363e-03, -1.4965e-01,
- 4.6189e-04, 2.9049e-01, 3.3573e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0164, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0164, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4069728069007397
- step: 52
- running loss: 0.027057169363475762
- Train Steps: 52/90 Loss: 0.0271 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6190, 0.4135, 0.8000, 0.4883, 0.3566, 0.3647, 0.5613, 0.5900],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019],
- [0.6257, 0.4167, 0.8775, 0.3433, 0.3563, 0.4133, 0.6200, 0.5667],
- [0.6168, 0.4029, 0.8523, 0.3417, 0.3588, 0.5000, 0.6125, 0.5400],
- [ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6113, 0.4088, 0.6859, 0.2208, 0.4363, 0.1700, 0.5188, 0.5533],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6663, -0.3088, 1.6004, 0.0154, -0.4911, -0.6876, 0.0623, 0.3197],
- [ 0.8698, -0.2878, 1.8808, -0.0299, -0.4834, -0.4837, 0.6643, 0.0741],
- [ 0.7847, -0.2747, 1.7890, -0.5887, -0.5645, -0.3924, 0.5472, 0.3043],
- [ 0.5769, -0.4142, 1.6525, -0.7106, -0.5385, 0.0847, 0.6281, 0.2195],
- [-2.3713, -2.3257, 1.1043, -1.3198, -0.1935, -1.3207, 0.1519, 0.2115],
- [ 0.7094, -0.3830, 1.8119, -0.5825, -0.2013, -0.9141, 0.9488, 0.0407],
- [ 0.3497, -0.5628, 0.9207, -1.2847, -0.1244, -1.5964, 0.2215, 0.2328],
- [ 0.5139, -0.4596, 1.3448, -0.5620, -0.5908, -0.3688, 0.0872, 0.2178]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5732, -0.3761, 1.4092, -0.0303, -0.6388, -0.6012, 0.3065,
- 0.4393],
- [ 0.6372, -0.4129, 1.8942, -0.0765, -0.6173, -0.4768, 0.6999,
- 0.0325],
- [ 0.6040, -0.3614, 1.7672, -0.7001, -0.6404, -0.3768, 0.5778,
- 0.3315],
- [ 0.5631, -0.4249, 1.6509, -0.7078, -0.6289, 0.0236, 0.5432,
- 0.2083],
- [-2.2859, -2.2859, 0.8516, -1.3112, -0.4326, -1.2851, 0.0755,
- 0.2930],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5377, -0.3978, 0.8824, -1.2663, -0.2709, -1.5007, 0.1102,
- 0.2699],
- [ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0103, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0103, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4172964449971914
- step: 53
- running loss: 0.026741442358437575
- Train Steps: 53/90 Loss: 0.0267 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6164, 0.4076, 0.8838, 0.4117, 0.3713, 0.5550, 0.6238, 0.5350],
- [0.6198, 0.4114, 0.8263, 0.3283, 0.3550, 0.3583, 0.5813, 0.5667],
- [0.6223, 0.3990, 0.8037, 0.2800, 0.4000, 0.2283, 0.5864, 0.5208],
- [0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5588, -0.4190, 1.8389, -0.3034, -0.4850, 0.2965, 0.6388, 0.2382],
- [ 0.5971, -0.3907, 1.5570, -0.6910, -0.6359, -0.7238, 0.2675, 0.2644],
- [ 0.5777, -0.4140, 1.4146, -1.0344, -0.2636, -1.2908, 0.4869, 0.0262],
- [ 0.3478, -0.5392, 0.8901, -1.1096, -0.2750, -1.3271, 0.4063, 0.2873],
- [ 0.6769, -0.3749, 1.8106, -0.0091, -0.1602, 0.2302, 0.5604, 0.2009],
- [ 0.6794, -0.3695, 1.8073, -0.6632, -0.4131, -0.8047, 0.6484, 0.1249],
- [-1.6592, -1.8379, 1.3722, -0.7628, -0.4710, -1.0075, -0.0038, 0.1739],
- [ 0.3202, -0.5807, 1.1559, -1.1361, -0.2836, -1.1998, 0.3835, 0.2031]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5614, -0.4032, 1.7961, -0.3844, -0.5711, 0.2776, 0.5952,
- 0.1852],
- [ 0.5768, -0.3857, 1.5305, -0.7694, -0.6462, -0.6308, 0.3988,
- 0.3315],
- [ 0.5885, -0.4429, 1.4266, -0.9926, -0.4383, -1.2313, 0.4228,
- 0.1195],
- [ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390],
- [-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0165, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0165, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4337830413132906
- step: 54
- running loss: 0.026551537802097975
- Train Steps: 54/90 Loss: 0.0266 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6264, 0.4035, 0.8888, 0.4883, 0.4050, 0.5217, 0.6361, 0.4791],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6179, 0.3993, 0.8925, 0.4789, 0.3879, 0.4900, 0.6041, 0.5279],
- [0.6154, 0.4112, 0.7037, 0.2317, 0.4238, 0.1833, 0.5350, 0.5600],
- [0.6197, 0.4050, 0.7527, 0.2000, 0.4042, 0.2249, 0.5895, 0.4995],
- [0.6286, 0.4055, 0.9000, 0.4717, 0.3763, 0.4683, 0.7018, 0.5494],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5739, -0.4075, 1.0099, -1.2497, -0.4887, -1.0567, 0.4248, 0.2145],
- [ 0.6474, -0.3825, 1.8956, 0.0107, -0.5090, 0.2286, 0.5319, 0.0127],
- [-2.0089, -2.0760, 1.3973, -1.0495, -0.2714, -1.0593, 0.1613, 0.2091],
- [ 0.5967, -0.3914, 1.8916, -0.0790, -0.5793, 0.0853, 0.3931, 0.1261],
- [ 0.6433, -0.3461, 0.9987, -1.1937, -0.3592, -1.3822, 0.1790, 0.2450],
- [ 0.6662, -0.3432, 1.2133, -1.2991, -0.4466, -1.1694, 0.4190, 0.0401],
- [ 0.4311, -0.5357, 1.9630, -0.0693, -0.5812, -0.0296, 0.7530, 0.2171],
- [ 0.4983, -0.3966, 1.1779, -0.7674, -0.0606, -1.3045, 0.2264, 0.4292]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.6074, -0.4223, 1.8192, -0.0303, -0.4152, 0.1236, 0.6524,
- -0.0731],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.5680, -0.4417, 1.8365, -0.0740, -0.4941, -0.0227, 0.5045,
- 0.1525],
- [ 0.5565, -0.3864, 0.9647, -1.2159, -0.3286, -1.4391, 0.1852,
- 0.3007],
- [ 0.5762, -0.4153, 1.1908, -1.3622, -0.4190, -1.2471, 0.4368,
- 0.0213],
- [ 0.6174, -0.4129, 1.8711, -0.1073, -0.5480, -0.1227, 0.9558,
- 0.2516],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4424332054331899
- step: 55
- running loss: 0.026226058280603454
- Train Steps: 55/90 Loss: 0.0262 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6097, 0.4024, 0.8488, 0.3717, 0.3875, 0.5517, 0.5836, 0.5591],
- [ nan, nan, 0.6469, 0.1943, 0.4025, 0.2000, 0.5125, 0.5533],
- [0.6346, 0.4086, 0.7938, 0.5500, 0.3962, 0.4867, 0.7343, 0.5702],
- [0.6132, 0.4037, 0.6963, 0.2217, 0.4100, 0.1950, 0.5395, 0.5175],
- [0.6164, 0.3956, 0.8757, 0.5088, 0.4300, 0.5320, 0.6022, 0.5202],
- [0.6200, 0.3913, 0.8788, 0.5217, 0.4075, 0.5100, 0.6060, 0.4913],
- [0.6127, 0.4115, 0.7163, 0.2883, 0.3625, 0.2950, 0.5327, 0.5495],
- [0.6197, 0.4050, 0.7527, 0.2000, 0.4042, 0.2249, 0.5895, 0.4995]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4872, -0.4469, 1.7565, -0.5619, -0.4863, 0.3109, 0.5125, 0.2862],
- [-1.7611, -1.9040, 0.9830, -1.3490, -0.3156, -1.5371, 0.0834, 0.3199],
- [ 0.5838, -0.4097, 1.6127, 0.1440, -0.5238, -0.0731, 0.8983, 0.3705],
- [ 0.5559, -0.3909, 1.0559, -1.3005, -0.4127, -1.4387, 0.2621, 0.0786],
- [ 0.6276, -0.3755, 1.8506, 0.0234, -0.3498, 0.1800, 0.3969, 0.1361],
- [ 0.4281, -0.5543, 1.8947, 0.0836, -0.3881, 0.0256, 0.3248, 0.0540],
- [ 0.5170, -0.4141, 1.1629, -1.0955, -0.6339, -0.8938, 0.1307, 0.2361],
- [ 0.7367, -0.3005, 1.2420, -1.3779, -0.4522, -1.2652, 0.4379, 0.0683]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5301, -0.4273, 1.6344, -0.5692, -0.4961, 0.2622, 0.4098,
- 0.2966],
- [-2.2859, -2.2859, 0.7023, -1.3883, -0.4268, -1.3621, 0.0813,
- 0.2699],
- [ 0.6454, -0.3984, 1.3804, 0.2545, -0.4557, -0.0380, 1.1057,
- 0.3478],
- [ 0.5465, -0.4214, 0.9300, -1.2620, -0.3921, -1.3852, 0.2062,
- 0.1043],
- [ 0.5614, -0.4586, 1.7587, 0.0641, -0.2998, 0.1712, 0.4958,
- 0.1170],
- [ 0.5776, -0.4784, 1.7730, 0.1236, -0.4037, 0.0697, 0.5132,
- -0.0168],
- [ 0.5442, -0.3855, 1.0224, -0.9541, -0.6115, -0.9233, 0.1745,
- 0.2522],
- [ 0.5762, -0.4153, 1.1908, -1.3622, -0.4190, -1.2471, 0.4368,
- 0.0213]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0146, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0146, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4570275181904435
- step: 56
- running loss: 0.026018348539115062
- Train Steps: 56/90 Loss: 0.0260 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6233, 0.4091, 0.8100, 0.2950, 0.3563, 0.3883, 0.6013, 0.5200],
- [0.6268, 0.4029, 0.8500, 0.2683, 0.3937, 0.3500, 0.6860, 0.5297],
- [0.6248, 0.4032, 0.7738, 0.1900, 0.4813, 0.1400, 0.5941, 0.4904],
- [0.6124, 0.4030, 0.8650, 0.4867, 0.4999, 0.5106, 0.5137, 0.5773],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6225, 0.4191, 0.8500, 0.4167, 0.3688, 0.3233, 0.5650, 0.5650],
- [0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4687, -0.4761, 1.3885, -0.9022, -0.7235, -0.3510, 0.5231, 0.1867],
- [ 0.5900, -0.4263, 1.4820, -1.0055, -0.6282, -0.5480, 0.8535, 0.1611],
- [ 0.5019, -0.4647, 1.1006, -1.1980, -0.0975, -1.5659, 0.3672, 0.0922],
- [ 0.2821, -0.6068, 1.6277, -0.0317, -0.0875, 0.1353, -0.0203, 0.2949],
- [ 0.4712, -0.5273, 1.7103, -0.5717, -0.4082, -0.7752, 0.8000, 0.1014],
- [ 0.3002, -0.5729, 1.6020, -0.7157, -0.7064, -0.2938, 0.3533, 0.2870],
- [ 0.1574, -0.6228, 1.4466, -0.3927, -0.6264, -0.6890, 0.0769, 0.4375],
- [ 0.1880, -0.6467, 1.4838, -1.2031, -0.1930, -1.1545, 0.4361, 0.0573]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5932, -0.3962, 1.4554, -0.9233, -0.6404, -0.4922, 0.4912,
- 0.1159],
- [ 0.6092, -0.4249, 1.6402, -1.0465, -0.4672, -0.6693, 0.8827,
- 0.1608],
- [ 0.5999, -0.4236, 1.2880, -1.4083, -0.0630, -1.6393, 0.4584,
- -0.0208],
- [ 0.5428, -0.4244, 1.7095, -0.0380, 0.0232, 0.0725, 0.0866,
- 0.3806],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.5894, -0.3503, 1.6402, -0.3614, -0.5827, -0.7925, 0.3238,
- 0.3238],
- [ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0224, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0224, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4794276067987084
- step: 57
- running loss: 0.02595487029471418
- Train Steps: 57/90 Loss: 0.0260 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4050, 0.9038, 0.3767, 0.3838, 0.3533, 0.7074, 0.5575],
- [0.6164, 0.3972, 0.7818, 0.3381, 0.3599, 0.3387, 0.5880, 0.5153],
- [0.6201, 0.4116, 0.8725, 0.4733, 0.3700, 0.3217, 0.5386, 0.5767],
- [0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517],
- [0.6168, 0.4029, 0.8523, 0.3417, 0.3588, 0.5000, 0.6125, 0.5400],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [0.6126, 0.4039, 0.8237, 0.3967, 0.3625, 0.3600, 0.5894, 0.6138],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5395, -0.4694, 1.7540, -0.6162, -0.3841, -0.6810, 0.9446, 0.2789],
- [ 0.5050, -0.4520, 1.3034, -1.0162, -0.6673, -0.7612, 0.3418, 0.0111],
- [ 0.4511, -0.4448, 1.5851, -0.2066, -0.5379, -0.8791, 0.2361, 0.2449],
- [ 0.3331, -0.6058, 1.6994, -0.1982, -0.0322, 0.0858, 0.1891, 0.1661],
- [ 0.4013, -0.5315, 1.5514, -0.8512, -0.5781, 0.1099, 0.5527, 0.2199],
- [ 0.5276, -0.4425, 1.3316, -1.2870, -0.3980, -1.1945, 0.4547, 0.0442],
- [ 0.3599, -0.5380, 1.4635, -0.6173, -0.6199, -0.5927, 0.3554, 0.4287],
- [ 0.3724, -0.5610, 1.7453, -0.5381, -0.5880, -0.0687, 0.4760, 0.2247]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4153, 1.8885, -0.5461, -0.5134, -0.6539, 0.9814,
- 0.2890],
- [ 0.5614, -0.4514, 1.3252, -0.7240, -0.6237, -0.7215, 0.4299,
- 0.0942],
- [ 0.5781, -0.3848, 1.7441, -0.0996, -0.5769, -0.8002, 0.2021,
- 0.3778],
- [ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622],
- [ 0.5631, -0.4249, 1.6509, -0.7078, -0.6289, 0.0236, 0.5432,
- 0.2083],
- [ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929],
- [ 0.5438, -0.4206, 1.5189, -0.4537, -0.6115, -0.6231, 0.4365,
- 0.5491],
- [ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0119, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0119, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.491361235268414
- step: 58
- running loss: 0.02571312474600714
- Train Steps: 58/90 Loss: 0.0257 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617],
- [0.6175, 0.3997, 0.6737, 0.2500, 0.4313, 0.1933, 0.5513, 0.5750],
- [0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6201, 0.4055, 0.7717, 0.1919, 0.4250, 0.2079, 0.5871, 0.5044],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6225, 0.4116, 0.8662, 0.3517, 0.3663, 0.3233, 0.5837, 0.5317],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.0428, -1.4417, 1.1209, -1.3021, -0.3286, -1.2498, 0.1953, 0.2366],
- [-0.5951, -1.1337, 0.8469, -1.3820, -0.3139, -1.3906, 0.1110, 0.3584],
- [ 0.7358, -0.2749, 1.8279, -0.0705, -0.3832, 0.1568, 0.2909, 0.1203],
- [ 0.6467, -0.3631, 1.2392, -1.4253, -0.4089, -1.1979, 0.4387, 0.0469],
- [ 0.7252, -0.3748, 1.8057, 0.2314, -0.5462, -0.1135, 0.6252, 0.0910],
- [ 0.7185, -0.2933, 1.6671, -0.7556, -0.6140, -0.6193, 0.4088, 0.1966],
- [ 0.6939, -0.3297, 1.2181, -1.2268, -0.5244, -0.8829, 0.6186, 0.3606],
- [ 0.6410, -0.4152, 1.8992, -0.1185, -0.5496, 0.0964, 0.7300, 0.1463]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084],
- [ 0.5663, -0.4396, 0.8261, -1.1312, -0.2940, -1.3929, 0.2603,
- 0.3700],
- [ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.5785, -0.4129, 1.2785, -1.3996, -0.3227, -1.3259, 0.4258,
- 0.0438],
- [ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [ 0.5893, -0.3847, 1.7152, -0.6616, -0.5942, -0.7925, 0.4104,
- 0.1698],
- [ 0.5915, -0.3682, 1.2187, -1.2313, -0.4326, -0.9541, 0.5778,
- 0.3777],
- [ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0705, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0705, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5618963139131665
- step: 59
- running loss: 0.026472818879884178
- Train Steps: 59/90 Loss: 0.0265 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6196, 0.4094, 0.7562, 0.2817, 0.3937, 0.3183, 0.6013, 0.6183],
- [0.6214, 0.4040, 0.8838, 0.3500, 0.3600, 0.5183, 0.6362, 0.5200],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [ nan, nan, 0.7425, 0.2117, 0.3937, 0.2433, 0.5438, 0.5567],
- [0.6037, 0.4020, 0.8300, 0.4033, 0.3575, 0.4883, 0.5647, 0.5631]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6830, -0.3160, 1.5683, -0.4438, -0.5378, -0.1399, 0.1804, 0.0487],
- [ 0.6534, -0.3342, 1.1585, -1.1978, -0.4531, -0.9316, 0.6637, 0.5017],
- [ 0.7875, -0.2731, 1.6926, -0.7453, -0.5681, 0.1508, 0.7731, 0.1326],
- [ 0.4850, -0.4299, 1.6709, -0.0909, -0.1282, -0.1104, 0.3001, 0.3511],
- [ 0.7053, -0.2990, 1.6272, 0.0098, -0.5108, -0.6850, 0.4937, 0.1394],
- [ 0.7281, -0.3108, 1.7384, -0.3913, -0.5842, -0.4616, 0.5438, -0.0194],
- [-2.2420, -2.2402, 1.1993, -1.3444, -0.4029, -1.2837, 0.1392, 0.1897],
- [ 0.4973, -0.4148, 1.5378, -0.5538, -0.5770, -0.0541, 0.4557, 0.2367]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.5759, -0.3948, 1.2072, -0.9849, -0.4672, -0.8156, 0.4912,
- 0.5701],
- [ 0.5841, -0.4199, 1.7961, -0.6693, -0.6231, 0.1082, 0.6529,
- 0.1159],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698],
- [ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [-2.2859, -2.2859, 1.1436, -1.3082, -0.4672, -1.1620, 0.2256,
- 0.2853],
- [ 0.5027, -0.4289, 1.5478, -0.4229, -0.6346, -0.0303, 0.3223,
- 0.3148]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0100, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0100, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5718588447198272
- step: 60
- running loss: 0.026197647411997118
- Train Steps: 60/90 Loss: 0.0262 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142],
- [0.6264, 0.4067, 0.9050, 0.4183, 0.3775, 0.4600, 0.6308, 0.4862],
- [ nan, nan, 0.6488, 0.1817, 0.4325, 0.1867, 0.5475, 0.5733],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [0.6164, 0.4076, 0.8838, 0.4117, 0.3713, 0.5550, 0.6238, 0.5350],
- [0.6271, 0.4040, 0.9000, 0.3250, 0.4938, 0.2300, 0.7192, 0.5413],
- [0.6284, 0.4029, 0.8838, 0.3783, 0.3975, 0.2850, 0.6335, 0.5090]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.9843, -1.3892, 1.0133, -1.2959, -0.4340, -1.2826, 0.1560, 0.2911],
- [-0.4680, -1.0566, 1.4186, -0.9007, -0.7730, -0.6571, 0.1447, 0.1514],
- [ 0.9725, -0.1446, 1.7648, -0.3390, -0.6778, -0.0703, 0.5688, 0.0236],
- [-1.6986, -1.8749, 0.8447, -1.3785, -0.2807, -1.4518, 0.1705, 0.4223],
- [ 0.9834, -0.1479, 1.6662, -0.1350, -0.1114, 0.1979, 0.4884, 0.2888],
- [ 1.0038, -0.0991, 1.7135, -0.3682, -0.5607, 0.4052, 0.5918, 0.2180],
- [ 0.6469, -0.3788, 1.8183, -0.9520, -0.1124, -1.1401, 0.9596, 0.2261],
- [ 1.1452, -0.0534, 1.6800, -0.5457, -0.5165, -0.8915, 0.6578, 0.0302]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084],
- [-2.2859, -2.2859, 1.5767, -0.7540, -0.6404, -0.7309, 0.1753,
- 0.0893],
- [ 0.6072, -0.4075, 1.8942, -0.3537, -0.5423, -0.1612, 0.6277,
- -0.0400],
- [-2.2859, -2.2859, 0.7106, -1.4468, -0.2882, -1.4237, 0.2430,
- 0.3623],
- [ 0.5598, -0.4273, 1.7152, -0.1227, -0.0065, 0.1917, 0.5143,
- 0.2853],
- [ 0.5614, -0.4032, 1.7961, -0.3844, -0.5711, 0.2776, 0.5952,
- 0.1852],
- [ 0.6108, -0.4201, 1.8711, -0.7848, -0.0053, -1.2236, 1.0362,
- 0.2142],
- [ 0.6165, -0.4249, 1.7961, -0.5384, -0.4499, -0.9695, 0.6401,
- 0.0652]]], device='cuda:0')
- loss_train_step before backward: tensor(0.1461, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.1461, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7179200714454055
- step: 61
- running loss: 0.028162624122055828
- Train Steps: 61/90 Loss: 0.0282 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272],
- [0.6307, 0.4045, 0.8025, 0.5833, 0.3775, 0.4867, 0.6892, 0.5459],
- [0.6182, 0.3967, 0.8263, 0.3065, 0.3526, 0.4161, 0.6192, 0.5290],
- [0.6226, 0.4103, 0.8575, 0.3450, 0.4388, 0.2067, 0.5787, 0.5383],
- [ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383],
- [0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183],
- [0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100],
- [0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4069, -0.4887, 1.5738, -0.8161, -0.4371, -0.9213, 0.2029, 0.1648],
- [ 0.6786, -0.3535, 1.4581, 0.0995, -0.4370, 0.0541, 0.8146, 0.2342],
- [ 0.4607, -0.4850, 1.5293, -0.9322, -0.6656, -0.2563, 0.6008, 0.1711],
- [ 0.8734, -0.1844, 1.6444, -0.8083, -0.2172, -1.0457, 0.3793, 0.2408],
- [-2.2036, -2.2297, 1.0053, -1.3418, -0.4457, -1.3178, 0.0063, 0.2084],
- [ 0.6053, -0.4024, 1.7200, -0.1428, -0.5565, -0.3159, 0.4685, 0.1977],
- [ 0.6157, -0.4194, 1.7860, -0.4878, -0.5991, -0.3522, 0.7102, 0.1331],
- [ 0.4235, -0.4903, 1.1394, -1.3845, -0.5097, -0.8972, 0.5451, 0.3152]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5711, -0.4015, 1.6979, -0.6770, -0.5365, -1.0619, 0.1712,
- 0.1494],
- [ 0.6273, -0.4177, 1.4208, 0.4085, -0.5423, -0.0380, 0.8973,
- 0.2356],
- [ 0.5697, -0.4538, 1.5308, -0.8703, -0.6572, -0.3639, 0.5739,
- 0.1576],
- [ 0.5898, -0.3908, 1.6748, -0.6924, -0.2594, -1.3313, 0.3873,
- 0.2006],
- [-2.2859, -2.2859, 1.0712, -1.2085, -0.3806, -1.3929, 0.0755,
- 0.2006],
- [ 0.5944, -0.4543, 1.8018, 0.0082, -0.6058, -0.4306, 0.4162,
- 0.1082],
- [ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697],
- [ 0.5697, -0.4466, 1.1973, -1.1871, -0.4571, -0.9965, 0.5219,
- 0.2032]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0114, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0114, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7292997566983104
- step: 62
- running loss: 0.027891931559650168
- Train Steps: 62/90 Loss: 0.0279 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6128, 0.4118, 0.8638, 0.5333, 0.4625, 0.5267, 0.5193, 0.5475],
- [0.6225, 0.4191, 0.8500, 0.4167, 0.3688, 0.3233, 0.5650, 0.5650],
- [0.6277, 0.4057, 0.8300, 0.2650, 0.4363, 0.1850, 0.6140, 0.4823],
- [0.6223, 0.3990, 0.8037, 0.2800, 0.4000, 0.2283, 0.5864, 0.5208],
- [0.6228, 0.4119, 0.7938, 0.2233, 0.4674, 0.1773, 0.6188, 0.5433],
- [0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383],
- [0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927],
- [0.6307, 0.4029, 0.8650, 0.5200, 0.3763, 0.4017, 0.7311, 0.5366]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4144, -0.4975, 1.6276, -0.0421, -0.2300, 0.1761, 0.1585, 0.3201],
- [ 0.0341, -0.7305, 1.5266, -0.5993, -0.6648, -0.6568, 0.3565, 0.5090],
- [ 0.5211, -0.4751, 1.5493, -1.3137, -0.4079, -1.2031, 0.6441, 0.0187],
- [ 0.1711, -0.6814, 1.3945, -1.2608, -0.5439, -1.0559, 0.4239, 0.1091],
- [ 0.6617, -0.3810, 1.2957, -1.4371, -0.2348, -1.3179, 0.5682, 0.2448],
- [ 0.4271, -0.5189, 1.6395, -0.2360, -0.3430, 0.0163, 0.2549, 0.3236],
- [ 0.4806, -0.5065, 1.6840, 0.0585, -0.6031, -0.1559, 0.5047, 0.0424],
- [ 0.3668, -0.6003, 1.7427, -0.1002, -0.6017, -0.3306, 0.8768, 0.1167]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.4475e-01, -3.8383e-01, 1.7037e+00, 1.7752e-01, -1.4965e-01,
- 1.4673e-01, 1.1283e-01, 2.4313e-01],
- [ 5.8942e-01, -3.5027e-01, 1.6402e+00, -3.6135e-01, -5.8268e-01,
- -7.9246e-01, 3.2379e-01, 3.2379e-01],
- [ 6.1316e-01, -4.1224e-01, 1.5478e+00, -1.0619e+00, -2.7090e-01,
- -1.4314e+00, 5.5000e-01, -5.8318e-02],
- [ 5.8851e-01, -4.4288e-01, 1.4266e+00, -9.9261e-01, -4.3834e-01,
- -1.2313e+00, 4.2276e-01, 1.1948e-01],
- [ 5.9076e-01, -3.8322e-01, 1.3804e+00, -1.2543e+00, -1.2695e-01,
- -1.4671e+00, 5.7206e-01, 2.2371e-01],
- [ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01],
- [ 6.3355e-01, -4.1617e-01, 1.7499e+00, 3.0839e-01, -4.9607e-01,
- -2.4588e-01, 6.5236e-01, -1.0225e-02],
- [ 6.2730e-01, -4.2490e-01, 1.7095e+00, 1.1594e-01, -5.4804e-01,
- -4.3064e-01, 1.0910e+00, 1.9283e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0269, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0269, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7561887232586741
- step: 63
- running loss: 0.027876011480296416
- Train Steps: 63/90 Loss: 0.0279 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6144, 0.4032, 0.8563, 0.3283, 0.3525, 0.4200, 0.5775, 0.5583],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6126, 0.4067, 0.8638, 0.5383, 0.4188, 0.4850, 0.5016, 0.5392],
- [0.6325, 0.4066, 0.8438, 0.5350, 0.3925, 0.5267, 0.7113, 0.5795],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6200, 0.4055, 0.8011, 0.2988, 0.3842, 0.2798, 0.5949, 0.5183],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6128, 0.4118, 0.8638, 0.5333, 0.4625, 0.5267, 0.5193, 0.5475]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6315, -0.3615, 1.5528, -0.9199, -0.7058, -0.4619, 0.3850, 0.2241],
- [ 0.3478, -0.5686, 1.4856, -0.6887, -0.6811, -0.5351, 0.2528, 0.0194],
- [ 0.2640, -0.5953, 1.5867, 0.0825, -0.2929, -0.1107, 0.1025, 0.2531],
- [ 0.5650, -0.4369, 1.6092, 0.0533, -0.3942, 0.1878, 1.0112, 0.3344],
- [-0.0204, -0.8140, 1.8506, -0.5604, -0.4599, -0.8181, 0.6399, 0.2778],
- [ 0.4338, -0.5079, 1.4570, -1.0512, -0.5840, -1.0056, 0.5227, 0.0549],
- [ 0.4230, -0.5198, 1.8174, -0.6804, -0.5331, -0.5642, 0.7121, 0.2244],
- [ 0.4962, -0.4371, 1.6423, 0.0260, -0.1258, -0.0331, 0.1809, 0.2964]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5520, -0.4237, 1.6691, -0.7694, -0.6577, -0.3460, 0.3815,
- 0.2930],
- [ 0.5206, -0.4603, 1.4670, -0.4768, -0.6577, -0.3998, 0.1836,
- 0.0021],
- [ 0.5436, -0.4076, 1.7037, 0.2006, -0.3517, -0.0457, 0.0311,
- 0.2048],
- [ 0.6355, -0.4080, 1.6113, 0.1852, -0.4730, 0.1467, 0.9996,
- 0.3905],
- [ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.5781, -0.4129, 1.4142, -0.9057, -0.5115, -0.9937, 0.4621,
- 0.1080],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.5447, -0.3838, 1.7037, 0.1775, -0.1497, 0.1467, 0.1128,
- 0.2431]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0206, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0206, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7767991842702031
- step: 64
- running loss: 0.027762487254221924
- Train Steps: 64/90 Loss: 0.0278 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6264, 0.4049, 0.8988, 0.4633, 0.3813, 0.4983, 0.6326, 0.4843],
- [0.6203, 0.4056, 0.8942, 0.4086, 0.3643, 0.3617, 0.5917, 0.5482],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822],
- [0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [0.6203, 0.4076, 0.8611, 0.2878, 0.4050, 0.2554, 0.5907, 0.5496],
- [0.6037, 0.4020, 0.8300, 0.4033, 0.3575, 0.4883, 0.5647, 0.5631]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4024, -0.5408, 1.8413, -0.2530, -0.4907, -0.0938, 0.6912, 0.0739],
- [ 0.2597, -0.6268, 1.7614, -0.5272, -0.5981, -0.6706, 0.5482, 0.1919],
- [ 0.5895, -0.4250, 1.6090, -0.0598, -0.2045, -0.0404, 0.6184, 0.3056],
- [ 0.3920, -0.5026, 1.5573, -0.3926, -0.6188, -0.8363, 0.1831, 0.3305],
- [ 0.3983, -0.5696, 1.7242, 0.0177, -0.4467, -0.0459, 0.4527, 0.1228],
- [ 0.2151, -0.6292, 1.6645, -0.2897, -0.3916, -0.1674, 0.1652, 0.1324],
- [ 0.7846, -0.2886, 1.6542, -1.0929, -0.3467, -1.0930, 0.6925, 0.1557],
- [ 0.0871, -0.7089, 1.5407, -0.4913, -0.5601, -0.1321, 0.4519, 0.3129]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0751e-01, -4.1586e-01, 1.8654e+00, -1.4580e-01, -5.2494e-01,
- 1.5858e-02, 6.3595e-01, -4.9015e-02],
- [ 5.7910e-01, -4.1270e-01, 1.8442e+00, -3.9854e-01, -6.0306e-01,
- -6.1538e-01, 4.4726e-01, 2.4636e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 5.6969e-01, -4.1132e-01, 1.7499e+00, -2.7667e-01, -6.4042e-01,
- -7.1547e-01, 1.5756e-01, 4.0319e-01],
- [ 5.7829e-01, -4.3634e-01, 1.7724e+00, 4.3211e-02, -5.1531e-01,
- 8.7136e-02, 4.8401e-01, 6.6263e-02],
- [ 5.7079e-01, -4.0747e-01, 1.7961e+00, -2.3048e-01, -4.2102e-01,
- -9.9615e-02, 1.2187e-01, 8.9251e-02],
- [ 5.7904e-01, -4.0308e-01, 1.6915e+00, -9.5640e-01, -4.1518e-01,
- -1.1063e+00, 4.4251e-01, 2.5281e-01],
- [ 5.0266e-01, -4.2895e-01, 1.5478e+00, -4.2294e-01, -6.3464e-01,
- -3.0331e-02, 3.2234e-01, 3.1483e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0183, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0183, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7951224902644753
- step: 65
- running loss: 0.02761726908099193
- Train Steps: 65/90 Loss: 0.0276 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550],
- [0.6120, 0.4014, 0.6863, 0.2817, 0.3700, 0.2783, 0.5513, 0.5667],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [0.6202, 0.4064, 0.7879, 0.2179, 0.4567, 0.1725, 0.5955, 0.5478],
- [0.6193, 0.3930, 0.8949, 0.4437, 0.3852, 0.5435, 0.6263, 0.5263]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 4.6338e-01, -5.2227e-01, 1.7455e+00, 1.4470e-01, -4.0361e-01,
- -9.9533e-03, 5.7737e-01, 2.0882e-01],
- [ 5.8856e-01, -4.1358e-01, 1.8907e+00, -1.4242e-01, -1.5540e-03,
- -1.9423e-01, 5.4205e-01, 3.0218e-01],
- [ 1.7223e-02, -7.4201e-01, 9.9789e-01, -9.9806e-01, -6.9489e-01,
- -9.9325e-01, 1.8740e-01, 3.3484e-01],
- [ 5.7928e-01, -4.4168e-01, 1.8534e+00, -2.0523e-01, -4.6537e-01,
- 1.8911e-01, 5.8318e-01, 4.3507e-02],
- [ 2.2809e-01, -6.4014e-01, 1.8076e+00, -2.9703e-01, -4.9680e-01,
- -3.3525e-01, 1.6166e-01, 8.1175e-02],
- [ 5.3848e-01, -4.6859e-01, 1.8441e+00, -2.2687e-01, -6.5880e-01,
- -4.8382e-01, 6.1689e-01, 7.4508e-02],
- [ 5.3224e-01, -4.6670e-01, 1.2817e+00, -1.2365e+00, -2.9876e-01,
- -1.5286e+00, 5.0515e-01, 2.2926e-01],
- [ 1.9818e-01, -6.9324e-01, 1.7965e+00, -2.1564e-01, -5.4197e-01,
- 4.7281e-02, 5.9085e-01, 1.7062e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5778, -0.4389, 1.7107, 0.1192, -0.3921, 0.0815, 0.4741,
- 0.0711],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776],
- [ 0.5410, -0.4321, 0.8838, -0.9849, -0.5769, -1.0003, 0.2603,
- 0.3315],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220],
- [ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [ 0.5787, -0.4086, 1.3535, -1.2794, -0.1764, -1.4891, 0.4645,
- 0.2442],
- [ 0.5746, -0.4706, 1.8476, -0.2365, -0.5068, 0.2245, 0.6069,
- 0.1449]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0180, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0180, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.813149274326861
- step: 66
- running loss: 0.027471958701922136
- Train Steps: 66/90 Loss: 0.0275 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000],
- [ nan, nan, 0.7850, 0.2700, 0.4288, 0.1717, 0.5199, 0.4999],
- [0.6128, 0.4116, 0.8450, 0.4583, 0.3675, 0.4867, 0.5337, 0.5446],
- [0.6268, 0.4061, 0.8350, 0.2433, 0.4575, 0.2283, 0.6350, 0.5300],
- [0.6317, 0.4038, 0.8287, 0.5900, 0.3800, 0.4717, 0.6295, 0.4986],
- [0.6069, 0.3975, 0.8625, 0.5083, 0.4388, 0.5483, 0.5650, 0.4967],
- [0.6357, 0.4118, 0.8400, 0.2500, 0.5413, 0.1633, 0.6725, 0.5586]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6237, -0.3526, 1.4897, -0.8053, -0.2185, -1.1974, 0.4056, 0.2007],
- [ 0.5143, -0.4223, 1.6992, -0.3952, -0.7517, -0.3259, 0.4469, 0.1026],
- [-2.5826, -2.4747, 1.1780, -1.1872, -0.4906, -1.2475, 0.2174, 0.1164],
- [ 0.4828, -0.4180, 1.6138, -0.1066, -0.5749, -0.0056, 0.3014, 0.1830],
- [ 0.1862, -0.6344, 1.5743, -1.1071, -0.3166, -1.1941, 0.6868, 0.2255],
- [ 0.8689, -0.2395, 1.6244, 0.4121, -0.6324, 0.0112, 0.6183, 0.0872],
- [ 0.6781, -0.3274, 1.6930, 0.1251, -0.3152, 0.2715, 0.3076, 0.0937],
- [ 0.8013, -0.2742, 1.6472, -1.0357, 0.1264, -1.3641, 0.7931, 0.2533]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236],
- [-2.2859, -2.2859, 1.3400, -1.0388, -0.3055, -1.4930, 0.1157,
- 0.0231],
- [ 0.5444, -0.3846, 1.6171, -0.1689, -0.5885, -0.0380, 0.1791,
- 0.2296],
- [ 0.6093, -0.4104, 1.5709, -1.1620, -0.1727, -1.2313, 0.6471,
- 0.1621],
- [ 0.6320, -0.4206, 1.5420, 0.4393, -0.5307, -0.1073, 0.6216,
- 0.0171],
- [ 0.5174, -0.4497, 1.6979, 0.0620, -0.2594, 0.2468, 0.3238,
- 0.0082],
- [ 0.6504, -0.3840, 1.5940, -1.1312, 0.2141, -1.5315, 0.8205,
- 0.2944]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0157, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0157, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8288374664261937
- step: 67
- running loss: 0.027296081588450653
- Train Steps: 67/90 Loss: 0.0273 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6226, 0.4185, 0.8850, 0.5500, 0.3800, 0.4250, 0.5625, 0.5617],
- [0.6197, 0.4118, 0.8688, 0.5517, 0.4037, 0.5233, 0.5875, 0.5600],
- [0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433],
- [0.6275, 0.4048, 0.8488, 0.2883, 0.4463, 0.2033, 0.6321, 0.5155],
- [0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167],
- [0.6304, 0.4024, 0.8925, 0.4800, 0.3937, 0.4817, 0.7485, 0.5297],
- [0.6122, 0.3993, 0.8738, 0.4667, 0.4517, 0.4879, 0.5155, 0.4927],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3651, -0.5206, 1.6997, 0.1684, -0.4600, -0.2442, 0.4703, 0.4270],
- [ 0.7231, -0.3273, 1.6843, 0.2476, -0.3617, 0.2596, 0.3580, 0.2787],
- [ 0.2887, -0.6014, 1.3200, -1.1043, -0.6701, -0.5464, 0.3305, 0.1842],
- [ 0.5762, -0.4481, 1.7215, -0.9292, -0.2008, -1.3391, 0.6507, 0.1040],
- [ 0.5290, -0.4668, 1.7715, -0.5002, -0.5998, -0.8600, 0.4262, 0.1119],
- [ 0.3244, -0.6056, 1.7703, -0.1491, -0.4256, 0.0223, 0.7856, 0.1435],
- [ 0.4005, -0.5445, 1.8250, -0.1427, -0.1946, 0.0466, 0.0948, 0.0051],
- [ 0.4814, -0.5089, 1.8568, 0.1708, -0.5595, -0.5359, 0.4954, -0.0020]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5897, -0.3527, 1.8018, 0.2545, -0.5307, -0.3229, 0.3122,
- 0.3084],
- [ 0.5762, -0.3840, 1.7268, 0.2622, -0.4210, 0.1313, 0.4277,
- 0.3007],
- [ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237],
- [ 0.6126, -0.4161, 1.6344, -0.9541, -0.2247, -1.3467, 0.6339,
- 0.0953],
- [ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005],
- [ 0.6257, -0.4273, 1.8365, -0.0688, -0.4672, -0.0611, 1.1715,
- 0.1608],
- [ 0.5417, -0.4417, 1.7499, -0.1304, -0.1994, -0.0324, 0.0951,
- -0.0099],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0129, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0129, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.8417663387954235
- step: 68
- running loss: 0.027084799099932697
- Train Steps: 68/90 Loss: 0.0271 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433],
- [0.6250, 0.4013, 0.8525, 0.5417, 0.4037, 0.5117, 0.6325, 0.5017],
- [0.6261, 0.4029, 0.8720, 0.3364, 0.3665, 0.3753, 0.6531, 0.5183],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [0.6129, 0.4069, 0.8750, 0.5067, 0.3875, 0.4233, 0.5235, 0.5881],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6136, 0.4060, 0.8025, 0.3217, 0.3650, 0.3000, 0.5060, 0.5646],
- [0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7885, -0.2755, 1.8507, -0.0734, -0.4111, 0.0235, 0.3560, 0.0462],
- [ 0.7404, -0.3294, 1.7126, 0.2041, -0.3258, 0.0337, 0.6419, -0.0222],
- [ 0.9415, -0.2172, 1.7814, -0.6563, -0.5288, -0.5494, 0.6929, 0.0388],
- [ 0.4125, -0.5422, 1.9219, -0.7111, -0.3666, -0.8003, 0.6918, 0.0999],
- [ 0.6063, -0.3980, 1.7486, 0.0841, -0.4291, -0.2599, 0.1080, 0.2190],
- [ 0.3537, -0.5790, 1.9364, -0.1108, -0.2992, -0.2235, 0.9382, 0.2877],
- [-1.0781, -1.4772, 1.3764, -0.8284, -0.6005, -0.8796, -0.0899, 0.0682],
- [ 0.6193, -0.3623, 1.5289, 0.2866, -0.4413, -0.5597, 0.1839, 0.4190]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237],
- [ 0.6010, -0.4321, 1.6517, 0.2160, -0.4210, 0.0774, 0.6356,
- 0.0313],
- [ 0.6059, -0.4249, 1.7420, -0.7321, -0.5931, -0.5523, 0.7307,
- 0.1080],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390],
- [ 0.5452, -0.4067, 1.7557, 0.0543, -0.4961, -0.3306, 0.1323,
- 0.4306],
- [ 0.6454, -0.3719, 1.9115, -0.1304, -0.5076, -0.2844, 1.0033,
- 0.4386],
- [ 0.5483, -0.4105, 1.4208, -0.8002, -0.6000, -0.9002, 0.0511,
- 0.3220],
- [ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0728, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0728, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.9145381338894367
- step: 69
- running loss: 0.027746929476658504
- Train Steps: 69/90 Loss: 0.0277 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6122, 0.4048, 0.7506, 0.3014, 0.3475, 0.3333, 0.5487, 0.5749],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250],
- [0.6246, 0.4028, 0.8738, 0.4867, 0.4088, 0.5667, 0.6362, 0.5200]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.1347, -0.8905, 1.3869, -1.0465, -0.3008, -1.1903, 0.5167, 0.1891],
- [ 0.5789, -0.4325, 1.7945, 0.1725, -0.3334, 0.1157, 0.6113, 0.0700],
- [ 0.1578, -0.6654, 1.5865, -0.2879, -0.4701, -0.9997, 0.2556, 0.2780],
- [ 0.3835, -0.5182, 1.2272, -0.7243, -0.6110, -0.6875, 0.1796, 0.2398],
- [ 0.6624, -0.3772, 2.0066, -0.3205, -0.2315, -1.2892, 0.5583, 0.0233],
- [ 1.0005, -0.1530, 1.7703, 0.2153, -0.3215, -0.0169, 0.2178, 0.0890],
- [ 0.5081, -0.4877, 1.9618, -0.1362, -0.5452, -0.0896, 0.5822, 0.0863],
- [ 0.6206, -0.3973, 1.8513, 0.1067, -0.3296, 0.4381, 0.6588, 0.0824]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698],
- [ 0.6010, -0.4562, 1.7198, -0.0090, -0.3464, 0.0108, 0.6294,
- 0.1627],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.5419, -0.4160, 1.1810, -0.8939, -0.6808, -0.7463, 0.2485,
- 0.3694],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854],
- [ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390],
- [ 0.5990, -0.4256, 1.7499, -0.0380, -0.3979, 0.3315, 0.6529,
- 0.1159]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0296, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0296, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.9441209603101015
- step: 70
- running loss: 0.027773156575858594
- Train Steps: 70/90 Loss: 0.0278 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6110, 0.3984, 0.8750, 0.4933, 0.4625, 0.4950, 0.5578, 0.5676],
- [0.6135, 0.3994, 0.7913, 0.3050, 0.3625, 0.3050, 0.5837, 0.5050],
- [0.6205, 0.4012, 0.8675, 0.3283, 0.3713, 0.3050, 0.5813, 0.5117],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6197, 0.4091, 0.8800, 0.4783, 0.3538, 0.4767, 0.5950, 0.5550],
- [0.6161, 0.4024, 0.8662, 0.4683, 0.4935, 0.5364, 0.6063, 0.5567],
- [0.6102, 0.3999, 0.8750, 0.5133, 0.3825, 0.4750, 0.5637, 0.5083],
- [0.6125, 0.3999, 0.8750, 0.4883, 0.4750, 0.4700, 0.5533, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6819, -0.3631, 1.7929, 0.0338, -0.1769, -0.1166, 0.2615, 0.2761],
- [ 0.2571, -0.6269, 1.4967, -0.7880, -0.6286, -0.9278, 0.4804, 0.0170],
- [ 0.1597, -0.7106, 1.8510, -0.6569, -0.5832, -0.9929, 0.5346, 0.0192],
- [ 0.6173, -0.4197, 1.6777, 0.3250, -0.4016, 0.1249, 0.7606, 0.0746],
- [ 0.7167, -0.3175, 1.7641, -0.0079, -0.6089, -0.2013, 0.5217, 0.2568],
- [ 0.8350, -0.2456, 1.8566, -0.0243, -0.0949, -0.0249, 0.4935, 0.2472],
- [ 0.5563, -0.4414, 1.8026, 0.2169, -0.5440, -0.1765, 0.2853, 0.0218],
- [ 0.5862, -0.4149, 1.7682, -0.0044, -0.1104, -0.1928, 0.2206, 0.2828]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3637e-01, -4.4573e-01, 1.7557e+00, -7.2363e-03, -1.4965e-01,
- 4.6189e-04, 2.9049e-01, 3.3573e-01],
- [ 5.4769e-01, -4.4126e-01, 1.3688e+00, -8.7714e-01, -6.1155e-01,
- -8.7714e-01, 4.1039e-01, 4.6651e-02],
- [ 5.7991e-01, -4.3295e-01, 1.7210e+00, -7.6936e-01, -5.7113e-01,
- -8.7714e-01, 3.9885e-01, 7.7444e-02],
- [ 6.0425e-01, -4.2731e-01, 1.7198e+00, 2.1845e-01, -3.4783e-01,
- 1.1492e-01, 8.0616e-01, 1.1755e-01],
- [ 5.7633e-01, -3.9630e-01, 1.7788e+00, -7.6520e-02, -6.5196e-01,
- -8.4219e-02, 4.6236e-01, 2.7760e-01],
- [ 5.5978e-01, -4.2731e-01, 1.7152e+00, -1.2271e-01, -6.4698e-03,
- 1.9169e-01, 5.1432e-01, 2.8530e-01],
- [ 5.3262e-01, -4.3895e-01, 1.7557e+00, 8.5142e-02, -5.1917e-01,
- -9.1917e-02, 3.1801e-01, 6.2048e-02],
- [ 5.4319e-01, -4.3880e-01, 1.7557e+00, -3.0331e-02, -9.1917e-02,
- -1.1501e-01, 2.6993e-01, 3.0867e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0130, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0130, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.9570787139236927
- step: 71
- running loss: 0.027564488928502714
- Train Steps: 71/90 Loss: 0.0276 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4055, 0.7717, 0.1919, 0.4250, 0.2079, 0.5871, 0.5044],
- [0.6102, 0.4020, 0.8638, 0.3717, 0.3625, 0.5017, 0.6038, 0.5500],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600],
- [0.6219, 0.3934, 0.8688, 0.5267, 0.4313, 0.4967, 0.5988, 0.4983],
- [0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901],
- [0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272],
- [0.6197, 0.3986, 0.8800, 0.4617, 0.4188, 0.4783, 0.5687, 0.5550],
- [0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2929, -0.6306, 1.3494, -1.0974, -0.2760, -1.2471, 0.5076, 0.1783],
- [ 0.5876, -0.4227, 1.8131, -0.3082, -0.5716, 0.0681, 0.6366, 0.1759],
- [ 0.4308, -0.4823, 1.2201, -0.8724, -0.5480, -0.7434, 0.4204, 0.2808],
- [ 0.5981, -0.4646, 1.8456, 0.3831, -0.3125, 0.1098, 0.6071, -0.0576],
- [ 0.3022, -0.6097, 1.5507, -0.6316, -0.4949, -1.1007, 0.2263, 0.1064],
- [ 0.7622, -0.3001, 1.7877, -0.3726, -0.3335, -1.0181, 0.2477, 0.1684],
- [ 0.6255, -0.4040, 1.8410, 0.0971, -0.3022, 0.0756, 0.4581, 0.1748],
- [ 0.5553, -0.4920, 1.8473, 0.2026, -0.5247, -0.4048, 0.5851, 0.1129]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5785, -0.4129, 1.2785, -1.3996, -0.3227, -1.3259, 0.4258,
- 0.0438],
- [ 0.5324, -0.4293, 1.7037, -0.5692, -0.6115, 0.0313, 0.5028,
- 0.2545],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007],
- [ 0.5864, -0.4690, 1.7268, 0.1467, -0.2940, 0.0082, 0.4797,
- 0.0159],
- [ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220],
- [ 0.5711, -0.4015, 1.6979, -0.6770, -0.5365, -1.0619, 0.1712,
- 0.1494],
- [ 0.5763, -0.4450, 1.7788, -0.1535, -0.3517, -0.0765, 0.3411,
- 0.2776],
- [ 0.5944, -0.4543, 1.8018, 0.0082, -0.6058, -0.4306, 0.4162,
- 0.1082]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0202, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0202, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.9772933050990105
- step: 72
- running loss: 0.027462407015264034
- Train Steps: 72/90 Loss: 0.0275 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6172, 0.4055, 0.8175, 0.2650, 0.3550, 0.3683, 0.5787, 0.5550],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933],
- [0.6204, 0.4049, 0.7975, 0.2700, 0.3937, 0.2567, 0.5700, 0.5183],
- [0.6107, 0.4050, 0.8700, 0.4850, 0.4470, 0.4848, 0.5043, 0.5431],
- [0.6189, 0.4029, 0.8375, 0.5767, 0.4745, 0.4829, 0.5551, 0.5598]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.5583e-01, -3.7811e-01, 1.2696e+00, -7.9125e-01, -4.5153e-01,
- -8.8227e-01, 6.5653e-01, 3.0809e-01],
- [ 9.5700e-01, -1.8838e-01, 2.0054e+00, -3.3127e-02, -6.0763e-01,
- -4.2334e-01, 6.2569e-01, 8.3709e-02],
- [ 9.3682e-01, -2.0342e-01, 1.6857e+00, -6.1299e-01, -6.7221e-01,
- -5.1084e-01, 4.4851e-01, 6.2466e-02],
- [ 2.5737e-01, -6.2742e-01, 1.3201e+00, -9.9313e-01, -2.6889e-01,
- -1.3625e+00, 3.5545e-01, 1.6475e-01],
- [-1.3298e+00, -1.6522e+00, 1.0108e+00, -9.3262e-01, -3.8892e-01,
- -1.2307e+00, 3.4897e-01, 3.3487e-01],
- [ 3.9808e-01, -5.5571e-01, 1.6143e+00, -7.6801e-01, -4.6551e-01,
- -1.0459e+00, 3.7800e-01, 6.5839e-02],
- [ 6.9094e-01, -3.6286e-01, 1.8962e+00, 1.8539e-01, -2.5873e-01,
- 7.2056e-02, 2.3904e-01, 1.9110e-02],
- [ 6.2867e-01, -4.1586e-01, 1.7483e+00, 5.4881e-01, -1.1541e-01,
- -1.4957e-03, 4.3051e-01, 1.5835e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700],
- [ 0.5880, -0.3887, 1.8423, -0.3306, -0.6231, -0.5230, 0.4046,
- 0.1544],
- [ 0.5647, -0.4129, 1.4901, -1.0619, -0.6462, -0.5846, 0.3873,
- 0.2776],
- [ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [-2.2859, -2.2859, 0.8033, -1.1250, -0.3864, -1.3082, 0.1126,
- 0.4543],
- [ 0.5798, -0.4156, 1.3977, -1.0388, -0.4672, -1.1004, 0.3469,
- 0.1082],
- [ 0.5351, -0.4153, 1.7326, -0.0457, -0.2214, -0.0466, 0.0434,
- 0.2228],
- [ 0.5726, -0.4249, 1.5824, 0.3777, -0.0942, -0.0556, 0.2782,
- 0.2997]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0512, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0512, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.028471328318119
- step: 73
- running loss: 0.02778727847011122
- Train Steps: 73/90 Loss: 0.0278 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6361, 0.4102, 0.8650, 0.2983, 0.4888, 0.2000, 0.6702, 0.5459],
- [0.6226, 0.4185, 0.8850, 0.5500, 0.3800, 0.4250, 0.5625, 0.5617],
- [ nan, nan, 0.6488, 0.1817, 0.4325, 0.1867, 0.5475, 0.5733],
- [0.6087, 0.3976, 0.8337, 0.3867, 0.3713, 0.3117, 0.5938, 0.5300],
- [0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6416, -0.3906, 1.7688, -0.7565, -0.0875, -1.1663, 0.7043, 0.1075],
- [ 0.7966, -0.2625, 1.7855, 0.4262, -0.6310, -0.1006, 0.5265, 0.2256],
- [-1.6472, -1.8579, 1.0097, -1.0127, -0.2529, -1.2835, 0.3738, 0.4171],
- [ 0.6535, -0.3692, 1.6415, -0.3841, -0.6620, -0.6364, 0.3850, 0.0625],
- [ 0.6294, -0.3894, 1.0214, -1.1424, -0.4673, -1.1836, 0.2044, 0.2154],
- [ 0.6398, -0.3967, 1.7436, 0.1655, -0.4967, 0.0448, 0.2775, 0.0594],
- [ 0.5993, -0.4317, 1.8031, -0.9034, 0.0458, -1.0696, 0.9169, 0.1342],
- [ 0.7419, -0.3240, 1.7888, 0.0059, -0.5461, 0.0415, 0.1476, 0.0034]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6520, -0.3912, 1.7095, -0.9079, -0.0284, -1.3621, 0.8096,
- 0.2356],
- [ 0.5897, -0.3527, 1.8018, 0.2545, -0.5307, -0.3229, 0.3122,
- 0.3084],
- [-2.2859, -2.2859, 0.7106, -1.4468, -0.2882, -1.4237, 0.2430,
- 0.3623],
- [ 0.5255, -0.4495, 1.5651, -0.4999, -0.5711, -0.8463, 0.4566,
- 0.1621],
- [ 0.5232, -0.4189, 0.9358, -1.4006, -0.3921, -1.3698, 0.2555,
- 0.2906],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854],
- [ 0.6207, -0.3936, 1.7788, -1.1235, 0.1448, -1.0850, 1.1459,
- 0.1982],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0284, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0284, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.056894328445196
- step: 74
- running loss: 0.027795869303313462
- Train Steps: 74/90 Loss: 0.0278 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6286, 0.4086, 0.8408, 0.2801, 0.4163, 0.2800, 0.6725, 0.5393],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320],
- [0.6159, 0.4085, 0.6900, 0.2283, 0.4088, 0.1950, 0.5123, 0.5397],
- [0.6146, 0.4124, 0.8800, 0.4867, 0.3800, 0.4500, 0.5106, 0.5524],
- [0.6136, 0.4060, 0.8025, 0.3217, 0.3650, 0.3000, 0.5060, 0.5646],
- [0.6364, 0.4092, 0.8525, 0.5633, 0.3875, 0.4950, 0.6599, 0.5285],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6127, 0.4084, 0.8700, 0.4467, 0.3987, 0.4317, 0.5013, 0.5471]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.8721e-01, -3.6603e-01, 1.6273e+00, -9.3910e-01, -2.3020e-01,
- -1.0608e+00, 8.1830e-01, 2.8156e-01],
- [ 2.6261e-01, -6.6006e-01, 1.7028e+00, 6.9278e-02, -5.0285e-01,
- 6.1316e-02, 9.1670e-01, 9.0216e-02],
- [ 7.8071e-02, -7.5531e-01, 9.7520e-01, -1.1406e+00, -3.2744e-01,
- -1.3896e+00, 1.0609e-01, 2.5919e-01],
- [ 6.7547e-01, -3.5546e-01, 1.7788e+00, -4.9031e-02, -5.2201e-01,
- -2.5553e-01, 2.1430e-01, 1.6061e-01],
- [ 9.4422e-01, -1.8775e-01, 1.4252e+00, -7.5169e-01, -5.3062e-01,
- -8.7556e-01, -3.1819e-02, 1.9832e-01],
- [ 4.0847e-01, -5.7434e-01, 1.6866e+00, 2.0950e-01, -4.9814e-01,
- 1.7580e-03, 7.6417e-01, 9.1559e-02],
- [ 6.3641e-01, -3.8298e-01, 1.7680e+00, -2.8971e-01, -5.1222e-01,
- -7.0556e-01, 4.5485e-01, 2.9442e-01],
- [ 4.7471e-01, -4.8755e-01, 1.7431e+00, -2.4426e-01, -4.8224e-01,
- -3.1786e-01, 1.0535e-01, 1.8711e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.1742e-01, -3.9842e-01, 1.5975e+00, -9.9214e-01, -3.6328e-01,
- -9.9261e-01, 8.2047e-01, 2.0505e-01],
- [ 6.2730e-01, -4.3934e-01, 1.6402e+00, 1.3133e-01, -5.0762e-01,
- 4.6651e-02, 1.1532e+00, 1.7146e-01],
- [ 5.5912e-01, -3.9900e-01, 9.0115e-01, -1.2313e+00, -3.9792e-01,
- -1.3852e+00, 8.0445e-02, 2.0706e-01],
- [ 5.5289e-01, -3.8106e-01, 1.7788e+00, -3.8029e-02, -5.3072e-01,
- -2.0739e-01, 7.2734e-02, 2.6568e-01],
- [ 5.4825e-01, -4.1045e-01, 1.4208e+00, -8.0015e-01, -6.0000e-01,
- -9.0023e-01, 5.1142e-02, 3.2204e-01],
- [ 6.5365e-01, -3.9601e-01, 1.6517e+00, 3.1609e-01, -4.9607e-01,
- 4.6189e-04, 7.6203e-01, 1.5543e-01],
- [ 6.0162e-01, -3.6328e-01, 1.7152e+00, -2.2279e-01, -6.1155e-01,
- -6.3849e-01, 5.0277e-01, 2.6990e-01],
- [ 5.4405e-01, -3.9969e-01, 1.7326e+00, -2.2279e-01, -4.4411e-01,
- -2.9207e-01, 2.9551e-02, 2.4088e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0174, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0174, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.0743119940161705
- step: 75
- running loss: 0.02765749325354894
- Train Steps: 75/90 Loss: 0.0277 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6286, 0.4097, 0.8107, 0.2414, 0.4425, 0.2483, 0.6745, 0.5385],
- [0.6262, 0.4163, 0.8850, 0.5183, 0.3763, 0.4150, 0.6025, 0.5500],
- [ nan, nan, 0.9088, 0.3783, 0.4562, 0.2617, 0.6741, 0.5575],
- [0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8649, -0.2485, 1.7574, -0.6191, -0.2934, -1.1843, 0.4251, 0.1032],
- [ 0.4361, -0.5190, 1.6486, -0.2521, -0.3616, -0.0664, 0.2143, 0.2285],
- [ 0.3731, -0.5932, 1.5713, 0.0356, -0.5071, -0.1149, 0.5145, 0.1103],
- [ 0.4553, -0.4569, 1.2745, -0.7047, -0.4934, -0.8180, 0.3275, 0.3119],
- [ 0.7047, -0.3546, 1.3248, -1.2568, -0.2380, -1.1344, 0.7567, 0.2289],
- [ 0.6938, -0.3485, 1.6413, -0.0666, -0.5683, -0.3663, 0.4046, 0.1990],
- [-0.3272, -1.0349, 1.8241, -0.5274, -0.2300, -0.9745, 0.7401, 0.2870],
- [ 0.7128, -0.3070, 1.5699, -0.2741, -0.6330, -0.5391, 0.0100, 0.2550]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469],
- [ 0.6174, -0.3936, 1.4586, -1.1709, -0.2420, -1.1389, 0.8296,
- 0.2012],
- [ 0.6066, -0.3632, 1.8018, 0.1082, -0.5480, -0.3691, 0.4970,
- 0.2545],
- [-2.2859, -2.2859, 1.9115, -0.5384, -0.1785, -1.0773, 0.8278,
- 0.2890],
- [ 0.5454, -0.4053, 1.6633, -0.1766, -0.6058, -0.5923, 0.1577,
- 0.4357]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0945, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0945, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.168797843158245
- step: 76
- running loss: 0.028536813725766382
- Train Steps: 76/90 Loss: 0.0285 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6245, 0.4115, 0.8700, 0.4883, 0.4625, 0.5517, 0.6100, 0.5217],
- [0.6154, 0.4112, 0.7037, 0.2317, 0.4238, 0.1833, 0.5350, 0.5600],
- [0.6168, 0.4055, 0.7575, 0.2950, 0.4050, 0.2717, 0.5938, 0.6167],
- [0.6201, 0.4027, 0.8029, 0.2728, 0.4042, 0.2310, 0.5980, 0.5391],
- [0.6250, 0.4116, 0.8700, 0.4850, 0.4650, 0.5567, 0.6088, 0.5183],
- [0.6197, 0.4090, 0.7825, 0.2500, 0.4200, 0.2483, 0.5988, 0.5667],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150],
- [0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5790, -0.4246, 1.7650, -0.0211, -0.2898, 0.0674, 0.4351, 0.2170],
- [ 0.7075, -0.3480, 1.0163, -1.1494, -0.3797, -1.5016, 0.1140, 0.3749],
- [ 0.5688, -0.4119, 1.2305, -0.8478, -0.4889, -1.1087, 0.3657, 0.4492],
- [ 0.5267, -0.4787, 1.4408, -0.9947, -0.4450, -1.2665, 0.3767, 0.1211],
- [ 0.5472, -0.4517, 1.7947, -0.1033, -0.2681, 0.1028, 0.4809, 0.2489],
- [ 0.4713, -0.5011, 1.2762, -1.1014, -0.4127, -1.2061, 0.4065, 0.3346],
- [ 0.3543, -0.5869, 1.7987, -0.0647, -0.3814, 0.2580, 0.5179, 0.1634],
- [ 0.3446, -0.6577, 1.8439, -0.3129, -0.6954, -0.6245, 0.6689, 0.0432]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5987, -0.3852, 1.7326, -0.0303, -0.1497, 0.2622, 0.5316,
- 0.1236],
- [ 0.5565, -0.3864, 0.9647, -1.2159, -0.3286, -1.4391, 0.1852,
- 0.3007],
- [ 0.5631, -0.4129, 1.2129, -0.9233, -0.4152, -1.0311, 0.4566,
- 0.5624],
- [ 0.5785, -0.4259, 1.4228, -1.0261, -0.4190, -1.2189, 0.4763,
- 0.2043],
- [ 0.6012, -0.3846, 1.7326, -0.0457, -0.1381, 0.2853, 0.5259,
- 0.1082],
- [ 0.5764, -0.3969, 1.3284, -1.1312, -0.3460, -1.1389, 0.4797,
- 0.3315],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928],
- [ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.1782545149326324
- step: 77
- running loss: 0.028289019674449772
- Train Steps: 77/90 Loss: 0.0283 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6117, 0.4019, 0.8538, 0.4067, 0.3513, 0.3583, 0.5663, 0.5133],
- [0.6221, 0.4107, 0.7788, 0.3033, 0.3950, 0.2817, 0.6075, 0.5517],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417],
- [ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433],
- [0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6206, 0.4001, 0.8900, 0.3933, 0.3588, 0.3567, 0.5837, 0.5083]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7530, -0.2939, 1.4046, -1.0338, -0.3590, -1.0755, 0.5026, 0.1979],
- [ 0.4401, -0.4800, 1.5502, -0.4485, -0.5286, -0.7753, 0.2494, 0.2087],
- [ 0.6913, -0.3143, 1.2940, -0.9489, -0.3650, -1.0523, 0.4405, 0.3707],
- [ 0.7265, -0.2805, 1.2429, -1.0236, -0.4281, -0.9581, 0.4826, 0.3433],
- [-1.5196, -1.7774, 1.3053, -0.7442, -0.5791, -0.8100, 0.3340, 0.2008],
- [ 0.4117, -0.5339, 1.6772, -0.1784, -0.2167, 0.0569, 0.6415, 0.2582],
- [ 0.5965, -0.3613, 1.5313, -0.4395, -0.3732, -1.0792, 0.1596, 0.1984],
- [ 0.6020, -0.4144, 1.7071, -0.4450, -0.5114, -0.7503, 0.4338, 0.1238]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7898e-01, -4.0793e-01, 1.5929e+00, -1.0630e+00, -4.7294e-01,
- -1.0725e+00, 4.1374e-01, 8.0707e-02],
- [ 5.3926e-01, -4.2941e-01, 1.6575e+00, -4.0754e-01, -6.6351e-01,
- -6.3079e-01, 3.2956e-01, 8.5142e-02],
- [ 5.8747e-01, -3.8876e-01, 1.3111e+00, -8.8483e-01, -4.6143e-01,
- -9.8491e-01, 5.2009e-01, 2.6220e-01],
- [ 5.9766e-01, -3.7916e-01, 1.2995e+00, -1.0311e+00, -5.1917e-01,
- -8.3865e-01, 5.8360e-01, 2.1601e-01],
- [-2.2859e+00, -2.2859e+00, 1.5478e+00, -8.3095e-01, -6.2887e-01,
- -7.2317e-01, 1.1982e-01, 1.1330e-01],
- [ 6.0087e-01, -4.1347e-01, 1.7651e+00, -1.0433e-01, -1.3233e-01,
- 1.9292e-01, 5.6051e-01, 2.2371e-01],
- [ 5.4850e-01, -4.2094e-01, 1.6691e+00, -4.1524e-01, -5.2494e-01,
- -1.1081e+00, 7.2521e-02, 2.0831e-03],
- [ 5.8072e-01, -4.3780e-01, 1.8249e+00, -4.6913e-01, -6.2887e-01,
- -6.3849e-01, 4.1039e-01, 6.2048e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0242, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0242, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.202410016208887
- step: 78
- running loss: 0.028236025848831885
- Train Steps: 78/90 Loss: 0.0282 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6218, 0.4098, 0.7238, 0.1850, 0.4238, 0.2517, 0.6288, 0.5350],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6271, 0.4024, 0.9000, 0.3517, 0.3700, 0.4517, 0.6931, 0.5285],
- [0.6325, 0.4066, 0.8438, 0.5350, 0.3925, 0.5267, 0.7113, 0.5795],
- [ nan, nan, 0.7553, 0.2722, 0.3875, 0.2550, 0.5125, 0.5633],
- [0.6203, 0.4021, 0.8780, 0.5031, 0.3667, 0.3882, 0.5842, 0.5405],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6716, -0.3651, 1.6848, 0.1993, -0.4682, -0.3016, 0.3698, 0.0878],
- [ 1.1126, -0.0634, 1.0590, -1.5005, -0.3099, -1.4083, 0.3902, 0.3436],
- [ 0.4396, -0.4931, 1.6367, -0.1527, -0.2727, -0.1796, 0.3804, 0.3029],
- [ 0.4806, -0.5013, 1.7506, -0.7712, -0.5522, -0.3615, 0.7104, 0.2110],
- [ 0.7517, -0.3017, 1.6676, 0.0399, -0.4713, 0.0236, 0.7962, 0.3514],
- [-1.4892, -1.7884, 1.1316, -1.2032, -0.4816, -1.2419, 0.0669, 0.3637],
- [ 0.8437, -0.2534, 1.7568, -0.0860, -0.5708, -0.6748, 0.2525, 0.1682],
- [ 0.6160, -0.3820, 1.7347, -0.3169, -0.5240, -0.3235, 0.1420, 0.2635]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.3949e-01, -4.2132e-01, 1.7037e+00, 3.6228e-01, -4.3256e-01,
- -1.0731e-01, 6.5602e-01, -4.8817e-03],
- [ 5.8614e-01, -3.9292e-01, 1.0570e+00, -1.4314e+00, -3.2864e-01,
- -1.1235e+00, 6.1824e-01, 1.8522e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 6.1083e-01, -4.2731e-01, 1.8711e+00, -6.6159e-01, -5.7691e-01,
- -1.9969e-01, 9.1557e-01, 1.5543e-01],
- [ 6.3554e-01, -4.0805e-01, 1.6113e+00, 1.8522e-01, -4.7298e-01,
- 1.4673e-01, 9.9965e-01, 3.9055e-01],
- [-2.2859e+00, -2.2859e+00, 1.2030e+00, -1.0288e+00, -4.9607e-01,
- -1.1081e+00, 8.1293e-02, 3.1609e-01],
- [ 5.7910e-01, -4.2887e-01, 1.7694e+00, 3.7905e-02, -5.9233e-01,
- -4.9270e-01, 4.1265e-01, 2.1070e-01],
- [ 5.3684e-01, -4.4057e-01, 1.7730e+00, -1.7660e-01, -5.2494e-01,
- -5.3426e-02, 2.3141e-01, 3.4688e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0364, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0364, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.2388419173657894
- step: 79
- running loss: 0.02833977110589607
- Train Steps: 79/90 Loss: 0.0283 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6198, 0.4115, 0.7762, 0.2717, 0.3713, 0.3200, 0.5837, 0.5683],
- [0.6199, 0.4071, 0.7600, 0.2117, 0.4037, 0.2767, 0.6138, 0.5550],
- [0.6226, 0.4098, 0.8912, 0.4100, 0.4025, 0.2383, 0.5763, 0.5367],
- [0.6203, 0.4073, 0.8189, 0.2398, 0.4400, 0.2054, 0.5929, 0.5501],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6199, 0.4093, 0.7913, 0.2533, 0.4288, 0.2467, 0.5975, 0.5700],
- [0.6148, 0.4076, 0.8666, 0.4820, 0.4138, 0.5067, 0.5250, 0.5767]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4967, -0.4445, 1.6899, -0.5683, -0.6099, -0.5226, 0.4112, 0.2618],
- [ 0.5527, -0.3992, 1.2878, -0.9653, -0.5312, -0.8909, 0.4131, 0.3071],
- [ 0.5773, -0.3801, 1.1588, -1.2760, -0.3543, -1.0148, 0.4385, 0.3473],
- [ 0.3141, -0.5629, 1.6904, -0.2649, -0.3561, -1.0912, 0.3390, 0.2800],
- [ 0.3397, -0.5448, 1.3303, -1.0739, -0.2484, -1.2535, 0.4785, 0.2336],
- [ 0.5512, -0.4214, 1.4524, -1.0067, -0.4021, -1.0468, 0.4744, 0.1392],
- [ 0.3694, -0.5477, 1.2548, -1.1035, -0.3461, -1.0229, 0.5109, 0.2759],
- [-0.1604, -0.9095, 1.6600, 0.0585, -0.4307, -0.0445, 0.3925, 0.2854]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.5768, -0.3852, 1.2995, -1.0311, -0.5711, -0.8079, 0.4104,
- 0.3392],
- [ 0.5775, -0.4054, 1.2245, -1.3082, -0.4210, -1.0080, 0.5490,
- 0.2776],
- [ 0.5900, -0.3932, 1.8307, -0.3921, -0.4268, -1.1851, 0.3758,
- 0.1929],
- [ 0.5792, -0.4048, 1.4965, -1.1781, -0.2534, -1.3371, 0.4528,
- 0.2549],
- [ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807],
- [ 0.5771, -0.3955, 1.3688, -1.1158, -0.3055, -1.1466, 0.4739,
- 0.3469],
- [ 0.5539, -0.4031, 1.7168, -0.0594, -0.3748, 0.0543, 0.1390,
- 0.3777]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0219, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0219, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.260784700512886
- step: 80
- running loss: 0.028259808756411077
- Train Steps: 80/90 Loss: 0.0283 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6224, 0.4097, 0.7438, 0.2267, 0.3850, 0.2850, 0.5988, 0.5250],
- [0.6153, 0.4117, 0.8688, 0.5167, 0.4895, 0.5647, 0.5524, 0.5136],
- [0.6117, 0.4018, 0.6562, 0.1967, 0.3738, 0.2550, 0.5280, 0.5103],
- [0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167],
- [0.6127, 0.4066, 0.8550, 0.5567, 0.4662, 0.5141, 0.5070, 0.5412],
- [0.6109, 0.4041, 0.6975, 0.3167, 0.3513, 0.3383, 0.5153, 0.5319],
- [0.6275, 0.4024, 0.8600, 0.2283, 0.5350, 0.1800, 0.7074, 0.5413],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5515, -0.4324, 1.2522, -1.1974, -0.5468, -0.9399, 0.5127, 0.2591],
- [ 0.2839, -0.6032, 1.8354, 0.1435, -0.2102, 0.1414, 0.4120, 0.2700],
- [ 0.4407, -0.4859, 0.9970, -1.3051, -0.5360, -1.1848, 0.2556, 0.2368],
- [ 0.4679, -0.5184, 1.8603, -0.3844, -0.6523, -0.8006, 0.4449, 0.1568],
- [ 0.4220, -0.5166, 1.7469, 0.3429, -0.2708, 0.0341, 0.1857, 0.3391],
- [ 0.7520, -0.3054, 1.1945, -0.9319, -0.6896, -0.7861, 0.1203, 0.2432],
- [-0.0073, -0.8187, 1.6968, -1.1910, 0.0458, -1.4126, 1.0480, 0.3479],
- [ 0.5477, -0.4101, 1.0520, -1.3807, -0.5342, -1.2166, 0.3435, 0.2966]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5891, -0.3937, 1.1494, -1.2390, -0.5076, -0.9695, 0.4797,
- 0.1390],
- [ 0.5564, -0.3842, 1.7268, 0.1005, -0.0250, 0.3225, 0.2658,
- 0.0862],
- [ 0.5393, -0.4299, 0.7453, -1.3775, -0.5596, -1.1081, 0.1527,
- 0.0712],
- [ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005],
- [ 0.5443, -0.4079, 1.6633, 0.2853, -0.1325, 0.0888, 0.0558,
- 0.2138],
- [ 0.5359, -0.4193, 0.9358, -0.8232, -0.6635, -0.7232, 0.0943,
- 0.1710],
- [ 0.6125, -0.4273, 1.6864, -1.2313, 0.1852, -1.4545, 0.9814,
- 0.2142],
- [ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0207, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0207, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.281465532258153
- step: 81
- running loss: 0.028166241138989544
- Train Steps: 81/90 Loss: 0.0282 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6111, 0.4033, 0.8300, 0.3267, 0.3588, 0.3333, 0.5444, 0.5637],
- [0.6176, 0.4017, 0.8788, 0.5100, 0.4075, 0.5250, 0.5913, 0.5033],
- [0.6129, 0.4114, 0.6950, 0.2467, 0.4050, 0.2133, 0.5363, 0.5550],
- [0.6132, 0.4118, 0.8200, 0.3633, 0.3563, 0.5400, 0.5787, 0.5136],
- [0.6197, 0.4090, 0.7825, 0.2500, 0.4200, 0.2483, 0.5988, 0.5667],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6236, 0.3977, 0.8985, 0.4806, 0.3835, 0.5216, 0.6613, 0.5166],
- [0.6168, 0.4029, 0.8523, 0.3417, 0.3588, 0.5000, 0.6125, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7174, -0.3203, 1.5432, -0.7647, -0.5346, -0.9745, 0.1499, 0.2596],
- [ 0.5553, -0.4361, 1.7861, 0.0788, -0.2853, 0.0368, 0.4463, 0.1984],
- [ 0.7145, -0.2997, 0.9637, -1.2829, -0.4113, -1.4868, 0.1665, 0.3214],
- [ 0.6634, -0.3140, 1.5424, -0.6475, -0.5232, -0.0573, 0.4547, 0.2503],
- [ 0.4377, -0.4985, 1.2757, -1.2660, -0.3435, -1.3296, 0.5275, 0.3440],
- [ 0.3779, -0.5464, 1.6157, -0.4544, -0.6177, -0.5650, 0.2407, 0.2846],
- [ 0.2344, -0.6762, 1.8225, -0.0604, -0.4420, 0.0151, 0.8304, 0.2047],
- [ 0.3008, -0.5890, 1.6560, -0.6949, -0.5658, -0.1531, 0.5472, 0.2596]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5366, -0.4232, 1.5478, -0.7771, -0.6289, -0.7463, 0.2288,
- 0.3177],
- [ 0.5668, -0.4306, 1.7730, 0.0697, -0.4037, 0.1390, 0.4450,
- 0.0390],
- [ 0.5448, -0.3859, 0.9242, -1.1466, -0.4152, -1.3005, 0.1910,
- 0.2776],
- [ 0.5466, -0.3840, 1.5016, -0.6077, -0.6404, 0.2083, 0.3871,
- 0.0862],
- [ 0.5764, -0.3969, 1.3284, -1.1312, -0.3460, -1.1389, 0.4797,
- 0.3315],
- [ 0.5367, -0.4294, 1.5709, -0.4999, -0.6693, -0.3075, 0.2455,
- 0.3559],
- [ 0.5944, -0.4490, 1.8643, -0.0659, -0.5147, 0.1235, 0.7684,
- 0.1004],
- [ 0.5631, -0.4249, 1.6509, -0.7078, -0.6289, 0.0236, 0.5432,
- 0.2083]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0148, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0148, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.2963146083056927
- step: 82
- running loss: 0.02800383668665479
- Train Steps: 82/90 Loss: 0.0280 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4024, 0.8600, 0.2283, 0.5350, 0.1800, 0.7074, 0.5413],
- [0.6163, 0.4006, 0.8788, 0.4683, 0.3663, 0.4883, 0.5887, 0.5017],
- [0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168],
- [0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6179, 0.4082, 0.6688, 0.2667, 0.3588, 0.3317, 0.5750, 0.5783],
- [0.6172, 0.4055, 0.8175, 0.2650, 0.3550, 0.3683, 0.5787, 0.5550],
- [0.6022, 0.3994, 0.8025, 0.3350, 0.3350, 0.4400, 0.5565, 0.5025],
- [0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1430, -0.7105, 1.6730, -1.2270, 0.1805, -1.4558, 0.9720, 0.2920],
- [ 0.5546, -0.4364, 1.8704, -0.0628, -0.5260, 0.0190, 0.3254, 0.1272],
- [-0.0603, -0.8157, 0.9096, -1.3470, -0.3919, -1.3662, 0.3169, 0.2475],
- [ 0.4587, -0.4335, 1.3221, -0.6188, -0.6190, -0.6014, 0.0964, 0.4070],
- [ 0.5745, -0.3822, 1.0003, -1.0263, -0.6209, -0.8339, 0.3180, 0.3165],
- [ 1.1199, -0.0309, 1.5450, -0.9909, -0.6218, -0.6417, 0.3637, 0.2202],
- [ 0.3321, -0.5572, 1.5391, -0.7018, -0.6428, -0.4123, 0.2566, 0.1486],
- [ 0.4968, -0.4762, 1.9318, 0.1283, -0.5195, -0.2110, 0.7342, 0.2339]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4273, 1.6864, -1.2313, 0.1852, -1.4545, 0.9814,
- 0.2142],
- [ 0.5609, -0.4354, 1.7730, -0.1227, -0.5942, -0.0303, 0.4335,
- 0.0313],
- [ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013],
- [ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.5680, -0.4005, 0.8030, -1.0542, -0.6289, -0.7540, 0.3700,
- 0.3854],
- [ 0.5647, -0.4129, 1.4901, -1.0619, -0.6462, -0.5846, 0.3873,
- 0.2776],
- [ 0.4956, -0.4413, 1.4208, -0.7386, -0.7386, -0.2536, 0.2843,
- 0.0351],
- [ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0251, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0251, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.3214387968182564
- step: 83
- running loss: 0.02796914213034044
- Train Steps: 83/90 Loss: 0.0280 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6201, 0.4055, 0.7717, 0.1919, 0.4250, 0.2079, 0.5871, 0.5044],
- [0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896],
- [0.6336, 0.4154, 0.8900, 0.2767, 0.4988, 0.2867, 0.7422, 0.5540],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [0.6102, 0.4020, 0.8638, 0.3717, 0.3625, 0.5017, 0.6038, 0.5500],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.2269, -2.2273, 1.1713, -1.1375, -0.4127, -1.0421, 0.1625, 0.2478],
- [ 0.6665, -0.2975, 1.0809, -1.5144, -0.3767, -1.2959, 0.3798, 0.1838],
- [ 0.8967, -0.1623, 1.6080, 0.1411, -0.5546, -0.2800, 0.2731, 0.0371],
- [ 0.8213, -0.1644, 1.3793, -0.6185, -0.5996, -0.8791, -0.0651, 0.3671],
- [ 0.6198, -0.3447, 1.5878, -1.1289, -0.0898, -1.0752, 0.8322, 0.2534],
- [ 0.5938, -0.3939, 1.7633, -0.2117, -0.4702, 0.2779, 0.6836, 0.1326],
- [ 0.6316, -0.3298, 1.6066, -0.6564, -0.6365, -0.0327, 0.4298, 0.2297],
- [ 0.6697, -0.3118, 1.7474, -0.2772, -0.3892, -0.8977, 0.6521, 0.3015]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.5785, -0.4129, 1.2785, -1.3996, -0.3227, -1.3259, 0.4258,
- 0.0438],
- [ 0.6335, -0.4162, 1.7499, 0.3084, -0.4961, -0.2459, 0.6524,
- -0.0102],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374],
- [ 0.6405, -0.3671, 1.8249, -1.0080, 0.0178, -0.9618, 1.1422,
- 0.2730],
- [ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554],
- [ 0.5324, -0.4293, 1.7037, -0.5692, -0.6115, 0.0313, 0.5028,
- 0.2545],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0214, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0214, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.342796679586172
- step: 84
- running loss: 0.027890436661740143
- Train Steps: 84/90 Loss: 0.0279 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6199, 0.4093, 0.7913, 0.2533, 0.4288, 0.2467, 0.5975, 0.5700],
- [0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168],
- [0.6180, 0.4064, 0.7200, 0.2467, 0.4275, 0.2367, 0.5963, 0.6217],
- [0.6275, 0.4157, 0.8337, 0.5800, 0.3763, 0.4200, 0.5547, 0.6125],
- [0.6109, 0.4041, 0.6975, 0.3167, 0.3513, 0.3383, 0.5153, 0.5319],
- [0.6226, 0.4185, 0.8850, 0.5500, 0.3800, 0.4250, 0.5625, 0.5617],
- [0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6604, -0.3537, 1.5051, -1.2975, -0.3695, -0.9548, 0.5446, 0.1394],
- [ 0.1555, -0.6838, 1.0384, -1.4719, -0.4375, -1.2605, 0.3670, 0.1280],
- [ 0.3765, -0.5204, 1.1939, -1.3233, -0.3674, -1.1210, 0.4890, 0.3674],
- [ 0.7508, -0.2881, 1.7318, 0.2697, -0.5587, -0.2539, 0.3189, 0.3947],
- [ 0.6070, -0.3943, 1.2620, -1.0188, -0.6195, -0.6756, 0.1259, 0.1423],
- [ 0.6287, -0.3404, 1.9525, 0.1932, -0.5037, -0.1270, 0.3228, 0.3000],
- [ 0.2278, -0.6186, 1.0869, -1.5289, -0.4159, -1.2903, 0.3593, 0.1703],
- [ 0.2406, -0.6494, 1.6878, 0.1533, -0.4699, 0.1824, 0.8549, 0.1438]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5771, -0.3955, 1.3688, -1.1158, -0.3055, -1.1466, 0.4739,
- 0.3469],
- [ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013],
- [ 0.5687, -0.4088, 1.0397, -1.1466, -0.3113, -1.1928, 0.4681,
- 0.5855],
- [ 0.6124, -0.3658, 1.5651, 0.3931, -0.5480, -0.3460, 0.2761,
- 0.5431],
- [ 0.5359, -0.4193, 0.9358, -0.8232, -0.6635, -0.7232, 0.0943,
- 0.1710],
- [ 0.5897, -0.3527, 1.8018, 0.2545, -0.5307, -0.3229, 0.3122,
- 0.3084],
- [ 0.5232, -0.4189, 0.9358, -1.4006, -0.3921, -1.3698, 0.2555,
- 0.2906],
- [ 0.6289, -0.4345, 1.3794, 0.3679, -0.4845, 0.0390, 0.9265,
- 0.1928]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0225, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0225, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.365304049104452
- step: 85
- running loss: 0.02782710646005238
- Train Steps: 85/90 Loss: 0.0278 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117],
- [0.6264, 0.4055, 0.8425, 0.2767, 0.4425, 0.2767, 0.7050, 0.5586],
- [0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6135, 0.4115, 0.8838, 0.4667, 0.4288, 0.6050, 0.5778, 0.5097],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4752, -0.4915, 1.7250, -0.1743, -0.5882, -0.1805, 0.7140, 0.1793],
- [ 0.5620, -0.3887, 1.4668, -1.3343, -0.3144, -1.1160, 0.8748, 0.2578],
- [ 0.6195, -0.3832, 1.7568, -0.3389, -0.4534, 0.3030, 0.6783, 0.1351],
- [ 0.3791, -0.4708, 1.6688, -0.3477, -0.2809, 0.3300, 0.3379, 0.1553],
- [ 0.5733, -0.3845, 1.6611, 0.1772, -0.4800, -0.0565, 0.3584, 0.3798],
- [ 0.5504, -0.4220, 1.6632, -0.1711, -0.4728, -0.0582, 0.2567, 0.2242],
- [ 0.5528, -0.4051, 0.8821, -1.4736, -0.3661, -1.5189, 0.3338, 0.2473],
- [ 0.6205, -0.3280, 1.5286, -0.6112, -0.6699, -0.7295, 0.0736, 0.2279]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774],
- [ 0.6075, -0.4129, 1.6055, -1.0080, -0.2420, -1.0080, 0.9704,
- 0.2944],
- [ 0.5934, -0.4276, 1.8192, -0.1458, -0.4499, 0.3777, 0.6702,
- 0.1082],
- [ 0.5477, -0.3851, 1.7961, -0.1304, -0.3055, 0.5085, 0.3830,
- 0.0682],
- [ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.5250, -0.4661, 1.7383, -0.0765, -0.4268, -0.0226, 0.2535,
- 0.2035],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.5433, -0.4032, 1.6344, -0.4922, -0.5769, -0.5846, 0.0357,
- 0.2567]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0094, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0094, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.374746444635093
- step: 86
- running loss: 0.027613330751570853
- Train Steps: 86/90 Loss: 0.0276 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901],
- [0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309],
- [0.6364, 0.4144, 0.8625, 0.3083, 0.4913, 0.2000, 0.6448, 0.5274],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6168, 0.4081, 0.8438, 0.3367, 0.3500, 0.4667, 0.6212, 0.5400],
- [0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683],
- [0.6311, 0.3998, 0.7975, 0.5767, 0.3838, 0.4850, 0.7327, 0.5343],
- [0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3664, -0.5092, 1.3370, -1.0725, -0.5688, -0.9993, 0.1131, 0.0623],
- [ 0.6189, -0.3521, 1.7721, -0.1010, -0.5002, -0.3470, 0.6110, 0.1564],
- [ 0.2863, -0.5541, 1.6003, -0.9776, -0.1182, -1.1446, 0.6046, 0.1507],
- [ 0.2970, -0.5251, 0.9866, -1.2467, -0.2565, -1.2284, 0.3042, 0.3866],
- [ 0.7506, -0.2783, 1.5889, -0.8261, -0.6352, 0.0359, 0.5611, 0.2387],
- [ 0.5821, -0.3698, 1.6028, 0.0332, -0.5533, -0.3493, 0.1958, 0.3500],
- [ 0.2462, -0.6476, 1.4856, 0.0648, -0.5149, 0.1151, 0.7020, 0.1618],
- [ 0.6343, -0.3339, 1.3000, -1.2645, -0.3201, -1.0795, 0.7160, 0.1657]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220],
- [ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661],
- [ 0.6537, -0.3719, 1.6979, -0.8617, -0.0169, -1.3621, 0.6926,
- 0.1501],
- [ 0.5862, -0.3530, 1.1032, -1.0619, -0.1497, -1.3852, 0.3411,
- 0.3931],
- [ 0.5631, -0.4008, 1.6113, -0.7309, -0.6693, -0.1304, 0.5836,
- 0.2083],
- [ 0.5777, -0.3915, 1.7961, 0.1698, -0.5192, -0.5307, 0.2141,
- 0.3392],
- [ 0.6289, -0.4393, 1.3977, 0.3777, -0.5134, -0.0457, 1.0984,
- 0.1821],
- [ 0.6174, -0.4225, 1.4975, -1.1709, -0.3174, -1.1806, 0.6539,
- 0.1879]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0211, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0211, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.3958609933033586
- step: 87
- running loss: 0.027538632106935156
- Train Steps: 87/90 Loss: 0.0275 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6101, 0.3977, 0.8550, 0.5667, 0.3862, 0.4417, 0.5128, 0.4869],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [0.6095, 0.4002, 0.8533, 0.5168, 0.5031, 0.5094, 0.5125, 0.5433],
- [0.6260, 0.4153, 0.9000, 0.4533, 0.4025, 0.2633, 0.6223, 0.4967],
- [0.6108, 0.4011, 0.8037, 0.3400, 0.3700, 0.2933, 0.5658, 0.5617],
- [0.6221, 0.4107, 0.7788, 0.3033, 0.3950, 0.2817, 0.6075, 0.5517],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3245, -0.5463, 1.6416, 0.1805, -0.5145, -0.1114, 0.1358, 0.1085],
- [ 0.4553, -0.4918, 1.4523, 0.0928, -0.5212, 0.1824, 0.9054, 0.2236],
- [ 0.5183, -0.4307, 1.6151, -0.0479, -0.1344, 0.1879, 0.0953, 0.3011],
- [ 0.4604, -0.4627, 1.8237, -0.4227, -0.4729, -0.9072, 0.5547, 0.1219],
- [ 0.5760, -0.3800, 1.4301, -0.8783, -0.5904, -0.7605, 0.3787, 0.2626],
- [ 0.4793, -0.4195, 1.2977, -1.1489, -0.4808, -0.8817, 0.4595, 0.2736],
- [ 0.5724, -0.3816, 1.1668, -1.3779, -0.2650, -1.3689, 0.5275, 0.1317],
- [ 0.6453, -0.3177, 1.3266, -1.2657, -0.3371, -1.0873, 0.7413, 0.1793]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5320, -0.4488, 1.6633, 0.3315, -0.5018, -0.2459, 0.0828,
- -0.0370],
- [ 0.6289, -0.4345, 1.3794, 0.3679, -0.4845, 0.0390, 0.9265,
- 0.1928],
- [ 0.5295, -0.4373, 1.6553, 0.1011, 0.0380, 0.0671, 0.0813,
- 0.2237],
- [ 0.6055, -0.3676, 1.8711, -0.1920, -0.4268, -1.0696, 0.5887,
- 0.0081],
- [ 0.5355, -0.4335, 1.4266, -0.7155, -0.5769, -0.9310, 0.3275,
- 0.3087],
- [ 0.5875, -0.3888, 1.3111, -0.8848, -0.4614, -0.9849, 0.5201,
- 0.2622],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [ 0.6174, -0.4225, 1.4975, -1.1709, -0.3174, -1.1806, 0.6539,
- 0.1879]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0118, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0118, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.4076440930366516
- step: 88
- running loss: 0.027359591966325588
- Train Steps: 88/90 Loss: 0.0274 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [0.6250, 0.4146, 0.8838, 0.3933, 0.3588, 0.4283, 0.6162, 0.5367],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6265, 0.4091, 0.8950, 0.3533, 0.3600, 0.3967, 0.6295, 0.4901],
- [0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8967, -0.1894, 1.6235, -0.5461, -0.5360, -0.7694, 0.4593, 0.1840],
- [ 0.7258, -0.2661, 1.1216, -1.2929, -0.1953, -1.4731, 0.5593, 0.1530],
- [ 0.6065, -0.3242, 1.6106, -0.5376, -0.5609, -0.3627, 0.5166, 0.2849],
- [ 0.7895, -0.2600, 1.5678, 0.1344, -0.4056, -0.0342, 0.4855, 0.2049],
- [ 0.5367, -0.4251, 1.5836, 0.1638, -0.4319, -0.0222, 0.6117, 0.1143],
- [ 0.9586, -0.0897, 1.6710, -0.7051, -0.5443, -0.4784, 0.5907, 0.1210],
- [ 0.7902, -0.2370, 1.5886, -0.1792, -0.1250, 0.1207, 0.3287, 0.1890],
- [-1.6931, -1.8893, 1.3705, -0.9159, -0.5961, -0.7617, 0.1850, 0.1535]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [ 0.6009, -0.3710, 1.7961, -0.4691, -0.6289, -0.3075, 0.5605,
- 0.1929],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.6077, -0.3965, 1.8480, -0.6539, -0.6231, -0.4537, 0.6216,
- -0.0220],
- [ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622],
- [-2.2859, -2.2859, 1.5767, -0.7540, -0.6404, -0.7309, 0.1753,
- 0.0893]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0235, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0235, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.4311892818659544
- step: 89
- running loss: 0.02731673350411185
- Train Steps: 89/90 Loss: 0.0273 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4133, 0.8800, 0.5117, 0.3713, 0.3283, 0.6223, 0.4967],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389],
- [0.6225, 0.4191, 0.8500, 0.4167, 0.3688, 0.3233, 0.5650, 0.5650],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6350, 0.4144, 0.9250, 0.4100, 0.4125, 0.3750, 0.7129, 0.5945],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433],
- [0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5705, -0.3882, 1.6861, -0.0167, -0.5071, -0.7305, 0.5165, 0.0700],
- [ 0.6534, -0.3226, 1.5963, -0.2239, -0.1139, 0.3164, 0.3441, 0.1890],
- [ 0.5489, -0.3968, 1.0934, -1.3142, -0.5016, -1.0165, 0.5338, 0.1896],
- [ 0.3663, -0.4814, 1.4985, -0.4219, -0.5752, -0.7880, 0.2258, 0.3580],
- [ 0.4916, -0.5086, 1.8622, -0.3967, -0.3204, -0.9278, 0.9234, 0.0578],
- [ 0.3684, -0.5708, 1.8041, -0.3877, -0.3142, -0.5238, 0.8517, 0.3063],
- [ 0.8468, -0.2431, 1.6189, -0.1766, -0.1639, 0.3228, 0.4935, 0.1421],
- [ 0.5632, -0.3607, 1.5821, -0.5251, -0.6298, -0.4061, 0.1537, 0.2595]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6054, -0.3767, 1.7788, 0.0774, -0.5711, -0.7694, 0.5887,
- 0.0081],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.5697, -0.4466, 1.1973, -1.1871, -0.4571, -0.9965, 0.5219,
- 0.2032],
- [ 0.5894, -0.3503, 1.6402, -0.3614, -0.5827, -0.7925, 0.3238,
- 0.3238],
- [ 0.6224, -0.4345, 1.9404, -0.2921, -0.3171, -0.8771, 1.0655,
- 0.2142],
- [ 0.6471, -0.3719, 1.9866, -0.3921, -0.3806, -0.5538, 1.0070,
- 0.4600],
- [ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237],
- [ 0.5757, -0.3917, 1.7095, -0.4768, -0.6346, -0.4229, 0.3931,
- 0.3238]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0120, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0120, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 2.443223439157009
- step: 90
- running loss: 0.027146927101744545
- Valid Steps: 10/10 Loss: nan 6.5902
- --------------------------------------------------
- Epoch: 7 Train Loss: 0.0271 Valid Loss: nan
- --------------------------------------------------
- size of train loader is: 90
- torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6199, 0.4112, 0.8475, 0.3717, 0.3550, 0.4350, 0.6063, 0.6083],
- [0.6271, 0.4020, 0.8375, 0.6083, 0.3925, 0.4867, 0.6037, 0.4626],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650],
- [ nan, nan, 0.9050, 0.3500, 0.5138, 0.2300, 0.7359, 0.5702],
- [0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7039, -0.2916, 1.6499, -0.5477, -0.5646, -0.1249, 0.4112, 0.3280],
- [ 0.8252, -0.2680, 1.5895, 0.4821, -0.4184, 0.0452, 0.6871, -0.0390],
- [ 0.7905, -0.2879, 1.7602, -0.0484, -0.4769, -0.2194, 0.5950, 0.1834],
- [ 0.7472, -0.2969, 1.6772, -0.1715, -0.5247, -0.3710, 0.4660, 0.2601],
- [ 0.8241, -0.2370, 1.0809, -1.1172, -0.4864, -1.0178, 0.3043, 0.0526],
- [ 0.7743, -0.2381, 1.6707, -0.4007, -0.5739, -0.3339, 0.3342, 0.2430],
- [-1.4220, -1.7229, 1.8686, -0.8128, 0.0823, -1.1515, 0.7699, 0.2080],
- [ 0.5649, -0.4012, 1.3509, -1.1256, -0.2297, -1.2799, 0.5118, 0.0663]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5774, -0.3868, 1.6286, -0.5692, -0.6462, -0.2767, 0.5143,
- 0.5239],
- [ 0.6105, -0.4293, 1.5824, 0.5239, -0.4730, -0.0380, 0.5025,
- -0.1492],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.5757, -0.3917, 1.7095, -0.4768, -0.6346, -0.4229, 0.3931,
- 0.3238],
- [-2.2859, -2.2859, 1.8942, -0.6693, 0.0871, -1.2236, 1.1130,
- 0.3478],
- [ 0.5796, -0.4326, 1.4439, -1.1774, -0.2940, -1.3390, 0.3931,
- 0.0928]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0307, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0307, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.030708475038409233
- step: 1
- running loss: 0.030708475038409233
- Train Steps: 1/90 Loss: 0.0307 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6138, 0.4054, 0.8750, 0.4750, 0.4363, 0.5017, 0.5086, 0.5822],
- [0.6364, 0.4165, 0.9088, 0.4367, 0.4075, 0.3150, 0.6448, 0.5297],
- [0.6296, 0.4060, 0.9100, 0.3267, 0.4726, 0.3367, 0.7446, 0.5355],
- [0.6189, 0.4033, 0.8650, 0.5267, 0.4487, 0.5150, 0.5925, 0.5050],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6142, 0.4127, 0.7575, 0.3067, 0.3438, 0.4383, 0.5778, 0.5207]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7814, -0.3283, 1.7350, 0.3717, -0.6610, -0.2527, 0.6901, 0.0986],
- [ 0.4466, -0.4523, 1.5270, -0.9148, -0.1701, -1.3227, 0.4904, 0.1241],
- [ 0.5207, -0.4192, 1.7305, 0.0228, -0.3488, 0.1860, 0.1492, 0.2329],
- [ 0.1713, -0.6661, 1.9103, -0.2153, -0.4411, -0.7909, 0.6483, 0.1719],
- [ 0.6538, -0.4094, 1.7988, -0.7230, -0.2640, -0.6816, 1.0283, 0.1449],
- [ 0.6401, -0.3794, 1.7304, 0.2279, -0.2252, 0.2746, 0.4670, 0.1517],
- [ 0.6683, -0.3525, 1.0400, -1.2745, -0.3444, -1.4572, 0.2691, 0.1660],
- [ 0.5483, -0.4067, 1.2148, -0.8755, -0.6606, -0.2426, 0.4862, 0.2548]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.5491, -0.4132, 1.7557, -0.0919, -0.2709, 0.0313, 0.0635,
- 0.4032],
- [ 0.6537, -0.3623, 1.9115, -0.2690, -0.4037, -0.8309, 0.6926,
- 0.1608],
- [ 0.6224, -0.4105, 1.9173, -0.7771, -0.1030, -0.7308, 1.1532,
- 0.1875],
- [ 0.5726, -0.4230, 1.7095, 0.1467, -0.2132, 0.0928, 0.4508,
- 0.0467],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.5509, -0.3798, 1.2129, -0.8694, -0.6982, -0.2613, 0.3830,
- 0.1193]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0126, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0126, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.043306597508490086
- step: 2
- running loss: 0.021653298754245043
- Train Steps: 2/90 Loss: 0.0217 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6183, 0.4076, 0.8838, 0.4517, 0.3813, 0.4483, 0.5775, 0.5633],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6267, 0.4094, 0.8712, 0.3083, 0.4400, 0.2267, 0.6250, 0.5200],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [0.6364, 0.4144, 0.8625, 0.3083, 0.4913, 0.2000, 0.6448, 0.5274]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5350, -0.4089, 1.6639, -0.7590, -0.2530, -1.2740, 0.3201, 0.0325],
- [ 0.6042, -0.3893, 1.6976, -0.0241, -0.5590, 0.0529, 0.2902, 0.2995],
- [ 0.5105, -0.4591, 1.2283, -1.1165, -0.2299, -1.4138, 0.3845, 0.1057],
- [ 0.5441, -0.4624, 1.8066, -0.1762, -0.5450, -0.5049, 0.5332, 0.1960],
- [ 0.2679, -0.5802, 1.7151, -0.9117, -0.2376, -1.0972, 0.5032, 0.1522],
- [ 0.6417, -0.3980, 1.3725, 0.3973, -0.4975, 0.1530, 0.8924, 0.2196],
- [ 0.7965, -0.3336, 1.8087, -0.2137, -0.5701, 0.3298, 0.9831, 0.1608],
- [ 0.3200, -0.5568, 1.6624, -0.7711, -0.0910, -1.2281, 0.5951, 0.1513]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.5700, -0.4032, 1.7961, -0.1997, -0.5249, -0.2151, 0.3815,
- 0.3161],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.6089, -0.3948, 1.7383, -0.8617, -0.2536, -1.2390, 0.6009,
- 0.1159],
- [ 0.6289, -0.4345, 1.3794, 0.3679, -0.4845, 0.0390, 0.9265,
- 0.1928],
- [ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715],
- [ 0.6537, -0.3719, 1.6979, -0.8617, -0.0169, -1.3621, 0.6926,
- 0.1501]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0125, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0125, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.055825852788984776
- step: 3
- running loss: 0.01860861759632826
- Train Steps: 3/90 Loss: 0.0186 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000],
- [0.6161, 0.4076, 0.8900, 0.4667, 0.4125, 0.5917, 0.6262, 0.5367],
- [0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064],
- [0.6222, 0.3957, 0.8838, 0.5017, 0.3937, 0.4600, 0.5900, 0.5017],
- [0.6267, 0.4065, 0.8313, 0.2467, 0.4788, 0.1733, 0.6312, 0.5133]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5987, -0.4661, 1.6236, -0.9908, -0.4434, -1.1789, 0.7196, 0.0251],
- [ 0.5112, -0.4525, 1.0903, -0.8345, -0.6445, -0.8166, 0.3535, 0.3133],
- [ 0.5388, -0.4309, 1.4379, -0.4941, -0.6618, -0.5530, 0.2934, 0.3137],
- [ 0.7562, -0.3317, 1.7788, -0.3304, -0.7112, -0.4528, 0.5880, 0.0941],
- [ 0.3007, -0.6169, 1.8800, 0.0517, -0.2237, 0.4643, 0.7065, 0.1734],
- [ 0.7015, -0.3517, 1.8053, 0.2233, 0.0085, 0.0399, 0.4138, 0.1027],
- [ 0.3611, -0.5971, 1.8793, 0.2300, -0.4435, -0.2307, 0.6075, 0.0287],
- [ 0.8464, -0.2693, 1.6988, -0.9977, -0.0583, -1.6439, 0.7758, 0.0953]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6042, -0.4225, 1.5420, -1.2082, -0.4730, -1.0311, 0.6380,
- -0.0220],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236],
- [ 0.5598, -0.4032, 1.8249, -0.1304, -0.3806, 0.4470, 0.6067,
- 0.1929],
- [ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532],
- [ 0.5879, -0.4583, 1.7961, 0.0313, -0.4672, -0.1612, 0.4393,
- 0.0313],
- [ 0.6085, -0.4084, 1.5536, -1.1466, -0.0746, -1.4853, 0.6298,
- 0.0851]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0153, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0153, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.07109592575579882
- step: 4
- running loss: 0.017773981438949704
- Train Steps: 4/90 Loss: 0.0178 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6109, 0.4041, 0.6975, 0.3167, 0.3513, 0.3383, 0.5153, 0.5319],
- [ nan, nan, 0.7192, 0.2346, 0.4037, 0.2050, 0.5138, 0.5650],
- [0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412],
- [0.6132, 0.3930, 0.8672, 0.4754, 0.3712, 0.5222, 0.5974, 0.5098],
- [ nan, nan, 0.7425, 0.2117, 0.3937, 0.2433, 0.5438, 0.5567],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7477, -0.3178, 1.2301, -0.7151, -0.6086, -0.7426, 0.3208, 0.1037],
- [-1.8269, -1.9756, 1.2389, -1.0472, -0.3977, -1.2951, 0.2191, 0.1287],
- [ 1.0299, -0.0680, 1.6474, -0.3865, -0.0985, -1.2672, 0.6018, 0.2814],
- [ 0.8511, -0.2321, 1.9707, -0.4202, -0.4431, -1.0457, 0.6645, 0.0311],
- [ 1.0978, -0.1226, 1.8701, 0.1610, -0.4615, 0.1962, 0.8286, 0.0466],
- [-1.8291, -1.9821, 1.4092, -1.0083, -0.3946, -1.1683, 0.3364, 0.0987],
- [ 0.9370, -0.1767, 1.8685, 0.0554, -0.0661, 0.2181, 0.7632, 0.1283],
- [ 1.0451, -0.1336, 1.0969, -0.9064, -0.4676, -1.1103, 0.4340, 0.2015]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5359, -0.4193, 0.9358, -0.8232, -0.6635, -0.7232, 0.0943,
- 0.1710],
- [-2.2859, -2.2859, 1.0361, -1.2021, -0.4210, -1.3390, 0.0871,
- 0.3238],
- [ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882],
- [ 0.5784, -0.4006, 1.7911, -0.5701, -0.5192, -1.0331, 0.4137,
- 0.2139],
- [ 0.5466, -0.4706, 1.7198, -0.0903, -0.5712, 0.1261, 0.4733,
- 0.0688],
- [-2.2859, -2.2859, 1.1436, -1.3082, -0.4672, -1.1620, 0.2256,
- 0.2853],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0528, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0528, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.12389781419187784
- step: 5
- running loss: 0.02477956283837557
- Train Steps: 5/90 Loss: 0.0248 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [0.6276, 0.4120, 0.8738, 0.3133, 0.4225, 0.2217, 0.6203, 0.4892],
- [0.6185, 0.4129, 0.8900, 0.4567, 0.3937, 0.5417, 0.5734, 0.5110],
- [ nan, nan, 0.7225, 0.2167, 0.3987, 0.2283, 0.5427, 0.5181],
- [0.6250, 0.4146, 0.8838, 0.3933, 0.3588, 0.4283, 0.6162, 0.5367],
- [0.6129, 0.4069, 0.8750, 0.5067, 0.3875, 0.4233, 0.5235, 0.5881],
- [0.6259, 0.4133, 0.8200, 0.2317, 0.5025, 0.1533, 0.6250, 0.4947]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4496, -0.4924, 1.7601, 0.0577, -0.3639, -0.1169, 0.4771, 0.1180],
- [ 0.6491, -0.3763, 1.2254, -0.8365, -0.5457, -0.9526, 0.2617, 0.0583],
- [ 0.7002, -0.3444, 1.8390, -0.6355, -0.3098, -1.3207, 0.7930, 0.0159],
- [ 0.8954, -0.2209, 1.8416, -0.0108, -0.3952, 0.1701, 0.6371, 0.1106],
- [-2.1383, -2.1947, 1.1317, -1.1560, -0.4116, -1.2603, 0.1670, 0.1438],
- [ 0.7399, -0.3151, 1.7280, -0.3167, -0.5821, -0.4310, 0.7327, 0.2390],
- [ 0.7173, -0.3291, 1.7100, 0.2690, -0.4947, -0.3467, 0.2674, 0.3007],
- [ 0.7036, -0.3440, 1.5613, -1.0072, 0.0162, -1.4975, 0.7395, 0.0891]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7460e-01, -3.6231e-01, 1.7961e+00, -1.1501e-01, -3.6905e-01,
- -3.8029e-02, 2.2079e-01, 1.4394e-01],
- [ 5.2194e-01, -4.5504e-01, 1.1415e+00, -9.1962e-01, -6.4042e-01,
- -9.3872e-01, 1.8562e-01, 1.4106e-02],
- [ 6.1282e-01, -3.8283e-01, 1.7499e+00, -8.3865e-01, -3.3441e-01,
- -1.2620e+00, 5.7925e-01, -2.6256e-02],
- [ 5.7113e-01, -3.7875e-01, 1.8249e+00, -1.7660e-01, -4.6721e-01,
- 2.1601e-01, 3.6246e-01, 7.4222e-02],
- [-2.2859e+00, -2.2859e+00, 1.0513e+00, -1.2851e+00, -4.4411e-01,
- -1.2313e+00, 2.2057e-01, 1.0729e-01],
- [ 6.0092e-01, -3.7098e-01, 1.7961e+00, -4.6913e-01, -6.2887e-01,
- -3.0747e-01, 5.6051e-01, 1.9292e-01],
- [ 5.4515e-01, -4.0670e-01, 1.7557e+00, 5.4350e-02, -4.9607e-01,
- -3.3056e-01, 1.3228e-01, 4.3063e-01],
- [ 6.0514e-01, -3.7714e-01, 1.5016e+00, -1.2159e+00, 3.5104e-02,
- -1.5777e+00, 6.0099e-01, -9.2270e-04]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0142, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0142, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.13813432399183512
- step: 6
- running loss: 0.02302238733197252
- Train Steps: 6/90 Loss: 0.0230 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6234, 0.4023, 0.8888, 0.4633, 0.3975, 0.5767, 0.6400, 0.5183],
- [0.6228, 0.4004, 0.8750, 0.5250, 0.3825, 0.5233, 0.6362, 0.5000],
- [0.6126, 0.4039, 0.8237, 0.3967, 0.3625, 0.3600, 0.5894, 0.6138],
- [0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6265, 0.4088, 0.8025, 0.1850, 0.4163, 0.2500, 0.6290, 0.4947],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650],
- [0.6307, 0.4029, 0.8650, 0.5200, 0.3763, 0.4017, 0.7311, 0.5366]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5177, -0.5142, 1.8768, -0.0287, -0.3660, 0.3563, 0.6098, 0.1759],
- [ 0.6169, -0.4642, 1.7793, 0.2798, -0.4347, 0.0232, 0.5844, 0.0657],
- [ 0.5446, -0.4551, 1.5762, -0.3746, -0.5773, -0.6849, 0.3518, 0.4858],
- [ 0.5052, -0.5054, 1.2804, -1.1753, -0.3175, -1.4387, 0.2268, 0.0866],
- [ 0.4783, -0.5226, 1.5799, -1.2029, -0.2695, -1.2095, 0.7283, 0.0610],
- [ 0.5183, -0.5346, 1.5911, -0.9976, -0.3980, -1.1554, 0.4931, 0.0675],
- [ 0.4365, -0.5086, 1.7467, -0.3318, -0.5918, -0.4954, 0.2639, 0.3172],
- [ 0.5277, -0.5272, 1.8241, 0.2649, -0.4634, -0.5806, 0.9055, 0.0354]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.9336e-01, -4.2756e-01, 1.8192e+00, -1.4580e-01, -4.4988e-01,
- 3.7768e-01, 6.7021e-01, 1.0824e-01],
- [ 5.9082e-01, -4.3664e-01, 1.7557e+00, 1.3903e-01, -5.1917e-01,
- 1.3133e-01, 6.5289e-01, 2.3557e-02],
- [ 5.4376e-01, -4.2055e-01, 1.5189e+00, -4.5373e-01, -6.1155e-01,
- -6.2309e-01, 4.3649e-01, 5.4914e-01],
- [ 5.5978e-01, -4.2008e-01, 1.1898e+00, -1.3005e+00, -3.8060e-01,
- -1.3313e+00, 3.8730e-01, 7.7444e-02],
- [ 6.0785e-01, -3.9761e-01, 1.4208e+00, -1.4314e+00, -3.6328e-01,
- -1.1312e+00, 6.1950e-01, -9.2270e-04],
- [ 6.0421e-01, -4.2248e-01, 1.5420e+00, -1.2082e+00, -4.7298e-01,
- -1.0311e+00, 6.3800e-01, -2.1963e-02],
- [ 5.7569e-01, -3.9169e-01, 1.7095e+00, -4.7683e-01, -6.3464e-01,
- -4.2294e-01, 3.9307e-01, 3.2379e-01],
- [ 6.2730e-01, -4.2490e-01, 1.7095e+00, 1.1594e-01, -5.4804e-01,
- -4.3064e-01, 1.0910e+00, 1.9283e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.14870771393179893
- step: 7
- running loss: 0.021243959133114134
- Train Steps: 7/90 Loss: 0.0212 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6216, 0.4099, 0.7225, 0.2033, 0.4188, 0.2217, 0.5975, 0.5283],
- [0.6275, 0.4071, 0.8075, 0.2100, 0.4700, 0.1533, 0.6148, 0.4834],
- [0.6129, 0.4063, 0.8738, 0.5250, 0.4313, 0.4733, 0.5230, 0.5874],
- [0.6353, 0.4128, 0.9138, 0.3533, 0.4688, 0.3250, 0.7145, 0.5991],
- [0.6264, 0.3972, 0.8853, 0.4771, 0.3853, 0.4511, 0.6293, 0.5334],
- [0.6258, 0.4143, 0.8525, 0.2617, 0.4950, 0.1667, 0.6219, 0.4967],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5176, -0.4837, 1.0740, -1.1601, -0.4477, -1.2307, 0.2159, 0.1664],
- [ 0.6377, -0.4477, 1.5012, -1.2757, -0.1299, -1.4302, 0.5491, -0.0120],
- [ 0.5548, -0.4542, 1.6038, 0.3398, -0.4500, 0.0341, 0.0801, 0.2882],
- [-0.2789, -1.0308, 1.9276, -0.5532, -0.2452, -0.9053, 1.0819, 0.2952],
- [ 0.6326, -0.4496, 1.7682, 0.0528, -0.6594, -0.1615, 0.5851, 0.0823],
- [ 0.3568, -0.5675, 1.6265, -1.0354, -0.0946, -1.2957, 0.5937, 0.0173],
- [ 0.4348, -0.5392, 1.7455, -0.7572, -0.4726, -0.8393, 0.7825, 0.1674],
- [ 0.4264, -0.4988, 1.6926, 0.0959, -0.7095, -0.5125, 0.3129, 0.2892]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.8505e-01, -3.9246e-01, 1.0513e+00, -1.3467e+00, -3.5173e-01,
- -1.2620e+00, 4.7390e-01, 1.5443e-01],
- [ 6.1264e-01, -4.0570e-01, 1.4439e+00, -1.3159e+00, -1.1501e-01,
- -1.5777e+00, 5.5366e-01, -5.2974e-02],
- [ 5.4527e-01, -4.0908e-01, 1.7499e+00, 1.3903e-01, -2.9400e-01,
- -9.9615e-02, 1.2997e-01, 4.2725e-01],
- [ 6.4871e-01, -3.7916e-01, 1.9346e+00, -6.5389e-01, -1.2079e-01,
- -7.8476e-01, 1.0143e+00, 4.8139e-01],
- [ 6.0754e-01, -4.5138e-01, 1.8032e+00, -8.2167e-02, -5.0606e-01,
- -2.0228e-01, 6.2076e-01, 1.7788e-01],
- [ 6.0479e-01, -3.7229e-01, 1.6517e+00, -1.0773e+00, 4.6189e-04,
- -1.5161e+00, 5.8660e-01, 8.0947e-03],
- [ 6.0260e-01, -4.0323e-01, 1.7326e+00, -7.7706e-01, -3.6905e-01,
- -8.6174e-01, 9.7040e-01, 3.0505e-01],
- [ 5.9024e-01, -3.4927e-01, 1.7961e+00, -7.2363e-03, -5.9423e-01,
- -5.6151e-01, 3.1801e-01, 3.1609e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0322, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0322, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.18095311149954796
- step: 8
- running loss: 0.022619138937443495
- Train Steps: 8/90 Loss: 0.0226 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4017, 0.8871, 0.4621, 0.3517, 0.4675, 0.5999, 0.5106],
- [0.6164, 0.4102, 0.8850, 0.4867, 0.4213, 0.5983, 0.5609, 0.5038],
- [0.6137, 0.4035, 0.8850, 0.4417, 0.3900, 0.4283, 0.5449, 0.5617],
- [0.6149, 0.4054, 0.6713, 0.2333, 0.4025, 0.2017, 0.5213, 0.5695],
- [0.6186, 0.3967, 0.7337, 0.1992, 0.4120, 0.2508, 0.6105, 0.5395],
- [0.6218, 0.4098, 0.7238, 0.1850, 0.4238, 0.2517, 0.6288, 0.5350],
- [0.6196, 0.4068, 0.7645, 0.2234, 0.4575, 0.1737, 0.5926, 0.5284],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3702, -0.6153, 1.9485, 0.0180, -0.6251, -0.1912, 0.5284, 0.1265],
- [ 0.3924, -0.5669, 1.9019, 0.1812, -0.2794, 0.3535, 0.4786, 0.1891],
- [ 0.4712, -0.5183, 1.9494, -0.0167, -0.5042, -0.3461, 0.3179, 0.1923],
- [ 0.0712, -0.7806, 1.0954, -1.0432, -0.4323, -1.4276, 0.1083, 0.2082],
- [ 0.5518, -0.4915, 1.3219, -1.1241, -0.4176, -1.2363, 0.5847, 0.1265],
- [ 0.8444, -0.3112, 1.2868, -1.1265, -0.3504, -1.2792, 0.7130, 0.1989],
- [ 0.5735, -0.4880, 1.4809, -1.0457, -0.1821, -1.5007, 0.5285, 0.0796],
- [ 0.3346, -0.6325, 1.4708, -0.8016, -0.6968, -0.4213, 0.6137, 0.1977]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5783, -0.4306, 1.8114, -0.1515, -0.6617, -0.1268, 0.4851,
- 0.0727],
- [ 0.5614, -0.3912, 1.8018, -0.0380, -0.3402, 0.4778, 0.3049,
- 0.0412],
- [ 0.5488, -0.4221, 1.8018, -0.2459, -0.4845, -0.3075, 0.2309,
- 0.3087],
- [ 0.5544, -0.4133, 0.8145, -1.2082, -0.4268, -1.3544, 0.1221,
- 0.3446],
- [ 0.5713, -0.4538, 1.1028, -1.3659, -0.3831, -1.1273, 0.5340,
- 0.2058],
- [ 0.5861, -0.3929, 1.0570, -1.4314, -0.3286, -1.1235, 0.6182,
- 0.1852],
- [ 0.5761, -0.4070, 1.2452, -1.2541, -0.1725, -1.4835, 0.4511,
- 0.1545],
- [ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0257, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0257, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2066050637513399
- step: 9
- running loss: 0.022956118194593325
- Train Steps: 9/90 Loss: 0.0230 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6202, 0.4066, 0.8746, 0.3376, 0.3717, 0.3090, 0.5842, 0.5165],
- [0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272],
- [0.6245, 0.4115, 0.8700, 0.4883, 0.4625, 0.5517, 0.6100, 0.5217],
- [0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986],
- [0.6186, 0.4013, 0.8191, 0.3188, 0.4279, 0.2060, 0.5767, 0.5411]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2651, -0.6157, 1.2570, -1.0635, -0.5154, -0.8834, 0.6192, 0.2747],
- [ 0.5491, -0.4932, 1.5250, 0.1891, -0.4677, 0.1058, 0.6565, 0.1900],
- [ 0.3616, -0.5737, 1.6377, -0.7714, -0.6248, -0.9259, 0.4174, 0.1688],
- [ 0.2940, -0.6039, 1.6172, -1.1136, -0.1859, -1.2848, 0.6837, 0.0900],
- [ 0.3842, -0.5400, 1.5422, -0.7308, -0.4851, -1.1094, 0.1827, 0.2126],
- [ 0.5567, -0.4578, 1.6566, -0.1017, -0.2839, 0.2518, 0.5364, 0.2497],
- [ 0.4010, -0.5515, 1.8669, -0.6154, -0.2421, -1.3174, 0.5998, 0.1543],
- [ 0.3493, -0.5728, 1.4240, -0.8848, -0.3612, -1.3296, 0.3260, 0.1651]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5977, -0.3792, 1.2995, -1.0311, -0.5192, -0.8386, 0.5836,
- 0.2160],
- [ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.5787, -0.4077, 1.7537, -0.7263, -0.5692, -0.8586, 0.4126,
- 0.1000],
- [ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [ 0.5711, -0.4015, 1.6979, -0.6770, -0.5365, -1.0619, 0.1712,
- 0.1494],
- [ 0.5987, -0.3852, 1.7326, -0.0303, -0.1497, 0.2622, 0.5316,
- 0.1236],
- [ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171],
- [ 0.5713, -0.4321, 1.4975, -0.8134, -0.3094, -1.3345, 0.3779,
- 0.2134]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.22040296904742718
- step: 10
- running loss: 0.02204029690474272
- Train Steps: 10/90 Loss: 0.0220 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901],
- [ nan, nan, 0.7525, 0.2291, 0.3838, 0.3017, 0.6050, 0.5667],
- [0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183],
- [0.6264, 0.3972, 0.8853, 0.4771, 0.3853, 0.4511, 0.6293, 0.5334],
- [0.6157, 0.3956, 0.8323, 0.4138, 0.3479, 0.4431, 0.5914, 0.5131],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960],
- [0.6274, 0.4087, 0.8375, 0.5700, 0.4025, 0.4800, 0.5700, 0.6117]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5814, -0.4430, 1.4538, -1.1155, -0.4904, -1.2293, 0.2936, -0.0107],
- [-1.1313, -1.5455, 1.3149, -1.3389, -0.4164, -1.0452, 0.6576, 0.2857],
- [ 0.7803, -0.3256, 1.7643, -0.2083, -0.5666, -0.6007, 0.4089, 0.1928],
- [ 0.7986, -0.3300, 1.8533, -0.2059, -0.5074, -0.3374, 0.7314, 0.1213],
- [ 0.5316, -0.4786, 1.6618, -0.5652, -0.6384, -0.3324, 0.4430, 0.1096],
- [ 0.5207, -0.4637, 1.5275, -0.6416, -0.4408, -1.1248, 0.3544, 0.2698],
- [ 0.5309, -0.4870, 1.8894, -0.3099, -0.1636, -0.0802, 0.3456, 0.0679],
- [ 0.7446, -0.3162, 1.5247, 0.2188, -0.4249, -0.1904, 0.4876, 0.4739]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220],
- [-2.2859, -2.2859, 1.1898, -1.2278, -0.5134, -0.8925, 0.5085,
- 0.3315],
- [ 0.5944, -0.4543, 1.8018, 0.0082, -0.6058, -0.4306, 0.4162,
- 0.1082],
- [ 0.6075, -0.4514, 1.8032, -0.0822, -0.5061, -0.2023, 0.6208,
- 0.1779],
- [ 0.5581, -0.4586, 1.5586, -0.3747, -0.6792, -0.2391, 0.4455,
- 0.0840],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051],
- [ 0.6118, -0.3983, 1.5824, 0.3469, -0.4268, -0.0688, 0.3469,
- 0.5393]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0416, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0416, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.26203702203929424
- step: 11
- running loss: 0.023821547458117657
- Train Steps: 11/90 Loss: 0.0238 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6286, 0.4055, 0.9000, 0.4717, 0.3763, 0.4683, 0.7018, 0.5494],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117],
- [0.6127, 0.4119, 0.8650, 0.5300, 0.4512, 0.5250, 0.5260, 0.5461],
- [0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6080, -0.3856, 1.7336, -0.8982, -0.5061, -1.2318, 0.4152, 0.1341],
- [ 0.4990, -0.4607, 1.6878, -0.4426, -0.1015, 0.0150, 0.4344, 0.2551],
- [ 0.6212, -0.4318, 1.8470, -0.4213, -0.5903, -0.3445, 0.9861, 0.1653],
- [ 0.5750, -0.4459, 1.7188, -0.4567, -0.6006, -0.5663, 0.4482, 0.2204],
- [ 0.3180, -0.5996, 1.5562, 0.1008, -0.2646, -0.2010, 0.1075, 0.1433],
- [ 0.6339, -0.4288, 1.7481, -0.3449, -0.6115, -0.3539, 0.6958, 0.1332],
- [ 0.5634, -0.4427, 1.6698, -0.0794, -0.2126, -0.0684, 0.2679, 0.2849],
- [ 0.5064, -0.4542, 1.6120, -0.4523, -0.6879, -0.7564, 0.0842, 0.3415]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5784, -0.4006, 1.7911, -0.5701, -0.5192, -1.0331, 0.4137,
- 0.2139],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.6174, -0.4129, 1.8711, -0.1073, -0.5480, -0.1227, 0.9558,
- 0.2516],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610],
- [ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774],
- [ 0.5443, -0.3831, 1.7095, 0.1621, -0.2016, 0.1390, 0.1437,
- 0.2364],
- [ 0.5454, -0.4053, 1.6633, -0.1766, -0.6058, -0.5923, 0.1577,
- 0.4357]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0211, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0211, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2831164803355932
- step: 12
- running loss: 0.0235930400279661
- Train Steps: 12/90 Loss: 0.0236 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3993, 0.8639, 0.3687, 0.3658, 0.3139, 0.6002, 0.5374],
- [0.6325, 0.4165, 0.9000, 0.4617, 0.3813, 0.4900, 0.7485, 0.5447],
- [0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683],
- [0.6284, 0.4127, 0.8538, 0.5867, 0.4363, 0.5083, 0.6038, 0.5433],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6201, 0.4055, 0.7717, 0.1919, 0.4250, 0.2079, 0.5871, 0.5044],
- [0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6903, -0.3651, 1.6474, -0.8374, -0.6341, -0.9663, 0.3582, 0.1343],
- [ 0.7031, -0.3525, 1.8754, -0.3281, -0.5801, -0.0378, 0.9083, 0.2030],
- [ 0.4434, -0.4866, 1.6338, -0.1192, -0.5832, -0.6506, 0.1753, 0.3616],
- [ 0.3422, -0.5627, 1.6275, 0.1652, -0.3122, -0.0549, 0.3189, 0.1649],
- [ 0.5406, -0.4587, 1.6440, -0.0238, -0.5029, -0.2293, 0.4639, 0.0663],
- [ 0.3885, -0.5420, 1.1723, -1.5524, -0.3675, -1.4431, 0.3967, 0.1097],
- [ 0.6008, -0.3660, 1.6981, -0.7082, -0.4588, -0.9460, 0.3118, 0.4068],
- [ 0.5218, -0.4222, 1.7080, -0.3800, -0.1079, 0.0906, 0.3519, 0.2555]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5777, -0.4416, 1.7044, -0.5827, -0.5962, -0.8361, 0.4862,
- 0.1963],
- [ 0.6355, -0.3623, 1.8711, -0.1535, -0.5249, -0.0226, 1.1715,
- 0.2302],
- [ 0.5777, -0.3915, 1.7961, 0.1698, -0.5192, -0.5307, 0.2141,
- 0.3392],
- [ 0.6166, -0.3795, 1.6575, 0.4239, -0.2709, 0.0620, 0.5028,
- 0.2237],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.5785, -0.4129, 1.2785, -1.3996, -0.3227, -1.3259, 0.4258,
- 0.0438],
- [ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0163, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0163, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.29939468391239643
- step: 13
- running loss: 0.02303036030095357
- Train Steps: 13/90 Loss: 0.0230 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285],
- [0.6087, 0.3976, 0.8337, 0.3867, 0.3713, 0.3117, 0.5938, 0.5300],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6201, 0.4116, 0.8725, 0.4733, 0.3700, 0.3217, 0.5386, 0.5767],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852],
- [ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933],
- [0.6164, 0.3956, 0.8757, 0.5088, 0.4300, 0.5320, 0.6022, 0.5202],
- [0.6197, 0.4118, 0.8688, 0.5517, 0.4037, 0.5233, 0.5875, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5391, -0.4051, 1.3651, -0.6155, -0.6337, -0.3972, 0.0932, 0.1569],
- [ 0.8467, -0.2399, 1.5689, -0.7761, -0.5896, -0.9227, 0.5025, 0.1898],
- [ 0.7796, -0.2896, 1.7108, -0.0121, -0.4627, -0.1891, 0.6232, 0.0323],
- [ 0.7671, -0.2283, 1.6868, -0.3612, -0.5792, -0.8732, 0.2958, 0.2853],
- [ 0.6036, -0.3718, 1.7765, -0.1932, -0.1691, -0.1180, 0.2454, 0.3723],
- [-2.3431, -2.3347, 0.9550, -1.3480, -0.4412, -1.4053, 0.1967, 0.3362],
- [ 0.7980, -0.2530, 1.7662, -0.1792, -0.2901, 0.0418, 0.4128, 0.0634],
- [ 0.7255, -0.2844, 1.7484, 0.0438, -0.4412, 0.1162, 0.4451, 0.2396]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552],
- [ 0.5255, -0.4495, 1.5651, -0.4999, -0.5711, -0.8463, 0.4566,
- 0.1621],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.5781, -0.3848, 1.7441, -0.0996, -0.5769, -0.8002, 0.2021,
- 0.3778],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171],
- [-2.2859, -2.2859, 0.8033, -1.1250, -0.3864, -1.3082, 0.1126,
- 0.4543],
- [ 0.5614, -0.4586, 1.7587, 0.0641, -0.2998, 0.1712, 0.4958,
- 0.1170],
- [ 0.5762, -0.3840, 1.7268, 0.2622, -0.4210, 0.1313, 0.4277,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0169, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0169, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3162560146301985
- step: 14
- running loss: 0.022589715330728462
- Train Steps: 14/90 Loss: 0.0226 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.6859, 0.2194, 0.4150, 0.1867, 0.5153, 0.5729],
- [0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364],
- [0.6113, 0.4088, 0.6859, 0.2208, 0.4363, 0.1700, 0.5188, 0.5533],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852],
- [0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6201, 0.4036, 0.8596, 0.3850, 0.3492, 0.3785, 0.5978, 0.5131],
- [0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.8744, -2.0117, 0.9690, -1.2382, -0.4163, -1.3135, 0.1242, 0.3034],
- [ 0.5949, -0.3956, 1.3967, -1.1633, -0.2750, -1.1070, 0.7447, 0.1486],
- [ 0.5934, -0.3800, 0.9055, -1.2207, -0.3371, -1.3213, 0.0684, 0.2919],
- [ 0.4608, -0.4651, 1.8125, 0.0842, -0.1831, 0.1602, 0.2000, 0.3755],
- [ 0.6010, -0.3735, 1.6042, -0.6678, -0.7322, -0.2244, 0.4141, 0.2086],
- [ 0.7164, -0.2757, 1.4874, -0.8664, -0.1305, -1.1824, 0.3852, 0.1847],
- [ 0.8126, -0.2737, 1.7947, -0.4520, -0.7088, -0.3828, 0.3791, 0.0978],
- [ 0.6742, -0.3727, 1.8206, 0.3258, -0.6526, -0.1677, 0.5933, 0.0655]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 0.8824, -1.2727, -0.3691, -1.4237, 0.0943,
- 0.3604],
- [ 0.6158, -0.3960, 1.4092, -1.2774, -0.2074, -1.1851, 0.8491,
- 0.1917],
- [ 0.5377, -0.3978, 0.8824, -1.2663, -0.2709, -1.5007, 0.1102,
- 0.2699],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171],
- [ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.5783, -0.4216, 1.6847, -0.5078, -0.6732, -0.5377, 0.4752,
- 0.0839],
- [ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0122, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0122, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.32841287832707167
- step: 15
- running loss: 0.021894191888471446
- Train Steps: 15/90 Loss: 0.0219 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6257, 0.4167, 0.8775, 0.3433, 0.3563, 0.4133, 0.6200, 0.5667],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6164, 0.4119, 0.7913, 0.2650, 0.3538, 0.3500, 0.5614, 0.5038],
- [0.6262, 0.4052, 0.8888, 0.4700, 0.3675, 0.5117, 0.6350, 0.5233],
- [0.6249, 0.4142, 0.8350, 0.3283, 0.3613, 0.3700, 0.6188, 0.5400],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6161, 0.4076, 0.8900, 0.4667, 0.4125, 0.5917, 0.6262, 0.5367]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6738, -0.3040, 1.6709, -0.5974, -0.5665, -0.4662, 0.4484, 0.4006],
- [ 0.6564, -0.3728, 1.6862, 0.2819, -0.4988, -0.4484, 0.4865, 0.1666],
- [ 0.4000, -0.5246, 1.2383, -1.0922, -0.6371, -0.8293, 0.3081, 0.1241],
- [ 0.6967, -0.3275, 1.8110, -0.0835, -0.4345, -0.0513, 0.5109, 0.1657],
- [ 0.5529, -0.3873, 1.5040, -0.7385, -0.5744, -0.7136, 0.4337, 0.3210],
- [ 0.6056, -0.3954, 1.7734, -0.2286, -0.3551, 0.1743, 0.3885, 0.1289],
- [ 0.3163, -0.5174, 1.6173, -0.2200, -0.4569, -0.3970, -0.0269, 0.4105],
- [ 0.4139, -0.4945, 1.7536, -0.1140, -0.1944, 0.3604, 0.4320, 0.2440]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6040, -0.3614, 1.7672, -0.7001, -0.6404, -0.3768, 0.5778,
- 0.3315],
- [ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [ 0.5614, -0.3834, 1.3688, -1.0619, -0.6520, -0.6693, 0.3069,
- 0.0412],
- [ 0.6063, -0.4142, 1.8192, -0.1150, -0.5885, 0.0774, 0.6471,
- 0.1313],
- [ 0.6006, -0.3728, 1.5709, -0.7694, -0.6173, -0.5769, 0.5721,
- 0.2083],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145],
- [ 0.5598, -0.4032, 1.8249, -0.1304, -0.3806, 0.4470, 0.6067,
- 0.1929]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0115, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0115, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3399082263931632
- step: 16
- running loss: 0.0212442641495727
- Train Steps: 16/90 Loss: 0.0212 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6179, 0.4008, 0.7505, 0.2678, 0.4368, 0.1891, 0.5831, 0.5263],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679],
- [0.6289, 0.4032, 0.8419, 0.5446, 0.4075, 0.5017, 0.6312, 0.5117],
- [0.6263, 0.4233, 0.7924, 0.4626, 0.3788, 0.2883, 0.5573, 0.6047],
- [0.6336, 0.4191, 0.8938, 0.5167, 0.3937, 0.3517, 0.7343, 0.5748],
- [0.6225, 0.4191, 0.8500, 0.4167, 0.3688, 0.3233, 0.5650, 0.5650],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7029, -0.2821, 1.0760, -1.2492, -0.3319, -1.2570, 0.1479, 0.1191],
- [ 0.7587, -0.2442, 1.6849, -0.0106, -0.3610, 0.4631, 0.6869, 0.2252],
- [ 0.5850, -0.3844, 1.6199, 0.0817, -0.3890, 0.0821, 0.4641, 0.0563],
- [ 0.7050, -0.2352, 1.3946, -0.2707, -0.4803, -0.7746, 0.0691, 0.4567],
- [ 0.6798, -0.3020, 1.7941, 0.0049, -0.3509, -0.4692, 0.7393, 0.2167],
- [ 0.6755, -0.2483, 1.5387, -0.4930, -0.5451, -0.6314, 0.1540, 0.3930],
- [ 0.6301, -0.3319, 1.7693, -0.2778, -0.5249, -0.2802, 0.2878, 0.1862],
- [-2.3278, -2.2892, 1.3048, -0.9943, -0.5037, -0.9370, 0.2053, 0.2479]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5680, -0.4345, 1.1806, -1.0490, -0.2683, -1.4127, 0.4074,
- 0.1449],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371],
- [ 0.6189, -0.4238, 1.6026, 0.2295, -0.4037, 0.0313, 0.6298,
- 0.0774],
- [ 0.6069, -0.3309, 1.3742, -0.1493, -0.5365, -0.9541, 0.2884,
- 0.5071],
- [ 0.6405, -0.3503, 1.8423, 0.1005, -0.4672, -0.6616, 1.1057,
- 0.3692],
- [ 0.5894, -0.3503, 1.6402, -0.3614, -0.5827, -0.7925, 0.3238,
- 0.3238],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0158, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0158, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3556927992030978
- step: 17
- running loss: 0.020923105835476342
- Train Steps: 17/90 Loss: 0.0209 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6296, 0.4076, 0.8400, 0.5583, 0.3700, 0.4367, 0.6876, 0.5494],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6200, 0.4071, 0.7338, 0.1917, 0.4200, 0.2450, 0.6150, 0.5517],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6203, 0.4096, 0.8862, 0.4267, 0.3538, 0.4117, 0.6025, 0.5650],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6109, 0.4009, 0.8725, 0.4700, 0.3550, 0.4167, 0.5650, 0.5117]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6595, -0.3308, 1.7053, 0.3498, -0.4550, -0.0557, 0.7336, 0.1286],
- [ 0.5503, -0.4199, 1.9664, -0.1062, -0.2386, -0.7404, 0.8984, 0.3366],
- [ 0.4786, -0.4499, 1.0389, -1.3838, -0.2911, -0.9671, 0.3889, 0.2742],
- [ 0.3249, -0.5281, 0.7924, -1.2884, -0.4708, -0.9557, 0.1264, 0.1307],
- [ 0.3706, -0.5244, 1.7032, -0.2031, -0.5283, -0.3190, 0.2407, 0.3382],
- [ 0.5839, -0.3540, 1.7904, -0.3297, -0.4904, -0.1844, 0.3168, 0.3530],
- [ 0.2467, -0.5829, 1.6835, -0.3035, -0.5309, -0.5152, 0.2587, 0.3660],
- [ 0.6686, -0.3094, 1.7843, -0.1293, -0.5499, -0.2107, 0.1485, 0.0952]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6224, -0.4032, 1.5940, 0.2930, -0.5769, -0.2690, 0.8900,
- 0.2516],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.5779, -0.4054, 1.1032, -1.4006, -0.3460, -1.1543, 0.5547,
- 0.2622],
- [ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.5790, -0.3940, 1.8076, -0.3152, -0.6520, -0.3844, 0.4970,
- 0.3238],
- [ 0.6016, -0.3633, 1.7152, -0.2228, -0.6115, -0.6385, 0.5028,
- 0.2699],
- [ 0.5356, -0.4343, 1.7441, -0.1150, -0.6462, -0.3614, 0.3238,
- 0.0774]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0152, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0152, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3708633789792657
- step: 18
- running loss: 0.020603521054403648
- Train Steps: 18/90 Loss: 0.0206 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6236, 0.3977, 0.8985, 0.4806, 0.3835, 0.5216, 0.6613, 0.5166],
- [0.6329, 0.4175, 0.8550, 0.2333, 0.5425, 0.2250, 0.7398, 0.5609],
- [0.6196, 0.4068, 0.7645, 0.2234, 0.4575, 0.1737, 0.5926, 0.5284],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6364, 0.4092, 0.8525, 0.5633, 0.3875, 0.4950, 0.6599, 0.5285],
- [0.6202, 0.3983, 0.8555, 0.5429, 0.3842, 0.4370, 0.5866, 0.5398],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6202, 0.4054, 0.8892, 0.4428, 0.3642, 0.3249, 0.5840, 0.5426]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3771, -0.5383, 1.7638, -0.0992, -0.5666, 0.2643, 0.5956, 0.2325],
- [ 0.7077, -0.2939, 1.6108, -1.1808, 0.1599, -1.2673, 0.9221, 0.3429],
- [ 0.5240, -0.3895, 1.0684, -1.2673, -0.2932, -1.2565, 0.1783, 0.2553],
- [ 0.3992, -0.4817, 1.6507, 0.1151, -0.2505, 0.2378, 0.2202, 0.2257],
- [ 0.4623, -0.4594, 1.6011, 0.3008, -0.5909, 0.1249, 0.5551, 0.2070],
- [ 0.6439, -0.3487, 1.6420, 0.2801, -0.6249, -0.1156, 0.3183, 0.2343],
- [ 0.3152, -0.5551, 1.8351, -0.2781, -0.6664, -0.0461, 0.4377, 0.0753],
- [ 0.6804, -0.2900, 1.7184, -0.2531, -0.6444, -0.5595, 0.2237, 0.3050]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.9436e-01, -4.4897e-01, 1.8643e+00, -6.5918e-02, -5.1472e-01,
- 1.2348e-01, 7.6842e-01, 1.0043e-01],
- [ 6.3718e-01, -3.5749e-01, 1.6633e+00, -1.2082e+00, 2.1986e-01,
- -1.2467e+00, 1.1313e+00, 3.0505e-01],
- [ 5.7610e-01, -4.0701e-01, 1.2452e+00, -1.2541e+00, -1.7255e-01,
- -1.4835e+00, 4.5107e-01, 1.5453e-01],
- [ 5.7864e-01, -4.1409e-01, 1.7037e+00, 1.5443e-01, -1.8624e-01,
- 7.3556e-02, 4.3926e-01, 8.5142e-02],
- [ 6.5365e-01, -3.9601e-01, 1.6517e+00, 3.1609e-01, -4.9607e-01,
- 4.6189e-04, 7.6203e-01, 1.5543e-01],
- [ 5.7864e-01, -4.4627e-01, 1.6655e+00, 2.2157e-01, -5.1146e-01,
- -2.6752e-01, 4.2362e-01, 2.0749e-01],
- [ 6.0716e-01, -4.2055e-01, 1.8711e+00, -2.5358e-01, -6.1155e-01,
- -1.3041e-01, 6.8119e-01, -6.7050e-02],
- [ 5.7875e-01, -4.1347e-01, 1.8214e+00, -2.4075e-01, -6.0389e-01,
- -7.8543e-01, 4.1155e-01, 2.2033e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0161, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0161, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3869306379929185
- step: 19
- running loss: 0.020364770420679922
- Train Steps: 19/90 Loss: 0.0204 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6022, 0.3994, 0.8025, 0.3350, 0.3350, 0.4400, 0.5565, 0.5025],
- [0.6250, 0.4131, 0.8688, 0.2983, 0.4275, 0.2367, 0.6162, 0.5367],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683],
- [0.6332, 0.4128, 0.9200, 0.3517, 0.4400, 0.3833, 0.7461, 0.5494],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4445, -0.4425, 1.4694, -0.9243, -0.4568, -0.9045, 0.4037, 0.1216],
- [ 0.5492, -0.4187, 1.6414, 0.1282, -0.3174, 0.0859, 0.3000, 0.2823],
- [ 0.4324, -0.4828, 1.6951, 0.0454, -0.1453, 0.1048, 0.2725, 0.0768],
- [ 0.2279, -0.6074, 1.3656, -0.6036, -0.7623, -0.1955, 0.3766, 0.1503],
- [ 0.1854, -0.5919, 1.6488, -0.8114, -0.3160, -1.0299, 0.6023, 0.2372],
- [ 0.5083, -0.4121, 1.3570, -0.8879, -0.4907, -0.6800, 0.4704, 0.3468],
- [ 0.5510, -0.4482, 1.7670, -0.4521, -0.4118, -0.3523, 1.0814, 0.2116],
- [ 0.7426, -0.2122, 1.5960, 0.2323, -0.3715, -0.8716, 0.3507, 0.4529]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.5525, -0.4463, 1.7326, 0.0313, -0.2536, -0.0688, 0.1968,
- 0.3700],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.4956, -0.4413, 1.4208, -0.7386, -0.7386, -0.2536, 0.2843,
- 0.0351],
- [ 0.6011, -0.3778, 1.7268, -0.9079, -0.3113, -1.1928, 0.5605,
- 0.1929],
- [ 0.5778, -0.3930, 1.5189, -0.9387, -0.4326, -0.9156, 0.4855,
- 0.3392],
- [ 0.6388, -0.3792, 1.9635, -0.6616, -0.2536, -0.5153, 1.1605,
- 0.2516],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0167, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0167, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.40361075196415186
- step: 20
- running loss: 0.020180537598207593
- Train Steps: 20/90 Loss: 0.0202 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6203, 0.4073, 0.8189, 0.2398, 0.4400, 0.2054, 0.5929, 0.5501],
- [ nan, nan, 0.8850, 0.2817, 0.5112, 0.2183, 0.7184, 0.5436],
- [0.6162, 0.4134, 0.6700, 0.2467, 0.3962, 0.2533, 0.5737, 0.5467],
- [0.6223, 0.4130, 0.8100, 0.2983, 0.3525, 0.3900, 0.5694, 0.5103],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [0.6145, 0.4008, 0.8750, 0.5383, 0.3975, 0.4650, 0.5563, 0.5533],
- [0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3955, -0.4749, 1.3898, -0.9947, -0.2276, -1.1466, 0.4303, 0.2292],
- [-0.9126, -1.3647, 1.7853, -1.0556, 0.1312, -1.1487, 1.0912, 0.3533],
- [ 0.5603, -0.3804, 0.8494, -1.0202, -0.4774, -1.0772, 0.3581, 0.2918],
- [ 0.5956, -0.3725, 1.5091, -0.6703, -0.6470, -0.3083, 0.4570, 0.1711],
- [ 0.4852, -0.4004, 1.4856, -0.3499, -0.6665, -0.7114, 0.0906, 0.3698],
- [ 0.6508, -0.3468, 1.8328, -0.0150, -0.5481, 0.1108, 0.3329, 0.1858],
- [ 0.6672, -0.3458, 1.7533, 0.3656, -0.3711, 0.0669, 0.4311, 0.2554],
- [ 0.7912, -0.3052, 1.8702, 0.3306, -0.6130, -0.0232, 0.8078, -0.0553]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5792, -0.4048, 1.4965, -1.1781, -0.2534, -1.3371, 0.4528,
- 0.2549],
- [-2.2859, -2.2859, 1.8018, -0.9849, 0.0755, -1.2774, 1.0326,
- 0.2249],
- [ 0.5603, -0.3764, 0.8088, -1.1466, -0.4557, -1.1158, 0.3642,
- 0.2391],
- [ 0.5886, -0.3784, 1.4554, -0.9079, -0.6577, -0.4845, 0.3440,
- 0.0712],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374],
- [ 0.5368, -0.4406, 1.7730, -0.1766, -0.5249, -0.0534, 0.2314,
- 0.3469],
- [ 0.5526, -0.4347, 1.7557, 0.2006, -0.4499, -0.1381, 0.2834,
- 0.2699],
- [ 0.6240, -0.4321, 1.8423, 0.1852, -0.5885, -0.1612, 0.6962,
- 0.0111]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0536, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0536, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.45722213480621576
- step: 21
- running loss: 0.021772482609819798
- Train Steps: 21/90 Loss: 0.0218 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268],
- [0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433],
- [0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6222, 0.3957, 0.8838, 0.5017, 0.3937, 0.4600, 0.5900, 0.5017],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [0.6314, 0.4050, 0.8227, 0.5431, 0.4150, 0.5517, 0.7121, 0.5690]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4032, -0.5113, 1.7258, -0.0226, -0.2910, -0.1014, 0.5768, 0.1624],
- [ 0.6011, -0.3635, 1.2942, -1.0630, -0.6989, -0.7143, 0.5331, 0.1748],
- [ 0.5156, -0.4315, 1.7901, -0.0675, -0.0420, -0.1152, 0.4514, 0.2772],
- [ 0.4480, -0.4534, 1.7835, -0.2277, -0.3825, 0.1414, 0.4593, 0.3638],
- [ 0.6085, -0.3451, 1.7743, 0.1849, -0.5596, -0.3062, 0.3344, 0.4080],
- [ 0.6167, -0.3827, 1.8204, 0.0671, -0.5119, -0.2906, 0.4843, -0.0131],
- [ 0.6249, -0.3598, 1.8184, -0.1034, -0.5868, -0.1451, 0.3213, 0.2177],
- [ 0.6213, -0.3791, 1.5676, 0.2199, -0.4263, 0.1815, 1.0220, 0.3422]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474],
- [ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237],
- [ 0.5371, -0.4252, 1.7037, -0.0688, -0.0342, 0.0620, 0.3758,
- 0.2853],
- [ 0.5772, -0.3913, 1.8480, -0.2459, -0.4326, 0.1929, 0.5374,
- 0.4701],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.5879, -0.4583, 1.7961, 0.0313, -0.4672, -0.1612, 0.4393,
- 0.0313],
- [ 0.5368, -0.4406, 1.7730, -0.1766, -0.5249, -0.0534, 0.2314,
- 0.3469],
- [ 0.6306, -0.4153, 1.5141, 0.2224, -0.3691, 0.2622, 1.0033,
- 0.3425]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0053, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0053, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.46247611194849014
- step: 22
- running loss: 0.021021641452204098
- Train Steps: 22/90 Loss: 0.0210 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4024, 0.8600, 0.2283, 0.5350, 0.1800, 0.7074, 0.5413],
- [0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168],
- [0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719],
- [0.6166, 0.4008, 0.8563, 0.5667, 0.4388, 0.4933, 0.5575, 0.5567],
- [0.6128, 0.4118, 0.8638, 0.5333, 0.4625, 0.5267, 0.5193, 0.5475],
- [0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [0.6182, 0.3967, 0.8263, 0.3065, 0.3526, 0.4161, 0.6192, 0.5290]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4703, -0.4666, 1.7351, -1.2481, 0.1270, -1.5093, 1.1119, 0.2799],
- [ 0.5133, -0.4259, 0.8617, -1.3169, -0.4766, -1.3048, 0.2593, 0.1520],
- [ 0.2934, -0.5855, 1.6849, 0.3353, -0.5495, 0.0145, 0.3481, 0.2117],
- [ 0.6970, -0.3230, 1.6839, 0.4214, -0.3112, 0.0768, 0.3962, 0.3051],
- [ 0.5945, -0.3530, 1.7217, 0.2182, -0.2332, 0.1234, 0.2404, 0.2736],
- [ 0.4804, -0.4319, 1.8376, -0.0268, -0.4507, -0.0504, 0.3362, 0.2036],
- [ 0.5193, -0.4420, 1.8981, 0.0182, -0.6593, -0.2885, 0.9374, 0.2386],
- [ 0.4919, -0.4813, 1.6119, -0.7349, -0.7653, -0.3712, 0.6983, 0.1223]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4273, 1.6864, -1.2313, 0.1852, -1.4545, 0.9814,
- 0.2142],
- [ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013],
- [ 0.5079, -0.4714, 1.6575, 0.2237, -0.4961, 0.0774, 0.1465,
- -0.1061],
- [ 0.5620, -0.4346, 1.6691, 0.3315, -0.2594, -0.0072, 0.2891,
- 0.2853],
- [ 0.5447, -0.3838, 1.7037, 0.1775, -0.1497, 0.1467, 0.1128,
- 0.2431],
- [ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499],
- [ 0.5697, -0.4538, 1.5308, -0.8703, -0.6572, -0.3639, 0.5739,
- 0.1576]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0083, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0083, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4708037283271551
- step: 23
- running loss: 0.02046972731857196
- Train Steps: 23/90 Loss: 0.0205 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6168, 0.4055, 0.7575, 0.2950, 0.4050, 0.2717, 0.5938, 0.6167],
- [0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6207, 0.4110, 0.8738, 0.5000, 0.4800, 0.5633, 0.6300, 0.5433],
- [0.6307, 0.4029, 0.8988, 0.4817, 0.3937, 0.3500, 0.7311, 0.5378],
- [0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3197, -0.5203, 1.6434, -1.0400, -0.1443, -1.1808, 0.6363, 0.1232],
- [ 0.2566, -0.6155, 1.8100, -0.6931, -0.4654, -0.6712, 0.7653, 0.2250],
- [ 0.5813, -0.4408, 1.7087, 0.2765, -0.6172, -0.2340, 0.6644, 0.1122],
- [ 0.4842, -0.4296, 1.1465, -0.9483, -0.4555, -1.0081, 0.4722, 0.4620],
- [ 0.5570, -0.3612, 1.5991, -0.3948, -0.5087, -1.0276, 0.1089, 0.1086],
- [ 0.5946, -0.3962, 1.6840, -0.0339, -0.1180, 0.3206, 0.4458, 0.3277],
- [ 0.5608, -0.4686, 1.8304, -0.0690, -0.4417, -0.6189, 1.0276, 0.1999],
- [ 0.3944, -0.5490, 1.7384, -0.1617, -0.3495, 0.2295, 0.4583, 0.2216]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390],
- [ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [ 0.5631, -0.4129, 1.2129, -0.9233, -0.4152, -1.0311, 0.4566,
- 0.5624],
- [ 0.5485, -0.4209, 1.6691, -0.4152, -0.5249, -1.1081, 0.0725,
- 0.0021],
- [ 0.5811, -0.3878, 1.7499, 0.0236, -0.0688, 0.3161, 0.6240,
- 0.2237],
- [ 0.6273, -0.4249, 1.8654, -0.0611, -0.4672, -0.6693, 1.0910,
- 0.1982],
- [ 0.5960, -0.4102, 1.8018, -0.1612, -0.3344, 0.1159, 0.5490,
- 0.2314]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0093, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0093, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4800864914432168
- step: 24
- running loss: 0.020003603810134035
- Train Steps: 24/90 Loss: 0.0200 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760],
- [0.6280, 0.4101, 0.9050, 0.4533, 0.3775, 0.3217, 0.6338, 0.4915],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6200, 0.4070, 0.8938, 0.4183, 0.3538, 0.4567, 0.6175, 0.5400],
- [0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6223, 0.4130, 0.8100, 0.2983, 0.3525, 0.3900, 0.5694, 0.5103],
- [0.6107, 0.4050, 0.8700, 0.4850, 0.4470, 0.4848, 0.5043, 0.5431]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2778, -0.6069, 1.9192, -0.2346, -0.2136, -0.9693, 1.0890, 0.4038],
- [ 0.6852, -0.3778, 1.5858, 0.2068, -0.3921, -0.0105, 1.0698, 0.2575],
- [ 0.4798, -0.4860, 1.8392, -0.2396, -0.4881, -0.8281, 0.6195, 0.1020],
- [ 0.6741, -0.3266, 1.7744, -0.3613, -0.5465, -0.4756, 0.5234, 0.2343],
- [ 0.3450, -0.5909, 1.8070, -0.4339, -0.5138, -0.2004, 0.6149, 0.1366],
- [ 0.2770, -0.5442, 1.5616, -0.4305, -0.5066, -0.8888, 0.0636, 0.2526],
- [ 0.6650, -0.3418, 1.4621, -0.8721, -0.5571, -0.4428, 0.4900, 0.1761],
- [ 0.5881, -0.3967, 1.6592, -0.0824, -0.1299, 0.0190, 0.0928, 0.1560]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745],
- [ 0.6148, -0.3918, 1.8942, -0.1920, -0.5423, -0.8002, 0.6414,
- -0.0156],
- [ 0.5880, -0.3887, 1.8423, -0.3306, -0.6231, -0.5230, 0.4046,
- 0.1544],
- [ 0.5778, -0.4061, 1.8423, -0.3537, -0.6520, -0.1766, 0.5663,
- 0.2083],
- [ 0.5470, -0.3952, 1.6377, -0.4253, -0.6289, -0.8079, 0.0249,
- 0.2116],
- [ 0.5886, -0.3784, 1.4554, -0.9079, -0.6577, -0.4845, 0.3440,
- 0.0712],
- [ 0.5351, -0.4153, 1.7326, -0.0457, -0.2214, -0.0466, 0.0434,
- 0.2228]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.49067305121570826
- step: 25
- running loss: 0.01962692204862833
- Train Steps: 25/90 Loss: 0.0196 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [0.6339, 0.4149, 0.8800, 0.5000, 0.3900, 0.5283, 0.7541, 0.5424],
- [0.6164, 0.3956, 0.8757, 0.5088, 0.4300, 0.5320, 0.6022, 0.5202],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [ nan, nan, 0.8938, 0.2850, 0.4662, 0.3117, 0.7406, 0.5528],
- [0.6364, 0.4092, 0.8525, 0.5633, 0.3875, 0.4950, 0.6599, 0.5285]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6417, -0.3648, 1.6992, -0.2418, -0.0921, -0.0855, 0.3235, 0.2546],
- [ 0.6611, -0.3901, 1.7823, -0.1001, -0.4637, 0.1033, 1.0566, 0.1444],
- [ 0.7788, -0.2877, 1.6923, -0.0969, -0.2912, -0.0103, 0.4486, 0.0481],
- [ 0.7775, -0.2282, 1.7290, -0.1277, -0.6033, -0.6896, 0.4119, 0.3582],
- [ 0.5787, -0.3499, 1.5670, 0.1464, -0.5430, -0.6805, 0.3149, 0.4437],
- [ 0.7507, -0.3013, 1.6025, -0.4169, -0.5600, -0.2327, 0.2022, 0.0216],
- [-1.5653, -1.8225, 1.9168, -1.0289, -0.1499, -1.0868, 1.0415, 0.2593],
- [ 0.8505, -0.2690, 1.6382, 0.1561, -0.4971, -0.1405, 0.8296, 0.0649]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.5052e-01, -4.2071e-01, 1.7095e+00, -5.3426e-02, -5.0936e-02,
- 1.0502e-01, 3.8730e-01, 3.0069e-01],
- [ 6.4212e-01, -3.6953e-01, 1.7788e+00, 2.3557e-02, -4.8453e-01,
- 1.5443e-01, 1.1971e+00, 2.1955e-01],
- [ 5.6143e-01, -4.5860e-01, 1.7587e+00, 6.4079e-02, -2.9982e-01,
- 1.7122e-01, 4.9584e-01, 1.1701e-01],
- [ 5.9024e-01, -3.4927e-01, 1.7961e+00, -7.2363e-03, -5.9423e-01,
- -5.6151e-01, 3.1801e-01, 3.1609e-01],
- [ 6.0554e-01, -3.3934e-01, 1.6575e+00, 2.5450e-01, -5.9423e-01,
- -5.4611e-01, 2.9492e-01, 4.7775e-01],
- [ 5.2448e-01, -4.3610e-01, 1.5940e+00, -2.9207e-01, -5.4804e-01,
- -9.1917e-02, 2.4319e-01, 5.0176e-02],
- [-2.2859e+00, -2.2859e+00, 1.8423e+00, -9.6952e-01, -1.3233e-01,
- -8.4634e-01, 1.1349e+00, 2.6764e-01],
- [ 6.5365e-01, -3.9601e-01, 1.6517e+00, 3.1609e-01, -4.9607e-01,
- 4.6189e-04, 7.6203e-01, 1.5543e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0227, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0227, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5133614921942353
- step: 26
- running loss: 0.019744672776701357
- Train Steps: 26/90 Loss: 0.0197 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [0.6196, 0.4090, 0.8450, 0.3900, 0.3575, 0.3933, 0.5975, 0.5583],
- [0.6311, 0.3998, 0.7975, 0.5767, 0.3838, 0.4850, 0.7327, 0.5343],
- [0.6274, 0.4087, 0.8375, 0.5700, 0.4025, 0.4800, 0.5700, 0.6117],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6289, 0.4081, 0.8720, 0.3487, 0.3900, 0.3183, 0.6703, 0.5376],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6258, 0.4143, 0.8525, 0.2617, 0.4950, 0.1667, 0.6219, 0.4967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4348, -0.5345, 1.8946, -0.7295, -0.4771, -0.7699, 0.7855, 0.1025],
- [ 0.5123, -0.4724, 1.6877, -0.4641, -0.6370, -0.5252, 0.5698, 0.2832],
- [ 0.5497, -0.4999, 1.5042, 0.1221, -0.5073, -0.1399, 0.8437, 0.0930],
- [ 0.3297, -0.5854, 1.5203, 0.3082, -0.4449, -0.1237, 0.4385, 0.4445],
- [ 0.6769, -0.3657, 1.7477, -0.1668, -0.0879, 0.0667, 0.3535, 0.1672],
- [ 0.7711, -0.3086, 1.7944, -0.5775, -0.4388, -0.8960, 0.9241, 0.1388],
- [ 0.2592, -0.5935, 1.7154, -0.2854, -0.5463, -0.3459, 0.0919, 0.3221],
- [ 0.2919, -0.5843, 1.6723, -1.1340, -0.0278, -1.4489, 0.6213, 0.0186]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0779e-01, -4.0570e-01, 1.8134e+00, -7.3087e-01, -4.4988e-01,
- -7.3857e-01, 6.2979e-01, 1.3903e-01],
- [ 5.7610e-01, -3.9661e-01, 1.6171e+00, -4.8453e-01, -6.3464e-01,
- -4.6913e-01, 4.7390e-01, 2.9299e-01],
- [ 6.2895e-01, -4.3934e-01, 1.3977e+00, 3.7768e-01, -5.1339e-01,
- -4.5727e-02, 1.0984e+00, 1.8214e-01],
- [ 6.1184e-01, -3.9831e-01, 1.5824e+00, 3.4688e-01, -4.2679e-01,
- -6.8822e-02, 3.4688e-01, 5.3934e-01],
- [ 5.5381e-01, -4.1386e-01, 1.7557e+00, -1.8430e-01, -4.5897e-02,
- 1.2417e-01, 4.2194e-01, 2.8530e-01],
- [ 6.1907e-01, -4.0082e-01, 1.7420e+00, -6.7528e-01, -4.8453e-01,
- -8.1555e-01, 8.1006e-01, 1.9744e-01],
- [ 5.5525e-01, -3.9923e-01, 1.7557e+00, -2.6898e-01, -4.9030e-01,
- -2.6898e-01, 5.4227e-02, 4.1446e-01],
- [ 6.0479e-01, -3.7229e-01, 1.6517e+00, -1.0773e+00, 4.6189e-04,
- -1.5161e+00, 5.8660e-01, 8.0947e-03]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0129, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0129, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5262837493792176
- step: 27
- running loss: 0.019491990717748802
- Train Steps: 27/90 Loss: 0.0195 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [0.6230, 0.4113, 0.7213, 0.1983, 0.4325, 0.2367, 0.6262, 0.5400],
- [0.6126, 0.4067, 0.8638, 0.5383, 0.4188, 0.4850, 0.5016, 0.5392],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6125, 0.3999, 0.8750, 0.4883, 0.4750, 0.4700, 0.5533, 0.5617],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.9047e-01, -4.3015e-01, 1.2829e+00, -8.2089e-01, -6.3280e-01,
- -8.4250e-01, 3.8176e-01, -1.2932e-03],
- [ 4.8052e-01, -4.9887e-01, 1.3317e+00, -1.0921e+00, -2.2815e-01,
- -1.4085e+00, 6.0268e-01, 1.1726e-01],
- [ 3.5900e-01, -5.5937e-01, 1.1690e+00, -1.2250e+00, -3.4386e-01,
- -1.1243e+00, 8.3362e-01, 2.8056e-01],
- [ 5.8892e-01, -4.3297e-01, 1.7712e+00, 4.5259e-01, -3.8018e-01,
- 1.1718e-01, 3.4146e-01, 2.2360e-01],
- [-5.7707e-02, -8.8170e-01, 1.5893e+00, -9.8783e-01, -4.7708e-01,
- -9.3724e-01, 8.6913e-01, 4.6581e-02],
- [ 6.0177e-01, -4.4187e-01, 1.8321e+00, 1.1511e-01, -1.2282e-01,
- 4.9209e-02, 3.9132e-01, 3.2597e-01],
- [ 3.7568e-01, -5.4407e-01, 1.9095e+00, -1.6609e-01, -6.2486e-01,
- -3.4410e-01, 4.6614e-01, 3.1886e-01],
- [ 8.9065e-01, -2.2668e-01, 1.8704e+00, -6.2750e-01, -2.4321e-01,
- -1.2563e+00, 6.0711e-01, 7.1932e-02]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [ 0.5917, -0.3861, 1.0455, -1.3698, -0.2882, -1.1928, 0.6067,
- 0.2083],
- [ 0.5436, -0.4076, 1.7037, 0.2006, -0.3517, -0.0457, 0.0311,
- 0.2048],
- [ 0.6042, -0.4225, 1.5420, -1.2082, -0.4730, -1.0311, 0.6380,
- -0.0220],
- [ 0.5432, -0.4388, 1.7557, -0.0303, -0.0919, -0.1150, 0.2699,
- 0.3087],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0293, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0293, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5556024992838502
- step: 28
- running loss: 0.019842946402994648
- Train Steps: 28/90 Loss: 0.0198 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6160, 0.4093, 0.8314, 0.4417, 0.3675, 0.4583, 0.5250, 0.5808],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114],
- [0.6200, 0.3993, 0.8639, 0.3687, 0.3658, 0.3139, 0.6002, 0.5374],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552],
- [ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933],
- [0.6083, 0.3957, 0.8638, 0.4950, 0.4363, 0.5083, 0.5346, 0.4980],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.9783, -0.1393, 1.6980, -0.1290, -0.5966, -0.2572, 0.4469, 0.3263],
- [ 1.0109, -0.1533, 1.7835, 0.1568, -0.2468, 0.1617, 0.6071, 0.1030],
- [ 1.0725, -0.1292, 1.8002, -0.4632, -0.6134, -0.8617, 0.7768, 0.0539],
- [ 1.0948, -0.0996, 1.6784, 0.3902, -0.3171, -0.0440, 0.3689, 0.1001],
- [-2.1774, -2.1930, 1.7331, -1.1181, 0.0143, -1.2074, 0.8685, 0.2866],
- [-2.1275, -2.1613, 0.9271, -1.1838, -0.4288, -1.2942, 0.1119, 0.3530],
- [ 1.1153, -0.1008, 1.8017, 0.0431, -0.3620, 0.0071, 0.4366, -0.0337],
- [ 0.8060, -0.2648, 1.0662, -1.2371, -0.4700, -1.2497, 0.5248, 0.1688]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.5924e-01, -3.9561e-01, 1.5543e+00, -2.4557e-01, -5.8845e-01,
- -1.6890e-01, 1.3922e-01, 3.9681e-01],
- [ 5.4496e-01, -4.7305e-01, 1.7420e+00, 1.3720e-01, -1.9186e-01,
- 2.6139e-01, 4.9757e-01, 7.6435e-02],
- [ 5.7771e-01, -4.4157e-01, 1.7044e+00, -5.8275e-01, -5.9618e-01,
- -8.3610e-01, 4.8621e-01, 1.9626e-01],
- [ 5.1928e-01, -4.6990e-01, 1.5767e+00, 4.0077e-01, -2.4203e-01,
- 7.7444e-02, 1.1776e-01, -6.1038e-02],
- [-2.2859e+00, -2.2859e+00, 1.7557e+00, -1.1466e+00, 8.7067e-02,
- -1.0773e+00, 1.1239e+00, 2.7833e-01],
- [-2.2859e+00, -2.2859e+00, 8.0331e-01, -1.1250e+00, -3.8637e-01,
- -1.3082e+00, 1.1262e-01, 4.5430e-01],
- [ 5.2379e-01, -4.5797e-01, 1.7037e+00, 4.6189e-04, -2.7090e-01,
- 6.2048e-02, 1.8356e-01, 1.4106e-02],
- [ 5.1853e-01, -4.2517e-01, 9.6467e-01, -1.2928e+00, -4.7875e-01,
- -1.2390e+00, 2.6170e-01, 2.5757e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0428, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0428, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5983591759577394
- step: 29
- running loss: 0.020633075033025496
- Train Steps: 29/90 Loss: 0.0206 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6275, 0.4024, 0.8600, 0.2283, 0.5350, 0.1800, 0.7074, 0.5413],
- [0.6034, 0.4011, 0.7350, 0.2533, 0.3438, 0.3367, 0.5516, 0.5084],
- [0.6201, 0.4116, 0.8725, 0.4733, 0.3700, 0.3217, 0.5386, 0.5767],
- [0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6336, 0.4086, 0.8900, 0.3950, 0.3900, 0.2950, 0.6504, 0.5066],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5075, -0.5088, 1.7788, -0.1351, -0.1265, -0.0524, 0.2179, 0.0959],
- [-0.2226, -0.9488, 1.6794, -1.3423, 0.1735, -1.5009, 1.0534, 0.2728],
- [ 0.4128, -0.5544, 1.1306, -1.1477, -0.6543, -0.7419, 0.3301, 0.0285],
- [ 0.5527, -0.3928, 1.7188, -0.1665, -0.5780, -0.7781, 0.2299, 0.3728],
- [ 0.6418, -0.4208, 1.8075, 0.0507, -0.4922, 0.0938, 0.3359, 0.1123],
- [ 0.7886, -0.3359, 1.8696, -0.5041, -0.4656, -0.9213, 0.8079, 0.0993],
- [ 0.9940, -0.1880, 1.8234, 0.1831, -0.6006, -0.5426, 0.5605, 0.0328],
- [ 0.3124, -0.6318, 1.6706, 0.0678, -0.3578, 0.2999, 0.9073, 0.3424]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.6125, -0.4273, 1.6864, -1.2313, 0.1852, -1.4545, 0.9814,
- 0.2142],
- [ 0.5009, -0.4333, 1.1090, -1.1158, -0.6982, -0.7309, 0.2617,
- 0.0622],
- [ 0.5781, -0.3848, 1.7441, -0.0996, -0.5769, -0.8002, 0.2021,
- 0.3778],
- [ 0.5783, -0.4363, 1.7724, 0.0432, -0.5153, 0.0871, 0.4840,
- 0.0663],
- [ 0.6405, -0.3984, 1.8249, -0.4614, -0.4845, -0.9233, 0.7182,
- 0.0539],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0240, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0240, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6223869854584336
- step: 30
- running loss: 0.020746232848614453
- Train Steps: 30/90 Loss: 0.0207 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960],
- [0.6339, 0.4081, 0.8425, 0.5417, 0.3850, 0.4833, 0.7335, 0.5760],
- [0.6226, 0.4103, 0.8575, 0.3450, 0.4388, 0.2067, 0.5787, 0.5383],
- [0.6239, 0.4107, 0.8162, 0.2763, 0.3625, 0.3600, 0.5988, 0.5700],
- [0.6097, 0.3988, 0.8650, 0.5250, 0.4213, 0.5200, 0.5675, 0.5050],
- [0.6163, 0.4006, 0.8788, 0.4683, 0.3663, 0.4883, 0.5887, 0.5017],
- [0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5228, -0.4469, 1.4481, -1.1311, -0.2062, -1.3879, 0.5930, 0.1343],
- [ 0.7055, -0.3875, 1.8804, -0.0432, -0.2867, 0.0243, 0.3167, 0.0265],
- [ 0.6936, -0.4046, 1.7164, 0.3293, -0.4862, -0.0240, 1.1612, 0.2557],
- [ 0.7628, -0.2640, 1.8340, -0.6504, -0.2326, -1.2022, 0.4564, 0.2166],
- [-0.6807, -1.2229, 1.4377, -0.8805, -0.5891, -0.6684, 0.4366, 0.2792],
- [ 0.5615, -0.4705, 1.7807, 0.2131, -0.3258, 0.1174, 0.3444, 0.0610],
- [ 0.6153, -0.4303, 1.8371, -0.0601, -0.5939, -0.0201, 0.5271, 0.1234],
- [ 0.4692, -0.5014, 1.0312, -1.2795, -0.2998, -1.5233, 0.2746, 0.1054]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051],
- [ 0.6421, -0.4008, 1.6055, 0.2160, -0.5076, -0.0534, 1.1020,
- 0.3745],
- [ 0.5898, -0.3908, 1.6748, -0.6924, -0.2594, -1.3313, 0.3873,
- 0.2006],
- [ 0.5960, -0.3888, 1.4840, -1.0095, -0.6115, -0.6231, 0.4797,
- 0.3469],
- [ 0.5303, -0.4440, 1.7095, 0.1390, -0.3402, 0.1159, 0.3353,
- 0.0467],
- [ 0.5609, -0.4354, 1.7730, -0.1227, -0.5942, -0.0303, 0.4335,
- 0.0313],
- [ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0424, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0424, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6647640662267804
- step: 31
- running loss: 0.021444002136347756
- Train Steps: 31/90 Loss: 0.0214 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650],
- [0.6245, 0.4115, 0.8700, 0.4883, 0.4625, 0.5517, 0.6100, 0.5217],
- [0.6240, 0.4217, 0.8150, 0.3133, 0.4425, 0.2650, 0.5650, 0.5817],
- [0.6264, 0.4055, 0.8425, 0.2767, 0.4425, 0.2767, 0.7050, 0.5586],
- [0.6203, 0.4072, 0.8892, 0.3523, 0.3783, 0.3017, 0.5898, 0.5478],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6193, 0.4034, 0.7757, 0.2347, 0.3733, 0.2919, 0.5930, 0.4926],
- [0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4506, -0.5275, 1.7686, -0.2940, -0.5330, -0.4536, 0.3733, 0.2598],
- [ 0.5407, -0.5005, 1.7940, 0.1318, -0.0543, 0.2764, 0.6289, 0.1438],
- [ 0.3794, -0.5269, 1.5145, -0.7211, -0.1765, -1.1368, 0.3719, 0.3084],
- [ 0.4169, -0.5643, 1.6879, -0.9827, -0.1545, -1.1282, 0.9710, 0.1931],
- [ 0.3583, -0.5941, 1.9093, -0.4784, -0.4752, -0.8796, 0.5574, 0.1085],
- [ 0.6627, -0.3939, 1.6628, -0.2187, -0.5922, -0.3117, 0.2554, 0.1885],
- [ 0.7224, -0.3879, 1.3228, -1.0733, -0.4802, -1.0500, 0.5118, -0.1316],
- [ 0.6318, -0.4254, 1.6359, -0.5419, -0.6135, -0.3646, 0.5188, 0.1461]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5757, -0.3917, 1.7095, -0.4768, -0.6346, -0.4229, 0.3931,
- 0.3238],
- [ 0.5987, -0.3852, 1.7326, -0.0303, -0.1497, 0.2622, 0.5316,
- 0.1236],
- [ 0.5964, -0.3380, 1.4785, -0.8386, -0.2420, -1.0619, 0.3238,
- 0.4008],
- [ 0.6075, -0.4129, 1.6055, -1.0080, -0.2420, -1.0080, 0.9704,
- 0.2944],
- [ 0.5792, -0.4052, 1.8214, -0.6587, -0.5384, -0.8924, 0.4381,
- 0.2442],
- [ 0.5367, -0.4294, 1.5709, -0.4999, -0.6693, -0.3075, 0.2455,
- 0.3559],
- [ 0.5746, -0.4225, 1.2968, -1.2019, -0.5615, -0.9374, 0.4533,
- -0.0108],
- [ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0130, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0130, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6778084067627788
- step: 32
- running loss: 0.021181512711336836
- Train Steps: 32/90 Loss: 0.0212 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6261, 0.4131, 0.8938, 0.3550, 0.4000, 0.2683, 0.6188, 0.5283],
- [0.6222, 0.3957, 0.8838, 0.5017, 0.3937, 0.4600, 0.5900, 0.5017],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507],
- [0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7188, -0.3367, 1.8920, -0.6707, -0.4313, -1.0907, 0.6629, 0.0770],
- [ 0.6263, -0.4435, 1.7797, 0.0100, -0.4408, -0.1267, 0.4634, -0.0449],
- [ 0.7959, -0.2640, 1.6776, 0.2334, -0.4097, -0.1032, 0.5011, 0.1118],
- [ 0.5462, -0.4585, 1.5470, -1.0173, -0.4050, -1.0583, 0.4912, -0.0234],
- [ 0.5802, -0.4418, 0.9874, -1.2851, -0.3965, -1.1792, 0.4044, 0.1651],
- [ 0.6268, -0.4222, 1.7376, -0.0928, -0.1368, 0.0947, 0.3983, 0.1281],
- [-2.0925, -2.1734, 1.2965, -0.9992, -0.5074, -0.9629, 0.3409, 0.2234],
- [ 0.7433, -0.2898, 1.7182, -0.0200, -0.2876, -0.9544, 0.3056, 0.4590]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6062, -0.3778, 1.8423, -0.6462, -0.4383, -1.0465, 0.5721,
- 0.1544],
- [ 0.5879, -0.4583, 1.7961, 0.0313, -0.4672, -0.1612, 0.4393,
- 0.0313],
- [ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391],
- [ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807],
- [ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576],
- [ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0063, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0063, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6841176045127213
- step: 33
- running loss: 0.020730836500385492
- Train Steps: 33/90 Loss: 0.0207 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482],
- [0.6258, 0.4143, 0.8525, 0.2617, 0.4950, 0.1667, 0.6219, 0.4967],
- [0.6336, 0.4154, 0.8900, 0.2767, 0.4988, 0.2867, 0.7422, 0.5540],
- [0.6311, 0.3998, 0.7975, 0.5767, 0.3838, 0.4850, 0.7327, 0.5343],
- [0.6246, 0.4028, 0.8738, 0.4867, 0.4088, 0.5667, 0.6362, 0.5200],
- [0.6268, 0.4052, 0.8175, 0.2250, 0.4688, 0.1917, 0.6375, 0.5267],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6255, -0.3592, 1.6984, -0.2594, -0.6196, -0.3773, 0.0322, 0.2342],
- [-1.9203, -2.0523, 1.4849, -1.2326, 0.1044, -1.1637, 0.8986, 0.2863],
- [ 0.6367, -0.3641, 1.5570, -1.0202, -0.1333, -1.3515, 0.3457, 0.0102],
- [ 0.5406, -0.4691, 1.7121, -0.8162, -0.1013, -0.9956, 0.8345, 0.1929],
- [ 0.6415, -0.4127, 1.3976, 0.2055, -0.5793, 0.0214, 0.5662, 0.1494],
- [ 0.6824, -0.3714, 1.7048, 0.0315, -0.4587, 0.4051, 0.4384, 0.1293],
- [ 0.6387, -0.3973, 1.3418, -1.1451, -0.2160, -1.4084, 0.4027, 0.0928],
- [ 0.6806, -0.3924, 1.7546, -0.0103, -0.6495, -0.3794, 0.3367, 0.0941]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0722e-01, -3.2394e-01, 1.8423e+00, -3.5366e-01, -4.9607e-01,
- -3.9215e-01, 2.0831e-01, 1.8522e-01],
- [-2.2859e+00, -2.2859e+00, 1.6517e+00, -1.2620e+00, 2.1409e-01,
- -1.1928e+00, 1.1166e+00, 2.4627e-01],
- [ 6.0479e-01, -3.7229e-01, 1.6517e+00, -1.0773e+00, 4.6189e-04,
- -1.5161e+00, 5.8660e-01, 8.0947e-03],
- [ 6.4048e-01, -3.6712e-01, 1.8249e+00, -1.0080e+00, 1.7783e-02,
- -9.6182e-01, 1.1422e+00, 2.7299e-01],
- [ 6.2895e-01, -4.3934e-01, 1.3977e+00, 3.7768e-01, -5.1339e-01,
- -4.5727e-02, 1.0984e+00, 1.8214e-01],
- [ 5.9902e-01, -4.2556e-01, 1.7499e+00, -3.8029e-02, -3.9792e-01,
- 3.3149e-01, 6.5289e-01, 1.1594e-01],
- [ 6.0918e-01, -4.1432e-01, 1.4901e+00, -1.2467e+00, -1.2079e-01,
- -1.4006e+00, 6.5866e-01, 1.4673e-01],
- [ 6.3718e-01, -4.1286e-01, 1.8942e+00, -7.6520e-02, -6.1732e-01,
- -4.7683e-01, 6.9989e-01, 3.2524e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0209, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0209, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7050670147873461
- step: 34
- running loss: 0.020737265140804297
- Train Steps: 34/90 Loss: 0.0207 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6069, 0.3975, 0.8625, 0.5083, 0.4388, 0.5483, 0.5650, 0.4967],
- [0.6198, 0.4130, 0.8762, 0.4117, 0.3650, 0.4900, 0.5707, 0.5103],
- [0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183],
- [0.6093, 0.3990, 0.8400, 0.4333, 0.3688, 0.4633, 0.5560, 0.5656],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413],
- [0.6168, 0.4055, 0.7575, 0.2950, 0.4050, 0.2717, 0.5938, 0.6167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4029, -0.5921, 1.6903, 0.1829, -0.3016, 0.2530, 0.2826, 0.0848],
- [ 0.5061, -0.4869, 1.8188, -0.2548, -0.6397, -0.0794, 0.3478, 0.0932],
- [ 0.5058, -0.5134, 1.2198, -1.2538, -0.3336, -1.4177, 0.3494, 0.0629],
- [ 0.5262, -0.4614, 1.6594, -0.1469, -0.6424, -0.1302, 0.2515, 0.2958],
- [ 0.5180, -0.4978, 1.7588, 0.0278, -0.3378, 0.0622, 0.4041, 0.2559],
- [ 0.5641, -0.4334, 1.6918, -0.9860, -0.2200, -1.3162, 0.6009, -0.0315],
- [ 0.4836, -0.5410, 1.7605, -1.1247, 0.1414, -1.2232, 1.1395, 0.1591],
- [ 0.5724, -0.4363, 1.2401, -0.8151, -0.4982, -0.9966, 0.3776, 0.4530]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5174, -0.4497, 1.6979, 0.0620, -0.2594, 0.2468, 0.3238,
- 0.0082],
- [ 0.5771, -0.3784, 1.7614, -0.3844, -0.6000, -0.0226, 0.3501,
- 0.0712],
- [ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082],
- [ 0.5283, -0.4429, 1.5940, -0.2844, -0.5827, -0.1458, 0.2823,
- 0.3267],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [ 0.6240, -0.3768, 1.6575, -1.2851, 0.2949, -1.2467, 1.1276,
- 0.2142],
- [ 0.5631, -0.4129, 1.2129, -0.9233, -0.4152, -1.0311, 0.4566,
- 0.5624]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0056, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0056, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7106758425943553
- step: 35
- running loss: 0.020305024074124437
- Train Steps: 35/90 Loss: 0.0203 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6276, 0.4095, 0.8237, 0.2250, 0.4662, 0.1783, 0.6171, 0.4869],
- [0.6219, 0.3934, 0.8688, 0.5267, 0.4313, 0.4967, 0.5988, 0.4983],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [ nan, nan, 0.6488, 0.1817, 0.4325, 0.1867, 0.5475, 0.5733],
- [0.6286, 0.4078, 0.8063, 0.2267, 0.4788, 0.1533, 0.5953, 0.4913],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250],
- [ nan, nan, 0.7335, 0.2569, 0.3788, 0.2667, 0.5066, 0.5578]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7175, -0.3182, 1.5601, -1.1531, -0.1840, -1.2262, 0.5677, -0.0161],
- [ 0.7583, -0.3484, 1.7435, 0.2148, -0.4061, 0.0851, 0.4845, -0.0334],
- [ 0.7261, -0.2570, 1.2147, -0.6802, -0.0970, -1.2256, 0.2552, 0.4634],
- [-1.5970, -1.8209, 0.9663, -1.2563, -0.2124, -1.3566, 0.3545, 0.4310],
- [ 0.6751, -0.3353, 1.3610, -1.0378, -0.1904, -1.4201, 0.3632, 0.0626],
- [ 0.9350, -0.1898, 1.9036, -0.2485, -0.6079, -0.5402, 0.5454, 0.1273],
- [ 0.9440, -0.1748, 1.8448, 0.0834, -0.4474, 0.3213, 0.6020, 0.1024],
- [-1.9158, -2.0474, 1.1735, -1.1139, -0.5103, -1.0042, 0.2081, 0.2710]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6127, -0.3944, 1.5189, -1.2467, -0.1323, -1.4622, 0.5646,
- -0.0369],
- [ 0.5864, -0.4690, 1.7268, 0.1467, -0.2940, 0.0082, 0.4797,
- 0.0159],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [-2.2859, -2.2859, 0.7106, -1.4468, -0.2882, -1.4237, 0.2430,
- 0.3623],
- [ 0.6177, -0.4022, 1.4381, -1.2390, -0.0746, -1.5777, 0.4639,
- -0.0168],
- [ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.5991, -0.3803, 1.8018, -0.0534, -0.3460, 0.1852, 0.5374,
- 0.1390],
- [-2.2859, -2.2859, 1.1020, -1.0994, -0.5365, -1.0542, 0.0542,
- 0.2905]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0280, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0280, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7386775049380958
- step: 36
- running loss: 0.02051881958161377
- Train Steps: 36/90 Loss: 0.0205 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6137, 0.4035, 0.8850, 0.4417, 0.3900, 0.4283, 0.5449, 0.5617],
- [0.6172, 0.4055, 0.8175, 0.2650, 0.3550, 0.3683, 0.5787, 0.5550],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [0.6189, 0.4029, 0.8375, 0.5767, 0.4745, 0.4829, 0.5551, 0.5598],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6080, 0.4010, 0.8750, 0.4500, 0.4825, 0.5617, 0.5837, 0.5583],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7026, -0.3475, 1.7143, -0.5126, -0.6330, -0.2998, 0.4079, 0.1759],
- [ 0.4452, -0.4884, 1.7904, -0.2999, -0.4580, -0.4563, 0.1168, 0.2451],
- [ 0.8208, -0.2664, 1.4383, -1.0448, -0.6861, -0.8196, 0.1715, 0.0891],
- [ 0.6975, -0.3649, 1.3709, 0.1070, -0.4919, -0.1339, 0.7419, 0.1754],
- [ 0.6489, -0.3556, 1.5818, 0.2273, -0.1254, -0.3050, 0.1784, 0.2450],
- [ 0.7435, -0.3313, 1.8172, -0.2670, -0.4164, -0.4652, 0.8118, 0.1959],
- [ 0.4413, -0.4983, 1.7393, -0.3508, -0.1120, -0.1085, 0.3081, 0.1528],
- [-1.9452, -2.0874, 1.6159, -1.3266, 0.2288, -1.4092, 1.0428, 0.2691]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.5488, -0.4221, 1.8018, -0.2459, -0.4845, -0.3075, 0.2309,
- 0.3087],
- [ 0.5647, -0.4129, 1.4901, -1.0619, -0.6462, -0.5846, 0.3873,
- 0.2776],
- [ 0.6289, -0.4345, 1.3794, 0.3679, -0.4845, 0.0390, 0.9265,
- 0.1928],
- [ 0.5726, -0.4249, 1.5824, 0.3777, -0.0942, -0.0556, 0.2782,
- 0.2997],
- [ 0.6454, -0.3719, 1.9115, -0.1304, -0.5076, -0.2844, 1.0033,
- 0.4386],
- [ 0.5223, -0.4336, 1.7557, -0.2074, -0.0573, 0.3084, 0.4104,
- 0.2930],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0191, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0191, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7578194118104875
- step: 37
- running loss: 0.02048160572460777
- Train Steps: 37/90 Loss: 0.0205 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7725, 0.2611, 0.3675, 0.2733, 0.5413, 0.5167],
- [0.6113, 0.4104, 0.8650, 0.4250, 0.3925, 0.5967, 0.5787, 0.5116],
- [0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892],
- [0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389],
- [0.6145, 0.4007, 0.8775, 0.4533, 0.4562, 0.5533, 0.6088, 0.5533],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.1001, -2.2408, 1.2620, -1.2134, -0.3332, -1.1288, 0.4092, 0.1548],
- [ 0.6518, -0.3524, 1.6998, -0.3598, -0.2870, 0.3224, 0.6301, 0.1420],
- [ 0.7167, -0.2884, 1.6898, -0.2260, -0.4900, -0.6974, 0.2223, 0.3375],
- [ 0.6266, -0.3632, 1.8030, -0.1496, -0.3881, -1.0460, 0.4562, 0.1622],
- [ 0.4956, -0.4900, 1.2467, -1.3024, -0.3474, -1.1994, 0.6318, 0.1830],
- [ 0.4585, -0.5079, 1.7557, -0.2600, -0.0559, 0.1161, 0.5975, 0.2690],
- [ 0.6890, -0.3344, 1.5034, -0.6130, -0.4778, -1.0306, 0.1694, 0.3261],
- [ 0.6072, -0.4055, 1.6764, -0.5149, -0.5742, -0.4923, 0.5001, 0.1026]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.2820, -1.0801, -0.5885, -1.0234, 0.2141,
- 0.1005],
- [ 0.5376, -0.3903, 1.7095, -0.3229, -0.4730, 0.4701, 0.3871,
- 0.0772],
- [ 0.5454, -0.4053, 1.6633, -0.1766, -0.6058, -0.5923, 0.1577,
- 0.4357],
- [ 0.5901, -0.4000, 1.8423, -0.0688, -0.5307, -0.9233, 0.3642,
- 0.1852],
- [ 0.5697, -0.4466, 1.1973, -1.1871, -0.4571, -0.9965, 0.5219,
- 0.2032],
- [ 0.5526, -0.4351, 1.7672, -0.1920, -0.1785, 0.2699, 0.5259,
- 0.2699],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0116, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0116, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7694539255462587
- step: 38
- running loss: 0.020248787514375227
- Train Steps: 38/90 Loss: 0.0202 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6125, 0.3999, 0.8750, 0.4883, 0.4750, 0.4700, 0.5533, 0.5617],
- [0.6286, 0.4040, 0.8696, 0.3047, 0.3924, 0.2887, 0.6300, 0.5367],
- [0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833],
- [0.6104, 0.4029, 0.8738, 0.4900, 0.4088, 0.4533, 0.5070, 0.5510],
- [0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3345, -0.5815, 1.6559, -0.2002, -0.0839, -0.0443, 0.3847, 0.3240],
- [ 0.6685, -0.3828, 1.5534, -1.0486, -0.4268, -0.9877, 0.7333, 0.1445],
- [ 0.6851, -0.3588, 1.4897, -1.1548, -0.4079, -1.0997, 0.4530, 0.0514],
- [ 0.4126, -0.5595, 1.6417, -0.2154, -0.5463, -0.0265, 0.4602, 0.1108],
- [ 0.5710, -0.4018, 1.6016, -0.0976, -0.4236, -0.0688, 0.2664, 0.2464],
- [ 0.6039, -0.3627, 1.6929, -0.2127, -0.5257, -0.8801, 0.3550, 0.1962],
- [-1.8773, -2.0152, 1.4880, -1.3270, 0.1654, -1.2752, 0.8369, 0.2394],
- [ 0.5513, -0.3570, 1.5899, -0.3356, -0.2179, -1.0885, 0.2876, 0.5012]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5432, -0.4388, 1.7557, -0.0303, -0.0919, -0.1150, 0.2699,
- 0.3087],
- [ 0.6174, -0.4201, 1.7309, -0.8784, -0.4735, -0.9524, 0.6242,
- 0.1931],
- [ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534],
- [ 0.5334, -0.4252, 1.7499, -0.0226, -0.3979, -0.1920, 0.0558,
- 0.2589],
- [ 0.5901, -0.4000, 1.8423, -0.0688, -0.5307, -0.9233, 0.3642,
- 0.1852],
- [-2.2859, -2.2859, 1.7210, -0.9772, 0.1852, -1.3698, 0.7986,
- 0.3104],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0153, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0153, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7847062242217362
- step: 39
- running loss: 0.020120672415941954
- Train Steps: 39/90 Loss: 0.0201 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6277, 0.4036, 0.8688, 0.3617, 0.3925, 0.2600, 0.6132, 0.4742],
- [ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6226, 0.4185, 0.8850, 0.5500, 0.3800, 0.4250, 0.5625, 0.5617],
- [0.6271, 0.4040, 0.9000, 0.3250, 0.4938, 0.2300, 0.7192, 0.5413],
- [0.6293, 0.4024, 0.8750, 0.5000, 0.4012, 0.5733, 0.7121, 0.5633],
- [0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528],
- [0.6104, 0.4029, 0.8738, 0.4900, 0.4088, 0.4533, 0.5070, 0.5510]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7944, -0.2597, 1.6941, -0.7817, -0.3957, -1.1214, 0.3668, 0.0797],
- [-1.2229, -1.5579, 1.2096, -0.8862, -0.5949, -0.8335, 0.0194, 0.3173],
- [-1.9269, -2.0668, 1.0180, -1.3759, -0.3612, -1.2341, 0.1503, 0.2716],
- [ 0.6871, -0.2951, 1.6941, 0.2017, -0.4402, -0.3049, 0.2864, 0.4204],
- [ 0.6057, -0.3936, 1.8279, -0.9332, 0.0218, -1.2719, 0.9551, 0.2071],
- [ 0.9779, -0.1799, 1.6772, 0.0201, -0.3361, 0.4483, 0.8435, 0.2974],
- [ 0.6400, -0.3831, 1.8871, -0.6828, -0.3191, -0.5734, 0.8844, 0.1991],
- [ 0.7648, -0.2619, 1.6307, 0.0055, -0.3844, -0.1027, 0.1083, 0.2771]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6134, -0.4218, 1.7268, -0.6154, -0.4730, -1.0850, 0.5463,
- -0.0957],
- [-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837],
- [-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373],
- [ 0.5897, -0.3527, 1.8018, 0.2545, -0.5307, -0.3229, 0.3122,
- 0.3084],
- [ 0.6108, -0.4201, 1.8711, -0.7848, -0.0053, -1.2236, 1.0362,
- 0.2142],
- [ 0.6207, -0.4273, 1.7557, 0.0236, -0.4326, 0.3623, 1.0033,
- 0.3157],
- [ 0.6388, -0.3623, 1.9173, -0.7386, -0.3517, -0.5846, 1.1495,
- 0.2676],
- [ 0.5334, -0.4252, 1.7499, -0.0226, -0.3979, -0.1920, 0.0558,
- 0.2589]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0405, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0405, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8252143119461834
- step: 40
- running loss: 0.020630357798654585
- Train Steps: 40/90 Loss: 0.0206 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6109, 0.4041, 0.6975, 0.3167, 0.3513, 0.3383, 0.5153, 0.5319],
- [0.6275, 0.4024, 0.7722, 0.2080, 0.4392, 0.2234, 0.6435, 0.5290],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750],
- [0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268],
- [0.6203, 0.4073, 0.8189, 0.2398, 0.4400, 0.2054, 0.5929, 0.5501],
- [0.6115, 0.4081, 0.6725, 0.2433, 0.4088, 0.1933, 0.5167, 0.5544],
- [0.6267, 0.4080, 0.8438, 0.2633, 0.4763, 0.1800, 0.6259, 0.5240]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2722, -0.6165, 1.1779, -0.8024, -0.6699, -0.6848, 0.0763, 0.2715],
- [ 0.2924, -0.6367, 1.3922, -1.1740, -0.2910, -1.2150, 0.6039, 0.1700],
- [ 0.3281, -0.5799, 1.9128, 0.0207, -0.1557, 0.4681, 0.6649, 0.3074],
- [ 0.3481, -0.5868, 1.1737, -1.0323, -0.5273, -0.8910, 0.5458, 0.4523],
- [ 0.2317, -0.6822, 1.8328, 0.0113, -0.2837, 0.2063, 0.6388, 0.2003],
- [ 0.4696, -0.4915, 1.5757, -1.0361, -0.2425, -1.3144, 0.4999, 0.2187],
- [ 0.2441, -0.6215, 1.0434, -1.0948, -0.4172, -1.3396, -0.0088, 0.3534],
- [ 0.6824, -0.3667, 1.7853, -0.9849, -0.1111, -1.4141, 0.6500, 0.1578]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5359, -0.4193, 0.9358, -0.8232, -0.6635, -0.7232, 0.0943,
- 0.1710],
- [ 0.6125, -0.4273, 1.2807, -1.3253, -0.2574, -1.2542, 0.6864,
- 0.1575],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700],
- [ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474],
- [ 0.5792, -0.4048, 1.4965, -1.1781, -0.2534, -1.3371, 0.4528,
- 0.2549],
- [ 0.5385, -0.4010, 0.8203, -1.1620, -0.3979, -1.3929, 0.1005,
- 0.2747],
- [ 0.6088, -0.4015, 1.6113, -1.0696, -0.0861, -1.4545, 0.6051,
- 0.1343]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0182, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0182, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.843462482560426
- step: 41
- running loss: 0.02057225567220551
- Train Steps: 41/90 Loss: 0.0206 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117],
- [0.6208, 0.4082, 0.8538, 0.3067, 0.3588, 0.3717, 0.6112, 0.5517],
- [0.6226, 0.4185, 0.8850, 0.5500, 0.3800, 0.4250, 0.5625, 0.5617],
- [0.6147, 0.4026, 0.6600, 0.2467, 0.4088, 0.2150, 0.5489, 0.5773],
- [0.6101, 0.3977, 0.8550, 0.5667, 0.3862, 0.4417, 0.5128, 0.4869],
- [0.6261, 0.4045, 0.8865, 0.5369, 0.3895, 0.4859, 0.6683, 0.5249],
- [0.6160, 0.4086, 0.8775, 0.3983, 0.3750, 0.3783, 0.5128, 0.4882]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4012, -0.5602, 1.1020, -1.4905, -0.3912, -1.1780, 0.3697, 0.3340],
- [ 0.3519, -0.6377, 1.8958, -0.1771, -0.4805, -0.0822, 0.7601, 0.2015],
- [ 0.7661, -0.3157, 1.7028, -0.8933, -0.5319, -0.5710, 0.5506, 0.2958],
- [ 0.3787, -0.5236, 1.8381, 0.0948, -0.3877, -0.3379, 0.4612, 0.4892],
- [ 0.3725, -0.5542, 0.9727, -1.3297, -0.2899, -1.4031, 0.1775, 0.5426],
- [ 0.1261, -0.7136, 1.7445, 0.1966, -0.3572, -0.2723, 0.2821, 0.1150],
- [ 0.5290, -0.4819, 1.8134, 0.0249, -0.4198, 0.0279, 0.8776, 0.1686],
- [ 0.4397, -0.5016, 1.8540, -0.5343, -0.5032, -0.5813, 0.1569, 0.1158]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853],
- [ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774],
- [ 0.5814, -0.4003, 1.6575, -0.8694, -0.6289, -0.5692, 0.5374,
- 0.2622],
- [ 0.5897, -0.3527, 1.8018, 0.2545, -0.5307, -0.3229, 0.3122,
- 0.3084],
- [ 0.5532, -0.4264, 0.7626, -1.1466, -0.3979, -1.2928, 0.2494,
- 0.3808],
- [ 0.5320, -0.4488, 1.6633, 0.3315, -0.5018, -0.2459, 0.0828,
- -0.0370],
- [ 0.6059, -0.4177, 1.8087, 0.1941, -0.4868, -0.0414, 0.8010,
- 0.1385],
- [ 0.5595, -0.3988, 1.7672, -0.4460, -0.5538, -0.5384, 0.0828,
- -0.0310]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0174, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0174, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.860898629296571
- step: 42
- running loss: 0.02049758641182312
- Train Steps: 42/90 Loss: 0.0205 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482],
- [0.6260, 0.4106, 0.8025, 0.2583, 0.4550, 0.1867, 0.6281, 0.4869],
- [ nan, nan, 0.7335, 0.2569, 0.3788, 0.2667, 0.5066, 0.5578],
- [0.6034, 0.4011, 0.7350, 0.2533, 0.3438, 0.3367, 0.5516, 0.5084],
- [0.6204, 0.4055, 0.8438, 0.5733, 0.4574, 0.4801, 0.5487, 0.5617],
- [0.6226, 0.4001, 0.8438, 0.5733, 0.3862, 0.4250, 0.5750, 0.5283],
- [0.6197, 0.4050, 0.7527, 0.2000, 0.4042, 0.2249, 0.5895, 0.4995],
- [0.6132, 0.4037, 0.6963, 0.2217, 0.4100, 0.1950, 0.5395, 0.5175]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.6744, -1.8164, 1.6428, -1.1464, 0.1531, -1.0890, 1.0504, 0.3297],
- [ 0.6633, -0.3424, 1.5168, -1.0733, -0.2207, -1.2959, 0.5642, 0.1678],
- [-2.1848, -2.1812, 1.1882, -1.0478, -0.5552, -0.9504, 0.1036, 0.2840],
- [ 0.5811, -0.4098, 1.1966, -1.0334, -0.6815, -0.6616, 0.3493, 0.1752],
- [ 0.7715, -0.2316, 1.6568, 0.3646, -0.2515, 0.0227, 0.3323, 0.3487],
- [ 0.6553, -0.3458, 1.7052, 0.3154, -0.5779, -0.2111, 0.3768, 0.2283],
- [ 0.7520, -0.3007, 1.2283, -1.2327, -0.4260, -1.1571, 0.4222, 0.2272],
- [ 0.6980, -0.3132, 1.0704, -1.2022, -0.4169, -1.3390, 0.2616, 0.2387]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.6517, -1.2620, 0.2141, -1.1928, 1.1166,
- 0.2463],
- [ 0.6058, -0.3892, 1.4208, -1.0927, -0.1843, -1.4237, 0.6154,
- -0.0370],
- [-2.2859, -2.2859, 1.1020, -1.0994, -0.5365, -1.0542, 0.0542,
- 0.2905],
- [ 0.5009, -0.4333, 1.1090, -1.1158, -0.6982, -0.7309, 0.2617,
- 0.0622],
- [ 0.5795, -0.4129, 1.6113, 0.3623, -0.1733, -0.0684, 0.2487,
- 0.3084],
- [ 0.5900, -0.4377, 1.6113, 0.3623, -0.5018, -0.3229, 0.3700,
- 0.1544],
- [ 0.5762, -0.4153, 1.1908, -1.3622, -0.4190, -1.2471, 0.4368,
- 0.0213],
- [ 0.5465, -0.4214, 0.9300, -1.2620, -0.3921, -1.3852, 0.2062,
- 0.1043]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0178, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0178, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8787088342942297
- step: 43
- running loss: 0.02043508916963325
- Train Steps: 43/90 Loss: 0.0204 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6147, 0.4107, 0.8137, 0.3333, 0.3750, 0.2683, 0.5006, 0.5412],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837],
- [0.6200, 0.4112, 0.8862, 0.4100, 0.3638, 0.4917, 0.6088, 0.6050],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6339, 0.4149, 0.8800, 0.5000, 0.3900, 0.5283, 0.7541, 0.5424],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0593, -0.7209, 1.4025, -0.7395, -0.5557, -1.0149, -0.0363, 0.1739],
- [ 0.2105, -0.6398, 0.9159, -0.9487, -0.5745, -1.0085, 0.1048, 0.4227],
- [ 0.5317, -0.4285, 1.8629, -0.3572, -0.4762, 0.0316, 0.7137, 0.3804],
- [ 0.4177, -0.4962, 1.5345, -0.5597, -0.5811, -0.9179, 0.0925, 0.3951],
- [ 0.6118, -0.4042, 1.9377, -0.8004, -0.4052, -0.8034, 0.7940, 0.1437],
- [ 0.0240, -0.7626, 1.1786, -1.3303, -0.2676, -1.4906, 0.2640, 0.2023],
- [ 0.5820, -0.4623, 1.8527, 0.0597, -0.4232, 0.3058, 1.2145, 0.1657],
- [ 0.2763, -0.6095, 1.8244, 0.0415, -0.1050, -0.0060, 0.3189, 0.3642]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5532, -0.3888, 1.4727, -0.7463, -0.5538, -1.0465, 0.0265,
- 0.2138],
- [ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103],
- [ 0.5777, -0.3868, 1.8076, -0.3921, -0.6058, -0.0149, 0.5259,
- 0.5085],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390],
- [ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.6421, -0.3695, 1.7788, 0.0236, -0.4845, 0.1544, 1.1971,
- 0.2196],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0223, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0223, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9010163084603846
- step: 44
- running loss: 0.02047764337409965
- Train Steps: 44/90 Loss: 0.0205 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6258, 0.4038, 0.8750, 0.4883, 0.3900, 0.5500, 0.6375, 0.5217],
- [0.6117, 0.4018, 0.6562, 0.1967, 0.3738, 0.2550, 0.5280, 0.5103],
- [ nan, nan, 0.8938, 0.2850, 0.4662, 0.3117, 0.7406, 0.5528],
- [0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6205, 0.4012, 0.8675, 0.3283, 0.3713, 0.3050, 0.5813, 0.5117],
- [0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100],
- [0.6132, 0.3930, 0.8672, 0.4754, 0.3712, 0.5222, 0.5974, 0.5098],
- [0.6151, 0.4125, 0.8738, 0.4417, 0.3575, 0.3783, 0.5138, 0.5483]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8081, -0.2567, 1.7158, -0.0621, -0.3863, 0.1884, 0.6553, 0.2262],
- [ 0.3466, -0.5509, 0.7081, -1.3252, -0.4496, -1.2155, 0.2184, 0.2836],
- [-2.5157, -2.3895, 1.8121, -0.7981, -0.1205, -0.9275, 0.9323, 0.2574],
- [ 0.4411, -0.4603, 1.7228, -0.2251, -0.3915, -0.2788, 0.1090, 0.1176],
- [ 0.5141, -0.4182, 1.6298, -0.8154, -0.5195, -0.9728, 0.3624, 0.1820],
- [ 0.7026, -0.3435, 1.6917, -0.4188, -0.5994, -0.5678, 0.6849, 0.2348],
- [ 0.5526, -0.4045, 1.6131, -0.0275, -0.4721, 0.1363, 0.4688, 0.2287],
- [ 0.6117, -0.3064, 1.5351, -0.2352, -0.5716, -0.6021, 0.0366, 0.4231]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6047, -0.4209, 1.7557, -0.0303, -0.4845, 0.2545, 0.6587,
- 0.1236],
- [ 0.5393, -0.4299, 0.7453, -1.3775, -0.5596, -1.1081, 0.1527,
- 0.0712],
- [-2.2859, -2.2859, 1.8423, -0.9695, -0.1323, -0.8463, 1.1349,
- 0.2676],
- [ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220],
- [ 0.5799, -0.4329, 1.7210, -0.7694, -0.5711, -0.8771, 0.3988,
- 0.0774],
- [ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697],
- [ 0.5466, -0.4706, 1.7198, -0.0903, -0.5712, 0.1261, 0.4733,
- 0.0688],
- [ 0.5551, -0.3807, 1.7499, -0.2459, -0.6346, -0.5384, 0.0871,
- 0.2468]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0107, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0107, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9117077724076807
- step: 45
- running loss: 0.020260172720170683
- Train Steps: 45/90 Loss: 0.0203 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4081, 0.8063, 0.2017, 0.4825, 0.1583, 0.6156, 0.4869],
- [0.6265, 0.4088, 0.8025, 0.1850, 0.4163, 0.2500, 0.6290, 0.4947],
- [0.6274, 0.4270, 0.8938, 0.4967, 0.3550, 0.4283, 0.5700, 0.5733],
- [0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6311, 0.3998, 0.7975, 0.5767, 0.3838, 0.4850, 0.7327, 0.5343],
- [0.6201, 0.4036, 0.8596, 0.3850, 0.3492, 0.3785, 0.5978, 0.5131],
- [0.6131, 0.4037, 0.6907, 0.2819, 0.3688, 0.2700, 0.5217, 0.5680],
- [0.6082, 0.4042, 0.6975, 0.1917, 0.4100, 0.1983, 0.5502, 0.5578]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3473, -0.5753, 1.5471, -1.2589, -0.0220, -1.4570, 0.6313, 0.0374],
- [ 0.3961, -0.5188, 1.5376, -1.2642, -0.3802, -1.0928, 0.6439, 0.0683],
- [ 0.5461, -0.3509, 1.9025, 0.1118, -0.6093, -0.2064, 0.1833, 0.4084],
- [ 0.5049, -0.4197, 1.1840, -1.0966, -0.3444, -1.0405, 0.4707, 0.5814],
- [ 0.3431, -0.6076, 1.5917, 0.3166, -0.5506, 0.0278, 0.8716, 0.1111],
- [ 0.4011, -0.5269, 1.8692, -0.4701, -0.7028, -0.4832, 0.3813, 0.0916],
- [-0.0361, -0.7732, 0.9775, -0.9902, -0.5846, -0.9177, 0.0684, 0.3048],
- [ 0.1723, -0.6870, 0.9510, -1.2858, -0.4434, -1.2211, 0.2540, 0.2577]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.1241e-01, -4.0100e-01, 1.4381e+00, -1.3544e+00, -5.7275e-02,
- -1.5546e+00, 5.5732e-01, -3.6943e-02],
- [ 6.0785e-01, -3.9761e-01, 1.4208e+00, -1.4314e+00, -3.6328e-01,
- -1.1312e+00, 6.1950e-01, -9.2270e-04],
- [ 6.1207e-01, -3.1378e-01, 1.8423e+00, 8.1601e-03, -6.4619e-01,
- -3.0747e-01, 3.4688e-01, 3.6228e-01],
- [ 5.7460e-01, -4.0208e-01, 1.0801e+00, -1.1312e+00, -3.2286e-01,
- -1.1081e+00, 4.8034e-01, 6.0842e-01],
- [ 6.2895e-01, -4.3934e-01, 1.3977e+00, 3.7768e-01, -5.1339e-01,
- -4.5727e-02, 1.0984e+00, 1.8214e-01],
- [ 5.7829e-01, -4.2163e-01, 1.6847e+00, -5.0778e-01, -6.7321e-01,
- -5.3774e-01, 4.7523e-01, 8.3916e-02],
- [ 5.4590e-01, -4.2148e-01, 9.0432e-01, -9.8382e-01, -5.8268e-01,
- -1.0388e+00, 1.2363e-01, 3.3782e-01],
- [ 5.2315e-01, -4.1886e-01, 9.3580e-01, -1.4006e+00, -3.9215e-01,
- -1.3698e+00, 2.5553e-01, 2.9064e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0213, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0213, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9329804559238255
- step: 46
- running loss: 0.020282183824430988
- Train Steps: 46/90 Loss: 0.0203 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6263, 0.4029, 0.8900, 0.4933, 0.4075, 0.5183, 0.6406, 0.4758],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6277, 0.4118, 0.8988, 0.3833, 0.3950, 0.2650, 0.6290, 0.4938],
- [0.6246, 0.4028, 0.8738, 0.4867, 0.4088, 0.5667, 0.6362, 0.5200],
- [0.6228, 0.4119, 0.7938, 0.2233, 0.4674, 0.1773, 0.6188, 0.5433],
- [0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6329, 0.4175, 0.8550, 0.2333, 0.5425, 0.2250, 0.7398, 0.5609]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 3.4157e-01, -5.4580e-01, 1.7160e+00, -9.9776e-02, -5.3122e-01,
- 1.0598e-01, 4.4080e-01, 7.4866e-02],
- [ 4.5865e-01, -4.6616e-01, 1.6395e+00, -8.6564e-01, -4.8457e-01,
- -8.4187e-01, 6.6704e-01, 2.8578e-01],
- [ 9.5002e-02, -7.0816e-01, 1.8023e+00, -5.4315e-01, -6.0169e-01,
- -1.0344e+00, 3.4626e-01, 5.6686e-02],
- [ 4.0714e-01, -5.0531e-01, 1.6999e+00, -8.3630e-02, -4.9261e-01,
- 3.0629e-01, 5.2600e-01, 1.3596e-01],
- [ 7.4241e-01, -3.0988e-01, 1.2474e+00, -1.2521e+00, -2.4741e-01,
- -1.4120e+00, 4.0297e-01, 2.0329e-01],
- [ 4.5643e-01, -4.0569e-01, 1.1043e+00, -6.4953e-01, -7.8430e-01,
- -5.2145e-01, -1.3662e-03, 4.6987e-01],
- [ 4.2579e-01, -5.1258e-01, 1.6009e+00, 2.2429e-01, -5.5763e-01,
- -8.9889e-02, 2.6553e-01, 1.4516e-01],
- [ 1.9168e-01, -6.7445e-01, 1.6228e+00, -1.1956e+00, 1.8815e-01,
- -1.3783e+00, 1.0478e+00, 3.1273e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6072, -0.4250, 1.8249, -0.0072, -0.4037, 0.1082, 0.6730,
- -0.0881],
- [ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.6135, -0.3841, 1.8654, -0.5153, -0.4614, -1.0619, 0.6195,
- -0.0049],
- [ 0.5990, -0.4256, 1.7499, -0.0380, -0.3979, 0.3315, 0.6529,
- 0.1159],
- [ 0.5908, -0.3832, 1.3804, -1.2543, -0.1270, -1.4671, 0.5721,
- 0.2237],
- [ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.6372, -0.3575, 1.6633, -1.2082, 0.2199, -1.2467, 1.1313,
- 0.3050]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0230, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0230, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9559376635588706
- step: 47
- running loss: 0.02033909922465682
- Train Steps: 47/90 Loss: 0.0203 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6143, 0.4040, 0.8237, 0.3333, 0.4075, 0.2117, 0.5137, 0.4973],
- [0.6198, 0.4101, 0.8838, 0.5283, 0.3763, 0.5267, 0.5913, 0.5567],
- [0.6185, 0.4079, 0.8838, 0.4617, 0.4838, 0.5650, 0.6175, 0.5850],
- [0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183],
- [0.6339, 0.4102, 0.8588, 0.3133, 0.4425, 0.2117, 0.6417, 0.5089],
- [ nan, nan, 0.6512, 0.1717, 0.4100, 0.1983, 0.5253, 0.5240],
- [0.6124, 0.4083, 0.6954, 0.3069, 0.3650, 0.2750, 0.5163, 0.5485],
- [0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.0754, -0.6728, 1.4005, -0.7200, -0.4994, -1.1253, 0.1119, 0.0342],
- [ 0.7075, -0.3032, 1.7789, 0.2578, -0.6072, 0.0754, 0.5373, 0.2307],
- [ 0.4886, -0.4103, 1.7513, -0.1235, -0.1657, 0.1917, 0.4897, 0.3107],
- [ 0.6213, -0.3726, 1.1898, -1.2564, -0.3196, -1.3608, 0.4322, 0.1022],
- [ 0.7552, -0.2970, 1.6800, -0.7415, -0.2835, -1.2006, 0.7921, 0.0721],
- [-2.7242, -2.5366, 0.9469, -1.2379, -0.4670, -1.2380, 0.2041, 0.1697],
- [ 0.4366, -0.4563, 0.9809, -0.8987, -0.6418, -0.8829, 0.1738, 0.2443],
- [ 0.6094, -0.3594, 1.1859, -1.1509, -0.5651, -0.9896, 0.5455, 0.2325]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5515, -0.4201, 1.5189, -0.7463, -0.4037, -1.3082, 0.0869,
- 0.0111],
- [ 0.5770, -0.3918, 1.7961, 0.1544, -0.5480, 0.1467, 0.4450,
- 0.2853],
- [ 0.5707, -0.4017, 1.7961, -0.1535, -0.0515, 0.3238, 0.5663,
- 0.4162],
- [ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082],
- [ 0.6421, -0.3912, 1.6806, -0.8386, -0.2420, -1.3082, 0.6780,
- 0.0646],
- [-2.2859, -2.2859, 0.7222, -1.4930, -0.3921, -1.3698, 0.1404,
- 0.1343],
- [ 0.5425, -0.3998, 0.9263, -0.8683, -0.6000, -1.0157, 0.0990,
- 0.2476],
- [ 0.5697, -0.4466, 1.1973, -1.1871, -0.4571, -0.9965, 0.5219,
- 0.2032]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0158, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0158, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9717486617155373
- step: 48
- running loss: 0.02024476378574036
- Train Steps: 48/90 Loss: 0.0202 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6296, 0.4060, 0.9100, 0.3267, 0.4726, 0.3367, 0.7446, 0.5355],
- [0.6279, 0.4050, 0.8025, 0.2200, 0.4313, 0.2733, 0.6820, 0.5320],
- [0.6203, 0.4073, 0.8189, 0.2398, 0.4400, 0.2054, 0.5929, 0.5501],
- [0.6221, 0.4107, 0.7788, 0.3033, 0.3950, 0.2817, 0.6075, 0.5517],
- [0.6167, 0.4048, 0.6831, 0.3639, 0.3763, 0.3017, 0.5700, 0.5883]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5644, -0.4089, 1.3489, -1.0632, -0.3554, -1.2604, 0.3068, 0.0804],
- [ 0.4687, -0.4830, 1.2589, -1.1416, -0.2557, -1.2853, 0.4649, 0.1626],
- [ 0.4234, -0.5423, 1.6914, 0.3361, -0.5816, -0.0057, 0.5142, -0.0243],
- [ 0.4425, -0.5201, 1.8059, -0.6999, -0.2962, -0.6888, 1.0130, 0.1350],
- [ 0.2810, -0.5907, 1.3972, -1.1654, -0.4216, -0.9874, 0.6505, 0.1130],
- [ 0.6097, -0.3620, 1.3889, -1.0538, -0.3057, -1.2302, 0.3695, 0.1679],
- [ 0.5667, -0.4004, 1.3944, -0.9263, -0.5386, -0.9340, 0.3893, 0.2306],
- [-0.0884, -0.7821, 1.0243, -0.7315, -0.6894, -0.8069, 0.1109, 0.4189]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5796, -0.4326, 1.4439, -1.1774, -0.2940, -1.3390, 0.3931,
- 0.0928],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.6224, -0.4105, 1.9173, -0.7771, -0.1030, -0.7308, 1.1532,
- 0.1875],
- [ 0.6141, -0.4153, 1.4208, -1.2697, -0.2940, -1.0234, 0.8644,
- 0.1715],
- [ 0.5792, -0.4048, 1.4965, -1.1781, -0.2534, -1.3371, 0.4528,
- 0.2549],
- [ 0.5875, -0.3888, 1.3111, -0.8848, -0.4614, -0.9849, 0.5201,
- 0.2622],
- [ 0.5626, -0.4162, 0.8692, -0.6051, -0.5480, -0.8925, 0.3469,
- 0.4316]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0207, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0207, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9924390469677746
- step: 49
- running loss: 0.020253858101383155
- Train Steps: 49/90 Loss: 0.0203 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6040, 0.4002, 0.7338, 0.2267, 0.3975, 0.2100, 0.5231, 0.4778],
- [0.6200, 0.4086, 0.8414, 0.2974, 0.4117, 0.2274, 0.5869, 0.5391],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [0.6264, 0.4049, 0.8988, 0.4633, 0.3813, 0.4983, 0.6326, 0.4843]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3356, -0.6246, 1.6195, 0.2512, -0.6513, -0.3454, 0.5973, 0.0688],
- [ 0.6370, -0.3359, 1.3365, -1.0085, -0.1253, -1.4134, 0.3820, 0.2478],
- [ 0.4861, -0.4302, 1.6799, -0.2698, -0.5321, -0.0616, 0.5071, 0.2307],
- [ 0.3765, -0.4768, 1.6450, -0.1350, -0.4039, -0.1223, 0.2556, 0.1823],
- [ 0.5536, -0.4313, 0.9053, -1.4331, -0.4854, -1.2592, 0.2617, 0.0754],
- [ 0.6161, -0.3597, 1.4798, -1.0293, -0.3282, -1.1504, 0.4840, 0.1977],
- [ 0.4582, -0.4561, 1.7081, -0.8268, -0.5026, -0.7563, 0.6979, 0.2014],
- [ 0.6078, -0.3919, 1.8090, -0.2198, -0.5793, -0.0794, 0.6614, 0.0188]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.5037, -0.4375, 1.1032, -1.2390, -0.4499, -1.3159, 0.1301,
- -0.0791],
- [ 0.5776, -0.3987, 1.6005, -0.9121, -0.3844, -1.2358, 0.4247,
- 0.2043],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390],
- [ 0.6075, -0.4159, 1.8654, -0.1458, -0.5249, 0.0159, 0.6359,
- -0.0490]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0099, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0099, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0023726033978164
- step: 50
- running loss: 0.02004745206795633
- Train Steps: 50/90 Loss: 0.0200 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6205, 0.4004, 0.8938, 0.4883, 0.3663, 0.5000, 0.6357, 0.4947],
- [0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [0.6153, 0.4117, 0.8688, 0.5167, 0.4895, 0.5647, 0.5524, 0.5136],
- [ nan, nan, 0.7192, 0.2346, 0.4037, 0.2050, 0.5138, 0.5650],
- [0.6219, 0.3934, 0.8688, 0.5267, 0.4313, 0.4967, 0.5988, 0.4983],
- [0.6292, 0.4010, 0.8988, 0.4800, 0.3638, 0.3817, 0.6357, 0.5051],
- [0.6240, 0.4217, 0.8150, 0.3133, 0.4425, 0.2650, 0.5650, 0.5817]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7533, -0.3049, 1.7014, -0.1500, -0.6275, -0.0598, 0.6284, -0.0155],
- [ 0.7431, -0.2644, 1.7102, -0.3314, -0.3599, 0.0643, 0.5581, 0.1520],
- [ 0.8763, -0.1319, 1.6137, -0.1098, -0.3940, -1.0488, 0.3424, 0.4068],
- [ 0.5484, -0.3760, 1.6466, -0.0710, -0.1373, 0.0032, 0.3874, 0.1075],
- [-2.5872, -2.4539, 0.9287, -1.3730, -0.5087, -1.2914, 0.1816, 0.1541],
- [ 0.5824, -0.4260, 1.6279, 0.0228, -0.3571, -0.1051, 0.5001, -0.1236],
- [ 0.7043, -0.3324, 1.7657, -0.3025, -0.6113, -0.6340, 0.7717, 0.0398],
- [ 0.6638, -0.2967, 1.3242, -1.0323, -0.3195, -1.1665, 0.3938, 0.3216]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.8031e-01, -4.3672e-01, 1.8423e+00, -3.0331e-02, -5.9423e-01,
- 2.3557e-02, 6.5034e-01, -9.2270e-04],
- [ 5.9602e-01, -4.1016e-01, 1.8018e+00, -1.6120e-01, -3.3441e-01,
- 1.1594e-01, 5.4896e-01, 2.3141e-01],
- [ 6.1386e-01, -3.2163e-01, 1.8134e+00, 3.1255e-02, -3.8637e-01,
- -1.0157e+00, 2.1441e-01, 5.7619e-01],
- [ 5.5635e-01, -3.8422e-01, 1.7268e+00, 1.0054e-01, -2.4997e-02,
- 3.2255e-01, 2.6581e-01, 8.6245e-02],
- [-2.2859e+00, -2.2859e+00, 1.0361e+00, -1.2021e+00, -4.2102e-01,
- -1.3390e+00, 8.7067e-02, 3.2379e-01],
- [ 5.8643e-01, -4.6898e-01, 1.7268e+00, 1.4673e-01, -2.9400e-01,
- 8.1601e-03, 4.7968e-01, 1.5858e-02],
- [ 6.2038e-01, -4.3356e-01, 1.8654e+00, -6.8822e-02, -6.0577e-01,
- -5.2302e-01, 6.5034e-01, 4.7170e-02],
- [ 5.9636e-01, -3.3795e-01, 1.4785e+00, -8.3865e-01, -2.4203e-01,
- -1.0619e+00, 3.2379e-01, 4.0077e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0161, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0161, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0185070815496147
- step: 51
- running loss: 0.01997072708920813
- Train Steps: 51/90 Loss: 0.0200 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6142, 0.4127, 0.7575, 0.3067, 0.3438, 0.4383, 0.5778, 0.5207],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550],
- [0.6250, 0.4131, 0.8688, 0.2983, 0.4275, 0.2367, 0.6162, 0.5367],
- [0.6200, 0.4024, 0.8390, 0.3139, 0.3525, 0.3833, 0.6162, 0.5383],
- [0.6188, 0.4099, 0.7400, 0.2433, 0.3962, 0.2750, 0.6162, 0.5467],
- [0.6236, 0.4081, 0.8575, 0.3000, 0.3713, 0.3033, 0.5550, 0.5633],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6201, 0.4050, 0.7757, 0.2234, 0.4459, 0.1798, 0.5975, 0.5426]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5031, -0.4425, 1.2040, -0.7772, -0.5495, -0.3063, 0.4339, 0.1906],
- [ 0.3934, -0.5387, 1.4250, -0.7125, -0.5673, -0.3420, 0.6277, 0.2145],
- [ 0.7193, -0.3086, 1.8446, -0.8237, -0.1610, -1.2996, 0.7239, 0.0600],
- [ 0.5397, -0.4609, 1.6552, -0.6620, -0.5809, -0.4994, 0.7042, 0.1421],
- [ 0.8671, -0.2594, 1.1405, -1.1521, -0.4197, -1.0268, 0.5970, 0.0799],
- [ 0.6018, -0.3752, 1.6716, -0.6814, -0.4735, -0.9963, 0.3710, 0.0484],
- [ 0.2039, -0.6625, 1.6431, -0.2761, -0.5604, -0.3393, 0.3029, 0.1686],
- [ 0.6398, -0.3982, 1.2430, -1.0984, -0.1309, -1.4904, 0.4661, 0.0773]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5509, -0.3798, 1.2129, -0.8694, -0.6982, -0.2613, 0.3830,
- 0.1193],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776],
- [ 0.6011, -0.3778, 1.7268, -0.9079, -0.3113, -1.1928, 0.5605,
- 0.1929],
- [ 0.5779, -0.4275, 1.5894, -0.8362, -0.6577, -0.5153, 0.5605,
- 0.2006],
- [ 0.5725, -0.3926, 1.1321, -1.1620, -0.4557, -1.0157, 0.5605,
- 0.2391],
- [ 0.5944, -0.4008, 1.6748, -0.9002, -0.5711, -0.8848, 0.2776,
- 0.3161],
- [ 0.5367, -0.4294, 1.5709, -0.4999, -0.6693, -0.3075, 0.2455,
- 0.3559],
- [ 0.5784, -0.4153, 1.2972, -1.2541, -0.2265, -1.4553, 0.4741,
- 0.2203]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0147, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0147, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0332335312850773
- step: 52
- running loss: 0.019869875601636104
- Train Steps: 52/90 Loss: 0.0199 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [0.6120, 0.4014, 0.6863, 0.2817, 0.3700, 0.2783, 0.5513, 0.5667],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6064, 0.4019, 0.8650, 0.4517, 0.4037, 0.5367, 0.5703, 0.5609],
- [0.6201, 0.4017, 0.8871, 0.4621, 0.3517, 0.4675, 0.5999, 0.5106],
- [0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901],
- [0.6113, 0.4104, 0.8650, 0.4250, 0.3925, 0.5967, 0.5787, 0.5116],
- [0.6095, 0.4002, 0.8533, 0.5168, 0.5031, 0.5094, 0.5125, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 7.2992e-01, -3.0336e-01, 1.6313e+00, -1.1895e+00, -1.6060e-01,
- -1.5407e+00, 7.1356e-01, -1.1083e-04],
- [ 8.3763e-01, -2.4081e-01, 9.4407e-01, -1.0714e+00, -6.1049e-01,
- -1.1304e+00, 3.6978e-01, 2.8219e-01],
- [ 5.3184e-01, -4.5070e-01, 1.7964e+00, -2.1034e-01, -4.5707e-01,
- 1.3236e-01, 5.4005e-01, 1.0987e-02],
- [ 4.1670e-01, -5.2252e-01, 1.7129e+00, -1.5120e-01, -3.7215e-01,
- 7.9002e-02, 4.5440e-01, 2.4114e-01],
- [ 6.4734e-01, -3.8203e-01, 1.7813e+00, -1.8053e-01, -6.0651e-01,
- -2.4972e-01, 4.8176e-01, 1.4656e-01],
- [ 8.4762e-01, -2.9559e-01, 1.6944e+00, -8.0337e-01, -6.5135e-01,
- -8.4902e-01, 8.1033e-01, -2.9006e-02],
- [ 5.1166e-01, -4.3294e-01, 1.7072e+00, -3.1367e-01, -4.1504e-01,
- 2.0122e-01, 4.7451e-01, 1.6525e-01],
- [ 5.3567e-01, -4.6310e-01, 1.7171e+00, 1.5583e-01, 6.1324e-03,
- -2.4901e-01, 2.7454e-01, 2.4279e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [ 0.5410, -0.4321, 0.8838, -0.9849, -0.5769, -1.0003, 0.2603,
- 0.3315],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.5151, -0.4296, 1.7095, -0.1997, -0.4210, 0.1929, 0.3484,
- 0.3047],
- [ 0.5783, -0.4306, 1.8114, -0.1515, -0.6617, -0.1268, 0.4851,
- 0.0727],
- [ 0.6031, -0.4307, 1.7268, -0.8002, -0.6058, -0.6462, 0.6442,
- -0.0220],
- [ 0.5376, -0.3903, 1.7095, -0.3229, -0.4730, 0.4701, 0.3871,
- 0.0772],
- [ 0.5295, -0.4373, 1.6553, 0.1011, 0.0380, 0.0671, 0.0813,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0120, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0120, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0451941271312535
- step: 53
- running loss: 0.019720643908136857
- Train Steps: 53/90 Loss: 0.0197 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6157, 0.3956, 0.8323, 0.4138, 0.3479, 0.4431, 0.5914, 0.5131],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402],
- [0.6183, 0.4076, 0.8838, 0.4517, 0.3813, 0.4483, 0.5775, 0.5633],
- [0.6364, 0.4154, 0.8938, 0.3717, 0.4500, 0.2583, 0.6448, 0.5285],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519],
- [0.6124, 0.4069, 0.8314, 0.5001, 0.3738, 0.4650, 0.5167, 0.5402],
- [0.6277, 0.4118, 0.8988, 0.3833, 0.3950, 0.2650, 0.6290, 0.4938]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6368, -0.3853, 1.6120, -0.5413, -0.6199, -0.2305, 0.5398, 0.0600],
- [ 0.5209, -0.4816, 1.6451, 0.1398, -0.3906, -0.1227, 0.5984, 0.0783],
- [ 0.9131, -0.1735, 1.7377, -0.3481, -0.4845, -0.1768, 0.5186, 0.2881],
- [ 0.6196, -0.3521, 1.8370, -0.8313, -0.1900, -1.2255, 0.7835, 0.2021],
- [ 0.6076, -0.3732, 1.5815, -0.3898, -0.5213, -0.1574, 0.3139, 0.2048],
- [ 0.6049, -0.3922, 1.6878, 0.0123, -0.1104, 0.0630, 0.2517, 0.1497],
- [ 0.7485, -0.2795, 1.5738, -0.0625, -0.4706, -0.1227, 0.3277, 0.2029],
- [ 0.6269, -0.3977, 1.9121, -0.6803, -0.4985, -1.1353, 0.7050, -0.0465]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5581, -0.4586, 1.5586, -0.3747, -0.6792, -0.2391, 0.4455,
- 0.0840],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091],
- [ 0.5700, -0.4032, 1.7961, -0.1997, -0.5249, -0.2151, 0.3815,
- 0.3161],
- [ 0.6537, -0.3671, 1.8423, -0.5692, -0.2074, -1.0927, 0.6926,
- 0.1554],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634],
- [ 0.5425, -0.4067, 1.5543, 0.0241, -0.5596, -0.1381, 0.1005,
- 0.2093],
- [ 0.6135, -0.3841, 1.8654, -0.5153, -0.4614, -1.0619, 0.6195,
- -0.0049]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0117, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0117, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0568622411228716
- step: 54
- running loss: 0.019571522983756882
- Train Steps: 54/90 Loss: 0.0196 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6286, 0.3977, 0.9038, 0.4733, 0.3900, 0.4150, 0.7074, 0.5320],
- [0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6201, 0.4050, 0.7757, 0.2234, 0.4459, 0.1798, 0.5975, 0.5426],
- [0.6147, 0.4107, 0.8137, 0.3333, 0.3750, 0.2683, 0.5006, 0.5412],
- [0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [ nan, nan, 0.7850, 0.2700, 0.4288, 0.1717, 0.5199, 0.4999],
- [0.6203, 0.4076, 0.8611, 0.2878, 0.4050, 0.2554, 0.5907, 0.5496]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7829, -0.3401, 1.8872, -0.0882, -0.5316, -0.3178, 0.9825, 0.0237],
- [ 0.7225, -0.2570, 1.2862, -0.5971, -0.6629, -0.5322, 0.2681, 0.3659],
- [ 0.7286, -0.3055, 1.2713, -1.2098, -0.2073, -1.4156, 0.5210, 0.1292],
- [ 0.6605, -0.3209, 1.4038, -0.7051, -0.5712, -0.9730, 0.0354, 0.1156],
- [ 0.8193, -0.2439, 1.8907, 0.0371, -0.3015, 0.2836, 0.6148, 0.1104],
- [ 0.8284, -0.2746, 1.8211, 0.1799, -0.3628, 0.1998, 0.5393, -0.0055],
- [-1.1938, -1.5406, 1.1514, -1.2486, -0.3078, -1.3804, 0.1980, 0.1326],
- [ 0.6440, -0.3563, 1.7050, -0.9408, -0.3089, -0.9373, 0.5627, 0.1509]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6174, -0.4490, 1.8885, -0.0996, -0.4845, -0.3691, 0.9814,
- 0.1715],
- [ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.5784, -0.4153, 1.2972, -1.2541, -0.2265, -1.4553, 0.4741,
- 0.2203],
- [ 0.5532, -0.3888, 1.4727, -0.7463, -0.5538, -1.0465, 0.0265,
- 0.2138],
- [ 0.5991, -0.3803, 1.8018, -0.0534, -0.3460, 0.1852, 0.5374,
- 0.1390],
- [ 0.5778, -0.4389, 1.7107, 0.1192, -0.3921, 0.0815, 0.4741,
- 0.0711],
- [-2.2859, -2.2859, 1.3400, -1.0388, -0.3055, -1.4930, 0.1157,
- 0.0231],
- [ 0.5790, -0.4031, 1.6915, -0.9564, -0.4152, -1.1063, 0.4425,
- 0.2528]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0374, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0374, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0942944386042655
- step: 55
- running loss: 0.019896262520077555
- Train Steps: 55/90 Loss: 0.0199 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6179, 0.4008, 0.7505, 0.2678, 0.4368, 0.1891, 0.5831, 0.5263],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019],
- [0.6115, 0.4081, 0.6725, 0.2433, 0.4088, 0.1933, 0.5167, 0.5544],
- [0.6284, 0.4029, 0.8838, 0.3783, 0.3975, 0.2850, 0.6335, 0.5090],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6246, 0.4008, 0.8757, 0.5088, 0.4101, 0.5392, 0.6644, 0.5133]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5863, -0.4174, 1.1850, -1.1955, -0.2477, -1.3069, 0.3485, 0.1124],
- [ 0.7275, -0.3032, 1.7512, -0.7057, -0.5881, -0.3120, 0.4027, 0.2238],
- [ 0.7115, -0.3709, 1.8298, -0.0473, -0.5467, -0.2742, 0.4645, 0.0810],
- [ 0.7314, -0.3769, 1.9074, -0.1583, -0.5646, -0.3302, 0.7126, 0.0212],
- [ 0.6423, -0.3575, 0.8832, -1.2260, -0.3278, -1.3196, 0.0393, 0.2563],
- [ 0.6789, -0.3773, 1.8181, -0.6685, -0.3828, -0.8828, 0.6894, 0.0553],
- [ 0.6784, -0.3066, 1.7560, -0.0381, -0.5392, -0.4158, 0.2583, 0.3503],
- [ 0.5078, -0.5002, 1.7740, -0.0045, -0.3177, 0.4120, 0.7222, 0.1060]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5680, -0.4345, 1.1806, -1.0490, -0.2683, -1.4127, 0.4074,
- 0.1449],
- [ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.5944, -0.4543, 1.8018, 0.0082, -0.6058, -0.4306, 0.4162,
- 0.1082],
- [ 0.6372, -0.4129, 1.8942, -0.0765, -0.6173, -0.4768, 0.6999,
- 0.0325],
- [ 0.5385, -0.4010, 0.8203, -1.1620, -0.3979, -1.3929, 0.1005,
- 0.2747],
- [ 0.6165, -0.4249, 1.7961, -0.5384, -0.4499, -0.9695, 0.6401,
- 0.0652],
- [ 0.5902, -0.3493, 1.7961, -0.0072, -0.5942, -0.5615, 0.3180,
- 0.3161],
- [ 0.5993, -0.4345, 1.7587, 0.0641, -0.3917, 0.2048, 0.7827,
- 0.0852]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0058, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0058, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1001434274949133
- step: 56
- running loss: 0.01964541834812345
- Train Steps: 56/90 Loss: 0.0196 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7850, 0.2700, 0.4288, 0.1717, 0.5199, 0.4999],
- [0.6126, 0.4067, 0.8638, 0.5383, 0.4188, 0.4850, 0.5016, 0.5392],
- [0.6245, 0.4100, 0.7762, 0.2583, 0.4963, 0.1517, 0.5875, 0.5417],
- [0.6202, 0.3983, 0.8555, 0.5429, 0.3842, 0.4370, 0.5866, 0.5398],
- [0.6200, 0.4055, 0.8011, 0.2988, 0.3842, 0.2798, 0.5949, 0.5183],
- [0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150],
- [0.6357, 0.4159, 0.8788, 0.5583, 0.3638, 0.4433, 0.6488, 0.5297],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.6910, -1.8692, 1.1434, -1.2963, -0.3648, -1.2930, 0.1842, 0.1635],
- [ 0.7451, -0.2723, 1.7204, 0.1904, -0.3997, 0.1187, 0.1519, 0.1289],
- [ 0.6097, -0.3337, 1.2839, -1.0808, -0.0696, -1.4428, 0.3928, 0.2679],
- [ 0.7237, -0.3450, 1.7572, 0.1803, -0.5939, -0.1386, 0.5070, 0.0829],
- [ 0.8000, -0.2632, 1.5401, -0.9677, -0.5274, -0.9060, 0.6557, 0.0595],
- [ 0.5654, -0.4025, 1.4035, -1.1105, -0.3297, -1.2302, 0.4009, 0.1177],
- [ 0.8601, -0.2298, 1.8324, 0.2251, -0.6189, -0.1713, 0.7841, 0.0564],
- [ 0.7536, -0.2819, 1.8000, -0.0206, -0.2430, 0.1234, 0.2688, 0.2988]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.3400, -1.0388, -0.3055, -1.4930, 0.1157,
- 0.0231],
- [ 0.5436, -0.4076, 1.7037, 0.2006, -0.3517, -0.0457, 0.0311,
- 0.2048],
- [ 0.5985, -0.3921, 1.2995, -1.0927, 0.0062, -1.5854, 0.4277,
- 0.2160],
- [ 0.5786, -0.4463, 1.6655, 0.2216, -0.5115, -0.2675, 0.4236,
- 0.2075],
- [ 0.5781, -0.4129, 1.4142, -0.9057, -0.5115, -0.9937, 0.4621,
- 0.1080],
- [ 0.5796, -0.4326, 1.4439, -1.1774, -0.2940, -1.3390, 0.3931,
- 0.0928],
- [ 0.6504, -0.3647, 1.7730, 0.2930, -0.6058, -0.2382, 0.7109,
- 0.1608],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0204, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0204, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1205182750709355
- step: 57
- running loss: 0.019658215352121675
- Train Steps: 57/90 Loss: 0.0197 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136],
- [0.6164, 0.3972, 0.7818, 0.3381, 0.3599, 0.3387, 0.5880, 0.5153],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [0.6271, 0.4040, 0.9000, 0.3250, 0.4938, 0.2300, 0.7192, 0.5413],
- [0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389],
- [0.6261, 0.4029, 0.8720, 0.3364, 0.3665, 0.3753, 0.6531, 0.5183],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7240, -0.3128, 1.8758, -0.1588, -0.5622, -0.1100, 0.3184, 0.3145],
- [ 0.6164, -0.3884, 0.9456, -1.0949, -0.4927, -1.2268, 0.0999, 0.0704],
- [ 0.5727, -0.4196, 1.4462, -0.6541, -0.6380, -0.7790, 0.2244, 0.1123],
- [ 0.7500, -0.3441, 1.8785, -0.1390, -0.5367, 0.1114, 0.7222, 0.2040],
- [ 0.3065, -0.6051, 1.9755, -0.6526, 0.0485, -1.1576, 0.8616, 0.2635],
- [ 0.5309, -0.4451, 1.2675, -1.0254, -0.4772, -1.0516, 0.3530, 0.2737],
- [ 0.7435, -0.3372, 1.7588, -0.5859, -0.5617, -0.5718, 0.6055, 0.1405],
- [ 0.4607, -0.4934, 1.8087, 0.0506, -0.2326, 0.4321, 0.3402, 0.1789]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862],
- [ 0.5614, -0.4514, 1.3252, -0.7240, -0.6237, -0.7215, 0.4299,
- 0.0942],
- [ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715],
- [ 0.6108, -0.4201, 1.8711, -0.7848, -0.0053, -1.2236, 1.0362,
- 0.2142],
- [ 0.5697, -0.4466, 1.1973, -1.1871, -0.4571, -0.9965, 0.5219,
- 0.2032],
- [ 0.6059, -0.4249, 1.7420, -0.7321, -0.5931, -0.5523, 0.7307,
- 0.1080],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.134296880569309
- step: 58
- running loss: 0.01955684276843636
- Train Steps: 58/90 Loss: 0.0196 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6163, 0.4001, 0.8788, 0.5033, 0.4012, 0.4633, 0.5338, 0.5767],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6264, 0.4035, 0.8888, 0.4883, 0.4050, 0.5217, 0.6361, 0.4791],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167],
- [0.6339, 0.4159, 0.8400, 0.5617, 0.3825, 0.4150, 0.7343, 0.5748],
- [0.6040, 0.4002, 0.7338, 0.2267, 0.3975, 0.2100, 0.5231, 0.4778]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6055, -0.4108, 1.7864, 0.0511, -0.3500, -0.0902, 0.1680, 0.2737],
- [ 0.7388, -0.3232, 1.8136, -0.4222, -0.5589, -0.1588, 0.5349, 0.2467],
- [ 0.7091, -0.3533, 1.8356, -0.0353, -0.3907, 0.1420, 0.4466, 0.0463],
- [ 0.5975, -0.3956, 1.4275, -0.8469, -0.5958, -0.3813, 0.4986, 0.3251],
- [ 0.5958, -0.3976, 1.8374, -0.2642, -0.5575, -0.5204, 0.3262, 0.2197],
- [ 0.6485, -0.3895, 1.7882, -0.4670, -0.5247, -0.8635, 0.3813, 0.1579],
- [ 0.4192, -0.5455, 1.6479, 0.2483, -0.4039, -0.3421, 0.7505, 0.3025],
- [ 0.4611, -0.4840, 1.1231, -1.3008, -0.3819, -1.3606, 0.1314, 0.0664]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5610, -0.4381, 1.7730, 0.0390, -0.4326, -0.1458, 0.1794,
- 0.3777],
- [ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.6074, -0.4223, 1.8192, -0.0303, -0.4152, 0.1236, 0.6524,
- -0.0731],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776],
- [ 0.5880, -0.3887, 1.8423, -0.3306, -0.6231, -0.5230, 0.4046,
- 0.1544],
- [ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005],
- [ 0.6421, -0.3647, 1.5940, 0.3084, -0.5192, -0.3691, 1.1057,
- 0.3692],
- [ 0.5037, -0.4375, 1.1032, -1.2390, -0.4499, -1.3159, 0.1301,
- -0.0791]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1419263114221394
- step: 59
- running loss: 0.01935468324444304
- Train Steps: 59/90 Loss: 0.0194 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6126, 0.4067, 0.8638, 0.5383, 0.4188, 0.4850, 0.5016, 0.5392],
- [0.6222, 0.3957, 0.8838, 0.5017, 0.3937, 0.4600, 0.5900, 0.5017],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533],
- [0.6168, 0.4029, 0.8523, 0.3417, 0.3588, 0.5000, 0.6125, 0.5400],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6053, 0.4035, 0.6897, 0.1916, 0.4000, 0.2117, 0.5440, 0.5168],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7460, -0.2874, 1.7264, 0.2952, -0.3615, 0.0596, 0.1324, 0.1865],
- [ 0.6967, -0.3833, 1.8552, 0.0737, -0.5081, -0.1024, 0.4197, 0.0161],
- [-1.7460, -1.9138, 1.3348, -0.9356, -0.5931, -0.9858, 0.2740, 0.2554],
- [ 0.8198, -0.2550, 1.6915, -0.6744, -0.5717, 0.0033, 0.7498, 0.2701],
- [ 0.5167, -0.4165, 1.8108, -0.7367, -0.2093, -1.3022, 0.3991, 0.0950],
- [ 0.5604, -0.4277, 0.9121, -1.2715, -0.3794, -1.3098, 0.3249, 0.1936],
- [ 0.7529, -0.2951, 1.8136, 0.2724, -0.5670, -0.5399, 0.4457, 0.1687],
- [ 0.4675, -0.4694, 1.3008, -1.0542, -0.3341, -1.1477, 0.4661, 0.2529]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5436, -0.4076, 1.7037, 0.2006, -0.3517, -0.0457, 0.0311,
- 0.2048],
- [ 0.5879, -0.4583, 1.7961, 0.0313, -0.4672, -0.1612, 0.4393,
- 0.0313],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699],
- [ 0.5631, -0.4249, 1.6509, -0.7078, -0.6289, 0.0236, 0.5432,
- 0.2083],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.5102, -0.4223, 0.8999, -1.4011, -0.4383, -1.3082, 0.2267,
- 0.1013],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0150, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0150, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.156885642092675
- step: 60
- running loss: 0.01928142736821125
- Train Steps: 60/90 Loss: 0.0193 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6125, 0.3974, 0.7725, 0.2517, 0.3538, 0.3317, 0.5887, 0.5500],
- [0.6147, 0.4112, 0.7988, 0.3200, 0.3775, 0.2767, 0.5150, 0.5550],
- [0.6357, 0.4159, 0.8788, 0.5583, 0.3638, 0.4433, 0.6488, 0.5297],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3795, -0.5493, 1.2779, -1.1011, -0.6506, -0.7793, 0.3970, 0.1421],
- [ 0.4603, -0.4661, 1.3833, -0.7593, -0.6260, -0.9102, -0.0130, 0.1901],
- [ 0.6507, -0.3994, 1.7494, 0.2472, -0.6366, -0.2860, 0.6668, 0.0906],
- [ 0.6627, -0.3054, 1.7199, -0.0872, -0.2746, -0.9652, 0.2201, 0.4598],
- [ 0.6057, -0.4007, 1.7335, -0.2720, -0.6664, -0.7356, 0.3688, 0.2675],
- [ 0.3530, -0.5622, 1.7161, -0.2440, -0.1596, 0.1293, 0.3165, 0.1964],
- [ 0.3366, -0.5876, 1.3808, -0.9403, -0.6362, -0.8249, 0.4522, 0.1936],
- [ 0.4302, -0.5576, 1.7444, -1.1820, 0.1793, -1.0450, 1.0431, 0.2124]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5430, -0.4503, 1.2822, -1.1235, -0.6520, -0.7540, 0.4335,
- 0.2545],
- [ 0.5532, -0.3864, 1.4035, -0.8079, -0.5423, -1.0080, 0.0928,
- 0.2776],
- [ 0.6504, -0.3647, 1.7730, 0.2930, -0.6058, -0.2382, 0.7109,
- 0.1608],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822],
- [ 0.6016, -0.3633, 1.7152, -0.2228, -0.6115, -0.6385, 0.5028,
- 0.2699],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853],
- [ 0.5697, -0.4442, 1.3529, -0.9515, -0.5774, -0.7801, 0.5253,
- 0.1931],
- [ 0.6240, -0.3768, 1.6575, -1.2851, 0.2949, -1.2467, 1.1276,
- 0.2142]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0086, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0086, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1654705270193517
- step: 61
- running loss: 0.019106074213431995
- Train Steps: 61/90 Loss: 0.0191 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [0.6090, 0.4045, 0.7250, 0.2100, 0.4075, 0.2300, 0.5476, 0.5663],
- [ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6274, 0.4270, 0.8938, 0.4967, 0.3550, 0.4283, 0.5700, 0.5733],
- [0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.1501, -2.1734, 1.3345, -0.8381, -0.5983, -0.9237, 0.1402, 0.2465],
- [ 0.4663, -0.4732, 1.1043, -1.1138, -0.4635, -1.1531, 0.1945, 0.2410],
- [-1.5817, -1.7912, 0.9501, -1.3078, -0.3975, -1.2940, 0.0970, 0.2340],
- [ 0.9681, -0.1215, 1.7032, 0.3756, -0.5523, -0.0942, 0.4063, 0.1481],
- [ 0.9483, -0.1109, 1.8560, 0.1420, -0.6529, -0.2599, 0.2922, 0.3571],
- [ 0.8209, -0.2577, 1.7611, -1.0484, 0.1209, -0.9930, 1.0093, 0.2220],
- [ 0.9207, -0.2333, 1.6936, 0.2770, -0.5535, -0.0371, 0.5384, 0.0420],
- [ 0.7437, -0.2797, 1.4979, -0.9594, -0.3998, -1.1038, 0.4923, 0.1269]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837],
- [ 0.5269, -0.4176, 1.0628, -1.3159, -0.4037, -1.2236, 0.2432,
- 0.3297],
- [-2.2859, -2.2859, 0.8516, -1.3112, -0.4326, -1.2851, 0.0755,
- 0.2930],
- [ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391],
- [ 0.6121, -0.3138, 1.8423, 0.0082, -0.6462, -0.3075, 0.3469,
- 0.3623],
- [ 0.6240, -0.3768, 1.6575, -1.2851, 0.2949, -1.2467, 1.1276,
- 0.2142],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0272, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0272, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.192680740263313
- step: 62
- running loss: 0.019236786133279244
- Train Steps: 62/90 Loss: 0.0192 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6289, 0.4024, 0.9088, 0.4567, 0.3937, 0.5633, 0.7058, 0.5609],
- [0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6261, 0.4045, 0.8865, 0.5369, 0.3895, 0.4859, 0.6683, 0.5249],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378],
- [0.6339, 0.4123, 0.8638, 0.5367, 0.4075, 0.5467, 0.7517, 0.5436],
- [ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422],
- [0.6339, 0.4112, 0.8838, 0.5067, 0.4000, 0.5433, 0.7549, 0.5378]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6573, -0.3446, 1.6966, -0.2443, -0.5200, 0.1686, 0.5751, 0.2832],
- [ 0.6415, -0.3198, 1.6393, -0.4346, -0.4965, -0.9133, 0.0875, 0.4190],
- [ 0.4149, -0.5455, 1.8838, -0.2986, -0.3281, -0.9900, 0.6586, 0.1507],
- [ 0.6257, -0.3637, 1.6404, 0.1075, -0.6028, -0.1368, 0.3381, 0.1363],
- [ 0.5099, -0.4514, 1.6478, -1.0770, 0.0905, -1.2842, 0.7178, 0.1467],
- [ 0.6812, -0.3321, 1.5995, 0.0378, -0.5077, 0.1190, 0.6594, 0.2596],
- [-2.2269, -2.2209, 1.0822, -1.1340, -0.4991, -1.3651, -0.0889, 0.1406],
- [ 0.5776, -0.4028, 1.6504, -0.0414, -0.5256, 0.1455, 0.5969, 0.2451]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6191, -0.4273, 1.9115, -0.1766, -0.4672, 0.3161, 0.9741,
- 0.3050],
- [ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928],
- [ 0.6224, -0.4345, 1.9404, -0.2921, -0.3171, -0.8771, 1.0655,
- 0.2142],
- [ 0.6059, -0.4177, 1.8087, 0.1941, -0.4868, -0.0414, 0.8010,
- 0.1385],
- [ 0.6207, -0.3936, 1.7788, -1.1235, 0.1448, -1.0850, 1.1459,
- 0.1982],
- [ 0.6421, -0.3816, 1.7037, 0.1929, -0.4037, 0.2391, 1.1861,
- 0.2249],
- [-2.2859, -2.2859, 1.2469, -1.0288, -0.4557, -1.2774, 0.0511,
- 0.2183],
- [ 0.6421, -0.3864, 1.7961, 0.0543, -0.4383, 0.2237, 1.2007,
- 0.1982]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0305, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0305, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2231627046130598
- step: 63
- running loss: 0.019415281025604123
- Train Steps: 63/90 Loss: 0.0194 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6126, 0.4039, 0.8237, 0.3967, 0.3625, 0.3600, 0.5894, 0.6138],
- [0.6179, 0.3961, 0.8347, 0.6020, 0.3887, 0.4624, 0.5714, 0.5373],
- [0.6282, 0.4029, 0.8988, 0.3000, 0.4250, 0.4183, 0.7042, 0.5575],
- [ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [0.6160, 0.4086, 0.8775, 0.3983, 0.3750, 0.3783, 0.5128, 0.4882],
- [ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [0.6350, 0.4043, 0.8738, 0.5650, 0.3850, 0.4750, 0.6401, 0.4950],
- [0.6241, 0.4143, 0.8938, 0.4650, 0.4075, 0.5350, 0.6250, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6864, -0.2961, 1.5186, -0.5382, -0.5293, -0.7114, 0.4208, 0.5046],
- [ 0.9863, -0.1553, 1.5130, 0.3829, -0.4618, -0.3696, 0.4101, 0.2275],
- [ 0.8880, -0.2267, 1.6710, -0.8084, -0.3837, -0.4435, 1.0219, 0.2616],
- [-2.2763, -2.2673, 1.3555, -0.8581, -0.5137, -0.9928, 0.1230, 0.2752],
- [ 0.7329, -0.2929, 1.7610, -0.4323, -0.5493, -0.6349, 0.1989, 0.0026],
- [-1.8182, -1.9563, 0.9659, -1.3406, -0.3299, -1.3894, 0.0819, 0.2440],
- [ 0.8465, -0.2613, 1.7509, 0.2371, -0.4888, -0.1813, 0.6480, 0.0601],
- [ 0.8349, -0.2106, 1.8088, -0.2125, -0.3730, 0.0472, 0.5546, 0.3162]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.4376e-01, -4.2055e-01, 1.5189e+00, -4.5373e-01, -6.1155e-01,
- -6.2309e-01, 4.3649e-01, 5.4914e-01],
- [ 5.6801e-01, -4.5619e-01, 1.5697e+00, 4.9469e-01, -4.9038e-01,
- -1.5026e-01, 3.5357e-01, 1.9563e-01],
- [ 6.1577e-01, -4.2490e-01, 1.8654e+00, -9.0023e-01, -3.2286e-01,
- -3.5366e-01, 9.6675e-01, 2.8902e-01],
- [-2.2859e+00, -2.2859e+00, 1.4006e+00, -8.1049e-01, -6.1155e-01,
- -8.2325e-01, 4.1889e-02, 2.8371e-01],
- [ 5.5953e-01, -3.9877e-01, 1.7672e+00, -4.4604e-01, -5.5381e-01,
- -5.3841e-01, 8.2802e-02, -3.0981e-02],
- [-2.2859e+00, -2.2859e+00, 8.5162e-01, -1.3112e+00, -4.3256e-01,
- -1.2851e+00, 7.5520e-02, 2.9299e-01],
- [ 6.4706e-01, -4.1832e-01, 1.7499e+00, 3.2379e-01, -5.0762e-01,
- -9.1917e-02, 6.7064e-01, 4.6189e-04],
- [ 5.9677e-01, -3.7252e-01, 1.8423e+00, -1.3811e-01, -4.0370e-01,
- 1.8522e-01, 6.0092e-01, 2.7760e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0195, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0195, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2426480515860021
- step: 64
- running loss: 0.019416375806031283
- Train Steps: 64/90 Loss: 0.0194 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [0.6357, 0.4118, 0.8400, 0.2500, 0.5413, 0.1633, 0.6725, 0.5586],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6197, 0.4091, 0.8800, 0.4783, 0.3538, 0.4767, 0.5950, 0.5550],
- [0.6224, 0.3964, 0.8225, 0.5717, 0.4150, 0.4617, 0.5775, 0.5267],
- [0.6339, 0.4102, 0.8588, 0.3133, 0.4425, 0.2117, 0.6417, 0.5089],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5682, -0.3837, 1.6764, -0.2138, -0.3883, -0.8902, 0.3701, 0.5147],
- [ 0.2831, -0.6369, 1.5697, -1.2638, 0.0999, -1.3712, 0.9089, 0.2294],
- [ 0.2783, -0.6375, 1.5955, -0.1216, -0.4374, 0.1521, 0.4283, 0.2186],
- [ 0.4816, -0.4981, 1.7127, -0.2116, -0.7235, -0.0632, 0.5347, 0.3418],
- [ 0.4290, -0.5700, 1.5273, 0.2132, -0.5799, -0.1178, 0.4299, 0.1792],
- [ 0.5207, -0.4917, 1.6541, -0.9631, -0.3377, -1.1652, 0.7063, 0.0927],
- [ 0.2865, -0.5891, 1.4471, -0.9866, -0.2524, -1.2922, 0.3699, 0.2180],
- [ 0.1956, -0.6708, 1.3196, -1.2192, -0.3199, -1.2378, 0.4614, 0.2082]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.1742e-01, -3.1175e-01, 1.6402e+00, -2.0739e-01, -1.9584e-01,
- -1.0927e+00, 2.2674e-01, 5.8220e-01],
- [ 6.5036e-01, -3.8397e-01, 1.5940e+00, -1.1312e+00, 2.1409e-01,
- -1.5315e+00, 8.2052e-01, 2.9436e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 5.7633e-01, -3.9630e-01, 1.7788e+00, -7.6520e-02, -6.5196e-01,
- -8.4219e-02, 4.6236e-01, 2.7760e-01],
- [ 5.8915e-01, -4.5504e-01, 1.5132e+00, 3.5458e-01, -3.6905e-01,
- -1.5350e-01, 3.8152e-01, 1.4673e-01],
- [ 6.4212e-01, -3.9120e-01, 1.6806e+00, -8.3865e-01, -2.4203e-01,
- -1.3082e+00, 6.7795e-01, 6.4585e-02],
- [ 5.9107e-01, -3.8879e-01, 1.4727e+00, -9.5412e-01, -9.1917e-02,
- -1.4930e+00, 3.9885e-01, 2.0831e-01],
- [ 5.7679e-01, -4.0308e-01, 1.3838e+00, -1.1527e+00, -2.1876e-01,
- -1.4216e+00, 4.3790e-01, 1.8502e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0199, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0199, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2625863901339471
- step: 65
- running loss: 0.019424406002060725
- Train Steps: 65/90 Loss: 0.0194 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [0.6267, 0.4080, 0.8438, 0.2633, 0.4763, 0.1800, 0.6259, 0.5240],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150],
- [0.6296, 0.4076, 0.8400, 0.5583, 0.3700, 0.4367, 0.6876, 0.5494],
- [0.6271, 0.4005, 0.8450, 0.6067, 0.3850, 0.4983, 0.6069, 0.4649],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5621, -0.4321, 1.7724, -0.4772, -0.5423, -0.1147, 0.8050, 0.2694],
- [ 0.5308, -0.4434, 1.6116, -1.1489, -0.1159, -1.5357, 0.5198, 0.1760],
- [ 0.5708, -0.3927, 1.7551, -0.3971, -0.4772, 0.0762, 0.3674, 0.0970],
- [ 0.3569, -0.5271, 1.7537, -0.2564, -0.2742, 0.1578, 0.5440, 0.2621],
- [ 0.5001, -0.4712, 1.6459, 0.1250, -0.5059, -0.5100, 0.7173, 0.2668],
- [ 0.5423, -0.4400, 1.6106, 0.2562, -0.5150, -0.2214, 0.4051, 0.1300],
- [ 0.4982, -0.4699, 1.4092, 0.0876, -0.4595, -0.2477, 0.7810, 0.3380],
- [-2.5799, -2.5101, 0.9946, -1.3201, -0.3054, -1.6528, 0.1075, 0.3432]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715],
- [ 0.6088, -0.4015, 1.6113, -1.0696, -0.0861, -1.4545, 0.6051,
- 0.1343],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928],
- [ 0.6224, -0.4032, 1.5940, 0.2930, -0.5769, -0.2690, 0.8900,
- 0.2516],
- [ 0.6105, -0.4360, 1.6171, 0.5162, -0.5076, 0.0159, 0.5171,
- -0.1385],
- [ 0.6289, -0.4345, 1.3794, 0.3679, -0.4845, 0.0390, 0.9265,
- 0.1928],
- [-2.2859, -2.2859, 0.6760, -1.4083, -0.3286, -1.4160, 0.2487,
- 0.3469]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0219, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0219, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2844528066925704
- step: 66
- running loss: 0.019461406162008643
- Train Steps: 66/90 Loss: 0.0195 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [0.6179, 0.4040, 0.7412, 0.1850, 0.3825, 0.2783, 0.5837, 0.5600],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [ nan, nan, 0.7225, 0.2167, 0.3987, 0.2283, 0.5427, 0.5181],
- [0.6268, 0.4052, 0.8175, 0.2250, 0.4688, 0.1917, 0.6375, 0.5267],
- [0.6212, 0.4033, 0.8938, 0.4167, 0.3813, 0.4267, 0.5613, 0.5583]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6355, -0.3365, 1.7837, 0.2189, -0.3321, -0.7855, 0.6329, 0.4986],
- [ 0.6189, -0.3985, 1.1583, -1.1676, -0.5172, -0.9246, 0.6778, 0.2728],
- [ 0.4296, -0.5356, 1.8003, -0.0223, -0.1546, 0.1135, 0.4732, 0.0954],
- [ 0.2051, -0.6368, 1.1759, -1.1390, -0.3122, -1.3071, 0.3184, 0.2113],
- [ 0.2515, -0.6269, 1.0781, -1.1108, -0.3976, -1.3259, 0.3161, 0.0738],
- [-2.5323, -2.4686, 1.0653, -1.2045, -0.4495, -1.2806, 0.2355, 0.2090],
- [ 0.6640, -0.3917, 1.4862, -1.1260, -0.1107, -1.3607, 0.8365, 0.1686],
- [ 0.4217, -0.5376, 1.9231, -0.1483, -0.5415, -0.2629, 0.6584, 0.2839]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762],
- [ 0.5680, -0.4201, 1.1379, -1.4314, -0.5192, -1.0003, 0.4104,
- 0.3007],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.5109, -0.4316, 1.1436, -1.3467, -0.3864, -1.4160, 0.1239,
- -0.0580],
- [-2.2859, -2.2859, 1.0513, -1.2851, -0.4441, -1.2313, 0.2206,
- 0.1073],
- [ 0.6092, -0.4143, 1.4901, -1.2467, -0.1208, -1.4006, 0.6587,
- 0.1467],
- [ 0.5832, -0.4231, 1.8423, -0.3614, -0.5249, -0.3152, 0.3065,
- 0.2930]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0230, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0230, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3074766951613128
- step: 67
- running loss: 0.019514577539721085
- Train Steps: 67/90 Loss: 0.0195 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683],
- [ nan, nan, 0.8850, 0.3000, 0.5363, 0.2250, 0.7343, 0.5771],
- [0.6162, 0.4014, 0.8800, 0.5333, 0.3750, 0.4817, 0.5988, 0.5283],
- [ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383],
- [0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933],
- [0.6212, 0.4033, 0.8938, 0.4167, 0.3813, 0.4267, 0.5613, 0.5583],
- [0.6083, 0.3957, 0.8638, 0.4950, 0.4363, 0.5083, 0.5346, 0.4980],
- [0.6339, 0.4159, 0.8400, 0.5617, 0.3825, 0.4150, 0.7343, 0.5748]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7521, -0.2619, 1.6282, -0.0429, -0.5543, -0.5445, 0.4366, 0.3235],
- [-2.1303, -2.1780, 1.7012, -1.1529, 0.1408, -1.2760, 0.8930, 0.4179],
- [ 0.7358, -0.3000, 1.6929, 0.0419, -0.5112, -0.0552, 0.5920, 0.1726],
- [-1.7605, -1.9247, 0.9663, -1.3456, -0.4302, -1.4783, 0.0266, 0.2209],
- [ 0.7053, -0.2838, 1.5613, 0.1993, -0.0641, -0.2609, 0.4373, 0.3980],
- [ 0.5957, -0.3948, 1.7999, -0.4406, -0.5199, -0.3977, 0.4966, 0.2283],
- [ 0.6119, -0.3963, 1.6988, -0.1708, -0.3118, 0.0706, 0.3834, -0.0591],
- [ 0.6916, -0.3560, 1.5536, 0.0555, -0.4908, -0.3907, 1.0764, 0.2408]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7771e-01, -3.9153e-01, 1.7961e+00, 1.6982e-01, -5.1917e-01,
- -5.3072e-01, 2.1409e-01, 3.3918e-01],
- [-2.2859e+00, -2.2859e+00, 1.8018e+00, -9.0023e-01, 1.9099e-01,
- -1.2467e+00, 1.1057e+00, 3.7986e-01],
- [ 5.6028e-01, -4.3195e-01, 1.7788e+00, 1.7752e-01, -5.5381e-01,
- -6.1124e-02, 4.7968e-01, 1.5443e-01],
- [-2.2859e+00, -2.2859e+00, 1.0712e+00, -1.2085e+00, -3.8060e-01,
- -1.3929e+00, 7.5520e-02, 2.0062e-01],
- [ 5.9590e-01, -3.5789e-01, 1.6055e+00, 3.6228e-01, -5.7275e-02,
- -2.0739e-01, 3.1224e-01, 4.5466e-01],
- [ 5.8320e-01, -4.2309e-01, 1.8423e+00, -3.6135e-01, -5.2494e-01,
- -3.1517e-01, 3.0647e-01, 2.9299e-01],
- [ 5.2379e-01, -4.5797e-01, 1.7037e+00, 4.6189e-04, -2.7090e-01,
- 6.2048e-02, 1.8356e-01, 1.4106e-02],
- [ 6.4212e-01, -3.6471e-01, 1.5940e+00, 3.0839e-01, -5.1917e-01,
- -3.6905e-01, 1.1057e+00, 3.6917e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0182, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0182, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.325725748669356
- step: 68
- running loss: 0.019495966892196414
- Train Steps: 68/90 Loss: 0.0195 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6336, 0.4086, 0.8900, 0.3950, 0.3900, 0.2950, 0.6504, 0.5066],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6263, 0.4233, 0.7924, 0.4626, 0.3788, 0.2883, 0.5573, 0.6047],
- [0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967],
- [0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5283, -0.5369, 1.9181, -0.6369, -0.4015, -0.9028, 0.8409, -0.0470],
- [ 0.2845, -0.6451, 1.9472, -0.3014, -0.4665, -0.3839, 0.4376, 0.1284],
- [ 0.5186, -0.4488, 1.4861, -0.1716, -0.4262, -0.8543, 0.4471, 0.4673],
- [ 0.3839, -0.5428, 1.3026, -1.1962, -0.0284, -1.3630, 0.4675, 0.3162],
- [ 0.1748, -0.7349, 1.7817, -0.0084, -0.4737, -0.1699, 0.6443, 0.3722],
- [ 0.4493, -0.5394, 1.7518, 0.1026, -0.4578, -0.4625, 0.4444, 0.3099],
- [ 0.4751, -0.5104, 1.9062, -0.2140, -0.2256, 0.3614, 0.8055, 0.2947],
- [ 0.3398, -0.6077, 0.9313, -1.0825, -0.4828, -0.9722, 0.4175, 0.3324]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6405, -0.3984, 1.8249, -0.4614, -0.4845, -0.9233, 0.7182,
- 0.0539],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [ 0.6069, -0.3309, 1.3742, -0.1493, -0.5365, -0.9541, 0.2884,
- 0.5071],
- [ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5777, -0.3915, 1.7961, 0.1698, -0.5192, -0.5307, 0.2141,
- 0.3392],
- [ 0.5772, -0.3913, 1.8480, -0.2459, -0.4326, 0.1929, 0.5374,
- 0.4701],
- [ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0231, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0231, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3488060417585075
- step: 69
- running loss: 0.01954791364867402
- Train Steps: 69/90 Loss: 0.0195 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6104, 0.4029, 0.8738, 0.4900, 0.4088, 0.4533, 0.5070, 0.5510],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6030, 0.3969, 0.7988, 0.3917, 0.3450, 0.3667, 0.5266, 0.4700],
- [ nan, nan, 0.7625, 0.2433, 0.3713, 0.2867, 0.5235, 0.5220],
- [0.6164, 0.4102, 0.8850, 0.4867, 0.4213, 0.5983, 0.5609, 0.5038],
- [0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183],
- [ nan, nan, 0.8463, 0.2550, 0.5850, 0.2133, 0.7129, 0.6072]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7772, -0.2527, 1.6278, 0.0698, -0.3793, -0.1506, 0.2176, 0.2853],
- [-1.7345, -1.9180, 1.5621, -1.2114, 0.2351, -1.2765, 1.1004, 0.4288],
- [ 0.8021, -0.2626, 1.6834, 0.2813, -0.5456, -0.5102, 0.6018, 0.0368],
- [ 0.8277, -0.2241, 1.4681, -0.4764, -0.6223, -0.5843, 0.2276, 0.0560],
- [-1.8877, -2.0528, 1.1652, -1.0918, -0.5150, -1.0408, 0.1515, 0.2189],
- [ 0.8518, -0.1916, 1.7081, -0.0081, -0.2950, 0.3841, 0.5503, 0.2017],
- [ 0.8578, -0.2372, 1.6706, 0.0083, -0.5515, -0.4213, 0.4641, 0.1731],
- [-0.7118, -1.2511, 1.4998, -1.1039, 0.2474, -1.1877, 1.1366, 0.5582]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5334, -0.4252, 1.7499, -0.0226, -0.3979, -0.1920, 0.0558,
- 0.2589],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.4992, -0.4525, 1.4035, -0.4768, -0.6924, -0.5923, 0.1465,
- -0.1151],
- [-2.2859, -2.2859, 1.2360, -1.1620, -0.5711, -0.9618, 0.1322,
- 0.1253],
- [ 0.5614, -0.3912, 1.8018, -0.0380, -0.3402, 0.4778, 0.3049,
- 0.0412],
- [ 0.5944, -0.4543, 1.8018, 0.0082, -0.6058, -0.4306, 0.4162,
- 0.1082],
- [-2.2859, -2.2859, 1.6229, -1.1081, 0.4162, -1.3005, 1.0070,
- 0.5188]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0804, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0804, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.429237652104348
- step: 70
- running loss: 0.020417680744347827
- Train Steps: 70/90 Loss: 0.0204 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6197, 0.4050, 0.7527, 0.2000, 0.4042, 0.2249, 0.5895, 0.4995],
- [0.6147, 0.4081, 0.8538, 0.3400, 0.3663, 0.3150, 0.5142, 0.4875],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [ nan, nan, 0.8850, 0.3000, 0.5363, 0.2250, 0.7343, 0.5771],
- [0.6153, 0.4117, 0.8688, 0.5167, 0.4895, 0.5647, 0.5524, 0.5136],
- [0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [0.6231, 0.3973, 0.8650, 0.3950, 0.3625, 0.3183, 0.5837, 0.5167],
- [0.6296, 0.4045, 0.9138, 0.4100, 0.4232, 0.4242, 0.7422, 0.5297]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4453, -0.4955, 1.0256, -1.3659, -0.3497, -1.3072, 0.4431, 0.1319],
- [ 0.5297, -0.4232, 1.5883, -0.5915, -0.5403, -0.8735, 0.1991, 0.1346],
- [ 0.4946, -0.4857, 1.7351, -0.2698, -0.5069, -0.1672, 0.6268, 0.2527],
- [-2.8201, -2.6383, 1.6262, -0.9551, 0.1732, -1.2972, 0.9966, 0.4400],
- [ 0.3718, -0.5021, 1.6190, 0.1805, -0.0066, 0.1725, 0.3168, 0.2959],
- [ 0.3578, -0.5493, 1.6804, -0.0961, -0.4836, -0.3701, 0.3286, 0.1494],
- [ 0.4327, -0.5074, 1.5830, -0.3938, -0.5189, -0.8052, 0.3056, 0.2072],
- [ 0.6953, -0.3807, 1.7009, -0.3156, -0.4463, -0.3204, 0.8906, 0.2495]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5762, -0.4153, 1.1908, -1.3622, -0.4190, -1.2471, 0.4368,
- 0.0213],
- [ 0.5532, -0.4008, 1.6575, -0.7155, -0.5942, -0.8309, 0.0890,
- -0.0340],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [-2.2859, -2.2859, 1.8018, -0.9002, 0.1910, -1.2467, 1.1057,
- 0.3799],
- [ 0.5564, -0.3842, 1.7268, 0.1005, -0.0250, 0.3225, 0.2658,
- 0.0862],
- [ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [ 0.5924, -0.4507, 1.7095, -0.4614, -0.6115, -0.8156, 0.4104,
- 0.1005],
- [ 0.6224, -0.4177, 1.9346, -0.3921, -0.3314, -0.3264, 1.1422,
- 0.1608]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0187, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0187, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4479092578403652
- step: 71
- running loss: 0.020393088138596693
- Train Steps: 71/90 Loss: 0.0204 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6160, 0.4093, 0.8314, 0.4417, 0.3675, 0.4583, 0.5250, 0.5808],
- [0.6182, 0.3930, 0.8841, 0.3892, 0.3556, 0.4967, 0.6222, 0.5279],
- [0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960],
- [0.6257, 0.4167, 0.8775, 0.3433, 0.3563, 0.4133, 0.6200, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4783, -0.4974, 1.6404, -0.4530, -0.5869, -0.5150, 0.2844, 0.1453],
- [ 0.3782, -0.5538, 0.8784, -0.8781, -0.4522, -1.1484, 0.3703, 0.4597],
- [ 0.4230, -0.5561, 2.0172, -0.3372, -0.3733, -0.6211, 0.8404, 0.1709],
- [ 0.3973, -0.5338, 1.6760, -0.0551, -0.4016, -0.2625, 0.2399, 0.4082],
- [ 0.4056, -0.5981, 1.8446, -0.3088, -0.4308, -0.1141, 0.7484, 0.1713],
- [ 0.4678, -0.5262, 1.4584, -0.9175, -0.4248, -1.0008, 0.6792, 0.2188],
- [ 0.0892, -0.7973, 1.9030, 0.0130, -0.0089, -0.0517, 0.2399, 0.0862],
- [ 0.2341, -0.6479, 1.8795, -0.4693, -0.4716, -0.5444, 0.6646, 0.3986]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.5592, -0.3956, 1.5543, -0.2456, -0.5885, -0.1689, 0.1392,
- 0.3968],
- [ 0.5697, -0.4706, 1.7976, -0.4884, -0.6433, 0.0081, 0.5878,
- 0.1525],
- [ 0.5697, -0.4442, 1.3529, -0.9515, -0.5774, -0.7801, 0.5253,
- 0.1931],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051],
- [ 0.6040, -0.3614, 1.7672, -0.7001, -0.6404, -0.3768, 0.5778,
- 0.3315]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0253, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0253, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4732359643094242
- step: 72
- running loss: 0.02046161061540867
- Train Steps: 72/90 Loss: 0.0205 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332],
- [0.6276, 0.4095, 0.8237, 0.2250, 0.4662, 0.1783, 0.6171, 0.4869],
- [0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268],
- [0.6263, 0.4057, 0.8800, 0.3833, 0.3650, 0.3717, 0.6375, 0.4804],
- [0.6114, 0.4018, 0.7213, 0.1967, 0.3763, 0.2700, 0.5875, 0.5533],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2125, -0.6521, 1.6035, -0.3375, -0.4940, -1.0512, 0.1700, 0.4392],
- [ 0.4755, -0.4858, 1.6933, -0.1396, -0.4665, -0.1380, 0.0999, 0.3121],
- [ 0.2651, -0.6861, 1.6333, 0.4201, -0.4264, 0.0603, 0.8666, 0.2789],
- [ 0.2776, -0.6162, 1.7169, -1.3630, 0.0578, -1.5248, 0.7481, 0.0741],
- [ 0.3534, -0.6021, 1.8435, 0.1591, -0.1584, 0.1252, 0.5831, 0.2369],
- [ 0.5017, -0.5179, 1.9070, -0.4184, -0.5838, -0.5517, 0.5219, -0.0068],
- [ 0.4388, -0.5183, 1.0337, -1.3808, -0.4962, -1.1296, 0.3308, 0.2330],
- [ 0.4061, -0.5370, 1.5350, -0.8161, -0.6444, -0.3295, 0.4475, 0.3008]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.6191, -0.4297, 1.4612, 0.2391, -0.4961, 0.0313, 1.1166,
- 0.1768],
- [ 0.6127, -0.3944, 1.5189, -1.2467, -0.1323, -1.4622, 0.5646,
- -0.0369],
- [ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474],
- [ 0.6071, -0.4119, 1.7788, -0.5153, -0.6000, -0.5692, 0.6586,
- -0.0670],
- [ 0.5380, -0.4302, 1.0455, -1.3775, -0.5480, -1.0388, 0.4277,
- 0.2699],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0192, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0192, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.4924231390468776
- step: 73
- running loss: 0.020444152589683254
- Train Steps: 73/90 Loss: 0.0204 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6171, 0.4127, 0.8900, 0.4800, 0.4325, 0.5783, 0.5769, 0.5090],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433],
- [0.6108, 0.4011, 0.8037, 0.3400, 0.3700, 0.2933, 0.5658, 0.5617],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433],
- [0.6240, 0.4217, 0.8150, 0.3133, 0.4425, 0.2650, 0.5650, 0.5817]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4990, -0.3994, 1.8878, 0.1543, -0.1967, 0.2972, 0.2611, 0.1317],
- [-2.5539, -2.4791, 1.3508, -0.8933, -0.3324, -1.1283, 0.5128, 0.2438],
- [ 0.5593, -0.4488, 1.0732, -1.2685, -0.3754, -1.2378, 0.5518, 0.1295],
- [ 0.4449, -0.4705, 1.3463, -0.9438, -0.6151, -0.6301, 0.4881, 0.1498],
- [ 0.4011, -0.5055, 1.4918, -0.5525, -0.5540, -0.9145, 0.3074, 0.2244],
- [ 0.5259, -0.4571, 1.8694, -0.2454, -0.5995, -0.1364, 0.5221, 0.1358],
- [ 0.4467, -0.4976, 1.9176, 0.1158, -0.4520, 0.0610, 0.3982, 0.1398],
- [ 0.4622, -0.4492, 1.5167, -0.7024, -0.1878, -1.1792, 0.4206, 0.3888]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5645, -0.3797, 1.8249, -0.0688, -0.2882, 0.3854, 0.3789,
- 0.0652],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237],
- [ 0.5355, -0.4335, 1.4266, -0.7155, -0.5769, -0.9310, 0.3275,
- 0.3087],
- [ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237],
- [ 0.5964, -0.3380, 1.4785, -0.8386, -0.2420, -1.0619, 0.3238,
- 0.4008]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0119, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0119, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5043019359000027
- step: 74
- running loss: 0.020328404539189226
- Train Steps: 74/90 Loss: 0.0203 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [0.6113, 0.4006, 0.8700, 0.5350, 0.3638, 0.3767, 0.5097, 0.4882],
- [0.6200, 0.4055, 0.8011, 0.2988, 0.3842, 0.2798, 0.5949, 0.5183],
- [0.6200, 0.3993, 0.8639, 0.3687, 0.3658, 0.3139, 0.6002, 0.5374],
- [0.6165, 0.4106, 0.7575, 0.1733, 0.3838, 0.2650, 0.5680, 0.5116],
- [0.6257, 0.4024, 0.8612, 0.5352, 0.4361, 0.5253, 0.6680, 0.5166],
- [0.6273, 0.4100, 0.7137, 0.2133, 0.4000, 0.2650, 0.6075, 0.5633],
- [0.6083, 0.3957, 0.8638, 0.4950, 0.4363, 0.5083, 0.5346, 0.4980]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7385, -0.2766, 1.7929, -0.4707, -0.3836, -0.8503, 0.2929, 0.5421],
- [ 0.4394, -0.5178, 1.7771, 0.1945, -0.5874, -0.4860, 0.1179, 0.1616],
- [ 0.5185, -0.4564, 1.5172, -0.9465, -0.5366, -0.9257, 0.4488, 0.1289],
- [ 0.4509, -0.5212, 1.7735, -0.5872, -0.6069, -0.7674, 0.4284, 0.1717],
- [ 0.3056, -0.6098, 1.1797, -1.4743, -0.5555, -1.1407, 0.5208, 0.0257],
- [ 0.5038, -0.4859, 1.7634, 0.1764, -0.2746, 0.2542, 0.7511, 0.2093],
- [-0.1378, -0.8732, 0.9934, -1.3726, -0.4818, -1.0837, 0.5299, 0.3079],
- [ 0.3289, -0.6140, 1.8338, 0.0409, -0.2576, 0.1750, 0.2668, 0.0221]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0075e-01, -3.2925e-01, 1.7037e+00, -5.4611e-01, -4.1524e-01,
- -8.3095e-01, 3.2339e-01, 3.9283e-01],
- [ 5.3788e-01, -4.3580e-01, 1.7326e+00, 1.8522e-01, -6.0577e-01,
- -5.4611e-01, 6.8408e-02, -3.0981e-02],
- [ 5.7806e-01, -4.1286e-01, 1.4142e+00, -9.0574e-01, -5.1146e-01,
- -9.9373e-01, 4.6205e-01, 1.0799e-01],
- [ 5.7771e-01, -4.4157e-01, 1.7044e+00, -5.8275e-01, -5.9618e-01,
- -8.3610e-01, 4.8621e-01, 1.9626e-01],
- [ 5.6184e-01, -3.8945e-01, 1.2129e+00, -1.4853e+00, -5.1339e-01,
- -1.0619e+00, 3.3778e-01, 7.7228e-02],
- [ 6.0425e-01, -4.2731e-01, 1.6920e+00, 1.8595e-01, -2.7171e-01,
- 1.4059e-01, 7.9965e-01, 1.0043e-01],
- [ 6.1155e-01, -3.9238e-01, 1.0109e+00, -1.3005e+00, -4.3834e-01,
- -1.0619e+00, 5.2009e-01, 3.1609e-01],
- [ 5.2379e-01, -4.5797e-01, 1.7037e+00, 4.6189e-04, -2.7090e-01,
- 6.2048e-02, 1.8356e-01, 1.4106e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0201, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0201, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5244338628835976
- step: 75
- running loss: 0.020325784838447967
- Train Steps: 75/90 Loss: 0.0203 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482],
- [0.6346, 0.4092, 0.7712, 0.5917, 0.4037, 0.4767, 0.7343, 0.5725],
- [0.6129, 0.4114, 0.6950, 0.2467, 0.4050, 0.2133, 0.5363, 0.5550],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6097, 0.3988, 0.8650, 0.5250, 0.4213, 0.5200, 0.5675, 0.5050],
- [0.6132, 0.4118, 0.8200, 0.3633, 0.3563, 0.5400, 0.5787, 0.5136],
- [ nan, nan, 0.7553, 0.2722, 0.3875, 0.2550, 0.5125, 0.5633],
- [0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.2324, -2.2225, 1.6292, -1.1904, 0.0917, -1.4040, 1.1599, 0.2829],
- [ 0.7922, -0.2702, 1.3739, 0.1742, -0.5550, -0.2331, 0.7177, 0.3121],
- [ 0.7121, -0.2939, 0.9677, -1.1365, -0.5669, -1.3134, 0.0549, 0.2373],
- [ 0.8527, -0.2089, 1.8690, -0.2207, -0.6001, 0.2241, 0.3038, -0.0493],
- [ 0.7100, -0.3023, 1.7268, 0.1583, -0.4271, 0.0640, 0.1201, 0.0194],
- [ 0.7107, -0.2707, 1.5645, -0.6021, -0.6825, 0.0030, 0.3742, 0.1236],
- [-2.0439, -2.0895, 1.1987, -1.0126, -0.5981, -1.1250, 0.1630, 0.2282],
- [ 0.7303, -0.3029, 1.7606, -0.0840, -0.2595, -0.0316, 0.2301, 0.1367]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.6517, -1.2620, 0.2141, -1.1928, 1.1166,
- 0.2463],
- [ 0.6454, -0.3960, 1.2764, 0.4470, -0.4210, -0.0842, 1.1057,
- 0.3585],
- [ 0.5448, -0.3859, 0.9242, -1.1466, -0.4152, -1.3005, 0.1910,
- 0.2776],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.5303, -0.4440, 1.7095, 0.1390, -0.3402, 0.1159, 0.3353,
- 0.0467],
- [ 0.5466, -0.3840, 1.5016, -0.6077, -0.6404, 0.2083, 0.3871,
- 0.0862],
- [-2.2859, -2.2859, 1.2030, -1.0288, -0.4961, -1.1081, 0.0813,
- 0.3161],
- [ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0172, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0172, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5416146223433316
- step: 76
- running loss: 0.020284402925570152
- Train Steps: 76/90 Loss: 0.0203 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390],
- [0.6101, 0.3977, 0.8550, 0.5667, 0.3862, 0.4417, 0.5128, 0.4869],
- [0.6286, 0.4055, 0.9000, 0.4717, 0.3763, 0.4683, 0.7018, 0.5494],
- [0.6150, 0.4097, 0.7468, 0.3194, 0.3825, 0.2633, 0.5463, 0.5800],
- [0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6226, 0.4185, 0.8850, 0.5500, 0.3800, 0.4250, 0.5625, 0.5617],
- [0.6206, 0.4001, 0.8900, 0.3933, 0.3588, 0.3567, 0.5837, 0.5083]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-0.0093, -0.8159, 1.5997, -0.9410, -0.6810, -0.3697, 0.5364, 0.1780],
- [ 0.4972, -0.5001, 1.7852, -0.1670, -0.4175, 0.0122, 0.3248, 0.1241],
- [ 0.5981, -0.3969, 1.6569, 0.3041, -0.4702, -0.2394, 0.2030, 0.0229],
- [ 0.5747, -0.4492, 1.8774, -0.2422, -0.5260, -0.1429, 1.0017, 0.2354],
- [ 0.7515, -0.2731, 1.0945, -0.9634, -0.6238, -1.0175, 0.1457, 0.3325],
- [ 0.5947, -0.4267, 1.8414, -0.3665, -0.3415, 0.1742, 0.4117, 0.1350],
- [ 0.6684, -0.3270, 1.7121, 0.1320, -0.5190, -0.3264, 0.3048, 0.4452],
- [ 0.4449, -0.5292, 1.8183, -0.6192, -0.6611, -0.6690, 0.3456, 0.0433]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391],
- [ 0.5250, -0.4661, 1.7383, -0.0765, -0.4268, -0.0226, 0.2535,
- 0.2035],
- [ 0.5320, -0.4488, 1.6633, 0.3315, -0.5018, -0.2459, 0.0828,
- -0.0370],
- [ 0.6174, -0.4129, 1.8711, -0.1073, -0.5480, -0.1227, 0.9558,
- 0.2516],
- [ 0.5548, -0.3936, 1.1634, -0.8105, -0.5192, -1.0696, 0.2372,
- 0.3931],
- [ 0.5604, -0.4620, 1.7961, -0.1997, -0.3229, 0.1082, 0.4104,
- 0.2545],
- [ 0.5897, -0.3527, 1.8018, 0.2545, -0.5307, -0.3229, 0.3122,
- 0.3084],
- [ 0.5807, -0.4378, 1.8249, -0.4691, -0.6289, -0.6385, 0.4104,
- 0.0620]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0129, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0129, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5544648678041995
- step: 77
- running loss: 0.020187855426028564
- Train Steps: 77/90 Loss: 0.0202 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7268, 0.2333, 0.4125, 0.1933, 0.5112, 0.5383],
- [0.6137, 0.4038, 0.8563, 0.4050, 0.3813, 0.2550, 0.5106, 0.4954],
- [0.6150, 0.4097, 0.7468, 0.3194, 0.3825, 0.2633, 0.5463, 0.5800],
- [ nan, nan, 0.6512, 0.1717, 0.4100, 0.1983, 0.5253, 0.5240],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6246, 0.4008, 0.8757, 0.5088, 0.4101, 0.5392, 0.6644, 0.5133],
- [0.6265, 0.4091, 0.8950, 0.3533, 0.3600, 0.3967, 0.6295, 0.4901]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.9846, -2.0517, 1.0544, -1.1034, -0.4659, -1.2181, 0.2361, 0.2311],
- [ 0.6813, -0.3112, 1.6636, -0.3705, -0.5481, -0.9216, 0.0807, 0.0184],
- [ 0.8089, -0.2280, 1.1881, -0.7429, -0.6169, -0.8958, 0.1599, 0.2940],
- [-1.8478, -1.9572, 0.9084, -1.2993, -0.4090, -1.2715, 0.3189, 0.2186],
- [ 0.6891, -0.3126, 1.0502, -1.2537, -0.5350, -0.9494, 0.4252, 0.2086],
- [ 0.7851, -0.2646, 1.7832, 0.0043, -0.2070, 0.1093, 0.2041, 0.1600],
- [ 0.7233, -0.3370, 1.8567, 0.0620, -0.4575, 0.3413, 0.8358, 0.1053],
- [ 0.7289, -0.2822, 1.9100, -0.5723, -0.6314, -0.3930, 0.5902, 0.0267]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859e+00, -2.2859e+00, 1.0712e+00, -1.2085e+00, -3.8060e-01,
- -1.3929e+00, 7.5520e-02, 2.0062e-01],
- [ 5.4850e-01, -4.2094e-01, 1.6691e+00, -4.1524e-01, -5.2494e-01,
- -1.1081e+00, 7.2521e-02, 2.0831e-03],
- [ 5.5484e-01, -3.9360e-01, 1.1634e+00, -8.1049e-01, -5.1917e-01,
- -1.0696e+00, 2.3718e-01, 3.9307e-01],
- [-2.2859e+00, -2.2859e+00, 7.2217e-01, -1.4930e+00, -3.9215e-01,
- -1.3698e+00, 1.4038e-01, 1.3434e-01],
- [ 5.3557e-01, -4.2171e-01, 1.0339e+00, -1.4776e+00, -5.0762e-01,
- -1.1081e+00, 4.2194e-01, 2.8530e-01],
- [ 5.3585e-01, -4.3703e-01, 1.7095e+00, -3.0331e-02, -8.0370e-02,
- -3.8029e-02, 1.0439e-01, 3.3918e-01],
- [ 5.9931e-01, -4.3453e-01, 1.7587e+00, 6.4079e-02, -3.9175e-01,
- 2.0479e-01, 7.8274e-01, 8.5217e-02],
- [ 6.0774e-01, -3.9646e-01, 1.8480e+00, -6.5389e-01, -6.2309e-01,
- -4.5373e-01, 6.2155e-01, -2.1963e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0198, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0198, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5742266257293522
- step: 78
- running loss: 0.020182392637555797
- Train Steps: 78/90 Loss: 0.0202 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3993, 0.8639, 0.3687, 0.3658, 0.3139, 0.6002, 0.5374],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6136, 0.4060, 0.8025, 0.3217, 0.3650, 0.3000, 0.5060, 0.5646],
- [0.6286, 0.4040, 0.8696, 0.3047, 0.3924, 0.2887, 0.6300, 0.5367],
- [0.6160, 0.4093, 0.8314, 0.4417, 0.3675, 0.4583, 0.5250, 0.5808],
- [0.6304, 0.4029, 0.8413, 0.5217, 0.4125, 0.5617, 0.7089, 0.5679],
- [0.6257, 0.4024, 0.8612, 0.5352, 0.4361, 0.5253, 0.6680, 0.5166],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3673, -0.5634, 1.6359, -0.6539, -0.6386, -0.7926, 0.4197, 0.1037],
- [ 0.7273, -0.2814, 1.7136, -0.3755, -0.6066, -0.4177, 0.0784, 0.1788],
- [ 0.4522, -0.4597, 1.3317, -0.7944, -0.7027, -0.8624, -0.1578, 0.1287],
- [ 0.5168, -0.4615, 1.5470, -0.9493, -0.4533, -0.9150, 0.5691, 0.2001],
- [ 0.4989, -0.4498, 1.5476, -0.3193, -0.5839, -0.1889, 0.1756, 0.3494],
- [ 0.5740, -0.4453, 1.6434, 0.0708, -0.4044, 0.3415, 0.9822, 0.2546],
- [ 0.4685, -0.5086, 1.6151, 0.0564, -0.3354, 0.2027, 0.7280, 0.0913],
- [ 0.3415, -0.5573, 1.7585, -0.8527, -0.4933, -0.7010, 0.5609, 0.1285]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5777, -0.4416, 1.7044, -0.5827, -0.5962, -0.8361, 0.4862,
- 0.1963],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [ 0.5483, -0.4105, 1.4208, -0.8002, -0.6000, -0.9002, 0.0511,
- 0.3220],
- [ 0.6174, -0.4201, 1.7309, -0.8784, -0.4735, -0.9524, 0.6242,
- 0.1931],
- [ 0.5592, -0.3956, 1.5543, -0.2456, -0.5885, -0.1689, 0.1392,
- 0.3968],
- [ 0.6257, -0.4249, 1.5998, 0.1236, -0.3806, 0.3084, 0.9887,
- 0.3371],
- [ 0.6042, -0.4273, 1.6920, 0.1860, -0.2717, 0.1406, 0.7997,
- 0.1004],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0083, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0083, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.5825061020441353
- step: 79
- running loss: 0.020031722810685257
- Train Steps: 79/90 Loss: 0.0200 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4102, 0.7288, 0.2417, 0.4150, 0.2383, 0.6100, 0.5500],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6314, 0.4050, 0.8227, 0.5431, 0.4150, 0.5517, 0.7121, 0.5690],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [ nan, nan, 0.7612, 0.3250, 0.4037, 0.2533, 0.5438, 0.5767],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6197, 0.4118, 0.8688, 0.5517, 0.4037, 0.5233, 0.5875, 0.5600],
- [0.6099, 0.4030, 0.8638, 0.5117, 0.4983, 0.4965, 0.5086, 0.5388]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6953, -0.3039, 1.0919, -1.2795, -0.5539, -1.2303, 0.4363, 0.1660],
- [ 0.6522, -0.3437, 1.6727, -0.4270, -0.6701, -0.1970, 0.1418, -0.0034],
- [ 0.4385, -0.5066, 1.6041, 0.0371, -0.4771, 0.1611, 1.0337, 0.2570],
- [ 0.6591, -0.3570, 1.7664, -0.3257, -0.6140, -0.1533, 0.2319, 0.1825],
- [-1.6271, -1.8176, 1.2880, -1.0241, -0.5292, -1.1064, 0.1217, 0.2967],
- [ 0.5874, -0.4156, 1.6548, 0.0828, -0.4626, 0.0553, 0.7523, 0.0670],
- [ 0.7439, -0.2793, 1.7456, 0.1186, -0.5632, 0.0915, 0.3984, 0.1757],
- [ 0.5422, -0.4143, 1.7436, -0.1030, -0.1236, -0.1744, 0.0474, 0.1065]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5784, -0.3913, 1.0801, -1.1697, -0.3691, -1.1851, 0.5316,
- 0.2545],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.6306, -0.4153, 1.5141, 0.2224, -0.3691, 0.2622, 1.0033,
- 0.3425],
- [ 0.5368, -0.4406, 1.7730, -0.1766, -0.5249, -0.0534, 0.2314,
- 0.3469],
- [-2.2859, -2.2859, 1.2303, -0.7848, -0.4210, -1.1158, 0.2256,
- 0.3777],
- [ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.5762, -0.3840, 1.7268, 0.2622, -0.4210, 0.1313, 0.4277,
- 0.3007],
- [ 0.5309, -0.4246, 1.7037, 0.0774, 0.0158, 0.0075, 0.0635,
- 0.2026]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0213, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0213, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.603833228815347
- step: 80
- running loss: 0.020047915360191838
- Train Steps: 80/90 Loss: 0.0200 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6168, 0.4055, 0.7575, 0.2950, 0.4050, 0.2717, 0.5938, 0.6167],
- [0.6279, 0.4050, 0.8025, 0.2200, 0.4313, 0.2733, 0.6820, 0.5320],
- [0.6201, 0.4036, 0.8596, 0.3850, 0.3492, 0.3785, 0.5978, 0.5131],
- [0.6275, 0.4050, 0.9038, 0.3767, 0.3838, 0.3533, 0.7074, 0.5575],
- [0.6204, 0.4049, 0.7975, 0.2700, 0.3937, 0.2567, 0.5700, 0.5183],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6223, 0.3990, 0.8037, 0.2800, 0.4000, 0.2283, 0.5864, 0.5208]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3734, -0.5190, 1.1472, -0.9185, -0.5289, -0.9328, 0.3290, 0.5005],
- [ 0.1556, -0.7042, 1.3313, -1.2112, -0.4058, -0.9651, 0.7235, 0.1515],
- [ 0.7030, -0.3688, 1.7134, -0.4899, -0.7348, -0.4037, 0.3331, 0.0598],
- [ 0.4604, -0.5118, 1.8374, -0.4674, -0.5123, -0.5352, 0.8484, 0.2562],
- [ 0.7388, -0.3145, 1.3714, -1.0121, -0.5813, -1.0120, 0.1488, 0.0172],
- [ 0.5941, -0.3950, 1.7309, -0.0935, -0.3420, 0.1457, 0.3144, 0.1970],
- [ 0.5540, -0.4376, 1.6098, 0.0253, -0.2959, 0.2028, 0.3435, 0.1415],
- [ 0.2762, -0.6084, 1.4040, -0.9958, -0.4741, -1.0922, 0.3900, 0.0834]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6307e-01, -4.1286e-01, 1.2129e+00, -9.2333e-01, -4.1524e-01,
- -1.0311e+00, 4.5658e-01, 5.6243e-01],
- [ 6.1413e-01, -4.1527e-01, 1.4208e+00, -1.2697e+00, -2.9400e-01,
- -1.0234e+00, 8.6439e-01, 1.7146e-01],
- [ 5.7829e-01, -4.2163e-01, 1.6847e+00, -5.0778e-01, -6.7321e-01,
- -5.3774e-01, 4.7523e-01, 8.3916e-02],
- [ 6.1248e-01, -4.1527e-01, 1.8885e+00, -5.4611e-01, -5.1339e-01,
- -6.5389e-01, 9.8137e-01, 2.8902e-01],
- [ 5.7985e-01, -4.1555e-01, 1.3977e+00, -1.0388e+00, -4.6721e-01,
- -1.1004e+00, 3.4688e-01, 1.0824e-01],
- [ 5.6374e-01, -4.1432e-01, 1.7519e+00, -7.8656e-02, -3.0554e-01,
- -1.4935e-02, 3.7575e-01, 3.0839e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 5.8851e-01, -4.4288e-01, 1.4266e+00, -9.9261e-01, -4.3834e-01,
- -1.2313e+00, 4.2276e-01, 1.1948e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0144, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0144, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6182256652973592
- step: 81
- running loss: 0.01997809463330073
- Train Steps: 81/90 Loss: 0.0200 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4081, 0.8063, 0.2017, 0.4825, 0.1583, 0.6156, 0.4869],
- [0.6125, 0.3974, 0.7725, 0.2517, 0.3538, 0.3317, 0.5887, 0.5500],
- [0.6183, 0.4076, 0.8838, 0.4517, 0.3813, 0.4483, 0.5775, 0.5633],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [0.6293, 0.4097, 0.8800, 0.2517, 0.5262, 0.2600, 0.7430, 0.5378]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2903, -0.5904, 1.3713, -1.2827, -0.1585, -1.3888, 0.3519, 0.0815],
- [ 0.3774, -0.5230, 1.1499, -0.9511, -0.7478, -0.5249, 0.2670, 0.2296],
- [ 0.6424, -0.3445, 1.6242, -0.1800, -0.6430, 0.0166, 0.1866, 0.3549],
- [ 0.3907, -0.5244, 1.1680, -1.1063, -0.3419, -1.1686, 0.3648, 0.2469],
- [ 0.7197, -0.3552, 1.6134, 0.0642, -0.4562, 0.1645, 0.5280, 0.1403],
- [ 0.5325, -0.4282, 1.7349, -0.4375, -0.4583, -1.1334, 0.2133, 0.0699],
- [ 0.4556, -0.5406, 1.7986, -0.2391, -0.6904, 0.2913, 0.8318, 0.1297],
- [ 0.4897, -0.4823, 1.6821, -1.0455, 0.0482, -1.0562, 0.9698, 0.2569]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6124, -0.4010, 1.4381, -1.3544, -0.0573, -1.5546, 0.5573,
- -0.0369],
- [ 0.5430, -0.4503, 1.2822, -1.1235, -0.6520, -0.7540, 0.4335,
- 0.2545],
- [ 0.5700, -0.4032, 1.7961, -0.1997, -0.5249, -0.2151, 0.3815,
- 0.3161],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.6010, -0.4562, 1.7198, -0.0090, -0.3464, 0.0108, 0.6294,
- 0.1627],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715],
- [ 0.6207, -0.3936, 1.7788, -1.1235, 0.1448, -1.0850, 1.1459,
- 0.1982]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0183, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0183, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6365177757106721
- step: 82
- running loss: 0.019957533850130146
- Train Steps: 82/90 Loss: 0.0200 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833],
- [0.6230, 0.4113, 0.7213, 0.1983, 0.4325, 0.2367, 0.6262, 0.5400],
- [0.6226, 0.4185, 0.8850, 0.5500, 0.3800, 0.4250, 0.5625, 0.5617],
- [ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552],
- [0.6199, 0.4071, 0.7600, 0.2117, 0.4037, 0.2767, 0.6138, 0.5550],
- [0.6260, 0.4253, 0.8938, 0.4400, 0.3675, 0.3650, 0.5675, 0.5767],
- [ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8619, -0.2506, 1.5938, 0.1987, -0.4249, 0.2109, 0.7913, 0.0299],
- [ 0.8644, -0.2650, 1.7133, -0.0653, -0.5711, -0.0539, 0.4279, -0.0110],
- [ 0.7838, -0.2591, 1.1059, -1.2509, -0.3135, -1.1064, 0.5907, 0.2126],
- [ 0.8056, -0.2161, 1.6969, 0.2063, -0.5404, -0.2532, 0.2598, 0.3049],
- [-2.0843, -2.1210, 1.6906, -1.1510, 0.0211, -1.1015, 0.7819, 0.1954],
- [ 0.7208, -0.2959, 1.2570, -1.1981, -0.3980, -0.8894, 0.5388, 0.1905],
- [ 0.8011, -0.2003, 1.7396, -0.2197, -0.6043, -0.5010, 0.2048, 0.3001],
- [-1.8934, -1.9678, 0.8566, -1.1885, -0.4570, -1.1944, 0.0053, 0.2951]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534],
- [ 0.5917, -0.3861, 1.0455, -1.3698, -0.2882, -1.1928, 0.6067,
- 0.2083],
- [ 0.5897, -0.3527, 1.8018, 0.2545, -0.5307, -0.3229, 0.3122,
- 0.3084],
- [-2.2859, -2.2859, 1.7557, -1.1466, 0.0871, -1.0773, 1.1239,
- 0.2783],
- [ 0.5775, -0.4054, 1.2245, -1.3082, -0.4210, -1.0080, 0.5490,
- 0.2776],
- [ 0.6058, -0.3216, 1.8423, -0.2536, -0.5885, -0.6000, 0.3353,
- 0.3777],
- [-2.2859, -2.2859, 0.8033, -1.1250, -0.3864, -1.3082, 0.1126,
- 0.4543]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0177, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0177, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.654198335018009
- step: 83
- running loss: 0.019930100421903723
- Train Steps: 83/90 Loss: 0.0199 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6325, 0.4165, 0.9000, 0.4617, 0.3813, 0.4900, 0.7485, 0.5447],
- [0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6197, 0.4091, 0.8800, 0.4783, 0.3538, 0.4767, 0.5950, 0.5550],
- [0.6271, 0.4005, 0.8450, 0.6067, 0.3850, 0.4983, 0.6069, 0.4649],
- [0.6261, 0.4131, 0.8938, 0.3550, 0.4000, 0.2683, 0.6188, 0.5283],
- [0.6147, 0.4081, 0.8538, 0.3400, 0.3663, 0.3150, 0.5142, 0.4875],
- [0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5667, -0.4106, 1.5669, 0.2542, -0.3283, -0.3381, 0.2532, 0.4962],
- [ 0.4737, -0.5161, 1.7973, -0.2878, -0.4420, 0.0469, 1.0790, 0.2255],
- [ 0.5542, -0.4182, 1.7720, -0.4122, -0.4295, 0.0192, 0.5208, 0.1936],
- [ 0.6322, -0.4009, 1.6925, -0.2425, -0.4987, -0.1369, 0.4719, 0.3256],
- [ 0.5740, -0.4672, 1.5395, 0.3276, -0.4665, 0.0108, 0.5626, -0.0387],
- [ 0.6924, -0.3483, 1.8122, -0.8088, -0.3993, -1.0986, 0.6015, 0.1548],
- [ 0.6318, -0.3645, 1.6522, -0.8129, -0.5442, -0.8673, 0.1077, -0.0085],
- [ 0.2982, -0.5549, 1.0625, -1.3172, -0.2534, -1.1064, 0.5091, 0.5923]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6038, -0.3464, 1.7037, 0.3931, -0.4441, -0.2613, 0.3007,
- 0.4624],
- [ 0.6355, -0.3623, 1.8711, -0.1535, -0.5249, -0.0226, 1.1715,
- 0.2302],
- [ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.5763, -0.3963, 1.7788, -0.0765, -0.6520, -0.0842, 0.4624,
- 0.2776],
- [ 0.6105, -0.4360, 1.6171, 0.5162, -0.5076, 0.0159, 0.5171,
- -0.1385],
- [ 0.6062, -0.3778, 1.8423, -0.6462, -0.4383, -1.0465, 0.5721,
- 0.1544],
- [ 0.5532, -0.4008, 1.6575, -0.7155, -0.5942, -0.8309, 0.0890,
- -0.0340],
- [ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6629118216224015
- step: 84
- running loss: 0.01979656930502859
- Train Steps: 84/90 Loss: 0.0198 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268],
- [0.6223, 0.4028, 0.8988, 0.4200, 0.3763, 0.5733, 0.6375, 0.5167],
- [0.6226, 0.4185, 0.8850, 0.5500, 0.3800, 0.4250, 0.5625, 0.5617],
- [0.6083, 0.3957, 0.8638, 0.4950, 0.4363, 0.5083, 0.5346, 0.4980],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6248, 0.4032, 0.7738, 0.1900, 0.4813, 0.1400, 0.5941, 0.4904],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852],
- [0.6280, 0.4101, 0.9050, 0.4533, 0.3775, 0.3217, 0.6338, 0.4915]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6178, -0.4126, 1.6719, -0.2266, -0.2874, 0.0123, 0.7712, 0.1822],
- [ 0.6737, -0.3681, 1.8302, -0.4825, -0.5582, 0.3299, 0.8426, 0.1744],
- [ 0.4683, -0.4485, 1.6903, 0.0651, -0.5430, -0.3624, 0.4358, 0.4397],
- [ 0.4677, -0.5152, 1.6872, -0.1683, -0.3159, 0.0394, 0.3376, 0.0098],
- [ 0.4362, -0.4553, 1.5300, 0.2041, -0.3977, -0.2993, 0.3835, 0.5141],
- [ 0.7591, -0.2939, 1.3000, -1.4181, -0.0185, -1.6864, 0.5949, 0.1079],
- [ 0.4794, -0.4698, 1.6591, -0.1646, -0.1904, -0.1011, 0.2546, 0.4571],
- [ 0.5885, -0.4134, 1.8501, -0.3274, -0.6333, -0.8266, 0.6597, 0.0587]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.8284e-01, -4.6823e-01, 1.7031e+00, -4.9668e-02, -2.4581e-01,
- 8.1770e-02, 6.3811e-01, 1.4745e-01],
- [ 5.8857e-01, -4.2525e-01, 1.8654e+00, -3.4596e-01, -5.4804e-01,
- 3.6228e-01, 6.5866e-01, 1.0054e-01],
- [ 5.8972e-01, -3.5273e-01, 1.8018e+00, 2.5450e-01, -5.3072e-01,
- -3.2286e-01, 3.1224e-01, 3.0839e-01],
- [ 5.2379e-01, -4.5797e-01, 1.7037e+00, 4.6189e-04, -2.7090e-01,
- 6.2048e-02, 1.8356e-01, 1.4106e-02],
- [ 5.8360e-01, -3.6490e-01, 1.7210e+00, 3.8537e-01, -3.9792e-01,
- -2.9207e-01, 3.0647e-01, 4.4696e-01],
- [ 5.9994e-01, -4.2363e-01, 1.2880e+00, -1.4083e+00, -6.3048e-02,
- -1.6393e+00, 4.5840e-01, -2.0790e-02],
- [ 5.5000e-01, -4.0600e-01, 1.7326e+00, 2.3557e-02, -1.5543e-01,
- -2.2633e-02, 1.4385e-01, 4.1710e-01],
- [ 6.1484e-01, -3.9184e-01, 1.8942e+00, -1.9199e-01, -5.4226e-01,
- -8.0015e-01, 6.4140e-01, -1.5569e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0094, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0094, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.6722815376706421
- step: 85
- running loss: 0.019673900443184025
- Train Steps: 85/90 Loss: 0.0197 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6200, 0.3913, 0.8788, 0.5217, 0.4075, 0.5100, 0.6060, 0.4913],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927],
- [0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136],
- [0.6142, 0.3982, 0.8650, 0.4883, 0.3912, 0.4317, 0.5315, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5126, -0.3980, 1.1951, -0.8997, 0.0626, -1.4429, 0.3967, 0.6447],
- [ 0.2296, -0.6835, 1.7541, -0.0746, -0.3331, 0.0221, 0.4547, 0.1091],
- [ 0.6355, -0.3560, 1.8110, -0.0143, -0.5147, -0.2400, 0.4765, 0.4517],
- [ 0.7565, -0.2948, 1.8072, -0.3907, -0.2909, 0.1811, 0.6384, 0.2877],
- [ 0.8373, -0.2884, 1.7138, 0.2233, -0.4457, -0.0877, 0.8785, 0.0349],
- [ 0.7473, -0.3367, 1.7696, 0.1339, -0.5059, -0.2661, 0.7932, 0.0021],
- [ 0.4635, -0.4734, 1.6943, -0.5987, -0.5666, -0.5225, 0.3460, 0.1546],
- [ 0.3809, -0.5535, 1.7622, -0.1957, -0.4246, -0.2758, 0.3063, 0.2153]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [ 0.5776, -0.4784, 1.7730, 0.1236, -0.4037, 0.0697, 0.5132,
- -0.0168],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.5960, -0.4102, 1.8018, -0.1612, -0.3344, 0.1159, 0.5490,
- 0.2314],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.6335, -0.4162, 1.7499, 0.3084, -0.4961, -0.2459, 0.6524,
- -0.0102],
- [ 0.5235, -0.4273, 1.7499, -0.4306, -0.5827, -0.4614, 0.1651,
- 0.0862],
- [ 0.5513, -0.4467, 1.7095, -0.0303, -0.4788, -0.2921, 0.1692,
- 0.1854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.686103314626962
- step: 86
- running loss: 0.019605852495662348
- Train Steps: 86/90 Loss: 0.0196 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986],
- [0.6131, 0.4064, 0.8638, 0.5200, 0.4788, 0.4783, 0.5258, 0.5867],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317],
- [0.6128, 0.4084, 0.8738, 0.4683, 0.3613, 0.3700, 0.4960, 0.5397],
- [ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [0.6147, 0.4112, 0.7988, 0.3200, 0.3775, 0.2767, 0.5150, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7564, -0.2589, 1.8650, -0.5441, -0.1607, -1.1702, 0.7058, 0.0304],
- [ 0.8369, -0.2295, 1.6265, 0.1115, -0.1178, 0.0394, 0.4619, 0.3338],
- [ 0.8468, -0.2518, 1.6333, -0.1932, -0.5100, -0.3991, 0.6418, 0.2949],
- [-1.7919, -1.9164, 1.2644, -0.9980, -0.3826, -0.9792, 0.2770, 0.2354],
- [ 0.7303, -0.2999, 1.2405, -1.0710, -0.2661, -1.0750, 0.7384, 0.1537],
- [ 0.7327, -0.2754, 1.6966, -0.0502, -0.5937, -0.4542, 0.3500, 0.1744],
- [-2.0738, -2.0970, 1.5829, -1.1729, 0.1751, -1.2038, 0.8197, 0.2315],
- [ 0.7515, -0.2552, 1.3871, -0.7542, -0.4840, -0.8544, 0.2894, 0.1536]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171],
- [ 0.5462, -0.4090, 1.7037, 0.1159, -0.0746, -0.0765, 0.1427,
- 0.4239],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698],
- [ 0.5443, -0.3994, 1.7499, -0.1227, -0.6173, -0.5769, 0.0049,
- 0.2071],
- [-2.2859, -2.2859, 1.7210, -0.9772, 0.1852, -1.3698, 0.7986,
- 0.3104],
- [ 0.5532, -0.3864, 1.4035, -0.8079, -0.5423, -1.0080, 0.0928,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0267, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0267, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7128123142756522
- step: 87
- running loss: 0.01968749786523738
- Train Steps: 87/90 Loss: 0.0197 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6120, 0.4014, 0.6863, 0.2817, 0.3700, 0.2783, 0.5513, 0.5667],
- [0.6262, 0.4163, 0.8850, 0.5183, 0.3763, 0.4150, 0.6025, 0.5500],
- [0.6075, 0.4007, 0.8275, 0.4917, 0.4050, 0.5100, 0.5167, 0.5280],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6126, 0.4067, 0.8638, 0.5383, 0.4188, 0.4850, 0.5016, 0.5392],
- [0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.6286e-01, -4.0666e-01, 1.1249e+00, -9.1667e-01, -4.4478e-01,
- -9.4364e-01, 4.9949e-01, 3.6532e-01],
- [ 4.0523e-01, -5.1145e-01, 1.8993e+00, 9.8369e-02, -4.8307e-01,
- -4.0015e-01, 6.8296e-01, 2.3447e-01],
- [ 4.5955e-01, -4.7740e-01, 1.6666e+00, -1.6434e-02, -2.6750e-01,
- 5.4508e-02, 3.1240e-01, 1.9984e-01],
- [ 2.8139e-01, -6.2768e-01, 2.0158e+00, -5.1398e-01, -2.0669e-01,
- -1.3062e+00, 6.4697e-01, 7.9610e-03],
- [ 5.3721e-01, -4.3750e-01, 1.7832e+00, 2.9688e-01, -2.3398e-01,
- -1.2247e-03, 2.8872e-01, 1.9452e-01],
- [ 6.6184e-01, -4.0100e-01, 1.3221e+00, -1.2418e+00, -4.3192e-01,
- -9.4730e-01, 6.9494e-01, 1.1958e-01],
- [ 5.3418e-01, -4.0369e-01, 1.3463e+00, -5.7753e-01, -5.9096e-01,
- -5.4382e-01, 4.0242e-01, 4.7178e-01],
- [ 5.9500e-01, -4.2295e-01, 1.4109e+00, -1.1970e+00, -6.6504e-02,
- -1.4813e+00, 6.5024e-01, 1.0265e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5410, -0.4321, 0.8838, -0.9849, -0.5769, -1.0003, 0.2603,
- 0.3315],
- [ 0.6066, -0.3632, 1.8018, 0.1082, -0.5480, -0.3691, 0.4970,
- 0.2545],
- [ 0.5200, -0.4353, 1.5363, -0.0149, -0.4152, 0.0697, 0.1005,
- 0.1530],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.5436, -0.4076, 1.7037, 0.2006, -0.3517, -0.0457, 0.0311,
- 0.2048],
- [ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0178, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0178, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7306391117163002
- step: 88
- running loss: 0.019666353542230685
- Train Steps: 88/90 Loss: 0.0197 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6197, 0.4118, 0.8688, 0.5517, 0.4037, 0.5233, 0.5875, 0.5600],
- [0.6031, 0.3986, 0.7462, 0.2833, 0.3638, 0.2717, 0.5253, 0.4726],
- [0.6364, 0.4092, 0.8525, 0.5633, 0.3875, 0.4950, 0.6599, 0.5285],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142],
- [0.6304, 0.4024, 0.8925, 0.4800, 0.3937, 0.4817, 0.7485, 0.5297],
- [0.6086, 0.3981, 0.8700, 0.4750, 0.4512, 0.5283, 0.5324, 0.5038]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6136, -0.3292, 1.7236, 0.2335, -0.3311, 0.0264, 0.4263, 0.2954],
- [ 0.6881, -0.2870, 1.2374, -1.0072, -0.4346, -1.1297, 0.2528, 0.0145],
- [ 0.5846, -0.4008, 1.6797, 0.3026, -0.3951, -0.1516, 0.7463, 0.1915],
- [ 0.4226, -0.4940, 1.8812, -0.1699, -0.2164, -0.4229, 0.9591, 0.3326],
- [ 0.6220, -0.3374, 1.7867, 0.2923, -0.3024, -0.1980, 0.3742, 0.3229],
- [-2.1704, -2.2239, 1.4709, -0.8261, -0.5161, -0.9168, 0.2164, 0.1590],
- [ 0.6821, -0.3473, 1.7859, -0.0876, -0.3812, -0.1809, 0.9278, 0.1667],
- [ 0.6349, -0.3336, 1.7291, -0.1699, -0.1123, -0.1446, 0.1518, 0.0453]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7625e-01, -3.8397e-01, 1.7268e+00, 2.6220e-01, -4.2102e-01,
- 1.3133e-01, 4.2771e-01, 3.0069e-01],
- [ 4.9971e-01, -4.4465e-01, 1.1610e+00, -9.7721e-01, -6.0577e-01,
- -1.0311e+00, 1.4038e-01, -1.0312e-01],
- [ 6.5365e-01, -3.9601e-01, 1.6517e+00, 3.1609e-01, -4.9607e-01,
- 4.6189e-04, 7.6203e-01, 1.5543e-01],
- [ 6.4542e-01, -3.7194e-01, 1.9115e+00, -1.3041e-01, -5.0762e-01,
- -2.8437e-01, 1.0033e+00, 4.3864e-01],
- [ 5.8834e-01, -3.5935e-01, 1.7557e+00, 2.5450e-01, -4.1524e-01,
- -6.1124e-02, 3.3533e-01, 3.0069e-01],
- [-2.2859e+00, -2.2859e+00, 1.5767e+00, -7.5396e-01, -6.4042e-01,
- -7.3087e-01, 1.7534e-01, 8.9251e-02],
- [ 6.2566e-01, -4.2731e-01, 1.8365e+00, -6.8822e-02, -4.6721e-01,
- -6.1124e-02, 1.1715e+00, 1.6077e-01],
- [ 5.2494e-01, -4.4734e-01, 1.7326e+00, -9.1917e-02, -2.0162e-01,
- 1.5443e-01, 1.7328e-01, 4.1158e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0114, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0114, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7420769152231514
- step: 89
- running loss: 0.019573897923855634
- Train Steps: 89/90 Loss: 0.0196 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960],
- [0.6147, 0.4026, 0.6600, 0.2467, 0.4088, 0.2150, 0.5489, 0.5773],
- [0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100],
- [0.6339, 0.4102, 0.9088, 0.4767, 0.3925, 0.5283, 0.7509, 0.5390],
- [0.6147, 0.4107, 0.8137, 0.3333, 0.3750, 0.2683, 0.5006, 0.5412],
- [0.6262, 0.4052, 0.8888, 0.4700, 0.3675, 0.5117, 0.6350, 0.5233],
- [0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4671, -0.5049, 1.8774, -0.1466, -0.0864, -0.0608, 0.2093, 0.0941],
- [ 0.5628, -0.4053, 0.9370, -1.2464, -0.2642, -1.3898, 0.2510, 0.3911],
- [ 0.7089, -0.3867, 1.8857, -0.3630, -0.5566, -0.5843, 0.7402, 0.1295],
- [ 0.5594, -0.4493, 1.9432, -0.0785, -0.3768, 0.2006, 1.0250, 0.2871],
- [ 0.0606, -0.7394, 1.5011, -0.7235, -0.4577, -1.0457, 0.0626, 0.1358],
- [ 0.7851, -0.2815, 1.9567, -0.1113, -0.4422, 0.0439, 0.6305, 0.0925],
- [ 0.4946, -0.4309, 1.6859, 0.3787, -0.4591, -0.5892, 0.3183, 0.4996],
- [ 0.5189, -0.5028, 1.7568, 0.4543, -0.5045, -0.1441, 0.6245, 0.0330]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051],
- [ 0.5532, -0.4264, 0.7626, -1.1466, -0.3979, -1.2928, 0.2494,
- 0.3808],
- [ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697],
- [ 0.6421, -0.3912, 1.9115, -0.0842, -0.4730, 0.1544, 1.1824,
- 0.2035],
- [ 0.5532, -0.3888, 1.4727, -0.7463, -0.5538, -1.0465, 0.0265,
- 0.2138],
- [ 0.6063, -0.4142, 1.8192, -0.1150, -0.5885, 0.0774, 0.6471,
- 0.1313],
- [ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778],
- [ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0123, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0123, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.7543703555129468
- step: 90
- running loss: 0.019493003950143854
- Valid Steps: 10/10 Loss: nan 3.8413
- --------------------------------------------------
- Epoch: 8 Train Loss: 0.0195 Valid Loss: nan
- --------------------------------------------------
- size of train loader is: 90
- torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6205, 0.4062, 0.8337, 0.2683, 0.3675, 0.4283, 0.6338, 0.5250],
- [0.6204, 0.4049, 0.7975, 0.2700, 0.3937, 0.2567, 0.5700, 0.5183],
- [0.6135, 0.3994, 0.7913, 0.3050, 0.3625, 0.3050, 0.5837, 0.5050],
- [0.6026, 0.3979, 0.8550, 0.4233, 0.3613, 0.5233, 0.5582, 0.4967],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402],
- [0.6357, 0.4139, 0.8450, 0.5883, 0.3775, 0.4950, 0.6488, 0.5297],
- [0.6353, 0.4128, 0.8488, 0.2600, 0.5525, 0.1616, 0.6694, 0.5540],
- [0.6127, 0.4115, 0.7163, 0.2883, 0.3625, 0.2950, 0.5327, 0.5495]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6140, -0.3871, 1.6023, -0.7491, -0.5414, -0.3933, 0.5851, 0.2746],
- [ 0.5138, -0.4631, 1.5303, -0.9427, -0.4560, -1.2151, 0.2397, 0.0554],
- [ 0.4892, -0.4673, 1.4873, -0.7423, -0.5505, -0.9114, 0.4542, 0.1094],
- [ 0.6205, -0.3850, 1.7838, -0.1074, -0.5529, -0.0047, 0.3428, 0.1468],
- [ 0.6016, -0.4252, 1.7565, 0.4326, -0.3560, -0.1970, 0.4493, 0.1951],
- [ 0.7335, -0.3303, 1.8009, 0.5891, -0.5046, -0.0271, 0.7923, 0.1182],
- [-0.4382, -1.0592, 1.7135, -1.0684, 0.3674, -1.3955, 0.8355, 0.2889],
- [ 0.4659, -0.4597, 1.1708, -0.8018, -0.5448, -0.9428, 0.1353, 0.3279]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7991e-01, -4.0985e-01, 1.5651e+00, -1.0465e+00, -5.8845e-01,
- -3.0747e-01, 6.4134e-01, 1.3903e-01],
- [ 5.7985e-01, -4.1555e-01, 1.3977e+00, -1.0388e+00, -4.6721e-01,
- -1.1004e+00, 3.4688e-01, 1.0824e-01],
- [ 5.4769e-01, -4.4126e-01, 1.3688e+00, -8.7714e-01, -6.1155e-01,
- -8.7714e-01, 4.1039e-01, 4.6651e-02],
- [ 4.9740e-01, -4.4819e-01, 1.6633e+00, -3.3056e-01, -6.1732e-01,
- 1.3133e-01, 2.9255e-01, 8.0947e-03],
- [ 5.7800e-01, -4.5651e-01, 1.6221e+00, 2.5323e-01, -3.7281e-01,
- -1.7182e-01, 4.3570e-01, 2.0910e-01],
- [ 6.5036e-01, -3.7434e-01, 1.6171e+00, 4.3156e-01, -5.4226e-01,
- 4.6189e-04, 7.1085e-01, 1.6077e-01],
- [ 6.4871e-01, -3.7916e-01, 1.6344e+00, -1.0850e+00, 2.6592e-01,
- -1.5397e+00, 8.0590e-01, 2.7299e-01],
- [ 5.4417e-01, -3.8545e-01, 1.0224e+00, -9.5412e-01, -6.1155e-01,
- -9.2333e-01, 1.7452e-01, 2.5215e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0350, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0350, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.035023126751184464
- step: 1
- running loss: 0.035023126751184464
- Train Steps: 1/90 Loss: 0.0350 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [0.6265, 0.4091, 0.8950, 0.3533, 0.3600, 0.3967, 0.6295, 0.4901],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [ nan, nan, 0.6488, 0.1817, 0.4325, 0.1867, 0.5475, 0.5733],
- [0.6187, 0.4104, 0.7126, 0.2069, 0.3950, 0.2517, 0.5913, 0.5683],
- [0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719],
- [0.6289, 0.4081, 0.8720, 0.3487, 0.3900, 0.3183, 0.6703, 0.5376]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.5893, -1.8049, 1.6660, -0.9662, 0.1232, -1.2444, 0.7696, 0.2287],
- [ 0.6649, -0.2618, 1.4795, -0.3987, -0.1274, -1.2144, 0.1891, 0.4023],
- [ 0.8279, -0.1916, 1.8551, -0.5088, -0.6687, -0.4753, 0.5482, -0.0837],
- [ 0.6262, -0.3445, 1.6581, 0.1843, -0.3398, 0.0883, 0.3233, 0.1184],
- [-2.2111, -2.2507, 0.9937, -1.0590, -0.2451, -1.3357, 0.2370, 0.3124],
- [ 0.7010, -0.3028, 1.0150, -1.1334, -0.4862, -1.0880, 0.4025, 0.2193],
- [ 0.6674, -0.3327, 1.6469, 0.4851, -0.5619, 0.0573, 0.2066, 0.0539],
- [ 0.6414, -0.3664, 1.7301, -0.4614, -0.4897, -0.7804, 0.7746, 0.1199]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859e+00, -2.2859e+00, 1.7210e+00, -9.7721e-01, 1.8522e-01,
- -1.3698e+00, 7.9859e-01, 3.1039e-01],
- [ 6.2367e-01, -2.9831e-01, 1.3919e+00, -4.6913e-01, -4.5727e-02,
- -1.2313e+00, 2.4525e-01, 5.8821e-01],
- [ 6.0774e-01, -3.9646e-01, 1.8480e+00, -6.5389e-01, -6.2309e-01,
- -4.5373e-01, 6.2155e-01, -2.1963e-02],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [-2.2859e+00, -2.2859e+00, 7.1062e-01, -1.4468e+00, -2.8822e-01,
- -1.4237e+00, 2.4296e-01, 3.6228e-01],
- [ 5.7182e-01, -3.9053e-01, 1.0053e+00, -1.3305e+00, -4.6143e-01,
- -1.1235e+00, 4.4503e-01, 3.3918e-01],
- [ 5.0785e-01, -4.7144e-01, 1.6575e+00, 2.2371e-01, -4.9607e-01,
- 7.7444e-02, 1.4655e-01, -1.0613e-01],
- [ 6.1907e-01, -4.0082e-01, 1.7420e+00, -6.7528e-01, -4.8453e-01,
- -8.1555e-01, 8.1006e-01, 1.9744e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0246, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0246, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.05961088836193085
- step: 2
- running loss: 0.029805444180965424
- Train Steps: 2/90 Loss: 0.0298 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6136, 0.4117, 0.8700, 0.5167, 0.4188, 0.5083, 0.5147, 0.5495],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6239, 0.4123, 0.8313, 0.2550, 0.4500, 0.2050, 0.6175, 0.5400],
- [0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467],
- [0.6141, 0.4038, 0.8650, 0.4833, 0.4839, 0.5176, 0.5787, 0.5600],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6113, 0.4006, 0.8700, 0.5350, 0.3638, 0.3767, 0.5097, 0.4882]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3977, -0.5190, 1.6397, 0.2275, -0.4261, 0.0427, 0.1332, 0.2363],
- [ 0.5997, -0.4350, 1.6313, 0.4328, -0.5913, -0.0857, 0.5645, -0.0069],
- [ 0.7061, -0.3038, 1.7128, -0.8875, -0.1318, -1.3337, 0.5031, 0.0823],
- [ 0.4596, -0.4776, 1.4336, -1.1474, -0.1885, -1.2794, 0.5792, 0.2321],
- [ 0.5913, -0.3999, 1.4971, -0.7091, -0.7462, -0.3909, 0.3259, 0.3171],
- [ 0.2882, -0.5980, 1.6730, -0.0155, -0.1775, 0.0260, 0.2027, 0.3243],
- [ 0.4600, -0.5590, 1.9039, -0.2497, -0.3810, -0.9030, 1.0014, 0.1673],
- [ 0.2536, -0.6194, 1.6788, 0.1859, -0.7088, -0.5439, 0.0605, 0.0532]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5482, -0.3841, 1.7326, 0.1005, -0.3517, 0.0620, 0.0912,
- 0.2522],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.5958, -0.3818, 1.5536, -1.1081, -0.2074, -1.3390, 0.5663,
- 0.2083],
- [ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391],
- [ 0.5505, -0.4207, 1.7095, -0.0534, -0.0509, 0.1050, 0.3873,
- 0.3007],
- [ 0.6224, -0.4345, 1.9404, -0.2921, -0.3171, -0.8771, 1.0655,
- 0.2142],
- [ 0.5379, -0.4358, 1.7326, 0.1852, -0.6058, -0.5461, 0.0684,
- -0.0310]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.06913623213768005
- step: 3
- running loss: 0.023045410712560017
- Train Steps: 3/90 Loss: 0.0230 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6229, 0.4086, 0.7538, 0.2600, 0.4775, 0.1617, 0.5900, 0.5383],
- [0.6128, 0.4115, 0.7934, 0.3778, 0.3450, 0.4033, 0.5337, 0.5456],
- [ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [0.6164, 0.3972, 0.7818, 0.3381, 0.3599, 0.3387, 0.5880, 0.5153],
- [0.6224, 0.3964, 0.8225, 0.5717, 0.4150, 0.4617, 0.5775, 0.5267],
- [0.6198, 0.4130, 0.8762, 0.4117, 0.3650, 0.4900, 0.5707, 0.5103]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7372, -0.2586, 1.7167, 0.3747, -0.4748, -0.1449, 0.4796, 0.0674],
- [ 0.8608, -0.2171, 1.2050, -1.0956, -0.1502, -1.5177, 0.4047, 0.2153],
- [ 0.5684, -0.3655, 1.4035, -0.4764, -0.7140, -0.4578, 0.2291, 0.2330],
- [-1.4736, -1.7437, 1.6607, -1.0372, 0.2038, -1.3720, 0.9044, 0.2716],
- [-2.0843, -2.1717, 1.3421, -0.6876, -0.6274, -0.8590, 0.1328, 0.2106],
- [ 0.6394, -0.3625, 1.4053, -0.7157, -0.6669, -0.7526, 0.3336, 0.1017],
- [ 0.7157, -0.3242, 1.5919, 0.4262, -0.4433, -0.1878, 0.3655, 0.0658],
- [ 0.7816, -0.2302, 1.8276, -0.3053, -0.6136, -0.1140, 0.3521, 0.0361]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391],
- [ 0.5911, -0.3984, 1.1956, -1.0850, -0.0804, -1.5392, 0.4393,
- 0.2006],
- [ 0.5444, -0.3852, 1.3786, -0.5409, -0.6924, -0.4229, 0.1791,
- 0.2341],
- [-2.2859, -2.2859, 1.7210, -0.9772, 0.1852, -1.3698, 0.7986,
- 0.3104],
- [-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837],
- [ 0.5614, -0.4514, 1.3252, -0.7240, -0.6237, -0.7215, 0.4299,
- 0.0942],
- [ 0.5891, -0.4550, 1.5132, 0.3546, -0.3691, -0.1535, 0.3815,
- 0.1467],
- [ 0.5771, -0.3784, 1.7614, -0.3844, -0.6000, -0.0226, 0.3501,
- 0.0712]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0222, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.0913512222468853
- step: 4
- running loss: 0.022837805561721325
- Train Steps: 4/90 Loss: 0.0228 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6267, 0.4065, 0.8313, 0.2467, 0.4788, 0.1733, 0.6312, 0.5133],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [0.6275, 0.4071, 0.8075, 0.2100, 0.4700, 0.1533, 0.6148, 0.4834],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6332, 0.4128, 0.9200, 0.3517, 0.4400, 0.3833, 0.7461, 0.5494],
- [0.6182, 0.3998, 0.8793, 0.4191, 0.3552, 0.4285, 0.6038, 0.5312]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3963, -0.5111, 1.5654, -0.2544, -0.6555, -0.8049, -0.0029, 0.2036],
- [ 0.6896, -0.3829, 1.4768, -0.9736, -0.0825, -1.4334, 0.5826, 0.1867],
- [ 0.5362, -0.5103, 1.6700, 0.3681, -0.4911, -0.0497, 0.4941, 0.1507],
- [ 0.2169, -0.6776, 1.1213, -0.8193, -0.6600, -0.8720, 0.0790, 0.0277],
- [ 0.5976, -0.4572, 1.3894, -1.2061, -0.0525, -1.4916, 0.4835, 0.0843],
- [ 0.4290, -0.4816, 1.7752, -0.1858, -0.6380, -0.3565, 0.2010, 0.2168],
- [ 0.3762, -0.6093, 1.8550, -0.4568, -0.4097, -0.4039, 1.0037, 0.2467],
- [ 0.5470, -0.4521, 1.7105, -0.2674, -0.6784, -0.2623, 0.4499, 0.1900]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5470, -0.3952, 1.6377, -0.4253, -0.6289, -0.8079, 0.0249,
- 0.2116],
- [ 0.6085, -0.4084, 1.5536, -1.1466, -0.0746, -1.4853, 0.6298,
- 0.0851],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141],
- [ 0.6126, -0.4057, 1.4439, -1.3159, -0.1150, -1.5777, 0.5537,
- -0.0530],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [ 0.6388, -0.3792, 1.9635, -0.6616, -0.2536, -0.5153, 1.1605,
- 0.2516],
- [ 0.5697, -0.4393, 1.7754, -0.3503, -0.6453, -0.3067, 0.5028,
- 0.1677]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0120, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0120, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.10337931476533413
- step: 5
- running loss: 0.020675862953066827
- Train Steps: 5/90 Loss: 0.0207 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [0.6101, 0.4042, 0.7775, 0.2617, 0.3713, 0.2817, 0.5440, 0.5650],
- [0.6149, 0.4054, 0.6713, 0.2333, 0.4025, 0.2017, 0.5213, 0.5695],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967],
- [0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6132, 0.3930, 0.8672, 0.4754, 0.3712, 0.5222, 0.5974, 0.5098],
- [0.6361, 0.4102, 0.8650, 0.2983, 0.4888, 0.2000, 0.6702, 0.5459],
- [0.6209, 0.3920, 0.8650, 0.5367, 0.4400, 0.5067, 0.6025, 0.4950]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5466, -0.4835, 1.6164, -0.6194, -0.7410, -0.6682, 0.3838, 0.1722],
- [ 0.4471, -0.5391, 1.2867, -0.9394, -0.5697, -1.0218, 0.1520, 0.2237],
- [ 0.1865, -0.6866, 0.8140, -1.0698, -0.4618, -1.3994, -0.0308, 0.2866],
- [ 0.4823, -0.4738, 1.8417, -0.0641, -0.4494, 0.1759, 0.4307, 0.3456],
- [ 0.5742, -0.4413, 1.7019, -0.8798, -0.3884, -1.1352, 0.6841, 0.1134],
- [ 0.5016, -0.4895, 1.7512, 0.0568, -0.5928, 0.0645, 0.3931, 0.1011],
- [ 0.5847, -0.4574, 1.7197, -0.7646, -0.0677, -1.3968, 0.8645, 0.1314],
- [ 0.3921, -0.5961, 1.7098, 0.3133, -0.3360, 0.0101, 0.3127, -0.0480]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6801e-01, -4.3934e-01, 1.5920e+00, -6.6715e-01, -6.4527e-01,
- -5.4566e-01, 5.1492e-01, 1.7534e-01],
- [ 5.3204e-01, -4.1886e-01, 1.3053e+00, -1.0773e+00, -5.7113e-01,
- -9.8491e-01, 2.2674e-01, 3.2370e-01],
- [ 5.5445e-01, -4.1332e-01, 8.1455e-01, -1.2082e+00, -4.2679e-01,
- -1.3544e+00, 1.2208e-01, 3.4458e-01],
- [ 5.7719e-01, -3.9130e-01, 1.8480e+00, -2.4588e-01, -4.3256e-01,
- 1.9292e-01, 5.3741e-01, 4.7005e-01],
- [ 6.0837e-01, -4.0762e-01, 1.6806e+00, -9.6182e-01, -2.9977e-01,
- -9.6952e-01, 6.3557e-01, 1.4673e-01],
- [ 5.4660e-01, -4.7064e-01, 1.7198e+00, -9.0292e-02, -5.7125e-01,
- 1.2613e-01, 4.7328e-01, 6.8827e-02],
- [ 6.5201e-01, -3.9120e-01, 1.7095e+00, -9.0793e-01, -2.8406e-02,
- -1.3621e+00, 8.0956e-01, 2.3558e-01],
- [ 5.8199e-01, -4.7544e-01, 1.7095e+00, 1.9292e-01, -2.5358e-01,
- 5.4350e-02, 4.9700e-01, 4.6189e-04]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.11402785126119852
- step: 6
- running loss: 0.01900464187686642
- Train Steps: 6/90 Loss: 0.0190 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6276, 0.4235, 0.8888, 0.5333, 0.3800, 0.3117, 0.5427, 0.6164],
- [0.6138, 0.4054, 0.8750, 0.4750, 0.4363, 0.5017, 0.5086, 0.5822],
- [0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6286, 0.3977, 0.9038, 0.4733, 0.3900, 0.4150, 0.7074, 0.5320],
- [0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533],
- [0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5891, -0.3890, 1.6729, 0.2073, -0.4570, -0.8302, 0.3594, 0.4329],
- [ 0.4704, -0.4707, 1.7011, -0.0808, -0.2426, -0.0565, 0.0563, 0.1839],
- [ 0.4041, -0.5540, 1.0145, -1.2279, -0.3703, -1.4565, 0.2284, -0.0221],
- [ 0.5034, -0.4659, 1.5339, -0.5810, -0.6889, -0.4715, 0.2772, 0.1573],
- [ 0.5466, -0.4884, 1.7959, -0.1409, -0.5534, -0.4282, 0.8416, 0.0456],
- [ 0.6808, -0.3508, 1.6525, -0.6522, -0.6313, -0.4027, 0.3507, 0.0792],
- [-2.1821, -2.2742, 1.3833, -0.8228, -0.5880, -1.0219, 0.2936, 0.2045],
- [ 0.6380, -0.4148, 1.7247, -0.0332, -0.2073, 0.0171, 0.6617, 0.0978]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6131, -0.3299, 1.8192, 0.1775, -0.5307, -0.8463, 0.2206,
- 0.5612],
- [ 0.5491, -0.4132, 1.7557, -0.0919, -0.2709, 0.0313, 0.0635,
- 0.4032],
- [ 0.5109, -0.4316, 1.1436, -1.3467, -0.3864, -1.4160, 0.1239,
- -0.0580],
- [ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.6174, -0.4490, 1.8885, -0.0996, -0.4845, -0.3691, 0.9814,
- 0.1715],
- [ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699],
- [ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0065, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0065, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.12055541574954987
- step: 7
- running loss: 0.017222202249935696
- Train Steps: 7/90 Loss: 0.0172 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6135, 0.3994, 0.7913, 0.3050, 0.3625, 0.3050, 0.5837, 0.5050],
- [0.6182, 0.3972, 0.8552, 0.5914, 0.3683, 0.4181, 0.5688, 0.5378],
- [0.6222, 0.3937, 0.8350, 0.5617, 0.4138, 0.4600, 0.5800, 0.5233],
- [0.6145, 0.4007, 0.8775, 0.4533, 0.4562, 0.5533, 0.6088, 0.5533],
- [0.6198, 0.4130, 0.8762, 0.4117, 0.3650, 0.4900, 0.5707, 0.5103]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6121, -0.4770, 1.8885, 0.0605, -0.6429, -0.2336, 0.6899, 0.0278],
- [ 0.7454, -0.3639, 1.0604, -1.3949, -0.4047, -1.2772, 0.4862, 0.2483],
- [ 0.2872, -0.6198, 1.6279, -0.3692, -0.5507, -0.2969, 0.1379, 0.3256],
- [ 0.3327, -0.6100, 1.4320, -0.9929, -0.6452, -0.9560, 0.4174, 0.0762],
- [ 0.7374, -0.3535, 1.6367, 0.2674, -0.6135, -0.4852, 0.2430, 0.1632],
- [ 0.5788, -0.5004, 1.6847, 0.2857, -0.3745, -0.2482, 0.4787, 0.2059],
- [ 0.5147, -0.4656, 1.8758, -0.2675, -0.1389, 0.0108, 0.3955, 0.3097],
- [ 0.5872, -0.4207, 1.8811, -0.4503, -0.5698, -0.2127, 0.3556, 0.0867]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6240, -0.4321, 1.8423, 0.1852, -0.5885, -0.1612, 0.6962,
- 0.0111],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.5477, -0.4413, 1.3688, -0.8771, -0.6115, -0.8771, 0.4104,
- 0.0467],
- [ 0.5697, -0.4514, 1.6642, 0.4459, -0.5850, -0.3546, 0.3414,
- 0.1982],
- [ 0.5880, -0.4676, 1.5709, 0.3084, -0.3748, -0.1612, 0.3931,
- 0.1313],
- [ 0.5526, -0.4351, 1.7672, -0.1920, -0.1785, 0.2699, 0.5259,
- 0.2699],
- [ 0.5771, -0.3784, 1.7614, -0.3844, -0.6000, -0.0226, 0.3501,
- 0.0712]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.1300251130014658
- step: 8
- running loss: 0.016253139125183225
- Train Steps: 8/90 Loss: 0.0163 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6200, 0.4055, 0.8011, 0.2988, 0.3842, 0.2798, 0.5949, 0.5183],
- [0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6182, 0.3972, 0.8552, 0.5914, 0.3683, 0.4181, 0.5688, 0.5378],
- [0.6343, 0.4097, 0.9287, 0.4367, 0.4313, 0.3600, 0.7248, 0.5841],
- [0.6176, 0.4017, 0.8788, 0.5100, 0.4075, 0.5250, 0.5913, 0.5033],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5256, -0.4524, 1.7984, 0.1450, -0.4289, -0.0788, 0.2589, 0.3393],
- [ 0.4977, -0.4804, 1.3995, -1.0820, -0.5870, -1.0135, 0.3227, 0.1081],
- [ 0.6630, -0.3975, 1.3779, -1.1074, -0.6264, -1.1621, 0.1380, -0.0220],
- [ 0.5724, -0.4312, 1.7310, -0.1963, -0.0846, -0.1130, 0.1525, 0.2617],
- [ 0.7592, -0.3167, 1.5519, 0.1764, -0.6423, -0.4017, 0.1510, 0.1811],
- [ 0.4704, -0.5583, 1.8519, -0.4481, -0.4448, -0.6366, 0.9631, 0.3323],
- [ 0.4849, -0.5122, 1.7827, -0.0995, -0.3785, 0.1170, 0.3145, 0.1204],
- [ 0.3807, -0.6192, 1.7006, 0.0377, -0.5237, 0.0271, 0.9367, 0.1956]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.5781, -0.4129, 1.4142, -0.9057, -0.5115, -0.9937, 0.4621,
- 0.1080],
- [ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220],
- [ 0.5359, -0.4370, 1.7095, -0.0303, -0.0804, -0.0380, 0.1044,
- 0.3392],
- [ 0.5697, -0.4514, 1.6642, 0.4459, -0.5850, -0.3546, 0.3414,
- 0.1982],
- [ 0.6438, -0.3936, 2.0039, -0.2690, -0.2940, -0.6231, 1.0618,
- 0.4119],
- [ 0.5668, -0.4306, 1.7730, 0.0697, -0.4037, 0.1390, 0.4450,
- 0.0390],
- [ 0.6273, -0.4393, 1.6402, 0.1313, -0.5076, 0.0467, 1.1532,
- 0.1715]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0117, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0117, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.14171226508915424
- step: 9
- running loss: 0.015745807232128248
- Train Steps: 9/90 Loss: 0.0157 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [0.6175, 0.3997, 0.6737, 0.2500, 0.4313, 0.1933, 0.5513, 0.5750],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402],
- [0.6100, 0.4071, 0.7601, 0.3444, 0.3400, 0.4117, 0.5625, 0.5617],
- [0.6218, 0.4137, 0.7263, 0.2233, 0.4075, 0.2650, 0.6212, 0.5783],
- [0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927],
- [0.6118, 0.4052, 0.8463, 0.3917, 0.3538, 0.3450, 0.5053, 0.5593],
- [0.6317, 0.4038, 0.8287, 0.5900, 0.3800, 0.4717, 0.6295, 0.4986]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5621, -0.4350, 1.8083, -0.7072, -0.6060, -0.3735, 0.3713, 0.1477],
- [-0.2548, -0.9666, 0.9694, -1.2713, -0.2514, -1.5026, 0.2285, 0.4129],
- [ 0.5501, -0.4866, 1.7867, 0.1286, -0.3355, -0.1685, 0.5161, 0.1942],
- [ 0.5518, -0.4300, 1.3952, -0.7849, -0.7246, -0.3439, 0.2739, 0.2838],
- [ 0.6204, -0.4182, 1.1728, -1.2457, -0.4112, -1.0753, 0.5919, 0.3851],
- [ 0.7153, -0.3850, 1.8950, 0.1854, -0.4805, -0.2747, 0.6471, -0.0912],
- [ 0.5189, -0.4356, 1.7161, -0.5475, -0.5983, -0.7178, -0.0889, 0.2347],
- [ 0.6597, -0.4147, 1.7465, 0.3388, -0.5111, -0.1052, 0.6238, 0.0331]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [ 0.5663, -0.4396, 0.8261, -1.1312, -0.2940, -1.3929, 0.2603,
- 0.3700],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091],
- [ 0.5318, -0.4056, 1.2249, -0.6949, -0.7155, -0.3844, 0.3122,
- 0.3084],
- [ 0.5863, -0.3749, 1.0686, -1.2543, -0.4037, -1.0619, 0.5836,
- 0.3854],
- [ 0.6335, -0.4162, 1.7499, 0.3084, -0.4961, -0.2459, 0.6524,
- -0.0102],
- [ 0.5399, -0.4142, 1.6229, -0.4768, -0.6520, -0.6924, 0.0481,
- 0.2972],
- [ 0.6320, -0.4206, 1.5420, 0.4393, -0.5307, -0.1073, 0.6216,
- 0.0171]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0208, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0208, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.16252397932112217
- step: 10
- running loss: 0.016252397932112216
- Train Steps: 10/90 Loss: 0.0163 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6257, 0.4060, 0.8300, 0.2333, 0.4688, 0.2583, 0.7050, 0.5633],
- [0.6022, 0.3994, 0.8025, 0.3350, 0.3350, 0.4400, 0.5565, 0.5025],
- [0.6289, 0.4081, 0.8720, 0.3487, 0.3900, 0.3183, 0.6703, 0.5376],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4952, -0.5065, 1.6989, -1.0779, -0.1564, -1.1010, 0.9690, 0.3485],
- [ 0.2338, -0.6378, 1.5139, -0.6089, -0.7161, -0.2372, 0.2462, 0.1252],
- [ 0.4851, -0.5297, 1.8211, -0.5048, -0.4673, -0.7272, 0.9266, 0.2146],
- [ 0.4475, -0.4619, 1.8858, -0.2298, -0.6026, -0.3046, 0.2469, 0.2047],
- [ 0.6366, -0.3988, 1.3170, -1.0641, -0.1947, -1.3048, 0.4453, 0.1899],
- [ 0.4931, -0.4868, 1.0749, -1.0042, -0.5348, -0.9385, 0.0530, 0.0562],
- [ 0.4666, -0.4832, 1.4160, -1.0601, -0.1922, -1.2653, 0.4001, 0.2308],
- [ 0.6729, -0.3513, 0.8831, -1.0201, -0.5309, -0.9789, 0.1976, 0.2888]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6042, -0.4105, 1.5478, -1.2082, -0.1208, -1.0927, 0.9704,
- 0.3157],
- [ 0.4956, -0.4413, 1.4208, -0.7386, -0.7386, -0.2536, 0.2843,
- 0.0351],
- [ 0.6191, -0.4008, 1.7420, -0.6753, -0.4845, -0.8156, 0.8101,
- 0.1974],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850],
- [ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0092, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0092, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.1717460323125124
- step: 11
- running loss: 0.015613275664773855
- Train Steps: 11/90 Loss: 0.0156 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933],
- [0.6193, 0.3930, 0.8949, 0.4437, 0.3852, 0.5435, 0.6263, 0.5263],
- [0.6250, 0.4008, 0.8950, 0.4183, 0.3550, 0.4383, 0.6361, 0.4927],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6275, 0.4081, 0.8063, 0.2017, 0.4825, 0.1583, 0.6156, 0.4869],
- [0.6104, 0.4029, 0.8738, 0.4900, 0.4088, 0.4533, 0.5070, 0.5510],
- [0.6059, 0.4002, 0.7562, 0.2767, 0.3538, 0.3033, 0.5529, 0.5455]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.1810, -2.2031, 0.8995, -1.0844, -0.4663, -1.2910, 0.0934, 0.3814],
- [ 0.4687, -0.4924, 1.8094, -0.1270, -0.4819, 0.2563, 0.5449, 0.1873],
- [ 0.4851, -0.4925, 1.7876, -0.3265, -0.6856, -0.1683, 0.4772, 0.1175],
- [ 0.7273, -0.2978, 0.9829, -1.2828, -0.4788, -1.0173, 0.3975, 0.2628],
- [ 0.4733, -0.5219, 1.8115, -0.5131, -0.3404, -0.8316, 0.9798, 0.1113],
- [ 0.9110, -0.2001, 1.4314, -1.2082, -0.0286, -1.5178, 0.5554, 0.0304],
- [ 0.4506, -0.4711, 1.7314, 0.1099, -0.3668, -0.0410, 0.1666, 0.2598],
- [ 0.4830, -0.4528, 1.1680, -0.8904, -0.5668, -0.7985, 0.3129, 0.1844]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 0.8033, -1.1250, -0.3864, -1.3082, 0.1126,
- 0.4543],
- [ 0.5746, -0.4706, 1.8476, -0.2365, -0.5068, 0.2245, 0.6069,
- 0.1449],
- [ 0.6010, -0.4345, 1.8480, -0.3537, -0.6462, -0.2613, 0.6524,
- -0.0099],
- [ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.6124, -0.4010, 1.4381, -1.3544, -0.0573, -1.5546, 0.5573,
- -0.0369],
- [ 0.5334, -0.4252, 1.7499, -0.0226, -0.3979, -0.1920, 0.0558,
- 0.2589],
- [ 0.5129, -0.4374, 1.2072, -1.0080, -0.6520, -0.8848, 0.2679,
- 0.2335]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0088, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0088, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.18051145039498806
- step: 12
- running loss: 0.015042620866249004
- Train Steps: 12/90 Loss: 0.0150 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.4034, 0.7757, 0.2347, 0.3733, 0.2919, 0.5930, 0.4926],
- [0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100],
- [0.6296, 0.4045, 0.9138, 0.4100, 0.4232, 0.4242, 0.7422, 0.5297],
- [0.6261, 0.4045, 0.8865, 0.5369, 0.3895, 0.4859, 0.6683, 0.5249],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019],
- [0.6202, 0.3983, 0.8555, 0.5429, 0.3842, 0.4370, 0.5866, 0.5398],
- [0.6286, 0.4060, 0.9188, 0.4333, 0.3675, 0.4167, 0.7034, 0.5528],
- [0.6166, 0.4008, 0.8563, 0.5667, 0.4388, 0.4933, 0.5575, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5929, -0.3831, 1.1603, -1.3763, -0.5561, -0.9755, 0.1903, 0.0160],
- [ 0.5210, -0.4703, 1.7357, -0.5514, -0.6131, -0.5008, 0.5070, 0.1872],
- [ 0.7239, -0.3509, 1.8028, -0.5375, -0.4470, -0.3079, 0.8961, 0.2300],
- [ 0.6579, -0.3626, 1.6936, 0.0632, -0.4777, 0.0678, 0.5938, 0.2176],
- [ 0.5153, -0.4594, 1.7896, -0.2423, -0.6064, -0.4672, 0.4889, 0.1511],
- [ 0.6139, -0.3908, 1.5405, 0.1031, -0.4729, -0.2208, 0.2265, 0.2722],
- [ 0.6588, -0.3622, 1.8513, -0.3756, -0.4461, -0.3094, 0.7993, 0.3618],
- [ 0.6246, -0.3740, 1.5466, 0.2366, -0.1276, 0.0130, 0.1270, 0.3720]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5746, -0.4225, 1.2968, -1.2019, -0.5615, -0.9374, 0.4533,
- -0.0108],
- [ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697],
- [ 0.6224, -0.4177, 1.9346, -0.3921, -0.3314, -0.3264, 1.1422,
- 0.1608],
- [ 0.6059, -0.4177, 1.8087, 0.1941, -0.4868, -0.0414, 0.8010,
- 0.1385],
- [ 0.6372, -0.4129, 1.8942, -0.0765, -0.6173, -0.4768, 0.6999,
- 0.0325],
- [ 0.5786, -0.4463, 1.6655, 0.2216, -0.5115, -0.2675, 0.4236,
- 0.2075],
- [ 0.6174, -0.4105, 1.9577, -0.2844, -0.5885, -0.3614, 0.9631,
- 0.2676],
- [ 0.5620, -0.4346, 1.6691, 0.3315, -0.2594, -0.0072, 0.2891,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0127, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0127, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.19325176812708378
- step: 13
- running loss: 0.01486552062516029
- Train Steps: 13/90 Loss: 0.0149 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6249, 0.4138, 0.9038, 0.4517, 0.3862, 0.4917, 0.6138, 0.5333],
- [0.6042, 0.3990, 0.6831, 0.2875, 0.3500, 0.3133, 0.5143, 0.5510],
- [0.6307, 0.4045, 0.8025, 0.5833, 0.3775, 0.4867, 0.6892, 0.5459],
- [0.6269, 0.4073, 0.8900, 0.4933, 0.3625, 0.4183, 0.6288, 0.5150],
- [0.6350, 0.4144, 0.9250, 0.4100, 0.4125, 0.3750, 0.7129, 0.5945],
- [0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973],
- [0.6371, 0.4092, 0.8337, 0.5850, 0.3950, 0.5117, 0.6559, 0.5262],
- [ nan, nan, 0.7240, 0.2722, 0.3900, 0.2567, 0.5168, 0.5933]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6185, -0.3156, 1.8265, -0.3126, -0.4726, 0.0292, 0.3994, 0.2266],
- [ 0.5336, -0.3887, 0.9285, -1.1986, -0.5366, -0.9300, 0.0476, 0.2809],
- [ 0.6409, -0.3765, 1.4790, 0.1583, -0.4156, -0.0975, 0.8417, 0.2170],
- [ 0.4891, -0.4365, 1.8433, -0.1204, -0.5106, -0.2830, 0.4545, 0.1341],
- [ 0.7050, -0.3099, 1.9224, -0.4509, -0.2300, -0.5580, 0.9066, 0.3912],
- [ 0.6713, -0.3561, 1.7889, 0.0174, -0.5189, -0.1541, 0.5659, 0.0518],
- [ 0.6875, -0.3242, 1.5716, 0.2427, -0.3745, 0.0493, 0.6705, 0.1036],
- [-1.9622, -2.0539, 1.0842, -1.0704, -0.5110, -1.1750, -0.0437, 0.3613]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6003, -0.3747, 1.8885, -0.1997, -0.5018, -0.0149, 0.5490,
- 0.1775],
- [ 0.5049, -0.4428, 0.8692, -0.9581, -0.6693, -0.8386, 0.0897,
- 0.2589],
- [ 0.6273, -0.4177, 1.4208, 0.4085, -0.5423, -0.0380, 0.8973,
- 0.2356],
- [ 0.6096, -0.4046, 1.8249, -0.0072, -0.6115, -0.3537, 0.6182,
- 0.0928],
- [ 0.6471, -0.3719, 1.9866, -0.3921, -0.3806, -0.5538, 1.0070,
- 0.4600],
- [ 0.6240, -0.4321, 1.8423, 0.1852, -0.5885, -0.1612, 0.6962,
- 0.0111],
- [ 0.6569, -0.3960, 1.5651, 0.4162, -0.4614, 0.0774, 0.7438,
- 0.1447],
- [-2.2859, -2.2859, 1.0580, -1.0288, -0.4845, -1.1004, 0.1011,
- 0.4543]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0104, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0104, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.20368763711303473
- step: 14
- running loss: 0.014549116936645337
- Train Steps: 14/90 Loss: 0.0145 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6109, 0.4009, 0.8725, 0.4700, 0.3550, 0.4167, 0.5650, 0.5117],
- [0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5511, -0.4068, 1.7669, -0.1467, -0.6737, -0.4046, 0.2705, 0.1262],
- [ 0.6452, -0.3602, 1.3157, -1.0661, -0.3160, -1.2657, 0.7119, 0.1955],
- [ 0.4112, -0.5618, 1.8742, -0.2422, -0.5652, 0.0540, 1.0783, 0.2086],
- [ 0.6926, -0.3282, 1.0094, -1.2379, -0.4402, -1.1454, 0.4859, 0.2555],
- [ 0.5699, -0.3966, 1.7770, -0.0380, -0.3195, 0.3987, 0.6858, 0.2068],
- [ 0.6017, -0.3589, 1.3299, -1.0885, -0.2723, -1.3584, 0.3951, 0.2467],
- [ 0.4981, -0.4629, 1.3449, -0.8999, -0.6521, -0.7340, 0.4934, 0.2876],
- [ 0.5636, -0.4064, 1.6784, 0.0822, -0.1191, 0.0233, 0.1590, 0.1523]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5356, -0.4343, 1.7441, -0.1150, -0.6462, -0.3614, 0.3238,
- 0.0774],
- [ 0.6158, -0.3960, 1.4092, -1.2774, -0.2074, -1.1851, 0.8491,
- 0.1917],
- [ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928],
- [ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850],
- [ 0.5697, -0.4442, 1.3529, -0.9515, -0.5774, -0.7801, 0.5253,
- 0.1931],
- [ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0061, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0061, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.20982471108436584
- step: 15
- running loss: 0.013988314072291057
- Train Steps: 15/90 Loss: 0.0140 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6207, 0.4110, 0.8738, 0.5000, 0.4800, 0.5633, 0.6300, 0.5433],
- [0.6261, 0.4131, 0.8938, 0.3550, 0.4000, 0.2683, 0.6188, 0.5283],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6314, 0.4050, 0.8227, 0.5431, 0.4150, 0.5517, 0.7121, 0.5690],
- [0.6092, 0.4001, 0.8638, 0.4867, 0.4288, 0.5367, 0.5484, 0.5064],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6161, 0.4055, 0.8675, 0.3867, 0.3713, 0.4033, 0.5195, 0.5162]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6932, -0.3002, 0.9223, -1.2076, -0.4446, -1.2456, 0.4461, 0.3540],
- [ 0.7345, -0.2983, 1.7527, -0.1153, -0.1353, 0.1659, 0.6014, 0.3645],
- [ 0.7010, -0.3145, 1.8317, -0.7354, -0.5536, -1.0791, 0.7113, 0.1254],
- [ 0.5710, -0.4561, 1.6580, 0.2129, -0.4870, -0.0747, 0.8136, 0.0383],
- [ 0.5815, -0.4396, 1.5620, 0.1961, -0.4041, 0.2234, 1.1959, 0.3494],
- [ 0.4783, -0.4605, 1.6568, -0.0696, -0.3142, 0.1620, 0.2832, 0.1092],
- [ 0.4819, -0.4050, 1.6976, -0.2870, -0.5468, -0.2685, 0.1350, 0.3684],
- [ 0.4149, -0.4674, 1.7142, -0.4944, -0.6656, -0.4889, 0.2146, 0.0351]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.5811, -0.3878, 1.7499, 0.0236, -0.0688, 0.3161, 0.6240,
- 0.2237],
- [ 0.6062, -0.3778, 1.8423, -0.6462, -0.4383, -1.0465, 0.5721,
- 0.1544],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.6306, -0.4153, 1.5141, 0.2224, -0.3691, 0.2622, 1.0033,
- 0.3425],
- [ 0.5278, -0.4377, 1.7037, -0.0380, -0.3055, 0.1929, 0.2473,
- 0.0532],
- [ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145],
- [ 0.5598, -0.4129, 1.7210, -0.4999, -0.5711, -0.4229, 0.1136,
- 0.0983]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0061, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0061, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.21596113871783018
- step: 16
- running loss: 0.013497571169864386
- Train Steps: 16/90 Loss: 0.0135 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8888, 0.3100, 0.5262, 0.2817, 0.7145, 0.6003],
- [0.6204, 0.4091, 0.8950, 0.4783, 0.3613, 0.4617, 0.6063, 0.5617],
- [0.6245, 0.4115, 0.8700, 0.4883, 0.4625, 0.5517, 0.6100, 0.5217],
- [0.6115, 0.4005, 0.8838, 0.3867, 0.3763, 0.4700, 0.5800, 0.5550],
- [0.6336, 0.4191, 0.8938, 0.5167, 0.3937, 0.3517, 0.7343, 0.5748],
- [0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301],
- [0.6274, 0.4003, 0.8638, 0.5967, 0.3688, 0.4900, 0.6108, 0.4661],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.4391e+00, -1.7195e+00, 1.6965e+00, -8.3241e-01, -7.4165e-03,
- -1.2388e+00, 9.2158e-01, 4.8889e-01],
- [ 6.7004e-01, -2.8881e-01, 1.7075e+00, -1.5896e-01, -6.4548e-01,
- -2.0172e-01, 4.7335e-01, 2.4208e-01],
- [ 5.9918e-01, -3.3162e-01, 1.6278e+00, -2.1823e-01, -2.4393e-01,
- 1.9002e-01, 4.4332e-01, 2.2739e-01],
- [ 6.2093e-01, -3.1903e-01, 1.6007e+00, -6.1591e-01, -6.5189e-01,
- -1.0654e-01, 3.2741e-01, 1.7108e-01],
- [ 6.0972e-01, -3.6531e-01, 1.7034e+00, 2.6776e-03, -4.8217e-01,
- -6.0080e-01, 8.8999e-01, 2.6616e-01],
- [ 5.8475e-01, -4.0440e-01, 1.6300e+00, -1.0673e-01, -3.9924e-01,
- -6.8137e-02, 7.1257e-01, 1.2286e-01],
- [ 7.6503e-01, -2.8282e-01, 1.5378e+00, 3.3768e-01, -5.7958e-01,
- -9.7698e-02, 6.3861e-01, 9.1715e-04],
- [ 5.7975e-01, -3.8051e-01, 1.5980e+00, 9.9984e-03, -2.3316e-01,
- 1.7962e-01, 4.1097e-01, 9.1926e-02]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.8192, -0.8540, 0.1448, -0.9849, 1.0143,
- 0.4867],
- [ 0.5797, -0.3965, 1.8480, -0.0765, -0.6173, -0.1535, 0.5143,
- 0.3084],
- [ 0.5987, -0.3852, 1.7326, -0.0303, -0.1497, 0.2622, 0.5316,
- 0.1236],
- [ 0.5384, -0.4361, 1.7961, -0.4999, -0.5480, -0.1150, 0.3931,
- 0.2776],
- [ 0.6405, -0.3503, 1.8423, 0.1005, -0.4672, -0.6616, 1.1057,
- 0.3692],
- [ 0.6010, -0.4562, 1.7198, -0.0090, -0.3464, 0.0108, 0.6294,
- 0.1627],
- [ 0.6120, -0.4371, 1.7037, 0.4701, -0.5827, -0.0226, 0.5354,
- -0.1331],
- [ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0266, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0266, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.24258825462311506
- step: 17
- running loss: 0.014269897330771475
- Train Steps: 17/90 Loss: 0.0143 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6204, 0.4091, 0.8950, 0.4783, 0.3613, 0.4617, 0.6063, 0.5617],
- [ nan, nan, 0.9088, 0.3783, 0.4562, 0.2617, 0.6741, 0.5575],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [0.6246, 0.4008, 0.8757, 0.5088, 0.4101, 0.5392, 0.6644, 0.5133],
- [0.6198, 0.3997, 0.8582, 0.5361, 0.4117, 0.5016, 0.5942, 0.5134],
- [0.6090, 0.4045, 0.7250, 0.2100, 0.4075, 0.2300, 0.5476, 0.5663],
- [0.6236, 0.4081, 0.8575, 0.3000, 0.3713, 0.3033, 0.5550, 0.5633]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7508, -0.2604, 1.7657, -0.0488, -0.5387, -0.0755, 0.5828, 0.2377],
- [-1.1455, -1.5244, 1.9091, -0.5219, -0.2446, -1.0815, 0.7982, 0.3013],
- [ 0.7796, -0.2696, 1.9089, -0.1851, -0.3437, -0.8657, 1.0811, 0.2547],
- [ 0.5238, -0.3978, 1.2088, -0.5805, -0.6452, -0.2787, 0.2464, 0.2178],
- [ 0.7354, -0.3332, 1.7281, 0.0586, -0.3375, 0.3449, 0.9514, 0.0944],
- [ 0.6988, -0.3625, 1.6217, 0.1694, -0.3268, 0.1545, 0.4640, 0.0219],
- [ 0.4548, -0.4465, 0.9776, -1.3111, -0.4202, -1.1218, 0.3172, 0.2431],
- [ 0.5979, -0.3529, 1.5608, -0.8263, -0.5420, -0.7747, 0.3924, 0.1048]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5797, -0.3965, 1.8480, -0.0765, -0.6173, -0.1535, 0.5143,
- 0.3084],
- [-2.2859, -2.2859, 1.9115, -0.5384, -0.1785, -1.0773, 0.8278,
- 0.2890],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386],
- [ 0.5993, -0.4345, 1.7587, 0.0641, -0.3917, 0.2048, 0.7827,
- 0.0852],
- [ 0.5767, -0.4396, 1.6782, 0.1905, -0.3844, 0.0308, 0.4588,
- 0.0855],
- [ 0.5269, -0.4176, 1.0628, -1.3159, -0.4037, -1.2236, 0.2432,
- 0.3297],
- [ 0.5944, -0.4008, 1.6748, -0.9002, -0.5711, -0.8848, 0.2776,
- 0.3161]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0362, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0362, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2787413029000163
- step: 18
- running loss: 0.015485627938889794
- Train Steps: 18/90 Loss: 0.0155 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6087, 0.3976, 0.8337, 0.3867, 0.3713, 0.3117, 0.5938, 0.5300],
- [0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6308, 0.3990, 0.8688, 0.5183, 0.3950, 0.4983, 0.6388, 0.5133],
- [0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505],
- [0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528],
- [0.6250, 0.4110, 0.7238, 0.2067, 0.4263, 0.1883, 0.5625, 0.5633],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6189, 0.4033, 0.8650, 0.5267, 0.4487, 0.5150, 0.5925, 0.5050]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5391, -0.4250, 1.5793, -0.5507, -0.5772, -0.7345, 0.5866, 0.2346],
- [ 0.6453, -0.3269, 1.6569, -0.9600, -0.3731, -0.8979, 0.7733, 0.1688],
- [ 0.6124, -0.4489, 1.7579, 0.1917, -0.4467, 0.1514, 0.9555, 0.0758],
- [ 0.4832, -0.4268, 1.5981, -0.4180, -0.6525, -0.4576, 0.0383, 0.2399],
- [ 0.6428, -0.4099, 1.9707, -0.5950, -0.3578, -0.4111, 1.2218, 0.2484],
- [ 0.4976, -0.4160, 0.9908, -1.2584, -0.3256, -1.3835, 0.4925, 0.2604],
- [ 0.6152, -0.4093, 1.6455, 0.1106, -0.3446, 0.0551, 0.3082, 0.1433],
- [ 0.5605, -0.4519, 1.6999, 0.1790, -0.2418, 0.2339, 0.5199, 0.1525]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5255, -0.4495, 1.5651, -0.4999, -0.5711, -0.8463, 0.4566,
- 0.1621],
- [ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [ 0.6275, -0.4430, 1.7268, 0.1082, -0.4614, 0.0159, 0.6644,
- 0.0851],
- [ 0.5433, -0.4032, 1.6344, -0.4922, -0.5769, -0.5846, 0.0357,
- 0.2567],
- [ 0.6388, -0.3623, 1.9173, -0.7386, -0.3517, -0.5846, 1.1495,
- 0.2676],
- [ 0.6010, -0.3875, 1.0570, -1.3313, -0.3171, -1.4160, 0.3122,
- 0.3161],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854],
- [ 0.5726, -0.4230, 1.7095, 0.1467, -0.2132, 0.0928, 0.4508,
- 0.0467]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0069, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0069, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2856500316411257
- step: 19
- running loss: 0.015034212191638193
- Train Steps: 19/90 Loss: 0.0150 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [0.6175, 0.4013, 0.7734, 0.2783, 0.4475, 0.1786, 0.5790, 0.5351],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519],
- [0.6040, 0.4002, 0.7338, 0.2267, 0.3975, 0.2100, 0.5231, 0.4778],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [0.6118, 0.4052, 0.8463, 0.3917, 0.3538, 0.3450, 0.5053, 0.5593],
- [0.6215, 0.4119, 0.7688, 0.2300, 0.4200, 0.2283, 0.5925, 0.5317]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6642, -0.3373, 1.6242, -0.4397, -0.5543, -0.7999, 0.5567, 0.2802],
- [ 0.3577, -0.5237, 1.7865, 0.0304, -0.3552, -0.8476, 0.5982, 0.5128],
- [ 0.7060, -0.3369, 1.3578, -1.1410, -0.2806, -1.2913, 0.6984, 0.0911],
- [ 0.4874, -0.5204, 1.7267, 0.1068, -0.1824, 0.2917, 0.4077, 0.1674],
- [ 0.6098, -0.4015, 1.1021, -1.3660, -0.4870, -1.1326, 0.5583, -0.0315],
- [ 0.5276, -0.4831, 1.8539, -0.0930, -0.3060, 0.1485, 0.5581, 0.2284],
- [ 0.2133, -0.6280, 1.6494, -0.4383, -0.6503, -0.4903, 0.2719, 0.2101],
- [ 0.6117, -0.4047, 1.2955, -1.1935, -0.3856, -0.9915, 0.8060, 0.1934]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762],
- [ 0.5664, -0.4321, 1.2862, -1.0003, -0.2189, -1.4608, 0.3883,
- 0.1855],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634],
- [ 0.5037, -0.4375, 1.1032, -1.2390, -0.4499, -1.3159, 0.1301,
- -0.0791],
- [ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057],
- [ 0.5399, -0.4142, 1.6229, -0.4768, -0.6520, -0.6924, 0.0481,
- 0.2972],
- [ 0.5849, -0.3836, 1.2649, -1.2236, -0.3460, -1.2313, 0.4508,
- 0.1698]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0265, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0265, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.31217335909605026
- step: 20
- running loss: 0.015608667954802513
- Train Steps: 20/90 Loss: 0.0156 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6227, 0.4083, 0.8938, 0.4800, 0.3800, 0.2950, 0.5737, 0.5350],
- [ nan, nan, 0.7240, 0.2722, 0.3900, 0.2567, 0.5168, 0.5933],
- [ nan, nan, 0.7525, 0.2291, 0.3838, 0.3017, 0.6050, 0.5667],
- [0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8436, -0.2661, 1.7293, 0.3506, -0.4195, -0.0204, 0.8849, -0.0659],
- [ 0.7133, -0.2538, 1.7154, -0.3090, -0.4786, -0.2038, 0.2791, 0.3428],
- [ 0.8705, -0.2223, 1.6207, -0.7515, -0.6418, -0.4919, 0.7847, 0.1622],
- [ 0.7293, -0.2986, 1.6384, -0.3508, -0.5085, -0.0364, 0.4600, 0.0077],
- [ 0.8734, -0.1393, 1.8251, -0.0868, -0.4825, -0.8209, 0.6283, 0.1497],
- [-2.1821, -2.1859, 1.0531, -1.0705, -0.4182, -1.0759, 0.1864, 0.3801],
- [-1.7686, -1.9233, 1.2301, -1.1572, -0.3819, -0.9255, 0.5486, 0.3583],
- [ 0.7109, -0.2645, 1.6211, 0.3448, -0.0315, -0.1749, 0.4552, 0.3760]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.5901, -0.4000, 1.8423, -0.0688, -0.5307, -0.9233, 0.3642,
- 0.1852],
- [-2.2859, -2.2859, 1.0580, -1.0288, -0.4845, -1.1004, 0.1011,
- 0.4543],
- [-2.2859, -2.2859, 1.1898, -1.2278, -0.5134, -0.8925, 0.5085,
- 0.3315],
- [ 0.5959, -0.3579, 1.6055, 0.3623, -0.0573, -0.2074, 0.3122,
- 0.4547]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0208, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0208, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.33298695273697376
- step: 21
- running loss: 0.015856521558903512
- Train Steps: 21/90 Loss: 0.0159 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6265, 0.4251, 0.7113, 0.3550, 0.4375, 0.2117, 0.5587, 0.6118],
- [0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683],
- [0.6131, 0.4064, 0.8638, 0.5200, 0.4788, 0.4783, 0.5258, 0.5867],
- [0.6058, 0.3986, 0.8324, 0.4626, 0.3838, 0.4983, 0.5147, 0.5466],
- [0.6165, 0.4106, 0.7575, 0.1733, 0.3838, 0.2650, 0.5680, 0.5116],
- [ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933],
- [0.6275, 0.4024, 0.8600, 0.2283, 0.5350, 0.1800, 0.7074, 0.5413],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5701, -0.3610, 1.1925, -0.5998, -0.3476, -1.1460, 0.3494, 0.4502],
- [ 0.5325, -0.4449, 1.5287, -0.8693, -0.5007, -0.7125, 0.5934, 0.2793],
- [ 0.4868, -0.4511, 1.6993, 0.1740, -0.1953, 0.0165, 0.2025, 0.3025],
- [ 0.3532, -0.5528, 1.6190, -0.0188, -0.5111, 0.0868, 0.3957, 0.1781],
- [ 0.5768, -0.4132, 1.2803, -1.2153, -0.5702, -1.0083, 0.5767, -0.0181],
- [-2.8392, -2.6520, 0.9217, -1.1096, -0.4011, -1.2152, 0.2435, 0.3523],
- [ 0.8260, -0.2496, 1.7667, -1.0595, 0.0952, -1.3712, 1.0684, 0.1354],
- [ 0.4822, -0.4867, 1.8582, 0.0209, -0.5483, 0.1714, 0.6321, 0.0669]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6077, -0.3226, 0.9993, -0.6462, -0.2651, -1.3082, 0.2946,
- 0.5401],
- [ 0.5778, -0.3930, 1.5189, -0.9387, -0.4326, -0.9156, 0.4855,
- 0.3392],
- [ 0.5462, -0.4090, 1.7037, 0.1159, -0.0746, -0.0765, 0.1427,
- 0.4239],
- [ 0.5124, -0.4446, 1.5587, -0.1493, -0.5134, 0.0159, 0.0912,
- 0.2386],
- [ 0.5618, -0.3895, 1.2129, -1.4853, -0.5134, -1.0619, 0.3378,
- 0.0772],
- [-2.2859, -2.2859, 0.8033, -1.1250, -0.3864, -1.3082, 0.1126,
- 0.4543],
- [ 0.6125, -0.4273, 1.6864, -1.2313, 0.1852, -1.4545, 0.9814,
- 0.2142],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0193, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0193, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3522569965571165
- step: 22
- running loss: 0.016011681661687115
- Train Steps: 22/90 Loss: 0.0160 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6211, 0.3993, 0.7650, 0.1933, 0.4575, 0.1550, 0.5965, 0.4895],
- [ nan, nan, 0.7625, 0.2433, 0.3713, 0.2867, 0.5235, 0.5220],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6226, 0.4125, 0.8800, 0.4900, 0.4512, 0.5600, 0.6275, 0.5517],
- [0.6239, 0.4123, 0.8313, 0.2550, 0.4500, 0.2050, 0.6175, 0.5400],
- [0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6197, 0.4118, 0.8688, 0.5517, 0.4037, 0.5233, 0.5875, 0.5600],
- [0.6277, 0.4029, 0.8250, 0.2433, 0.4325, 0.2100, 0.6366, 0.5207]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4190, -0.5163, 1.2306, -1.3445, -0.2406, -1.4644, 0.4430, 0.0780],
- [-2.6366, -2.5332, 1.1538, -1.0783, -0.4702, -1.0118, 0.2247, 0.2517],
- [ 0.5058, -0.4009, 1.6188, 0.4193, -0.4515, -0.2694, 0.3354, 0.4193],
- [ 0.4812, -0.4535, 1.7116, -0.0511, -0.2676, 0.2715, 0.4280, 0.3389],
- [ 0.3823, -0.4933, 1.4640, -1.0505, -0.2502, -1.2271, 0.5584, 0.2454],
- [ 0.5660, -0.4228, 1.7460, -0.1628, -0.4159, 0.1009, 0.4308, 0.1760],
- [ 0.6304, -0.3654, 1.6921, 0.3137, -0.4992, 0.1335, 0.4860, 0.2379],
- [ 0.4978, -0.4602, 1.4664, -1.0301, -0.3268, -1.2302, 0.7269, 0.1300]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5828, -0.4417, 1.2476, -1.3929, -0.1727, -1.5700, 0.4694,
- -0.0248],
- [-2.2859, -2.2859, 1.2360, -1.1620, -0.5711, -0.9618, 0.1322,
- 0.1253],
- [ 0.5836, -0.3649, 1.7210, 0.3854, -0.3979, -0.2921, 0.3065,
- 0.4470],
- [ 0.5897, -0.3804, 1.7788, -0.0226, -0.2016, 0.3007, 0.6125,
- 0.2622],
- [ 0.5958, -0.3818, 1.5536, -1.1081, -0.2074, -1.3390, 0.5663,
- 0.2083],
- [ 0.5604, -0.4620, 1.7961, -0.1997, -0.3229, 0.1082, 0.4104,
- 0.2545],
- [ 0.5762, -0.3840, 1.7268, 0.2622, -0.4210, 0.1313, 0.4277,
- 0.3007],
- [ 0.6132, -0.4248, 1.5247, -1.1620, -0.2882, -1.3159, 0.6545,
- 0.1193]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0088, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0088, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.36108022555708885
- step: 23
- running loss: 0.01569914024161256
- Train Steps: 23/90 Loss: 0.0157 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717],
- [0.6176, 0.4017, 0.8788, 0.5100, 0.4075, 0.5250, 0.5913, 0.5033],
- [0.6260, 0.4120, 0.8013, 0.2350, 0.4888, 0.1533, 0.6281, 0.4895],
- [0.6109, 0.4015, 0.7668, 0.3639, 0.3513, 0.3667, 0.5200, 0.5641],
- [0.6203, 0.4072, 0.8892, 0.3523, 0.3783, 0.3017, 0.5898, 0.5478],
- [0.6223, 0.3990, 0.8037, 0.2800, 0.4000, 0.2283, 0.5864, 0.5208]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.3397e-01, -4.4429e-01, 1.6449e+00, 3.7041e-01, -4.7376e-01,
- -5.6033e-01, 1.8569e-01, 5.2465e-01],
- [ 4.3509e-01, -5.2979e-01, 1.8832e+00, -7.4444e-01, -4.3511e-01,
- -6.0733e-01, 6.5849e-01, 1.9896e-01],
- [ 6.8811e-01, -3.3548e-01, 9.0652e-01, -1.1236e+00, -4.8432e-01,
- -1.0859e+00, 2.8385e-01, 3.4144e-01],
- [ 2.5779e-01, -7.2311e-01, 1.8132e+00, 1.1183e-01, -3.2180e-01,
- 2.0947e-01, 4.4570e-01, 1.2317e-01],
- [ 1.6847e-01, -7.1877e-01, 1.4626e+00, -1.2881e+00, 1.9532e-02,
- -1.5018e+00, 6.9486e-01, 1.8900e-01],
- [ 5.0582e-01, -4.5344e-01, 1.3273e+00, -6.3100e-01, -6.1505e-01,
- -5.4265e-01, 1.8403e-01, 2.8166e-01],
- [-1.5590e-03, -8.3832e-01, 1.8570e+00, -6.9928e-01, -4.9101e-01,
- -7.3952e-01, 5.2433e-01, 3.0981e-01],
- [ 6.4362e-01, -3.8651e-01, 1.5309e+00, -1.0483e+00, -3.1909e-01,
- -1.2067e+00, 4.5029e-01, 1.2443e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778],
- [ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390],
- [ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546],
- [ 0.5668, -0.4306, 1.7730, 0.0697, -0.4037, 0.1390, 0.4450,
- 0.0390],
- [ 0.6053, -0.3830, 1.4150, -1.2005, -0.0284, -1.5777, 0.6154,
- -0.0250],
- [ 0.5357, -0.4315, 1.2557, -0.6051, -0.6635, -0.5923, 0.1159,
- 0.3198],
- [ 0.5792, -0.4052, 1.8214, -0.6587, -0.5384, -0.8924, 0.4381,
- 0.2442],
- [ 0.5885, -0.4429, 1.4266, -0.9926, -0.4383, -1.2313, 0.4228,
- 0.1195]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0212, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0212, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.38229384645819664
- step: 24
- running loss: 0.015928910269091528
- Train Steps: 24/90 Loss: 0.0159 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6175, 0.4013, 0.8900, 0.4500, 0.4375, 0.5850, 0.6175, 0.5383],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433],
- [0.6336, 0.4086, 0.8900, 0.3950, 0.3900, 0.2950, 0.6504, 0.5066],
- [0.6125, 0.3983, 0.8750, 0.4867, 0.4275, 0.4783, 0.5413, 0.5717]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5587, -0.4226, 1.6160, -0.5043, -0.5054, -1.1615, 0.1804, 0.3907],
- [ 0.5253, -0.4484, 1.8616, 0.1801, -0.5132, -0.3568, 0.1869, 0.3852],
- [ 0.4857, -0.4937, 1.0900, -1.4514, -0.3567, -1.3147, 0.4973, 0.2626],
- [ 0.3560, -0.5321, 1.2411, -0.6366, -0.6568, -0.6283, 0.1718, 0.4453],
- [ 0.4202, -0.5697, 1.8400, -0.1959, -0.2509, 0.3437, 0.5042, 0.1765],
- [ 0.6044, -0.4654, 1.8214, -0.0966, -0.1333, 0.1111, 0.4976, 0.2188],
- [ 0.5153, -0.4811, 1.9231, -0.4981, -0.4574, -1.0707, 0.7030, 0.0785],
- [ 0.4016, -0.6046, 1.7845, -0.0465, -0.3021, -0.1544, 0.2956, 0.2958]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.5664, -0.4321, 1.8249, -0.2074, -0.2651, 0.4162, 0.5663,
- 0.2006],
- [ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237],
- [ 0.6405, -0.3984, 1.8249, -0.4614, -0.4845, -0.9233, 0.7182,
- 0.0539],
- [ 0.5432, -0.4462, 1.7557, -0.0380, -0.3113, -0.0765, 0.2141,
- 0.3546]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0074, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0074, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.38968273205682635
- step: 25
- running loss: 0.015587309282273054
- Train Steps: 25/90 Loss: 0.0156 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6139, 0.4019, 0.7137, 0.2150, 0.4375, 0.1533, 0.5293, 0.5006],
- [0.6357, 0.4118, 0.8400, 0.2500, 0.5413, 0.1633, 0.6725, 0.5586],
- [0.6193, 0.4108, 0.7425, 0.2350, 0.3887, 0.2750, 0.5900, 0.5717],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250],
- [0.6260, 0.4133, 0.8800, 0.5117, 0.3713, 0.3283, 0.6223, 0.4967],
- [0.6263, 0.4030, 0.9000, 0.4767, 0.3800, 0.5167, 0.6415, 0.4771],
- [0.6275, 0.4024, 0.7722, 0.2080, 0.4392, 0.2234, 0.6435, 0.5290]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.1355, -0.7047, 1.0245, -1.2254, -0.2885, -1.5247, 0.0463, 0.1693],
- [ 0.5920, -0.4166, 1.6155, -1.0675, 0.1942, -1.4872, 0.6072, 0.3212],
- [ 0.5402, -0.4191, 1.1201, -1.1080, -0.5136, -0.9845, 0.2833, 0.4162],
- [ 0.3849, -0.5270, 1.8260, -0.2820, -0.6391, -0.4763, 0.2228, 0.3612],
- [ 0.4151, -0.5447, 1.8440, 0.0276, -0.4044, 0.1932, 0.3751, 0.2620],
- [ 0.3779, -0.5567, 1.8440, 0.0828, -0.5188, -0.7754, 0.3708, 0.2907],
- [ 0.4404, -0.5536, 1.8745, -0.0533, -0.5073, 0.0109, 0.4361, 0.1189],
- [ 0.6545, -0.3770, 1.2548, -1.1872, -0.2788, -1.2400, 0.5030, 0.2161]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5496, -0.4295, 1.0109, -1.2928, -0.2651, -1.5777, 0.1589,
- 0.0261],
- [ 0.6504, -0.3840, 1.5940, -1.1312, 0.2141, -1.5315, 0.8205,
- 0.2944],
- [ 0.5746, -0.3882, 1.1436, -1.2005, -0.4903, -1.0157, 0.4393,
- 0.3546],
- [ 0.5880, -0.3887, 1.8423, -0.3306, -0.6231, -0.5230, 0.4046,
- 0.1544],
- [ 0.5991, -0.3803, 1.8018, -0.0534, -0.3460, 0.1852, 0.5374,
- 0.1390],
- [ 0.6054, -0.3767, 1.7788, 0.0774, -0.5711, -0.7694, 0.5887,
- 0.0081],
- [ 0.6072, -0.4247, 1.8711, -0.0842, -0.5307, 0.1005, 0.6771,
- -0.0821],
- [ 0.6125, -0.4273, 1.2807, -1.3253, -0.2574, -1.2542, 0.6864,
- 0.1575]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0168, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0168, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4064830797724426
- step: 26
- running loss: 0.015633964606632408
- Train Steps: 26/90 Loss: 0.0156 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6273, 0.4105, 0.8988, 0.4517, 0.3912, 0.2550, 0.5894, 0.4811],
- [0.6069, 0.3975, 0.8625, 0.5083, 0.4388, 0.5483, 0.5650, 0.4967],
- [0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [0.6265, 0.4091, 0.8950, 0.3533, 0.3600, 0.3967, 0.6295, 0.4901],
- [0.6193, 0.3930, 0.8949, 0.4437, 0.3852, 0.5435, 0.6263, 0.5263],
- [0.6164, 0.4119, 0.7913, 0.2650, 0.3538, 0.3500, 0.5614, 0.5038],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 4.7468e-01, -4.2405e-01, 1.7528e+00, -2.6740e-01, -3.7613e-01,
- -1.1936e+00, 2.4123e-01, 2.4698e-01],
- [ 7.2586e-01, -3.2563e-01, 1.5593e+00, 8.7796e-02, -1.8891e-01,
- 8.3703e-02, 2.8491e-02, 2.2675e-01],
- [ 5.2334e-01, -4.4256e-01, 1.7059e+00, -2.4735e-01, -4.9585e-01,
- -5.1626e-01, 2.3367e-01, 1.9561e-01],
- [ 6.6523e-01, -3.2866e-01, 1.7513e+00, -6.5725e-01, -5.1157e-01,
- -6.0597e-01, 5.1856e-01, 2.0810e-01],
- [ 7.9341e-01, -2.9291e-01, 1.7165e+00, -1.9395e-01, -4.3940e-01,
- -2.4226e-03, 4.1576e-01, 2.1084e-01],
- [ 4.6099e-01, -4.5544e-01, 1.2891e+00, -1.0755e+00, -5.3392e-01,
- -7.8686e-01, 2.7304e-01, 1.8630e-01],
- [ 3.8093e-01, -5.4834e-01, 1.8468e+00, -3.5344e-01, -2.1268e-01,
- -1.0234e+00, 6.6850e-01, 4.1560e-01],
- [-2.5304e+00, -2.3983e+00, 9.7836e-01, -1.2369e+00, -2.7478e-01,
- -1.2620e+00, 2.4886e-01, 3.5089e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6116, -0.3898, 1.8654, -0.1997, -0.4788, -1.1081, 0.4367,
- -0.0637],
- [ 0.5174, -0.4497, 1.6979, 0.0620, -0.2594, 0.2468, 0.3238,
- 0.0082],
- [ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [ 0.6077, -0.3965, 1.8480, -0.6539, -0.6231, -0.4537, 0.6216,
- -0.0220],
- [ 0.5746, -0.4706, 1.8476, -0.2365, -0.5068, 0.2245, 0.6069,
- 0.1449],
- [ 0.5614, -0.3834, 1.3688, -1.0619, -0.6520, -0.6693, 0.3069,
- 0.0412],
- [ 0.6224, -0.4345, 1.9404, -0.2921, -0.3171, -0.8771, 1.0655,
- 0.2142],
- [-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0215, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0215, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.427944693248719
- step: 27
- running loss: 0.015849803453656258
- Train Steps: 27/90 Loss: 0.0158 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6197, 0.4118, 0.8688, 0.5517, 0.4037, 0.5233, 0.5875, 0.5600],
- [0.6224, 0.3964, 0.8225, 0.5717, 0.4150, 0.4617, 0.5775, 0.5267],
- [0.6128, 0.4118, 0.8638, 0.5333, 0.4625, 0.5267, 0.5193, 0.5475],
- [0.6258, 0.4038, 0.8750, 0.4883, 0.3900, 0.5500, 0.6375, 0.5217],
- [0.6214, 0.4112, 0.7838, 0.2117, 0.3650, 0.3133, 0.5675, 0.5083],
- [0.6339, 0.4102, 0.9088, 0.4767, 0.3925, 0.5283, 0.7509, 0.5390],
- [0.6282, 0.4029, 0.8988, 0.3000, 0.4250, 0.4183, 0.7042, 0.5575]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4570, -0.5229, 1.7586, -0.0554, -0.5715, -0.5387, 0.2527, 0.4641],
- [ 0.6345, -0.3882, 1.7560, 0.2261, -0.4069, -0.0963, 0.2293, 0.2826],
- [ 0.6074, -0.4412, 1.6149, 0.2421, -0.3680, -0.4795, 0.1157, 0.2424],
- [ 0.6530, -0.3447, 1.7055, 0.0860, -0.1344, -0.2192, -0.1011, 0.2670],
- [ 0.4935, -0.5104, 1.8558, -0.0979, -0.4405, -0.0116, 0.3747, 0.1274],
- [ 0.2789, -0.6232, 1.3619, -1.4057, -0.5798, -1.1723, 0.3074, 0.1082],
- [ 0.5152, -0.4956, 1.8940, -0.1752, -0.4296, -0.0322, 0.8230, 0.2762],
- [ 0.9435, -0.1969, 1.7871, -0.7499, -0.3294, -0.5972, 0.8296, 0.2128]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5762, -0.3840, 1.7268, 0.2622, -0.4210, 0.1313, 0.4277,
- 0.3007],
- [ 0.5891, -0.4550, 1.5132, 0.3546, -0.3691, -0.1535, 0.3815,
- 0.1467],
- [ 0.5447, -0.3838, 1.7037, 0.1775, -0.1497, 0.1467, 0.1128,
- 0.2431],
- [ 0.6047, -0.4209, 1.7557, -0.0303, -0.4845, 0.2545, 0.6587,
- 0.1236],
- [ 0.5845, -0.3864, 1.3342, -1.3082, -0.6000, -0.8386, 0.3353,
- 0.0620],
- [ 0.6421, -0.3912, 1.9115, -0.0842, -0.4730, 0.1544, 1.1824,
- 0.2035],
- [ 0.6158, -0.4249, 1.8654, -0.9002, -0.3229, -0.3537, 0.9667,
- 0.2890]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0249, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0249, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4528018026612699
- step: 28
- running loss: 0.01617149295218821
- Train Steps: 28/90 Loss: 0.0162 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6164, 0.4076, 0.8838, 0.4117, 0.3713, 0.5550, 0.6238, 0.5350],
- [ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552],
- [0.6239, 0.4123, 0.8313, 0.2550, 0.4500, 0.2050, 0.6175, 0.5400],
- [0.6339, 0.4102, 0.9088, 0.4767, 0.3925, 0.5283, 0.7509, 0.5390],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6100, 0.4016, 0.8600, 0.5067, 0.4612, 0.5233, 0.5086, 0.5519],
- [0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7551, -0.2866, 1.7308, -0.2989, -0.4932, 0.0477, 0.4730, 0.0802],
- [-2.5839, -2.4586, 1.5701, -1.1338, 0.0178, -1.2622, 0.7895, 0.2292],
- [ 0.4940, -0.4422, 1.4511, -1.0838, -0.1897, -1.4387, 0.3430, 0.1239],
- [ 0.6857, -0.3570, 1.8496, -0.0496, -0.4596, 0.0302, 0.8218, 0.1607],
- [ 0.3040, -0.5759, 1.3551, -0.7724, -0.6065, -0.6449, 0.2698, 0.4399],
- [ 0.7371, -0.2424, 1.7519, 0.0958, -0.6039, -0.7225, 0.1211, 0.2988],
- [ 0.6786, -0.3373, 1.6232, 0.1325, -0.1826, -0.1288, -0.1010, 0.1699],
- [ 0.4422, -0.4763, 1.2160, -1.1084, -0.4471, -1.0021, 0.4260, 0.1637]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5614, -0.4032, 1.7961, -0.3844, -0.5711, 0.2776, 0.5952,
- 0.1852],
- [-2.2859, -2.2859, 1.7557, -1.1466, 0.0871, -1.0773, 1.1239,
- 0.2783],
- [ 0.5958, -0.3818, 1.5536, -1.1081, -0.2074, -1.3390, 0.5663,
- 0.2083],
- [ 0.6421, -0.3912, 1.9115, -0.0842, -0.4730, 0.1544, 1.1824,
- 0.2035],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.5902, -0.3493, 1.7961, -0.0072, -0.5942, -0.5615, 0.3180,
- 0.3161],
- [ 0.5318, -0.4310, 1.6864, 0.0543, -0.1554, 0.1313, 0.0635,
- 0.2634],
- [ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0203, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.47314505791291595
- step: 29
- running loss: 0.01631534682458331
- Train Steps: 29/90 Loss: 0.0163 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6214, 0.4040, 0.8838, 0.3500, 0.3600, 0.5183, 0.6362, 0.5200],
- [0.6280, 0.4055, 0.8600, 0.5317, 0.3800, 0.4700, 0.6275, 0.5133],
- [0.6182, 0.3972, 0.8720, 0.5527, 0.3638, 0.3582, 0.5696, 0.5395],
- [0.6179, 0.3961, 0.8347, 0.6020, 0.3887, 0.4624, 0.5714, 0.5373],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6136, 0.4060, 0.8025, 0.3217, 0.3650, 0.3000, 0.5060, 0.5646],
- [0.6286, 0.4078, 0.8063, 0.2267, 0.4788, 0.1533, 0.5953, 0.4913]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6479, -0.3862, 1.7959, -0.6655, -0.4468, 0.0505, 0.8091, 0.0969],
- [ 0.6897, -0.3558, 1.7876, 0.1890, -0.4869, -0.1190, 0.6193, 0.0967],
- [ 0.6158, -0.3547, 1.7291, 0.0648, -0.5078, -0.6661, 0.3796, 0.2100],
- [ 0.7697, -0.3150, 1.5880, 0.3262, -0.4156, -0.3101, 0.4204, 0.1966],
- [-2.2058, -2.1965, 1.4134, -1.0115, -0.5480, -0.8214, 0.3142, 0.1668],
- [ 0.5060, -0.3896, 1.7413, -0.3907, -0.4397, -0.3701, 0.1312, 0.3688],
- [ 0.6487, -0.3205, 1.4211, -0.8362, -0.5193, -0.9339, 0.0720, 0.2005],
- [ 0.5592, -0.4004, 1.3866, -1.2150, -0.0363, -1.5537, 0.4363, 0.0868]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5841, -0.4199, 1.7961, -0.6693, -0.6231, 0.1082, 0.6529,
- 0.1159],
- [ 0.6148, -0.4130, 1.6864, 0.1698, -0.5307, -0.1150, 0.6125,
- 0.0851],
- [ 0.5697, -0.4514, 1.7420, 0.2672, -0.6055, -0.6312, 0.3449,
- 0.2058],
- [ 0.5680, -0.4562, 1.5697, 0.4947, -0.4904, -0.1503, 0.3536,
- 0.1956],
- [-2.2859, -2.2859, 1.5767, -0.7540, -0.6404, -0.7309, 0.1753,
- 0.0893],
- [ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145],
- [ 0.5483, -0.4105, 1.4208, -0.8002, -0.6000, -0.9002, 0.0511,
- 0.3220],
- [ 0.6177, -0.4022, 1.4381, -1.2390, -0.0746, -1.5777, 0.4639,
- -0.0168]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0082, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0082, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.48136191023513675
- step: 30
- running loss: 0.016045397007837893
- Train Steps: 30/90 Loss: 0.0160 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6214, 0.4175, 0.7300, 0.2883, 0.4338, 0.2167, 0.5698, 0.5773],
- [0.6200, 0.4049, 0.8638, 0.5617, 0.4125, 0.5100, 0.6013, 0.5317],
- [0.6268, 0.4029, 0.8500, 0.2683, 0.3937, 0.3500, 0.6860, 0.5297],
- [0.6136, 0.4060, 0.8025, 0.3217, 0.3650, 0.3000, 0.5060, 0.5646],
- [0.6127, 0.4115, 0.7163, 0.2883, 0.3625, 0.2950, 0.5327, 0.5495],
- [0.6201, 0.4098, 0.8575, 0.3617, 0.3700, 0.3550, 0.5988, 0.5667],
- [0.6162, 0.4014, 0.8800, 0.5333, 0.3750, 0.4817, 0.5988, 0.5283],
- [0.6307, 0.4045, 0.8025, 0.5833, 0.3775, 0.4867, 0.6892, 0.5459]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5437, -0.4190, 1.2397, -0.9936, -0.2858, -1.3147, 0.3011, 0.3873],
- [ 0.4616, -0.5160, 1.8487, 0.2746, -0.3216, 0.0470, 0.4399, 0.0933],
- [ 0.7601, -0.3446, 1.7578, -1.0177, -0.4505, -0.7159, 0.8616, 0.0667],
- [ 0.6262, -0.3732, 1.5641, -0.8014, -0.5630, -0.9078, 0.0116, 0.1624],
- [ 0.4152, -0.5154, 1.2256, -1.0323, -0.5391, -0.9245, 0.1490, 0.2274],
- [ 0.7800, -0.3039, 1.8411, -0.6873, -0.4801, -0.6690, 0.4628, 0.2354],
- [ 0.6368, -0.4049, 1.9289, 0.1807, -0.4706, -0.0622, 0.4105, 0.1088],
- [ 0.6706, -0.3962, 1.6757, 0.2004, -0.4664, -0.1117, 0.8264, 0.1407]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5841, -0.3574, 1.0859, -0.9541, -0.2824, -1.2851, 0.3460,
- 0.3808],
- [ 0.5776, -0.4159, 1.7037, 0.3084, -0.3806, 0.0697, 0.4912,
- 0.1698],
- [ 0.6092, -0.4249, 1.6402, -1.0465, -0.4672, -0.6693, 0.8827,
- 0.1608],
- [ 0.5483, -0.4105, 1.4208, -0.8002, -0.6000, -0.9002, 0.0511,
- 0.3220],
- [ 0.5442, -0.3855, 1.0224, -0.9541, -0.6115, -0.9233, 0.1745,
- 0.2522],
- [ 0.5783, -0.3933, 1.6748, -0.6154, -0.5769, -0.6462, 0.4797,
- 0.3315],
- [ 0.5603, -0.4319, 1.7788, 0.1775, -0.5538, -0.0611, 0.4797,
- 0.1544],
- [ 0.6273, -0.4177, 1.4208, 0.4085, -0.5423, -0.0380, 0.8973,
- 0.2356]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0089, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0089, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.49028233671560884
- step: 31
- running loss: 0.015815559248890606
- Train Steps: 31/90 Loss: 0.0158 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6267, 0.4094, 0.8712, 0.3083, 0.4400, 0.2267, 0.6250, 0.5200],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6179, 0.3993, 0.8925, 0.4789, 0.3879, 0.4900, 0.6041, 0.5279],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083],
- [0.6126, 0.4039, 0.8237, 0.3967, 0.3625, 0.3600, 0.5894, 0.6138],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6163, 0.4001, 0.8788, 0.5033, 0.4012, 0.4633, 0.5338, 0.5767]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5666, -0.4195, 1.8396, -1.0422, -0.1928, -1.2118, 0.5643, 0.1019],
- [ 0.6211, -0.3755, 1.3698, -0.6980, -0.6557, -0.4697, 0.1725, 0.2209],
- [ 0.5803, -0.4509, 1.9152, -0.6383, -0.3041, -0.8855, 0.8861, 0.0336],
- [ 0.7501, -0.3202, 1.8613, -0.1640, -0.4751, 0.0098, 0.5408, 0.0257],
- [ 0.6964, -0.3375, 1.5899, 0.3226, -0.3722, -0.0324, 0.4169, 0.4082],
- [ 0.4888, -0.4642, 1.5771, -0.5611, -0.5993, -0.5054, 0.4021, 0.4605],
- [ 0.7757, -0.3521, 1.8301, 0.2087, -0.5843, -0.1622, 0.5861, 0.0187],
- [ 0.6856, -0.3810, 1.8420, -0.0025, -0.4428, -0.0025, 0.2644, 0.1522]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6089, -0.3948, 1.7383, -0.8617, -0.2536, -1.2390, 0.6009,
- 0.1159],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5680, -0.4417, 1.8365, -0.0740, -0.4941, -0.0227, 0.5045,
- 0.1525],
- [ 0.6134, -0.3910, 1.4497, 0.3546, -0.3517, -0.0919, 0.3296,
- 0.5239],
- [ 0.5438, -0.4206, 1.5189, -0.4537, -0.6115, -0.6231, 0.4365,
- 0.5491],
- [ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [ 0.5610, -0.4381, 1.7730, 0.0390, -0.4326, -0.1458, 0.1794,
- 0.3777]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0072, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0072, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.49751824559643865
- step: 32
- running loss: 0.015547445174888708
- Train Steps: 32/90 Loss: 0.0155 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6173, 0.4114, 0.7325, 0.2500, 0.4213, 0.1917, 0.5338, 0.5700],
- [0.6277, 0.4029, 0.8250, 0.2433, 0.4325, 0.2100, 0.6366, 0.5207],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6357, 0.4118, 0.8400, 0.2500, 0.5413, 0.1633, 0.6725, 0.5586],
- [0.6346, 0.4092, 0.7712, 0.5917, 0.4037, 0.4767, 0.7343, 0.5725],
- [0.6196, 0.4090, 0.8450, 0.3900, 0.3575, 0.3933, 0.5975, 0.5583],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7685, -0.2554, 1.8790, -0.0951, -0.5094, 0.1064, 0.3453, 0.0740],
- [ 0.2122, -0.6466, 1.1111, -1.1771, -0.4408, -1.2101, 0.1642, 0.2664],
- [ 0.5185, -0.4993, 1.5777, -1.2015, -0.3875, -1.1538, 0.6529, 0.0108],
- [ 0.6000, -0.4405, 1.6702, 0.3551, -0.3816, 0.2087, 0.2137, -0.0261],
- [ 0.5316, -0.4604, 1.7136, -1.2135, 0.0900, -1.4193, 0.7995, 0.1499],
- [ 0.7085, -0.3692, 1.4491, 0.1493, -0.6077, 0.0302, 0.9614, 0.2498],
- [ 0.6815, -0.3494, 1.7495, -0.4778, -0.7476, -0.3255, 0.4961, 0.2621],
- [ 0.6221, -0.3105, 1.7768, -0.2247, -0.3396, -0.9656, 0.3741, 0.4538]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.5652, -0.3858, 1.0975, -1.1312, -0.3402, -1.4006, 0.1794,
- 0.3469],
- [ 0.6132, -0.4248, 1.5247, -1.1620, -0.2882, -1.3159, 0.6545,
- 0.1193],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610],
- [ 0.6504, -0.3840, 1.5940, -1.1312, 0.2141, -1.5315, 0.8205,
- 0.2944],
- [ 0.6454, -0.3960, 1.2764, 0.4470, -0.4210, -0.0842, 1.1057,
- 0.3585],
- [ 0.5761, -0.3966, 1.6171, -0.4845, -0.6346, -0.4691, 0.4739,
- 0.2930],
- [ 0.6174, -0.3118, 1.6402, -0.2074, -0.1958, -1.0927, 0.2267,
- 0.5822]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0149, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0149, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5123806581832469
- step: 33
- running loss: 0.01552668661161354
- Train Steps: 33/90 Loss: 0.0155 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6042, 0.3990, 0.6831, 0.2875, 0.3500, 0.3133, 0.5143, 0.5510],
- [0.6307, 0.4045, 0.8025, 0.5833, 0.3775, 0.4867, 0.6892, 0.5459],
- [0.6236, 0.4081, 0.8575, 0.3000, 0.3713, 0.3033, 0.5550, 0.5633],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [0.6038, 0.3946, 0.8413, 0.4883, 0.3563, 0.4550, 0.5266, 0.4693],
- [0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6101, -0.3861, 1.0465, -1.0171, -0.5287, -0.9288, 0.1636, 0.3035],
- [ 0.7595, -0.3106, 1.5950, 0.3544, -0.4964, -0.0664, 0.8690, 0.1603],
- [ 0.5309, -0.4411, 1.7549, -0.7236, -0.5169, -0.8883, 0.3679, 0.1458],
- [ 0.7765, -0.3054, 1.7333, 0.3088, -0.3553, 0.1623, 0.7348, 0.0983],
- [ 0.6907, -0.3104, 1.9679, -0.2774, -0.5666, -0.0387, 0.7282, 0.2893],
- [ 0.5866, -0.3977, 1.7523, 0.0307, -0.5445, -0.1638, 0.2781, 0.1210],
- [ 0.5182, -0.4533, 1.4693, -0.9037, -0.5433, -0.7623, 0.4990, 0.2418],
- [ 0.3984, -0.5048, 1.4839, -1.1185, -0.1541, -1.3903, 0.4739, 0.2252]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5049, -0.4428, 0.8692, -0.9581, -0.6693, -0.8386, 0.0897,
- 0.2589],
- [ 0.6273, -0.4177, 1.4208, 0.4085, -0.5423, -0.0380, 0.8973,
- 0.2356],
- [ 0.5944, -0.4008, 1.6748, -0.9002, -0.5711, -0.8848, 0.2776,
- 0.3161],
- [ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [ 0.5030, -0.4631, 1.5998, -0.0303, -0.6404, -0.1843, 0.1465,
- -0.1181],
- [ 0.5697, -0.4442, 1.3529, -0.9515, -0.5774, -0.7801, 0.5253,
- 0.1931],
- [ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0088, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0088, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5212206044234335
- step: 34
- running loss: 0.01533001777715981
- Train Steps: 34/90 Loss: 0.0153 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6225, 0.4191, 0.8500, 0.4167, 0.3688, 0.3233, 0.5650, 0.5650],
- [ nan, nan, 0.7612, 0.3250, 0.4037, 0.2533, 0.5438, 0.5767],
- [0.6200, 0.3993, 0.8519, 0.4923, 0.3962, 0.4717, 0.6013, 0.5433],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6226, 0.4103, 0.8575, 0.3450, 0.4388, 0.2067, 0.5787, 0.5383],
- [0.6150, 0.3935, 0.8696, 0.5158, 0.4647, 0.5329, 0.6041, 0.5153],
- [0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7662, -0.2341, 1.5781, -0.2909, -0.6309, -0.7669, 0.4134, 0.3638],
- [-1.5719, -1.7703, 1.2770, -0.8794, -0.4331, -1.0268, 0.3747, 0.3496],
- [ 0.7161, -0.3501, 1.6823, 0.1277, -0.5211, -0.0683, 0.6721, 0.1472],
- [ 0.6450, -0.3428, 1.7448, 0.0048, -0.3701, 0.0794, 0.4939, 0.2061],
- [ 0.6709, -0.3055, 1.6780, -0.6860, -0.2785, -1.2521, 0.4511, 0.2015],
- [ 0.6167, -0.3962, 1.6691, 0.1726, -0.2480, 0.1969, 0.6600, 0.0819],
- [ 0.7405, -0.2789, 1.8078, 0.0298, -0.4354, 0.2576, 0.7490, 0.0836],
- [ 0.5992, -0.3914, 1.5422, -0.4336, -0.6631, -0.2964, 0.3682, 0.2405]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5894, -0.3503, 1.6402, -0.3614, -0.5827, -0.7925, 0.3238,
- 0.3238],
- [-2.2859, -2.2859, 1.2303, -0.7848, -0.4210, -1.1158, 0.2256,
- 0.3777],
- [ 0.5777, -0.4415, 1.6491, -0.0122, -0.4557, -0.1073, 0.4912,
- 0.2237],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.5898, -0.3908, 1.6748, -0.6924, -0.2594, -1.3313, 0.3873,
- 0.2006],
- [ 0.5548, -0.4682, 1.7309, 0.0966, -0.1394, 0.1757, 0.5045,
- 0.0942],
- [ 0.5991, -0.3803, 1.8018, -0.0534, -0.3460, 0.1852, 0.5374,
- 0.1390],
- [ 0.5367, -0.4294, 1.5709, -0.4999, -0.6693, -0.3075, 0.2455,
- 0.3559]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0195, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0195, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5407532160170376
- step: 35
- running loss: 0.015450091886201075
- Train Steps: 35/90 Loss: 0.0155 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6282, 0.4034, 0.7830, 0.2080, 0.4532, 0.2080, 0.6404, 0.5323],
- [0.6034, 0.4011, 0.7350, 0.2533, 0.3438, 0.3367, 0.5516, 0.5084],
- [0.6273, 0.4100, 0.7137, 0.2133, 0.4000, 0.2650, 0.6075, 0.5633],
- [ nan, nan, 0.7192, 0.2346, 0.4037, 0.2050, 0.5138, 0.5650],
- [0.6286, 0.4097, 0.8107, 0.2414, 0.4425, 0.2483, 0.6745, 0.5385],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [0.6219, 0.3934, 0.8688, 0.5267, 0.4313, 0.4967, 0.5988, 0.4983],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5688, -0.4102, 1.3362, -1.0214, -0.2337, -1.2387, 0.5933, 0.2205],
- [ 0.5588, -0.4107, 1.1360, -0.9133, -0.6114, -0.6363, 0.2814, 0.1120],
- [ 0.4255, -0.4792, 1.0315, -1.0336, -0.4536, -1.0128, 0.4469, 0.3514],
- [-2.0111, -2.0337, 1.0375, -1.0228, -0.4102, -1.0963, 0.2566, 0.2898],
- [ 0.5824, -0.3836, 1.4738, -0.8859, -0.2546, -1.1444, 0.8461, 0.2488],
- [ 0.6641, -0.3520, 1.6249, -0.4234, -0.6472, -0.4652, 0.5174, 0.2492],
- [ 0.7156, -0.3546, 1.7393, 0.3704, -0.3914, 0.1171, 0.5668, -0.0235],
- [ 0.6197, -0.3639, 1.9170, -0.2692, -0.5723, -0.3346, 0.6970, 0.2217]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6158, -0.4225, 1.3307, -1.3253, -0.1924, -1.3252, 0.6721,
- 0.1727],
- [ 0.5009, -0.4333, 1.1090, -1.1158, -0.6982, -0.7309, 0.2617,
- 0.0622],
- [ 0.6115, -0.3924, 1.0109, -1.3005, -0.4383, -1.0619, 0.5201,
- 0.3161],
- [-2.2859, -2.2859, 1.0361, -1.2021, -0.4210, -1.3390, 0.0871,
- 0.3238],
- [ 0.6174, -0.3936, 1.4586, -1.1709, -0.2420, -1.1389, 0.8296,
- 0.2012],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753],
- [ 0.5864, -0.4690, 1.7268, 0.1467, -0.2940, 0.0082, 0.4797,
- 0.0159],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0145, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0145, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5552862132899463
- step: 36
- running loss: 0.015424617035831843
- Train Steps: 36/90 Loss: 0.0154 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833],
- [0.6336, 0.4191, 0.8938, 0.5167, 0.3937, 0.3517, 0.7343, 0.5748],
- [0.6179, 0.4008, 0.7505, 0.2678, 0.4368, 0.1891, 0.5831, 0.5263],
- [0.6258, 0.4038, 0.8750, 0.4883, 0.3900, 0.5500, 0.6375, 0.5217],
- [0.6200, 0.3993, 0.8639, 0.3687, 0.3658, 0.3139, 0.6002, 0.5374],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6339, 0.4102, 0.9088, 0.4767, 0.3925, 0.5283, 0.7509, 0.5390],
- [0.6200, 0.4055, 0.8011, 0.2988, 0.3842, 0.2798, 0.5949, 0.5183]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6712, -0.3344, 0.8852, -0.8259, -0.5781, -0.8849, 0.2836, 0.4461],
- [ 0.6448, -0.3840, 1.7873, 0.1943, -0.3306, -0.5015, 0.9702, 0.3116],
- [ 0.6883, -0.3586, 1.1804, -1.1055, -0.3317, -1.3468, 0.3378, 0.1692],
- [ 0.6501, -0.3811, 1.7949, 0.0383, -0.4651, 0.4288, 0.6228, 0.0843],
- [ 0.5457, -0.4620, 1.6727, -0.5455, -0.5818, -0.7265, 0.4503, 0.1704],
- [ 0.5400, -0.4457, 1.6360, -0.1206, -0.5851, -0.3272, 0.2805, 0.3501],
- [ 0.6369, -0.3821, 1.8384, -0.0908, -0.4472, 0.3384, 1.0631, 0.2493],
- [ 0.4643, -0.4946, 1.4443, -0.9013, -0.4899, -0.8918, 0.3928, 0.1161]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085],
- [ 0.6405, -0.3503, 1.8423, 0.1005, -0.4672, -0.6616, 1.1057,
- 0.3692],
- [ 0.5680, -0.4345, 1.1806, -1.0490, -0.2683, -1.4127, 0.4074,
- 0.1449],
- [ 0.6047, -0.4209, 1.7557, -0.0303, -0.4845, 0.2545, 0.6587,
- 0.1236],
- [ 0.5777, -0.4416, 1.7044, -0.5827, -0.5962, -0.8361, 0.4862,
- 0.1963],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.6421, -0.3912, 1.9115, -0.0842, -0.4730, 0.1544, 1.1824,
- 0.2035],
- [ 0.5781, -0.4129, 1.4142, -0.9057, -0.5115, -0.9937, 0.4621,
- 0.1080]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0055, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0055, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5607698620297015
- step: 37
- running loss: 0.015155942217018959
- Train Steps: 37/90 Loss: 0.0152 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6085, 0.4008, 0.8588, 0.5200, 0.4959, 0.4977, 0.5175, 0.5283],
- [0.6256, 0.4199, 0.8638, 0.5800, 0.3987, 0.4383, 0.5600, 0.5950],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6275, 0.4081, 0.8063, 0.2017, 0.4825, 0.1583, 0.6156, 0.4869],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6250, 0.4103, 0.8950, 0.4400, 0.3912, 0.5650, 0.6050, 0.5133],
- [0.6308, 0.3990, 0.8688, 0.5183, 0.3950, 0.4983, 0.6388, 0.5133],
- [0.6127, 0.4115, 0.7163, 0.2883, 0.3625, 0.2950, 0.5327, 0.5495]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5668, -0.4175, 1.6320, 0.0660, -0.1801, -0.0683, 0.3313, 0.2419],
- [ 0.5908, -0.3833, 1.6107, 0.3380, -0.5683, -0.2802, 0.4929, 0.5329],
- [ 0.4183, -0.5385, 1.5019, 0.3227, -0.3926, 0.0037, 0.3500, 0.1237],
- [ 0.6590, -0.4055, 1.4546, -1.4122, -0.1601, -1.6383, 0.7569, 0.0479],
- [ 0.5319, -0.4480, 1.6853, -0.0332, -0.2487, -0.0303, 0.4169, 0.3119],
- [ 0.5453, -0.4142, 1.7662, -0.3417, -0.5738, 0.2874, 0.7600, 0.2159],
- [ 0.5614, -0.4496, 1.7109, 0.0886, -0.5932, -0.0110, 0.9051, 0.1374],
- [ 0.5893, -0.4026, 1.0282, -1.0175, -0.7196, -0.9125, 0.2854, 0.3452]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5245, -0.4347, 1.6806, 0.1159, 0.0046, 0.0129, 0.1044,
- 0.1544],
- [ 0.6038, -0.3464, 1.7037, 0.3931, -0.4441, -0.2613, 0.3007,
- 0.4624],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610],
- [ 0.6124, -0.4010, 1.4381, -1.3544, -0.0573, -1.5546, 0.5573,
- -0.0369],
- [ 0.5359, -0.4370, 1.7095, -0.0303, -0.0804, -0.0380, 0.1044,
- 0.3392],
- [ 0.6010, -0.3909, 1.8480, -0.2536, -0.4788, 0.3238, 0.5085,
- 0.0851],
- [ 0.6275, -0.4430, 1.7268, 0.1082, -0.4614, 0.0159, 0.6644,
- 0.0851],
- [ 0.5442, -0.3855, 1.0224, -0.9541, -0.6115, -0.9233, 0.1745,
- 0.2522]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0117, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0117, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5724910940043628
- step: 38
- running loss: 0.01506555510537797
- Train Steps: 38/90 Loss: 0.0151 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064],
- [0.6274, 0.4117, 0.8100, 0.5801, 0.4000, 0.4583, 0.5582, 0.6118],
- [0.6193, 0.4050, 0.7313, 0.2433, 0.4075, 0.2117, 0.5649, 0.5656],
- [0.6226, 0.4125, 0.8800, 0.4900, 0.4512, 0.5600, 0.6275, 0.5517],
- [0.6268, 0.4052, 0.8175, 0.2250, 0.4688, 0.1917, 0.6375, 0.5267],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6068, 0.3963, 0.8650, 0.4317, 0.4037, 0.5083, 0.5253, 0.4999],
- [0.6275, 0.4024, 0.7722, 0.2080, 0.4392, 0.2234, 0.6435, 0.5290]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5787, -0.4111, 1.6932, 0.0974, -0.2177, 0.0952, 0.3047, 0.1366],
- [ 0.3868, -0.5064, 1.4697, 0.3451, -0.5825, -0.1846, 0.5391, 0.5011],
- [ 0.5121, -0.4486, 1.0925, -1.1847, -0.4936, -1.2274, 0.4309, 0.2811],
- [ 0.5534, -0.3974, 1.7306, -0.0548, -0.2913, 0.2534, 0.6129, 0.3512],
- [ 0.6356, -0.4325, 1.4172, -1.2141, -0.2466, -1.5455, 0.7136, 0.1865],
- [ 0.6097, -0.4405, 1.6402, 0.2651, -0.5443, -0.1034, 0.6511, 0.1993],
- [ 0.5489, -0.4402, 1.6364, -0.2779, -0.5507, 0.0573, 0.3002, 0.0735],
- [ 0.5650, -0.4668, 1.2234, -1.2461, -0.3808, -1.2864, 0.7092, 0.1740]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532],
- [ 0.6121, -0.3844, 1.4556, 0.3936, -0.4383, -0.1689, 0.2925,
- 0.5401],
- [ 0.5746, -0.4153, 1.0917, -1.1620, -0.4037, -1.3082, 0.3234,
- 0.3267],
- [ 0.5897, -0.3804, 1.7788, -0.0226, -0.2016, 0.3007, 0.6125,
- 0.2622],
- [ 0.6092, -0.4143, 1.4901, -1.2467, -0.1208, -1.4006, 0.6587,
- 0.1467],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.5168, -0.4556, 1.7095, -0.2921, -0.4210, 0.0620, 0.1404,
- 0.0231],
- [ 0.6125, -0.4273, 1.2807, -1.3253, -0.2574, -1.2542, 0.6864,
- 0.1575]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0072, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0072, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5796950873918831
- step: 39
- running loss: 0.014863976599791875
- Train Steps: 39/90 Loss: 0.0149 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6199, 0.4015, 0.8716, 0.5228, 0.3833, 0.4772, 0.5883, 0.5148],
- [0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6110, 0.3984, 0.8750, 0.4933, 0.4625, 0.4950, 0.5578, 0.5676],
- [0.6104, 0.4029, 0.8738, 0.4900, 0.4088, 0.4533, 0.5070, 0.5510],
- [0.6273, 0.4105, 0.8988, 0.4517, 0.3912, 0.2550, 0.5894, 0.4811],
- [0.6150, 0.3935, 0.8696, 0.5158, 0.4647, 0.5329, 0.6041, 0.5153],
- [0.6203, 0.4078, 0.8800, 0.5083, 0.3900, 0.5000, 0.6100, 0.5583]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4518, -0.5226, 1.5759, 0.0545, -0.5464, -0.0713, 0.5453, 0.1484],
- [ 0.5803, -0.3962, 1.6064, -0.1914, -0.6144, -0.1679, 0.5165, 0.2878],
- [ 0.5458, -0.4359, 1.6392, -0.9572, -0.1208, -1.4111, 0.6136, 0.1508],
- [ 0.5890, -0.4114, 1.5700, -0.1026, -0.1931, 0.0397, 0.3496, 0.4049],
- [ 0.5104, -0.4378, 1.5383, -0.0344, -0.4701, -0.0894, 0.2003, 0.3153],
- [ 0.5813, -0.4163, 1.6899, -0.3295, -0.5267, -1.1116, 0.5762, 0.1261],
- [ 0.4640, -0.5030, 1.4995, 0.0286, -0.2245, 0.1700, 0.5783, 0.2438],
- [ 0.5282, -0.4285, 1.5917, -0.0356, -0.4829, 0.0265, 0.5645, 0.2748]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7725e-01, -4.3156e-01, 1.7399e+00, 1.2871e-01, -5.1531e-01,
- -8.1749e-02, 4.3131e-01, 9.1941e-02],
- [ 5.8655e-01, -3.9731e-01, 1.8423e+00, -6.8822e-02, -5.1917e-01,
- -2.3048e-01, 4.1617e-01, 1.1594e-01],
- [ 6.0520e-01, -3.6628e-01, 1.7845e+00, -8.1555e-01, -8.0370e-02,
- -1.4237e+00, 5.8660e-01, 5.0889e-03],
- [ 5.3637e-01, -4.4573e-01, 1.7557e+00, -7.2363e-03, -1.4965e-01,
- 4.6189e-04, 2.9049e-01, 3.3573e-01],
- [ 5.3343e-01, -4.2517e-01, 1.7499e+00, -2.2633e-02, -3.9792e-01,
- -1.9199e-01, 5.5769e-02, 2.5891e-01],
- [ 6.1161e-01, -3.8976e-01, 1.8654e+00, -1.9969e-01, -4.7875e-01,
- -1.1081e+00, 4.3668e-01, -6.3661e-02],
- [ 5.5484e-01, -4.6823e-01, 1.7309e+00, 9.6578e-02, -1.3942e-01,
- 1.7573e-01, 5.0451e-01, 9.4188e-02],
- [ 5.7939e-01, -4.0231e-01, 1.7788e+00, 6.2048e-02, -4.8453e-01,
- 2.3557e-02, 5.3164e-01, 2.9299e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0105, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0105, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5902246865443885
- step: 40
- running loss: 0.014755617163609713
- Train Steps: 40/90 Loss: 0.0148 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [0.6040, 0.4002, 0.7338, 0.2267, 0.3975, 0.2100, 0.5231, 0.4778],
- [0.6127, 0.4118, 0.8650, 0.5083, 0.4088, 0.5367, 0.5300, 0.5456],
- [ nan, nan, 0.6935, 0.1930, 0.4150, 0.2250, 0.5450, 0.5650],
- [0.6157, 0.4102, 0.8513, 0.3817, 0.3613, 0.3667, 0.5096, 0.5890],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6250, 0.4110, 0.7238, 0.2067, 0.4263, 0.1883, 0.5625, 0.5633],
- [0.6203, 0.4073, 0.8189, 0.2398, 0.4400, 0.2054, 0.5929, 0.5501]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6170, -0.4152, 1.4426, -0.9766, -0.4147, -0.9239, 0.6348, 0.1055],
- [ 0.6492, -0.3891, 1.0826, -1.1782, -0.4910, -1.1525, 0.3329, -0.0206],
- [ 0.5339, -0.4025, 1.6845, 0.1729, -0.3956, 0.3507, 0.3930, 0.1802],
- [-1.7459, -1.8893, 1.0771, -1.0959, -0.2213, -1.1079, 0.4450, 0.2548],
- [ 0.4224, -0.4749, 1.6062, -0.3164, -0.6632, -0.3488, 0.2345, 0.2554],
- [ 0.5959, -0.3686, 1.0819, -0.8909, -0.2060, -1.2524, 0.4956, 0.3669],
- [ 0.4865, -0.4776, 1.0770, -1.1057, -0.3190, -1.2749, 0.5400, 0.2507],
- [ 0.5668, -0.4227, 1.4136, -0.9592, -0.2372, -1.2127, 0.6334, 0.2023]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929],
- [ 0.5037, -0.4375, 1.1032, -1.2390, -0.4499, -1.3159, 0.1301,
- -0.0791],
- [ 0.5443, -0.3840, 1.7095, 0.0620, -0.3979, 0.1929, 0.1622,
- 0.2341],
- [-2.2859, -2.2859, 0.9175, -1.3947, -0.3691, -1.2467, 0.2314,
- 0.3238],
- [ 0.5581, -0.3912, 1.6460, -0.5230, -0.6173, -0.5923, 0.0681,
- 0.4348],
- [ 0.5862, -0.3530, 1.1032, -1.0619, -0.1497, -1.3852, 0.3411,
- 0.3931],
- [ 0.6010, -0.3875, 1.0570, -1.3313, -0.3171, -1.4160, 0.3122,
- 0.3161],
- [ 0.5792, -0.4048, 1.4965, -1.1781, -0.2534, -1.3371, 0.4528,
- 0.2549]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0225, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0225, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6126899658702314
- step: 41
- running loss: 0.014943657704151986
- Train Steps: 41/90 Loss: 0.0149 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4098, 0.8237, 0.2917, 0.4012, 0.2967, 0.6000, 0.5683],
- [0.6125, 0.4010, 0.8650, 0.4567, 0.3663, 0.3900, 0.5600, 0.5567],
- [0.6205, 0.4012, 0.8675, 0.3283, 0.3713, 0.3050, 0.5813, 0.5117],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [0.6223, 0.3990, 0.8037, 0.2800, 0.4000, 0.2283, 0.5864, 0.5208],
- [0.6176, 0.4030, 0.8850, 0.4850, 0.3688, 0.4050, 0.5312, 0.5783],
- [0.6204, 0.4110, 0.7913, 0.2667, 0.4062, 0.2500, 0.6225, 0.5367],
- [0.6197, 0.3930, 0.8793, 0.4736, 0.4152, 0.5464, 0.6308, 0.5235]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7854, -0.2991, 1.3389, -0.9233, -0.3140, -0.9295, 0.4933, 0.3211],
- [ 0.4851, -0.4815, 1.5754, -0.1447, -0.5025, -0.4698, 0.2705, 0.3721],
- [ 0.4202, -0.5532, 1.6440, -0.7332, -0.4721, -0.9302, 0.4453, 0.0863],
- [ 0.5706, -0.4244, 1.7125, -0.0341, -0.3242, 0.3171, 0.8492, 0.1820],
- [ 0.4740, -0.5121, 1.3715, -1.0159, -0.2918, -1.2467, 0.4170, 0.1064],
- [ 0.6055, -0.4053, 1.6585, 0.0100, -0.4876, -0.3127, 0.2278, 0.2346],
- [ 0.6458, -0.3923, 1.3370, -1.0757, -0.2966, -1.1584, 0.5205, 0.1678],
- [ 0.4434, -0.5174, 1.6545, -0.0345, -0.2625, 0.2169, 0.5201, 0.1693]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5778, -0.3930, 1.5189, -0.9387, -0.4326, -0.9156, 0.4855,
- 0.3392],
- [ 0.5432, -0.4336, 1.7095, -0.1766, -0.5942, -0.4845, 0.3007,
- 0.2853],
- [ 0.5799, -0.4329, 1.7210, -0.7694, -0.5711, -0.8771, 0.3988,
- 0.0774],
- [ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554],
- [ 0.5885, -0.4429, 1.4266, -0.9926, -0.4383, -1.2313, 0.4228,
- 0.1195],
- [ 0.5667, -0.4246, 1.8018, -0.0457, -0.5827, -0.4152, 0.1679,
- 0.3854],
- [ 0.5796, -0.3878, 1.3688, -1.0542, -0.4095, -1.1312, 0.5894,
- 0.1929],
- [ 0.5762, -0.4706, 1.7754, -0.0984, -0.3680, 0.2380, 0.6277,
- 0.1322]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0071, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0071, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6198296854272485
- step: 42
- running loss: 0.014757849653029726
- Train Steps: 42/90 Loss: 0.0148 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833],
- [0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6102, 0.4020, 0.8638, 0.3717, 0.3625, 0.5017, 0.6038, 0.5500],
- [0.6271, 0.4005, 0.8450, 0.6067, 0.3850, 0.4983, 0.6069, 0.4649],
- [0.6271, 0.4040, 0.9000, 0.3250, 0.4938, 0.2300, 0.7192, 0.5413],
- [0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6239, 0.4107, 0.8162, 0.2763, 0.3625, 0.3600, 0.5988, 0.5700]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6620, -0.3627, 0.8645, -0.8953, -0.4624, -1.0615, 0.2567, 0.4448],
- [ 0.5483, -0.4709, 1.3646, -0.9783, -0.4312, -0.9092, 0.4041, 0.2567],
- [ 0.4148, -0.5180, 1.6699, -0.6309, -0.4510, -0.0706, 0.5017, 0.2699],
- [ 0.6890, -0.3866, 1.6303, 0.5067, -0.3901, -0.0298, 0.3656, -0.0725],
- [ 0.2361, -0.6919, 2.0030, -0.9078, 0.1716, -1.4575, 1.0266, 0.2301],
- [ 0.6916, -0.4029, 1.8254, 0.1274, -0.4539, -0.1698, 0.5178, -0.0051],
- [ 0.3982, -0.5432, 1.5778, -0.5003, -0.5175, -0.3889, 0.1091, 0.2928],
- [ 0.6824, -0.3615, 1.3945, -0.8919, -0.4078, -0.7204, 0.3479, 0.2505]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085],
- [ 0.5697, -0.4442, 1.3529, -0.9515, -0.5774, -0.7801, 0.5253,
- 0.1931],
- [ 0.5324, -0.4293, 1.7037, -0.5692, -0.6115, 0.0313, 0.5028,
- 0.2545],
- [ 0.6105, -0.4360, 1.6171, 0.5162, -0.5076, 0.0159, 0.5171,
- -0.1385],
- [ 0.6108, -0.4201, 1.8711, -0.7848, -0.0053, -1.2236, 1.0362,
- 0.2142],
- [ 0.6240, -0.4321, 1.8423, 0.1852, -0.5885, -0.1612, 0.6962,
- 0.0111],
- [ 0.5367, -0.4294, 1.5709, -0.4999, -0.6693, -0.3075, 0.2455,
- 0.3559],
- [ 0.5960, -0.3888, 1.4840, -1.0095, -0.6115, -0.6231, 0.4797,
- 0.3469]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0128, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0128, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.632617355324328
- step: 43
- running loss: 0.014712031519170417
- Train Steps: 43/90 Loss: 0.0147 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6357, 0.4139, 0.8450, 0.5883, 0.3775, 0.4950, 0.6488, 0.5297],
- [0.6211, 0.4069, 0.8750, 0.5117, 0.4150, 0.4900, 0.5875, 0.5167],
- [0.6185, 0.4129, 0.8900, 0.4567, 0.3937, 0.5417, 0.5734, 0.5110],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6086, 0.3981, 0.8700, 0.4750, 0.4512, 0.5283, 0.5324, 0.5038],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833],
- [0.6286, 0.4086, 0.8408, 0.2801, 0.4163, 0.2800, 0.6725, 0.5393]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3379, -0.5900, 1.6437, 0.3095, -0.4413, -0.1503, 0.5774, 0.1200],
- [ 0.3663, -0.5576, 1.8159, -0.0059, -0.3329, -0.1042, 0.3667, 0.1079],
- [ 0.6056, -0.3637, 1.8744, -0.2903, -0.3508, 0.0614, 0.2247, 0.1056],
- [ 0.6311, -0.4130, 1.2440, -1.1452, -0.4054, -1.1107, 0.4740, 0.3398],
- [ 0.5598, -0.4292, 1.7782, -0.1508, -0.1658, -0.0379, 0.0746, -0.0257],
- [ 0.6271, -0.3747, 1.7128, -0.3489, -0.4974, -0.8574, 0.3628, 0.2517],
- [ 0.6131, -0.4009, 0.8760, -0.9219, -0.4928, -1.0583, 0.2549, 0.4428],
- [ 0.4163, -0.5562, 1.5630, -0.9899, -0.1836, -1.1978, 0.7956, 0.2543]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.5036e-01, -3.7434e-01, 1.6171e+00, 4.3156e-01, -5.4226e-01,
- 4.6189e-04, 7.1085e-01, 1.6077e-01],
- [ 5.8279e-01, -4.0662e-01, 1.7557e+00, 7.7444e-02, -3.6905e-01,
- -2.2633e-02, 4.2771e-01, 1.0054e-01],
- [ 5.7113e-01, -3.7875e-01, 1.8249e+00, -1.7660e-01, -4.6721e-01,
- 2.1601e-01, 3.6246e-01, 7.4222e-02],
- [ 5.9151e-01, -3.6821e-01, 1.2187e+00, -1.2313e+00, -4.3256e-01,
- -9.5412e-01, 5.7783e-01, 3.7768e-01],
- [ 5.2494e-01, -4.4734e-01, 1.7326e+00, -9.1917e-02, -2.0162e-01,
- 1.5443e-01, 1.7328e-01, 4.1158e-02],
- [ 6.0162e-01, -3.6328e-01, 1.7152e+00, -2.2279e-01, -6.1155e-01,
- -6.3849e-01, 5.0277e-01, 2.6990e-01],
- [ 5.6143e-01, -4.0805e-01, 7.7413e-01, -8.8483e-01, -5.4226e-01,
- -9.1563e-01, 3.5843e-01, 4.0847e-01],
- [ 6.1742e-01, -3.9842e-01, 1.5975e+00, -9.9214e-01, -3.6328e-01,
- -9.9261e-01, 8.2047e-01, 2.0505e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0122, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0122, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6447693314403296
- step: 44
- running loss: 0.014653848441825672
- Train Steps: 44/90 Loss: 0.0147 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6189, 0.4049, 0.8888, 0.4417, 0.4213, 0.5200, 0.5988, 0.5633],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6186, 0.4154, 0.6825, 0.2633, 0.4150, 0.2300, 0.5713, 0.5517],
- [0.6193, 0.4108, 0.7438, 0.2700, 0.3650, 0.3683, 0.6238, 0.5717],
- [0.6257, 0.4024, 0.8612, 0.5352, 0.4361, 0.5253, 0.6680, 0.5166],
- [0.6275, 0.4013, 0.8850, 0.2833, 0.4975, 0.2233, 0.7058, 0.5343],
- [0.6268, 0.4061, 0.8350, 0.2433, 0.4575, 0.2283, 0.6350, 0.5300],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6149, -0.3487, 1.7814, -0.2349, -0.4272, 0.1006, 0.3636, 0.1894],
- [-2.3590, -2.3096, 1.0946, -1.1491, -0.4777, -1.3035, 0.0823, 0.1859],
- [ 0.5637, -0.4276, 0.9312, -1.0148, -0.4159, -1.2547, 0.2622, 0.2948],
- [ 0.5444, -0.4137, 1.1649, -0.9891, -0.6327, -0.5801, 0.3558, 0.2762],
- [ 0.5295, -0.4331, 1.6803, 0.1782, -0.3518, 0.1204, 0.6046, 0.0747],
- [ 0.5971, -0.4446, 1.7483, -0.9914, -0.0197, -1.3498, 0.8464, 0.0536],
- [ 0.6495, -0.3742, 1.5860, -1.0997, -0.2983, -1.3387, 0.5325, 0.0691],
- [ 0.3837, -0.5090, 1.7551, -0.1794, -0.0704, -0.0786, 0.2862, 0.2237]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5726, -0.4159, 1.8192, -0.2459, -0.3402, 0.1159, 0.4797,
- 0.3161],
- [-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373],
- [ 0.5713, -0.3671, 0.8665, -1.0696, -0.3691, -1.2236, 0.3527,
- 0.2622],
- [ 0.5747, -0.3886, 1.1494, -1.0388, -0.6000, -0.5846, 0.5952,
- 0.3546],
- [ 0.6042, -0.4273, 1.6920, 0.1860, -0.2717, 0.1406, 0.7997,
- 0.1004],
- [ 0.6125, -0.4321, 1.8018, -0.9772, 0.0120, -1.2543, 0.9741,
- 0.1821],
- [ 0.6093, -0.4104, 1.5709, -1.1620, -0.1727, -1.2313, 0.6471,
- 0.1621],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0080, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0080, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.652790330350399
- step: 45
- running loss: 0.014506451785564423
- Train Steps: 45/90 Loss: 0.0145 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4055, 0.7717, 0.1919, 0.4250, 0.2079, 0.5871, 0.5044],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6205, 0.4062, 0.8337, 0.2683, 0.3675, 0.4283, 0.6338, 0.5250],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [0.6299, 0.4008, 0.8450, 0.5350, 0.4213, 0.5000, 0.6350, 0.5100],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6284, 0.4093, 0.8900, 0.4700, 0.3650, 0.3850, 0.6212, 0.5167],
- [0.6262, 0.4163, 0.8850, 0.5183, 0.3763, 0.4150, 0.6025, 0.5500]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4966, -0.4927, 1.2699, -1.3492, -0.2894, -1.4053, 0.3474, 0.0317],
- [ 0.7391, -0.3165, 1.2395, -1.1042, -0.4034, -1.0465, 0.4538, 0.3428],
- [ 0.3934, -0.5337, 1.5320, -1.0319, -0.4831, -0.3558, 0.4587, 0.2339],
- [ 0.6288, -0.3821, 1.3381, -1.0664, -0.5571, -0.4490, 0.3945, 0.2473],
- [ 0.5720, -0.4417, 1.6718, 0.1874, -0.2753, -0.1033, 0.4886, 0.1259],
- [ 0.5163, -0.4307, 1.8413, 0.3058, -0.3082, -0.0951, 0.2531, 0.2563],
- [ 0.4386, -0.5311, 1.9441, -0.1007, -0.5351, -0.5640, 0.4021, 0.0828],
- [ 0.6740, -0.3230, 1.7757, 0.1000, -0.4700, -0.4806, 0.4102, 0.2311]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5785, -0.4129, 1.2785, -1.3996, -0.3227, -1.3259, 0.4258,
- 0.0438],
- [ 0.5915, -0.3682, 1.2187, -1.2313, -0.4326, -0.9541, 0.5778,
- 0.3777],
- [ 0.5799, -0.4099, 1.5651, -1.0465, -0.5885, -0.3075, 0.6413,
- 0.1390],
- [ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545],
- [ 0.6236, -0.4344, 1.6171, 0.1852, -0.3402, 0.0236, 0.6471,
- 0.0697],
- [ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.6164, -0.3956, 1.8249, -0.1150, -0.6000, -0.5076, 0.5836,
- 0.1005],
- [ 0.6066, -0.3632, 1.8018, 0.1082, -0.5480, -0.3691, 0.4970,
- 0.2545]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0074, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0074, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6602126411162317
- step: 46
- running loss: 0.01435244871991808
- Train Steps: 46/90 Loss: 0.0144 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933],
- [0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6159, 0.4085, 0.6900, 0.2283, 0.4088, 0.1950, 0.5123, 0.5397],
- [0.6218, 0.4137, 0.7263, 0.2233, 0.4075, 0.2650, 0.6212, 0.5783],
- [0.6260, 0.4106, 0.8025, 0.2583, 0.4550, 0.1867, 0.6281, 0.4869],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3742, -0.5066, 1.7244, 0.3687, -0.0828, -0.2045, 0.3595, 0.3976],
- [ 0.5415, -0.4194, 1.6979, -0.4614, -0.7064, -0.5221, -0.0144, 0.2012],
- [ 0.8284, -0.2728, 0.9806, -1.3618, -0.4925, -1.1502, 0.3888, 0.2431],
- [ 0.6283, -0.4036, 2.0047, -0.3204, -0.6244, -0.0814, 0.6591, -0.0324],
- [ 0.4902, -0.4794, 0.9705, -1.2138, -0.4393, -1.3999, 0.1626, 0.1567],
- [ 0.3520, -0.5710, 1.1238, -1.1896, -0.5240, -0.9904, 0.5083, 0.3460],
- [ 0.6551, -0.3947, 1.5409, -1.1691, -0.2441, -1.3841, 0.6094, -0.0491],
- [ 0.4091, -0.5144, 1.8871, -0.2143, -0.0562, -0.0024, 0.4478, 0.2442]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5959, -0.3579, 1.6055, 0.3623, -0.0573, -0.2074, 0.3122,
- 0.4547],
- [ 0.5433, -0.4032, 1.6344, -0.4922, -0.5769, -0.5846, 0.0357,
- 0.2567],
- [ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.5591, -0.3990, 0.9012, -1.2313, -0.3979, -1.3852, 0.0804,
- 0.2071],
- [ 0.5863, -0.3749, 1.0686, -1.2543, -0.4037, -1.0619, 0.5836,
- 0.3854],
- [ 0.6058, -0.3892, 1.4208, -1.0927, -0.1843, -1.4237, 0.6154,
- -0.0370],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.66893858788535
- step: 47
- running loss: 0.014232735912454254
- Train Steps: 47/90 Loss: 0.0142 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6346, 0.4165, 0.9138, 0.3983, 0.3875, 0.4317, 0.7469, 0.5471],
- [0.6226, 0.4098, 0.8912, 0.4100, 0.4025, 0.2383, 0.5763, 0.5367],
- [0.6275, 0.4024, 0.7722, 0.2080, 0.4392, 0.2234, 0.6435, 0.5290],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [0.6361, 0.4076, 0.8862, 0.5350, 0.3713, 0.4650, 0.6654, 0.5297],
- [ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6188, 0.4099, 0.7400, 0.2433, 0.3962, 0.2750, 0.6162, 0.5467]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4051, -0.5136, 1.8833, -0.4045, -0.3325, -0.2155, 0.8739, 0.2710],
- [ 0.6273, -0.3338, 1.7346, -0.3649, -0.3575, -1.0852, 0.2452, 0.2073],
- [ 0.7074, -0.3350, 1.1613, -1.3132, -0.2587, -1.1944, 0.5886, 0.1082],
- [ 0.5802, -0.3619, 1.6835, -0.1829, -0.4105, -0.0615, -0.0204, 0.1170],
- [ 0.7498, -0.3204, 1.7780, 0.1821, -0.5136, -0.1024, 0.6707, 0.1807],
- [-2.0482, -2.0954, 1.0732, -1.2049, -0.3920, -1.2881, 0.1151, 0.2374],
- [ 0.6764, -0.3235, 1.8600, -0.5183, -0.5283, -0.4335, 0.4900, 0.1452],
- [ 0.7032, -0.3257, 1.0644, -1.2792, -0.4724, -0.9075, 0.4405, 0.1871]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6454, -0.3623, 1.9346, -0.4460, -0.4961, -0.2921, 1.1642,
- 0.2409],
- [ 0.5900, -0.3932, 1.8307, -0.3921, -0.4268, -1.1851, 0.3758,
- 0.1929],
- [ 0.6125, -0.4273, 1.2807, -1.3253, -0.2574, -1.2542, 0.6864,
- 0.1575],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893],
- [ 0.6520, -0.4032, 1.8076, 0.1852, -0.5711, -0.1381, 0.7876,
- 0.1608],
- [-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.5725, -0.3926, 1.1321, -1.1620, -0.4557, -1.0157, 0.5605,
- 0.2391]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0102, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0102, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6791246416978538
- step: 48
- running loss: 0.014148430035371954
- Train Steps: 48/90 Loss: 0.0141 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6296, 0.4076, 0.8400, 0.5583, 0.3700, 0.4367, 0.6876, 0.5494],
- [0.6200, 0.3978, 0.8900, 0.4550, 0.3775, 0.5200, 0.6150, 0.5367],
- [0.6129, 0.4063, 0.8738, 0.5250, 0.4313, 0.4733, 0.5230, 0.5874],
- [0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783],
- [0.6120, 0.4014, 0.6863, 0.2817, 0.3700, 0.2783, 0.5513, 0.5667],
- [0.6200, 0.3993, 0.8519, 0.4923, 0.3962, 0.4717, 0.6013, 0.5433],
- [0.6302, 0.4007, 0.8850, 0.5500, 0.3613, 0.4517, 0.6335, 0.5012],
- [0.6124, 0.4069, 0.8314, 0.5001, 0.3738, 0.4650, 0.5167, 0.5402]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6016, -0.4220, 1.7538, 0.1703, -0.5186, -0.2837, 0.8657, 0.1236],
- [ 0.4566, -0.5206, 1.9691, -0.3438, -0.5333, 0.1213, 0.4083, 0.1729],
- [ 0.6168, -0.3684, 1.7965, 0.0535, -0.2900, -0.0179, 0.1753, 0.3332],
- [ 0.4819, -0.4374, 1.2815, -1.2242, -0.1292, -1.3188, 0.3799, 0.3378],
- [ 0.5563, -0.3974, 1.0200, -1.2427, -0.5874, -0.9715, 0.2051, 0.2670],
- [ 0.6416, -0.3941, 1.8055, -0.1221, -0.4340, -0.1021, 0.5248, 0.1909],
- [ 0.6706, -0.4074, 1.8877, 0.1244, -0.5731, -0.2325, 0.6664, 0.0643],
- [ 0.6742, -0.3256, 1.6654, -0.1106, -0.4677, -0.1163, 0.1835, 0.2330]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6224, -0.4032, 1.5940, 0.2930, -0.5769, -0.2690, 0.8900,
- 0.2516],
- [ 0.5776, -0.4484, 1.8249, -0.1843, -0.5423, 0.1159, 0.5547,
- 0.1929],
- [ 0.5453, -0.4091, 1.7499, 0.1390, -0.2940, -0.0996, 0.1300,
- 0.4272],
- [ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854],
- [ 0.5410, -0.4321, 0.8838, -0.9849, -0.5769, -1.0003, 0.2603,
- 0.3315],
- [ 0.5777, -0.4415, 1.6491, -0.0122, -0.4557, -0.1073, 0.4912,
- 0.2237],
- [ 0.6249, -0.4352, 1.8018, 0.2545, -0.6173, -0.1997, 0.6401,
- 0.0291],
- [ 0.5425, -0.4067, 1.5543, 0.0241, -0.5596, -0.1381, 0.1005,
- 0.2093]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6867347755469382
- step: 49
- running loss: 0.01401499541932527
- Train Steps: 49/90 Loss: 0.0140 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719],
- [0.6200, 0.3913, 0.8788, 0.5217, 0.4075, 0.5100, 0.6060, 0.4913],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [0.6333, 0.4037, 0.8638, 0.5733, 0.4012, 0.4717, 0.6369, 0.4938],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6162, 0.4014, 0.8800, 0.5333, 0.3750, 0.4817, 0.5988, 0.5283],
- [0.6190, 0.4135, 0.8000, 0.4883, 0.3566, 0.3647, 0.5613, 0.5900],
- [ nan, nan, 0.7512, 0.2117, 0.4288, 0.2000, 0.5600, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5894, -0.3801, 1.6447, 0.1531, -0.4084, 0.0639, 0.3153, 0.1972],
- [ 0.6713, -0.3791, 1.7616, 0.0157, -0.3227, 0.1243, 0.4278, 0.0049],
- [ 0.6451, -0.3785, 1.8642, -0.5306, -0.5431, -0.1355, 0.8136, 0.2384],
- [ 0.7246, -0.3287, 1.7098, 0.2364, -0.4094, -0.0973, 0.7035, 0.0385],
- [ 0.7551, -0.2297, 1.7543, -0.4982, -0.5698, -0.4273, 0.2666, 0.2370],
- [ 0.6142, -0.3619, 1.7747, 0.0742, -0.4392, -0.0329, 0.5170, 0.2477],
- [ 0.6024, -0.3271, 1.5167, -0.2986, -0.5872, -0.5988, 0.2826, 0.4324],
- [-2.1414, -2.1944, 1.1244, -1.3578, -0.3550, -1.3435, 0.2133, 0.2454]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5079, -0.4714, 1.6575, 0.2237, -0.4961, 0.0774, 0.1465,
- -0.1061],
- [ 0.5776, -0.4784, 1.7730, 0.1236, -0.4037, 0.0697, 0.5132,
- -0.0168],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [ 0.6395, -0.4213, 1.7037, 0.3623, -0.4326, -0.1073, 0.6560,
- -0.0049],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [ 0.5603, -0.4319, 1.7788, 0.1775, -0.5538, -0.0611, 0.4797,
- 0.1544],
- [ 0.5732, -0.3761, 1.4092, -0.0303, -0.6388, -0.6012, 0.3065,
- 0.4393],
- [-2.2859, -2.2859, 1.1841, -1.3082, -0.3055, -1.3621, 0.3007,
- 0.3084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6954759764485061
- step: 50
- running loss: 0.013909519528970122
- Train Steps: 50/90 Loss: 0.0139 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7225, 0.2167, 0.3987, 0.2283, 0.5427, 0.5181],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6179, 0.3993, 0.8925, 0.4789, 0.3879, 0.4900, 0.6041, 0.5279],
- [0.6364, 0.4144, 0.8625, 0.3083, 0.4913, 0.2000, 0.6448, 0.5274],
- [0.6208, 0.4082, 0.8538, 0.3067, 0.3588, 0.3717, 0.6112, 0.5517],
- [0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [0.6259, 0.4133, 0.8200, 0.2317, 0.5025, 0.1533, 0.6250, 0.4947],
- [0.6128, 0.4115, 0.7934, 0.3778, 0.3450, 0.4033, 0.5337, 0.5456]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.0417, -2.1145, 0.9707, -1.2916, -0.5134, -1.1903, 0.1369, 0.1892],
- [ 0.6440, -0.3374, 1.2099, -1.0695, -0.4862, -0.8650, 0.6079, 0.3799],
- [ 0.6411, -0.3589, 1.8196, -0.0150, -0.5245, 0.0290, 0.5910, 0.1434],
- [ 0.5620, -0.3825, 1.6482, -0.7810, -0.0810, -1.1873, 0.6928, 0.2009],
- [ 0.5983, -0.3833, 1.6245, -0.7267, -0.6526, -0.4460, 0.5589, 0.2570],
- [ 0.3943, -0.4981, 1.0297, -1.1695, -0.4064, -1.2838, 0.2265, 0.0506],
- [ 0.5851, -0.3882, 1.4568, -1.1265, -0.0250, -1.3781, 0.6049, 0.1001],
- [ 0.5941, -0.3497, 1.4244, -0.4968, -0.6755, -0.3567, 0.3419, 0.2971]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859e+00, -2.2859e+00, 1.0513e+00, -1.2851e+00, -4.4411e-01,
- -1.2313e+00, 2.2057e-01, 1.0729e-01],
- [ 5.9151e-01, -3.6821e-01, 1.2187e+00, -1.2313e+00, -4.3256e-01,
- -9.5412e-01, 5.7783e-01, 3.7768e-01],
- [ 5.6801e-01, -4.4175e-01, 1.8365e+00, -7.4042e-02, -4.9414e-01,
- -2.2653e-02, 5.0451e-01, 1.5252e-01],
- [ 6.5365e-01, -3.7194e-01, 1.6979e+00, -8.6174e-01, -1.6859e-02,
- -1.3621e+00, 6.9257e-01, 1.5008e-01],
- [ 5.8135e-01, -4.0031e-01, 1.6575e+00, -8.6944e-01, -6.2887e-01,
- -5.6921e-01, 5.3741e-01, 2.6220e-01],
- [ 5.1085e-01, -4.3164e-01, 1.1436e+00, -1.3467e+00, -3.8637e-01,
- -1.4160e+00, 1.2393e-01, -5.8033e-02],
- [ 6.0514e-01, -3.7714e-01, 1.5016e+00, -1.2159e+00, 3.5104e-02,
- -1.5777e+00, 6.0099e-01, -9.2270e-04],
- [ 5.4440e-01, -3.8522e-01, 1.3786e+00, -5.4087e-01, -6.9238e-01,
- -4.2294e-01, 1.7915e-01, 2.3412e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7030893294140697
- step: 51
- running loss: 0.013786065282628816
- Train Steps: 51/90 Loss: 0.0138 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [0.6175, 0.4013, 0.7734, 0.2783, 0.4475, 0.1786, 0.5790, 0.5351],
- [0.6159, 0.4085, 0.6900, 0.2283, 0.4088, 0.1950, 0.5123, 0.5397],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6279, 0.4008, 0.8600, 0.4883, 0.4325, 0.5283, 0.7010, 0.5378],
- [0.6364, 0.4144, 0.8625, 0.3083, 0.4913, 0.2000, 0.6448, 0.5274],
- [0.6196, 0.4088, 0.8888, 0.4583, 0.4500, 0.5683, 0.6138, 0.5883],
- [0.6275, 0.4024, 0.7722, 0.2080, 0.4392, 0.2234, 0.6435, 0.5290]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6494, -0.3643, 1.7888, -0.1043, -0.6878, -0.2725, 0.3839, 0.0475],
- [ 0.5098, -0.4389, 1.2404, -1.0108, -0.3349, -1.3609, 0.3059, 0.1627],
- [ 0.3660, -0.5140, 0.8387, -1.1964, -0.4575, -1.3360, 0.0789, 0.2320],
- [ 0.6436, -0.3578, 1.8885, -0.4588, -0.6509, -0.4069, 0.5721, 0.1984],
- [ 0.5306, -0.4581, 1.7036, 0.0555, -0.3979, 0.1843, 0.7566, 0.2434],
- [ 0.4884, -0.4214, 1.6386, -0.8014, -0.1471, -1.2295, 0.6100, 0.2176],
- [ 0.5705, -0.3704, 1.7832, -0.1546, -0.3066, 0.3843, 0.5200, 0.3995],
- [ 0.5586, -0.4240, 1.1823, -1.2662, -0.3523, -1.1681, 0.6554, 0.1749]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [ 0.5664, -0.4321, 1.2862, -1.0003, -0.2189, -1.4608, 0.3883,
- 0.1855],
- [ 0.5591, -0.3990, 0.9012, -1.2313, -0.3979, -1.3852, 0.0804,
- 0.2071],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.6141, -0.4345, 1.6864, -0.0303, -0.2882, 0.1544, 0.9521,
- 0.1982],
- [ 0.6537, -0.3719, 1.6979, -0.8617, -0.0169, -1.3621, 0.6926,
- 0.1501],
- [ 0.5759, -0.3978, 1.8192, -0.1689, -0.2074, 0.3392, 0.5490,
- 0.4316],
- [ 0.6125, -0.4273, 1.2807, -1.3253, -0.2574, -1.2542, 0.6864,
- 0.1575]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0058, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0058, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7088540904223919
- step: 52
- running loss: 0.013631809431199845
- Train Steps: 52/90 Loss: 0.0136 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6197, 0.4091, 0.8800, 0.4783, 0.3538, 0.4767, 0.5950, 0.5550],
- [0.6125, 0.3983, 0.8750, 0.4867, 0.4275, 0.4783, 0.5413, 0.5717],
- [0.6339, 0.4159, 0.8400, 0.5617, 0.3825, 0.4150, 0.7343, 0.5748],
- [0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6276, 0.4095, 0.8237, 0.2250, 0.4662, 0.1783, 0.6171, 0.4869],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896],
- [0.6124, 0.4083, 0.6954, 0.3069, 0.3650, 0.2750, 0.5163, 0.5485],
- [0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6867, -0.3530, 1.8893, -0.0427, -0.6081, -0.0703, 0.5564, 0.2224],
- [ 0.6031, -0.4232, 1.8380, -0.0181, -0.3122, 0.0297, 0.3452, 0.1512],
- [ 0.5968, -0.4435, 1.6900, 0.2695, -0.5246, -0.3441, 1.0880, 0.1359],
- [ 0.5225, -0.4026, 1.1519, -1.2220, -0.3218, -1.0692, 0.5546, 0.5218],
- [ 0.4358, -0.4869, 1.6258, -1.2949, -0.0756, -1.2650, 0.5801, -0.0843],
- [ 0.5571, -0.4031, 1.5258, -0.5435, -0.6655, -0.8282, 0.1144, 0.2918],
- [ 0.5077, -0.4292, 1.0523, -0.9469, -0.5805, -0.9220, 0.1200, 0.1878],
- [ 0.4917, -0.4569, 1.0927, -1.1920, -0.5139, -0.9124, 0.5999, 0.2974]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5763, -0.3963, 1.7788, -0.0765, -0.6520, -0.0842, 0.4624,
- 0.2776],
- [ 0.5432, -0.4462, 1.7557, -0.0380, -0.3113, -0.0765, 0.2141,
- 0.3546],
- [ 0.6421, -0.3647, 1.5940, 0.3084, -0.5192, -0.3691, 1.1057,
- 0.3692],
- [ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084],
- [ 0.6127, -0.3944, 1.5189, -1.2467, -0.1323, -1.4622, 0.5646,
- -0.0369],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374],
- [ 0.5425, -0.3998, 0.9263, -0.8683, -0.6000, -1.0157, 0.0990,
- 0.2476],
- [ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0067, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0067, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7155375052243471
- step: 53
- running loss: 0.013500707645742398
- Train Steps: 53/90 Loss: 0.0135 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6249, 0.4142, 0.8350, 0.3283, 0.3613, 0.3700, 0.6188, 0.5400],
- [0.6216, 0.4167, 0.8588, 0.5583, 0.3975, 0.5167, 0.5775, 0.5667],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6236, 0.4081, 0.8575, 0.3000, 0.3713, 0.3033, 0.5550, 0.5633],
- [0.6149, 0.4054, 0.6713, 0.2333, 0.4025, 0.2017, 0.5213, 0.5695],
- [0.6163, 0.4114, 0.7650, 0.2017, 0.3763, 0.2867, 0.5631, 0.5071],
- [ nan, nan, 0.7425, 0.2117, 0.3937, 0.2433, 0.5438, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6952, -0.2909, 1.5572, -0.7564, -0.6099, -0.5721, 0.5670, 0.2324],
- [ 1.0167, -0.1225, 1.6744, 0.3887, -0.4468, 0.1252, 0.6714, 0.2086],
- [-1.5770, -1.8032, 1.3967, -0.9103, -0.6525, -0.7907, 0.2326, 0.1734],
- [ 0.7683, -0.2966, 1.9082, -0.1257, -0.2844, -0.8902, 1.1414, 0.2348],
- [ 0.6591, -0.3276, 1.5648, -0.8208, -0.4872, -0.8877, 0.3777, 0.1544],
- [ 0.7440, -0.2497, 0.8397, -1.2438, -0.3175, -1.3303, 0.1661, 0.2908],
- [ 0.8056, -0.2475, 1.1913, -1.2971, -0.4608, -0.9584, 0.5213, 0.0536],
- [-1.8084, -1.9542, 1.1200, -1.2545, -0.4184, -1.0982, 0.2681, 0.2478]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6006, -0.3728, 1.5709, -0.7694, -0.6173, -0.5769, 0.5721,
- 0.2083],
- [ 0.5853, -0.3614, 1.6806, 0.2930, -0.4499, 0.1005, 0.3815,
- 0.3315],
- [-2.2859, -2.2859, 1.5767, -0.7540, -0.6404, -0.7309, 0.1753,
- 0.0893],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.5944, -0.4008, 1.6748, -0.9002, -0.5711, -0.8848, 0.2776,
- 0.3161],
- [ 0.5544, -0.4133, 0.8145, -1.2082, -0.4268, -1.3544, 0.1221,
- 0.3446],
- [ 0.5606, -0.3856, 1.2476, -1.3544, -0.5480, -0.9618, 0.3152,
- 0.0562],
- [-2.2859, -2.2859, 1.1436, -1.3082, -0.4672, -1.1620, 0.2256,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0291, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0291, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7446684222668409
- step: 54
- running loss: 0.013790155967904462
- Train Steps: 54/90 Loss: 0.0138 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6176, 0.4030, 0.8850, 0.4850, 0.3688, 0.4050, 0.5312, 0.5783],
- [0.6192, 0.3980, 0.7078, 0.2750, 0.4250, 0.2100, 0.5450, 0.5783],
- [0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973],
- [0.6137, 0.4035, 0.8850, 0.4417, 0.3900, 0.4283, 0.5449, 0.5617],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136],
- [0.6081, 0.3950, 0.8538, 0.4667, 0.3850, 0.4917, 0.5342, 0.4954],
- [0.6162, 0.4014, 0.8800, 0.5333, 0.3750, 0.4817, 0.5988, 0.5283]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 7.3151e-01, -3.2304e-01, 1.8139e+00, -4.3675e-01, -6.3154e-01,
- -1.5793e-01, 7.0981e-01, 2.7233e-01],
- [ 7.2082e-01, -3.2855e-01, 1.7849e+00, 1.5959e-03, -5.5553e-01,
- -3.2249e-01, 3.4367e-01, 2.8201e-01],
- [-1.0985e+00, -1.4875e+00, 1.0323e+00, -1.1355e+00, -2.6686e-01,
- -1.3477e+00, 3.6026e-01, 3.8651e-01],
- [ 7.9492e-01, -3.2222e-01, 1.8341e+00, 2.6112e-01, -5.6936e-01,
- -1.4523e-01, 8.4170e-01, 6.2565e-02],
- [ 7.0293e-01, -3.1763e-01, 1.8348e+00, -1.5597e-01, -4.7494e-01,
- -2.8655e-01, 4.0996e-01, 2.7960e-01],
- [ 5.9462e-01, -3.7201e-01, 8.9894e-01, -1.3596e+00, -4.7997e-01,
- -1.1826e+00, 4.1370e-01, 1.1764e-01],
- [ 5.3425e-01, -4.5420e-01, 1.7221e+00, -1.7401e-01, -5.0341e-01,
- -2.0689e-02, 3.2310e-01, 3.1694e-02],
- [ 5.7662e-01, -4.1861e-01, 1.7637e+00, 2.6550e-01, -4.6343e-01,
- -2.3559e-02, 5.5708e-01, 2.2800e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.5667, -0.4246, 1.8018, -0.0457, -0.5827, -0.4152, 0.1679,
- 0.3854],
- [ 0.5742, -0.4474, 0.9834, -1.0159, -0.3229, -1.3159, 0.2314,
- 0.3854],
- [ 0.6240, -0.4321, 1.8423, 0.1852, -0.5885, -0.1612, 0.6962,
- 0.0111],
- [ 0.5488, -0.4221, 1.8018, -0.2459, -0.4845, -0.3075, 0.2309,
- 0.3087],
- [ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862],
- [ 0.5227, -0.4615, 1.6575, -0.1304, -0.5076, -0.0149, 0.1815,
- 0.0021],
- [ 0.5603, -0.4319, 1.7788, 0.1775, -0.5538, -0.0611, 0.4797,
- 0.1544]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0676, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0676, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8122478108853102
- step: 55
- running loss: 0.01476814201609655
- Train Steps: 55/90 Loss: 0.0148 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6279, 0.4050, 0.8025, 0.2200, 0.4313, 0.2733, 0.6820, 0.5320],
- [0.6068, 0.3963, 0.8650, 0.4317, 0.4037, 0.5083, 0.5253, 0.4999],
- [0.6197, 0.3986, 0.8800, 0.4617, 0.4188, 0.4783, 0.5687, 0.5550],
- [0.6198, 0.4076, 0.7945, 0.2453, 0.4475, 0.1871, 0.5897, 0.5350],
- [0.6205, 0.4016, 0.8350, 0.2717, 0.3987, 0.2550, 0.5787, 0.5133],
- [0.6227, 0.4193, 0.8838, 0.4933, 0.3663, 0.3733, 0.5637, 0.5633],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6733, -0.3537, 1.3374, -1.3534, -0.3475, -1.1263, 0.9403, 0.1226],
- [ 0.4557, -0.5294, 1.6551, -0.3730, -0.5073, 0.0383, 0.2753, 0.0239],
- [ 0.4782, -0.5015, 1.6879, -0.2401, -0.4075, -0.0528, 0.4632, 0.2047],
- [ 0.5682, -0.3980, 1.3429, -1.2565, -0.1921, -1.4071, 0.6248, 0.1764],
- [ 0.6195, -0.3795, 1.5597, -1.1509, -0.4353, -1.0833, 0.5308, 0.0891],
- [ 0.5529, -0.4028, 1.6849, -0.0855, -0.6844, -0.5984, 0.4197, 0.4058],
- [ 0.5639, -0.4079, 1.6461, 0.3015, -0.4696, -0.1384, 0.3921, 0.3661],
- [ 0.4199, -0.5031, 1.5452, 0.2457, -0.6153, -0.5789, 0.3617, 0.4921]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6141, -0.4153, 1.4208, -1.2697, -0.2940, -1.0234, 0.8644,
- 0.1715],
- [ 0.5168, -0.4556, 1.7095, -0.2921, -0.4210, 0.0620, 0.1404,
- 0.0231],
- [ 0.5763, -0.4450, 1.7788, -0.1535, -0.3517, -0.0765, 0.3411,
- 0.2776],
- [ 0.5768, -0.4031, 1.3838, -1.1527, -0.2188, -1.4216, 0.4379,
- 0.1850],
- [ 0.5800, -0.4312, 1.5709, -1.0311, -0.4441, -1.1081, 0.3873,
- 0.0851],
- [ 0.5902, -0.3493, 1.7961, -0.0072, -0.5942, -0.5615, 0.3180,
- 0.3161],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007],
- [ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0060, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0060, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8182361843064427
- step: 56
- running loss: 0.01461136043404362
- Train Steps: 56/90 Loss: 0.0146 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6059, 0.4002, 0.7562, 0.2767, 0.3538, 0.3033, 0.5529, 0.5455],
- [ nan, nan, 0.8888, 0.3100, 0.5262, 0.2817, 0.7145, 0.6003],
- [0.6246, 0.4126, 0.8850, 0.4833, 0.4200, 0.5350, 0.6112, 0.5250],
- [0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5556, -0.4120, 1.6844, -0.5043, -0.7045, -0.3261, 0.6481, 0.2448],
- [ 0.5690, -0.3721, 1.1256, -1.0251, -0.6030, -1.0134, 0.4253, 0.1987],
- [-2.3533, -2.3378, 1.6712, -0.8950, 0.0409, -1.2024, 1.0081, 0.3700],
- [ 0.5131, -0.4205, 1.6990, -0.0872, -0.4344, 0.0332, 0.6334, 0.1912],
- [ 0.4841, -0.4213, 1.5142, -0.6901, -0.7282, -0.4796, 0.3208, 0.1235],
- [ 0.6184, -0.3216, 1.5878, -0.5615, -0.4717, -0.9574, 0.4515, 0.4288],
- [ 0.4499, -0.4935, 1.6163, -0.1788, -0.1629, -0.1551, 0.3060, 0.1087],
- [ 0.5053, -0.4721, 1.4499, 0.3496, -0.3156, -0.0978, 0.2096, 0.0571]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.5129, -0.4374, 1.2072, -1.0080, -0.6520, -0.8848, 0.2679,
- 0.2335],
- [-2.2859, -2.2859, 1.8192, -0.8540, 0.1448, -0.9849, 1.0143,
- 0.4867],
- [ 0.5991, -0.3803, 1.8018, -0.0534, -0.3460, 0.1852, 0.5374,
- 0.1390],
- [ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8277576519176364
- step: 57
- running loss: 0.014522064068730463
- Train Steps: 57/90 Loss: 0.0145 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6132, 0.4118, 0.8200, 0.3633, 0.3563, 0.5400, 0.5787, 0.5136],
- [0.6199, 0.4071, 0.7600, 0.2117, 0.4037, 0.2767, 0.6138, 0.5550],
- [0.6200, 0.3998, 0.8850, 0.3950, 0.3500, 0.4650, 0.6138, 0.5400],
- [0.6307, 0.4029, 0.8650, 0.5200, 0.3763, 0.4017, 0.7311, 0.5366],
- [0.6332, 0.4118, 0.9238, 0.4267, 0.4012, 0.4733, 0.7525, 0.5436],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [0.6204, 0.4091, 0.8950, 0.4783, 0.3613, 0.4617, 0.6063, 0.5617],
- [0.6206, 0.4123, 0.7175, 0.2400, 0.3887, 0.2933, 0.6225, 0.5750]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3653, -0.5301, 1.4886, -0.5390, -0.5928, 0.0507, 0.2721, 0.2364],
- [ 0.5125, -0.4335, 1.2703, -1.2746, -0.3055, -1.0842, 0.5741, 0.3149],
- [ 0.4835, -0.5004, 1.7939, -0.4004, -0.6551, -0.2711, 0.3656, 0.2429],
- [ 0.5630, -0.4785, 1.7487, 0.2648, -0.5154, -0.5629, 0.8131, 0.0676],
- [ 0.5086, -0.5020, 1.8854, -0.2616, -0.4566, -0.1313, 0.9231, 0.2956],
- [ 0.6456, -0.3688, 1.2962, -1.1110, -0.1280, -1.6545, 0.3240, 0.0716],
- [ 0.6136, -0.3784, 1.8307, 0.0766, -0.5950, -0.2757, 0.2350, 0.2931],
- [ 0.6073, -0.3740, 1.0229, -1.0454, -0.4761, -1.0501, 0.4470, 0.4177]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5466, -0.3840, 1.5016, -0.6077, -0.6404, 0.2083, 0.3871,
- 0.0862],
- [ 0.5775, -0.4054, 1.2245, -1.3082, -0.4210, -1.0080, 0.5490,
- 0.2776],
- [ 0.5778, -0.4393, 1.8018, -0.4614, -0.6693, -0.1381, 0.5490,
- 0.2083],
- [ 0.6273, -0.4249, 1.7095, 0.1159, -0.5480, -0.4306, 1.0910,
- 0.1928],
- [ 0.6388, -0.3840, 1.9808, -0.3152, -0.4326, -0.0996, 1.1897,
- 0.2249],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [ 0.5797, -0.3965, 1.8480, -0.0765, -0.6173, -0.1535, 0.5143,
- 0.3084],
- [ 0.5805, -0.3818, 1.0282, -1.1774, -0.4903, -0.9310, 0.5894,
- 0.3700]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0118, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0118, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8395349802449346
- step: 58
- running loss: 0.014474741038705769
- Train Steps: 58/90 Loss: 0.0145 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.4101, 0.8838, 0.5283, 0.3763, 0.5267, 0.5913, 0.5567],
- [0.6133, 0.4066, 0.6787, 0.2617, 0.3800, 0.2433, 0.5147, 0.5358],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6185, 0.4129, 0.8900, 0.4567, 0.3937, 0.5417, 0.5734, 0.5110],
- [0.6263, 0.4029, 0.8900, 0.4933, 0.4075, 0.5183, 0.6406, 0.4758],
- [0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183],
- [0.6151, 0.4125, 0.8738, 0.4417, 0.3575, 0.3783, 0.5138, 0.5483],
- [0.6160, 0.4093, 0.8314, 0.4417, 0.3675, 0.4583, 0.5250, 0.5808]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4477, -0.5367, 1.7819, 0.2281, -0.5142, 0.0859, 0.5084, 0.3027],
- [ 0.6758, -0.3454, 0.9215, -1.0019, -0.4842, -1.2423, 0.1739, 0.2234],
- [ 0.5677, -0.3958, 1.6321, -0.6215, -0.5877, -0.8491, 0.4111, 0.2329],
- [ 0.5721, -0.4261, 1.8749, -0.1511, -0.4139, 0.1686, 0.4451, 0.1869],
- [ 0.5606, -0.4834, 1.8408, -0.0444, -0.3999, 0.0658, 0.7831, 0.1033],
- [ 0.5758, -0.4490, 1.2640, -1.3102, -0.1981, -1.5140, 0.5301, 0.1225],
- [ 0.4960, -0.4478, 1.6744, -0.1980, -0.6295, -0.6053, 0.1823, 0.3388],
- [ 0.3174, -0.5879, 1.6093, -0.2038, -0.5482, -0.2282, 0.3039, 0.4434]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5770, -0.3918, 1.7961, 0.1544, -0.5480, 0.1467, 0.4450,
- 0.2853],
- [ 0.5470, -0.4081, 0.8492, -1.0773, -0.5307, -1.1620, 0.0912,
- 0.1890],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.5711, -0.3788, 1.8249, -0.1766, -0.4672, 0.2160, 0.3625,
- 0.0742],
- [ 0.6072, -0.4250, 1.8249, -0.0072, -0.4037, 0.1082, 0.6730,
- -0.0881],
- [ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082],
- [ 0.5551, -0.3807, 1.7499, -0.2459, -0.6346, -0.5384, 0.0871,
- 0.2468],
- [ 0.5592, -0.3956, 1.5543, -0.2456, -0.5885, -0.1689, 0.1392,
- 0.3968]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0073, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0073, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8468739879317582
- step: 59
- running loss: 0.01435379640562302
- Train Steps: 59/90 Loss: 0.0144 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672],
- [0.6153, 0.4119, 0.8463, 0.3833, 0.3600, 0.3200, 0.5106, 0.5563],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [0.6264, 0.4035, 0.8888, 0.4883, 0.4050, 0.5217, 0.6361, 0.4791],
- [0.6138, 0.4101, 0.8800, 0.5083, 0.4637, 0.5950, 0.5587, 0.5077],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5716, -0.4683, 1.6745, 0.2683, -0.6068, -0.3342, 0.6065, 0.1156],
- [ 0.6136, -0.3583, 1.5699, -0.5214, -0.5876, -0.8559, 0.1447, 0.3616],
- [ 0.3707, -0.5752, 1.5958, -0.3524, -0.5343, -0.1191, 0.1477, 0.1774],
- [ 0.6514, -0.3415, 1.3220, -0.9513, -0.3702, -1.1556, 0.1929, 0.4177],
- [ 0.5917, -0.4493, 1.7983, -0.0735, -0.4208, 0.0744, 0.7328, 0.0889],
- [ 0.4227, -0.5170, 1.7463, 0.0612, -0.0829, 0.3032, 0.4088, 0.2015],
- [ 0.4966, -0.4800, 1.8956, -0.3544, -0.4724, -0.7064, 0.7027, 0.3024],
- [ 0.4521, -0.4958, 1.6725, -0.5166, -0.7062, -0.4615, 0.4067, 0.1989]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278],
- [ 0.5561, -0.3834, 1.6229, -0.5153, -0.6231, -0.8079, 0.0727,
- 0.2837],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.5695, -0.3927, 1.3226, -0.9002, -0.4672, -1.1928, 0.1737,
- 0.3700],
- [ 0.6074, -0.4223, 1.8192, -0.0303, -0.4152, 0.1236, 0.6524,
- -0.0731],
- [ 0.5491, -0.3918, 1.7788, 0.0620, -0.1439, 0.4624, 0.2946,
- 0.0592],
- [ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0074, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0074, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8542463732883334
- step: 60
- running loss: 0.014237439554805557
- Train Steps: 60/90 Loss: 0.0142 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.4108, 0.7438, 0.2700, 0.3650, 0.3683, 0.6238, 0.5717],
- [0.6201, 0.4050, 0.7757, 0.2234, 0.4459, 0.1798, 0.5975, 0.5426],
- [0.6164, 0.4076, 0.8838, 0.4117, 0.3713, 0.5550, 0.6238, 0.5350],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6350, 0.4043, 0.8738, 0.5650, 0.3850, 0.4750, 0.6401, 0.4950],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6277, 0.4036, 0.8688, 0.3617, 0.3925, 0.2600, 0.6132, 0.4742],
- [0.6267, 0.4094, 0.8712, 0.3083, 0.4400, 0.2267, 0.6250, 0.5200]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4369, -0.4964, 1.1146, -1.0326, -0.6546, -0.4766, 0.3411, 0.4066],
- [ 0.6392, -0.3629, 1.1748, -1.1745, -0.2520, -1.3513, 0.2541, 0.2668],
- [ 0.3895, -0.5462, 1.7361, -0.3636, -0.5520, 0.3711, 0.4176, 0.3004],
- [ 0.4097, -0.5550, 1.7841, 0.0594, -0.5848, -0.4577, 0.3989, 0.0598],
- [ 0.4349, -0.5610, 1.6784, 0.2591, -0.5321, 0.0387, 0.3806, 0.0563],
- [ 0.5784, -0.4637, 1.9492, -0.3301, -0.2924, -0.8105, 0.9900, 0.2359],
- [ 0.6130, -0.4002, 1.7304, -0.6834, -0.4741, -1.0336, 0.3487, 0.0620],
- [ 0.6742, -0.3106, 1.7169, -0.9077, -0.2364, -1.1439, 0.4729, 0.2411]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7471e-01, -3.8861e-01, 1.1494e+00, -1.0388e+00, -6.0000e-01,
- -5.8460e-01, 5.9515e-01, 3.5458e-01],
- [ 5.7841e-01, -4.1532e-01, 1.2972e+00, -1.2541e+00, -2.2647e-01,
- -1.4553e+00, 4.7413e-01, 2.2033e-01],
- [ 5.6143e-01, -4.0323e-01, 1.7961e+00, -3.8445e-01, -5.7113e-01,
- 2.7760e-01, 5.9515e-01, 1.8522e-01],
- [ 6.2730e-01, -4.1045e-01, 1.8480e+00, 1.0824e-01, -5.5381e-01,
- -5.0762e-01, 6.4140e-01, -4.8817e-03],
- [ 6.4706e-01, -4.1832e-01, 1.7499e+00, 3.2379e-01, -5.0762e-01,
- -9.1917e-02, 6.7064e-01, 4.6189e-04],
- [ 6.2236e-01, -4.3453e-01, 1.9404e+00, -2.9207e-01, -3.1709e-01,
- -8.7714e-01, 1.0655e+00, 2.1421e-01],
- [ 6.1339e-01, -4.2179e-01, 1.7268e+00, -6.1540e-01, -4.7298e-01,
- -1.0850e+00, 5.4635e-01, -9.5723e-02],
- [ 6.0889e-01, -3.9477e-01, 1.7383e+00, -8.6174e-01, -2.5358e-01,
- -1.2390e+00, 6.0092e-01, 1.1594e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0121, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0121, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8663500072434545
- step: 61
- running loss: 0.014202459135138597
- Train Steps: 61/90 Loss: 0.0142 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6058, 0.3986, 0.8324, 0.4626, 0.3838, 0.4983, 0.5147, 0.5466],
- [ nan, nan, 0.6488, 0.1817, 0.4325, 0.1867, 0.5475, 0.5733],
- [0.6102, 0.4020, 0.8638, 0.3717, 0.3625, 0.5017, 0.6038, 0.5500],
- [0.6353, 0.4128, 0.9138, 0.3533, 0.4688, 0.3250, 0.7145, 0.5991],
- [0.6284, 0.4127, 0.8538, 0.5867, 0.4363, 0.5083, 0.6038, 0.5433],
- [0.6179, 0.3993, 0.8925, 0.4789, 0.3879, 0.4900, 0.6041, 0.5279],
- [0.6275, 0.4024, 0.8600, 0.2283, 0.5350, 0.1800, 0.7074, 0.5413],
- [0.6129, 0.4063, 0.8738, 0.5250, 0.4313, 0.4733, 0.5230, 0.5874]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3651, -0.5405, 1.6259, -0.0620, -0.5820, 0.0492, 0.1450, 0.1149],
- [-1.0391, -1.4939, 0.8902, -1.3470, -0.2780, -1.4584, 0.2304, 0.4114],
- [ 0.5111, -0.4410, 1.7324, -0.5073, -0.7363, 0.0249, 0.4958, 0.1436],
- [ 0.6049, -0.4107, 1.9642, -0.5052, -0.2794, -0.8110, 1.0207, 0.2991],
- [ 0.5173, -0.4620, 1.6767, 0.4512, -0.4031, 0.0677, 0.3231, 0.0606],
- [ 0.4464, -0.4915, 1.8005, -0.0707, -0.6219, -0.0269, 0.3503, 0.0424],
- [ 0.9084, -0.1551, 1.7199, -1.1509, 0.0133, -1.4654, 0.9196, 0.0301],
- [ 0.5109, -0.4292, 1.6963, 0.2100, -0.4320, -0.0112, -0.0428, 0.2638]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5124, -0.4446, 1.5587, -0.1493, -0.5134, 0.0159, 0.0912,
- 0.2386],
- [-2.2859, -2.2859, 0.7106, -1.4468, -0.2882, -1.4237, 0.2430,
- 0.3623],
- [ 0.5324, -0.4293, 1.7037, -0.5692, -0.6115, 0.0313, 0.5028,
- 0.2545],
- [ 0.6487, -0.3792, 1.9346, -0.6539, -0.1208, -0.7848, 1.0143,
- 0.4814],
- [ 0.6166, -0.3795, 1.6575, 0.4239, -0.2709, 0.0620, 0.5028,
- 0.2237],
- [ 0.5680, -0.4417, 1.8365, -0.0740, -0.4941, -0.0227, 0.5045,
- 0.1525],
- [ 0.6125, -0.4273, 1.6864, -1.2313, 0.1852, -1.4545, 0.9814,
- 0.2142],
- [ 0.5453, -0.4091, 1.7499, 0.1390, -0.2940, -0.0996, 0.1300,
- 0.4272]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0454, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0454, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9117817068472505
- step: 62
- running loss: 0.014706156562052427
- Train Steps: 62/90 Loss: 0.0147 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6183, 0.4076, 0.8838, 0.4517, 0.3813, 0.4483, 0.5775, 0.5633],
- [0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356],
- [0.6122, 0.3993, 0.8738, 0.4667, 0.4517, 0.4879, 0.5155, 0.4927],
- [0.6249, 0.4142, 0.8350, 0.3283, 0.3613, 0.3700, 0.6188, 0.5400],
- [ nan, nan, 0.8463, 0.2550, 0.5850, 0.2133, 0.7129, 0.6072],
- [0.6264, 0.4067, 0.9050, 0.4183, 0.3775, 0.4600, 0.6308, 0.4862],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3876, -0.5186, 1.7843, -0.1677, -0.5246, -0.2142, 0.2486, 0.2402],
- [ 0.5521, -0.4131, 1.4844, -0.9581, -0.3242, -1.1722, 0.6436, 0.1172],
- [ 0.4796, -0.4917, 1.7374, -0.0860, -0.2325, 0.0316, 0.0268, -0.1138],
- [ 0.5655, -0.4131, 1.6269, -0.6418, -0.6632, -0.6151, 0.5047, 0.1447],
- [-2.0073, -2.1644, 1.5982, -0.9444, 0.2918, -1.1021, 1.0624, 0.3503],
- [ 0.5611, -0.4074, 1.8383, -0.2921, -0.6295, -0.2790, 0.4994, -0.0903],
- [ 0.4352, -0.4581, 1.0209, -0.8541, -0.6522, -0.8294, 0.1052, 0.2289],
- [ 0.6101, -0.3655, 1.4231, -0.7075, -0.6191, -0.5375, 0.3774, 0.4063]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5700, -0.4032, 1.7961, -0.1997, -0.5249, -0.2151, 0.3815,
- 0.3161],
- [ 0.6174, -0.4225, 1.4975, -1.1709, -0.3174, -1.1806, 0.6539,
- 0.1879],
- [ 0.5417, -0.4417, 1.7499, -0.1304, -0.1994, -0.0324, 0.0951,
- -0.0099],
- [ 0.6006, -0.3728, 1.5709, -0.7694, -0.6173, -0.5769, 0.5721,
- 0.2083],
- [-2.2859, -2.2859, 1.6229, -1.1081, 0.4162, -1.3005, 1.0070,
- 0.5188],
- [ 0.6072, -0.4075, 1.8942, -0.3537, -0.5423, -0.1612, 0.6277,
- -0.0400],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0091, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0091, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9208882143720984
- step: 63
- running loss: 0.014617273244001562
- Train Steps: 63/90 Loss: 0.0146 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6060, 0.3924, 0.8450, 0.5717, 0.4200, 0.5217, 0.5253, 0.4752],
- [0.6198, 0.4114, 0.8263, 0.3283, 0.3550, 0.3583, 0.5813, 0.5667],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533],
- [0.6146, 0.4124, 0.8800, 0.4867, 0.3800, 0.4500, 0.5106, 0.5524],
- [0.6275, 0.4013, 0.8850, 0.2833, 0.4975, 0.2233, 0.7058, 0.5343],
- [0.6271, 0.4005, 0.8450, 0.6067, 0.3850, 0.4983, 0.6069, 0.4649],
- [0.6240, 0.4217, 0.8150, 0.3133, 0.4425, 0.2650, 0.5650, 0.5817],
- [0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.7482e-01, -4.2917e-01, 1.6042e+00, 2.2142e-01, -3.1746e-01,
- 1.4282e-01, 2.6080e-01, 1.7529e-02],
- [ 6.0468e-01, -3.6197e-01, 1.5752e+00, -8.6071e-01, -7.0272e-01,
- -5.9898e-01, 3.5001e-01, 2.9250e-01],
- [-1.7935e+00, -2.0138e+00, 1.3754e+00, -1.0052e+00, -5.5799e-01,
- -9.4388e-01, 3.6216e-01, 2.2459e-01],
- [ 5.5556e-01, -3.8595e-01, 1.7956e+00, -1.1016e-01, -5.4520e-01,
- -1.9756e-01, 1.6915e-01, 1.7724e-01],
- [ 6.3051e-01, -3.9541e-01, 1.8195e+00, -1.0585e+00, -4.3913e-02,
- -1.2640e+00, 9.3094e-01, -8.6043e-04],
- [ 6.1835e-01, -3.9714e-01, 1.6594e+00, 3.2765e-01, -5.4559e-01,
- 2.5476e-02, 5.1325e-01, -1.0676e-01],
- [ 5.3566e-01, -3.9626e-01, 1.4632e+00, -9.3724e-01, -2.8451e-01,
- -1.0030e+00, 3.8548e-01, 3.7415e-01],
- [ 6.2579e-01, -3.6221e-01, 1.8387e+00, 1.5004e-01, -4.5995e-01,
- 4.2121e-03, 3.9129e-01, 2.0803e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5132, -0.4736, 1.6171, 0.3546, -0.3460, 0.1236, 0.1404,
- -0.0911],
- [ 0.5768, -0.3857, 1.5305, -0.7694, -0.6462, -0.6308, 0.3988,
- 0.3315],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699],
- [ 0.5529, -0.3811, 1.7788, -0.0380, -0.5307, -0.2074, 0.0727,
- 0.2657],
- [ 0.6125, -0.4321, 1.8018, -0.9772, 0.0120, -1.2543, 0.9741,
- 0.1821],
- [ 0.6105, -0.4360, 1.6171, 0.5162, -0.5076, 0.0159, 0.5171,
- -0.1385],
- [ 0.5964, -0.3380, 1.4785, -0.8386, -0.2420, -1.0619, 0.3238,
- 0.4008],
- [ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0095, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9303545439615846
- step: 64
- running loss: 0.014536789749399759
- Train Steps: 64/90 Loss: 0.0145 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6098, 0.3991, 0.8638, 0.4717, 0.4263, 0.4967, 0.5212, 0.5650],
- [0.6250, 0.3993, 0.9138, 0.4333, 0.3763, 0.5217, 0.6995, 0.5320],
- [0.6111, 0.3995, 0.8788, 0.4567, 0.3813, 0.4833, 0.5450, 0.5700],
- [0.6284, 0.4127, 0.8538, 0.5867, 0.4363, 0.5083, 0.6038, 0.5433],
- [0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [0.6250, 0.4103, 0.8950, 0.4400, 0.3912, 0.5650, 0.6050, 0.5133],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5780, -0.3837, 1.8008, -0.3853, -0.6711, -0.5892, 0.4198, 0.1675],
- [ 0.3405, -0.5710, 1.7168, -0.1329, -0.2891, -0.0377, 0.1252, 0.1673],
- [ 0.5260, -0.4815, 1.9134, -0.3315, -0.5640, 0.0606, 0.8162, 0.1185],
- [ 0.5334, -0.4503, 1.7712, -0.2396, -0.5370, -0.1090, 0.2359, 0.1811],
- [ 0.4991, -0.4842, 1.6608, 0.3424, -0.2535, -0.0699, 0.4387, 0.1371],
- [ 0.7833, -0.1901, 1.4691, -0.5761, -0.1424, -1.3454, 0.1815, 0.5077],
- [ 0.6020, -0.3899, 1.8731, -0.2790, -0.4664, 0.2550, 0.5829, 0.0653],
- [ 0.7122, -0.3456, 1.8752, -0.1186, -0.4541, 0.2055, 0.8527, 0.0780]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5880, -0.3887, 1.8423, -0.3306, -0.6231, -0.5230, 0.4046,
- 0.1544],
- [ 0.5308, -0.4425, 1.7037, -0.1073, -0.3171, 0.0082, 0.1217,
- 0.3238],
- [ 0.6010, -0.4417, 1.9346, -0.2844, -0.5480, 0.1236, 0.9448,
- 0.1715],
- [ 0.5368, -0.4406, 1.7730, -0.1766, -0.5249, -0.0534, 0.2314,
- 0.3469],
- [ 0.6166, -0.3795, 1.6575, 0.4239, -0.2709, 0.0620, 0.5028,
- 0.2237],
- [ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882],
- [ 0.6010, -0.3909, 1.8480, -0.2536, -0.4788, 0.3238, 0.5085,
- 0.0851],
- [ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0056, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0056, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9359597829170525
- step: 65
- running loss: 0.014399381275646961
- Train Steps: 65/90 Loss: 0.0144 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6087, 0.3976, 0.8337, 0.3867, 0.3713, 0.3117, 0.5938, 0.5300],
- [0.6199, 0.4093, 0.7913, 0.2533, 0.4288, 0.2467, 0.5975, 0.5700],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6275, 0.4048, 0.8488, 0.2883, 0.4463, 0.2033, 0.6321, 0.5155],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [0.6064, 0.3953, 0.8738, 0.4417, 0.3663, 0.4683, 0.5511, 0.5416],
- [0.6200, 0.3913, 0.8788, 0.5217, 0.4075, 0.5100, 0.6060, 0.4913]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5275, -0.4615, 1.7064, -0.4898, -0.5600, -0.7662, 0.4385, 0.1653],
- [ 0.6013, -0.4050, 1.4063, -1.1010, -0.3349, -0.9689, 0.5350, 0.2417],
- [ 0.7457, -0.3026, 1.4962, -0.7248, -0.5378, -0.3920, 0.5202, 0.4871],
- [ 0.6284, -0.3964, 1.7495, -0.9078, -0.1837, -1.2448, 0.6356, 0.0381],
- [ 0.6329, -0.3909, 1.8370, 0.0075, -0.5290, -0.1658, 0.4625, 0.3722],
- [ 0.4794, -0.4647, 1.0918, -1.3628, -0.4908, -1.0768, 0.3367, 0.2329],
- [ 0.5490, -0.4589, 1.8760, -0.1951, -0.5087, -0.0391, 0.3459, 0.0651],
- [ 0.5781, -0.4790, 1.8421, 0.1577, -0.2619, 0.1640, 0.4385, -0.0967]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5255, -0.4495, 1.5651, -0.4999, -0.5711, -0.8463, 0.4566,
- 0.1621],
- [ 0.5771, -0.3955, 1.3688, -1.1158, -0.3055, -1.1466, 0.4739,
- 0.3469],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.6126, -0.4161, 1.6344, -0.9541, -0.2247, -1.3467, 0.6339,
- 0.0953],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853],
- [ 0.5149, -0.4603, 1.7499, -0.2459, -0.5942, -0.1227, 0.2596,
- 0.2155],
- [ 0.5776, -0.4784, 1.7730, 0.1236, -0.4037, 0.0697, 0.5132,
- -0.0168]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0063, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0063, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9422118063084781
- step: 66
- running loss: 0.014275936459219365
- Train Steps: 66/90 Loss: 0.0143 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6176, 0.3911, 0.8738, 0.4217, 0.3488, 0.4033, 0.6025, 0.4817],
- [ nan, nan, 0.9088, 0.3783, 0.4562, 0.2617, 0.6741, 0.5575],
- [0.6371, 0.4092, 0.8337, 0.5850, 0.3950, 0.5117, 0.6559, 0.5262],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5496, -0.4349, 1.7507, -0.4072, -0.6231, -0.4261, 0.2851, 0.0239],
- [-2.2059, -2.2517, 1.9492, -0.6269, -0.1311, -1.0656, 0.8054, 0.3096],
- [ 0.7202, -0.2978, 1.6103, 0.3138, -0.4298, 0.0925, 0.7170, 0.0483],
- [ 0.4686, -0.4632, 1.3834, -0.9696, -0.6548, -0.4049, 0.5023, 0.1962],
- [ 0.5204, -0.4374, 1.2730, -1.2612, -0.2085, -1.4333, 0.2908, 0.1288],
- [ 0.7266, -0.2690, 1.8107, -0.2156, -0.3568, 0.3110, 0.4842, 0.3369],
- [ 0.6644, -0.3100, 1.8463, 0.0909, -0.5299, -0.1275, 0.2842, 0.2648],
- [ 0.6698, -0.3349, 1.6451, -0.0235, -0.1953, 0.1273, 0.3835, 0.2099]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6692e-01, -4.7937e-01, 1.7499e+00, -3.3826e-01, -6.7506e-01,
- -4.2294e-01, 4.9700e-01, -6.1124e-02],
- [-2.2859e+00, -2.2859e+00, 1.9115e+00, -5.3841e-01, -1.7852e-01,
- -1.0773e+00, 8.2783e-01, 2.8902e-01],
- [ 6.5695e-01, -3.9601e-01, 1.5651e+00, 4.1617e-01, -4.6143e-01,
- 7.7444e-02, 7.4375e-01, 1.4474e-01],
- [ 5.7633e-01, -4.1470e-01, 1.3226e+00, -1.0619e+00, -6.6351e-01,
- -4.1524e-01, 5.3741e-01, 2.5450e-01],
- [ 5.7956e-01, -4.3510e-01, 1.3342e+00, -1.3159e+00, -2.1894e-01,
- -1.4853e+00, 4.0462e-01, 1.0054e-01],
- [ 5.7719e-01, -3.9130e-01, 1.8480e+00, -2.4588e-01, -4.3256e-01,
- 1.9292e-01, 5.3741e-01, 4.7005e-01],
- [ 5.8793e-01, -3.5912e-01, 1.8018e+00, 1.2363e-01, -5.5958e-01,
- -1.6120e-01, 3.4688e-01, 3.1609e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0051, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0051, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9472914161160588
- step: 67
- running loss: 0.01413867785247849
- Train Steps: 67/90 Loss: 0.0141 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4086, 0.8414, 0.2974, 0.4117, 0.2274, 0.5869, 0.5391],
- [0.6212, 0.4033, 0.8938, 0.4167, 0.3813, 0.4267, 0.5613, 0.5583],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6131, 0.4064, 0.8638, 0.5200, 0.4788, 0.4783, 0.5258, 0.5867],
- [0.6222, 0.4169, 0.8638, 0.5650, 0.4313, 0.4783, 0.5637, 0.5633],
- [0.6200, 0.3913, 0.8788, 0.5217, 0.4075, 0.5100, 0.6060, 0.4913],
- [0.6286, 0.4086, 0.8408, 0.2801, 0.4163, 0.2800, 0.6725, 0.5393],
- [0.6175, 0.4013, 0.7734, 0.2783, 0.4475, 0.1786, 0.5790, 0.5351]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6238, -0.3564, 1.6074, -1.0028, -0.3612, -1.0364, 0.5066, 0.2033],
- [ 0.4492, -0.5054, 1.8988, -0.3947, -0.5929, -0.2613, 0.4276, 0.2535],
- [ 0.4352, -0.4504, 1.6065, -0.7117, -0.6445, -0.7353, 0.2741, 0.2579],
- [ 0.5791, -0.3839, 1.6990, 0.0284, -0.1213, 0.0138, 0.2417, 0.3851],
- [ 0.6664, -0.3126, 1.7192, 0.2874, -0.3526, 0.0089, 0.3909, 0.3248],
- [ 0.5155, -0.5051, 1.7634, 0.0443, -0.3976, 0.1064, 0.4973, -0.0456],
- [ 0.6025, -0.4032, 1.5351, -1.0168, -0.3299, -0.9217, 0.9194, 0.3174],
- [ 0.5629, -0.4255, 1.3034, -1.1134, -0.3056, -1.3572, 0.3460, 0.2011]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5776, -0.3987, 1.6005, -0.9121, -0.3844, -1.2358, 0.4247,
- 0.2043],
- [ 0.5832, -0.4231, 1.8423, -0.3614, -0.5249, -0.3152, 0.3065,
- 0.2930],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.5462, -0.4090, 1.7037, 0.1159, -0.0746, -0.0765, 0.1427,
- 0.4239],
- [ 0.5880, -0.3605, 1.7037, 0.3238, -0.2940, -0.0765, 0.3180,
- 0.3161],
- [ 0.5776, -0.4784, 1.7730, 0.1236, -0.4037, 0.0697, 0.5132,
- -0.0168],
- [ 0.6174, -0.3984, 1.5975, -0.9921, -0.3633, -0.9926, 0.8205,
- 0.2050],
- [ 0.5664, -0.4321, 1.2862, -1.0003, -0.2189, -1.4608, 0.3883,
- 0.1855]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0051, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0051, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9523771712556481
- step: 68
- running loss: 0.014005546636112472
- Train Steps: 68/90 Loss: 0.0140 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [0.6201, 0.4116, 0.8725, 0.4733, 0.3700, 0.3217, 0.5386, 0.5767],
- [0.6197, 0.4090, 0.7825, 0.2500, 0.4200, 0.2483, 0.5988, 0.5667],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6339, 0.4118, 0.7988, 0.5800, 0.3912, 0.4583, 0.7343, 0.5760],
- [0.6275, 0.4013, 0.8850, 0.2833, 0.4975, 0.2233, 0.7058, 0.5343],
- [0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783],
- [0.6250, 0.3961, 0.8672, 0.4929, 0.4199, 0.4972, 0.6312, 0.5301]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5940, -0.3932, 1.7485, -0.6328, -0.6519, -0.2000, 0.3192, 0.1487],
- [ 0.5559, -0.3739, 1.8155, -0.1258, -0.5492, -0.6956, 0.2571, 0.4124],
- [ 0.3322, -0.5662, 1.3762, -1.2519, -0.3556, -1.0895, 0.3842, 0.3540],
- [ 0.5320, -0.3999, 1.7015, -0.6836, -0.5756, -0.6865, 0.1819, 0.2674],
- [ 0.4998, -0.4898, 1.5446, 0.2641, -0.4719, -0.0665, 1.0350, 0.3207],
- [ 0.6239, -0.4223, 1.8720, -1.0206, 0.0367, -1.1793, 0.8825, 0.1049],
- [ 0.5348, -0.4411, 0.8810, -1.2536, -0.4768, -1.1120, 0.1583, 0.3431],
- [ 0.6846, -0.3897, 1.8536, 0.0126, -0.2834, 0.1039, 0.6548, 0.1332]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [ 0.5781, -0.3848, 1.7441, -0.0996, -0.5769, -0.8002, 0.2021,
- 0.3778],
- [ 0.5764, -0.3969, 1.3284, -1.1312, -0.3460, -1.1389, 0.4797,
- 0.3315],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.6421, -0.3840, 1.4035, 0.3931, -0.4788, -0.1689, 1.1057,
- 0.3745],
- [ 0.6125, -0.4321, 1.8018, -0.9772, 0.0120, -1.2543, 0.9741,
- 0.1821],
- [ 0.5483, -0.3991, 0.8030, -1.2159, -0.5018, -1.1928, 0.2624,
- 0.3852],
- [ 0.6010, -0.4562, 1.7198, -0.0090, -0.3464, 0.0108, 0.6294,
- 0.1627]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0058, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0058, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9581651668995619
- step: 69
- running loss: 0.013886451694196549
- Train Steps: 69/90 Loss: 0.0139 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6224, 0.4097, 0.7438, 0.2267, 0.3850, 0.2850, 0.5988, 0.5250],
- [0.6145, 0.4007, 0.8775, 0.4533, 0.4562, 0.5533, 0.6088, 0.5533],
- [0.6199, 0.4015, 0.8716, 0.5228, 0.3833, 0.4772, 0.5883, 0.5148],
- [ nan, nan, 0.7850, 0.2700, 0.4288, 0.1717, 0.5199, 0.4999],
- [0.6284, 0.4127, 0.8538, 0.5867, 0.4363, 0.5083, 0.6038, 0.5433],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6299, 0.4008, 0.8450, 0.5350, 0.4213, 0.5000, 0.6350, 0.5100],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3494, -0.5370, 1.2136, -1.3041, -0.4782, -0.9355, 0.5158, 0.2729],
- [ 0.8429, -0.2129, 1.8653, -0.2707, -0.1902, 0.2714, 0.5865, 0.2691],
- [ 0.6540, -0.3671, 1.8191, 0.0418, -0.4627, -0.0966, 0.4293, 0.1704],
- [-2.2697, -2.2630, 1.3518, -1.1985, -0.3079, -1.3828, 0.2759, 0.2044],
- [ 0.6816, -0.3238, 1.6917, 0.3073, -0.2383, 0.0343, 0.5112, 0.2740],
- [ 0.5939, -0.3918, 1.9143, -0.0183, -0.5544, -0.5055, 0.6066, 0.0947],
- [ 0.7189, -0.3206, 1.6926, 0.0616, -0.3304, -0.0278, 0.7087, 0.2288],
- [ 0.7295, -0.2626, 1.5060, -0.6643, -0.5875, -0.8475, 0.1134, 0.5077]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5891, -0.3937, 1.1494, -1.2390, -0.5076, -0.9695, 0.4797,
- 0.1390],
- [ 0.5526, -0.4351, 1.7672, -0.1920, -0.1785, 0.2699, 0.5259,
- 0.2699],
- [ 0.5773, -0.4316, 1.7399, 0.1287, -0.5153, -0.0817, 0.4313,
- 0.0919],
- [-2.2859, -2.2859, 1.3400, -1.0388, -0.3055, -1.4930, 0.1157,
- 0.0231],
- [ 0.6166, -0.3795, 1.6575, 0.4239, -0.2709, 0.0620, 0.5028,
- 0.2237],
- [ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.6236, -0.4344, 1.6171, 0.1852, -0.3402, 0.0236, 0.6471,
- 0.0697],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0097, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0097, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9678890649229288
- step: 70
- running loss: 0.013826986641756125
- Train Steps: 70/90 Loss: 0.0138 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6361, 0.4076, 0.8862, 0.5350, 0.3713, 0.4650, 0.6654, 0.5297],
- [0.6161, 0.4076, 0.8900, 0.4667, 0.4125, 0.5917, 0.6262, 0.5367],
- [0.6128, 0.4084, 0.8738, 0.4683, 0.3613, 0.3700, 0.4960, 0.5397],
- [ nan, nan, 0.7725, 0.2611, 0.3675, 0.2733, 0.5413, 0.5167],
- [ nan, nan, 0.7612, 0.3250, 0.4037, 0.2533, 0.5438, 0.5767],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8417, -0.2317, 1.7029, 0.0056, -0.2438, -0.0576, 0.3418, 0.2024],
- [ 0.8047, -0.2616, 1.6958, 0.0687, -0.2768, -0.1067, 0.3044, 0.2138],
- [ 0.9906, -0.1865, 1.8446, 0.1755, -0.5234, -0.1499, 0.9876, 0.1798],
- [ 0.9552, -0.1518, 1.8578, -0.1127, -0.2964, 0.4549, 0.7682, 0.1948],
- [ 0.7645, -0.2472, 1.7689, -0.1310, -0.5927, -0.5891, 0.2263, 0.2930],
- [-1.9544, -2.0844, 1.3214, -1.0800, -0.4260, -1.0365, 0.2664, 0.2658],
- [-1.9802, -2.0483, 1.3216, -0.8993, -0.3252, -1.1174, 0.3076, 0.4552],
- [ 0.7517, -0.2976, 1.0570, -1.4151, -0.4750, -1.1065, 0.4526, 0.2947]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01],
- [ 5.4348e-01, -4.5974e-01, 1.6575e+00, 1.5858e-02, -3.2286e-01,
- -1.1501e-01, 1.8767e-01, 1.8544e-01],
- [ 6.5201e-01, -4.0323e-01, 1.8076e+00, 1.8522e-01, -5.7113e-01,
- -1.3811e-01, 7.8762e-01, 1.6077e-01],
- [ 5.5978e-01, -4.0323e-01, 1.8249e+00, -1.3041e-01, -3.8060e-01,
- 4.4696e-01, 6.0670e-01, 1.9292e-01],
- [ 5.4434e-01, -3.9938e-01, 1.7499e+00, -1.2271e-01, -6.1732e-01,
- -5.7691e-01, 4.8756e-03, 2.0706e-01],
- [-2.2859e+00, -2.2859e+00, 1.2820e+00, -1.0801e+00, -5.8845e-01,
- -1.0234e+00, 2.1409e-01, 1.0054e-01],
- [-2.2859e+00, -2.2859e+00, 1.2303e+00, -7.8476e-01, -4.2102e-01,
- -1.1158e+00, 2.2564e-01, 3.7768e-01],
- [ 5.3557e-01, -4.2171e-01, 1.0339e+00, -1.4776e+00, -5.0762e-01,
- -1.1081e+00, 4.2194e-01, 2.8530e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0211, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0211, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9890259634703398
- step: 71
- running loss: 0.013929943147469575
- Train Steps: 71/90 Loss: 0.0139 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6200, 0.4039, 0.8880, 0.4799, 0.3625, 0.4285, 0.5866, 0.5148],
- [0.6164, 0.4102, 0.8850, 0.4867, 0.4213, 0.5983, 0.5609, 0.5038],
- [0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285],
- [0.6224, 0.4061, 0.8988, 0.4300, 0.3838, 0.4750, 0.6112, 0.5483],
- [ nan, nan, 0.9088, 0.3783, 0.4562, 0.2617, 0.6741, 0.5575]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7189, -0.2970, 1.5169, -1.1217, -0.1463, -1.2613, 0.6161, 0.1181],
- [-1.8552, -1.9676, 1.3940, -0.8310, -0.5719, -0.8433, 0.2184, 0.2288],
- [ 0.9510, -0.1599, 1.5640, 0.3152, -0.3457, 0.1025, 0.5300, 0.3758],
- [ 0.7031, -0.3321, 1.7282, -0.0901, -0.5328, -0.3028, 0.4581, 0.1999],
- [ 0.9493, -0.1386, 1.6505, -0.0565, -0.2638, 0.3398, 0.5135, 0.2446],
- [ 0.6160, -0.3527, 1.2463, -0.4025, -0.5617, -0.2948, 0.1490, 0.2339],
- [ 0.8022, -0.2647, 1.6993, -0.3240, -0.4886, -0.0780, 0.6073, 0.2791],
- [-1.9800, -2.0272, 1.8216, -0.6188, -0.1633, -1.1645, 0.7517, 0.3891]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [-2.2859, -2.2859, 1.5478, -0.8309, -0.6289, -0.7232, 0.1198,
- 0.1133],
- [ 0.5742, -0.3792, 1.6460, 0.3084, -0.3460, 0.1467, 0.4162,
- 0.3161],
- [ 0.5781, -0.4205, 1.8159, -0.0691, -0.6116, -0.3069, 0.4236,
- 0.0919],
- [ 0.5614, -0.3912, 1.8018, -0.0380, -0.3402, 0.4778, 0.3049,
- 0.0412],
- [ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552],
- [ 0.5888, -0.4101, 1.8654, -0.2998, -0.5134, -0.0919, 0.5374,
- 0.2468],
- [-2.2859, -2.2859, 1.9115, -0.5384, -0.1785, -1.0773, 0.8278,
- 0.2890]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0214, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0214, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.010474806651473
- step: 72
- running loss: 0.014034372314603792
- Train Steps: 72/90 Loss: 0.0140 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364],
- [0.6266, 0.4070, 0.8712, 0.5600, 0.3713, 0.4783, 0.5775, 0.6100],
- [0.6266, 0.4101, 0.8350, 0.2333, 0.3950, 0.2950, 0.6264, 0.4921],
- [0.6147, 0.4026, 0.6600, 0.2467, 0.4088, 0.2150, 0.5489, 0.5773],
- [0.6124, 0.4030, 0.8650, 0.4867, 0.4999, 0.5106, 0.5137, 0.5773],
- [0.6199, 0.4015, 0.8716, 0.5228, 0.3833, 0.4772, 0.5883, 0.5148],
- [0.6262, 0.4163, 0.8850, 0.5183, 0.3763, 0.4150, 0.6025, 0.5500],
- [0.6263, 0.4233, 0.7924, 0.4626, 0.3788, 0.2883, 0.5573, 0.6047]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3368, -0.5989, 1.3968, -1.2855, -0.2685, -1.2013, 0.8332, 0.1484],
- [ 0.6560, -0.3918, 1.6908, 0.3211, -0.5529, -0.0343, 0.5810, 0.3675],
- [ 0.6294, -0.4085, 1.5747, -1.2638, -0.4456, -0.9089, 0.5935, 0.0504],
- [ 0.3532, -0.5393, 0.8963, -1.2529, -0.4193, -1.2993, 0.1756, 0.4006],
- [ 0.4367, -0.4870, 1.7705, -0.0473, -0.0324, 0.0032, 0.1528, 0.2683],
- [ 0.6308, -0.4134, 1.8200, 0.1440, -0.5066, -0.0720, 0.4684, 0.0920],
- [ 0.4497, -0.4605, 1.8136, 0.0541, -0.5289, -0.4077, 0.5499, 0.3112],
- [ 0.4315, -0.4607, 1.4753, -0.1712, -0.5194, -0.8658, 0.3211, 0.5433]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6158, -0.3960, 1.4092, -1.2774, -0.2074, -1.1851, 0.8491,
- 0.1917],
- [ 0.6081, -0.4059, 1.7383, 0.3007, -0.5711, -0.0765, 0.3815,
- 0.5316],
- [ 0.6081, -0.3918, 1.5709, -1.2082, -0.4614, -0.9233, 0.6072,
- -0.0129],
- [ 0.5532, -0.4264, 0.7626, -1.1466, -0.3979, -1.2928, 0.2494,
- 0.3808],
- [ 0.5428, -0.4244, 1.7095, -0.0380, 0.0232, 0.0725, 0.0866,
- 0.3806],
- [ 0.5773, -0.4316, 1.7399, 0.1287, -0.5153, -0.0817, 0.4313,
- 0.0919],
- [ 0.6066, -0.3632, 1.8018, 0.1082, -0.5480, -0.3691, 0.4970,
- 0.2545],
- [ 0.6069, -0.3309, 1.3742, -0.1493, -0.5365, -0.9541, 0.2884,
- 0.5071]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0073, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0073, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0177430603653193
- step: 73
- running loss: 0.01394168575842903
- Train Steps: 73/90 Loss: 0.0139 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6068, 0.3963, 0.8650, 0.4317, 0.4037, 0.5083, 0.5253, 0.4999],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142],
- [0.6307, 0.4029, 0.8988, 0.4817, 0.3937, 0.3500, 0.7311, 0.5378],
- [0.6279, 0.4008, 0.8600, 0.4883, 0.4325, 0.5283, 0.7010, 0.5378],
- [ nan, nan, 0.8850, 0.3000, 0.5363, 0.2250, 0.7343, 0.5771],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136],
- [0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114],
- [0.6125, 0.3974, 0.7725, 0.2517, 0.3538, 0.3317, 0.5887, 0.5500]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6748, -0.3631, 1.6707, -0.1981, -0.4835, 0.0423, 0.1712, 0.0683],
- [-2.2875, -2.2457, 1.4929, -0.7364, -0.6461, -0.9071, 0.2137, 0.1640],
- [ 0.6945, -0.3782, 1.8052, -0.0300, -0.4562, -0.6526, 0.8941, 0.2610],
- [ 0.8142, -0.2674, 1.6115, 0.0434, -0.3279, 0.1068, 0.7172, 0.3199],
- [-1.7700, -1.8789, 1.6584, -0.8913, 0.1248, -1.2807, 0.8153, 0.4383],
- [ 0.5807, -0.4324, 0.8782, -1.2439, -0.5315, -1.1515, 0.2223, 0.1654],
- [ 0.8077, -0.2522, 1.6139, 0.1851, -0.2085, 0.1892, 0.3838, 0.1822],
- [ 0.6567, -0.3555, 1.2165, -1.0069, -0.6257, -0.7862, 0.3077, 0.2937]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5168, -0.4556, 1.7095, -0.2921, -0.4210, 0.0620, 0.1404,
- 0.0231],
- [-2.2859, -2.2859, 1.5767, -0.7540, -0.6404, -0.7309, 0.1753,
- 0.0893],
- [ 0.6273, -0.4249, 1.8654, -0.0611, -0.4672, -0.6693, 1.0910,
- 0.1982],
- [ 0.6141, -0.4345, 1.6864, -0.0303, -0.2882, 0.1544, 0.9521,
- 0.1982],
- [-2.2859, -2.2859, 1.8018, -0.9002, 0.1910, -1.2467, 1.1057,
- 0.3799],
- [ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862],
- [ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764],
- [ 0.5430, -0.4503, 1.2822, -1.1235, -0.6520, -0.7540, 0.4335,
- 0.2545]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0173, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0173, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0350117646157742
- step: 74
- running loss: 0.013986645467780731
- Train Steps: 74/90 Loss: 0.0140 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6223, 0.4171, 0.8750, 0.5500, 0.4050, 0.4817, 0.5675, 0.5600],
- [0.6293, 0.3982, 0.8700, 0.5300, 0.3763, 0.4717, 0.7050, 0.5297],
- [0.6060, 0.3924, 0.8450, 0.5717, 0.4200, 0.5217, 0.5253, 0.4752],
- [0.6085, 0.4008, 0.8588, 0.5200, 0.4959, 0.4977, 0.5175, 0.5283],
- [0.6130, 0.4072, 0.8550, 0.4567, 0.3638, 0.3667, 0.5290, 0.5892],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6147, 0.4107, 0.8137, 0.3333, 0.3750, 0.2683, 0.5006, 0.5412],
- [ nan, nan, 0.7612, 0.3250, 0.4037, 0.2533, 0.5438, 0.5767]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5489, -0.4094, 1.6848, 0.0984, -0.4474, -0.0212, 0.4780, 0.3581],
- [ 0.3928, -0.5698, 1.6149, -0.0116, -0.5300, -0.1118, 0.9159, 0.1718],
- [ 0.4654, -0.4955, 1.4773, 0.1833, -0.3214, 0.1166, 0.3754, 0.1369],
- [ 0.3872, -0.5270, 1.5909, -0.0495, -0.0616, -0.0460, 0.1934, 0.1892],
- [ 0.4057, -0.4826, 1.6038, -0.3406, -0.6131, -0.5951, 0.2500, 0.4324],
- [ 0.6082, -0.4286, 1.7353, -0.7135, -0.3392, -1.2192, 0.5991, 0.0500],
- [ 0.4191, -0.5111, 1.3351, -0.8959, -0.5430, -0.9761, 0.1950, 0.1611],
- [-2.9487, -2.6707, 1.2990, -0.8877, -0.3762, -1.1304, 0.2738, 0.2526]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5883, -0.3594, 1.7557, 0.2545, -0.4152, -0.0611, 0.3353,
- 0.3007],
- [ 0.6207, -0.4466, 1.7326, 0.1621, -0.5480, -0.1073, 0.9704,
- 0.1608],
- [ 0.5132, -0.4736, 1.6171, 0.3546, -0.3460, 0.1236, 0.1404,
- -0.0911],
- [ 0.5245, -0.4347, 1.6806, 0.1159, 0.0046, 0.0129, 0.1044,
- 0.1544],
- [ 0.5454, -0.4053, 1.6633, -0.1766, -0.6058, -0.5923, 0.1577,
- 0.4357],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.5532, -0.3888, 1.4727, -0.7463, -0.5538, -1.0465, 0.0265,
- 0.2138],
- [-2.2859, -2.2859, 1.2303, -0.7848, -0.4210, -1.1158, 0.2256,
- 0.3777]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0204, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0204, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.055397231131792
- step: 75
- running loss: 0.014071963081757228
- Train Steps: 75/90 Loss: 0.0141 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6179, 0.4040, 0.7412, 0.1850, 0.3825, 0.2783, 0.5837, 0.5600],
- [0.6273, 0.4143, 0.8750, 0.5700, 0.3987, 0.4717, 0.6013, 0.5467],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [0.6080, 0.4010, 0.8750, 0.4500, 0.4825, 0.5617, 0.5837, 0.5583],
- [ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422],
- [0.6211, 0.3993, 0.7650, 0.1933, 0.4575, 0.1550, 0.5965, 0.4895],
- [0.6037, 0.4020, 0.8300, 0.4033, 0.3575, 0.4883, 0.5647, 0.5631],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5642, -0.4334, 1.1553, -1.3390, -0.5370, -1.0926, 0.5606, 0.3162],
- [ 0.4602, -0.4550, 1.6843, 0.3836, -0.4767, -0.2140, 0.6209, 0.2623],
- [ 0.5543, -0.4040, 1.6614, 0.1765, -0.3040, 0.0755, 0.2942, 0.1887],
- [ 0.5686, -0.4199, 1.7921, -0.1467, -0.1592, 0.0329, 0.5323, 0.2321],
- [-2.2041, -2.1930, 1.2239, -0.9110, -0.5603, -1.3102, 0.1554, 0.1432],
- [ 0.1707, -0.7323, 1.2666, -1.3800, -0.2301, -1.5770, 0.5435, 0.0338],
- [ 0.3557, -0.5347, 1.6182, -0.3355, -0.6558, -0.1010, 0.4520, 0.2669],
- [ 0.2443, -0.6425, 1.5739, 0.4655, -0.2944, -0.0588, 0.2487, 0.0846]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5680, -0.4201, 1.1379, -1.4314, -0.5192, -1.0003, 0.4104,
- 0.3007],
- [ 0.6115, -0.3724, 1.7557, 0.3469, -0.4441, -0.1073, 0.4912,
- 0.2391],
- [ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386],
- [ 0.5223, -0.4336, 1.7557, -0.2074, -0.0573, 0.3084, 0.4104,
- 0.2930],
- [-2.2859, -2.2859, 1.2469, -1.0288, -0.4557, -1.2774, 0.0511,
- 0.2183],
- [ 0.5828, -0.4417, 1.2476, -1.3929, -0.1727, -1.5700, 0.4694,
- -0.0248],
- [ 0.5027, -0.4289, 1.5478, -0.4229, -0.6346, -0.0303, 0.3223,
- 0.3148],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0134, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0134, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.068770982325077
- step: 76
- running loss: 0.014062776083224699
- Train Steps: 76/90 Loss: 0.0141 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6264, 0.4049, 0.8988, 0.4633, 0.3813, 0.4983, 0.6326, 0.4843],
- [0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350],
- [0.6161, 0.4024, 0.8838, 0.4583, 0.3688, 0.3733, 0.5311, 0.5344],
- [0.6250, 0.4116, 0.8700, 0.4850, 0.4650, 0.5567, 0.6088, 0.5183],
- [ nan, nan, 0.7097, 0.2346, 0.4250, 0.1850, 0.5175, 0.5583],
- [0.6268, 0.4052, 0.8175, 0.2250, 0.4688, 0.1917, 0.6375, 0.5267],
- [0.6270, 0.4267, 0.7150, 0.3317, 0.4900, 0.1817, 0.5560, 0.6183]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.2556e-01, -4.7396e-01, 1.8801e+00, -7.8138e-03, -6.2453e-01,
- 3.7464e-02, 5.8580e-01, -1.2725e-02],
- [ 4.0192e-01, -5.3662e-01, 1.7949e+00, 9.5810e-02, -6.1910e-01,
- -1.8867e-01, 7.2414e-01, 2.0731e-01],
- [ 3.6289e-01, -5.6181e-01, 1.6216e+00, 1.5098e-01, -3.6548e-01,
- -4.9103e-02, 1.0999e-01, 1.4797e-01],
- [ 3.2511e-01, -5.8615e-01, 1.7849e+00, -4.6862e-02, -6.3078e-01,
- -4.6994e-01, 1.9428e-01, 1.4981e-01],
- [ 5.6330e-01, -4.2058e-01, 1.7289e+00, -4.8876e-04, -2.2566e-01,
- 3.5793e-01, 5.1060e-01, 1.5541e-01],
- [-2.2766e+00, -2.2593e+00, 1.0513e+00, -1.0888e+00, -4.0269e-01,
- -1.3363e+00, 1.0775e-01, 1.6716e-01],
- [ 4.5438e-01, -5.3729e-01, 1.4152e+00, -1.1941e+00, -1.9299e-01,
- -1.4076e+00, 7.0603e-01, 1.0110e-01],
- [ 4.3415e-01, -4.6464e-01, 1.0296e+00, -7.9853e-01, -1.5969e-01,
- -1.3360e+00, 3.0307e-01, 4.9511e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6075, -0.4159, 1.8654, -0.1458, -0.5249, 0.0159, 0.6359,
- -0.0490],
- [ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854],
- [ 0.5598, -0.4273, 1.7961, -0.1689, -0.5827, -0.5615, 0.1671,
- 0.1824],
- [ 0.6012, -0.3846, 1.7326, -0.0457, -0.1381, 0.2853, 0.5259,
- 0.1082],
- [-2.2859, -2.2859, 0.9922, -1.2021, -0.3229, -1.4314, 0.1044,
- 0.2930],
- [ 0.6092, -0.4143, 1.4901, -1.2467, -0.1208, -1.4006, 0.6587,
- 0.1467],
- [ 0.6101, -0.3152, 1.0166, -0.7540, -0.0226, -1.4468, 0.2823,
- 0.5702]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0775065701454878
- step: 77
- running loss: 0.01399359182007127
- Train Steps: 77/90 Loss: 0.0140 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6266, 0.4070, 0.8712, 0.5600, 0.3713, 0.4783, 0.5775, 0.6100],
- [0.6336, 0.4154, 0.8900, 0.2767, 0.4988, 0.2867, 0.7422, 0.5540],
- [0.6117, 0.4018, 0.6562, 0.1967, 0.3738, 0.2550, 0.5280, 0.5103],
- [0.6214, 0.4040, 0.8838, 0.3500, 0.3600, 0.5183, 0.6362, 0.5200],
- [0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [0.6176, 0.3911, 0.8738, 0.4217, 0.3488, 0.4033, 0.6025, 0.4817],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6179, 0.4040, 0.7412, 0.1850, 0.3825, 0.2783, 0.5837, 0.5600]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 1.8696e-01, -6.7659e-01, 1.6200e+00, 5.9537e-01, -5.5009e-01,
- -5.2045e-02, 3.8511e-01, 3.9849e-01],
- [ 5.6669e-01, -4.5132e-01, 1.8657e+00, -7.5506e-01, 5.8507e-02,
- -1.1113e+00, 1.0879e+00, 2.1666e-01],
- [ 2.5891e-01, -6.3093e-01, 8.7453e-01, -1.1247e+00, -5.1247e-01,
- -1.2483e+00, 1.9881e-01, 7.4780e-02],
- [ 3.3157e-01, -6.2260e-01, 1.7811e+00, -3.8282e-01, -5.5791e-01,
- 1.4175e-01, 6.5250e-01, 1.2902e-01],
- [ 1.8281e-01, -6.8666e-01, 1.7092e+00, 2.4061e-01, -2.2659e-01,
- -6.1996e-02, 1.3360e-01, 2.7632e-01],
- [ 5.4019e-01, -4.9346e-01, 1.7867e+00, -1.4107e-01, -6.5465e-01,
- -4.7040e-01, 4.3645e-01, 1.0568e-03],
- [ 3.5157e-01, -5.6563e-01, 1.6518e+00, -7.9254e-02, -5.3259e-01,
- -8.8585e-02, 6.9966e-02, 4.8155e-02],
- [ 6.5928e-01, -3.6591e-01, 1.1671e+00, -1.2855e+00, -5.0414e-01,
- -1.1079e+00, 4.3803e-01, 2.8392e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6081, -0.4059, 1.7383, 0.3007, -0.5711, -0.0765, 0.3815,
- 0.5316],
- [ 0.6405, -0.3671, 1.8249, -1.0080, 0.0178, -0.9618, 1.1422,
- 0.2730],
- [ 0.5393, -0.4299, 0.7453, -1.3775, -0.5596, -1.1081, 0.1527,
- 0.0712],
- [ 0.5841, -0.4199, 1.7961, -0.6693, -0.6231, 0.1082, 0.6529,
- 0.1159],
- [ 0.5525, -0.4463, 1.7326, 0.0313, -0.2536, -0.0688, 0.1968,
- 0.3700],
- [ 0.5669, -0.4794, 1.7499, -0.3383, -0.6751, -0.4229, 0.4970,
- -0.0611],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.5680, -0.4201, 1.1379, -1.4314, -0.5192, -1.0003, 0.4104,
- 0.3007]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0214, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0214, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0989203732460737
- step: 78
- running loss: 0.014088722733924022
- Train Steps: 78/90 Loss: 0.0141 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6199, 0.4015, 0.8716, 0.5228, 0.3833, 0.4772, 0.5883, 0.5148],
- [0.6201, 0.4027, 0.8029, 0.2728, 0.4042, 0.2310, 0.5980, 0.5391],
- [0.6272, 0.4071, 0.8738, 0.5600, 0.3675, 0.3783, 0.5926, 0.4742],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417],
- [0.6175, 0.3997, 0.6737, 0.2500, 0.4313, 0.1933, 0.5513, 0.5750],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6260, 0.4133, 0.8800, 0.5117, 0.3713, 0.3283, 0.6223, 0.4967],
- [0.6179, 0.4082, 0.6688, 0.2667, 0.3588, 0.3317, 0.5750, 0.5783]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3864, -0.5742, 1.8245, 0.1772, -0.4627, 0.0362, 0.2757, 0.0727],
- [ 0.3755, -0.5567, 1.5371, -1.0199, -0.3176, -1.1191, 0.5130, 0.1074],
- [ 0.5378, -0.4717, 1.8535, 0.2957, -0.5370, -0.3660, 0.3919, -0.0246],
- [ 0.4323, -0.5277, 1.4370, -1.0060, -0.4104, -0.7326, 0.5667, 0.1857],
- [ 0.3792, -0.5396, 0.9493, -1.1634, -0.2327, -1.3323, 0.2315, 0.4083],
- [-2.9494, -2.7514, 1.1699, -1.0930, -0.3473, -1.1785, 0.1991, 0.1744],
- [ 0.4948, -0.4716, 1.8885, 0.0686, -0.4718, -0.5718, 0.5318, 0.0908],
- [ 0.4863, -0.4793, 1.0017, -0.9466, -0.5978, -0.7258, 0.2886, 0.2970]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5773, -0.4316, 1.7399, 0.1287, -0.5153, -0.0817, 0.4313,
- 0.0919],
- [ 0.5785, -0.4259, 1.4228, -1.0261, -0.4190, -1.2189, 0.4763,
- 0.2043],
- [ 0.6113, -0.4057, 1.7499, 0.3007, -0.5885, -0.5384, 0.4513,
- -0.0957],
- [ 0.5977, -0.3792, 1.2995, -1.0311, -0.5192, -0.8386, 0.5836,
- 0.2160],
- [ 0.5663, -0.4396, 0.8261, -1.1312, -0.2940, -1.3929, 0.2603,
- 0.3700],
- [-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373],
- [ 0.6054, -0.3767, 1.7788, 0.0774, -0.5711, -0.7694, 0.5887,
- 0.0081],
- [ 0.5680, -0.4005, 0.8030, -1.0542, -0.6289, -0.7540, 0.3700,
- 0.3854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0227, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0227, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.121607020497322
- step: 79
- running loss: 0.01419755722148509
- Train Steps: 79/90 Loss: 0.0142 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6236, 0.3966, 0.8850, 0.4967, 0.3638, 0.4017, 0.5850, 0.5183],
- [0.6122, 0.3993, 0.8738, 0.4667, 0.4517, 0.4879, 0.5155, 0.4927],
- [0.6239, 0.4107, 0.8162, 0.2763, 0.3625, 0.3600, 0.5988, 0.5700],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6299, 0.4008, 0.8450, 0.5350, 0.4213, 0.5000, 0.6350, 0.5100],
- [0.6276, 0.4235, 0.8888, 0.5333, 0.3800, 0.3117, 0.5427, 0.6164]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5590, -0.4708, 1.7701, -0.0477, -0.5697, -0.4099, 0.3951, 0.1113],
- [ 0.2881, -0.6408, 1.7571, -0.1415, -0.1678, 0.0765, -0.0485, -0.0760],
- [ 0.6762, -0.3469, 1.4456, -0.9191, -0.5233, -0.5599, 0.4077, 0.1622],
- [ 0.5194, -0.5119, 1.8524, -0.1059, -0.5642, -0.4052, 0.7536, 0.1808],
- [ 0.5069, -0.4797, 1.2314, -1.3000, -0.4628, -0.9837, 0.5290, 0.1718],
- [ 0.5393, -0.4442, 1.7180, 0.1891, -0.5061, -0.5392, 0.3002, 0.1873],
- [ 0.4009, -0.5789, 1.6085, 0.1635, -0.3419, 0.0440, 0.5460, 0.1089],
- [ 0.1756, -0.6277, 1.6535, 0.0782, -0.4525, -0.8386, 0.2303, 0.5210]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5944, -0.4543, 1.8018, 0.0082, -0.6058, -0.4306, 0.4162,
- 0.1082],
- [ 0.5417, -0.4417, 1.7499, -0.1304, -0.1994, -0.0324, 0.0951,
- -0.0099],
- [ 0.5960, -0.3888, 1.4840, -1.0095, -0.6115, -0.6231, 0.4797,
- 0.3469],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [ 0.5697, -0.4466, 1.1973, -1.1871, -0.4571, -0.9965, 0.5219,
- 0.2032],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698],
- [ 0.6236, -0.4344, 1.6171, 0.1852, -0.3402, 0.0236, 0.6471,
- 0.0697],
- [ 0.6131, -0.3299, 1.8192, 0.1775, -0.5307, -0.8463, 0.2206,
- 0.5612]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0109, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0109, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1325139282271266
- step: 80
- running loss: 0.014156424102839082
- Train Steps: 80/90 Loss: 0.0142 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.8625, 0.2550, 0.5487, 0.2200, 0.7335, 0.5737],
- [0.6182, 0.3982, 0.7541, 0.2379, 0.3959, 0.2792, 0.6079, 0.5389],
- [0.6193, 0.4108, 0.7425, 0.2350, 0.3887, 0.2750, 0.5900, 0.5717],
- [0.6184, 0.4079, 0.8350, 0.3700, 0.3675, 0.2883, 0.5312, 0.5783],
- [0.6254, 0.3993, 0.8988, 0.4767, 0.3987, 0.5517, 0.6955, 0.5285],
- [0.6317, 0.4038, 0.8287, 0.5900, 0.3800, 0.4717, 0.6295, 0.4986],
- [0.6353, 0.4128, 0.8488, 0.2600, 0.5525, 0.1616, 0.6694, 0.5540],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.6894, -2.5739, 1.5702, -1.0534, 0.0456, -1.2679, 0.7645, 0.2454],
- [ 0.4485, -0.5052, 1.2072, -1.1342, -0.5686, -0.9861, 0.4241, 0.1608],
- [ 0.4587, -0.4829, 1.0750, -1.1103, -0.5859, -0.9948, 0.3502, 0.2427],
- [ 0.5041, -0.4563, 1.5614, -0.5170, -0.6090, -0.8942, -0.1033, 0.2127],
- [ 0.5302, -0.4635, 1.8460, -0.0276, -0.5491, 0.2861, 0.7146, 0.0353],
- [ 0.5678, -0.4470, 1.5932, 0.4860, -0.6324, -0.0811, 0.4204, -0.0671],
- [ 0.2759, -0.6126, 1.5074, -1.1428, 0.0767, -1.4070, 0.6163, 0.1728],
- [ 0.5419, -0.3975, 1.6601, -0.1506, -0.1774, 0.0067, 0.1483, 0.1847]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.6979, -1.1081, 0.2487, -1.2697, 1.1020,
- 0.3638],
- [ 0.5697, -0.4466, 1.1973, -1.1871, -0.4571, -0.9965, 0.5219,
- 0.2032],
- [ 0.5746, -0.3882, 1.1436, -1.2005, -0.4903, -1.0157, 0.4393,
- 0.3546],
- [ 0.5704, -0.4019, 1.5709, -0.5769, -0.5885, -0.9541, 0.1679,
- 0.3854],
- [ 0.6026, -0.4417, 1.8654, -0.0842, -0.4441, 0.2622, 0.9265,
- 0.1554],
- [ 0.6320, -0.4206, 1.5420, 0.4393, -0.5307, -0.1073, 0.6216,
- 0.0171],
- [ 0.6487, -0.3792, 1.6344, -1.0850, 0.2659, -1.5397, 0.8059,
- 0.2730],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0195, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0195, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1520524276420474
- step: 81
- running loss: 0.014222869477062314
- Train Steps: 81/90 Loss: 0.0142 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6248, 0.4185, 0.8500, 0.5767, 0.4463, 0.4550, 0.5613, 0.5917],
- [0.6189, 0.4049, 0.8888, 0.4417, 0.4213, 0.5200, 0.5988, 0.5633],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136],
- [0.6038, 0.3946, 0.8413, 0.4883, 0.3563, 0.4550, 0.5266, 0.4693],
- [ nan, nan, 0.7612, 0.3250, 0.4037, 0.2533, 0.5438, 0.5767],
- [0.6271, 0.4081, 0.9081, 0.4894, 0.3700, 0.4283, 0.6661, 0.5274],
- [0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822],
- [0.6164, 0.4066, 0.6625, 0.3033, 0.3775, 0.2967, 0.5725, 0.5833]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6217, -0.3808, 1.7538, 0.3747, -0.1461, -0.2232, 0.3396, 0.3322],
- [ 0.5353, -0.4553, 1.8654, -0.2540, -0.3222, 0.1267, 0.5113, 0.1193],
- [ 0.5424, -0.4752, 0.9778, -1.3349, -0.4621, -1.2054, 0.3810, -0.0704],
- [ 0.5623, -0.4164, 1.7162, -0.0753, -0.5480, -0.1832, 0.1149, -0.0326],
- [-2.3358, -2.3530, 1.2751, -0.9237, -0.3565, -1.1791, 0.2657, 0.3107],
- [ 0.6190, -0.3997, 1.9346, -0.0340, -0.5371, -0.2918, 0.8412, 0.0862],
- [ 0.6145, -0.3911, 1.7789, -0.3152, -0.5916, -0.7133, 0.2112, 0.2105],
- [ 0.3885, -0.5344, 0.9275, -0.9589, -0.5050, -1.0208, 0.3161, 0.3429]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5999, -0.3530, 1.6402, 0.3777, -0.2247, -0.1843, 0.3065,
- 0.4470],
- [ 0.5726, -0.4159, 1.8192, -0.2459, -0.3402, 0.1159, 0.4797,
- 0.3161],
- [ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862],
- [ 0.5030, -0.4631, 1.5998, -0.0303, -0.6404, -0.1843, 0.1465,
- -0.1181],
- [-2.2859, -2.2859, 1.2303, -0.7848, -0.4210, -1.1158, 0.2256,
- 0.3777],
- [ 0.6108, -0.4008, 1.9088, -0.0253, -0.5769, -0.3075, 0.7905,
- 0.1499],
- [ 0.5697, -0.4113, 1.7499, -0.2767, -0.6404, -0.7155, 0.1576,
- 0.4032],
- [ 0.5614, -0.4080, 0.7741, -0.8848, -0.5423, -0.9156, 0.3584,
- 0.4085]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0059, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0059, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.157949673011899
- step: 82
- running loss: 0.014121337475754865
- Train Steps: 82/90 Loss: 0.0141 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413],
- [0.6251, 0.4163, 0.8662, 0.4467, 0.3625, 0.3567, 0.6038, 0.5533],
- [0.6185, 0.4129, 0.8900, 0.4567, 0.3937, 0.5417, 0.5734, 0.5110],
- [0.6200, 0.3999, 0.8653, 0.5207, 0.4100, 0.5125, 0.5975, 0.5103],
- [0.6129, 0.4114, 0.6950, 0.2467, 0.4050, 0.2133, 0.5363, 0.5550],
- [0.6172, 0.4055, 0.8175, 0.2650, 0.3550, 0.3683, 0.5787, 0.5550],
- [0.6264, 0.4069, 0.7900, 0.1650, 0.4275, 0.2267, 0.6290, 0.4947],
- [0.6126, 0.4039, 0.8237, 0.3967, 0.3625, 0.3600, 0.5894, 0.6138]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4949, -0.4959, 1.6806, -1.2023, 0.2291, -1.2822, 1.0642, 0.2773],
- [ 0.5310, -0.4471, 1.7362, -0.1250, -0.6350, -0.7626, 0.4045, 0.2777],
- [ 0.6785, -0.3380, 1.8746, -0.0475, -0.4372, 0.1743, 0.2674, 0.1442],
- [ 0.6515, -0.4176, 1.7337, 0.2529, -0.4218, 0.0880, 0.3510, 0.0264],
- [ 0.6312, -0.3546, 0.8784, -0.9879, -0.4545, -1.2742, 0.0937, 0.2359],
- [ 0.6393, -0.3567, 1.4234, -0.8862, -0.6905, -0.6463, 0.3072, 0.2282],
- [ 0.4639, -0.5364, 1.3209, -1.2894, -0.3382, -1.3093, 0.6672, -0.0192],
- [ 0.6300, -0.3829, 1.5335, -0.3926, -0.6023, -0.6566, 0.4111, 0.4879]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.2401e-01, -3.7675e-01, 1.6575e+00, -1.2851e+00, 2.9492e-01,
- -1.2467e+00, 1.1276e+00, 2.1421e-01],
- [ 6.0162e-01, -3.6328e-01, 1.7152e+00, -2.2279e-01, -6.1155e-01,
- -6.3849e-01, 5.0277e-01, 2.6990e-01],
- [ 5.7113e-01, -3.7875e-01, 1.8249e+00, -1.7660e-01, -4.6721e-01,
- 2.1601e-01, 3.6246e-01, 7.4222e-02],
- [ 5.7777e-01, -4.3888e-01, 1.7107e+00, 1.1921e-01, -3.9207e-01,
- 8.1507e-02, 4.7413e-01, 7.1077e-02],
- [ 5.4480e-01, -3.8591e-01, 9.2425e-01, -1.1466e+00, -4.1524e-01,
- -1.3005e+00, 1.9099e-01, 2.7760e-01],
- [ 5.6472e-01, -4.1286e-01, 1.4901e+00, -1.0619e+00, -6.4619e-01,
- -5.8460e-01, 3.8730e-01, 2.7760e-01],
- [ 6.0739e-01, -4.0631e-01, 1.3630e+00, -1.5238e+00, -3.1132e-01,
- -1.2390e+00, 6.1950e-01, -9.2270e-04],
- [ 5.4376e-01, -4.2055e-01, 1.5189e+00, -4.5373e-01, -6.1155e-01,
- -6.2309e-01, 4.3649e-01, 5.4914e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0066, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0066, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.164504498243332
- step: 83
- running loss: 0.014030174677630505
- Train Steps: 83/90 Loss: 0.0140 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6213, 0.4001, 0.7712, 0.2117, 0.4338, 0.1800, 0.5850, 0.5183],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6200, 0.4112, 0.8862, 0.4100, 0.3638, 0.4917, 0.6088, 0.6050],
- [0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6216, 0.4167, 0.8588, 0.5583, 0.3975, 0.5167, 0.5775, 0.5667],
- [0.6201, 0.4116, 0.8725, 0.4733, 0.3700, 0.3217, 0.5386, 0.5767],
- [0.6339, 0.4102, 0.9088, 0.4767, 0.3925, 0.5283, 0.7509, 0.5390],
- [0.6284, 0.4029, 0.8838, 0.3783, 0.3975, 0.2850, 0.6335, 0.5090]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4710, -0.5196, 1.1014, -1.3862, -0.2148, -1.5333, 0.3553, 0.0757],
- [ 0.7050, -0.3146, 1.6446, -0.7035, -0.6196, -0.5095, 0.3162, 0.2193],
- [ 0.7285, -0.2890, 1.6976, -0.3794, -0.5090, -0.0404, 0.3566, 0.4185],
- [ 0.3456, -0.5866, 1.2864, -0.9612, -0.5184, -0.8896, 0.4405, 0.2379],
- [ 0.8533, -0.2240, 1.6473, 0.3931, -0.4096, 0.0653, 0.3822, 0.2566],
- [ 0.5720, -0.3857, 1.6606, -0.0632, -0.5481, -0.8728, 0.1911, 0.3357],
- [ 0.6318, -0.4071, 1.8293, -0.1566, -0.4036, 0.1316, 1.0711, 0.2480],
- [ 0.7790, -0.3147, 1.7108, -0.5726, -0.3795, -1.0664, 0.6091, 0.0483]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5838, -0.4379, 1.2764, -1.3082, -0.2824, -1.4545, 0.4162,
- 0.1082],
- [ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.5777, -0.3868, 1.8076, -0.3921, -0.6058, -0.0149, 0.5259,
- 0.5085],
- [ 0.5697, -0.4442, 1.3529, -0.9515, -0.5774, -0.7801, 0.5253,
- 0.1931],
- [ 0.5853, -0.3614, 1.6806, 0.2930, -0.4499, 0.1005, 0.3815,
- 0.3315],
- [ 0.5781, -0.3848, 1.7441, -0.0996, -0.5769, -0.8002, 0.2021,
- 0.3778],
- [ 0.6421, -0.3912, 1.9115, -0.0842, -0.4730, 0.1544, 1.1824,
- 0.2035],
- [ 0.6165, -0.4249, 1.7961, -0.5384, -0.4499, -0.9695, 0.6401,
- 0.0652]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0089, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0089, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.173423076979816
- step: 84
- running loss: 0.013969322344997809
- Train Steps: 84/90 Loss: 0.0140 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4087, 0.8375, 0.5700, 0.4025, 0.4800, 0.5700, 0.6117],
- [0.6229, 0.4107, 0.8137, 0.2883, 0.4750, 0.1717, 0.5813, 0.5400],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837],
- [0.6109, 0.4003, 0.8650, 0.4883, 0.4775, 0.4867, 0.5175, 0.5683],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [ nan, nan, 0.8213, 0.2700, 0.3775, 0.2817, 0.5425, 0.5533]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7204, -0.3026, 1.6254, 0.1875, -0.4491, -0.1383, 0.5085, 0.3773],
- [ 0.5447, -0.3915, 1.5013, -1.1210, -0.1454, -1.4991, 0.5017, 0.1182],
- [ 0.5424, -0.3848, 1.6505, -0.8521, -0.5953, -0.8380, 0.3130, 0.1086],
- [ 0.5663, -0.3752, 0.8812, -1.0786, -0.5915, -1.0182, 0.2309, 0.2956],
- [ 0.5830, -0.4065, 1.7906, -0.2180, -0.1265, -0.1026, 0.2333, 0.1712],
- [ 0.7589, -0.2776, 1.6148, 0.1922, -0.3882, -0.1449, 0.5068, 0.4028],
- [ 0.5904, -0.4397, 1.7383, 0.0546, -0.4931, -0.0932, 0.7224, 0.0257],
- [-2.2089, -2.2824, 1.3509, -1.0788, -0.5869, -1.0564, 0.3734, 0.2143]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6118, -0.3983, 1.5824, 0.3469, -0.4268, -0.0688, 0.3469,
- 0.5393],
- [ 0.5911, -0.3888, 1.4727, -0.9541, -0.0919, -1.4930, 0.3988,
- 0.2083],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103],
- [ 0.5359, -0.4370, 1.7095, -0.0303, -0.0804, -0.0380, 0.1044,
- 0.3392],
- [ 0.6134, -0.3910, 1.4497, 0.3546, -0.3517, -0.0919, 0.3296,
- 0.5239],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [-2.2859, -2.2859, 1.5074, -1.0388, -0.5423, -0.9849, 0.2199,
- 0.2699]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0103, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0103, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1836915975436568
- step: 85
- running loss: 0.01392578350051361
- Train Steps: 85/90 Loss: 0.0139 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6146, 0.4124, 0.8800, 0.4867, 0.3800, 0.4500, 0.5106, 0.5524],
- [0.6199, 0.4065, 0.7598, 0.2385, 0.4317, 0.1981, 0.5933, 0.5221],
- [0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833],
- [0.6233, 0.4091, 0.8100, 0.2950, 0.3563, 0.3883, 0.6013, 0.5200],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6361, 0.4165, 0.9038, 0.5017, 0.3750, 0.3783, 0.6448, 0.5309],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [ nan, nan, 0.7525, 0.2291, 0.3838, 0.3017, 0.6050, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7068, -0.2859, 1.7985, 0.0704, -0.5089, -0.2907, 0.1986, 0.2836],
- [ 0.8525, -0.2179, 1.2044, -1.2195, -0.2814, -1.3455, 0.5076, 0.1458],
- [ 0.8303, -0.2774, 1.7756, -0.0766, -0.5239, -0.1445, 0.4094, 0.0691],
- [ 0.5732, -0.3968, 1.4232, -0.9013, -0.6039, -0.4955, 0.5431, 0.2011],
- [ 0.6532, -0.3466, 1.7894, -0.0737, -0.2789, -0.0805, 0.4266, 0.3231],
- [ 0.7064, -0.3133, 1.8737, 0.0259, -0.4378, -0.6395, 0.7381, 0.2126],
- [ 0.5834, -0.3917, 1.1698, -1.2030, -0.4331, -1.0781, 0.5621, 0.4097],
- [-1.9059, -2.0811, 1.1619, -1.2046, -0.4429, -1.0495, 0.4665, 0.3691]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5529, -0.3811, 1.7788, -0.0380, -0.5307, -0.2074, 0.0727,
- 0.2657],
- [ 0.5774, -0.4082, 1.2235, -1.1844, -0.2919, -1.3709, 0.4544,
- 0.1256],
- [ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534],
- [ 0.5932, -0.3962, 1.4554, -0.9233, -0.6404, -0.4922, 0.4912,
- 0.1159],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.6520, -0.3623, 1.8885, 0.0313, -0.5538, -0.5384, 0.6926,
- 0.1661],
- [ 0.5915, -0.3682, 1.2187, -1.2313, -0.4326, -0.9541, 0.5778,
- 0.3777],
- [-2.2859, -2.2859, 1.1898, -1.2278, -0.5134, -0.8925, 0.5085,
- 0.3315]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0100, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0100, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1936637787148356
- step: 86
- running loss: 0.013879811380405066
- Train Steps: 86/90 Loss: 0.0139 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6138, 0.4054, 0.8750, 0.4750, 0.4363, 0.5017, 0.5086, 0.5822],
- [0.6202, 0.4066, 0.8398, 0.2648, 0.3925, 0.2627, 0.5845, 0.5124],
- [0.6286, 0.4086, 0.8408, 0.2801, 0.4163, 0.2800, 0.6725, 0.5393],
- [0.6199, 0.4015, 0.8716, 0.5228, 0.3833, 0.4772, 0.5883, 0.5148],
- [0.6222, 0.3937, 0.8350, 0.5617, 0.4138, 0.4600, 0.5800, 0.5233],
- [0.6245, 0.4100, 0.7762, 0.2583, 0.4963, 0.1517, 0.5875, 0.5417],
- [ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6049, -0.3581, 1.7083, -0.1912, -0.1407, -0.0747, 0.4234, 0.3232],
- [ 0.5529, -0.3694, 1.6717, -0.1886, -0.3450, -0.0744, 0.1994, 0.3777],
- [ 0.5310, -0.4016, 1.4163, -1.1305, -0.4936, -1.0623, 0.4344, 0.1493],
- [ 0.5927, -0.3553, 1.4686, -1.0565, -0.3416, -1.0536, 0.8740, 0.3268],
- [ 0.6938, -0.3412, 1.7493, 0.0628, -0.5975, -0.1540, 0.5167, 0.1286],
- [ 0.7393, -0.3116, 1.5618, 0.2102, -0.4959, -0.1859, 0.4538, 0.2352],
- [ 0.6530, -0.2842, 1.1949, -1.1057, -0.1295, -1.5413, 0.4090, 0.3264],
- [-2.0142, -2.1302, 1.3487, -0.9828, -0.6667, -0.8325, 0.3096, 0.2276]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5371, -0.4252, 1.7037, -0.0688, -0.0342, 0.0620, 0.3758,
- 0.2853],
- [ 0.5491, -0.4132, 1.7557, -0.0919, -0.2709, 0.0313, 0.0635,
- 0.4032],
- [ 0.5790, -0.4079, 1.5929, -1.0630, -0.4729, -1.0725, 0.4137,
- 0.0807],
- [ 0.6174, -0.3984, 1.5975, -0.9921, -0.3633, -0.9926, 0.8205,
- 0.2050],
- [ 0.5773, -0.4316, 1.7399, 0.1287, -0.5153, -0.0817, 0.4313,
- 0.0919],
- [ 0.5880, -0.4676, 1.5709, 0.3084, -0.3748, -0.1612, 0.3931,
- 0.1313],
- [ 0.5985, -0.3921, 1.2995, -1.0927, 0.0062, -1.5854, 0.4277,
- 0.2160],
- [-2.2859, -2.2859, 1.5478, -0.8309, -0.6289, -0.7232, 0.1198,
- 0.1133]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0096, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0096, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2032914850860834
- step: 87
- running loss: 0.013830936610184866
- Train Steps: 87/90 Loss: 0.0138 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [0.6186, 0.3967, 0.7337, 0.1992, 0.4120, 0.2508, 0.6105, 0.5395],
- [0.6068, 0.3963, 0.8650, 0.4317, 0.4037, 0.5083, 0.5253, 0.4999],
- [0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6216, 0.4099, 0.7225, 0.2033, 0.4188, 0.2217, 0.5975, 0.5283],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [0.6136, 0.4117, 0.8700, 0.5167, 0.4188, 0.5083, 0.5147, 0.5495],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6805, -0.3348, 1.9624, -0.4664, -0.5938, -0.4975, 0.7282, 0.2224],
- [ 0.6928, -0.3152, 1.1401, -1.1865, -0.4487, -1.0811, 0.5481, 0.2631],
- [ 0.6312, -0.3960, 1.7950, -0.2478, -0.4948, 0.0599, 0.3920, 0.0744],
- [ 0.6732, -0.2913, 1.1461, -1.0722, -0.3414, -1.0949, 0.5678, 0.6630],
- [ 0.7776, -0.2525, 1.1160, -1.1318, -0.3865, -1.2380, 0.4634, 0.2548],
- [ 0.6188, -0.3683, 1.3264, -1.0727, -0.2328, -1.4757, 0.4626, 0.1861],
- [ 0.6825, -0.3208, 1.7779, 0.1908, -0.3806, 0.0440, 0.3096, 0.2934],
- [-1.7795, -1.9782, 1.0829, -1.3242, -0.4301, -1.2074, 0.2961, 0.3201]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [ 0.5713, -0.4538, 1.1028, -1.3659, -0.3831, -1.1273, 0.5340,
- 0.2058],
- [ 0.5168, -0.4556, 1.7095, -0.2921, -0.4210, 0.0620, 0.1404,
- 0.0231],
- [ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084],
- [ 0.5850, -0.3925, 1.0513, -1.3467, -0.3517, -1.2620, 0.4739,
- 0.1544],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [ 0.5482, -0.3841, 1.7326, 0.1005, -0.3517, 0.0620, 0.0912,
- 0.2522],
- [-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0152, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0152, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2185192555189133
- step: 88
- running loss: 0.013846809721805832
- Train Steps: 88/90 Loss: 0.0138 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6305, 0.3983, 0.8950, 0.4833, 0.3688, 0.4683, 0.6375, 0.5117],
- [0.6346, 0.4144, 0.9088, 0.4667, 0.3850, 0.4333, 0.7121, 0.5899],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6175, 0.4093, 0.8800, 0.4433, 0.4075, 0.4367, 0.5128, 0.4901],
- [0.6214, 0.4112, 0.7838, 0.2117, 0.3650, 0.3133, 0.5675, 0.5083],
- [0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6163, -0.3500, 1.1294, -1.2303, -0.3170, -1.3979, 0.2945, 0.2448],
- [ 0.6485, -0.4020, 1.5849, 0.4244, -0.2580, 0.0700, 0.1429, 0.1287],
- [ 0.7184, -0.3741, 1.8447, -0.0733, -0.5955, -0.0847, 0.7442, 0.2117],
- [ 0.6278, -0.3934, 1.9591, -0.1562, -0.4049, -0.2477, 1.0511, 0.4199],
- [ 0.6740, -0.3122, 1.5357, -0.4706, -0.5488, -0.9307, 0.1926, 0.4752],
- [ 0.6191, -0.4002, 1.8453, -0.2578, -0.4682, -0.1923, 0.1952, 0.1291],
- [ 0.6972, -0.3326, 1.3606, -1.3241, -0.5866, -0.8800, 0.4947, 0.1693],
- [ 0.6533, -0.3122, 1.0827, -1.2065, -0.3026, -1.0755, 0.5253, 0.7117]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610],
- [ 0.6262, -0.4461, 1.8480, -0.0534, -0.5827, -0.1227, 0.6587,
- 0.0774],
- [ 0.6454, -0.3719, 1.9115, -0.1304, -0.5076, -0.2844, 1.0033,
- 0.4386],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.5663, -0.3955, 1.7788, -0.2382, -0.4037, -0.2690, 0.0828,
- -0.0220],
- [ 0.5845, -0.3864, 1.3342, -1.3082, -0.6000, -0.8386, 0.3353,
- 0.0620],
- [ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0050, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0050, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.223516573663801
- step: 89
- running loss: 0.01374737723217754
- Train Steps: 89/90 Loss: 0.0137 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4059, 0.8700, 0.4900, 0.4163, 0.5000, 0.6162, 0.5467],
- [0.6228, 0.4004, 0.8750, 0.5250, 0.3825, 0.5233, 0.6362, 0.5000],
- [0.6198, 0.4115, 0.7762, 0.2717, 0.3713, 0.3200, 0.5837, 0.5683],
- [0.6263, 0.4057, 0.8800, 0.3833, 0.3650, 0.3717, 0.6375, 0.4804],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6202, 0.3983, 0.8555, 0.5429, 0.3842, 0.4370, 0.5866, 0.5398],
- [0.6271, 0.4024, 0.9000, 0.3517, 0.3700, 0.4517, 0.6931, 0.5285],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6415, -0.3877, 1.6808, -0.0775, -0.2826, 0.0688, 0.3160, 0.3622],
- [ 0.6123, -0.4358, 1.6953, 0.1156, -0.4587, 0.1190, 0.5347, 0.1479],
- [ 0.6549, -0.3544, 1.2989, -1.1406, -0.5752, -0.8567, 0.2800, 0.4329],
- [ 0.7401, -0.3168, 1.7433, -0.5885, -0.5935, -0.5729, 0.5201, 0.0110],
- [ 0.6757, -0.2947, 1.5974, -0.2606, -0.4664, -0.2819, -0.0857, 0.5438],
- [ 0.6677, -0.3903, 1.5811, 0.2361, -0.5221, -0.2246, 0.3248, 0.2999],
- [ 0.6235, -0.3914, 1.6939, -0.7000, -0.5345, -0.1516, 0.8339, 0.3037],
- [ 0.6724, -0.3755, 1.9923, -0.5858, -0.0867, -1.1977, 0.9983, 0.3048]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5776, -0.4109, 1.7326, -0.0226, -0.3633, 0.0236, 0.5605,
- 0.2391],
- [ 0.5908, -0.4366, 1.7557, 0.1390, -0.5192, 0.1313, 0.6529,
- 0.0236],
- [ 0.5768, -0.3852, 1.2995, -1.0311, -0.5711, -0.8079, 0.4104,
- 0.3392],
- [ 0.6071, -0.4119, 1.7788, -0.5153, -0.6000, -0.5692, 0.6586,
- -0.0670],
- [ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145],
- [ 0.5786, -0.4463, 1.6655, 0.2216, -0.5115, -0.2675, 0.4236,
- 0.2075],
- [ 0.6108, -0.4273, 1.8711, -0.6616, -0.5769, -0.1997, 0.9156,
- 0.1554],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0074, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0074, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.230874984525144
- step: 90
- running loss: 0.013676388716946045
- Valid Steps: 10/10 Loss: nan 37
- --------------------------------------------------
- Epoch: 9 Train Loss: 0.0137 Valid Loss: nan
- --------------------------------------------------
- size of train loader is: 90
- torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6321, 0.4048, 0.8738, 0.5617, 0.3875, 0.4417, 0.6361, 0.4927],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6087, 0.3976, 0.8337, 0.3867, 0.3713, 0.3117, 0.5938, 0.5300],
- [0.6200, 0.4055, 0.8011, 0.2988, 0.3842, 0.2798, 0.5949, 0.5183],
- [0.6264, 0.4067, 0.9050, 0.4183, 0.3775, 0.4600, 0.6308, 0.4862],
- [0.6201, 0.4004, 0.8786, 0.5043, 0.3833, 0.5138, 0.5997, 0.5092],
- [0.6198, 0.4164, 0.8700, 0.5067, 0.4625, 0.5650, 0.5464, 0.5197]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6679, -0.4081, 1.7148, 0.2545, -0.5189, -0.2133, 0.6253, 0.0836],
- [ 0.6459, -0.3418, 1.8009, -0.9554, -0.0123, -1.4325, 0.6081, 0.2512],
- [ 0.5468, -0.3994, 1.2339, -0.7346, -0.6692, -0.4572, 0.1664, 0.4841],
- [ 0.6728, -0.3519, 1.5303, -0.6012, -0.5453, -0.7852, 0.4589, 0.3779],
- [ 0.6847, -0.3457, 1.3959, -1.0181, -0.4633, -0.9420, 0.4814, 0.2562],
- [ 0.7260, -0.3154, 1.8198, -0.4252, -0.6058, -0.1752, 0.6467, 0.0982],
- [ 0.5705, -0.4656, 1.7108, 0.0119, -0.4905, 0.1314, 0.3809, 0.2373],
- [ 0.5818, -0.3801, 1.7161, -0.0282, -0.1464, 0.2088, 0.2626, 0.3285]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6335, -0.4162, 1.7499, 0.3084, -0.4961, -0.2459, 0.6524,
- -0.0102],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.5255, -0.4495, 1.5651, -0.4999, -0.5711, -0.8463, 0.4566,
- 0.1621],
- [ 0.5781, -0.4129, 1.4142, -0.9057, -0.5115, -0.9937, 0.4621,
- 0.1080],
- [ 0.6072, -0.4075, 1.8942, -0.3537, -0.5423, -0.1612, 0.6277,
- -0.0400],
- [ 0.5783, -0.4363, 1.7724, 0.0432, -0.5153, 0.0871, 0.4840,
- 0.0663],
- [ 0.5770, -0.3624, 1.7326, 0.0543, -0.1497, 0.3238, 0.2378,
- 0.1146]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0075, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0075, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.0074615185149014
- step: 1
- running loss: 0.0074615185149014
- Train Steps: 1/90 Loss: 0.0075 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4098, 0.8575, 0.3617, 0.3700, 0.3550, 0.5988, 0.5667],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320],
- [0.6086, 0.3981, 0.8700, 0.4750, 0.4512, 0.5283, 0.5324, 0.5038],
- [0.6339, 0.4118, 0.7988, 0.5800, 0.3912, 0.4583, 0.7343, 0.5760],
- [0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6280, 0.4101, 0.9050, 0.4533, 0.3775, 0.3217, 0.6338, 0.4915],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329],
- [0.6263, 0.4065, 0.9038, 0.4317, 0.3588, 0.4550, 0.6325, 0.5250]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6624, -0.3220, 1.6149, -0.7544, -0.4754, -0.6492, 0.2969, 0.4028],
- [ 0.6560, -0.3936, 1.6232, 0.0781, -0.4228, 0.0890, 0.8605, 0.1984],
- [ 0.6372, -0.3622, 1.6846, -0.1787, -0.1467, 0.0819, 0.0969, 0.0911],
- [ 0.5155, -0.4592, 1.3896, 0.2184, -0.4286, -0.1787, 0.9320, 0.3922],
- [ 0.6200, -0.3272, 1.5840, -0.5143, -0.5560, -0.8308, -0.1218, 0.2780],
- [ 0.5761, -0.4320, 1.9143, -0.2647, -0.5046, -0.7932, 0.4572, 0.0386],
- [ 0.6207, -0.3765, 1.6158, -0.8149, -0.6073, -0.5826, 0.3833, 0.2445],
- [ 0.4286, -0.5189, 1.9060, -0.4003, -0.5482, -0.1925, 0.5965, 0.2424]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5783, -0.3933, 1.6748, -0.6154, -0.5769, -0.6462, 0.4797,
- 0.3315],
- [ 0.6273, -0.4393, 1.6402, 0.1313, -0.5076, 0.0467, 1.1532,
- 0.1715],
- [ 0.5249, -0.4473, 1.7326, -0.0919, -0.2016, 0.1544, 0.1733,
- 0.0412],
- [ 0.6421, -0.3840, 1.4035, 0.3931, -0.4788, -0.1689, 1.1057,
- 0.3745],
- [ 0.5470, -0.3952, 1.6377, -0.4253, -0.6289, -0.8079, 0.0249,
- 0.2116],
- [ 0.6148, -0.3918, 1.8942, -0.1920, -0.5423, -0.8002, 0.6414,
- -0.0156],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753],
- [ 0.6070, -0.4085, 1.8885, -0.2921, -0.6289, -0.1843, 0.6356,
- 0.1390]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0085, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0085, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.015985853504389524
- step: 2
- running loss: 0.007992926752194762
- Train Steps: 2/90 Loss: 0.0080 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4087, 0.8375, 0.5700, 0.4025, 0.4800, 0.5700, 0.6117],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [0.6214, 0.4112, 0.7838, 0.2117, 0.3650, 0.3133, 0.5675, 0.5083],
- [0.6166, 0.4008, 0.8563, 0.5667, 0.4388, 0.4933, 0.5575, 0.5567],
- [0.6250, 0.4103, 0.8950, 0.4400, 0.3912, 0.5650, 0.6050, 0.5133],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6501, -0.3832, 1.6167, 0.3688, -0.4298, -0.0988, 0.5013, 0.4481],
- [ 0.5538, -0.4239, 1.8637, -0.2437, -0.4463, -0.1293, 0.2182, 0.0533],
- [ 0.6812, -0.3732, 1.5055, -1.3821, -0.6032, -0.9587, 0.6223, -0.0342],
- [ 0.5355, -0.4735, 1.7399, 0.3028, -0.2351, 0.0205, 0.4293, 0.2091],
- [ 0.6038, -0.3897, 1.9341, -0.3007, -0.4164, 0.3271, 0.8311, 0.0847],
- [ 0.4763, -0.4982, 1.6687, -0.5504, -0.7019, -0.4592, 0.3585, -0.0793],
- [ 0.5035, -0.3993, 1.2370, -0.6837, -0.6868, -0.5294, 0.2725, 0.3857],
- [ 0.6356, -0.3197, 1.2649, -0.7366, -0.0193, -1.4324, 0.3899, 0.5164]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6118, -0.3983, 1.5824, 0.3469, -0.4268, -0.0688, 0.3469,
- 0.5393],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893],
- [ 0.5845, -0.3864, 1.3342, -1.3082, -0.6000, -0.8386, 0.3353,
- 0.0620],
- [ 0.5620, -0.4346, 1.6691, 0.3315, -0.2594, -0.0072, 0.2891,
- 0.2853],
- [ 0.6010, -0.3909, 1.8480, -0.2536, -0.4788, 0.3238, 0.5085,
- 0.0851],
- [ 0.5206, -0.4603, 1.4670, -0.4768, -0.6577, -0.3998, 0.1836,
- 0.0021],
- [ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0084, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0084, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.02438948256894946
- step: 3
- running loss: 0.008129827522983154
- Train Steps: 3/90 Loss: 0.0081 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.4110, 0.7238, 0.2067, 0.4263, 0.1883, 0.5625, 0.5633],
- [0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136],
- [0.6221, 0.4107, 0.7788, 0.3033, 0.3950, 0.2817, 0.6075, 0.5517],
- [0.6250, 0.4054, 0.8770, 0.4723, 0.4662, 0.5367, 0.6162, 0.5433],
- [0.6262, 0.4052, 0.8888, 0.4700, 0.3675, 0.5117, 0.6350, 0.5233],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6250, 0.4008, 0.8950, 0.4183, 0.3550, 0.4383, 0.6361, 0.4927],
- [ nan, nan, 0.7335, 0.2569, 0.3788, 0.2667, 0.5066, 0.5578]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.5326e-01, -4.2534e-01, 1.0864e+00, -1.1242e+00, -2.1720e-01,
- -1.4227e+00, 3.4412e-01, 1.9398e-01],
- [ 4.9047e-01, -4.4277e-01, 1.7040e+00, -3.5306e-01, -6.0177e-01,
- -3.8281e-01, 2.0634e-01, 6.6394e-03],
- [ 4.8487e-01, -4.6687e-01, 1.4244e+00, -8.3061e-01, -4.0475e-01,
- -9.4819e-01, 4.2932e-01, 2.4966e-01],
- [ 6.4183e-01, -3.5516e-01, 1.7091e+00, -1.8088e-02, -1.4074e-01,
- 2.4657e-01, 5.6285e-01, 1.6769e-01],
- [ 6.6839e-01, -3.6778e-01, 1.8622e+00, -2.1339e-03, -5.3337e-01,
- 1.3466e-01, 7.2328e-01, 5.1827e-02],
- [ 5.6919e-01, -3.9543e-01, 1.7194e+00, -7.2178e-01, -3.5065e-01,
- -8.3430e-01, 7.7734e-01, 2.2063e-01],
- [ 6.5032e-01, -3.9691e-01, 1.7636e+00, -2.2513e-01, -6.1440e-01,
- -2.3528e-01, 4.7430e-01, 3.8070e-02],
- [-2.1769e+00, -2.2064e+00, 1.1524e+00, -1.1870e+00, -5.2893e-01,
- -9.4108e-01, 8.8347e-02, 2.0739e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6010, -0.3875, 1.0570, -1.3313, -0.3171, -1.4160, 0.3122,
- 0.3161],
- [ 0.5235, -0.4273, 1.7499, -0.4306, -0.5827, -0.4614, 0.1651,
- 0.0862],
- [ 0.5875, -0.3888, 1.3111, -0.8848, -0.4614, -0.9849, 0.5201,
- 0.2622],
- [ 0.6009, -0.4135, 1.7651, -0.1043, -0.1323, 0.1929, 0.5605,
- 0.2237],
- [ 0.6063, -0.4142, 1.8192, -0.1150, -0.5885, 0.0774, 0.6471,
- 0.1313],
- [ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.6010, -0.4345, 1.8480, -0.3537, -0.6462, -0.2613, 0.6524,
- -0.0099],
- [-2.2859, -2.2859, 1.1020, -1.0994, -0.5365, -1.0542, 0.0542,
- 0.2905]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0057, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0057, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.030109287705272436
- step: 4
- running loss: 0.007527321926318109
- Train Steps: 4/90 Loss: 0.0075 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.4116, 0.8700, 0.4850, 0.4650, 0.5567, 0.6088, 0.5183],
- [0.6259, 0.4156, 0.8812, 0.3183, 0.4775, 0.1867, 0.6219, 0.4960],
- [0.6274, 0.4270, 0.8938, 0.4967, 0.3550, 0.4283, 0.5700, 0.5733],
- [0.6175, 0.3957, 0.8700, 0.4817, 0.4662, 0.5133, 0.5800, 0.5517],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600],
- [0.6151, 0.4058, 0.7068, 0.2680, 0.3400, 0.4083, 0.5775, 0.5733],
- [0.6097, 0.4000, 0.7325, 0.2667, 0.3450, 0.3517, 0.5284, 0.5045],
- [0.6095, 0.4002, 0.8533, 0.5168, 0.5031, 0.5094, 0.5125, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5593, -0.4216, 1.8539, 0.0233, -0.2211, 0.3257, 0.6745, 0.1137],
- [ 0.5730, -0.4056, 1.9641, -0.6987, -0.1147, -1.4240, 0.6490, -0.0203],
- [ 0.4865, -0.4275, 1.9484, 0.1791, -0.6681, -0.2434, 0.3987, 0.2926],
- [ 0.6164, -0.4252, 1.7836, 0.0594, -0.1791, 0.0937, 0.4037, 0.1212],
- [ 0.5586, -0.4110, 1.1464, -1.0049, -0.6523, -0.8031, 0.6228, 0.1723],
- [ 0.4328, -0.5101, 1.1184, -0.9284, -0.7571, -0.4165, 0.4390, 0.2188],
- [ 0.4403, -0.5205, 1.1551, -0.9474, -0.7202, -0.6720, 0.2133, -0.0217],
- [ 0.5382, -0.4574, 1.7547, 0.2606, -0.0796, 0.0177, 0.1632, 0.2114]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6012, -0.3846, 1.7326, -0.0457, -0.1381, 0.2853, 0.5259,
- 0.1082],
- [ 0.6052, -0.3663, 1.7845, -0.8156, -0.0804, -1.4237, 0.5866,
- 0.0051],
- [ 0.6121, -0.3138, 1.8423, 0.0082, -0.6462, -0.3075, 0.3469,
- 0.3623],
- [ 0.5662, -0.4581, 1.7326, -0.0611, -0.1323, 0.0851, 0.3931,
- 0.2622],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007],
- [ 0.5552, -0.4113, 0.9790, -1.0480, -0.7155, -0.3998, 0.3815,
- 0.3623],
- [ 0.5303, -0.4384, 1.0975, -1.0542, -0.6924, -0.6616, 0.1548,
- 0.0442],
- [ 0.5295, -0.4373, 1.6553, 0.1011, 0.0380, 0.0671, 0.0813,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.037714792881160975
- step: 5
- running loss: 0.0075429585762321946
- Train Steps: 5/90 Loss: 0.0075 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6250, 0.4013, 0.8525, 0.5417, 0.4037, 0.5117, 0.6325, 0.5017],
- [0.6098, 0.3991, 0.8638, 0.4717, 0.4263, 0.4967, 0.5212, 0.5650],
- [0.6197, 0.4050, 0.7527, 0.2000, 0.4042, 0.2249, 0.5895, 0.4995],
- [0.6283, 0.4283, 0.7477, 0.3542, 0.5125, 0.1917, 0.5500, 0.6167],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450],
- [0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356],
- [0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4132, -0.5643, 1.6594, 0.1873, -0.5100, 0.1605, 0.4882, 0.0102],
- [ 0.3890, -0.5216, 1.6669, -0.0909, -0.3992, 0.1360, 0.0391, 0.1829],
- [ 0.6699, -0.3643, 1.1973, -1.2907, -0.4870, -1.1349, 0.3494, -0.0741],
- [ 0.5747, -0.3525, 1.1663, -0.6527, -0.0847, -1.2640, 0.1935, 0.5250],
- [ 0.5285, -0.4959, 1.8223, -0.5301, -0.4036, -0.7936, 0.8845, 0.0212],
- [ 0.5436, -0.4472, 1.8121, -0.1635, -0.4170, 0.2699, 0.4882, 0.1760],
- [ 0.5868, -0.4045, 1.4909, -1.0393, -0.3746, -1.0620, 0.5821, 0.1467],
- [ 0.5726, -0.4658, 1.8135, 0.1574, -0.6902, -0.0282, 0.5791, -0.0738]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6010, -0.4321, 1.6517, 0.2160, -0.4210, 0.0774, 0.6356,
- 0.0313],
- [ 0.5308, -0.4425, 1.7037, -0.1073, -0.3171, 0.0082, 0.1217,
- 0.3238],
- [ 0.5762, -0.4153, 1.1908, -1.3622, -0.4190, -1.2471, 0.4368,
- 0.0213],
- [ 0.6161, -0.3075, 1.1678, -0.6500, 0.0813, -1.4006, 0.2545,
- 0.5624],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [ 0.5960, -0.4102, 1.8018, -0.1612, -0.3344, 0.1159, 0.5490,
- 0.2314],
- [ 0.6174, -0.4225, 1.4975, -1.1709, -0.3174, -1.1806, 0.6539,
- 0.1879],
- [ 0.6240, -0.4321, 1.8423, 0.1852, -0.5885, -0.1612, 0.6962,
- 0.0111]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.04535488644614816
- step: 6
- running loss: 0.0075591477410246926
- Train Steps: 6/90 Loss: 0.0076 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300],
- [0.6161, 0.4024, 0.8838, 0.4583, 0.3688, 0.3733, 0.5311, 0.5344],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550],
- [0.6072, 0.4029, 0.7037, 0.2150, 0.3912, 0.2267, 0.5516, 0.5507],
- [0.6228, 0.4004, 0.8750, 0.5250, 0.3825, 0.5233, 0.6362, 0.5000],
- [0.6164, 0.4076, 0.8838, 0.4117, 0.3713, 0.5550, 0.6238, 0.5350],
- [0.6128, 0.4084, 0.8738, 0.4683, 0.3613, 0.3700, 0.4960, 0.5397],
- [0.6118, 0.4052, 0.8463, 0.3917, 0.3538, 0.3450, 0.5053, 0.5593]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5229, -0.4542, 1.2582, -1.1586, -0.4608, -0.8265, 0.7056, 0.1246],
- [ 0.5421, -0.4597, 1.8666, -0.0591, -0.5739, -0.4832, 0.3031, 0.0289],
- [ 0.4805, -0.4632, 1.7951, -0.0926, 0.0650, 0.0324, 0.5053, 0.2165],
- [ 0.5597, -0.4463, 0.9369, -1.2581, -0.4177, -1.2128, 0.3655, 0.1525],
- [ 0.5571, -0.4922, 1.7682, 0.2352, -0.4721, 0.1582, 0.8246, -0.0903],
- [ 0.5490, -0.4201, 1.7635, -0.2910, -0.4697, 0.3081, 0.7349, 0.2057],
- [ 0.5234, -0.4357, 1.7965, -0.0241, -0.6572, -0.5234, 0.0793, 0.1230],
- [ 0.4955, -0.4340, 1.6476, -0.4130, -0.6548, -0.6279, -0.0118, 0.1236]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621],
- [ 0.5598, -0.4273, 1.7961, -0.1689, -0.5827, -0.5615, 0.1671,
- 0.1824],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776],
- [ 0.5185, -0.4252, 0.9647, -1.2928, -0.4788, -1.2390, 0.2617,
- 0.2576],
- [ 0.5908, -0.4366, 1.7557, 0.1390, -0.5192, 0.1313, 0.6529,
- 0.0236],
- [ 0.5614, -0.4032, 1.7961, -0.3844, -0.5711, 0.2776, 0.5952,
- 0.1852],
- [ 0.5443, -0.3994, 1.7499, -0.1227, -0.6173, -0.5769, 0.0049,
- 0.2071],
- [ 0.5399, -0.4142, 1.6229, -0.4768, -0.6520, -0.6924, 0.0481,
- 0.2972]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0054, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0054, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.050726964604109526
- step: 7
- running loss: 0.007246709229158503
- Train Steps: 7/90 Loss: 0.0072 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6102, 0.4020, 0.8638, 0.3717, 0.3625, 0.5017, 0.6038, 0.5500],
- [0.6248, 0.4032, 0.7738, 0.1900, 0.4813, 0.1400, 0.5941, 0.4904],
- [0.6277, 0.4118, 0.8988, 0.3833, 0.3950, 0.2650, 0.6290, 0.4938],
- [0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064],
- [0.6200, 0.4071, 0.7338, 0.1917, 0.4200, 0.2450, 0.6150, 0.5517],
- [0.6343, 0.4097, 0.9287, 0.4367, 0.4313, 0.3600, 0.7248, 0.5841],
- [0.6226, 0.4185, 0.8850, 0.5500, 0.3800, 0.4250, 0.5625, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5618, -0.3972, 1.6424, -0.7693, -0.2796, -1.2178, 0.3273, -0.0264],
- [ 0.5328, -0.4261, 1.5671, -0.5516, -0.6503, 0.1277, 0.5681, 0.2026],
- [ 0.5060, -0.4781, 1.2327, -1.2211, -0.0598, -1.4889, 0.3843, 0.0457],
- [ 0.5032, -0.4759, 1.8289, -0.4374, -0.5492, -0.8765, 0.5361, -0.0238],
- [ 0.4846, -0.4673, 1.5984, 0.0356, -0.1474, 0.2934, 0.0812, -0.0116],
- [ 0.4216, -0.5137, 1.0399, -1.3717, -0.4076, -0.9645, 0.4648, 0.2284],
- [ 0.5114, -0.4945, 1.7288, -0.2810, -0.4495, -0.4657, 1.0433, 0.2603],
- [ 0.4677, -0.4175, 1.6918, 0.2672, -0.5914, -0.1755, 0.2433, 0.2968]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.5324, -0.4293, 1.7037, -0.5692, -0.6115, 0.0313, 0.5028,
- 0.2545],
- [ 0.5999, -0.4236, 1.2880, -1.4083, -0.0630, -1.6393, 0.4584,
- -0.0208],
- [ 0.6135, -0.3841, 1.8654, -0.5153, -0.4614, -1.0619, 0.6195,
- -0.0049],
- [ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532],
- [ 0.5779, -0.4054, 1.1032, -1.4006, -0.3460, -1.1543, 0.5547,
- 0.2622],
- [ 0.6438, -0.3936, 2.0039, -0.2690, -0.2940, -0.6231, 1.0618,
- 0.4119],
- [ 0.5897, -0.3527, 1.8018, 0.2545, -0.5307, -0.3229, 0.3122,
- 0.3084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0097, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0097, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.06042189570143819
- step: 8
- running loss: 0.007552736962679774
- Train Steps: 8/90 Loss: 0.0076 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6060, 0.3924, 0.8450, 0.5717, 0.4200, 0.5217, 0.5253, 0.4752],
- [0.6196, 0.4094, 0.7562, 0.2817, 0.3937, 0.3183, 0.6013, 0.6183],
- [0.6241, 0.4143, 0.8938, 0.4650, 0.4075, 0.5350, 0.6250, 0.5550],
- [0.6198, 0.4105, 0.7950, 0.3267, 0.3675, 0.3767, 0.6038, 0.6167],
- [0.6277, 0.4036, 0.8688, 0.3617, 0.3925, 0.2600, 0.6132, 0.4742],
- [0.6293, 0.4024, 0.8750, 0.5000, 0.4012, 0.5733, 0.7121, 0.5633],
- [0.6250, 0.4008, 0.8950, 0.4183, 0.3550, 0.4383, 0.6361, 0.4927],
- [0.6307, 0.4029, 0.8988, 0.4817, 0.3937, 0.3500, 0.7311, 0.5378]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5586, -0.4238, 1.5115, 0.3208, -0.2791, 0.0410, 0.1167, -0.0283],
- [ 0.5033, -0.4404, 1.2076, -0.9702, -0.4188, -0.7943, 0.3816, 0.4679],
- [ 0.4406, -0.4708, 1.8064, -0.1864, -0.3350, 0.1449, 0.4002, 0.2132],
- [ 0.3537, -0.5212, 1.3906, -0.8135, -0.5936, -0.4937, 0.3015, 0.4278],
- [ 0.6171, -0.4108, 1.7262, -0.7019, -0.3729, -1.1624, 0.4567, -0.1241],
- [ 0.6642, -0.3580, 1.7266, 0.0246, -0.3493, 0.3263, 0.7752, 0.1696],
- [ 0.5559, -0.4502, 1.7533, -0.3565, -0.6303, -0.3345, 0.4073, 0.0063],
- [ 0.5353, -0.4811, 1.8886, -0.0844, -0.4030, -0.6989, 0.9185, 0.0296]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5132, -0.4736, 1.6171, 0.3546, -0.3460, 0.1236, 0.1404,
- -0.0911],
- [ 0.5759, -0.3948, 1.2072, -0.9849, -0.4672, -0.8156, 0.4912,
- 0.5701],
- [ 0.5968, -0.3725, 1.8423, -0.1381, -0.4037, 0.1852, 0.6009,
- 0.2776],
- [ 0.5768, -0.3899, 1.3861, -0.7771, -0.5885, -0.5461, 0.5028,
- 0.5624],
- [ 0.6134, -0.4218, 1.7268, -0.6154, -0.4730, -1.0850, 0.5463,
- -0.0957],
- [ 0.6207, -0.4273, 1.7557, 0.0236, -0.4326, 0.3623, 1.0033,
- 0.3157],
- [ 0.6010, -0.4345, 1.8480, -0.3537, -0.6462, -0.2613, 0.6524,
- -0.0099],
- [ 0.6273, -0.4249, 1.8654, -0.0611, -0.4672, -0.6693, 1.0910,
- 0.1982]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.06914769345894456
- step: 9
- running loss: 0.00768307705099384
- Train Steps: 9/90 Loss: 0.0077 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6179, 0.4082, 0.6688, 0.2667, 0.3588, 0.3317, 0.5750, 0.5783],
- [0.6277, 0.4057, 0.8300, 0.2650, 0.4363, 0.1850, 0.6140, 0.4823],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600],
- [0.6173, 0.4114, 0.7325, 0.2500, 0.4213, 0.1917, 0.5338, 0.5700],
- [0.6325, 0.4165, 0.9000, 0.4617, 0.3813, 0.4900, 0.7485, 0.5447],
- [0.6080, 0.4010, 0.8750, 0.4500, 0.4825, 0.5617, 0.5837, 0.5583],
- [0.6201, 0.4116, 0.8725, 0.4733, 0.3700, 0.3217, 0.5386, 0.5767]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6758, -0.3886, 2.0155, -0.1198, -0.3333, -0.9112, 1.0076, 0.2153],
- [ 0.4710, -0.4752, 0.8565, -1.0481, -0.6728, -0.7240, 0.1994, 0.2647],
- [ 0.6471, -0.4175, 1.6222, -1.1371, -0.1707, -1.3694, 0.5561, -0.1339],
- [ 0.5243, -0.4422, 1.0698, -1.1698, -0.5872, -0.7628, 0.4584, 0.1919],
- [ 0.2242, -0.6373, 1.0507, -1.1499, -0.3268, -1.2658, 0.1022, 0.2798],
- [ 0.6209, -0.4149, 1.9634, -0.0922, -0.5583, 0.1483, 1.0144, 0.0745],
- [ 0.4827, -0.4694, 1.8218, -0.1977, -0.0812, 0.2834, 0.3023, 0.1277],
- [ 0.4675, -0.4487, 1.7748, -0.0849, -0.5758, -0.7073, 0.1284, 0.2613]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.5680, -0.4005, 0.8030, -1.0542, -0.6289, -0.7540, 0.3700,
- 0.3854],
- [ 0.6132, -0.4122, 1.5478, -1.0619, -0.2709, -1.4314, 0.5500,
- -0.0583],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007],
- [ 0.5652, -0.3858, 1.0975, -1.1312, -0.3402, -1.4006, 0.1794,
- 0.3469],
- [ 0.6355, -0.3623, 1.8711, -0.1535, -0.5249, -0.0226, 1.1715,
- 0.2302],
- [ 0.5223, -0.4336, 1.7557, -0.2074, -0.0573, 0.3084, 0.4104,
- 0.2930],
- [ 0.5781, -0.3848, 1.7441, -0.0996, -0.5769, -0.8002, 0.2021,
- 0.3778]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0086, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0086, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.07775980466976762
- step: 10
- running loss: 0.007775980466976762
- Train Steps: 10/90 Loss: 0.0078 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6263, 0.4029, 0.8900, 0.4933, 0.4075, 0.5183, 0.6406, 0.4758],
- [0.6205, 0.4081, 0.8950, 0.4017, 0.3788, 0.4700, 0.5963, 0.5667],
- [ nan, nan, 0.6900, 0.1917, 0.3937, 0.2367, 0.5240, 0.5246],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6201, 0.4027, 0.8029, 0.2728, 0.4042, 0.2310, 0.5980, 0.5391],
- [ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482],
- [0.6311, 0.4008, 0.7935, 0.5746, 0.3900, 0.5033, 0.6955, 0.5366],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7300, -0.3389, 1.8162, -0.1014, -0.4492, 0.1221, 0.6110, -0.0020],
- [ 0.7533, -0.2762, 1.7339, -0.4205, -0.6105, -0.0945, 0.4151, 0.1860],
- [-1.9275, -2.0202, 1.1070, -1.3464, -0.4541, -1.1237, 0.0258, 0.1614],
- [ 0.7344, -0.2898, 1.6910, 0.2567, -0.4216, 0.0965, 0.2483, 0.2432],
- [ 0.7589, -0.2931, 1.4342, -1.0748, -0.3835, -1.2520, 0.3794, 0.0544],
- [-1.2442, -1.5647, 1.6967, -1.2537, 0.2367, -1.2305, 0.9762, 0.3556],
- [ 0.7263, -0.3257, 1.4122, 0.2276, -0.4983, -0.0615, 0.8091, 0.2047],
- [ 0.6517, -0.3751, 1.6237, 0.1518, -0.4258, -0.1553, 0.3233, 0.1533]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6072, -0.4250, 1.8249, -0.0072, -0.4037, 0.1082, 0.6730,
- -0.0881],
- [ 0.5799, -0.4012, 1.8480, -0.4306, -0.5365, -0.1150, 0.4681,
- 0.3315],
- [-2.2859, -2.2859, 0.9012, -1.4006, -0.4672, -1.1928, 0.1342,
- 0.1373],
- [ 0.5742, -0.3792, 1.6460, 0.3084, -0.3460, 0.1467, 0.4162,
- 0.3161],
- [ 0.5785, -0.4259, 1.4228, -1.0261, -0.4190, -1.2189, 0.4763,
- 0.2043],
- [-2.2859, -2.2859, 1.6517, -1.2620, 0.2141, -1.1928, 1.1166,
- 0.2463],
- [ 0.6289, -0.4345, 1.3794, 0.3679, -0.4845, 0.0390, 0.9265,
- 0.1928],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0361, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0361, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.11386870825663209
- step: 11
- running loss: 0.010351700750602917
- Train Steps: 11/90 Loss: 0.0104 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6216, 0.4167, 0.8588, 0.5583, 0.3975, 0.5167, 0.5775, 0.5667],
- [0.6125, 0.3974, 0.7725, 0.2517, 0.3538, 0.3317, 0.5887, 0.5500],
- [0.6143, 0.4040, 0.8237, 0.3333, 0.4075, 0.2117, 0.5137, 0.4973],
- [0.6307, 0.4045, 0.8025, 0.5833, 0.3775, 0.4867, 0.6892, 0.5459],
- [0.6200, 0.3993, 0.8639, 0.3687, 0.3658, 0.3139, 0.6002, 0.5374],
- [0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6214, -0.3683, 1.7116, 0.2403, -0.3745, 0.1278, 0.5127, 0.3513],
- [ 0.5240, -0.4460, 1.2222, -1.2010, -0.5856, -0.7551, 0.4109, 0.2644],
- [ 0.3942, -0.5166, 1.4681, -0.8702, -0.3608, -1.2539, 0.1577, 0.0390],
- [ 0.6655, -0.3826, 1.4986, 0.1916, -0.4446, -0.0653, 1.0156, 0.2396],
- [ 0.5855, -0.4402, 1.7162, -0.6379, -0.5470, -0.8691, 0.5754, 0.1821],
- [ 0.3844, -0.5187, 1.5678, -0.6282, -0.6653, -0.4034, 0.3104, 0.2106],
- [ 0.7308, -0.3358, 1.6167, -1.1079, 0.0411, -1.5191, 0.8376, 0.1383],
- [ 0.5378, -0.4333, 1.7431, -0.5032, -0.4683, 0.0062, 0.4150, 0.2511]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5853, -0.3614, 1.6806, 0.2930, -0.4499, 0.1005, 0.3815,
- 0.3315],
- [ 0.5430, -0.4503, 1.2822, -1.1235, -0.6520, -0.7540, 0.4335,
- 0.2545],
- [ 0.5515, -0.4201, 1.5189, -0.7463, -0.4037, -1.3082, 0.0869,
- 0.0111],
- [ 0.6273, -0.4177, 1.4208, 0.4085, -0.5423, -0.0380, 0.8973,
- 0.2356],
- [ 0.5777, -0.4416, 1.7044, -0.5827, -0.5962, -0.8361, 0.4862,
- 0.1963],
- [ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.5546, -0.4620, 1.7788, -0.4229, -0.5192, -0.0226, 0.4277,
- 0.2468]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0045, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0045, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.11839128797873855
- step: 12
- running loss: 0.009865940664894879
- Train Steps: 12/90 Loss: 0.0099 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300],
- [0.6122, 0.4048, 0.7506, 0.3014, 0.3475, 0.3333, 0.5487, 0.5749],
- [0.6263, 0.4057, 0.8800, 0.3833, 0.3650, 0.3717, 0.6375, 0.4804],
- [0.6282, 0.4092, 0.8000, 0.2183, 0.4500, 0.2383, 0.6787, 0.5364],
- [0.6124, 0.4075, 0.7696, 0.4153, 0.3475, 0.3767, 0.5157, 0.5427],
- [0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [0.6262, 0.4052, 0.8888, 0.4700, 0.3675, 0.5117, 0.6350, 0.5233],
- [ nan, nan, 0.8938, 0.2850, 0.4662, 0.3117, 0.7406, 0.5528]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5405, -0.4378, 1.2390, -1.2238, -0.4669, -0.9086, 0.6034, 0.2385],
- [ 0.4922, -0.4575, 1.1102, -0.8623, -0.6171, -0.7878, 0.2250, 0.3683],
- [ 0.6639, -0.4080, 1.8109, -0.4827, -0.5623, -0.6579, 0.6498, -0.0299],
- [ 0.6907, -0.3644, 1.3608, -1.1843, -0.1819, -1.3539, 0.8125, 0.1886],
- [ 0.5084, -0.4234, 1.3220, -0.4061, -0.6198, -0.5928, 0.0843, 0.2962],
- [ 0.6199, -0.3647, 1.6480, 0.1301, -0.1844, 0.1268, 0.2038, 0.2411],
- [ 0.6979, -0.3626, 1.8685, -0.0838, -0.4998, 0.0246, 0.6699, 0.1938],
- [-1.8352, -1.9717, 1.9515, -0.8713, -0.0920, -0.9134, 1.1173, 0.3375]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621],
- [ 0.5419, -0.4160, 1.1810, -0.8939, -0.6808, -0.7463, 0.2485,
- 0.3694],
- [ 0.6071, -0.4119, 1.7788, -0.5153, -0.6000, -0.5692, 0.6586,
- -0.0670],
- [ 0.6158, -0.3960, 1.4092, -1.2774, -0.2074, -1.1851, 0.8491,
- 0.1917],
- [ 0.5427, -0.4035, 1.2688, -0.3675, -0.6808, -0.5461, 0.0959,
- 0.2206],
- [ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386],
- [ 0.6063, -0.4142, 1.8192, -0.1150, -0.5885, 0.0774, 0.6471,
- 0.1313],
- [-2.2859, -2.2859, 1.8423, -0.9695, -0.1323, -0.8463, 1.1349,
- 0.2676]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0085, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0085, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.1268529868684709
- step: 13
- running loss: 0.009757922066805454
- Train Steps: 13/90 Loss: 0.0098 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6258, 0.4143, 0.8525, 0.2617, 0.4950, 0.1667, 0.6219, 0.4967],
- [0.6198, 0.3997, 0.8582, 0.5361, 0.4117, 0.5016, 0.5942, 0.5134],
- [0.6189, 0.4049, 0.8888, 0.4417, 0.4213, 0.5200, 0.5988, 0.5633],
- [ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6265, 0.4251, 0.7113, 0.3550, 0.4375, 0.2117, 0.5587, 0.6118],
- [0.6239, 0.4123, 0.8313, 0.2550, 0.4500, 0.2050, 0.6175, 0.5400],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6785, -0.3535, 1.6476, -1.0522, -0.1186, -1.3418, 0.6660, 0.0418],
- [ 0.7046, -0.3856, 1.7158, 0.1641, -0.4697, 0.1161, 0.5764, 0.0492],
- [ 0.5759, -0.4075, 1.8268, -0.2217, -0.4446, 0.2211, 0.6357, 0.2442],
- [-1.3988, -1.6600, 0.8677, -1.2617, -0.4867, -1.2016, 0.2127, 0.3967],
- [-2.0198, -2.1018, 0.9475, -1.3378, -0.4070, -1.3136, 0.2460, 0.3122],
- [ 0.7884, -0.2667, 1.1408, -0.7161, -0.3460, -1.2052, 0.4208, 0.4894],
- [ 0.5331, -0.4345, 1.5298, -1.1118, -0.2301, -1.2077, 0.6807, 0.1989],
- [ 0.6563, -0.3672, 1.7630, -0.0805, -0.3497, -0.0191, 0.3838, 0.3103]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0479e-01, -3.7229e-01, 1.6517e+00, -1.0773e+00, 4.6189e-04,
- -1.5161e+00, 5.8660e-01, 8.0947e-03],
- [ 5.7673e-01, -4.3957e-01, 1.6782e+00, 1.9046e-01, -3.8437e-01,
- 3.0841e-02, 4.5876e-01, 8.5521e-02],
- [ 5.7258e-01, -4.1594e-01, 1.8192e+00, -2.4588e-01, -3.4018e-01,
- 1.1594e-01, 4.7968e-01, 3.1609e-01],
- [-2.2859e+00, -2.2859e+00, 8.0331e-01, -1.1250e+00, -3.8637e-01,
- -1.3082e+00, 1.1262e-01, 4.5430e-01],
- [-2.2859e+00, -2.2859e+00, 6.7598e-01, -1.4083e+00, -3.2864e-01,
- -1.4160e+00, 2.4873e-01, 3.4688e-01],
- [ 6.0774e-01, -3.2256e-01, 9.9931e-01, -6.4619e-01, -2.6513e-01,
- -1.3082e+00, 2.9460e-01, 5.4012e-01],
- [ 5.9579e-01, -3.8176e-01, 1.5536e+00, -1.1081e+00, -2.0739e-01,
- -1.3390e+00, 5.6628e-01, 2.0831e-01],
- [ 5.4908e-01, -4.2902e-01, 1.7788e+00, -1.0731e-01, -2.6513e-01,
- -1.0731e-01, 2.5553e-01, 3.0567e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0278, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0278, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.15469072526320815
- step: 14
- running loss: 0.011049337518800582
- Train Steps: 14/90 Loss: 0.0110 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6163, 0.4001, 0.8788, 0.5033, 0.4012, 0.4633, 0.5338, 0.5767],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6276, 0.4235, 0.8888, 0.5333, 0.3800, 0.3117, 0.5427, 0.6164],
- [ nan, nan, 0.7225, 0.2167, 0.3987, 0.2283, 0.5427, 0.5181],
- [0.6161, 0.4099, 0.8738, 0.4383, 0.3788, 0.5483, 0.5605, 0.5019],
- [0.6282, 0.4029, 0.8988, 0.3000, 0.4250, 0.4183, 0.7042, 0.5575]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4819, -0.5002, 1.7234, -0.0345, -0.4618, -0.2502, 0.4140, 0.3121],
- [ 0.5653, -0.4157, 1.7060, -0.1241, -0.2971, -0.1908, 0.3426, 0.3553],
- [ 0.4675, -0.4928, 1.6902, 0.0889, -0.2267, 0.0156, 0.6133, 0.1752],
- [ 0.4411, -0.5210, 1.7248, -0.1622, -0.1141, -0.0964, 0.2784, 0.0968],
- [ 0.8619, -0.1851, 1.7110, 0.1010, -0.5076, -0.9692, 0.5351, 0.5752],
- [-2.5459, -2.4300, 0.9579, -1.4209, -0.5373, -1.2719, 0.2063, 0.1480],
- [ 0.5651, -0.4017, 1.7222, -0.3233, -0.5675, 0.1211, 0.5236, 0.2215],
- [ 0.6173, -0.4305, 1.7402, -0.8352, -0.4560, -0.4488, 1.1476, 0.3046]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5610, -0.4381, 1.7730, 0.0390, -0.4326, -0.1458, 0.1794,
- 0.3777],
- [ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.6131, -0.3299, 1.8192, 0.1775, -0.5307, -0.8463, 0.2206,
- 0.5612],
- [-2.2859, -2.2859, 1.0513, -1.2851, -0.4441, -1.2313, 0.2206,
- 0.1073],
- [ 0.5597, -0.3928, 1.7499, -0.2613, -0.5365, 0.2468, 0.3028,
- 0.0321],
- [ 0.6158, -0.4249, 1.8654, -0.9002, -0.3229, -0.3537, 0.9667,
- 0.2890]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0121, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0121, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.1667864709161222
- step: 15
- running loss: 0.011119098061074813
- Train Steps: 15/90 Loss: 0.0111 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6339, 0.4149, 0.8800, 0.5000, 0.3900, 0.5283, 0.7541, 0.5424],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6275, 0.4024, 0.8500, 0.5383, 0.3912, 0.4883, 0.6288, 0.5100],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6104, 0.4029, 0.8738, 0.4900, 0.4088, 0.4533, 0.5070, 0.5510],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4449, -0.5116, 1.8714, -0.0182, -0.5449, 0.1283, 1.1254, 0.3308],
- [ 0.4398, -0.5067, 1.7343, 0.0700, -0.2354, 0.0319, 0.5108, 0.2732],
- [ 0.5798, -0.4334, 1.6550, 0.1778, -0.5138, -0.1207, 0.6395, 0.2326],
- [ 0.6115, -0.3747, 1.8011, -0.1351, -0.3285, -0.0546, 0.4288, 0.4305],
- [ 0.4821, -0.4128, 1.7874, -0.1781, -0.4174, -0.1471, 0.2563, 0.2960],
- [ 0.7083, -0.3171, 1.1143, -1.4232, -0.4142, -1.5667, 0.2975, 0.2217],
- [ 0.5611, -0.3972, 1.7412, -0.0379, -0.4422, -0.2419, 0.1391, 0.3804],
- [ 0.5256, -0.4907, 1.7250, 0.1376, -0.4755, -0.2529, 0.5504, 0.2964]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6421, -0.3695, 1.7788, 0.0236, -0.4845, 0.1544, 1.1971,
- 0.2196],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.6125, -0.4273, 1.6402, 0.2006, -0.4788, -0.0303, 0.6182,
- 0.0697],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.5334, -0.4252, 1.7499, -0.0226, -0.3979, -0.1920, 0.0558,
- 0.2589],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0075, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0075, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.17424756661057472
- step: 16
- running loss: 0.01089047291316092
- Train Steps: 16/90 Loss: 0.0109 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6350, 0.4118, 0.8363, 0.2717, 0.4850, 0.1700, 0.6441, 0.5170],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6139, 0.4019, 0.7137, 0.2150, 0.4375, 0.1533, 0.5293, 0.5006],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6201, 0.4029, 0.8776, 0.4602, 0.3683, 0.4203, 0.5938, 0.5450],
- [0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6300, 0.4133, 0.8538, 0.2167, 0.5587, 0.2250, 0.7390, 0.5413]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6731, -0.3697, 1.5592, -1.0559, -0.0977, -1.3919, 0.7334, 0.1976],
- [ 0.6478, -0.3031, 1.7829, -0.3219, -0.6307, -0.3326, 0.2386, 0.3660],
- [ 0.4876, -0.4768, 0.8883, -1.4431, -0.3269, -1.5276, 0.1864, 0.1209],
- [ 0.5578, -0.4057, 1.6874, 0.1956, -0.5940, -0.4984, 0.3570, 0.3694],
- [ 0.5158, -0.4404, 1.7622, -0.1544, -0.6287, -0.2941, 0.4888, 0.3767],
- [ 0.3452, -0.5299, 1.5267, -0.5571, -0.7532, -0.3578, 0.2696, 0.2688],
- [ 0.6597, -0.3832, 1.6093, 0.2269, -0.3988, 0.2980, 0.7815, 0.2671],
- [ 0.0781, -0.7422, 1.6755, -1.3120, 0.2650, -1.0328, 1.1781, 0.4177]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6471, -0.3840, 1.5767, -1.0311, -0.0457, -1.5007, 0.6889,
- 0.1020],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [ 0.5496, -0.4295, 1.0109, -1.2928, -0.2651, -1.5777, 0.1589,
- 0.0261],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698],
- [ 0.5785, -0.4252, 1.7676, -0.1602, -0.5845, -0.3446, 0.4566,
- 0.2314],
- [ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.6240, -0.3768, 1.6575, -1.2851, 0.2949, -1.2467, 1.1276,
- 0.2142]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.18802320212125778
- step: 17
- running loss: 0.011060188360073987
- Train Steps: 17/90 Loss: 0.0111 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6239, 0.4123, 0.8313, 0.2550, 0.4500, 0.2050, 0.6175, 0.5400],
- [0.6073, 0.3932, 0.8363, 0.5817, 0.4425, 0.5117, 0.5204, 0.4817],
- [0.6168, 0.4111, 0.6517, 0.2875, 0.3688, 0.2817, 0.5228, 0.5837],
- [0.6329, 0.4196, 0.9238, 0.4583, 0.4263, 0.2933, 0.7343, 0.5748],
- [0.6125, 0.3999, 0.8750, 0.4883, 0.4750, 0.4700, 0.5533, 0.5617],
- [0.6115, 0.4005, 0.8838, 0.3867, 0.3763, 0.4700, 0.5800, 0.5550],
- [0.6229, 0.4066, 0.8513, 0.5717, 0.4500, 0.5050, 0.6025, 0.5350],
- [0.6200, 0.4059, 0.8700, 0.4900, 0.4163, 0.5000, 0.6162, 0.5467]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4197, -0.4912, 1.5191, -1.2403, -0.1832, -1.3755, 0.6581, 0.2036],
- [ 0.4366, -0.5245, 1.5805, 0.2763, -0.2760, 0.0055, 0.1530, 0.0448],
- [ 0.6062, -0.3579, 0.8087, -1.0960, -0.6512, -1.0815, 0.1575, 0.4478],
- [ 0.6468, -0.3977, 1.9815, -0.2081, -0.4123, -1.0205, 1.1302, 0.3880],
- [ 0.5060, -0.4523, 1.7256, -0.1588, -0.0872, -0.1265, 0.2759, 0.3836],
- [ 0.4599, -0.4719, 1.7737, -0.5584, -0.6429, -0.1395, 0.4024, 0.2736],
- [ 0.3906, -0.5403, 1.6324, 0.2416, -0.2583, -0.0143, 0.4181, 0.2703],
- [ 0.4343, -0.5111, 1.7783, -0.1365, -0.3835, 0.0125, 0.5447, 0.2704]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5958, -0.3818, 1.5536, -1.1081, -0.2074, -1.3390, 0.5663,
- 0.2083],
- [ 0.5193, -0.4699, 1.5767, 0.4008, -0.2420, 0.0774, 0.1178,
- -0.0610],
- [ 0.5629, -0.3871, 0.7243, -0.9581, -0.5827, -0.9849, 0.1288,
- 0.4103],
- [ 0.6372, -0.3479, 1.9808, -0.1689, -0.3171, -0.9310, 1.1057,
- 0.3692],
- [ 0.5432, -0.4388, 1.7557, -0.0303, -0.0919, -0.1150, 0.2699,
- 0.3087],
- [ 0.5384, -0.4361, 1.7961, -0.4999, -0.5480, -0.1150, 0.3931,
- 0.2776],
- [ 0.5911, -0.4080, 1.6460, 0.3546, -0.2074, 0.0467, 0.4970,
- 0.1852],
- [ 0.5776, -0.4109, 1.7326, -0.0226, -0.3633, 0.0236, 0.5605,
- 0.2391]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0057, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0057, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.19375777710229158
- step: 18
- running loss: 0.010764320950127311
- Train Steps: 18/90 Loss: 0.0108 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6102, 0.3999, 0.8750, 0.5133, 0.3825, 0.4750, 0.5637, 0.5083],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332],
- [0.6201, 0.4064, 0.8688, 0.5050, 0.4225, 0.5100, 0.6138, 0.5500],
- [0.6127, 0.4084, 0.8700, 0.4467, 0.3987, 0.4317, 0.5013, 0.5471],
- [0.6129, 0.3930, 0.8769, 0.5105, 0.4146, 0.5642, 0.6000, 0.5082]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5465, -0.4084, 1.7556, -0.0485, -0.4730, -0.2105, 0.2575, 0.1068],
- [ 0.5799, -0.3872, 1.7264, 0.1061, -0.5587, -0.7446, 0.4119, 0.3354],
- [ 0.6407, -0.3606, 1.6930, -0.0809, -0.2809, -0.1557, 0.2516, 0.2782],
- [ 0.3428, -0.5433, 1.7698, -0.2409, 0.0305, -0.0332, 0.5215, 0.3632],
- [ 0.5239, -0.4432, 1.5371, 0.1497, -0.5197, -0.1187, 0.9927, 0.2922],
- [ 0.6542, -0.3643, 1.7341, -0.0491, -0.3532, -0.0352, 0.5050, 0.3007],
- [ 0.4386, -0.4446, 1.6842, -0.2906, -0.4989, -0.4463, 0.0770, 0.3000],
- [ 0.5291, -0.4532, 1.7692, 0.0302, -0.3224, 0.2420, 0.4699, 0.1400]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.3262e-01, -4.3895e-01, 1.7557e+00, 8.5142e-02, -5.1917e-01,
- -9.1917e-02, 3.1801e-01, 6.2048e-02],
- [ 5.9013e-01, -4.1570e-01, 1.7557e+00, 1.9292e-01, -5.4226e-01,
- -5.9230e-01, 3.5843e-01, 1.6982e-01],
- [ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01],
- [ 5.3508e-01, -4.3212e-01, 1.7326e+00, -1.3811e-01, 4.6329e-02,
- 1.0491e-01, 4.1617e-01, 2.7760e-01],
- [ 6.1907e-01, -4.2971e-01, 1.4612e+00, 2.3911e-01, -4.9607e-01,
- 3.1255e-02, 1.1166e+00, 1.7680e-01],
- [ 5.7841e-01, -4.0878e-01, 1.7268e+00, 4.6651e-02, -3.3441e-01,
- 6.9746e-02, 5.4896e-01, 2.5450e-01],
- [ 5.4405e-01, -3.9969e-01, 1.7326e+00, -2.2279e-01, -4.4411e-01,
- -2.9207e-01, 2.9551e-02, 2.4088e-01],
- [ 5.4496e-01, -4.7064e-01, 1.7643e+00, 7.2204e-02, -3.7076e-01,
- 3.2001e-01, 4.8543e-01, 6.1219e-02]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0066, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0066, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.20038292463868856
- step: 19
- running loss: 0.010546469717825713
- Train Steps: 19/90 Loss: 0.0105 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6179, 0.3993, 0.8925, 0.4789, 0.3879, 0.4900, 0.6041, 0.5279],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6109, 0.4015, 0.7668, 0.3639, 0.3513, 0.3667, 0.5200, 0.5641],
- [0.6189, 0.4049, 0.8888, 0.4417, 0.4213, 0.5200, 0.5988, 0.5633],
- [0.6125, 0.3999, 0.8750, 0.4883, 0.4750, 0.4700, 0.5533, 0.5617],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6239, 0.4123, 0.8313, 0.2550, 0.4500, 0.2050, 0.6175, 0.5400],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.1788e-01, -3.8531e-01, 1.8123e+00, -8.3692e-03, -5.4274e-01,
- -1.6935e-01, 6.3533e-01, 1.3633e-01],
- [ 6.6055e-01, -3.6350e-01, 1.0613e+00, -1.3483e+00, -4.5378e-01,
- -1.2605e+00, 5.3864e-01, 2.7648e-01],
- [ 5.9610e-01, -3.5126e-01, 1.2928e+00, -6.1835e-01, -7.0665e-01,
- -7.1798e-01, 2.0525e-01, 3.0496e-01],
- [ 4.8049e-01, -4.5639e-01, 1.7886e+00, -1.5145e-01, -3.8253e-01,
- 7.3162e-02, 5.6609e-01, 2.5449e-01],
- [ 5.1796e-01, -4.4070e-01, 1.7000e+00, 3.3282e-02, -6.6376e-02,
- -1.6498e-01, 2.8864e-01, 3.2839e-01],
- [ 3.5361e-01, -5.8107e-01, 1.7022e+00, -2.0324e-02, -9.5997e-02,
- -7.1067e-02, 2.6798e-01, 6.7780e-02],
- [ 5.0432e-01, -4.3011e-01, 1.5125e+00, -1.0313e+00, -1.7659e-01,
- -1.4298e+00, 6.5754e-01, 1.9049e-01],
- [ 3.6905e-01, -5.3715e-01, 1.7403e+00, -5.2373e-02, -1.7687e-02,
- -1.7288e-03, 5.1245e-01, 2.9648e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5680, -0.4417, 1.8365, -0.0740, -0.4941, -0.0227, 0.5045,
- 0.1525],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.5357, -0.4315, 1.2557, -0.6051, -0.6635, -0.5923, 0.1159,
- 0.3198],
- [ 0.5726, -0.4159, 1.8192, -0.2459, -0.3402, 0.1159, 0.4797,
- 0.3161],
- [ 0.5432, -0.4388, 1.7557, -0.0303, -0.0919, -0.1150, 0.2699,
- 0.3087],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.5958, -0.3818, 1.5536, -1.1081, -0.2074, -1.3390, 0.5663,
- 0.2083],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0057, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0057, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.20609361585229635
- step: 20
- running loss: 0.010304680792614818
- Train Steps: 20/90 Loss: 0.0103 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3993, 0.8519, 0.4923, 0.3962, 0.4717, 0.6013, 0.5433],
- [0.6258, 0.4038, 0.8750, 0.4883, 0.3900, 0.5500, 0.6375, 0.5217],
- [0.6131, 0.4037, 0.6907, 0.2819, 0.3688, 0.2700, 0.5217, 0.5680],
- [0.6176, 0.3911, 0.8738, 0.4217, 0.3488, 0.4033, 0.6025, 0.4817],
- [0.6200, 0.4086, 0.8414, 0.2974, 0.4117, 0.2274, 0.5869, 0.5391],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567],
- [0.6127, 0.4118, 0.8650, 0.5083, 0.4088, 0.5367, 0.5300, 0.5456],
- [0.6267, 0.4065, 0.8313, 0.2467, 0.4788, 0.1733, 0.6312, 0.5133]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4543, -0.5039, 1.7159, 0.1398, -0.3645, -0.0678, 0.5161, 0.2738],
- [ 0.4250, -0.5397, 1.8740, 0.0648, -0.3627, 0.3088, 0.6891, 0.1285],
- [ 0.5432, -0.4129, 0.9103, -1.0536, -0.5007, -1.0081, 0.1384, 0.3272],
- [ 0.5651, -0.4690, 1.7934, -0.2742, -0.5696, -0.4388, 0.4848, 0.0175],
- [ 0.6463, -0.3446, 1.6692, -0.8134, -0.2191, -1.0957, 0.4650, 0.1368],
- [ 0.3467, -0.5055, 1.0952, -0.9362, -0.3407, -1.0388, 0.3832, 0.4081],
- [ 0.6227, -0.3434, 1.7308, 0.1826, -0.2582, 0.2336, 0.2736, 0.2280],
- [ 0.6184, -0.4156, 1.5748, -1.0515, -0.0119, -1.4673, 0.5948, 0.1301]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5777, -0.4415, 1.6491, -0.0122, -0.4557, -0.1073, 0.4912,
- 0.2237],
- [ 0.6047, -0.4209, 1.7557, -0.0303, -0.4845, 0.2545, 0.6587,
- 0.1236],
- [ 0.5459, -0.4215, 0.9043, -0.9838, -0.5827, -1.0388, 0.1236,
- 0.3378],
- [ 0.5669, -0.4794, 1.7499, -0.3383, -0.6751, -0.4229, 0.4970,
- -0.0611],
- [ 0.5776, -0.3987, 1.6005, -0.9121, -0.3844, -1.2358, 0.4247,
- 0.2043],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853],
- [ 0.5443, -0.3840, 1.7095, 0.0620, -0.3979, 0.1929, 0.1622,
- 0.2341],
- [ 0.6085, -0.4084, 1.5536, -1.1466, -0.0746, -1.4853, 0.6298,
- 0.0851]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0070, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0070, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.21307452209293842
- step: 21
- running loss: 0.010146405813949448
- Train Steps: 21/90 Loss: 0.0101 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6196, 0.4090, 0.8450, 0.3900, 0.3575, 0.3933, 0.5975, 0.5583],
- [0.6353, 0.4128, 0.9138, 0.3533, 0.4688, 0.3250, 0.7145, 0.5991],
- [0.6198, 0.4114, 0.8263, 0.3283, 0.3550, 0.3583, 0.5813, 0.5667],
- [0.6339, 0.4149, 0.8800, 0.5000, 0.3900, 0.5283, 0.7541, 0.5424],
- [0.6236, 0.4084, 0.7738, 0.2133, 0.3663, 0.3233, 0.5813, 0.5567],
- [0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960],
- [0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6394, -0.3515, 1.6325, -0.3776, -0.5088, -0.5482, 0.3714, 0.3221],
- [-0.5533, -1.1389, 1.7858, -0.4646, 0.0421, -0.8907, 1.0837, 0.3898],
- [ 0.6300, -0.3319, 1.5087, -0.6143, -0.5620, -0.6781, 0.2920, 0.3042],
- [ 0.6850, -0.3482, 1.7831, 0.1664, -0.3374, 0.1902, 1.0232, 0.1071],
- [ 0.5287, -0.4297, 1.2409, -1.1871, -0.4289, -0.8006, 0.4612, 0.2733],
- [ 0.4822, -0.4647, 1.5526, 0.3464, -0.3086, 0.0092, 0.1336, -0.0019],
- [ 0.5267, -0.4623, 1.7165, -0.0318, -0.0668, 0.0993, 0.1245, -0.0606],
- [ 0.4854, -0.4350, 1.5649, -0.3733, -0.5061, -0.6786, -0.0152, 0.1631]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5761, -0.3966, 1.6171, -0.4845, -0.6346, -0.4691, 0.4739,
- 0.2930],
- [ 0.6487, -0.3792, 1.9346, -0.6539, -0.1208, -0.7848, 1.0143,
- 0.4814],
- [ 0.5768, -0.3857, 1.5305, -0.7694, -0.6462, -0.6308, 0.3988,
- 0.3315],
- [ 0.6421, -0.3695, 1.7788, 0.0236, -0.4845, 0.1544, 1.1971,
- 0.2196],
- [ 0.5946, -0.3995, 1.2880, -1.3005, -0.5942, -0.7925, 0.3988,
- 0.2853],
- [ 0.5079, -0.4714, 1.6575, 0.2237, -0.4961, 0.0774, 0.1465,
- -0.1061],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051],
- [ 0.5433, -0.4032, 1.6344, -0.4922, -0.5769, -0.5846, 0.0357,
- 0.2567]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0396, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0396, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2526354994624853
- step: 22
- running loss: 0.011483431793749332
- Train Steps: 22/90 Loss: 0.0115 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6179, 0.4008, 0.8600, 0.4015, 0.3932, 0.2515, 0.5711, 0.5438],
- [0.6090, 0.4010, 0.7838, 0.3483, 0.3538, 0.3783, 0.5462, 0.5077],
- [0.6265, 0.4088, 0.8025, 0.1850, 0.4163, 0.2500, 0.6290, 0.4947],
- [0.6286, 0.4274, 0.8500, 0.4500, 0.4525, 0.2583, 0.5440, 0.6209],
- [0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6081, 0.3950, 0.8538, 0.4667, 0.3850, 0.4917, 0.5342, 0.4954],
- [ nan, nan, 0.7097, 0.2346, 0.4250, 0.1850, 0.5175, 0.5583]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6787, -0.3309, 1.5545, -0.5017, -0.2383, -1.0141, 0.2557, 0.2843],
- [ 0.6255, -0.3809, 1.3916, -0.6963, -0.5258, -0.5401, 0.1449, 0.1210],
- [ 0.5222, -0.4661, 1.4427, -1.3368, -0.2490, -0.9906, 0.6824, 0.1045],
- [ 0.5180, -0.3779, 1.5996, -0.1218, -0.0797, -0.9529, 0.3193, 0.5528],
- [ 0.6336, -0.4351, 1.7194, 0.2918, -0.4420, -0.1731, 0.6663, 0.0219],
- [ 0.5221, -0.4751, 1.8630, -0.2368, -0.4454, -0.0110, 0.6517, -0.0719],
- [ 0.5000, -0.4733, 1.6916, -0.1716, -0.3797, 0.0467, 0.2077, -0.0549],
- [-2.3529, -2.2758, 0.7838, -1.2784, -0.2598, -1.3029, 0.2252, 0.2022]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.6801e-01, -4.3453e-01, 1.6864e+00, -4.3153e-01, -4.6981e-01,
- -1.1241e+00, 3.5183e-01, 2.2607e-01],
- [ 5.2702e-01, -4.3356e-01, 1.3342e+00, -6.7698e-01, -6.5196e-01,
- -5.3841e-01, 2.3702e-01, 5.9193e-02],
- [ 6.0785e-01, -3.9761e-01, 1.4208e+00, -1.4314e+00, -3.6328e-01,
- -1.1312e+00, 6.1950e-01, -9.2270e-04],
- [ 6.1742e-01, -3.1175e-01, 1.6402e+00, -2.0739e-01, -1.9584e-01,
- -1.0927e+00, 2.2674e-01, 5.8220e-01],
- [ 6.1276e-01, -4.3749e-01, 1.7788e+00, 2.6990e-01, -6.3464e-01,
- -2.5358e-01, 5.4635e-01, -1.2778e-01],
- [ 6.0716e-01, -4.2055e-01, 1.8711e+00, -2.5358e-01, -6.1155e-01,
- -1.3041e-01, 6.8119e-01, -6.7050e-02],
- [ 5.2269e-01, -4.6151e-01, 1.6575e+00, -1.3041e-01, -5.0762e-01,
- -1.4935e-02, 1.8150e-01, 2.0831e-03],
- [-2.2859e+00, -2.2859e+00, 9.9216e-01, -1.2021e+00, -3.2286e-01,
- -1.4314e+00, 1.0439e-01, 2.9299e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0088, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0088, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.2614791188389063
- step: 23
- running loss: 0.011368657340822012
- Train Steps: 23/90 Loss: 0.0114 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [0.6276, 0.4095, 0.8237, 0.2250, 0.4662, 0.1783, 0.6171, 0.4869],
- [0.6138, 0.4101, 0.8800, 0.5083, 0.4637, 0.5950, 0.5587, 0.5077],
- [0.6154, 0.4112, 0.7037, 0.2317, 0.4238, 0.1833, 0.5350, 0.5600],
- [0.6300, 0.4102, 0.9088, 0.4433, 0.4088, 0.3067, 0.6820, 0.5540],
- [0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585],
- [0.6219, 0.4114, 0.8175, 0.2817, 0.3925, 0.2783, 0.5900, 0.5350],
- [0.6189, 0.3911, 0.8800, 0.4917, 0.3713, 0.4800, 0.6038, 0.4833]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5986, -0.4155, 1.1743, -0.8504, -0.5632, -0.9299, 0.2082, -0.0190],
- [ 0.5951, -0.3816, 1.5139, -1.1340, -0.0489, -1.3246, 0.5283, -0.0248],
- [ 0.5897, -0.3812, 1.7208, 0.1187, -0.1267, 0.3652, 0.2454, 0.0767],
- [ 0.5437, -0.4196, 0.9207, -1.1098, -0.2944, -1.4431, 0.1695, 0.3735],
- [-0.3162, -1.0101, 1.7977, -0.1153, -0.3829, -0.8892, 0.7867, 0.1933],
- [ 0.5377, -0.4063, 1.7138, -0.2150, -0.2550, 0.3595, 0.3530, 0.1509],
- [ 0.4393, -0.4756, 1.4093, -0.8884, -0.4290, -0.8770, 0.4530, 0.2676],
- [ 0.7966, -0.3260, 1.7673, 0.0466, -0.4888, -0.0788, 0.4975, -0.0127]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141],
- [ 0.6127, -0.3944, 1.5189, -1.2467, -0.1323, -1.4622, 0.5646,
- -0.0369],
- [ 0.5491, -0.3918, 1.7788, 0.0620, -0.1439, 0.4624, 0.2946,
- 0.0592],
- [ 0.5565, -0.3864, 0.9647, -1.2159, -0.3286, -1.4391, 0.1852,
- 0.3007],
- [ 0.6240, -0.3912, 1.9115, -0.2382, -0.3979, -0.8694, 0.8644,
- 0.2730],
- [ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936],
- [ 0.5868, -0.3858, 1.4901, -0.9849, -0.4730, -1.0003, 0.4393,
- 0.1852],
- [ 0.5726, -0.4795, 1.7788, -0.0149, -0.5711, -0.0688, 0.5028,
- -0.0534]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0251, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0251, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.28657217137515545
- step: 24
- running loss: 0.011940507140631476
- Train Steps: 24/90 Loss: 0.0119 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6339, 0.4118, 0.7988, 0.5800, 0.3912, 0.4583, 0.7343, 0.5760],
- [ nan, nan, 0.6469, 0.1943, 0.4025, 0.2000, 0.5125, 0.5533],
- [0.6314, 0.4107, 0.8750, 0.5100, 0.3788, 0.4900, 0.7121, 0.5864],
- [0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [0.6229, 0.4086, 0.7538, 0.2600, 0.4775, 0.1617, 0.5900, 0.5383],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083],
- [0.6275, 0.4050, 0.9038, 0.3767, 0.3838, 0.3533, 0.7074, 0.5575]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5811, -0.4193, 1.4924, 0.1970, -0.4542, -0.1588, 0.7031, 0.1884],
- [-2.8906, -2.6697, 0.7015, -1.2611, -0.3438, -1.3725, 0.1311, 0.1136],
- [ 0.6681, -0.3650, 1.8313, 0.0553, -0.4647, 0.0585, 0.6894, 0.1858],
- [ 0.6057, -0.3989, 1.8592, -0.2476, -0.4531, 0.3330, 0.2408, -0.1263],
- [ 0.4641, -0.5044, 1.3320, -1.1807, -0.1842, -1.3894, 0.2375, -0.0075],
- [ 0.5326, -0.4550, 1.2745, -1.0956, -0.1449, -1.3885, 0.2040, 0.1259],
- [ 0.6367, -0.3457, 1.5546, 0.2843, -0.3334, -0.0293, 0.0620, 0.3324],
- [ 0.6460, -0.3894, 1.9278, -0.4953, -0.4726, -0.5593, 0.6979, 0.1154]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6421, -0.3840, 1.4035, 0.3931, -0.4788, -0.1689, 1.1057,
- 0.3745],
- [-2.2859, -2.2859, 0.7023, -1.3883, -0.4268, -1.3621, 0.0813,
- 0.2699],
- [ 0.6306, -0.3888, 1.7557, 0.0697, -0.5365, -0.0226, 1.0033,
- 0.4226],
- [ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [ 0.5911, -0.3984, 1.1956, -1.0850, -0.0804, -1.5392, 0.4393,
- 0.2006],
- [ 0.6134, -0.3910, 1.4497, 0.3546, -0.3517, -0.0919, 0.3296,
- 0.5239],
- [ 0.6125, -0.4153, 1.8885, -0.5461, -0.5134, -0.6539, 0.9814,
- 0.2890]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0235, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0235, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3100247588008642
- step: 25
- running loss: 0.012400990352034569
- Train Steps: 25/90 Loss: 0.0124 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.7412, 0.2200, 0.4450, 0.1517, 0.5312, 0.4983],
- [0.6162, 0.3949, 0.8838, 0.4517, 0.4250, 0.5183, 0.5837, 0.5500],
- [0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006],
- [0.6254, 0.4076, 0.8700, 0.3267, 0.4150, 0.3083, 0.7050, 0.5609],
- [0.6200, 0.4071, 0.7338, 0.1917, 0.4200, 0.2450, 0.6150, 0.5517],
- [0.6038, 0.3946, 0.8413, 0.4883, 0.3563, 0.4550, 0.5266, 0.4693],
- [ nan, nan, 0.8037, 0.2483, 0.3975, 0.2517, 0.5575, 0.5600],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-1.1408, -1.5361, 1.0224, -1.2326, -0.2584, -1.5380, 0.1721, 0.0526],
- [ 0.7885, -0.2863, 1.8197, -0.1257, -0.3346, 0.1764, 0.3208, 0.1103],
- [ 0.9252, -0.2396, 1.6979, 0.4724, -0.5313, -0.0838, 0.6209, -0.0663],
- [ 0.8023, -0.2682, 1.7999, -0.7008, -0.3828, -0.8174, 0.7808, 0.2176],
- [ 0.7457, -0.3124, 1.2050, -1.2411, -0.3297, -1.1018, 0.5817, 0.2070],
- [ 0.6664, -0.3476, 1.6641, 0.0448, -0.5411, -0.1716, 0.1976, 0.0074],
- [-1.9553, -2.0598, 1.1571, -0.9643, -0.3716, -1.0576, 0.3377, 0.1847],
- [ 0.8637, -0.2172, 1.7630, -0.1238, -0.3981, -0.1083, 0.0655, 0.0336]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 1.1379, -1.2697, -0.2305, -1.5854, 0.1679,
- 0.0159],
- [ 0.5604, -0.4620, 1.7961, -0.1997, -0.3229, 0.1082, 0.4104,
- 0.2545],
- [ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261],
- [ 0.6026, -0.4032, 1.7326, -0.7771, -0.3691, -0.8617, 0.9704,
- 0.3050],
- [ 0.5779, -0.4054, 1.1032, -1.4006, -0.3460, -1.1543, 0.5547,
- 0.2622],
- [ 0.5030, -0.4631, 1.5998, -0.0303, -0.6404, -0.1843, 0.1465,
- -0.1181],
- [-2.2859, -2.2859, 1.4266, -1.1389, -0.4499, -1.1235, 0.2891,
- 0.3007],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0447, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0447, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3547267075628042
- step: 26
- running loss: 0.013643334906261701
- Train Steps: 26/90 Loss: 0.0136 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6364, 0.4165, 0.9088, 0.4367, 0.4075, 0.3150, 0.6448, 0.5297],
- [0.6273, 0.4110, 0.8900, 0.3817, 0.4188, 0.2167, 0.5858, 0.4835],
- [0.6300, 0.4013, 0.8938, 0.5350, 0.3675, 0.4600, 0.6456, 0.4973],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6169, 0.4052, 0.8742, 0.4779, 0.4288, 0.4917, 0.5763, 0.5617],
- [0.6137, 0.4084, 0.8076, 0.3889, 0.3650, 0.3150, 0.5356, 0.5896],
- [0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [0.6127, 0.4118, 0.8650, 0.5083, 0.4088, 0.5367, 0.5300, 0.5456]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6620, -0.3677, 1.8076, -0.3847, -0.4292, -0.8024, 0.6340, 0.0763],
- [ 0.5911, -0.4249, 1.7981, -0.6270, -0.3402, -1.2767, 0.5001, -0.1651],
- [ 0.5506, -0.5222, 1.7383, 0.0278, -0.5927, -0.0961, 0.7650, -0.1522],
- [ 0.6720, -0.3217, 1.5277, 0.2882, -0.4061, -0.1802, 0.2474, 0.3197],
- [ 0.5696, -0.4438, 1.7033, -0.2212, -0.2888, 0.0774, 0.3652, 0.1547],
- [ 0.5928, -0.4065, 1.4053, -0.6018, -0.6057, -0.8218, 0.1283, 0.2768],
- [ 0.4583, -0.4911, 1.6556, -0.6127, -0.3981, -0.7983, 0.3458, 0.3686],
- [ 0.5000, -0.4589, 1.6152, -0.0718, -0.3428, 0.2066, 0.2578, 0.1003]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6537, -0.3623, 1.9115, -0.2690, -0.4037, -0.8309, 0.6926,
- 0.1608],
- [ 0.6117, -0.3877, 1.8249, -0.5230, -0.3517, -1.2851, 0.4200,
- -0.0529],
- [ 0.6240, -0.4321, 1.8423, 0.1852, -0.5885, -0.1612, 0.6962,
- 0.0111],
- [ 0.5836, -0.3649, 1.7210, 0.3854, -0.3979, -0.2921, 0.3065,
- 0.4470],
- [ 0.5637, -0.4143, 1.7519, -0.0787, -0.3055, -0.0149, 0.3758,
- 0.3084],
- [ 0.5485, -0.3997, 1.4445, -0.4895, -0.6000, -0.8309, 0.1878,
- 0.4374],
- [ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928],
- [ 0.5443, -0.3840, 1.7095, 0.0620, -0.3979, 0.1929, 0.1622,
- 0.2341]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0073, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0073, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3619840261526406
- step: 27
- running loss: 0.013406815783431133
- Train Steps: 27/90 Loss: 0.0134 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6125, 0.3983, 0.8750, 0.4867, 0.4275, 0.4783, 0.5413, 0.5717],
- [0.6263, 0.4233, 0.7924, 0.4626, 0.3788, 0.2883, 0.5573, 0.6047],
- [0.6153, 0.4117, 0.8688, 0.5167, 0.4895, 0.5647, 0.5524, 0.5136],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917],
- [0.6205, 0.4012, 0.8675, 0.3283, 0.3713, 0.3050, 0.5813, 0.5117],
- [0.6257, 0.4167, 0.8775, 0.3433, 0.3563, 0.4133, 0.6200, 0.5667],
- [0.6258, 0.4143, 0.8525, 0.2617, 0.4950, 0.1667, 0.6219, 0.4967],
- [0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5315, -0.4936, 1.6832, -0.1068, -0.3618, -0.0711, 0.2883, 0.1341],
- [ 0.4543, -0.4824, 1.3927, -0.0890, -0.5153, -0.8565, 0.3237, 0.4404],
- [ 0.5046, -0.4862, 1.6858, 0.0078, -0.1689, 0.1853, 0.3572, 0.0407],
- [ 0.6492, -0.3580, 1.2518, -0.4897, -0.6171, -0.7439, 0.2897, 0.3618],
- [ 0.4292, -0.5649, 1.7148, -0.7917, -0.5799, -0.9039, 0.4304, -0.0680],
- [ 0.7026, -0.3457, 1.7546, -0.6548, -0.7039, -0.3940, 0.5502, 0.2667],
- [ 0.4067, -0.5144, 1.6281, -1.1137, -0.1014, -1.4728, 0.5556, -0.0738],
- [ 0.5445, -0.4663, 1.4199, -1.1006, -0.3598, -1.2067, 0.7418, 0.0817]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.4319e-01, -4.4619e-01, 1.7557e+00, -3.8029e-02, -3.1132e-01,
- -7.6520e-02, 2.1409e-01, 3.5458e-01],
- [ 6.0687e-01, -3.3095e-01, 1.3742e+00, -1.4927e-01, -5.3649e-01,
- -9.5412e-01, 2.8843e-01, 5.0705e-01],
- [ 5.5635e-01, -3.8422e-01, 1.7268e+00, 1.0054e-01, -2.4997e-02,
- 3.2255e-01, 2.6581e-01, 8.6245e-02],
- [ 5.6801e-01, -3.8397e-01, 1.0756e+00, -3.2902e-01, -6.2887e-01,
- -7.1547e-01, 3.3533e-01, 4.4696e-01],
- [ 5.7991e-01, -4.3295e-01, 1.7210e+00, -7.6936e-01, -5.7113e-01,
- -8.7714e-01, 3.9885e-01, 7.7444e-02],
- [ 6.0404e-01, -3.6135e-01, 1.7672e+00, -7.0008e-01, -6.4042e-01,
- -3.7675e-01, 5.7783e-01, 3.3149e-01],
- [ 6.0479e-01, -3.7229e-01, 1.6517e+00, -1.0773e+00, 4.6189e-04,
- -1.5161e+00, 5.8660e-01, 8.0947e-03],
- [ 6.1742e-01, -4.2249e-01, 1.4975e+00, -1.1709e+00, -3.1736e-01,
- -1.1806e+00, 6.5391e-01, 1.8793e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.36961828637868166
- step: 28
- running loss: 0.013200653084952916
- Train Steps: 28/90 Loss: 0.0132 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6154, 0.4048, 0.7100, 0.2067, 0.4338, 0.1667, 0.5413, 0.5220],
- [0.6279, 0.4008, 0.8600, 0.4883, 0.4325, 0.5283, 0.7010, 0.5378],
- [0.6211, 0.3993, 0.7650, 0.1933, 0.4575, 0.1550, 0.5965, 0.4895],
- [0.6139, 0.4019, 0.7137, 0.2150, 0.4375, 0.1533, 0.5293, 0.5006],
- [ nan, nan, 0.6512, 0.1717, 0.4100, 0.1983, 0.5253, 0.5240],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967],
- [0.6267, 0.4065, 0.8313, 0.2467, 0.4788, 0.1733, 0.6312, 0.5133],
- [0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4313, -0.5155, 1.1067, -1.0634, -0.3721, -1.3428, 0.2664, 0.1494],
- [ 0.6846, -0.3799, 1.7791, 0.0894, -0.4743, 0.2629, 0.8621, 0.1941],
- [ 0.4842, -0.4876, 1.2978, -1.2284, -0.2565, -1.3639, 0.4528, 0.0666],
- [ 0.3270, -0.5922, 1.1034, -1.0974, -0.3443, -1.4218, 0.2423, 0.0650],
- [-2.2664, -2.2828, 0.8286, -1.3220, -0.4161, -1.3312, 0.1259, 0.1718],
- [ 0.7369, -0.3070, 1.8857, -0.0652, -0.5453, 0.3664, 0.5765, 0.3902],
- [ 0.5477, -0.4333, 1.6124, -0.9366, -0.2165, -1.2759, 0.6049, 0.1203],
- [ 0.4474, -0.5065, 1.5137, -0.7639, -0.6022, -0.9738, 0.1621, 0.0091]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5566, -0.4160, 0.9935, -1.3313, -0.2824, -1.5161, 0.2144,
- 0.1253],
- [ 0.6141, -0.4345, 1.6864, -0.0303, -0.2882, 0.1544, 0.9521,
- 0.1982],
- [ 0.5828, -0.4417, 1.2476, -1.3929, -0.1727, -1.5700, 0.4694,
- -0.0248],
- [ 0.5496, -0.4295, 1.0109, -1.2928, -0.2651, -1.5777, 0.1589,
- 0.0261],
- [-2.2859, -2.2859, 0.7222, -1.4930, -0.3921, -1.3698, 0.1404,
- 0.1343],
- [ 0.5772, -0.3913, 1.8480, -0.2459, -0.4326, 0.1929, 0.5374,
- 0.4701],
- [ 0.6085, -0.4084, 1.5536, -1.1466, -0.0746, -1.4853, 0.6298,
- 0.0851],
- [ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0138, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3834594888612628
- step: 29
- running loss: 0.013222740995215958
- Train Steps: 29/90 Loss: 0.0132 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6198, 0.3997, 0.8582, 0.5361, 0.4117, 0.5016, 0.5942, 0.5134],
- [0.6132, 0.4037, 0.6963, 0.2217, 0.4100, 0.1950, 0.5395, 0.5175],
- [0.6173, 0.4114, 0.7325, 0.2500, 0.4213, 0.1917, 0.5338, 0.5700],
- [0.6076, 0.3953, 0.8125, 0.3917, 0.3525, 0.4083, 0.5346, 0.4954],
- [0.6163, 0.4006, 0.8788, 0.4683, 0.3663, 0.4883, 0.5887, 0.5017],
- [0.6138, 0.4054, 0.8750, 0.4750, 0.4363, 0.5017, 0.5086, 0.5822],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6854, -0.4110, 1.7386, 0.2029, -0.4442, 0.0123, 0.5923, 0.0250],
- [ 0.4848, -0.4617, 1.0833, -1.2358, -0.3939, -1.4464, 0.3869, 0.0485],
- [ 0.2746, -0.5979, 1.1664, -1.1019, -0.3426, -1.3615, 0.2957, 0.2825],
- [ 0.5144, -0.4763, 1.6270, -0.4498, -0.6939, -0.4958, 0.4341, 0.0083],
- [ 0.6393, -0.4014, 1.8480, -0.1163, -0.6229, -0.0050, 0.5811, 0.1108],
- [ 0.6124, -0.3778, 1.7751, -0.0631, -0.2991, -0.0231, 0.2343, 0.2695],
- [-2.0529, -2.1630, 0.8682, -1.3244, -0.3408, -1.5010, 0.1745, 0.3081],
- [ 0.6295, -0.3719, 1.8619, -0.2724, -0.5806, -0.4473, 0.4335, 0.2207]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7673e-01, -4.3957e-01, 1.6782e+00, 1.9046e-01, -3.8437e-01,
- 3.0841e-02, 4.5876e-01, 8.5521e-02],
- [ 5.4648e-01, -4.2140e-01, 9.3002e-01, -1.2620e+00, -3.9215e-01,
- -1.3852e+00, 2.0618e-01, 1.0428e-01],
- [ 5.6518e-01, -3.8584e-01, 1.0975e+00, -1.1312e+00, -3.4018e-01,
- -1.4006e+00, 1.7945e-01, 3.4688e-01],
- [ 5.2061e-01, -4.6028e-01, 1.4670e+00, -4.7683e-01, -6.5774e-01,
- -3.9985e-01, 1.8356e-01, 2.0831e-03],
- [ 5.6091e-01, -4.3541e-01, 1.7730e+00, -1.2271e-01, -5.9423e-01,
- -3.0331e-02, 4.3349e-01, 3.1255e-02],
- [ 5.4908e-01, -4.1324e-01, 1.7557e+00, -9.1917e-02, -2.7090e-01,
- 3.1255e-02, 6.3480e-02, 4.0319e-01],
- [-2.2859e+00, -2.2859e+00, 6.7598e-01, -1.4083e+00, -3.2864e-01,
- -1.4160e+00, 2.4873e-01, 3.4688e-01],
- [ 6.0722e-01, -3.2394e-01, 1.8423e+00, -3.5366e-01, -4.9607e-01,
- -3.9215e-01, 2.0831e-01, 1.8522e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0104, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0104, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.3938441062346101
- step: 30
- running loss: 0.013128136874487002
- Train Steps: 30/90 Loss: 0.0131 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6286, 0.4034, 0.8191, 0.2414, 0.4262, 0.2393, 0.6365, 0.5356],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142],
- [0.6087, 0.3951, 0.8387, 0.5833, 0.4188, 0.4933, 0.5146, 0.4830],
- [ nan, nan, 0.7850, 0.2700, 0.4288, 0.1717, 0.5199, 0.4999],
- [0.6339, 0.4159, 0.8400, 0.5617, 0.3825, 0.4150, 0.7343, 0.5748],
- [0.6167, 0.4048, 0.6831, 0.3639, 0.3763, 0.3017, 0.5700, 0.5883],
- [0.6176, 0.3911, 0.8738, 0.4217, 0.3488, 0.4033, 0.6025, 0.4817]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7876, -0.2612, 1.4754, -1.2204, -0.3086, -1.2458, 0.6964, 0.1785],
- [ 0.6606, -0.3845, 1.8579, -0.6968, -0.3384, -0.8934, 0.8674, 0.1214],
- [-1.1621, -1.5742, 1.4177, -0.9740, -0.6736, -0.8677, 0.1271, 0.1619],
- [ 0.8018, -0.2644, 1.5804, 0.3024, -0.3906, -0.0824, 0.0951, 0.0740],
- [-1.5449, -1.8026, 1.1856, -1.3089, -0.3308, -1.4579, 0.0138, 0.1254],
- [ 0.8893, -0.2169, 1.6289, 0.1542, -0.5249, -0.3488, 0.8478, 0.2615],
- [ 0.5511, -0.3979, 1.0464, -0.8318, -0.5398, -0.8926, 0.2521, 0.5029],
- [ 0.6511, -0.3953, 1.7895, -0.4373, -0.6443, -0.5113, 0.4277, 0.0741]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6174, -0.4225, 1.4975, -1.1709, -0.3174, -1.1806, 0.6539,
- 0.1879],
- [ 0.6125, -0.4369, 1.9173, -0.5384, -0.2594, -0.8386, 0.9741,
- 0.1821],
- [-2.2859, -2.2859, 1.5767, -0.7540, -0.6404, -0.7309, 0.1753,
- 0.0893],
- [ 0.5258, -0.4610, 1.5882, 0.4085, -0.3517, -0.0072, 0.0910,
- -0.0550],
- [-2.2859, -2.2859, 1.3400, -1.0388, -0.3055, -1.4930, 0.1157,
- 0.0231],
- [ 0.6421, -0.3647, 1.5940, 0.3084, -0.5192, -0.3691, 1.1057,
- 0.3692],
- [ 0.5626, -0.4162, 0.8692, -0.6051, -0.5480, -0.8925, 0.3469,
- 0.4316],
- [ 0.5669, -0.4794, 1.7499, -0.3383, -0.6751, -0.4229, 0.4970,
- -0.0611]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0529, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0529, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.44675440434366465
- step: 31
- running loss: 0.01441143239818273
- Train Steps: 31/90 Loss: 0.0144 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4101, 0.8838, 0.5317, 0.3825, 0.3800, 0.5413, 0.5683],
- [0.6202, 0.4053, 0.8638, 0.5283, 0.4546, 0.5108, 0.5900, 0.5133],
- [0.6167, 0.4048, 0.6831, 0.3639, 0.3763, 0.3017, 0.5700, 0.5883],
- [0.6197, 0.4090, 0.7825, 0.2500, 0.4200, 0.2483, 0.5988, 0.5667],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413],
- [0.6131, 0.4037, 0.6907, 0.2819, 0.3688, 0.2700, 0.5217, 0.5680],
- [0.6200, 0.3913, 0.8788, 0.5217, 0.4075, 0.5100, 0.6060, 0.4913],
- [0.6185, 0.4080, 0.8625, 0.3483, 0.3788, 0.2650, 0.5320, 0.5272]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6828, -0.3442, 1.7467, 0.0619, -0.5904, -0.5502, 0.3114, 0.3808],
- [ 0.6513, -0.3840, 1.7281, 0.0559, -0.2999, 0.0749, 0.4490, 0.1396],
- [ 0.3418, -0.5646, 1.0523, -0.8296, -0.6099, -0.9134, 0.3042, 0.4728],
- [ 0.4111, -0.5128, 1.3451, -1.2935, -0.4238, -1.2114, 0.4419, 0.3051],
- [ 0.6906, -0.3634, 2.1027, -0.7261, -0.1946, -1.2085, 1.1692, 0.1020],
- [ 0.2817, -0.5988, 0.9430, -1.1393, -0.5916, -1.0603, 0.1292, 0.2920],
- [ 0.5299, -0.5242, 1.7526, 0.0204, -0.4859, 0.0421, 0.3819, -0.0368],
- [ 0.6846, -0.3526, 1.7553, -0.8015, -0.5090, -1.1177, 0.3066, 0.1300]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5777, -0.3915, 1.7961, 0.1698, -0.5192, -0.5307, 0.2141,
- 0.3392],
- [ 0.5786, -0.4141, 1.7037, 0.1544, -0.1862, 0.0736, 0.4393,
- 0.0851],
- [ 0.5626, -0.4162, 0.8692, -0.6051, -0.5480, -0.8925, 0.3469,
- 0.4316],
- [ 0.5764, -0.3969, 1.3284, -1.1312, -0.3460, -1.1389, 0.4797,
- 0.3315],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142],
- [ 0.5459, -0.4215, 0.9043, -0.9838, -0.5827, -1.0388, 0.1236,
- 0.3378],
- [ 0.5776, -0.4784, 1.7730, 0.1236, -0.4037, 0.0697, 0.5132,
- -0.0168],
- [ 0.5711, -0.4015, 1.6979, -0.6770, -0.5365, -1.0619, 0.1712,
- 0.1494]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0103, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0103, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4570535933598876
- step: 32
- running loss: 0.014282924792496487
- Train Steps: 32/90 Loss: 0.0143 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6245, 0.4115, 0.8700, 0.4883, 0.4625, 0.5517, 0.6100, 0.5217],
- [0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142],
- [0.6156, 0.4125, 0.8850, 0.4833, 0.4550, 0.5817, 0.5765, 0.5084],
- [0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6199, 0.4060, 0.8888, 0.4667, 0.3800, 0.5050, 0.6188, 0.5433],
- [0.6219, 0.4114, 0.8175, 0.2817, 0.3925, 0.2783, 0.5900, 0.5350],
- [0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.4178e-01, -3.5956e-01, 1.7213e+00, -5.5002e-03, -2.6230e-01,
- 1.4525e-02, 5.9712e-01, 2.7523e-01],
- [ 4.7999e-01, -4.8110e-01, 1.1534e+00, -9.4995e-01, -6.3442e-01,
- -1.0915e+00, 2.5093e-01, -2.0075e-03],
- [-1.9991e+00, -2.1325e+00, 1.3982e+00, -9.4794e-01, -7.2812e-01,
- -9.9760e-01, 1.5390e-01, 1.4590e-01],
- [ 5.6458e-01, -3.8236e-01, 1.7374e+00, -7.4091e-02, -2.8748e-01,
- 1.2523e-01, 3.6424e-01, 2.4052e-01],
- [ 4.8428e-01, -4.9028e-01, 1.7524e+00, -8.4647e-02, -1.1400e-01,
- -2.1300e-01, 4.6986e-01, 2.6927e-01],
- [ 5.5946e-01, -4.2733e-01, 1.7709e+00, -1.3355e-01, -5.6999e-01,
- -1.6277e-01, 5.4718e-01, 2.3423e-01],
- [ 4.1153e-01, -5.0251e-01, 1.4305e+00, -1.0455e+00, -5.0402e-01,
- -1.1267e+00, 5.4868e-01, 2.4379e-01],
- [ 4.6093e-01, -4.3216e-01, 1.2042e+00, -1.0118e+00, -2.2142e-01,
- -1.4587e+00, 3.7473e-01, 3.7239e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5987, -0.3852, 1.7326, -0.0303, -0.1497, 0.2622, 0.5316,
- 0.1236],
- [ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141],
- [-2.2859, -2.2859, 1.5767, -0.7540, -0.6404, -0.7309, 0.1753,
- 0.0893],
- [ 0.5573, -0.3808, 1.8018, -0.0534, -0.1843, 0.4008, 0.3769,
- 0.0622],
- [ 0.5371, -0.4252, 1.7037, -0.0688, -0.0342, 0.0620, 0.3758,
- 0.2853],
- [ 0.5773, -0.4105, 1.8192, -0.1304, -0.5307, 0.0467, 0.5721,
- 0.2237],
- [ 0.5868, -0.3858, 1.4901, -0.9849, -0.4730, -1.0003, 0.4393,
- 0.1852],
- [ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0128, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0128, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.46980485040694475
- step: 33
- running loss: 0.014236510618392265
- Train Steps: 33/90 Loss: 0.0142 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6265, 0.4071, 0.8875, 0.3367, 0.3975, 0.3350, 0.6312, 0.5250],
- [0.6148, 0.4053, 0.8750, 0.4550, 0.4850, 0.5218, 0.5863, 0.5567],
- [0.6222, 0.4108, 0.8938, 0.4233, 0.3600, 0.3817, 0.5825, 0.5283],
- [0.6277, 0.4029, 0.8250, 0.2433, 0.4325, 0.2100, 0.6366, 0.5207],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6112, 0.4029, 0.8638, 0.4800, 0.4875, 0.5083, 0.5763, 0.5567],
- [0.6276, 0.4235, 0.8888, 0.5333, 0.3800, 0.3117, 0.5427, 0.6164],
- [0.6126, 0.3954, 0.8538, 0.4983, 0.4250, 0.4700, 0.5355, 0.5350]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6651, -0.3525, 1.8387, -0.8408, -0.5424, -0.8504, 0.6656, 0.1439],
- [ 0.4286, -0.4925, 1.7124, -0.2640, -0.1381, -0.0479, 0.2317, 0.3010],
- [ 0.7453, -0.2869, 1.7095, -0.4168, -0.7020, -0.5912, 0.4953, 0.2396],
- [ 0.3159, -0.6062, 1.4369, -1.2623, -0.3676, -1.3907, 0.5356, 0.0811],
- [ 0.6956, -0.3403, 1.5745, 0.1918, -0.4717, 0.0982, 0.7810, 0.1632],
- [ 0.3613, -0.5702, 1.7576, -0.1592, -0.1135, -0.0804, 0.3595, 0.2578],
- [ 0.6153, -0.3244, 1.6121, 0.1522, -0.5642, -0.8442, 0.2849, 0.5926],
- [ 0.5132, -0.4749, 1.6085, -0.0092, -0.4199, -0.1877, 0.1662, 0.1904]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6078, -0.4057, 1.8134, -0.7309, -0.4499, -0.7386, 0.6298,
- 0.1390],
- [ 0.5538, -0.4139, 1.7557, -0.1843, -0.0459, 0.1242, 0.4219,
- 0.2853],
- [ 0.5880, -0.3887, 1.8423, -0.3306, -0.6231, -0.5230, 0.4046,
- 0.1544],
- [ 0.6132, -0.4248, 1.5247, -1.1620, -0.2882, -1.3159, 0.6545,
- 0.1193],
- [ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.5371, -0.4252, 1.7037, -0.0688, -0.0342, 0.0620, 0.3758,
- 0.2853],
- [ 0.6131, -0.3299, 1.8192, 0.1775, -0.5307, -0.8463, 0.2206,
- 0.5612],
- [ 0.5435, -0.4597, 1.6575, 0.0159, -0.3229, -0.1150, 0.1877,
- 0.1854]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0096, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0096, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.4794104462489486
- step: 34
- running loss: 0.014100307242616135
- Train Steps: 34/90 Loss: 0.0141 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6241, 0.4143, 0.8938, 0.4650, 0.4075, 0.5350, 0.6250, 0.5550],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6226, 0.4103, 0.8575, 0.3450, 0.4388, 0.2067, 0.5787, 0.5383],
- [0.6064, 0.4019, 0.8650, 0.4517, 0.4037, 0.5367, 0.5703, 0.5609],
- [ nan, nan, 0.8850, 0.3000, 0.5363, 0.2250, 0.7343, 0.5771],
- [0.6201, 0.4050, 0.7757, 0.2234, 0.4459, 0.1798, 0.5975, 0.5426],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [ nan, nan, 0.6859, 0.2194, 0.4150, 0.1867, 0.5153, 0.5729]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7850, -0.2571, 1.8136, -0.0547, -0.3984, 0.1662, 0.5929, 0.2880],
- [ 0.7754, -0.2955, 1.7141, 0.1585, -0.6337, -0.1329, 0.4963, 0.4172],
- [ 0.7866, -0.2281, 1.6788, -0.6753, -0.2686, -1.2191, 0.3706, 0.1885],
- [ 0.6878, -0.3163, 1.6834, -0.0576, -0.4463, 0.3079, 0.4258, 0.2013],
- [-1.5394, -1.7654, 1.7289, -0.9718, 0.0645, -1.2452, 0.7936, 0.3608],
- [ 0.6353, -0.3698, 1.2097, -1.1548, -0.2937, -1.3861, 0.3841, 0.0950],
- [ 0.6473, -0.3535, 0.9732, -1.2908, -0.5212, -1.0675, 0.3539, 0.1694],
- [-1.8065, -1.9688, 0.9073, -1.2550, -0.4676, -1.4095, -0.0827, 0.2193]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5968, -0.3725, 1.8423, -0.1381, -0.4037, 0.1852, 0.6009,
- 0.2776],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5898, -0.3908, 1.6748, -0.6924, -0.2594, -1.3313, 0.3873,
- 0.2006],
- [ 0.5151, -0.4296, 1.7095, -0.1997, -0.4210, 0.1929, 0.3484,
- 0.3047],
- [-2.2859, -2.2859, 1.8018, -0.9002, 0.1910, -1.2467, 1.1057,
- 0.3799],
- [ 0.5784, -0.4153, 1.2972, -1.2541, -0.2265, -1.4553, 0.4741,
- 0.2203],
- [ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853],
- [-2.2859, -2.2859, 0.8824, -1.2727, -0.3691, -1.4237, 0.0943,
- 0.3604]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0284, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0284, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5077718859538436
- step: 35
- running loss: 0.014507768170109818
- Train Steps: 35/90 Loss: 0.0145 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6102, 0.3999, 0.8750, 0.5133, 0.3825, 0.4750, 0.5637, 0.5083],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [0.6102, 0.4020, 0.8638, 0.3717, 0.3625, 0.5017, 0.6038, 0.5500],
- [0.6161, 0.4055, 0.8675, 0.3867, 0.3713, 0.4033, 0.5195, 0.5162],
- [0.6144, 0.4032, 0.8563, 0.3283, 0.3525, 0.4200, 0.5775, 0.5583],
- [0.6286, 0.4078, 0.8063, 0.2267, 0.4788, 0.1533, 0.5953, 0.4913],
- [0.6204, 0.4055, 0.8438, 0.5733, 0.4574, 0.4801, 0.5487, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5011, -0.4467, 1.7422, 0.0539, -0.4417, -0.0660, 0.3217, 0.0671],
- [ 0.5066, -0.4254, 1.6587, 0.2854, -0.3504, 0.1462, 0.4665, 0.3451],
- [ 0.6545, -0.3217, 1.6254, 0.3260, -0.5526, -0.4922, 0.4399, 0.5244],
- [ 0.5252, -0.4480, 1.6938, -0.6012, -0.5887, -0.0047, 0.6975, 0.3762],
- [ 0.2379, -0.6145, 1.7338, -0.5259, -0.5895, -0.5340, 0.2075, 0.1188],
- [ 0.6845, -0.3041, 1.5760, -0.7952, -0.6356, -0.4136, 0.3729, 0.3453],
- [ 0.4442, -0.4868, 1.3857, -1.2715, -0.0972, -1.6168, 0.4491, 0.0212],
- [ 0.4995, -0.4227, 1.5819, 0.3630, -0.1269, -0.0922, 0.3253, 0.3093]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5326, -0.4390, 1.7557, 0.0851, -0.5192, -0.0919, 0.3180,
- 0.0620],
- [ 0.5742, -0.3792, 1.6460, 0.3084, -0.3460, 0.1467, 0.4162,
- 0.3161],
- [ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778],
- [ 0.5324, -0.4293, 1.7037, -0.5692, -0.6115, 0.0313, 0.5028,
- 0.2545],
- [ 0.5598, -0.4129, 1.7210, -0.4999, -0.5711, -0.4229, 0.1136,
- 0.0983],
- [ 0.5520, -0.4237, 1.6691, -0.7694, -0.6577, -0.3460, 0.3815,
- 0.2930],
- [ 0.6177, -0.4022, 1.4381, -1.2390, -0.0746, -1.5777, 0.4639,
- -0.0168],
- [ 0.5795, -0.4129, 1.6113, 0.3623, -0.1733, -0.0684, 0.2487,
- 0.3084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0061, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0061, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5138542065396905
- step: 36
- running loss: 0.014273727959435847
- Train Steps: 36/90 Loss: 0.0143 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4151, 0.8588, 0.5467, 0.3700, 0.3950, 0.5637, 0.5933],
- [0.6364, 0.4154, 0.8938, 0.3717, 0.4500, 0.2583, 0.6448, 0.5285],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6205, 0.4062, 0.8337, 0.2683, 0.3675, 0.4283, 0.6338, 0.5250],
- [0.6325, 0.4066, 0.8438, 0.5350, 0.3925, 0.5267, 0.7113, 0.5795],
- [0.6332, 0.4118, 0.9238, 0.4267, 0.4012, 0.4733, 0.7525, 0.5436],
- [0.6200, 0.4024, 0.8390, 0.3139, 0.3525, 0.3833, 0.6162, 0.5383],
- [0.6125, 0.3974, 0.7725, 0.2517, 0.3538, 0.3317, 0.5887, 0.5500]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7667, -0.1900, 1.5827, 0.3425, -0.4211, -0.4493, 0.1190, 0.4417],
- [ 0.6480, -0.3304, 1.8848, -0.5738, -0.0757, -1.2094, 0.5862, 0.2134],
- [ 0.4521, -0.4451, 1.6456, -0.5933, -0.4995, -0.8002, -0.0086, 0.1792],
- [ 0.3198, -0.5839, 1.5540, -0.9149, -0.5333, -0.2930, 0.5008, 0.2137],
- [ 0.6390, -0.3685, 1.5938, 0.3059, -0.3143, 0.2177, 0.8638, 0.3779],
- [ 0.6208, -0.3873, 1.8751, -0.2567, -0.3241, 0.0175, 0.9235, 0.2965],
- [ 0.2887, -0.6082, 1.6301, -0.7104, -0.5966, -0.4797, 0.4536, 0.2508],
- [ 0.2123, -0.6332, 1.2361, -1.0201, -0.5911, -0.7614, 0.2279, 0.2410]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5785, -0.3687, 1.6806, 0.2391, -0.5769, -0.4614, 0.3180,
- 0.4547],
- [ 0.6537, -0.3671, 1.8423, -0.5692, -0.2074, -1.0927, 0.6926,
- 0.1554],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.5799, -0.4099, 1.5651, -1.0465, -0.5885, -0.3075, 0.6413,
- 0.1390],
- [ 0.6355, -0.4080, 1.6113, 0.1852, -0.4730, 0.1467, 0.9996,
- 0.3905],
- [ 0.6388, -0.3840, 1.9808, -0.3152, -0.4326, -0.0996, 1.1897,
- 0.2249],
- [ 0.5779, -0.4275, 1.5894, -0.8362, -0.6577, -0.5153, 0.5605,
- 0.2006],
- [ 0.5430, -0.4503, 1.2822, -1.1235, -0.6520, -0.7540, 0.4335,
- 0.2545]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0156, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0156, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5294085452333093
- step: 37
- running loss: 0.01430833906035971
- Train Steps: 37/90 Loss: 0.0143 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6182, 0.3972, 0.8552, 0.5914, 0.3683, 0.4181, 0.5688, 0.5378],
- [0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783],
- [0.6109, 0.4036, 0.7188, 0.1750, 0.3850, 0.2550, 0.5863, 0.5567],
- [0.6164, 0.4119, 0.7913, 0.2650, 0.3538, 0.3500, 0.5614, 0.5038],
- [ nan, nan, 0.7553, 0.2722, 0.3875, 0.2550, 0.5125, 0.5633],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6224, 0.3964, 0.8225, 0.5717, 0.4150, 0.4617, 0.5775, 0.5267],
- [ nan, nan, 0.7648, 0.2722, 0.3962, 0.2183, 0.5060, 0.5422]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.8430, -0.2584, 1.6744, 0.3633, -0.4802, -0.2443, 0.5070, 0.2324],
- [ 0.6614, -0.2980, 1.3332, -0.9772, -0.0668, -1.1946, 0.3990, 0.4166],
- [ 0.6807, -0.3319, 1.1142, -1.3562, -0.3954, -1.0487, 0.4462, 0.3111],
- [ 0.4883, -0.4645, 1.4983, -1.0006, -0.5738, -0.6161, 0.4483, 0.1922],
- [-2.2025, -2.2211, 1.3382, -0.9761, -0.4328, -0.9994, 0.1234, 0.2760],
- [ 0.7368, -0.3126, 1.1983, -1.3563, -0.3093, -1.0961, 0.5613, 0.3036],
- [ 0.7644, -0.3089, 1.6260, 0.3014, -0.3090, -0.0725, 0.5097, 0.2114],
- [-2.1184, -2.1461, 1.3030, -1.0153, -0.4229, -1.1399, 0.1310, 0.2134]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5697, -0.4514, 1.6642, 0.4459, -0.5850, -0.3546, 0.3414,
- 0.1982],
- [ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854],
- [ 0.5356, -0.4217, 1.0339, -1.4776, -0.5076, -1.1081, 0.4219,
- 0.2853],
- [ 0.5614, -0.3834, 1.3688, -1.0619, -0.6520, -0.6693, 0.3069,
- 0.0412],
- [-2.2859, -2.2859, 1.2030, -1.0288, -0.4961, -1.1081, 0.0813,
- 0.3161],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.5891, -0.4550, 1.5132, 0.3546, -0.3691, -0.1535, 0.3815,
- 0.1467],
- [-2.2859, -2.2859, 1.2469, -1.0288, -0.4557, -1.2774, 0.0511,
- 0.2183]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0101, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0101, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5395098840817809
- step: 38
- running loss: 0.014197628528467919
- Train Steps: 38/90 Loss: 0.0142 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064],
- [0.6175, 0.4013, 0.7734, 0.2783, 0.4475, 0.1786, 0.5790, 0.5351],
- [0.6026, 0.3979, 0.8550, 0.4233, 0.3613, 0.5233, 0.5582, 0.4967],
- [0.6193, 0.4165, 0.8838, 0.4700, 0.4150, 0.4867, 0.5427, 0.5261],
- [0.6269, 0.4073, 0.8900, 0.4933, 0.3625, 0.4183, 0.6288, 0.5150],
- [0.6107, 0.4013, 0.8700, 0.4650, 0.5049, 0.5176, 0.5850, 0.5550],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5074, -0.4429, 1.7396, -0.2308, -0.5302, 0.2456, 0.5146, 0.1426],
- [ 0.4840, -0.4596, 1.6347, 0.0541, -0.1286, 0.0975, 0.1633, 0.1393],
- [ 0.8040, -0.2417, 1.2190, -1.0917, -0.2926, -1.5079, 0.3536, 0.2265],
- [ 0.4214, -0.4987, 1.6166, -0.2834, -0.6564, 0.0112, 0.3598, 0.2051],
- [ 0.5247, -0.3758, 1.7237, -0.0731, -0.4058, -0.1434, 0.2149, 0.2700],
- [ 0.5961, -0.3832, 1.7580, 0.0120, -0.6258, -0.3360, 0.6261, 0.2390],
- [ 0.6072, -0.3421, 1.6672, -0.1749, -0.0391, -0.0249, 0.3669, 0.3491],
- [-2.1279, -2.1510, 1.6978, -1.1259, 0.1169, -1.2127, 1.0293, 0.4119]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532],
- [ 0.5664, -0.4321, 1.2862, -1.0003, -0.2189, -1.4608, 0.3883,
- 0.1855],
- [ 0.4974, -0.4482, 1.6633, -0.3306, -0.6173, 0.1313, 0.2925,
- 0.0081],
- [ 0.5746, -0.3623, 1.7961, -0.1150, -0.3691, -0.0380, 0.2208,
- 0.1439],
- [ 0.6096, -0.4046, 1.8249, -0.0072, -0.6115, -0.3537, 0.6182,
- 0.0928],
- [ 0.5351, -0.4321, 1.7326, -0.1381, 0.0463, 0.1049, 0.4162,
- 0.2776],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0078, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0078, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5473534110933542
- step: 39
- running loss: 0.014034702848547544
- Train Steps: 39/90 Loss: 0.0140 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6214, 0.4112, 0.7838, 0.2117, 0.3650, 0.3133, 0.5675, 0.5083],
- [0.6124, 0.4083, 0.6954, 0.3069, 0.3650, 0.2750, 0.5163, 0.5485],
- [0.6059, 0.4002, 0.7562, 0.2767, 0.3538, 0.3033, 0.5529, 0.5455],
- [0.6156, 0.4125, 0.8850, 0.4833, 0.4550, 0.5817, 0.5765, 0.5084],
- [0.6193, 0.4079, 0.7288, 0.2500, 0.4250, 0.2550, 0.5989, 0.6266],
- [0.6222, 0.4169, 0.8638, 0.5650, 0.4313, 0.4783, 0.5637, 0.5633],
- [0.6343, 0.4097, 0.9287, 0.4367, 0.4313, 0.3600, 0.7248, 0.5841],
- [0.6268, 0.4094, 0.9038, 0.4300, 0.3700, 0.3483, 0.6262, 0.5167]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.9239e-01, -4.0632e-01, 1.4173e+00, -1.3067e+00, -5.8054e-01,
- -8.9081e-01, 4.0951e-01, 3.0051e-02],
- [ 5.6247e-01, -3.9829e-01, 1.0312e+00, -8.5208e-01, -5.4845e-01,
- -9.3977e-01, 1.0921e-01, 2.4333e-01],
- [ 1.8700e-01, -6.6109e-01, 1.2660e+00, -9.8780e-01, -5.7842e-01,
- -8.6583e-01, 2.8633e-01, 2.2913e-01],
- [ 9.6982e-02, -6.9610e-01, 1.8964e+00, -3.7301e-02, -1.7227e-01,
- 4.4360e-01, 2.9462e-01, 1.1563e-01],
- [ 6.4631e-01, -3.3672e-01, 1.1264e+00, -1.1566e+00, -2.4963e-01,
- -1.0645e+00, 4.9173e-01, 6.1092e-01],
- [ 3.9081e-01, -4.9535e-01, 1.7839e+00, 4.0092e-01, -2.1903e-01,
- -9.6561e-04, 2.6731e-01, 3.3483e-01],
- [ 6.0249e-01, -4.3258e-01, 1.9517e+00, -2.0927e-01, -2.7999e-01,
- -5.2175e-01, 1.0574e+00, 3.2433e-01],
- [ 4.8791e-01, -4.7775e-01, 1.9963e+00, -2.2164e-01, -4.3256e-01,
- -5.8975e-01, 5.4839e-01, 1.9789e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5845, -0.3864, 1.3342, -1.3082, -0.6000, -0.8386, 0.3353,
- 0.0620],
- [ 0.5425, -0.3998, 0.9263, -0.8683, -0.6000, -1.0157, 0.0990,
- 0.2476],
- [ 0.5129, -0.4374, 1.2072, -1.0080, -0.6520, -0.8848, 0.2679,
- 0.2335],
- [ 0.5573, -0.3808, 1.8018, -0.0534, -0.1843, 0.4008, 0.3769,
- 0.0622],
- [ 0.5746, -0.4021, 1.0801, -1.1312, -0.3229, -1.1081, 0.4803,
- 0.6084],
- [ 0.5880, -0.3605, 1.7037, 0.3238, -0.2940, -0.0765, 0.3180,
- 0.3161],
- [ 0.6438, -0.3936, 2.0039, -0.2690, -0.2940, -0.6231, 1.0618,
- 0.4119],
- [ 0.6094, -0.3947, 1.8885, -0.2998, -0.5769, -0.6770, 0.6067,
- 0.1005]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0117, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0117, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5590283134952188
- step: 40
- running loss: 0.01397570783738047
- Train Steps: 40/90 Loss: 0.0140 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6178, 0.4012, 0.8900, 0.4495, 0.3891, 0.5617, 0.5972, 0.4879],
- [0.6139, 0.4019, 0.7137, 0.2150, 0.4375, 0.1533, 0.5293, 0.5006],
- [0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6201, 0.4151, 0.8588, 0.5467, 0.3700, 0.3950, 0.5637, 0.5933],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6153, 0.4119, 0.8463, 0.3833, 0.3600, 0.3200, 0.5106, 0.5563],
- [0.6310, 0.4017, 0.8563, 0.5800, 0.3688, 0.4750, 0.6326, 0.5006],
- [0.6223, 0.3990, 0.8037, 0.2800, 0.4000, 0.2283, 0.5864, 0.5208]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4518, -0.4911, 1.8600, -0.2607, -0.4129, 0.5143, 0.4992, 0.0568],
- [-0.3478, -1.0317, 1.0285, -1.3610, -0.1993, -1.5180, 0.1647, 0.1402],
- [ 0.6369, -0.3709, 1.4022, -0.9832, -0.4957, -0.6442, 0.4793, 0.2773],
- [ 0.5346, -0.3425, 1.6216, 0.1690, -0.4701, -0.3177, 0.2371, 0.5411],
- [ 0.7848, -0.2854, 1.9911, -0.3355, -0.1997, -0.7153, 1.0501, 0.2050],
- [ 0.3135, -0.5431, 1.6285, -0.5310, -0.5323, -0.6793, 0.0580, 0.3620],
- [ 0.3326, -0.6026, 1.6768, 0.3122, -0.5380, 0.0443, 0.6050, 0.0992],
- [ 0.5161, -0.4441, 1.4498, -1.1177, -0.3233, -1.1196, 0.4261, 0.2058]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5676, -0.4327, 1.8252, -0.2095, -0.4886, 0.3084, 0.4727,
- -0.0322],
- [ 0.5496, -0.4295, 1.0109, -1.2928, -0.2651, -1.5777, 0.1589,
- 0.0261],
- [ 0.5697, -0.4442, 1.3529, -0.9515, -0.5774, -0.7801, 0.5253,
- 0.1931],
- [ 0.5785, -0.3687, 1.6806, 0.2391, -0.5769, -0.4614, 0.3180,
- 0.4547],
- [ 0.6224, -0.4345, 1.9404, -0.2921, -0.3171, -0.8771, 1.0655,
- 0.2142],
- [ 0.5561, -0.3834, 1.6229, -0.5153, -0.6231, -0.8079, 0.0727,
- 0.2837],
- [ 0.6286, -0.4303, 1.6691, 0.3931, -0.5827, -0.0919, 0.6359,
- 0.0261],
- [ 0.5885, -0.4429, 1.4266, -0.9926, -0.4383, -1.2313, 0.4228,
- 0.1195]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0280, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0280, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.5870048655197024
- step: 41
- running loss: 0.014317191841943962
- Train Steps: 41/90 Loss: 0.0143 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3993, 0.8519, 0.4923, 0.3962, 0.4717, 0.6013, 0.5433],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6311, 0.3998, 0.7975, 0.5767, 0.3838, 0.4850, 0.7327, 0.5343],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6157, 0.4102, 0.8513, 0.3817, 0.3613, 0.3667, 0.5096, 0.5890],
- [0.6125, 0.4076, 0.8488, 0.3883, 0.3700, 0.3683, 0.5026, 0.5505],
- [0.6225, 0.4191, 0.8500, 0.4167, 0.3688, 0.3233, 0.5650, 0.5650],
- [0.6250, 0.4110, 0.7238, 0.2067, 0.4263, 0.1883, 0.5625, 0.5633]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5652, -0.4335, 1.8033, 0.0198, -0.3656, 0.0220, 0.6305, 0.2068],
- [ 0.4282, -0.5123, 1.7713, -0.2921, -0.4786, 0.0357, 0.2857, 0.0134],
- [ 0.5329, -0.4882, 1.5947, 0.2187, -0.4321, 0.0283, 1.0430, 0.1789],
- [ 0.7813, -0.2962, 1.0969, -1.1801, -0.4818, -1.0343, 0.2377, 0.0690],
- [ 0.2271, -0.6053, 1.7303, -0.5249, -0.5412, -0.5170, 0.1491, 0.3835],
- [-0.0694, -0.8135, 1.7507, -0.5003, -0.5468, -0.5444, 0.1607, 0.2659],
- [ 0.7761, -0.2279, 1.7220, -0.3759, -0.4916, -0.7065, 0.3742, 0.4468],
- [ 0.3328, -0.5700, 1.1195, -1.3783, -0.1821, -1.4471, 0.4467, 0.2584]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5777, -0.4415, 1.6491, -0.0122, -0.4557, -0.1073, 0.4912,
- 0.2237],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.6289, -0.4393, 1.3977, 0.3777, -0.5134, -0.0457, 1.0984,
- 0.1821],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.5581, -0.3912, 1.6460, -0.5230, -0.6173, -0.5923, 0.0681,
- 0.4348],
- [ 0.5433, -0.4032, 1.6344, -0.4922, -0.5769, -0.5846, 0.0357,
- 0.2567],
- [ 0.5894, -0.3503, 1.6402, -0.3614, -0.5827, -0.7925, 0.3238,
- 0.3238],
- [ 0.6010, -0.3875, 1.0570, -1.3313, -0.3171, -1.4160, 0.3122,
- 0.3161]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0210, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0210, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.6079580811783671
- step: 42
- running loss: 0.014475192409008741
- Train Steps: 42/90 Loss: 0.0145 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.6859, 0.2194, 0.4150, 0.1867, 0.5153, 0.5729],
- [0.6201, 0.4102, 0.7288, 0.2417, 0.4150, 0.2383, 0.6100, 0.5500],
- [0.6216, 0.4099, 0.7225, 0.2033, 0.4188, 0.2217, 0.5975, 0.5283],
- [0.6153, 0.4119, 0.8463, 0.3833, 0.3600, 0.3200, 0.5106, 0.5563],
- [ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [0.6139, 0.4019, 0.7137, 0.2150, 0.4375, 0.1533, 0.5293, 0.5006],
- [0.6224, 0.4097, 0.7438, 0.2267, 0.3850, 0.2850, 0.5988, 0.5250],
- [0.6200, 0.3961, 0.8461, 0.5497, 0.4142, 0.4577, 0.5892, 0.5402]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.1093, -2.1695, 1.0704, -1.1241, -0.2829, -1.2708, 0.3400, 0.3245],
- [ 0.9580, -0.1868, 1.1554, -1.1501, -0.3985, -1.0545, 0.5273, 0.2618],
- [ 0.7270, -0.3383, 1.1407, -1.2378, -0.3266, -1.1439, 0.4433, 0.2077],
- [ 0.6284, -0.3472, 1.6952, -0.4482, -0.5650, -0.6186, 0.1774, 0.2573],
- [-2.4414, -2.3598, 1.4631, -0.6933, -0.5079, -0.7125, 0.2897, 0.2286],
- [ 0.6782, -0.3799, 1.1089, -1.2278, -0.2241, -1.4500, 0.2807, 0.0738],
- [ 0.7070, -0.3484, 1.2289, -1.1555, -0.4486, -0.8100, 0.5723, 0.2394],
- [ 0.6015, -0.4189, 1.7105, 0.1990, -0.4040, 0.0405, 0.5011, 0.1426]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 0.8824, -1.2727, -0.3691, -1.4237, 0.0943,
- 0.3604],
- [ 0.5784, -0.3913, 1.0801, -1.1697, -0.3691, -1.1851, 0.5316,
- 0.2545],
- [ 0.5850, -0.3925, 1.0513, -1.3467, -0.3517, -1.2620, 0.4739,
- 0.1544],
- [ 0.5561, -0.3834, 1.6229, -0.5153, -0.6231, -0.8079, 0.0727,
- 0.2837],
- [-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837],
- [ 0.5496, -0.4295, 1.0109, -1.2928, -0.2651, -1.5777, 0.1589,
- 0.0261],
- [ 0.5891, -0.3937, 1.1494, -1.2390, -0.5076, -0.9695, 0.4797,
- 0.1390],
- [ 0.5780, -0.4565, 1.6221, 0.2532, -0.3728, -0.1718, 0.4357,
- 0.2091]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0137, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0137, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.621706354431808
- step: 43
- running loss: 0.014458287312367629
- Train Steps: 43/90 Loss: 0.0145 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6212, 0.4171, 0.7875, 0.3633, 0.3813, 0.2933, 0.5675, 0.5700],
- [0.6264, 0.4071, 0.9038, 0.3867, 0.3663, 0.3917, 0.6338, 0.5283],
- [ nan, nan, 0.8888, 0.3100, 0.5262, 0.2817, 0.7145, 0.6003],
- [ nan, nan, 0.7850, 0.2700, 0.4288, 0.1717, 0.5199, 0.4999],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [ nan, nan, 0.7625, 0.2433, 0.3713, 0.2867, 0.5235, 0.5220],
- [0.6325, 0.4165, 0.9000, 0.4617, 0.3813, 0.4900, 0.7485, 0.5447],
- [0.6214, 0.4112, 0.7838, 0.2117, 0.3650, 0.3133, 0.5675, 0.5083]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7925, -0.2197, 1.2258, -0.5560, -0.5129, -0.8534, 0.1539, 0.3959],
- [ 0.9824, -0.1438, 1.7369, -0.4676, -0.5488, -0.3900, 0.4122, 0.0837],
- [-0.5162, -1.0907, 1.6477, -0.7800, 0.1305, -1.0171, 0.9095, 0.4633],
- [-2.3501, -2.3298, 1.2146, -1.0448, -0.2453, -1.2660, 0.2259, 0.1469],
- [ 0.8942, -0.2418, 1.7042, -0.0787, -0.5784, -0.3624, 0.6176, 0.1548],
- [-2.1278, -2.1791, 1.1879, -1.0004, -0.4103, -0.9609, 0.1842, 0.1987],
- [ 0.9677, -0.1782, 1.7206, -0.1119, -0.5416, 0.0892, 0.8351, 0.1302],
- [ 0.9036, -0.2227, 1.1927, -1.2856, -0.5745, -0.8649, 0.2551, 0.0385]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5832, -0.3593, 1.3515, -0.6077, -0.5249, -0.9310, 0.3353,
- 0.3469],
- [ 0.6073, -0.4058, 1.8885, -0.4999, -0.5942, -0.4768, 0.6413,
- 0.1544],
- [-2.2859, -2.2859, 1.8192, -0.8540, 0.1448, -0.9849, 1.0143,
- 0.4867],
- [-2.2859, -2.2859, 1.3400, -1.0388, -0.3055, -1.4930, 0.1157,
- 0.0231],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [-2.2859, -2.2859, 1.2360, -1.1620, -0.5711, -0.9618, 0.1322,
- 0.1253],
- [ 0.6355, -0.3623, 1.8711, -0.1535, -0.5249, -0.0226, 1.1715,
- 0.2302],
- [ 0.5845, -0.3864, 1.3342, -1.3082, -0.6000, -0.8386, 0.3353,
- 0.0620]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0917, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0917, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7134014395996928
- step: 44
- running loss: 0.0162136690818112
- Train Steps: 44/90 Loss: 0.0162 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6037, 0.4020, 0.8300, 0.4033, 0.3575, 0.4883, 0.5647, 0.5631],
- [0.6204, 0.4007, 0.7838, 0.2100, 0.4475, 0.1733, 0.5825, 0.5167],
- [ nan, nan, 0.6488, 0.1817, 0.4325, 0.1867, 0.5475, 0.5733],
- [0.6149, 0.4054, 0.6713, 0.2333, 0.4025, 0.2017, 0.5213, 0.5695],
- [0.6293, 0.4024, 0.8750, 0.5000, 0.4012, 0.5733, 0.7121, 0.5633],
- [0.6252, 0.4158, 0.8988, 0.4083, 0.3788, 0.4783, 0.6225, 0.5633],
- [0.6229, 0.4086, 0.7538, 0.2600, 0.4775, 0.1617, 0.5900, 0.5383],
- [0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2478, -0.5887, 1.5762, -0.3955, -0.6159, -0.0235, 0.3924, 0.1690],
- [ 0.5105, -0.4776, 1.2312, -1.2097, -0.2561, -1.4255, 0.4250, 0.0495],
- [-2.8672, -2.7021, 0.9996, -1.1260, -0.2092, -1.3250, 0.2963, 0.3326],
- [ 0.4463, -0.5024, 0.8379, -1.1694, -0.4447, -1.2806, 0.0816, 0.2743],
- [ 0.4706, -0.4984, 1.7752, 0.0275, -0.4758, 0.3607, 0.8918, 0.1198],
- [ 0.3932, -0.5023, 1.7916, -0.3608, -0.5850, -0.0992, 0.6404, 0.2181],
- [ 0.5354, -0.4506, 1.1401, -1.1430, -0.1944, -1.4443, 0.3866, 0.1853],
- [ 0.5298, -0.4138, 1.7078, -0.5646, -0.5096, -0.9205, 0.3372, 0.1251]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5027, -0.4289, 1.5478, -0.4229, -0.6346, -0.0303, 0.3223,
- 0.3148],
- [ 0.5796, -0.4351, 1.3342, -1.3159, -0.2189, -1.4853, 0.4046,
- 0.1005],
- [-2.2859, -2.2859, 0.7106, -1.4468, -0.2882, -1.4237, 0.2430,
- 0.3623],
- [ 0.5544, -0.4133, 0.8145, -1.2082, -0.4268, -1.3544, 0.1221,
- 0.3446],
- [ 0.6207, -0.4273, 1.7557, 0.0236, -0.4326, 0.3623, 1.0033,
- 0.3157],
- [ 0.6017, -0.3654, 1.8654, -0.3998, -0.5365, -0.0765, 0.5894,
- 0.3161],
- [ 0.5911, -0.3984, 1.1956, -1.0850, -0.0804, -1.5392, 0.4393,
- 0.2006],
- [ 0.5784, -0.4006, 1.7911, -0.5701, -0.5192, -1.0331, 0.4137,
- 0.2139]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0179, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0179, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7312849136069417
- step: 45
- running loss: 0.016250775857932036
- Train Steps: 45/90 Loss: 0.0163 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6109, 0.4009, 0.8725, 0.4700, 0.3550, 0.4167, 0.5650, 0.5117],
- [0.6182, 0.3972, 0.8720, 0.5527, 0.3638, 0.3582, 0.5696, 0.5395],
- [0.6161, 0.4040, 0.7525, 0.2133, 0.4125, 0.2067, 0.5787, 0.5117],
- [0.6250, 0.4236, 0.8638, 0.3767, 0.4050, 0.3150, 0.5649, 0.5799],
- [0.6198, 0.4101, 0.8838, 0.5283, 0.3763, 0.5267, 0.5913, 0.5567],
- [0.6122, 0.4006, 0.8850, 0.4217, 0.4088, 0.5517, 0.6063, 0.5517],
- [0.6086, 0.3981, 0.8700, 0.4750, 0.4512, 0.5283, 0.5324, 0.5038],
- [0.6098, 0.3991, 0.8638, 0.4717, 0.4263, 0.4967, 0.5212, 0.5650]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4556, -0.5099, 1.7766, -0.1818, -0.6755, -0.4903, 0.4060, 0.0858],
- [ 0.5090, -0.4585, 1.5956, 0.1512, -0.6968, -0.7298, 0.3877, 0.2242],
- [ 0.5739, -0.4423, 1.0018, -1.4543, -0.4832, -1.4066, 0.4122, 0.0719],
- [ 0.1892, -0.6547, 1.6334, -0.6650, -0.4261, -0.9657, 0.4471, 0.4533],
- [ 0.6028, -0.4209, 1.7416, 0.1237, -0.5934, -0.0068, 0.6176, 0.2581],
- [ 0.5751, -0.4199, 1.8006, -0.3498, -0.4233, 0.1760, 0.6082, 0.1663],
- [ 0.5965, -0.4214, 1.7018, -0.1034, -0.2478, -0.0326, 0.2991, -0.0696],
- [ 0.4331, -0.5177, 1.6583, -0.1081, -0.3108, -0.0744, 0.3485, 0.2111]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5356, -0.4343, 1.7441, -0.1150, -0.6462, -0.3614, 0.3238,
- 0.0774],
- [ 0.5697, -0.4514, 1.7420, 0.2672, -0.6055, -0.6312, 0.3449,
- 0.2058],
- [ 0.5598, -0.4201, 1.1898, -1.3005, -0.3806, -1.3313, 0.3873,
- 0.0774],
- [ 0.6008, -0.3293, 1.7037, -0.5461, -0.4152, -0.8309, 0.3234,
- 0.3928],
- [ 0.5770, -0.3918, 1.7961, 0.1544, -0.5480, 0.1467, 0.4450,
- 0.2853],
- [ 0.5417, -0.4355, 1.8018, -0.3383, -0.3979, 0.2622, 0.5143,
- 0.2622],
- [ 0.5249, -0.4473, 1.7326, -0.0919, -0.2016, 0.1544, 0.1733,
- 0.0412],
- [ 0.5308, -0.4425, 1.7037, -0.1073, -0.3171, 0.0082, 0.1217,
- 0.3238]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0118, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0118, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7431346783414483
- step: 46
- running loss: 0.016155101703074964
- Train Steps: 46/90 Loss: 0.0162 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6150, 0.4097, 0.7468, 0.3194, 0.3825, 0.2633, 0.5463, 0.5800],
- [0.6275, 0.4008, 0.8047, 0.2484, 0.4037, 0.2777, 0.6506, 0.5212],
- [0.6164, 0.4119, 0.7913, 0.2650, 0.3538, 0.3500, 0.5614, 0.5038],
- [0.6186, 0.3967, 0.7337, 0.1992, 0.4120, 0.2508, 0.6105, 0.5395],
- [0.6127, 0.4084, 0.8700, 0.4467, 0.3987, 0.4317, 0.5013, 0.5471],
- [0.6200, 0.4059, 0.8700, 0.4900, 0.4163, 0.5000, 0.6162, 0.5467],
- [0.6048, 0.3987, 0.7620, 0.3861, 0.3475, 0.4167, 0.5137, 0.5466],
- [ nan, nan, 0.9050, 0.3500, 0.5138, 0.2300, 0.7359, 0.5702]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5993, -0.3803, 1.1228, -0.8423, -0.6056, -1.1385, 0.0585, 0.3099],
- [ 0.6539, -0.4069, 1.4465, -1.0566, -0.4854, -1.0856, 0.7644, 0.0711],
- [ 0.5155, -0.4606, 1.3688, -1.0454, -0.7240, -0.7367, 0.3963, 0.0150],
- [ 0.5882, -0.4315, 1.0366, -1.3716, -0.4745, -1.2091, 0.5045, 0.1624],
- [ 0.4665, -0.4729, 1.7877, -0.1115, -0.4896, -0.3687, 0.1220, 0.2028],
- [ 0.4432, -0.5455, 1.8228, 0.0551, -0.3761, -0.0258, 0.6413, 0.1544],
- [ 0.5897, -0.4017, 1.2351, -0.5070, -0.7453, -0.4400, 0.1918, 0.1963],
- [-1.4964, -1.7881, 1.8678, -0.7517, 0.1144, -1.2951, 1.0231, 0.3557]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5548, -0.3936, 1.1634, -0.8105, -0.5192, -1.0696, 0.2372,
- 0.3931],
- [ 0.6125, -0.4345, 1.4308, -1.1384, -0.4213, -1.0031, 0.7190,
- 0.1214],
- [ 0.5614, -0.3834, 1.3688, -1.0619, -0.6520, -0.6693, 0.3069,
- 0.0412],
- [ 0.5713, -0.4538, 1.1028, -1.3659, -0.3831, -1.1273, 0.5340,
- 0.2058],
- [ 0.5441, -0.3997, 1.7326, -0.2228, -0.4441, -0.2921, 0.0296,
- 0.2409],
- [ 0.5776, -0.4109, 1.7326, -0.0226, -0.3633, 0.0236, 0.5605,
- 0.2391],
- [ 0.5076, -0.4443, 1.2337, -0.5023, -0.6808, -0.3614, 0.0866,
- 0.2386],
- [-2.2859, -2.2859, 1.8942, -0.6693, 0.0871, -1.2236, 1.1130,
- 0.3478]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0182, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0182, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7613579174503684
- step: 47
- running loss: 0.016199104626603584
- Train Steps: 47/90 Loss: 0.0162 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6202, 0.4079, 0.8025, 0.2500, 0.3763, 0.3217, 0.6125, 0.5533],
- [0.6218, 0.4137, 0.7263, 0.2233, 0.4075, 0.2650, 0.6212, 0.5783],
- [0.6282, 0.4034, 0.7830, 0.2080, 0.4532, 0.2080, 0.6404, 0.5323],
- [0.6129, 0.3930, 0.8769, 0.5105, 0.4146, 0.5642, 0.6000, 0.5082],
- [0.6226, 0.4103, 0.8575, 0.3450, 0.4388, 0.2067, 0.5787, 0.5383],
- [0.6224, 0.4179, 0.8700, 0.5683, 0.4037, 0.4683, 0.5650, 0.5600],
- [0.6057, 0.4011, 0.8750, 0.4267, 0.4400, 0.5800, 0.5845, 0.5585],
- [0.6187, 0.4104, 0.7126, 0.2069, 0.3950, 0.2517, 0.5913, 0.5683]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4333, -0.5111, 1.4087, -1.0953, -0.6372, -0.8462, 0.4818, 0.1355],
- [ 0.3784, -0.5491, 1.0189, -1.2366, -0.5795, -1.2066, 0.4777, 0.2933],
- [ 0.6046, -0.4339, 1.2734, -1.2354, -0.3520, -1.3963, 0.5953, 0.0656],
- [ 0.2975, -0.6681, 1.8023, 0.2010, -0.3973, 0.1365, 0.5395, 0.0553],
- [ 0.5932, -0.3750, 1.7603, -0.6885, -0.3399, -1.3641, 0.3018, 0.1969],
- [ 0.2940, -0.5934, 1.7302, 0.3709, -0.5093, -0.3573, 0.4433, 0.4143],
- [ 0.3846, -0.5253, 1.8102, -0.2334, -0.3446, 0.1800, 0.4126, 0.1311],
- [ 0.4028, -0.5231, 0.8992, -1.3941, -0.5818, -1.2945, 0.3011, 0.2570]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5788, -0.4020, 1.4208, -1.1312, -0.5480, -0.8002, 0.5432,
- 0.2699],
- [ 0.5863, -0.3749, 1.0686, -1.2543, -0.4037, -1.0619, 0.5836,
- 0.3854],
- [ 0.6158, -0.4225, 1.3307, -1.3253, -0.1924, -1.3252, 0.6721,
- 0.1727],
- [ 0.5450, -0.4706, 1.7643, 0.0722, -0.3708, 0.3200, 0.4854,
- 0.0612],
- [ 0.5898, -0.3908, 1.6748, -0.6924, -0.2594, -1.3313, 0.3873,
- 0.2006],
- [ 0.5891, -0.3557, 1.7326, 0.3392, -0.4210, -0.1227, 0.3238,
- 0.3007],
- [ 0.5115, -0.4332, 1.7557, -0.3152, -0.2536, 0.3931, 0.4139,
- 0.2936],
- [ 0.5718, -0.3905, 1.0053, -1.3305, -0.4614, -1.1235, 0.4450,
- 0.3392]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0146, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0146, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7759437253698707
- step: 48
- running loss: 0.016165494278538972
- Train Steps: 48/90 Loss: 0.0162 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6264, 0.4069, 0.7900, 0.1650, 0.4275, 0.2267, 0.6290, 0.4947],
- [0.6271, 0.4040, 0.9000, 0.3250, 0.4938, 0.2300, 0.7192, 0.5413],
- [0.6275, 0.4048, 0.8488, 0.2883, 0.4463, 0.2033, 0.6321, 0.5155],
- [0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412],
- [0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822],
- [0.6115, 0.3998, 0.7063, 0.2383, 0.4037, 0.1950, 0.5320, 0.4993],
- [0.6175, 0.3997, 0.6737, 0.2500, 0.4313, 0.1933, 0.5513, 0.5750],
- [0.6182, 0.3930, 0.8841, 0.3892, 0.3556, 0.4967, 0.6222, 0.5279]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 4.9754e-01, -5.1794e-01, 1.2568e+00, -1.4171e+00, -3.8776e-01,
- -1.1685e+00, 5.8964e-01, 4.4017e-02],
- [ 2.9641e-01, -6.1759e-01, 1.8639e+00, -7.7632e-01, -1.0200e-01,
- -1.1415e+00, 9.5960e-01, 2.4079e-01],
- [ 6.7223e-01, -3.8713e-01, 1.6328e+00, -9.0681e-01, -2.8939e-01,
- -1.2221e+00, 7.3069e-01, 1.4676e-01],
- [ 5.1888e-01, -4.4076e-01, 1.7888e+00, -4.8815e-01, -5.6462e-01,
- -9.1237e-01, 3.5224e-01, 2.3299e-01],
- [ 3.6893e-01, -5.5036e-01, 1.7233e+00, -2.2765e-01, -7.2718e-01,
- -5.9332e-01, 1.3962e-01, 3.8474e-01],
- [ 6.5524e-01, -3.6731e-01, 9.2200e-01, -1.1836e+00, -5.1188e-01,
- -1.2763e+00, 6.7762e-02, -1.0445e-03],
- [ 2.2790e-01, -6.3644e-01, 6.8836e-01, -1.2796e+00, -3.6872e-01,
- -1.4031e+00, 1.8171e-01, 4.5589e-01],
- [-1.6043e-02, -8.6411e-01, 1.7684e+00, -4.3701e-01, -6.8084e-01,
- 4.7354e-02, 7.2844e-01, 1.9542e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0739e-01, -4.0631e-01, 1.3630e+00, -1.5238e+00, -3.1132e-01,
- -1.2390e+00, 6.1950e-01, -9.2270e-04],
- [ 6.1083e-01, -4.2008e-01, 1.8711e+00, -7.8476e-01, -5.3118e-03,
- -1.2236e+00, 1.0362e+00, 2.1421e-01],
- [ 6.1259e-01, -4.1609e-01, 1.6344e+00, -9.5412e-01, -2.2471e-01,
- -1.3467e+00, 6.3389e-01, 9.5262e-02],
- [ 5.7841e-01, -4.0062e-01, 1.7911e+00, -5.7008e-01, -5.1916e-01,
- -1.0331e+00, 4.1374e-01, 2.1391e-01],
- [ 5.6969e-01, -4.1132e-01, 1.7499e+00, -2.7667e-01, -6.4042e-01,
- -7.1547e-01, 1.5756e-01, 4.0319e-01],
- [ 5.3837e-01, -4.3934e-01, 9.7621e-01, -1.1851e+00, -4.2102e-01,
- -1.3852e+00, 1.7122e-01, 2.0118e-02],
- [ 5.6634e-01, -4.3965e-01, 8.2610e-01, -1.1312e+00, -2.9400e-01,
- -1.3929e+00, 2.6028e-01, 3.6998e-01],
- [ 5.6966e-01, -4.7064e-01, 1.7976e+00, -4.8841e-01, -6.4332e-01,
- 8.0865e-03, 5.8780e-01, 1.5252e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0183, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0183, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.7942405147477984
- step: 49
- running loss: 0.016208990096893847
- Train Steps: 49/90 Loss: 0.0162 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6271, 0.4024, 0.9000, 0.3517, 0.3700, 0.4517, 0.6931, 0.5285],
- [0.6329, 0.4175, 0.8550, 0.2333, 0.5425, 0.2250, 0.7398, 0.5609],
- [0.6058, 0.3978, 0.8287, 0.3600, 0.3400, 0.4050, 0.5529, 0.5461],
- [0.6273, 0.4105, 0.8988, 0.4517, 0.3912, 0.2550, 0.5894, 0.4811],
- [0.6325, 0.4165, 0.9000, 0.4617, 0.3813, 0.4900, 0.7485, 0.5447],
- [0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [0.6128, 0.4118, 0.8638, 0.5333, 0.4625, 0.5267, 0.5193, 0.5475],
- [0.6222, 0.4169, 0.8638, 0.5650, 0.4313, 0.4783, 0.5637, 0.5633]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.3633, -0.6099, 1.7727, -0.7365, -0.6176, -0.1545, 0.9483, 0.1723],
- [ 0.3857, -0.5608, 1.5930, -1.4546, 0.1408, -1.4649, 1.0670, 0.2022],
- [ 0.3727, -0.5318, 1.4499, -0.7004, -0.8048, -0.4178, 0.1934, 0.1654],
- [ 0.6114, -0.3969, 1.8067, -0.3324, -0.5716, -1.1282, 0.2881, -0.0128],
- [ 0.6555, -0.3796, 1.7888, -0.1854, -0.6189, 0.0533, 0.9555, 0.2217],
- [ 0.5391, -0.4041, 1.6146, -0.1128, -0.4412, -1.0803, 0.1859, 0.5246],
- [ 0.6420, -0.3471, 1.5873, 0.1019, -0.2799, 0.1364, -0.0239, 0.1821],
- [ 0.5307, -0.4357, 1.6244, 0.2666, -0.3907, -0.1204, 0.2242, 0.3382]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6108, -0.4273, 1.8711, -0.6616, -0.5769, -0.1997, 0.9156,
- 0.1554],
- [ 0.6372, -0.3575, 1.6633, -1.2082, 0.2199, -1.2467, 1.1313,
- 0.3050],
- [ 0.5124, -0.4483, 1.5420, -0.6231, -0.7155, -0.4152, 0.2679,
- 0.2365],
- [ 0.6116, -0.3898, 1.8654, -0.1997, -0.4788, -1.1081, 0.4367,
- -0.0637],
- [ 0.6355, -0.3623, 1.8711, -0.1535, -0.5249, -0.0226, 1.1715,
- 0.2302],
- [ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762],
- [ 0.5447, -0.3838, 1.7037, 0.1775, -0.1497, 0.1467, 0.1128,
- 0.2431],
- [ 0.5880, -0.3605, 1.7037, 0.3238, -0.2940, -0.0765, 0.3180,
- 0.3161]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0114, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0114, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8056448325514793
- step: 50
- running loss: 0.016112896651029586
- Train Steps: 50/90 Loss: 0.0161 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6296, 0.3989, 0.9000, 0.4150, 0.3613, 0.3867, 0.6400, 0.5100],
- [0.6172, 0.4055, 0.8175, 0.2650, 0.3550, 0.3683, 0.5787, 0.5550],
- [0.6153, 0.4119, 0.8463, 0.3833, 0.3600, 0.3200, 0.5106, 0.5563],
- [0.6127, 0.4084, 0.8700, 0.4467, 0.3987, 0.4317, 0.5013, 0.5471],
- [0.6101, 0.4042, 0.7775, 0.2617, 0.3713, 0.2817, 0.5440, 0.5650],
- [0.6225, 0.4191, 0.8500, 0.4167, 0.3688, 0.3233, 0.5650, 0.5650],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390],
- [0.6132, 0.3930, 0.8672, 0.4754, 0.3712, 0.5222, 0.5974, 0.5098]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.5583e-01, -4.2216e-01, 1.9241e+00, -2.7688e-01, -5.5424e-01,
- -6.0314e-01, 9.0408e-01, 1.5943e-01],
- [ 4.8151e-01, -4.7833e-01, 1.4162e+00, -1.0737e+00, -6.1699e-01,
- -6.1750e-01, 5.0363e-01, 2.4150e-01],
- [ 5.1282e-01, -4.4040e-01, 1.6403e+00, -5.1735e-01, -5.4924e-01,
- -9.2146e-01, 2.4885e-01, 2.8576e-01],
- [ 5.4983e-01, -4.1066e-01, 1.7298e+00, -1.6783e-01, -4.0936e-01,
- -3.1518e-01, 1.2596e-01, 2.7520e-01],
- [ 7.2211e-01, -3.1429e-01, 1.2671e+00, -1.0859e+00, -4.3973e-01,
- -1.0486e+00, 2.4254e-01, 2.3212e-01],
- [ 4.2025e-01, -4.8194e-01, 1.6345e+00, -3.9314e-01, -5.3136e-01,
- -8.7824e-01, 4.3722e-01, 4.3657e-01],
- [ 5.2567e-01, -4.8861e-01, 1.7370e+00, -2.4383e-02, -3.7794e-01,
- 1.3738e-02, 3.3758e-01, 1.4508e-01],
- [ 6.6609e-01, -3.9081e-01, 1.7458e+00, 3.7987e-04, -4.4658e-01,
- 1.1860e-01, 6.7305e-01, 1.6379e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6220, -0.4436, 1.8711, -0.3691, -0.6173, -0.4999, 0.6702,
- 0.0697],
- [ 0.5647, -0.4129, 1.4901, -1.0619, -0.6462, -0.5846, 0.3873,
- 0.2776],
- [ 0.5561, -0.3834, 1.6229, -0.5153, -0.6231, -0.8079, 0.0727,
- 0.2837],
- [ 0.5441, -0.3997, 1.7326, -0.2228, -0.4441, -0.2921, 0.0296,
- 0.2409],
- [ 0.5320, -0.4189, 1.3053, -1.0773, -0.5711, -0.9849, 0.2267,
- 0.3237],
- [ 0.5894, -0.3503, 1.6402, -0.3614, -0.5827, -0.7925, 0.3238,
- 0.3238],
- [ 0.5250, -0.4661, 1.7383, -0.0765, -0.4268, -0.0226, 0.2535,
- 0.2035],
- [ 0.5466, -0.4706, 1.7198, -0.0903, -0.5712, 0.1261, 0.4733,
- 0.0688]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0073, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0073, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8129367139190435
- step: 51
- running loss: 0.01593993556704007
- Train Steps: 51/90 Loss: 0.0159 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6278, 0.4253, 0.8875, 0.5017, 0.4113, 0.2750, 0.5413, 0.6196],
- [0.6219, 0.4114, 0.8175, 0.2817, 0.3925, 0.2783, 0.5900, 0.5350],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [ nan, nan, 0.9050, 0.3500, 0.5138, 0.2300, 0.7359, 0.5702],
- [0.6219, 0.4097, 0.8738, 0.3400, 0.3563, 0.4117, 0.5975, 0.5683],
- [0.6222, 0.3937, 0.8350, 0.5617, 0.4138, 0.4600, 0.5800, 0.5233],
- [0.6086, 0.3998, 0.8788, 0.4450, 0.4025, 0.4650, 0.5306, 0.5103],
- [0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6138, -0.3162, 1.6757, -0.0385, -0.3359, -1.0177, 0.2893, 0.4970],
- [ 0.3506, -0.4950, 1.3670, -1.0617, -0.4420, -0.9203, 0.3976, 0.2195],
- [ 0.6430, -0.3678, 1.8338, -0.3944, -0.5642, -0.1219, 0.7280, 0.2425],
- [-2.2223, -2.2294, 1.5857, -0.9523, 0.0569, -1.1593, 0.6875, 0.2897],
- [ 0.6182, -0.3403, 1.7174, -0.7481, -0.6678, -0.3971, 0.4993, 0.2399],
- [ 0.5614, -0.4385, 1.5351, 0.2646, -0.4268, -0.1418, 0.4188, 0.1910],
- [ 0.5111, -0.4241, 1.6742, -0.2531, -0.4758, -0.0282, 0.2072, 0.1283],
- [ 0.6298, -0.3722, 1.3168, -1.1760, -0.2614, -1.3129, 0.4119, 0.1058]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6139, -0.3216, 1.8134, 0.0313, -0.3864, -1.0157, 0.2144,
- 0.5762],
- [ 0.5868, -0.3858, 1.4901, -0.9849, -0.4730, -1.0003, 0.4393,
- 0.1852],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [-2.2859, -2.2859, 1.8942, -0.6693, 0.0871, -1.2236, 1.1130,
- 0.3478],
- [ 0.5867, -0.3937, 1.7499, -0.7155, -0.6404, -0.3844, 0.4739,
- 0.3392],
- [ 0.5880, -0.4676, 1.5709, 0.3084, -0.3748, -0.1612, 0.3931,
- 0.1313],
- [ 0.5253, -0.4392, 1.7730, -0.2305, -0.4268, -0.1381, 0.1651,
- 0.0712],
- [ 0.5796, -0.4326, 1.4439, -1.1774, -0.2940, -1.3390, 0.3931,
- 0.0928]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0099, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0099, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8228131141513586
- step: 52
- running loss: 0.015823329118295357
- Train Steps: 52/90 Loss: 0.0158 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6229, 0.4066, 0.8513, 0.5717, 0.4500, 0.5050, 0.6025, 0.5350],
- [0.6164, 0.4119, 0.7913, 0.2650, 0.3538, 0.3500, 0.5614, 0.5038],
- [0.6082, 0.4024, 0.8738, 0.4017, 0.3688, 0.3950, 0.5306, 0.5136],
- [ nan, nan, 0.7612, 0.3250, 0.4037, 0.2533, 0.5438, 0.5767],
- [0.6160, 0.4086, 0.8775, 0.3983, 0.3750, 0.3783, 0.5128, 0.4882],
- [0.6284, 0.4029, 0.8838, 0.3783, 0.3975, 0.2850, 0.6335, 0.5090],
- [0.6145, 0.3983, 0.8700, 0.5017, 0.4400, 0.4800, 0.5375, 0.5750],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6219, -0.3752, 1.6652, 0.2994, -0.1866, 0.0215, 0.3743, 0.2948],
- [ 0.7644, -0.2511, 1.4535, -1.0984, -0.6169, -0.6842, 0.4383, 0.1393],
- [ 0.5370, -0.4199, 1.7934, -0.4256, -0.5711, -0.4423, 0.2916, 0.1527],
- [-1.5907, -1.8032, 1.1504, -1.0188, -0.3355, -1.0750, 0.1658, 0.3443],
- [ 0.7614, -0.2678, 1.8735, -0.4168, -0.5463, -0.5598, 0.2533, 0.0554],
- [ 0.7204, -0.3163, 1.9029, -0.5729, -0.3919, -1.0855, 0.6899, 0.2262],
- [ 0.5421, -0.4263, 1.7555, -0.0450, -0.2282, -0.0415, 0.2501, 0.3798],
- [ 0.6803, -0.3359, 1.5785, 0.1547, -0.4745, -0.0506, 0.9300, 0.3099]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5911, -0.4080, 1.6460, 0.3546, -0.2074, 0.0467, 0.4970,
- 0.1852],
- [ 0.5614, -0.3834, 1.3688, -1.0619, -0.6520, -0.6693, 0.3069,
- 0.0412],
- [ 0.5235, -0.4273, 1.7499, -0.4306, -0.5827, -0.4614, 0.1651,
- 0.0862],
- [-2.2859, -2.2859, 1.2303, -0.7848, -0.4210, -1.1158, 0.2256,
- 0.3777],
- [ 0.5595, -0.3988, 1.7672, -0.4460, -0.5538, -0.5384, 0.0828,
- -0.0310],
- [ 0.6165, -0.4249, 1.7961, -0.5384, -0.4499, -0.9695, 0.6401,
- 0.0652],
- [ 0.5525, -0.4463, 1.7326, 0.0313, -0.2536, -0.0688, 0.1968,
- 0.3700],
- [ 0.6191, -0.4297, 1.4612, 0.2391, -0.4961, 0.0313, 1.1166,
- 0.1768]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0193, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0193, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8421134296804667
- step: 53
- running loss: 0.015888932635480503
- Train Steps: 53/90 Loss: 0.0159 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6115, 0.4081, 0.6725, 0.2433, 0.4088, 0.1933, 0.5167, 0.5544],
- [0.6204, 0.4091, 0.8950, 0.4783, 0.3613, 0.4617, 0.6063, 0.5617],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767],
- [0.6106, 0.4022, 0.7125, 0.2650, 0.3713, 0.2700, 0.5431, 0.5123],
- [0.6150, 0.3935, 0.8696, 0.5158, 0.4647, 0.5329, 0.6041, 0.5153],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6222, 0.4169, 0.8638, 0.5650, 0.4313, 0.4783, 0.5637, 0.5633],
- [0.6277, 0.4103, 0.8087, 0.5717, 0.4188, 0.4750, 0.5663, 0.6083]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6673, -0.3268, 1.1403, -1.1721, -0.3743, -1.4599, 0.3026, 0.2220],
- [ 0.6780, -0.3399, 2.0108, -0.0608, -0.5956, -0.1336, 0.5727, 0.2934],
- [ 0.3732, -0.5323, 1.4563, -1.1377, -0.4467, -0.9829, 0.7052, 0.2905],
- [ 0.6293, -0.3675, 1.2879, -1.0773, -0.5225, -1.0735, 0.2881, 0.0723],
- [ 0.4728, -0.4933, 1.8535, 0.0456, -0.1775, 0.2102, 0.4344, 0.1276],
- [-2.0240, -2.1459, 0.9359, -1.3552, -0.3156, -1.4738, 0.2332, 0.2545],
- [ 0.6209, -0.3482, 1.8802, 0.2570, -0.3235, -0.0597, 0.2850, 0.2831],
- [ 0.6233, -0.3547, 1.7081, 0.2817, -0.3935, -0.0655, 0.3343, 0.4167]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5385, -0.4010, 0.8203, -1.1620, -0.3979, -1.3929, 0.1005,
- 0.2747],
- [ 0.5797, -0.3965, 1.8480, -0.0765, -0.6173, -0.1535, 0.5143,
- 0.3084],
- [ 0.5915, -0.3682, 1.2187, -1.2313, -0.4326, -0.9541, 0.5778,
- 0.3777],
- [ 0.5342, -0.4280, 1.0051, -1.0619, -0.5711, -1.0388, 0.2226,
- 0.0802],
- [ 0.5548, -0.4682, 1.7309, 0.0966, -0.1394, 0.1757, 0.5045,
- 0.0942],
- [-2.2859, -2.2859, 0.6760, -1.4083, -0.3286, -1.4160, 0.2487,
- 0.3469],
- [ 0.5880, -0.3605, 1.7037, 0.3238, -0.2940, -0.0765, 0.3180,
- 0.3161],
- [ 0.6134, -0.3910, 1.4497, 0.3546, -0.3517, -0.0919, 0.3296,
- 0.5239]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0127, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0127, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8548482032492757
- step: 54
- running loss: 0.015830522282393993
- Train Steps: 54/90 Loss: 0.0158 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.3993, 0.8519, 0.4923, 0.3962, 0.4717, 0.6013, 0.5433],
- [0.6274, 0.4270, 0.8938, 0.4967, 0.3550, 0.4283, 0.5700, 0.5733],
- [ nan, nan, 0.6688, 0.2513, 0.4113, 0.2117, 0.5193, 0.5933],
- [ nan, nan, 0.7725, 0.2611, 0.3675, 0.2733, 0.5413, 0.5167],
- [0.6182, 0.3972, 0.8720, 0.5527, 0.3638, 0.3582, 0.5696, 0.5395],
- [0.6140, 0.4034, 0.8850, 0.4317, 0.4288, 0.5067, 0.5825, 0.5533],
- [0.6097, 0.3988, 0.8650, 0.5250, 0.4213, 0.5200, 0.5675, 0.5050],
- [0.6126, 0.4067, 0.8638, 0.5383, 0.4188, 0.4850, 0.5016, 0.5392]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7359, -0.2978, 1.7785, -0.0222, -0.3996, -0.1070, 0.5864, 0.2958],
- [ 0.7449, -0.2164, 1.9379, -0.0315, -0.5511, -0.3084, 0.5034, 0.3601],
- [-1.3691, -1.6667, 0.8612, -1.2613, -0.3751, -1.3698, 0.2615, 0.4043],
- [-1.9919, -2.1095, 1.2635, -1.0819, -0.4544, -1.0682, 0.2091, 0.1249],
- [ 0.7430, -0.2650, 1.8531, 0.1796, -0.5756, -0.6459, 0.5336, 0.1851],
- [ 0.8209, -0.2462, 1.8957, -0.2638, -0.2654, 0.1714, 0.5637, 0.2248],
- [ 0.7045, -0.3207, 1.8143, 0.1388, -0.2615, 0.2025, 0.2661, 0.0781],
- [ 0.8117, -0.2041, 1.7769, 0.2153, -0.3169, 0.0384, 0.1559, 0.1889]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5777, -0.4415, 1.6491, -0.0122, -0.4557, -0.1073, 0.4912,
- 0.2237],
- [ 0.6121, -0.3138, 1.8423, 0.0082, -0.6462, -0.3075, 0.3469,
- 0.3623],
- [-2.2859, -2.2859, 0.8033, -1.1250, -0.3864, -1.3082, 0.1126,
- 0.4543],
- [-2.2859, -2.2859, 1.2820, -1.0801, -0.5885, -1.0234, 0.2141,
- 0.1005],
- [ 0.5697, -0.4514, 1.7420, 0.2672, -0.6055, -0.6312, 0.3449,
- 0.2058],
- [ 0.5499, -0.4225, 1.8018, -0.2921, -0.3055, 0.0543, 0.4046,
- 0.2699],
- [ 0.5303, -0.4440, 1.7095, 0.1390, -0.3402, 0.1159, 0.3353,
- 0.0467],
- [ 0.5436, -0.4076, 1.7037, 0.2006, -0.3517, -0.0457, 0.0311,
- 0.2048]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0322, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0322, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.8870698222890496
- step: 55
- running loss: 0.016128542223437264
- Train Steps: 55/90 Loss: 0.0161 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6204, 0.4049, 0.7975, 0.2700, 0.3937, 0.2567, 0.5700, 0.5183],
- [0.6279, 0.4050, 0.8025, 0.2200, 0.4313, 0.2733, 0.6820, 0.5320],
- [0.6142, 0.3982, 0.8650, 0.4883, 0.3912, 0.4317, 0.5315, 0.5350],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6205, 0.4081, 0.8950, 0.4017, 0.3788, 0.4700, 0.5963, 0.5667],
- [0.6201, 0.4064, 0.8688, 0.5050, 0.4225, 0.5100, 0.6138, 0.5500],
- [ nan, nan, 0.7850, 0.2700, 0.4288, 0.1717, 0.5199, 0.4999],
- [ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6181, -0.3578, 1.4846, -0.8818, -0.5126, -1.1168, 0.2677, 0.1355],
- [ 0.7924, -0.2418, 1.4436, -1.0710, -0.3385, -1.0589, 0.8068, 0.2151],
- [ 0.7669, -0.2631, 1.7523, 0.1436, -0.4913, -0.1816, 0.1077, 0.1780],
- [ 0.7052, -0.3087, 1.6060, 0.0755, -0.2882, 0.1245, 0.3166, 0.2797],
- [ 0.8958, -0.1547, 1.7925, -0.2482, -0.5676, -0.0407, 0.4489, 0.3177],
- [ 0.8621, -0.2202, 1.7208, 0.1235, -0.3645, 0.0783, 0.3573, 0.2843],
- [-1.8809, -1.9708, 1.1639, -1.0491, -0.3869, -1.3121, 0.1169, 0.1347],
- [-2.1162, -2.1510, 1.5890, -1.0452, 0.0364, -1.0503, 0.8505, 0.3124]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7985e-01, -4.1555e-01, 1.3977e+00, -1.0388e+00, -4.6721e-01,
- -1.1004e+00, 3.4688e-01, 1.0824e-01],
- [ 6.1413e-01, -4.1527e-01, 1.4208e+00, -1.2697e+00, -2.9400e-01,
- -1.0234e+00, 8.6439e-01, 1.7146e-01],
- [ 5.5127e-01, -4.4673e-01, 1.7095e+00, -3.0331e-02, -4.7875e-01,
- -2.9207e-01, 1.6917e-01, 1.8544e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 5.7991e-01, -4.0115e-01, 1.8480e+00, -4.3064e-01, -5.3649e-01,
- -1.1501e-01, 4.6813e-01, 3.3149e-01],
- [ 5.7841e-01, -4.0878e-01, 1.7268e+00, 4.6651e-02, -3.3441e-01,
- 6.9746e-02, 5.4896e-01, 2.5450e-01],
- [-2.2859e+00, -2.2859e+00, 1.3400e+00, -1.0388e+00, -3.0554e-01,
- -1.4930e+00, 1.1570e-01, 2.3124e-02],
- [-2.2859e+00, -2.2859e+00, 1.7557e+00, -1.1466e+00, 8.7067e-02,
- -1.0773e+00, 1.1239e+00, 2.7833e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0196, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0196, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9066562773659825
- step: 56
- running loss: 0.01619029066724969
- Train Steps: 56/90 Loss: 0.0162 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6339, 0.4123, 0.8638, 0.5367, 0.4075, 0.5467, 0.7517, 0.5436],
- [0.6214, 0.4040, 0.8838, 0.3500, 0.3600, 0.5183, 0.6362, 0.5200],
- [0.6224, 0.4061, 0.8988, 0.4300, 0.3838, 0.4750, 0.6112, 0.5483],
- [0.6239, 0.4107, 0.8162, 0.2763, 0.3625, 0.3600, 0.5988, 0.5700],
- [0.6102, 0.4005, 0.8688, 0.5100, 0.4813, 0.5400, 0.5404, 0.5064],
- [0.6160, 0.4086, 0.8775, 0.3983, 0.3750, 0.3783, 0.5128, 0.4882],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600],
- [0.6204, 0.4049, 0.7975, 0.2700, 0.3937, 0.2567, 0.5700, 0.5183]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6829, -0.3401, 1.7066, 0.2011, -0.3595, 0.1086, 0.9120, 0.4105],
- [ 0.8049, -0.2839, 1.7544, -0.5074, -0.5299, 0.0459, 0.5987, 0.2734],
- [ 0.5709, -0.4311, 1.8545, -0.0998, -0.4727, -0.0623, 0.4352, 0.2658],
- [ 0.4772, -0.4543, 1.5151, -0.7104, -0.5211, -0.7383, 0.4856, 0.3452],
- [ 0.5559, -0.4322, 1.7555, 0.1990, -0.0483, 0.0407, 0.1077, 0.1094],
- [ 0.5763, -0.4048, 1.8463, -0.2421, -0.5570, -0.6607, 0.1426, 0.0537],
- [ 0.6002, -0.3517, 1.1491, -0.9855, -0.5656, -0.8767, 0.4835, 0.3959],
- [ 0.1484, -0.6880, 1.5266, -0.8485, -0.4765, -1.2423, 0.2979, 0.1321]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6421, -0.3816, 1.7037, 0.1929, -0.4037, 0.2391, 1.1861,
- 0.2249],
- [ 0.5841, -0.4199, 1.7961, -0.6693, -0.6231, 0.1082, 0.6529,
- 0.1159],
- [ 0.5888, -0.4101, 1.8654, -0.2998, -0.5134, -0.0919, 0.5374,
- 0.2468],
- [ 0.5960, -0.3888, 1.4840, -1.0095, -0.6115, -0.6231, 0.4797,
- 0.3469],
- [ 0.5328, -0.4361, 1.7268, 0.0697, -0.0630, 0.2083, 0.2103,
- 0.0532],
- [ 0.5595, -0.3988, 1.7672, -0.4460, -0.5538, -0.5384, 0.0828,
- -0.0310],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007],
- [ 0.5798, -0.4156, 1.3977, -1.0388, -0.4672, -1.1004, 0.3469,
- 0.1082]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0156, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0156, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9222497092559934
- step: 57
- running loss: 0.01617981946063146
- Train Steps: 57/90 Loss: 0.0162 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6204, 0.4013, 0.8075, 0.2400, 0.4313, 0.2050, 0.5800, 0.5150],
- [0.6263, 0.4039, 0.9000, 0.4400, 0.3625, 0.4667, 0.6424, 0.4804],
- [0.6189, 0.4029, 0.8375, 0.5767, 0.4745, 0.4829, 0.5551, 0.5598],
- [0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6040, 0.4002, 0.7338, 0.2267, 0.3975, 0.2100, 0.5231, 0.4778],
- [0.6197, 0.4051, 0.7812, 0.2650, 0.3513, 0.4050, 0.6112, 0.5500],
- [0.6216, 0.4099, 0.7225, 0.2033, 0.4188, 0.2217, 0.5975, 0.5283],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4680, -0.5072, 1.4882, -0.9610, -0.2499, -1.2891, 0.5355, 0.2191],
- [ 0.7412, -0.3457, 1.8500, -0.2045, -0.5993, -0.0566, 0.4874, 0.0658],
- [ 0.4956, -0.4700, 1.5806, 0.3873, -0.1192, -0.0546, 0.2357, 0.3602],
- [-0.0531, -0.8100, 1.7826, -0.8371, -0.3113, -1.0016, 0.6913, 0.2936],
- [ 0.5888, -0.4092, 1.1607, -1.1879, -0.4697, -1.2295, 0.2869, 0.0801],
- [ 0.7139, -0.3403, 1.3548, -0.9276, -0.6617, -0.3502, 0.5011, 0.3473],
- [ 0.2308, -0.6215, 1.1084, -1.1688, -0.3470, -1.2640, 0.4619, 0.3078],
- [ 0.5495, -0.4039, 1.7657, -0.5195, -0.6240, -0.7107, 0.2321, 0.2657]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5796, -0.4326, 1.4439, -1.1774, -0.2940, -1.3390, 0.3931,
- 0.0928],
- [ 0.6072, -0.4206, 1.8711, -0.2536, -0.6115, -0.1304, 0.6812,
- -0.0670],
- [ 0.5726, -0.4249, 1.5824, 0.3777, -0.0942, -0.0556, 0.2782,
- 0.2997],
- [ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [ 0.5037, -0.4375, 1.1032, -1.2390, -0.4499, -1.3159, 0.1301,
- -0.0791],
- [ 0.5763, -0.4147, 1.3226, -1.0619, -0.6635, -0.4152, 0.5374,
- 0.2545],
- [ 0.5850, -0.3925, 1.0513, -1.3467, -0.3517, -1.2620, 0.4739,
- 0.1544],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0198, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0198, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9420179659500718
- step: 58
- running loss: 0.016241689068104685
- Train Steps: 58/90 Loss: 0.0162 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6193, 0.3930, 0.8949, 0.4437, 0.3852, 0.5435, 0.6263, 0.5263],
- [0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [0.6126, 0.4039, 0.8237, 0.3967, 0.3625, 0.3600, 0.5894, 0.6138],
- [ nan, nan, 0.6412, 0.1900, 0.4238, 0.1883, 0.5487, 0.5700],
- [0.6199, 0.3952, 0.8573, 0.5374, 0.4075, 0.4687, 0.5942, 0.5377],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783],
- [0.6204, 0.4091, 0.8950, 0.4783, 0.3613, 0.4617, 0.6063, 0.5617]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5118, -0.4744, 1.7803, -0.3263, -0.4744, 0.1388, 0.4503, 0.0420],
- [ 0.4170, -0.4748, 1.6589, 0.1730, -0.5230, -0.5177, 0.1883, 0.3262],
- [ 0.4429, -0.4836, 1.5923, -0.5252, -0.5800, -0.5985, 0.3619, 0.4124],
- [-2.6916, -2.5599, 0.9813, -1.2277, -0.3173, -1.4307, 0.2689, 0.2390],
- [ 0.5146, -0.5080, 1.7030, 0.0749, -0.3763, -0.1399, 0.4489, 0.0463],
- [ 0.4605, -0.4835, 1.7907, -0.1342, -0.1387, 0.3505, 0.4464, 0.1572],
- [ 0.4982, -0.4772, 0.9685, -1.1748, -0.4223, -1.1938, 0.3804, 0.2206],
- [ 0.5921, -0.4157, 1.8043, -0.1693, -0.5291, -0.1697, 0.5353, 0.2092]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5746, -0.4706, 1.8476, -0.2365, -0.5068, 0.2245, 0.6069,
- 0.1449],
- [ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778],
- [ 0.5438, -0.4206, 1.5189, -0.4537, -0.6115, -0.6231, 0.4365,
- 0.5491],
- [-2.2859, -2.2859, 0.6760, -1.4083, -0.3286, -1.4160, 0.2487,
- 0.3469],
- [ 0.5775, -0.4607, 1.6741, 0.1962, -0.4036, -0.1212, 0.4588,
- 0.1979],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.5483, -0.3991, 0.8030, -1.2159, -0.5018, -1.1928, 0.2624,
- 0.3852],
- [ 0.5797, -0.3965, 1.8480, -0.0765, -0.6173, -0.1535, 0.5143,
- 0.3084]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0128, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0128, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.95481295324862
- step: 59
- running loss: 0.016183270394044408
- Train Steps: 59/90 Loss: 0.0162 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6222, 0.4172, 0.8850, 0.5217, 0.3738, 0.4600, 0.5700, 0.5633],
- [0.6266, 0.4070, 0.8712, 0.5600, 0.3713, 0.4783, 0.5775, 0.6100],
- [0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719],
- [0.6307, 0.4060, 0.8950, 0.5183, 0.3750, 0.3850, 0.6338, 0.4938],
- [0.6084, 0.3981, 0.8588, 0.5233, 0.4600, 0.5367, 0.5680, 0.5006],
- [0.6178, 0.4059, 0.7525, 0.2250, 0.4313, 0.1783, 0.5404, 0.5240],
- [0.6134, 0.4090, 0.6926, 0.2819, 0.3538, 0.3233, 0.5563, 0.5667]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6028, -0.3939, 1.3621, -0.7628, -0.6650, -0.5480, 0.2328, 0.2807],
- [ 0.6330, -0.3729, 1.8560, -0.0090, -0.5082, -0.1894, 0.3534, 0.3002],
- [ 0.6191, -0.4331, 1.6896, 0.1688, -0.5121, -0.0415, 0.5741, 0.4011],
- [ 0.1725, -0.7107, 1.6695, 0.1222, -0.4347, 0.0672, 0.2956, 0.1270],
- [ 0.5586, -0.4837, 1.9110, -0.0396, -0.5220, -0.5785, 0.6986, -0.0429],
- [ 0.4333, -0.5479, 1.6927, 0.0067, -0.1288, 0.1918, 0.2718, 0.0897],
- [ 0.2199, -0.6341, 1.2996, -1.2787, -0.2926, -1.4296, 0.4385, 0.1184],
- [ 0.6418, -0.3789, 1.0555, -1.0573, -0.6544, -0.7649, 0.3476, 0.3257]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.5879, -0.3591, 1.8018, 0.1236, -0.5596, -0.1612, 0.3469,
- 0.3161],
- [ 0.6081, -0.4059, 1.7383, 0.3007, -0.5711, -0.0765, 0.3815,
- 0.5316],
- [ 0.5079, -0.4714, 1.6575, 0.2237, -0.4961, 0.0774, 0.1465,
- -0.1061],
- [ 0.6273, -0.4105, 1.8480, 0.1082, -0.5538, -0.5076, 0.6414,
- -0.0049],
- [ 0.5241, -0.4470, 1.6806, 0.1313, -0.1612, 0.1929, 0.3378,
- 0.0261],
- [ 0.5676, -0.4112, 1.1898, -1.2467, -0.2940, -1.4622, 0.2103,
- 0.1343],
- [ 0.5473, -0.3966, 0.9131, -0.9838, -0.6520, -0.7925, 0.2834,
- 0.3315]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0120, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0120, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9668285604566336
- step: 60
- running loss: 0.016113809340943894
- Train Steps: 60/90 Loss: 0.0161 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6189, 0.3961, 0.7589, 0.2256, 0.3759, 0.3280, 0.6184, 0.5334],
- [0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6364, 0.4092, 0.8525, 0.5633, 0.3875, 0.4950, 0.6599, 0.5285],
- [0.6212, 0.4159, 0.8675, 0.5783, 0.4088, 0.4317, 0.5613, 0.5917],
- [0.6122, 0.3993, 0.8738, 0.4667, 0.4517, 0.4879, 0.5155, 0.4927],
- [0.6275, 0.4003, 0.9100, 0.3783, 0.4388, 0.3133, 0.7058, 0.5343],
- [0.6124, 0.4083, 0.6954, 0.3069, 0.3650, 0.2750, 0.5163, 0.5485],
- [0.6307, 0.3998, 0.8500, 0.5233, 0.3850, 0.5050, 0.7446, 0.5320]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5016, -0.4703, 1.2278, -1.2672, -0.5932, -0.8244, 0.4420, 0.1547],
- [ 0.2497, -0.6129, 1.6548, -0.4681, -0.5996, -0.8265, -0.0047, 0.1981],
- [ 0.5524, -0.4908, 1.6442, 0.1523, -0.4874, 0.0100, 0.6921, 0.1528],
- [ 0.4261, -0.4861, 1.6044, 0.1678, -0.3794, -0.2528, 0.2993, 0.4646],
- [ 0.5280, -0.4921, 1.7713, -0.2128, -0.2425, 0.0598, 0.1536, 0.0179],
- [ 0.4659, -0.5584, 1.8527, -0.6520, -0.3224, -0.8461, 0.8426, 0.0868],
- [ 0.4742, -0.4619, 0.9765, -0.9842, -0.6332, -0.9425, 0.0351, 0.2549],
- [ 0.5845, -0.4672, 1.6357, 0.0128, -0.4951, 0.0762, 0.8753, 0.1771]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 5.7296e-01, -4.5619e-01, 1.2195e+00, -1.2440e+00, -5.4966e-01,
- -7.7109e-01, 5.7045e-01, 1.7788e-01],
- [ 5.4700e-01, -3.9515e-01, 1.6377e+00, -4.2531e-01, -6.2887e-01,
- -8.0785e-01, 2.4925e-02, 2.1157e-01],
- [ 6.5365e-01, -3.9601e-01, 1.6517e+00, 3.1609e-01, -4.9607e-01,
- 4.6189e-04, 7.6203e-01, 1.5543e-01],
- [ 5.8360e-01, -3.6490e-01, 1.7210e+00, 3.8537e-01, -3.9792e-01,
- -2.9207e-01, 3.0647e-01, 4.4696e-01],
- [ 5.4166e-01, -4.4175e-01, 1.7499e+00, -1.3041e-01, -1.9942e-01,
- -3.2367e-02, 9.5140e-02, -9.9401e-03],
- [ 6.1248e-01, -4.3693e-01, 1.9173e+00, -5.3841e-01, -2.5935e-01,
- -8.3865e-01, 9.7406e-01, 1.8214e-01],
- [ 5.4249e-01, -3.9977e-01, 9.2628e-01, -8.6826e-01, -6.0000e-01,
- -1.0157e+00, 9.8951e-02, 2.4764e-01],
- [ 6.2730e-01, -4.3934e-01, 1.6402e+00, 1.3133e-01, -5.0762e-01,
- 4.6651e-02, 1.1532e+00, 1.7146e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.9755781814455986
- step: 61
- running loss: 0.015993084941731125
- Train Steps: 61/90 Loss: 0.0160 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6079, 0.3964, 0.7420, 0.2958, 0.3563, 0.2917, 0.5351, 0.4980],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679],
- [0.6192, 0.4128, 0.8513, 0.5617, 0.4200, 0.5267, 0.5850, 0.5633],
- [0.6182, 0.3972, 0.8552, 0.5914, 0.3683, 0.4181, 0.5688, 0.5378],
- [0.6111, 0.4019, 0.8350, 0.3867, 0.3500, 0.4283, 0.5480, 0.5719],
- [0.6219, 0.4089, 0.8938, 0.4800, 0.3825, 0.4450, 0.5850, 0.5200],
- [0.6164, 0.3972, 0.7818, 0.3381, 0.3599, 0.3387, 0.5880, 0.5153],
- [0.6147, 0.4081, 0.8538, 0.3400, 0.3663, 0.3150, 0.5142, 0.4875]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4429, -0.5028, 1.0489, -1.0283, -0.5719, -0.9001, 0.1954, 0.0527],
- [-2.7236, -2.5519, 1.5574, -1.1088, 0.1584, -1.0457, 1.0681, 0.3034],
- [ 0.5035, -0.4616, 1.5655, 0.1601, -0.3356, 0.1247, 0.5148, 0.2402],
- [ 0.3959, -0.5453, 1.4629, 0.1522, -0.5350, -0.4185, 0.3992, 0.2318],
- [ 0.3227, -0.5639, 1.4088, -0.5852, -0.6078, -0.3931, 0.1804, 0.2821],
- [ 0.5485, -0.4297, 1.6632, -0.2612, -0.4895, -0.2028, 0.5109, 0.1559],
- [ 0.5043, -0.4713, 1.2385, -0.8179, -0.6154, -0.6846, 0.3148, 0.1545],
- [ 0.5604, -0.4319, 1.6134, -0.8001, -0.5624, -0.7814, 0.1643, 0.0606]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5219, -0.4550, 1.1415, -0.9196, -0.6404, -0.9387, 0.1856,
- 0.0141],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371],
- [ 0.5742, -0.3792, 1.6460, 0.3084, -0.3460, 0.1467, 0.4162,
- 0.3161],
- [ 0.5697, -0.4514, 1.6642, 0.4459, -0.5850, -0.3546, 0.3414,
- 0.1982],
- [ 0.5367, -0.4294, 1.5709, -0.4999, -0.6693, -0.3075, 0.2455,
- 0.3559],
- [ 0.5865, -0.3973, 1.8423, -0.0688, -0.5192, -0.2305, 0.4162,
- 0.1159],
- [ 0.5614, -0.4514, 1.3252, -0.7240, -0.6237, -0.7215, 0.4299,
- 0.0942],
- [ 0.5532, -0.4008, 1.6575, -0.7155, -0.5942, -0.8309, 0.0890,
- -0.0340]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0143, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0143, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 0.989847207441926
- step: 62
- running loss: 0.015965277539385904
- Train Steps: 62/90 Loss: 0.0160 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6114, 0.4018, 0.7213, 0.1967, 0.3763, 0.2700, 0.5875, 0.5533],
- [0.6225, 0.4116, 0.8662, 0.3517, 0.3663, 0.3233, 0.5837, 0.5317],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413],
- [0.6332, 0.4165, 0.9100, 0.3350, 0.4188, 0.3683, 0.7438, 0.5528],
- [ nan, nan, 0.8675, 0.2833, 0.5350, 0.1983, 0.6678, 0.5621],
- [0.6200, 0.4118, 0.8287, 0.4017, 0.3775, 0.2833, 0.5391, 0.5799],
- [0.6261, 0.4045, 0.8865, 0.5369, 0.3895, 0.4859, 0.6683, 0.5249],
- [0.6198, 0.4075, 0.8650, 0.5617, 0.4150, 0.4367, 0.5450, 0.5650]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.8423e-01, -4.0921e-01, 8.4051e-01, -1.3952e+00, -5.2819e-01,
- -9.3486e-01, 2.4505e-01, 2.1930e-01],
- [ 6.5671e-01, -3.4788e-01, 1.5112e+00, -7.5554e-01, -6.2020e-01,
- -6.4918e-01, 2.0874e-01, 1.8594e-01],
- [ 4.3317e-01, -5.3625e-01, 1.7903e+00, -5.5146e-01, -1.9873e-01,
- -9.7180e-01, 7.4523e-01, 9.3876e-02],
- [ 2.9301e-01, -5.9894e-01, 1.7694e+00, -6.2905e-01, -4.0333e-01,
- -5.2115e-01, 8.2788e-01, 2.0718e-01],
- [-2.7556e+00, -2.5627e+00, 1.4485e+00, -1.1333e+00, -9.4608e-04,
- -1.1536e+00, 7.4314e-01, 2.3110e-01],
- [ 3.9862e-01, -4.9696e-01, 1.3710e+00, -4.8671e-01, -5.6165e-01,
- -8.6880e-01, 1.3935e-02, 3.0212e-01],
- [ 6.2111e-01, -4.1407e-01, 1.5811e+00, 4.3570e-02, -5.5322e-01,
- 2.0704e-02, 6.7610e-01, 4.8456e-02],
- [ 3.0005e-01, -5.6617e-01, 1.4875e+00, 2.0248e-01, -4.3813e-01,
- -2.1301e-01, 2.2350e-01, 2.4070e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5380, -0.4302, 1.0455, -1.3775, -0.5480, -1.0388, 0.4277,
- 0.2699],
- [ 0.5893, -0.3847, 1.7152, -0.6616, -0.5942, -0.7925, 0.4104,
- 0.1698],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142],
- [ 0.6388, -0.3623, 1.9173, -0.7386, -0.3517, -0.5846, 1.1495,
- 0.2676],
- [-2.2859, -2.2859, 1.7210, -0.9772, 0.1852, -1.3698, 0.7986,
- 0.3104],
- [ 0.5779, -0.3840, 1.5420, -0.4306, -0.5423, -0.9772, 0.2041,
- 0.3928],
- [ 0.6059, -0.4177, 1.8087, 0.1941, -0.4868, -0.0414, 0.8010,
- 0.1385],
- [ 0.5770, -0.4036, 1.7095, 0.3084, -0.3691, -0.2690, 0.2314,
- 0.3238]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0255, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0255, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.015299854800105
- step: 63
- running loss: 0.01611587071111278
- Train Steps: 63/90 Loss: 0.0161 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6086, 0.4019, 0.7782, 0.4278, 0.3625, 0.4350, 0.5150, 0.5285],
- [0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097],
- [0.6182, 0.3987, 0.7878, 0.2889, 0.3699, 0.3260, 0.6086, 0.5367],
- [0.6095, 0.3970, 0.8688, 0.4767, 0.4860, 0.4879, 0.5191, 0.4940],
- [0.6170, 0.4102, 0.7468, 0.3695, 0.3463, 0.3767, 0.5238, 0.5823],
- [0.6296, 0.4008, 0.9150, 0.4317, 0.4263, 0.3050, 0.7256, 0.5413],
- [0.6266, 0.4067, 0.8588, 0.2867, 0.4300, 0.2850, 0.6325, 0.5267],
- [0.6289, 0.4019, 0.8113, 0.5467, 0.3875, 0.5017, 0.7367, 0.5332]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4983, -0.4283, 1.2735, -0.4185, -0.6757, -0.3920, -0.0064, 0.1818],
- [ 0.4463, -0.4745, 1.7635, -0.0741, -0.2198, 0.0709, 0.3045, 0.0067],
- [ 0.4358, -0.5093, 1.3346, -1.0143, -0.6237, -0.7973, 0.4648, 0.2807],
- [ 0.4776, -0.4860, 1.7109, -0.0809, -0.1550, -0.0845, 0.2076, 0.0665],
- [ 0.3352, -0.4959, 1.1055, -0.6256, -0.7360, -0.6334, 0.1003, 0.4328],
- [ 0.3745, -0.5841, 1.9757, -0.3037, -0.3375, -0.9440, 1.0600, 0.1346],
- [ 0.7117, -0.3051, 1.6728, -0.9691, -0.3679, -1.0242, 0.5702, 0.1927],
- [ 0.6047, -0.4263, 1.4712, 0.2078, -0.5265, -0.0830, 1.0713, 0.1632]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5253, -0.4296, 1.3084, -0.3098, -0.6115, -0.2767, 0.0928,
- 0.1552],
- [ 0.5515, -0.4225, 1.7788, -0.0534, -0.2016, 0.1929, 0.1568,
- 0.0682],
- [ 0.5697, -0.4442, 1.3529, -0.9515, -0.5774, -0.7801, 0.5253,
- 0.1931],
- [ 0.5292, -0.4520, 1.7268, -0.0842, -0.0413, -0.0324, 0.1116,
- -0.0039],
- [ 0.5639, -0.3911, 1.1634, -0.5794, -0.6866, -0.5461, 0.1334,
- 0.4036],
- [ 0.6224, -0.4345, 1.9404, -0.2921, -0.3171, -0.8771, 1.0655,
- 0.2142],
- [ 0.6084, -0.4076, 1.6806, -0.9618, -0.2998, -0.9695, 0.6356,
- 0.1467],
- [ 0.6191, -0.4297, 1.4612, 0.2391, -0.4961, 0.0313, 1.1166,
- 0.1768]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0063, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0063, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.021567848045379
- step: 64
- running loss: 0.015961997625709046
- Train Steps: 64/90 Loss: 0.0160 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6260, 0.4161, 0.9000, 0.3833, 0.4450, 0.2133, 0.6237, 0.4986],
- [0.6175, 0.4091, 0.7863, 0.2800, 0.3638, 0.3583, 0.6188, 0.5433],
- [0.6179, 0.3993, 0.8925, 0.4789, 0.3879, 0.4900, 0.6041, 0.5279],
- [0.6250, 0.4106, 0.8700, 0.3717, 0.3588, 0.4967, 0.6038, 0.5167],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679],
- [0.6125, 0.3983, 0.8750, 0.4867, 0.4275, 0.4783, 0.5413, 0.5717],
- [0.6214, 0.3982, 0.8938, 0.4517, 0.3663, 0.4083, 0.5863, 0.5050],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4823, -0.4508, 1.8019, -0.3918, -0.2574, -1.2008, 0.5459, 0.0978],
- [ 0.5608, -0.3837, 1.1220, -1.0028, -0.6592, -0.5652, 0.3373, 0.2505],
- [ 0.4518, -0.4826, 1.6649, -0.0459, -0.5189, -0.0822, 0.5264, 0.1332],
- [ 0.6273, -0.3566, 1.5237, -0.4565, -0.6031, -0.0520, 0.6042, 0.1593],
- [-2.3707, -2.2975, 1.5311, -1.0986, 0.2021, -1.1821, 1.1018, 0.3396],
- [ 0.4574, -0.4729, 1.5861, 0.0844, -0.3624, -0.0815, 0.3012, 0.2289],
- [ 0.5393, -0.4603, 1.6396, -0.0924, -0.6100, -0.3728, 0.4136, 0.0178],
- [ 0.6332, -0.3491, 1.5950, -0.1174, -0.4494, -0.1350, 0.1423, 0.0673]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6054, -0.3641, 1.8711, -0.5153, -0.2305, -1.3005, 0.5948,
- 0.0171],
- [ 0.5665, -0.3963, 1.3457, -0.9926, -0.6058, -0.6308, 0.5721,
- 0.2237],
- [ 0.5680, -0.4417, 1.8365, -0.0740, -0.4941, -0.0227, 0.5045,
- 0.1525],
- [ 0.6010, -0.3896, 1.7326, -0.5692, -0.6289, 0.0082, 0.5028,
- 0.1005],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371],
- [ 0.5432, -0.4462, 1.7557, -0.0380, -0.3113, -0.0765, 0.2141,
- 0.3546],
- [ 0.5844, -0.4466, 1.8423, -0.1997, -0.5942, -0.3998, 0.4219,
- 0.0467],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0087, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0303018535487354
- step: 65
- running loss: 0.015850797746903622
- Train Steps: 65/90 Loss: 0.0159 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6070, 0.3979, 0.8575, 0.5083, 0.4350, 0.5400, 0.5180, 0.5466],
- [0.6185, 0.4067, 0.8838, 0.4450, 0.4037, 0.4733, 0.5213, 0.5142],
- [0.6277, 0.4036, 0.8688, 0.3617, 0.3925, 0.2600, 0.6132, 0.4742],
- [0.6201, 0.4151, 0.8588, 0.5467, 0.3700, 0.3950, 0.5637, 0.5933],
- [0.6243, 0.4128, 0.7762, 0.2717, 0.3825, 0.3133, 0.6212, 0.5417],
- [0.6257, 0.4034, 0.8287, 0.2333, 0.3925, 0.2717, 0.6330, 0.4901],
- [0.6332, 0.4128, 0.9200, 0.3517, 0.4400, 0.3833, 0.7461, 0.5494],
- [0.6160, 0.4093, 0.8314, 0.4417, 0.3675, 0.4583, 0.5250, 0.5808]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5700, -0.4073, 1.6418, 0.1862, -0.2623, 0.1250, 0.4004, 0.1653],
- [ 0.7220, -0.3087, 1.7366, -0.1382, -0.4315, -0.1361, 0.2370, 0.0559],
- [ 0.4944, -0.4977, 1.6870, -0.6288, -0.4603, -1.0396, 0.5128, -0.0098],
- [ 0.5891, -0.3530, 1.6456, 0.2662, -0.5829, -0.4358, 0.4185, 0.4307],
- [ 0.7564, -0.2906, 1.2859, -1.0564, -0.4996, -0.8074, 0.6509, 0.2876],
- [ 0.5688, -0.4439, 1.4126, -1.1453, -0.4822, -0.9829, 0.5486, -0.0029],
- [ 0.5284, -0.4687, 1.8423, -0.5432, -0.2972, -0.5283, 1.1121, 0.2081],
- [ 0.4286, -0.4599, 1.5429, -0.1226, -0.6268, -0.2642, 0.2406, 0.3169]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5178, -0.4480, 1.6748, 0.0620, -0.2767, 0.2083, 0.1067,
- 0.2386],
- [ 0.5708, -0.4075, 1.7961, -0.2305, -0.4210, -0.0996, 0.1219,
- 0.0893],
- [ 0.6134, -0.4218, 1.7268, -0.6154, -0.4730, -1.0850, 0.5463,
- -0.0957],
- [ 0.5785, -0.3687, 1.6806, 0.2391, -0.5769, -0.4614, 0.3180,
- 0.4547],
- [ 0.5977, -0.3792, 1.2995, -1.0311, -0.5192, -0.8386, 0.5836,
- 0.2160],
- [ 0.6042, -0.4225, 1.5420, -1.2082, -0.4730, -1.0311, 0.6380,
- -0.0220],
- [ 0.6388, -0.3792, 1.9635, -0.6616, -0.2536, -0.5153, 1.1605,
- 0.2516],
- [ 0.5592, -0.3956, 1.5543, -0.2456, -0.5885, -0.1689, 0.1392,
- 0.3968]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0066, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0066, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0369146969169378
- step: 66
- running loss: 0.015710828741165726
- Train Steps: 66/90 Loss: 0.0157 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6136, 0.4085, 0.6688, 0.2317, 0.3862, 0.2367, 0.5517, 0.5783],
- [0.6136, 0.4117, 0.8700, 0.5167, 0.4188, 0.5083, 0.5147, 0.5495],
- [0.6299, 0.4303, 0.7963, 0.3933, 0.4850, 0.2283, 0.5480, 0.6222],
- [0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [0.6196, 0.4094, 0.7562, 0.2817, 0.3937, 0.3183, 0.6013, 0.6183],
- [0.6129, 0.4063, 0.8738, 0.5250, 0.4313, 0.4733, 0.5230, 0.5874],
- [0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783],
- [0.6085, 0.4008, 0.8588, 0.5200, 0.4959, 0.4977, 0.5175, 0.5283]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4583, -0.4992, 0.8629, -1.1960, -0.6042, -1.1037, 0.4087, 0.1988],
- [ 0.5696, -0.4237, 1.8692, 0.1697, -0.4560, 0.0712, 0.5130, 0.0453],
- [ 0.6720, -0.3059, 1.6680, -0.4913, -0.1965, -1.1177, 0.5406, 0.3097],
- [ 0.2959, -0.5816, 1.4801, -0.9764, -0.5421, -1.1075, 0.2888, 0.1507],
- [ 0.6170, -0.3856, 1.3985, -0.9501, -0.5622, -0.8307, 0.7079, 0.3880],
- [ 0.6378, -0.3512, 1.8180, 0.2329, -0.4365, -0.0178, 0.4217, 0.2185],
- [ 0.4949, -0.4244, 1.3233, -1.0124, -0.2559, -1.2040, 0.5447, 0.3054],
- [ 0.5707, -0.4398, 1.8753, 0.1660, -0.1342, 0.0073, 0.5037, 0.0035]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5483, -0.3991, 0.8030, -1.2159, -0.5018, -1.1928, 0.2624,
- 0.3852],
- [ 0.5482, -0.3841, 1.7326, 0.1005, -0.3517, 0.0620, 0.0912,
- 0.2522],
- [ 0.6237, -0.2983, 1.3919, -0.4691, -0.0457, -1.2313, 0.2453,
- 0.5882],
- [ 0.5695, -0.3927, 1.3226, -0.9002, -0.4672, -1.1928, 0.1737,
- 0.3700],
- [ 0.5759, -0.3948, 1.2072, -0.9849, -0.4672, -0.8156, 0.4912,
- 0.5701],
- [ 0.5453, -0.4091, 1.7499, 0.1390, -0.2940, -0.0996, 0.1300,
- 0.4272],
- [ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854],
- [ 0.5245, -0.4347, 1.6806, 0.1159, 0.0046, 0.0129, 0.1044,
- 0.1544]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0235, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0235, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.060399990528822
- step: 67
- running loss: 0.015826865530280926
- Train Steps: 67/90 Loss: 0.0158 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6229, 0.4066, 0.8513, 0.5717, 0.4500, 0.5050, 0.6025, 0.5350],
- [0.6361, 0.4071, 0.9100, 0.4783, 0.3738, 0.3967, 0.6670, 0.5332],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6214, 0.4116, 0.7750, 0.2317, 0.4487, 0.1883, 0.6200, 0.5400],
- [0.6150, 0.3949, 0.8800, 0.4033, 0.3825, 0.4900, 0.5875, 0.5483],
- [0.6274, 0.4099, 0.8625, 0.3233, 0.4400, 0.1983, 0.5876, 0.4869],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6203, 0.4072, 0.8892, 0.3523, 0.3783, 0.3017, 0.5898, 0.5478]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6211, -0.3934, 1.7261, 0.4591, -0.2666, 0.0805, 0.5491, 0.1778],
- [ 0.6831, -0.3731, 1.9187, -0.0112, -0.6001, -0.3599, 0.7898, 0.1492],
- [ 0.3914, -0.5190, 0.9761, -1.2904, -0.5236, -1.0034, 0.4221, 0.2727],
- [ 0.5367, -0.4494, 1.2859, -1.1676, -0.2218, -1.2896, 0.5564, 0.1938],
- [ 0.7263, -0.3094, 1.7982, -0.3888, -0.5521, 0.0118, 0.5007, 0.1631],
- [ 0.6938, -0.3546, 1.8083, -0.7449, -0.2507, -1.3090, 0.5148, 0.0247],
- [ 0.5000, -0.4189, 1.0990, -1.0047, -0.2257, -1.2594, 0.3236, 0.4026],
- [ 0.6410, -0.3702, 1.9326, -0.6053, -0.5425, -0.7602, 0.4749, 0.2303]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5911, -0.4080, 1.6460, 0.3546, -0.2074, 0.0467, 0.4970,
- 0.1852],
- [ 0.6520, -0.4056, 1.9173, -0.0765, -0.5596, -0.4537, 0.7949,
- 0.1768],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.5842, -0.3848, 1.2938, -1.2159, -0.2132, -1.4160, 0.5778,
- 0.2083],
- [ 0.5546, -0.4620, 1.7788, -0.4229, -0.5192, -0.0226, 0.4277,
- 0.2468],
- [ 0.6119, -0.3927, 1.6979, -0.7925, -0.2536, -1.3698, 0.4282,
- -0.0368],
- [ 0.5862, -0.3530, 1.1032, -1.0619, -0.1497, -1.3852, 0.3411,
- 0.3931],
- [ 0.5792, -0.4052, 1.8214, -0.6587, -0.5384, -0.8924, 0.4381,
- 0.2442]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0052, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0052, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0655750310979784
- step: 68
- running loss: 0.015670221045558506
- Train Steps: 68/90 Loss: 0.0157 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6201, 0.4151, 0.8588, 0.5467, 0.3700, 0.3950, 0.5637, 0.5933],
- [0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6179, 0.4008, 0.8600, 0.4015, 0.3932, 0.2515, 0.5711, 0.5438],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [0.6168, 0.4055, 0.7575, 0.2950, 0.4050, 0.2717, 0.5938, 0.6167],
- [0.6353, 0.4128, 0.8488, 0.2600, 0.5525, 0.1616, 0.6694, 0.5540],
- [0.6198, 0.4164, 0.8700, 0.5067, 0.4625, 0.5650, 0.5464, 0.5197]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 6.4932e-01, -3.1109e-01, 1.7652e+00, 1.8150e-01, -6.3398e-01,
- -3.7820e-01, 2.5626e-01, 3.6480e-01],
- [ 5.5427e-01, -3.8678e-01, 1.2450e+00, -1.0782e+00, -2.0523e-01,
- -1.2230e+00, 4.0710e-01, 3.8396e-01],
- [ 5.8606e-01, -4.1393e-01, 1.8375e+00, 1.7360e-01, -6.3783e-01,
- -4.7665e-01, 3.0656e-01, 1.1668e-01],
- [ 7.0116e-01, -3.3979e-01, 1.6863e+00, -5.5139e-01, -4.7769e-01,
- -1.0744e+00, 2.7354e-01, 1.6098e-01],
- [ 6.5969e-01, -3.8419e-01, 1.7464e+00, 1.3380e-01, -3.9393e-01,
- 1.2854e-01, 9.0110e-01, 1.3022e-01],
- [ 5.2570e-01, -4.2665e-01, 1.2532e+00, -1.0416e+00, -5.1630e-01,
- -9.5149e-01, 4.5284e-01, 4.6819e-01],
- [ 5.8049e-01, -4.0814e-01, 1.6135e+00, -1.3326e+00, 2.0178e-01,
- -1.3246e+00, 9.6435e-01, 2.3764e-01],
- [ 6.0887e-01, -3.5483e-01, 1.8828e+00, -1.1708e-03, -2.4791e-01,
- 1.8476e-01, 4.1590e-01, 7.2394e-02]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5785, -0.3687, 1.6806, 0.2391, -0.5769, -0.4614, 0.3180,
- 0.4547],
- [ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698],
- [ 0.5680, -0.4345, 1.6864, -0.4315, -0.4698, -1.1241, 0.3518,
- 0.2261],
- [ 0.6042, -0.4273, 1.7198, 0.2184, -0.3478, 0.1149, 0.8062,
- 0.1176],
- [ 0.5631, -0.4129, 1.2129, -0.9233, -0.4152, -1.0311, 0.4566,
- 0.5624],
- [ 0.6487, -0.3792, 1.6344, -1.0850, 0.2659, -1.5397, 0.8059,
- 0.2730],
- [ 0.5770, -0.3624, 1.7326, 0.0543, -0.1497, 0.3238, 0.2378,
- 0.1146]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0071, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0071, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0726689579896629
- step: 69
- running loss: 0.01554592692738642
- Train Steps: 69/90 Loss: 0.0155 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6048, 0.3928, 0.8538, 0.5433, 0.3875, 0.5117, 0.5266, 0.4719],
- [0.6055, 0.4015, 0.7425, 0.2033, 0.4113, 0.1883, 0.5217, 0.4823],
- [0.6148, 0.3996, 0.8488, 0.3867, 0.3488, 0.4067, 0.5863, 0.5000],
- [0.6136, 0.3955, 0.8400, 0.3267, 0.3500, 0.4200, 0.5863, 0.5467],
- [0.6223, 0.4130, 0.8100, 0.2983, 0.3525, 0.3900, 0.5694, 0.5103],
- [0.6361, 0.4102, 0.8650, 0.2983, 0.4888, 0.2000, 0.6702, 0.5459],
- [0.6271, 0.4040, 0.9138, 0.3750, 0.4625, 0.2617, 0.7232, 0.5413],
- [0.6214, 0.4112, 0.7838, 0.2117, 0.3650, 0.3133, 0.5675, 0.5083]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5832, -0.4019, 1.7111, 0.4521, -0.3889, 0.0626, 0.1651, 0.1931],
- [ 0.5105, -0.4393, 0.9998, -1.1845, -0.3595, -1.3779, 0.1922, 0.1342],
- [ 0.6528, -0.3713, 1.6885, -0.3394, -0.6116, -0.3599, 0.3463, 0.2027],
- [ 0.6792, -0.3349, 1.6055, -0.6538, -0.5749, -0.4010, 0.4729, 0.3349],
- [ 0.8564, -0.2081, 1.5162, -0.6773, -0.5229, -0.5072, 0.3051, 0.3393],
- [ 0.6010, -0.4154, 1.7584, -0.8380, 0.0861, -1.3183, 0.9652, 0.2800],
- [ 0.6342, -0.4167, 2.0447, -0.4159, -0.0362, -1.1658, 1.0665, 0.2960],
- [ 0.6628, -0.3548, 1.3875, -1.2671, -0.5098, -0.9115, 0.4795, 0.2078]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5079, -0.4714, 1.6575, 0.2237, -0.4961, 0.0774, 0.1465,
- -0.1061],
- [ 0.5109, -0.4316, 1.1436, -1.3467, -0.3864, -1.4160, 0.1239,
- -0.0580],
- [ 0.5538, -0.4400, 1.6344, -0.4999, -0.6751, -0.4075, 0.4219,
- 0.0236],
- [ 0.5484, -0.4591, 1.5940, -0.7771, -0.6693, -0.3460, 0.4219,
- 0.2391],
- [ 0.5886, -0.3784, 1.4554, -0.9079, -0.6577, -0.4845, 0.3440,
- 0.0712],
- [ 0.6520, -0.3912, 1.7095, -0.9079, -0.0284, -1.3621, 0.8096,
- 0.2356],
- [ 0.6108, -0.4201, 1.9346, -0.5538, -0.1497, -1.0773, 1.0545,
- 0.2142],
- [ 0.5845, -0.3864, 1.3342, -1.3082, -0.6000, -0.8386, 0.3353,
- 0.0620]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0132, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0132, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.0858652363531291
- step: 70
- running loss: 0.015512360519330417
- Train Steps: 70/90 Loss: 0.0155 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6277, 0.4036, 0.8688, 0.3617, 0.3925, 0.2600, 0.6132, 0.4742],
- [0.6076, 0.3958, 0.8700, 0.4667, 0.4546, 0.5046, 0.5231, 0.4960],
- [0.6275, 0.4071, 0.8075, 0.2100, 0.4700, 0.1533, 0.6148, 0.4834],
- [0.6189, 0.4029, 0.8375, 0.5767, 0.4745, 0.4829, 0.5551, 0.5598],
- [0.6140, 0.4070, 0.8700, 0.5000, 0.4612, 0.4900, 0.5260, 0.5852],
- [0.6147, 0.4112, 0.7988, 0.3200, 0.3775, 0.2767, 0.5150, 0.5550],
- [ nan, nan, 0.7515, 0.2708, 0.3987, 0.2267, 0.5162, 0.5567],
- [0.6276, 0.4002, 0.8800, 0.5533, 0.3575, 0.4400, 0.6132, 0.4672]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7524, -0.3138, 1.7652, -0.7036, -0.4591, -1.0202, 0.6242, 0.0644],
- [ 0.7036, -0.3337, 1.8266, -0.1566, -0.2323, 0.0085, 0.2252, 0.1692],
- [ 0.7085, -0.3449, 1.4596, -1.4295, -0.0171, -1.5700, 0.9127, 0.0854],
- [ 0.6374, -0.3348, 1.6523, 0.3809, -0.1125, -0.1319, 0.3967, 0.3868],
- [ 0.5919, -0.3661, 1.7851, 0.0314, -0.1617, -0.0299, 0.3320, 0.4521],
- [ 0.8570, -0.1832, 1.4189, -0.8228, -0.5323, -0.9292, 0.0790, 0.3344],
- [-1.1760, -1.5135, 1.1183, -1.1627, -0.3278, -1.3243, 0.1392, 0.3514],
- [ 0.7340, -0.3614, 1.7749, 0.2905, -0.6121, -0.2406, 0.7003, 0.1101]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6134, -0.4218, 1.7268, -0.6154, -0.4730, -1.0850, 0.5463,
- -0.0957],
- [ 0.5205, -0.4577, 1.7326, -0.1304, -0.1862, 0.0447, 0.1301,
- 0.0051],
- [ 0.6126, -0.4057, 1.4439, -1.3159, -0.1150, -1.5777, 0.5537,
- -0.0530],
- [ 0.5726, -0.4249, 1.5824, 0.3777, -0.0942, -0.0556, 0.2782,
- 0.2997],
- [ 0.5500, -0.4060, 1.7326, 0.0236, -0.1554, -0.0226, 0.1438,
- 0.4171],
- [ 0.5532, -0.3864, 1.4035, -0.8079, -0.5423, -1.0080, 0.0928,
- 0.2776],
- [-2.2859, -2.2859, 1.1854, -1.0352, -0.4441, -1.2390, 0.0986,
- 0.2853],
- [ 0.6128, -0.4375, 1.7788, 0.2699, -0.6346, -0.2536, 0.5463,
- -0.1278]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0401, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0401, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1259842771105468
- step: 71
- running loss: 0.015858933480430236
- Train Steps: 71/90 Loss: 0.0159 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6200, 0.4112, 0.8862, 0.4100, 0.3638, 0.4917, 0.6088, 0.6050],
- [0.6151, 0.4085, 0.8750, 0.4367, 0.3887, 0.4367, 0.5066, 0.5846],
- [0.6108, 0.4011, 0.8037, 0.3400, 0.3700, 0.2933, 0.5658, 0.5617],
- [0.6236, 0.4084, 0.7738, 0.2133, 0.3663, 0.3233, 0.5813, 0.5567],
- [0.6138, 0.4020, 0.8800, 0.4717, 0.4375, 0.4717, 0.5502, 0.5611],
- [0.6339, 0.4123, 0.8638, 0.5367, 0.4075, 0.5467, 0.7517, 0.5436],
- [0.6161, 0.4055, 0.8675, 0.3867, 0.3713, 0.4033, 0.5195, 0.5162],
- [ nan, nan, 0.8488, 0.2300, 0.5563, 0.2100, 0.7390, 0.5679]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7596, -0.2490, 1.7706, -0.2143, -0.4673, -0.0846, 0.3435, 0.3957],
- [ 0.6366, -0.3010, 1.7074, -0.1447, -0.4508, -0.3138, -0.0450, 0.2564],
- [ 0.6255, -0.3668, 1.4270, -0.6066, -0.5383, -0.9385, 0.2893, 0.1861],
- [ 0.6542, -0.3571, 1.2980, -1.1232, -0.4624, -0.9424, 0.4861, 0.2497],
- [ 0.6544, -0.3270, 1.7445, -0.0034, -0.2148, -0.1705, 0.1259, 0.2557],
- [ 0.6998, -0.3413, 1.7491, 0.2611, -0.3304, 0.1221, 1.0424, 0.2210],
- [ 0.7470, -0.2772, 1.7329, -0.3159, -0.5494, -0.5700, -0.0321, 0.0129],
- [-1.4784, -1.7442, 1.6703, -1.2390, 0.4985, -1.6062, 1.2554, 0.3567]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5777, -0.3868, 1.8076, -0.3921, -0.6058, -0.0149, 0.5259,
- 0.5085],
- [ 0.5553, -0.3992, 1.7557, -0.2690, -0.4903, -0.2690, 0.0542,
- 0.4145],
- [ 0.5355, -0.4335, 1.4266, -0.7155, -0.5769, -0.9310, 0.3275,
- 0.3087],
- [ 0.5946, -0.3995, 1.2880, -1.3005, -0.5942, -0.7925, 0.3988,
- 0.2853],
- [ 0.5491, -0.4290, 1.7788, -0.1073, -0.2651, -0.1073, 0.2555,
- 0.3057],
- [ 0.6421, -0.3816, 1.7037, 0.1929, -0.4037, 0.2391, 1.1861,
- 0.2249],
- [ 0.5598, -0.4129, 1.7210, -0.4999, -0.5711, -0.4229, 0.1136,
- 0.0983],
- [-2.2859, -2.2859, 1.6344, -1.2236, 0.2834, -1.3159, 1.1276,
- 0.3371]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0265, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0265, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1525277267210186
- step: 72
- running loss: 0.016007329537791923
- Train Steps: 72/90 Loss: 0.0160 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6132, 0.4118, 0.8200, 0.3633, 0.3563, 0.5400, 0.5787, 0.5136],
- [ nan, nan, 0.8750, 0.2467, 0.5138, 0.2617, 0.7382, 0.5552],
- [0.6110, 0.4047, 0.8700, 0.4483, 0.3713, 0.3967, 0.5088, 0.5517],
- [0.6185, 0.4042, 0.7700, 0.2250, 0.4062, 0.2117, 0.5763, 0.5150],
- [0.6292, 0.4010, 0.8988, 0.4800, 0.3638, 0.3817, 0.6357, 0.5051],
- [0.6270, 0.4267, 0.7150, 0.3317, 0.4900, 0.1817, 0.5560, 0.6183],
- [0.6264, 0.4248, 0.8938, 0.4183, 0.3875, 0.4100, 0.5400, 0.5350],
- [0.6276, 0.4095, 0.8237, 0.2250, 0.4662, 0.1783, 0.6171, 0.4869]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 5.9427e-01, -3.5149e-01, 1.5071e+00, -5.8090e-01, -4.9336e-01,
- 8.7826e-02, 4.6292e-01, 2.3218e-01],
- [-1.9318e+00, -2.0346e+00, 1.7207e+00, -1.2192e+00, 2.7853e-01,
- -1.4549e+00, 1.0914e+00, 3.3141e-01],
- [ 5.7028e-01, -3.5766e-01, 1.7352e+00, -9.0277e-02, -5.7938e-01,
- -3.2921e-01, -1.3246e-01, 2.5256e-01],
- [ 5.9285e-01, -4.0147e-01, 1.2382e+00, -1.1689e+00, -3.9334e-01,
- -1.2938e+00, 3.6060e-01, 1.1863e-01],
- [ 6.5827e-01, -3.9911e-01, 1.8256e+00, -1.6101e-03, -5.0889e-01,
- -4.0250e-01, 5.7856e-01, 1.1106e-01],
- [ 6.9346e-01, -2.9639e-01, 1.0066e+00, -7.7031e-01, -1.1378e-01,
- -1.3372e+00, 2.4999e-01, 5.3997e-01],
- [ 6.0116e-01, -3.3656e-01, 1.8278e+00, -2.2940e-01, -5.3385e-01,
- -3.2537e-01, 7.8322e-02, 2.2241e-01],
- [ 6.2932e-01, -3.6683e-01, 1.5071e+00, -1.2220e+00, -3.4216e-03,
- -1.4243e+00, 6.1705e-01, 7.4610e-02]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5466, -0.3840, 1.5016, -0.6077, -0.6404, 0.2083, 0.3871,
- 0.0862],
- [-2.2859, -2.2859, 1.7557, -1.1466, 0.0871, -1.0773, 1.1239,
- 0.2783],
- [ 0.5363, -0.4168, 1.7326, -0.2151, -0.5711, -0.4537, 0.0640,
- 0.2622],
- [ 0.5707, -0.4189, 1.2707, -1.2467, -0.4095, -1.3082, 0.3758,
- 0.0928],
- [ 0.6204, -0.4336, 1.8654, -0.0688, -0.6058, -0.5230, 0.6503,
- 0.0472],
- [ 0.6101, -0.3152, 1.0166, -0.7540, -0.0226, -1.4468, 0.2823,
- 0.5702],
- [ 0.6072, -0.3239, 1.8423, -0.3537, -0.4961, -0.3921, 0.2083,
- 0.1852],
- [ 0.6127, -0.3944, 1.5189, -1.2467, -0.1323, -1.4622, 0.5646,
- -0.0369]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1630904129706323
- step: 73
- running loss: 0.015932745383159346
- Train Steps: 73/90 Loss: 0.0159 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6182, 0.4058, 0.8738, 0.4350, 0.3563, 0.3400, 0.5290, 0.5822],
- [0.6164, 0.3972, 0.7818, 0.3381, 0.3599, 0.3387, 0.5880, 0.5153],
- [0.6314, 0.4107, 0.8750, 0.5100, 0.3788, 0.4900, 0.7121, 0.5864],
- [0.6109, 0.4015, 0.7668, 0.3639, 0.3513, 0.3667, 0.5200, 0.5641],
- [0.6226, 0.4001, 0.8438, 0.5733, 0.3862, 0.4250, 0.5750, 0.5283],
- [0.6163, 0.4001, 0.8788, 0.5033, 0.4012, 0.4633, 0.5338, 0.5767],
- [0.6201, 0.4065, 0.7300, 0.1933, 0.4075, 0.2533, 0.6162, 0.5483],
- [0.6153, 0.4119, 0.8463, 0.3833, 0.3600, 0.3200, 0.5106, 0.5563]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6199, -0.3633, 1.7836, -0.3836, -0.5077, -0.7211, 0.0684, 0.2759],
- [ 0.6889, -0.3536, 1.4425, -0.8366, -0.5263, -0.7809, 0.4121, 0.0883],
- [ 0.6030, -0.4272, 1.9410, 0.0582, -0.2889, -0.1175, 1.1050, 0.3419],
- [ 0.5360, -0.4069, 1.3305, -0.7654, -0.5238, -0.6782, 0.1789, 0.2832],
- [ 0.7144, -0.3519, 1.7441, 0.3128, -0.3675, -0.3465, 0.3809, 0.1663],
- [ 0.5369, -0.4524, 1.8689, -0.0673, -0.2455, -0.2268, 0.1903, 0.2530],
- [ 0.6427, -0.3706, 1.1777, -1.4482, -0.2613, -1.2697, 0.5720, 0.2102],
- [ 0.6546, -0.3343, 1.7212, -0.5872, -0.4739, -0.9253, 0.0056, 0.2401]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5697, -0.4113, 1.7499, -0.2767, -0.6404, -0.7155, 0.1576,
- 0.4032],
- [ 0.5614, -0.4514, 1.3252, -0.7240, -0.6237, -0.7215, 0.4299,
- 0.0942],
- [ 0.6306, -0.3888, 1.7557, 0.0697, -0.5365, -0.0226, 1.0033,
- 0.4226],
- [ 0.5357, -0.4315, 1.2557, -0.6051, -0.6635, -0.5923, 0.1159,
- 0.3198],
- [ 0.5900, -0.4377, 1.6113, 0.3623, -0.5018, -0.3229, 0.3700,
- 0.1544],
- [ 0.5610, -0.4381, 1.7730, 0.0390, -0.4326, -0.1458, 0.1794,
- 0.3777],
- [ 0.5784, -0.4085, 1.0859, -1.3929, -0.4037, -1.1158, 0.5605,
- 0.2468],
- [ 0.5561, -0.3834, 1.6229, -0.5153, -0.6231, -0.8079, 0.0727,
- 0.2837]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0091, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0091, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1722305989824235
- step: 74
- running loss: 0.015840954040303022
- Train Steps: 74/90 Loss: 0.0158 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6139, 0.4019, 0.7137, 0.2150, 0.4375, 0.1533, 0.5293, 0.5006],
- [0.6200, 0.4055, 0.8011, 0.2988, 0.3842, 0.2798, 0.5949, 0.5183],
- [ nan, nan, 0.8525, 0.2217, 0.5413, 0.2367, 0.7367, 0.5482],
- [0.6138, 0.4054, 0.8750, 0.4750, 0.4363, 0.5017, 0.5086, 0.5822],
- [0.6275, 0.4157, 0.8337, 0.5800, 0.3763, 0.4200, 0.5547, 0.6125],
- [0.6128, 0.4022, 0.8738, 0.5067, 0.4983, 0.5231, 0.5364, 0.5064],
- [0.6097, 0.4024, 0.8488, 0.3717, 0.3875, 0.5517, 0.5836, 0.5591],
- [0.6239, 0.4061, 0.8850, 0.4600, 0.4225, 0.5200, 0.6138, 0.5450]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7868, -0.2607, 1.0193, -1.2032, -0.3471, -1.6748, 0.2052, 0.0175],
- [ 0.5743, -0.4024, 1.4428, -0.8914, -0.5607, -0.9998, 0.4252, 0.0937],
- [-2.1559, -2.1935, 1.5947, -1.3129, 0.2300, -1.5728, 1.0261, 0.3095],
- [ 0.5379, -0.3878, 1.7228, -0.1518, -0.3012, -0.0963, 0.0473, 0.2776],
- [ 0.6823, -0.3246, 1.6219, 0.3853, -0.6079, -0.4308, 0.2746, 0.4834],
- [ 0.4335, -0.4857, 1.7522, -0.0481, -0.0376, -0.1753, 0.0980, 0.1554],
- [ 0.4807, -0.4354, 1.6261, -0.5759, -0.4750, 0.1213, 0.4935, 0.1963],
- [ 0.5461, -0.4275, 1.8113, -0.2441, -0.3507, 0.0115, 0.4388, 0.1897]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5496, -0.4295, 1.0109, -1.2928, -0.2651, -1.5777, 0.1589,
- 0.0261],
- [ 0.5781, -0.4129, 1.4142, -0.9057, -0.5115, -0.9937, 0.4621,
- 0.1080],
- [-2.2859, -2.2859, 1.6517, -1.2620, 0.2141, -1.1928, 1.1166,
- 0.2463],
- [ 0.5491, -0.4132, 1.7557, -0.0919, -0.2709, 0.0313, 0.0635,
- 0.4032],
- [ 0.6124, -0.3658, 1.5651, 0.3931, -0.5480, -0.3460, 0.2761,
- 0.5431],
- [ 0.5446, -0.4280, 1.7499, 0.0543, 0.0156, 0.1301, 0.1918,
- 0.0532],
- [ 0.5301, -0.4273, 1.6344, -0.5692, -0.4961, 0.2622, 0.4098,
- 0.2966],
- [ 0.5960, -0.4102, 1.8018, -0.1612, -0.3344, 0.1159, 0.5490,
- 0.2314]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0091, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0091, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1813519136048853
- step: 75
- running loss: 0.01575135884806514
- Train Steps: 75/90 Loss: 0.0158 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6117, 0.4018, 0.6562, 0.1967, 0.3738, 0.2550, 0.5280, 0.5103],
- [0.6264, 0.4049, 0.8988, 0.4633, 0.3813, 0.4983, 0.6326, 0.4843],
- [0.6124, 0.4030, 0.8650, 0.4867, 0.4999, 0.5106, 0.5137, 0.5773],
- [0.6086, 0.3981, 0.8700, 0.4750, 0.4512, 0.5283, 0.5324, 0.5038],
- [0.6201, 0.4082, 0.8827, 0.3715, 0.3825, 0.2712, 0.5845, 0.5412],
- [0.6222, 0.4072, 0.7164, 0.2166, 0.3738, 0.3167, 0.6100, 0.5533],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6240, 0.4217, 0.8150, 0.3133, 0.4425, 0.2650, 0.5650, 0.5817]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 4.3204e-01, -4.9423e-01, 8.7678e-01, -1.1577e+00, -4.9900e-01,
- -1.2377e+00, 1.9630e-01, 1.8164e-01],
- [ 6.9072e-01, -3.8656e-01, 2.0222e+00, -1.1292e-01, -5.2003e-01,
- -2.3396e-02, 6.1781e-01, 6.3428e-02],
- [ 3.8911e-01, -5.3941e-01, 1.7964e+00, -3.4572e-02, -9.5070e-03,
- -9.4753e-02, 7.4348e-02, 3.2933e-01],
- [ 6.1090e-01, -4.0105e-01, 1.8377e+00, -9.8769e-02, -1.8835e-01,
- -1.7130e-03, 5.7579e-02, 8.1385e-02],
- [ 6.4133e-01, -3.7246e-01, 1.8869e+00, -5.2754e-01, -5.2379e-01,
- -1.0953e+00, 4.3551e-01, 2.3086e-01],
- [ 6.0569e-01, -3.8838e-01, 1.1288e+00, -1.1754e+00, -5.1845e-01,
- -9.1244e-01, 5.3057e-01, 2.8769e-01],
- [ 5.0548e-01, -4.6197e-01, 1.0753e+00, -1.1920e+00, -4.6685e-01,
- -1.2029e+00, 4.8190e-01, 3.0180e-01],
- [ 5.9589e-01, -3.6515e-01, 1.5408e+00, -7.3261e-01, -3.0263e-01,
- -1.2108e+00, 2.8190e-01, 4.1174e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5393, -0.4299, 0.7453, -1.3775, -0.5596, -1.1081, 0.1527,
- 0.0712],
- [ 0.6075, -0.4159, 1.8654, -0.1458, -0.5249, 0.0159, 0.6359,
- -0.0490],
- [ 0.5428, -0.4244, 1.7095, -0.0380, 0.0232, 0.0725, 0.0866,
- 0.3806],
- [ 0.5249, -0.4473, 1.7326, -0.0919, -0.2016, 0.1544, 0.1733,
- 0.0412],
- [ 0.5784, -0.4006, 1.7911, -0.5701, -0.5192, -1.0331, 0.4137,
- 0.2139],
- [ 0.5878, -0.4052, 1.0229, -1.2855, -0.5596, -0.8232, 0.5316,
- 0.2699],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.5964, -0.3380, 1.4785, -0.8386, -0.2420, -1.0619, 0.3238,
- 0.4008]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0076, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.1889062826521695
- step: 76
- running loss: 0.015643503719107492
- Train Steps: 76/90 Loss: 0.0156 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [ nan, nan, 0.7981, 0.3194, 0.3625, 0.3167, 0.5040, 0.5563],
- [0.6213, 0.4131, 0.8438, 0.3550, 0.3513, 0.4400, 0.5716, 0.5123],
- [0.6043, 0.4022, 0.6887, 0.1983, 0.3775, 0.2483, 0.5480, 0.5136],
- [0.6275, 0.4008, 0.8047, 0.2484, 0.4037, 0.2777, 0.6506, 0.5212],
- [0.6179, 0.4008, 0.7505, 0.2678, 0.4368, 0.1891, 0.5831, 0.5263],
- [ nan, nan, 0.7425, 0.2117, 0.3937, 0.2433, 0.5438, 0.5567],
- [0.6249, 0.4142, 0.8350, 0.3283, 0.3613, 0.3700, 0.6188, 0.5400]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6656, -0.3252, 1.6314, -0.9552, -0.1234, -1.3587, 0.5424, 0.1389],
- [-1.9349, -2.0047, 1.2804, -0.8710, -0.5206, -0.9053, 0.0334, 0.3029],
- [ 0.6856, -0.3197, 1.6577, -0.5465, -0.5838, -0.3089, 0.2376, 0.2427],
- [ 0.6226, -0.3830, 0.9294, -1.1398, -0.4260, -1.1295, 0.2986, 0.2081],
- [ 0.6480, -0.3885, 1.4777, -0.9221, -0.3241, -0.9524, 0.6381, 0.1960],
- [ 0.8055, -0.2722, 1.2072, -0.9814, -0.2422, -1.3769, 0.3189, 0.2356],
- [-1.9116, -2.0022, 1.1542, -1.1836, -0.3305, -1.1824, 0.2033, 0.3075],
- [ 0.7482, -0.2765, 1.6254, -0.7034, -0.5661, -0.6050, 0.4760, 0.3313]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [-2.2859, -2.2859, 1.4006, -0.8105, -0.6115, -0.8232, 0.0419,
- 0.2837],
- [ 0.5841, -0.3778, 1.6113, -0.6462, -0.6635, -0.2536, 0.3542,
- 0.0802],
- [ 0.5053, -0.4281, 0.8954, -1.3698, -0.5423, -1.1389, 0.2453,
- 0.0862],
- [ 0.6125, -0.4345, 1.4308, -1.1384, -0.4213, -1.0031, 0.7190,
- 0.1214],
- [ 0.5680, -0.4345, 1.1806, -1.0490, -0.2683, -1.4127, 0.4074,
- 0.1449],
- [-2.2859, -2.2859, 1.1436, -1.3082, -0.4672, -1.1620, 0.2256,
- 0.2853],
- [ 0.6006, -0.3728, 1.5709, -0.7694, -0.6173, -0.5769, 0.5721,
- 0.2083]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0152, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0152, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2041279622353613
- step: 77
- running loss: 0.01563802548357612
- Train Steps: 77/90 Loss: 0.0156 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6207, 0.4110, 0.8738, 0.5000, 0.4800, 0.5633, 0.6300, 0.5433],
- [0.6138, 0.4101, 0.8800, 0.5083, 0.4637, 0.5950, 0.5587, 0.5077],
- [0.6164, 0.3956, 0.8757, 0.5088, 0.4300, 0.5320, 0.6022, 0.5202],
- [0.6185, 0.4098, 0.8838, 0.4900, 0.4537, 0.5800, 0.6288, 0.5400],
- [0.6132, 0.4037, 0.6963, 0.2217, 0.4100, 0.1950, 0.5395, 0.5175],
- [0.6086, 0.3940, 0.8712, 0.4783, 0.4025, 0.4900, 0.5498, 0.5390],
- [0.6272, 0.4120, 0.9038, 0.4117, 0.3725, 0.3200, 0.6175, 0.5250],
- [0.6230, 0.4152, 0.7588, 0.2283, 0.4012, 0.2883, 0.6200, 0.5767]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.6204, -0.3751, 1.7329, -0.1243, -0.1641, 0.0857, 0.3867, 0.3459],
- [ 0.4157, -0.4951, 1.7252, -0.0288, -0.2015, 0.1722, 0.2050, 0.1587],
- [ 0.5428, -0.4652, 1.6959, -0.0345, -0.3847, 0.0054, 0.4109, 0.1474],
- [ 0.4291, -0.5127, 1.7436, -0.1355, -0.2256, 0.2061, 0.4410, 0.2915],
- [ 0.5248, -0.4014, 0.9346, -1.2211, -0.4824, -1.5760, 0.1073, 0.0700],
- [ 0.6467, -0.4007, 1.6932, -0.1175, -0.4838, -0.1200, 0.0742, 0.1444],
- [ 0.4221, -0.5190, 1.8474, -0.4413, -0.6660, -0.8941, 0.4888, 0.2100],
- [ 0.5255, -0.4244, 1.2112, -1.0893, -0.5817, -1.1285, 0.5264, 0.3707]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5811, -0.3878, 1.7499, 0.0236, -0.0688, 0.3161, 0.6240,
- 0.2237],
- [ 0.5491, -0.3918, 1.7788, 0.0620, -0.1439, 0.4624, 0.2946,
- 0.0592],
- [ 0.5614, -0.4586, 1.7587, 0.0641, -0.2998, 0.1712, 0.4958,
- 0.1170],
- [ 0.5709, -0.3933, 1.7961, -0.0226, -0.1901, 0.3931, 0.6182,
- 0.2083],
- [ 0.5465, -0.4214, 0.9300, -1.2620, -0.3921, -1.3852, 0.2062,
- 0.1043],
- [ 0.5250, -0.4661, 1.7383, -0.0765, -0.4268, -0.0226, 0.2535,
- 0.2035],
- [ 0.6111, -0.3828, 1.8885, -0.3844, -0.5654, -0.8079, 0.5663,
- 0.1390],
- [ 0.5915, -0.3682, 1.2187, -1.2313, -0.4326, -0.9541, 0.5778,
- 0.3777]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0122, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0122, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2163738389499485
- step: 78
- running loss: 0.015594536396794213
- Train Steps: 78/90 Loss: 0.0156 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6286, 0.4086, 0.8408, 0.2801, 0.4163, 0.2800, 0.6725, 0.5393],
- [0.6202, 0.3983, 0.8555, 0.5429, 0.3842, 0.4370, 0.5866, 0.5398],
- [ nan, nan, 0.6992, 0.2791, 0.3950, 0.2383, 0.5483, 0.5819],
- [0.6157, 0.4102, 0.8513, 0.3817, 0.3613, 0.3667, 0.5096, 0.5890],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6207, 0.4110, 0.8738, 0.5000, 0.4800, 0.5633, 0.6300, 0.5433],
- [0.6224, 0.4061, 0.8988, 0.4300, 0.3838, 0.4750, 0.6112, 0.5483],
- [0.6101, 0.4042, 0.7775, 0.2617, 0.3713, 0.2817, 0.5440, 0.5650]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4975, -0.4646, 1.5513, -0.9666, -0.3757, -1.0631, 0.8349, 0.2213],
- [ 0.6141, -0.4448, 1.6689, 0.2204, -0.5464, -0.2051, 0.5138, 0.0906],
- [-2.0015, -2.1097, 0.9544, -1.1119, -0.4661, -1.1842, 0.0765, 0.2852],
- [ 0.3614, -0.5117, 1.6381, -0.5652, -0.6371, -0.5385, 0.0116, 0.2458],
- [ 0.4667, -0.4367, 1.1219, -1.0317, -0.2074, -1.4519, 0.2099, 0.2799],
- [ 0.6129, -0.3851, 1.7800, -0.1275, -0.1141, 0.2926, 0.4631, 0.2179],
- [ 0.5707, -0.4434, 1.8278, -0.3480, -0.5337, 0.0171, 0.5358, 0.1088],
- [ 0.4578, -0.4612, 1.2927, -1.0640, -0.5458, -1.0070, 0.0641, 0.2277]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6174, -0.3984, 1.5975, -0.9921, -0.3633, -0.9926, 0.8205,
- 0.2050],
- [ 0.5786, -0.4463, 1.6655, 0.2216, -0.5115, -0.2675, 0.4236,
- 0.2075],
- [-2.2859, -2.2859, 0.9438, -0.9967, -0.4614, -1.1851, 0.2468,
- 0.4019],
- [ 0.5581, -0.3912, 1.6460, -0.5230, -0.6173, -0.5923, 0.0681,
- 0.4348],
- [ 0.5862, -0.3530, 1.1032, -1.0619, -0.1497, -1.3852, 0.3411,
- 0.3931],
- [ 0.5811, -0.3878, 1.7499, 0.0236, -0.0688, 0.3161, 0.6240,
- 0.2237],
- [ 0.5888, -0.4101, 1.8654, -0.2998, -0.5134, -0.0919, 0.5374,
- 0.2468],
- [ 0.5320, -0.4189, 1.3053, -1.0773, -0.5711, -0.9849, 0.2267,
- 0.3237]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0080, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0080, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.224423078354448
- step: 79
- running loss: 0.015499026308284152
- Train Steps: 79/90 Loss: 0.0155 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6091, 0.3997, 0.8314, 0.4334, 0.3788, 0.4550, 0.5213, 0.5656],
- [0.6250, 0.4103, 0.8950, 0.4400, 0.3912, 0.5650, 0.6050, 0.5133],
- [0.6225, 0.4196, 0.8788, 0.3467, 0.3750, 0.3400, 0.5413, 0.5433],
- [0.6153, 0.4119, 0.8463, 0.3833, 0.3600, 0.3200, 0.5106, 0.5563],
- [0.6138, 0.4101, 0.8800, 0.5083, 0.4637, 0.5950, 0.5587, 0.5077],
- [0.6175, 0.4013, 0.7734, 0.2783, 0.4475, 0.1786, 0.5790, 0.5351],
- [0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901],
- [0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4846, -0.4914, 1.4329, -0.3428, -0.5375, -0.1980, 0.2619, 0.3136],
- [ 0.5042, -0.4608, 1.7244, -0.3396, -0.4670, 0.3175, 0.5917, 0.2142],
- [ 0.4605, -0.4590, 1.5435, -0.6849, -0.6888, -0.7761, 0.2342, 0.2381],
- [ 0.2250, -0.6154, 1.5077, -0.5573, -0.6753, -0.8241, 0.0768, 0.3247],
- [ 0.4591, -0.4868, 1.6747, -0.0019, -0.1548, 0.3512, 0.3674, 0.1820],
- [ 0.7229, -0.3085, 1.1871, -1.0721, -0.2958, -1.5675, 0.3066, 0.1513],
- [ 0.5734, -0.4881, 1.6056, -0.7844, -0.6470, -0.6891, 0.7640, 0.0590],
- [ 0.4164, -0.5204, 1.6723, -0.1330, -0.2016, 0.1220, 0.1374, 0.1163]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5275, -0.4396, 1.5543, -0.2841, -0.5365, -0.1843, 0.1221,
- 0.3265],
- [ 0.6010, -0.3909, 1.8480, -0.2536, -0.4788, 0.3238, 0.5085,
- 0.0851],
- [ 0.5894, -0.3479, 1.7730, -0.6847, -0.5538, -0.7155, 0.2141,
- 0.2237],
- [ 0.5561, -0.3834, 1.6229, -0.5153, -0.6231, -0.8079, 0.0727,
- 0.2837],
- [ 0.5491, -0.3918, 1.7788, 0.0620, -0.1439, 0.4624, 0.2946,
- 0.0592],
- [ 0.5664, -0.4321, 1.2862, -1.0003, -0.2189, -1.4608, 0.3883,
- 0.1855],
- [ 0.6031, -0.4307, 1.7268, -0.8002, -0.6058, -0.6462, 0.6442,
- -0.0220],
- [ 0.5515, -0.4225, 1.7788, -0.0534, -0.2016, 0.1929, 0.1568,
- 0.0682]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0099, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0099, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.234301685821265
- step: 80
- running loss: 0.015428771072765812
- Train Steps: 80/90 Loss: 0.0154 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6284, 0.4029, 0.8838, 0.3783, 0.3975, 0.2850, 0.6335, 0.5090],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150],
- [0.6087, 0.3951, 0.8387, 0.5833, 0.4188, 0.4933, 0.5146, 0.4830],
- [0.6143, 0.4034, 0.8800, 0.4833, 0.4512, 0.5367, 0.5289, 0.5097],
- [0.6257, 0.4024, 0.8672, 0.5422, 0.4196, 0.5198, 0.6694, 0.5204],
- [ nan, nan, 0.8363, 0.3317, 0.3563, 0.3367, 0.5329, 0.5142],
- [0.6109, 0.3943, 0.8525, 0.4950, 0.4338, 0.4800, 0.5449, 0.5383],
- [ nan, nan, 0.8300, 0.3150, 0.3588, 0.3383, 0.5208, 0.5194]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7503, -0.2881, 1.6015, -0.6986, -0.4646, -1.0654, 0.6464, 0.1602],
- [ 0.7804, -0.2480, 1.6838, -0.2485, -0.3454, 0.3726, 0.5508, 0.1698],
- [ 0.6938, -0.3236, 1.5166, 0.2860, -0.4027, -0.0619, 0.1601, 0.1312],
- [ 0.5815, -0.3628, 1.6414, -0.2030, -0.2527, 0.0957, 0.1165, 0.0985],
- [ 0.6586, -0.3652, 1.5467, 0.0677, -0.4092, 0.0632, 0.8244, 0.2094],
- [-2.3185, -2.3273, 1.3828, -0.8933, -0.6300, -0.7669, 0.1151, 0.2224],
- [ 0.6937, -0.3142, 1.5366, -0.1562, -0.3201, -0.1382, 0.1785, 0.2307],
- [-2.0555, -2.1445, 1.3345, -0.8968, -0.6410, -0.7471, 0.0783, 0.2306]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.1645e-01, -4.2487e-01, 1.7961e+00, -5.3841e-01, -4.4988e-01,
- -9.6952e-01, 6.4006e-01, 6.5205e-02],
- [ 6.0139e-01, -3.8830e-01, 1.8192e+00, -1.1501e-01, -2.8822e-01,
- 4.0077e-01, 5.2009e-01, 9.2841e-02],
- [ 5.2575e-01, -4.6105e-01, 1.5882e+00, 4.0847e-01, -3.5173e-01,
- -7.2363e-03, 9.1027e-02, -5.5027e-02],
- [ 5.5155e-01, -4.2249e-01, 1.7788e+00, -5.3426e-02, -2.0162e-01,
- 1.9292e-01, 1.5683e-01, 6.8210e-02],
- [ 6.0425e-01, -4.2731e-01, 1.7198e+00, 2.1845e-01, -3.4783e-01,
- 1.1492e-01, 8.0616e-01, 1.1755e-01],
- [-2.2859e+00, -2.2859e+00, 1.5767e+00, -7.5396e-01, -6.4042e-01,
- -7.3087e-01, 1.7534e-01, 8.9251e-02],
- [ 5.3603e-01, -4.6490e-01, 1.6517e+00, 4.6189e-04, -2.8245e-01,
- -6.8822e-02, 2.3086e-01, 2.0046e-01],
- [-2.2859e+00, -2.2859e+00, 1.5478e+00, -8.3095e-01, -6.2887e-01,
- -7.2317e-01, 1.1982e-01, 1.1330e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0122, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0122, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.246514733415097
- step: 81
- running loss: 0.015389070782902432
- Train Steps: 81/90 Loss: 0.0154 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6196, 0.4088, 0.8888, 0.4583, 0.4500, 0.5683, 0.6138, 0.5883],
- [0.6211, 0.3935, 0.8636, 0.4841, 0.4417, 0.5126, 0.6331, 0.5268],
- [0.6251, 0.4108, 0.8888, 0.4700, 0.4325, 0.5817, 0.6075, 0.5150],
- [0.6086, 0.3981, 0.8700, 0.4750, 0.4512, 0.5283, 0.5324, 0.5038],
- [0.6227, 0.4049, 0.8750, 0.5367, 0.3775, 0.3667, 0.5725, 0.5317],
- [0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5458, -0.3904, 1.5569, -0.4111, -0.6724, -0.8364, 0.0369, 0.1736],
- [ 0.5075, -0.4487, 1.7388, -0.2161, -0.2107, 0.4441, 0.5468, 0.3925],
- [ 0.4614, -0.5432, 1.6451, -0.0553, -0.2670, 0.1172, 0.6347, 0.1695],
- [ 0.6589, -0.3599, 1.7563, -0.1110, -0.3010, 0.5124, 0.5206, 0.1081],
- [ 0.6011, -0.4098, 1.6841, -0.1285, -0.2343, 0.1414, 0.1193, -0.0113],
- [ 0.6166, -0.4051, 1.6784, 0.2026, -0.5937, -0.5582, 0.3913, 0.1671],
- [-0.2581, -0.9259, 1.3314, -0.9020, -0.5419, -1.0859, 0.0955, 0.2768],
- [ 0.4362, -0.5123, 1.3979, -0.9372, -0.7266, -0.3351, 0.5126, 0.2498]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5470, -0.3952, 1.6377, -0.4253, -0.6289, -0.8079, 0.0249,
- 0.2116],
- [ 0.5759, -0.3978, 1.8192, -0.1689, -0.2074, 0.3392, 0.5490,
- 0.4316],
- [ 0.5828, -0.4682, 1.7031, -0.0497, -0.2458, 0.0818, 0.6381,
- 0.1474],
- [ 0.6014, -0.3883, 1.8192, -0.1150, -0.2882, 0.4008, 0.5201,
- 0.0928],
- [ 0.5249, -0.4473, 1.7326, -0.0919, -0.2016, 0.1544, 0.1733,
- 0.0412],
- [ 0.5901, -0.4157, 1.7557, 0.1929, -0.5423, -0.5923, 0.3584,
- 0.1698],
- [ 0.5695, -0.3927, 1.3226, -0.9002, -0.4672, -1.1928, 0.1737,
- 0.3700],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0178, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0178, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.264311611186713
- step: 82
- running loss: 0.015418434282764792
- Train Steps: 82/90 Loss: 0.0154 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6264, 0.4067, 0.9050, 0.4183, 0.3775, 0.4600, 0.6308, 0.4862],
- [0.6329, 0.4055, 0.9050, 0.4783, 0.3613, 0.3917, 0.6464, 0.5019],
- [0.6117, 0.4019, 0.8538, 0.4067, 0.3513, 0.3583, 0.5663, 0.5133],
- [0.6250, 0.4013, 0.8525, 0.5417, 0.4037, 0.5117, 0.6325, 0.5017],
- [0.6128, 0.4118, 0.8638, 0.5333, 0.4625, 0.5267, 0.5193, 0.5475],
- [0.6201, 0.3970, 0.8413, 0.4950, 0.4413, 0.5183, 0.6088, 0.5400],
- [0.6260, 0.4253, 0.8938, 0.4400, 0.3675, 0.3650, 0.5675, 0.5767],
- [0.6161, 0.4024, 0.8838, 0.4583, 0.3688, 0.3733, 0.5311, 0.5344]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5646, -0.4298, 1.7529, -0.4515, -0.6126, -0.0544, 0.5670, 0.0215],
- [ 0.6456, -0.4035, 1.7761, -0.2404, -0.5597, -0.4027, 0.7305, 0.0774],
- [ 0.5289, -0.4356, 1.5772, -0.5750, -0.6524, -0.5157, 0.1538, 0.1797],
- [ 0.3057, -0.6639, 1.6080, 0.1656, -0.4046, 0.2433, 0.5498, 0.0604],
- [ 0.4928, -0.4453, 1.6299, 0.1164, -0.1336, 0.3010, 0.0359, 0.2095],
- [ 0.4195, -0.5548, 1.5906, -0.0317, -0.2286, 0.2824, 0.4368, 0.2617],
- [ 0.5216, -0.3977, 1.7365, -0.2763, -0.5934, -0.4298, 0.2651, 0.4191],
- [ 0.5687, -0.4265, 1.7573, -0.2624, -0.5796, -0.3919, 0.1728, 0.1689]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 6.0722e-01, -4.0747e-01, 1.8942e+00, -3.5366e-01, -5.4226e-01,
- -1.6120e-01, 6.2772e-01, -3.9998e-02],
- [ 6.3718e-01, -4.1286e-01, 1.8942e+00, -7.6520e-02, -6.1732e-01,
- -4.7683e-01, 6.9989e-01, 3.2524e-02],
- [ 5.3926e-01, -4.2941e-01, 1.6575e+00, -4.0754e-01, -6.6351e-01,
- -6.3079e-01, 3.2956e-01, 8.5142e-02],
- [ 6.0095e-01, -4.3212e-01, 1.6517e+00, 2.1601e-01, -4.2102e-01,
- 7.7444e-02, 6.3557e-01, 3.1255e-02],
- [ 5.4475e-01, -3.8383e-01, 1.7037e+00, 1.7752e-01, -1.4965e-01,
- 1.4673e-01, 1.1283e-01, 2.4313e-01],
- [ 5.7812e-01, -4.5219e-01, 1.5998e+00, 4.6189e-04, -2.4781e-01,
- 1.0824e-01, 5.2587e-01, 2.0831e-01],
- [ 6.0577e-01, -3.2156e-01, 1.8423e+00, -2.5358e-01, -5.8845e-01,
- -6.0000e-01, 3.3533e-01, 3.7768e-01],
- [ 5.5978e-01, -4.2731e-01, 1.7961e+00, -1.6890e-01, -5.8268e-01,
- -5.6151e-01, 1.6711e-01, 1.8243e-01]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0094, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0094, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.2737219003029168
- step: 83
- running loss: 0.015346046991601407
- Train Steps: 83/90 Loss: 0.0153 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6275, 0.4013, 0.8850, 0.2833, 0.4975, 0.2233, 0.7058, 0.5343],
- [0.6085, 0.4005, 0.8400, 0.4317, 0.3763, 0.4750, 0.5476, 0.5058],
- [0.6115, 0.3998, 0.7063, 0.2383, 0.4037, 0.1950, 0.5320, 0.4993],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6090, 0.4045, 0.7250, 0.2100, 0.4075, 0.2300, 0.5476, 0.5663],
- [0.6218, 0.4185, 0.7338, 0.2650, 0.4625, 0.1950, 0.5687, 0.5800],
- [0.6026, 0.3979, 0.8550, 0.4233, 0.3613, 0.5233, 0.5582, 0.4967],
- [0.6058, 0.3986, 0.8324, 0.4626, 0.3838, 0.4983, 0.5147, 0.5466]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5970, -0.4386, 1.7881, -0.9258, 0.0162, -1.1350, 1.0138, 0.0702],
- [ 0.6098, -0.4173, 1.6972, -0.2384, -0.5802, 0.1849, 0.2896, 0.0095],
- [ 0.4448, -0.4789, 1.0836, -1.2496, -0.4350, -1.2242, 0.1473, -0.0186],
- [ 0.7261, -0.3231, 1.8236, 0.1307, -0.6321, 0.0100, 0.5721, 0.3982],
- [-0.4759, -1.1006, 1.1537, -1.1627, -0.4673, -0.9932, 0.2593, 0.3161],
- [ 0.4587, -0.4426, 1.1685, -1.0159, -0.1973, -1.2388, 0.2722, 0.3378],
- [ 0.6435, -0.4056, 1.7502, -0.2111, -0.6102, 0.3916, 0.4004, -0.0266],
- [ 0.5203, -0.4551, 1.6436, -0.0169, -0.4993, 0.3264, 0.1976, 0.1663]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6125, -0.4321, 1.8018, -0.9772, 0.0120, -1.2543, 0.9741,
- 0.1821],
- [ 0.5245, -0.4361, 1.5940, -0.2921, -0.5480, -0.0919, 0.2432,
- 0.0502],
- [ 0.5384, -0.4393, 0.9762, -1.1851, -0.4210, -1.3852, 0.1712,
- 0.0201],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.5269, -0.4176, 1.0628, -1.3159, -0.4037, -1.2236, 0.2432,
- 0.3297],
- [ 0.5862, -0.3530, 1.1032, -1.0619, -0.1497, -1.3852, 0.3411,
- 0.3931],
- [ 0.4974, -0.4482, 1.6633, -0.3306, -0.6173, 0.1313, 0.2925,
- 0.0081],
- [ 0.5124, -0.4446, 1.5587, -0.1493, -0.5134, 0.0159, 0.0912,
- 0.2386]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0350, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0350, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3087662127800286
- step: 84
- running loss: 0.015580550152143198
- Train Steps: 84/90 Loss: 0.0156 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6229, 0.4198, 0.7662, 0.2700, 0.4700, 0.2133, 0.5675, 0.5783],
- [0.6133, 0.4094, 0.8495, 0.4028, 0.3588, 0.3200, 0.5003, 0.5407],
- [0.6144, 0.4032, 0.8563, 0.3283, 0.3525, 0.4200, 0.5775, 0.5583],
- [0.6261, 0.4029, 0.8720, 0.3364, 0.3665, 0.3753, 0.6531, 0.5183],
- [0.6210, 0.4164, 0.7202, 0.2930, 0.4025, 0.2483, 0.5687, 0.5567],
- [0.6201, 0.4098, 0.8575, 0.3617, 0.3700, 0.3550, 0.5988, 0.5667],
- [0.6095, 0.4002, 0.8533, 0.5168, 0.5031, 0.5094, 0.5125, 0.5433],
- [0.6275, 0.4050, 0.9038, 0.3767, 0.3838, 0.3533, 0.7074, 0.5575]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 3.2658e-01, -5.4547e-01, 1.2428e+00, -1.0113e+00, -1.1611e-01,
- -1.1311e+00, 2.8831e-01, 3.0673e-01],
- [ 4.7822e-01, -4.6463e-01, 1.6447e+00, -3.4109e-01, -5.8065e-01,
- -7.2646e-01, 7.7841e-02, 1.1476e-01],
- [ 4.7345e-01, -4.9782e-01, 1.6934e+00, -7.2943e-01, -6.7776e-01,
- -2.3362e-01, 3.6246e-01, 1.4206e-01],
- [ 6.8065e-01, -4.0213e-01, 1.7472e+00, -7.7854e-01, -5.4823e-01,
- -4.4851e-01, 7.6350e-01, 1.3053e-03],
- [ 7.1257e-02, -7.2028e-01, 1.1654e+00, -9.3299e-01, -4.1678e-01,
- -8.6892e-01, 3.4502e-01, 2.8765e-01],
- [ 5.8301e-01, -4.2550e-01, 1.6716e+00, -5.7195e-01, -5.2895e-01,
- -4.7976e-01, 4.6565e-01, 2.3277e-01],
- [ 5.5077e-01, -4.6933e-01, 1.6813e+00, 2.6087e-01, 1.6187e-02,
- 3.4759e-01, 3.8679e-03, 1.3716e-01],
- [ 4.8412e-01, -5.3995e-01, 1.8609e+00, -4.5671e-01, -4.9464e-01,
- -4.9666e-01, 1.0809e+00, 2.0112e-01]], device='cuda:0',
- grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5912, -0.3467, 1.2533, -1.0388, -0.1150, -1.3005, 0.3353,
- 0.3854],
- [ 0.5470, -0.3952, 1.6377, -0.4253, -0.6289, -0.8079, 0.0249,
- 0.2116],
- [ 0.5520, -0.4237, 1.6691, -0.7694, -0.6577, -0.3460, 0.3815,
- 0.2930],
- [ 0.6059, -0.4249, 1.7420, -0.7321, -0.5931, -0.5523, 0.7307,
- 0.1080],
- [ 0.5824, -0.3625, 1.0405, -0.9325, -0.4268, -1.1389, 0.3411,
- 0.2853],
- [ 0.5783, -0.3933, 1.6748, -0.6154, -0.5769, -0.6462, 0.4797,
- 0.3315],
- [ 0.5295, -0.4373, 1.6553, 0.1011, 0.0380, 0.0671, 0.0813,
- 0.2237],
- [ 0.6125, -0.4153, 1.8885, -0.5461, -0.5134, -0.6539, 0.9814,
- 0.2890]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0155, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0155, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3242887216620147
- step: 85
- running loss: 0.015579867313670762
- Train Steps: 85/90 Loss: 0.0156 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6126, 0.4073, 0.8750, 0.5133, 0.3800, 0.4333, 0.4986, 0.5378],
- [0.6197, 0.4118, 0.8688, 0.5517, 0.4037, 0.5233, 0.5875, 0.5600],
- [0.6246, 0.4090, 0.6964, 0.2027, 0.3925, 0.2683, 0.6088, 0.5583],
- [0.6040, 0.4002, 0.7338, 0.2267, 0.3975, 0.2100, 0.5231, 0.4778],
- [0.6260, 0.4214, 0.8538, 0.5500, 0.3663, 0.3767, 0.5587, 0.5983],
- [0.6147, 0.4081, 0.8538, 0.3400, 0.3663, 0.3150, 0.5142, 0.4875],
- [0.6195, 0.4101, 0.8650, 0.3917, 0.3575, 0.4033, 0.5800, 0.5650],
- [0.6207, 0.4081, 0.7662, 0.2067, 0.3962, 0.3200, 0.6312, 0.5300]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.4749, -0.4938, 1.8200, 0.1421, -0.4529, -0.0682, 0.2061, 0.1641],
- [ 0.4511, -0.5150, 1.8038, 0.3654, -0.4010, 0.4584, 0.4593, 0.1613],
- [ 0.4468, -0.5258, 1.0679, -1.3597, -0.4119, -0.9884, 0.5265, 0.2220],
- [ 0.4100, -0.5387, 1.1669, -1.3780, -0.4079, -1.1665, 0.2400, -0.0464],
- [ 0.4981, -0.4564, 1.6810, 0.3457, -0.4863, -0.4005, 0.3559, 0.3938],
- [ 0.6217, -0.4062, 1.7781, -0.7077, -0.5463, -0.6803, 0.2540, -0.0090],
- [ 0.4976, -0.4992, 1.7516, -0.4075, -0.5858, -0.2284, 0.3990, 0.2871],
- [ 0.5423, -0.4508, 1.3129, -1.3409, -0.4253, -0.7361, 0.7393, 0.1741]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5435, -0.4045, 1.7557, 0.0851, -0.5307, -0.2844, 0.0172,
- 0.1980],
- [ 0.5762, -0.3840, 1.7268, 0.2622, -0.4210, 0.1313, 0.4277,
- 0.3007],
- [ 0.5992, -0.3968, 0.9307, -1.3497, -0.4730, -1.0465, 0.5259,
- 0.2930],
- [ 0.5037, -0.4375, 1.1032, -1.2390, -0.4499, -1.3159, 0.1301,
- -0.0791],
- [ 0.6055, -0.3393, 1.6575, 0.2545, -0.5942, -0.5461, 0.2949,
- 0.4778],
- [ 0.5532, -0.4008, 1.6575, -0.7155, -0.5942, -0.8309, 0.0890,
- -0.0340],
- [ 0.5757, -0.3917, 1.7095, -0.4768, -0.6346, -0.4229, 0.3931,
- 0.3238],
- [ 0.5809, -0.4011, 1.2533, -1.3313, -0.4557, -0.8079, 0.6298,
- 0.1621]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3348985011689365
- step: 86
- running loss: 0.015522075594987634
- Train Steps: 86/90 Loss: 0.0155 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[ nan, nan, 0.6793, 0.2110, 0.4012, 0.2167, 0.5112, 0.5583],
- [0.6250, 0.4131, 0.8688, 0.2983, 0.4275, 0.2367, 0.6162, 0.5367],
- [0.6169, 0.4108, 0.6821, 0.2722, 0.3825, 0.2550, 0.5550, 0.5717],
- [0.6059, 0.4002, 0.7562, 0.2767, 0.3538, 0.3033, 0.5529, 0.5455],
- [0.6271, 0.4020, 0.8375, 0.6083, 0.3925, 0.4867, 0.6037, 0.4626],
- [0.6228, 0.4119, 0.7938, 0.2233, 0.4674, 0.1773, 0.6188, 0.5433],
- [0.6102, 0.4001, 0.7738, 0.3583, 0.3463, 0.3800, 0.5524, 0.5689],
- [0.6199, 0.4102, 0.8950, 0.4417, 0.4012, 0.5367, 0.6112, 0.5967]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[-2.6211, -2.5212, 1.0486, -1.1651, -0.4450, -0.9817, 0.1317, 0.2193],
- [ 0.6544, -0.3648, 1.8017, -0.8586, -0.2291, -1.0695, 0.6752, 0.0898],
- [ 0.6067, -0.3832, 0.9838, -1.1507, -0.4227, -1.1178, 0.2797, 0.2733],
- [ 0.4072, -0.5191, 1.3219, -1.0060, -0.5335, -0.8429, 0.3747, 0.1266],
- [ 0.4585, -0.5462, 1.6707, 0.4150, -0.4383, 0.0116, 0.4843, -0.0506],
- [ 0.5655, -0.4500, 1.4585, -1.1970, -0.0922, -1.2743, 0.6101, 0.1232],
- [ 0.3720, -0.5204, 1.4266, -0.6348, -0.6016, -0.5184, 0.2348, 0.2190],
- [ 0.3983, -0.5229, 1.9028, -0.1224, -0.4020, 0.3903, 0.5228, 0.3090]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[-2.2859, -2.2859, 0.8516, -1.3112, -0.4326, -1.2851, 0.0755,
- 0.2930],
- [ 0.6011, -0.3778, 1.7268, -0.9079, -0.3113, -1.1928, 0.5605,
- 0.1929],
- [ 0.5633, -0.3883, 0.8648, -1.0288, -0.5192, -1.1081, 0.2776,
- 0.3546],
- [ 0.5129, -0.4374, 1.2072, -1.0080, -0.6520, -0.8848, 0.2679,
- 0.2335],
- [ 0.6105, -0.4293, 1.5824, 0.5239, -0.4730, -0.0380, 0.5025,
- -0.1492],
- [ 0.5908, -0.3832, 1.3804, -1.2543, -0.1270, -1.4671, 0.5721,
- 0.2237],
- [ 0.5327, -0.4381, 1.2880, -0.6308, -0.6866, -0.5307, 0.2658,
- 0.3417],
- [ 0.5772, -0.3913, 1.8480, -0.2459, -0.4326, 0.1929, 0.5374,
- 0.4701]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0132, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0132, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3481103568337858
- step: 87
- running loss: 0.015495521342917078
- Train Steps: 87/90 Loss: 0.0155 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6255, 0.4017, 0.8688, 0.3217, 0.3638, 0.3550, 0.6344, 0.4901],
- [0.6218, 0.4098, 0.7238, 0.1850, 0.4238, 0.2517, 0.6288, 0.5350],
- [0.6275, 0.4111, 0.8463, 0.2617, 0.4487, 0.1983, 0.6211, 0.4904],
- [0.6258, 0.4038, 0.8750, 0.4883, 0.3900, 0.5500, 0.6375, 0.5217],
- [0.6173, 0.4013, 0.8713, 0.4640, 0.4800, 0.5700, 0.6025, 0.5600],
- [0.6292, 0.4010, 0.8988, 0.4800, 0.3638, 0.3817, 0.6357, 0.5051],
- [0.6143, 0.4055, 0.8150, 0.2767, 0.3825, 0.2567, 0.5173, 0.4901],
- [0.6179, 0.3998, 0.8396, 0.3505, 0.3552, 0.3768, 0.6064, 0.5329]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.2906, -0.6409, 1.6966, -0.7035, -0.6151, -0.6604, 0.4858, 0.0571],
- [ 0.6713, -0.3423, 1.0141, -1.4186, -0.3000, -1.2024, 0.4727, 0.3262],
- [ 0.7239, -0.2935, 1.5762, -1.0051, -0.1290, -1.1708, 0.5538, 0.0640],
- [ 0.5389, -0.4618, 1.8069, 0.0371, -0.4642, 0.2881, 0.5613, 0.1595],
- [ 0.3526, -0.5561, 1.7288, -0.0225, -0.1932, 0.3346, 0.4374, 0.2839],
- [ 0.3348, -0.6103, 1.7632, -0.0713, -0.5542, -0.5833, 0.5219, 0.1729],
- [ 0.6104, -0.3794, 1.3945, -0.9225, -0.5040, -1.0243, 0.0740, 0.1107],
- [ 0.6039, -0.4215, 1.6051, -0.6760, -0.6397, -0.5594, 0.4211, 0.2860]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.6031, -0.4307, 1.7268, -0.8002, -0.6058, -0.6462, 0.6442,
- -0.0220],
- [ 0.5861, -0.3929, 1.0570, -1.4314, -0.3286, -1.1235, 0.6182,
- 0.1852],
- [ 0.6126, -0.3871, 1.6229, -1.0773, -0.2132, -1.3698, 0.5829,
- -0.0209],
- [ 0.6047, -0.4209, 1.7557, -0.0303, -0.4845, 0.2545, 0.6587,
- 0.1236],
- [ 0.5652, -0.4325, 1.7388, -0.1429, -0.0688, 0.3469, 0.4970,
- 0.3007],
- [ 0.6204, -0.4336, 1.8654, -0.0688, -0.6058, -0.5230, 0.6503,
- 0.0472],
- [ 0.5515, -0.4129, 1.4785, -1.0080, -0.5192, -1.1004, 0.1034,
- -0.0220],
- [ 0.5680, -0.4393, 1.5920, -0.6672, -0.6453, -0.5457, 0.5149,
- 0.1753]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0106, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3586613987572491
- step: 88
- running loss: 0.015439334076786921
- Train Steps: 88/90 Loss: 0.0154 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6182, 0.4099, 0.7812, 0.3000, 0.3937, 0.2367, 0.5325, 0.5750],
- [ nan, nan, 0.7515, 0.2708, 0.3987, 0.2267, 0.5162, 0.5567],
- [0.6078, 0.4033, 0.8019, 0.3055, 0.3450, 0.4200, 0.6025, 0.5550],
- [0.6168, 0.4081, 0.8438, 0.3367, 0.3500, 0.4667, 0.6212, 0.5400],
- [0.6239, 0.4174, 0.8425, 0.5733, 0.4825, 0.4500, 0.5625, 0.5933],
- [0.6346, 0.4092, 0.7712, 0.5917, 0.4037, 0.4767, 0.7343, 0.5725],
- [0.6186, 0.3967, 0.7337, 0.1992, 0.4120, 0.2508, 0.6105, 0.5395],
- [0.6179, 0.4118, 0.7278, 0.4237, 0.3588, 0.3400, 0.5675, 0.5917]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.7738, -0.2730, 1.4386, -1.0415, -0.3418, -1.2038, 0.2729, 0.1546],
- [-2.5829, -2.4818, 1.2752, -1.0553, -0.4747, -1.0027, 0.1306, 0.1917],
- [ 0.3546, -0.5509, 1.5533, -0.9022, -0.6610, -0.4159, 0.4727, 0.1213],
- [ 0.2965, -0.5853, 1.7268, -0.7251, -0.6103, -0.2529, 0.5206, 0.1225],
- [ 0.4197, -0.4712, 1.7078, 0.2913, -0.0776, -0.2989, 0.2674, 0.2726],
- [ 0.4849, -0.4927, 1.4642, 0.1457, -0.4687, -0.2381, 0.8454, 0.2116],
- [ 0.5784, -0.4329, 1.1811, -1.4583, -0.3245, -1.2026, 0.5778, 0.1081],
- [ 0.6367, -0.3579, 1.3764, -0.5841, -0.5169, -0.8698, 0.3198, 0.3117]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5695, -0.3927, 1.3226, -0.9002, -0.4672, -1.1928, 0.1737,
- 0.3700],
- [-2.2859, -2.2859, 1.1854, -1.0352, -0.4441, -1.2390, 0.0986,
- 0.2853],
- [ 0.5215, -0.4232, 1.4182, -0.8747, -0.6924, -0.3460, 0.4970,
- 0.2776],
- [ 0.5631, -0.4008, 1.6113, -0.7309, -0.6693, -0.1304, 0.5836,
- 0.2083],
- [ 0.5959, -0.3579, 1.6055, 0.3623, -0.0573, -0.2074, 0.3122,
- 0.4547],
- [ 0.6454, -0.3960, 1.2764, 0.4470, -0.4210, -0.0842, 1.1057,
- 0.3585],
- [ 0.5713, -0.4538, 1.1028, -1.3659, -0.3831, -1.1273, 0.5340,
- 0.2058],
- [ 0.5680, -0.3840, 1.0756, -0.3290, -0.6289, -0.7155, 0.3353,
- 0.4470]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0193, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0193, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3779842429794371
- step: 89
- running loss: 0.015482969022240866
- Train Steps: 89/90 Loss: 0.0155 torch.Size([8, 600, 800])
- torch.Size([8, 8])
- tensor([[0.6129, 0.3925, 0.8720, 0.5246, 0.4534, 0.5515, 0.6026, 0.5114],
- [ nan, nan, 0.7553, 0.2722, 0.3875, 0.2550, 0.5125, 0.5633],
- [0.6186, 0.4060, 0.8750, 0.5050, 0.3538, 0.4367, 0.5813, 0.6083],
- [0.6261, 0.3987, 0.9045, 0.4208, 0.3600, 0.4633, 0.6570, 0.5162],
- [0.6132, 0.4066, 0.7259, 0.2402, 0.3588, 0.3300, 0.6000, 0.5600],
- [0.6286, 0.3977, 0.9038, 0.4733, 0.3900, 0.4150, 0.7074, 0.5320],
- [0.6203, 0.4021, 0.8780, 0.5031, 0.3667, 0.3882, 0.5842, 0.5405],
- [0.6261, 0.4066, 0.8325, 0.2150, 0.4763, 0.2667, 0.7002, 0.5633]],
- device='cuda:0', dtype=torch.float64)
- predictions are: tensor([[ 0.5745, -0.4073, 1.7057, 0.1399, -0.1932, 0.2375, 0.3406, 0.0806],
- [-2.1859, -2.2379, 1.2122, -1.0418, -0.5679, -0.9760, 0.1158, 0.2787],
- [ 0.5295, -0.4313, 1.7288, 0.0070, -0.5881, -0.3866, 0.3565, 0.3972],
- [ 0.4754, -0.4928, 1.8224, -0.3816, -0.6022, -0.2334, 0.5597, 0.0926],
- [ 0.5929, -0.3855, 1.1233, -1.2087, -0.5744, -0.8558, 0.3877, 0.1927],
- [ 0.5226, -0.4764, 1.7320, -0.1790, -0.4929, -0.5515, 0.6222, 0.1111],
- [ 0.6722, -0.3593, 1.7096, -0.0041, -0.5225, -0.5976, 0.3466, 0.1197],
- [ 0.4967, -0.4938, 1.5374, -1.2878, -0.1328, -1.1846, 0.8814, 0.1625]],
- device='cuda:0', grad_fn=<AddmmBackward>)
- landmarks are: tensor([[[ 0.5450, -0.4730, 1.7420, 0.1372, -0.1919, 0.2614, 0.4976,
- 0.0764],
- [-2.2859, -2.2859, 1.2030, -1.0288, -0.4961, -1.1081, 0.0813,
- 0.3161],
- [ 0.5713, -0.4105, 1.7557, 0.0467, -0.6520, -0.2690, 0.3988,
- 0.5239],
- [ 0.6059, -0.4442, 1.8921, -0.3422, -0.6230, -0.1461, 0.7489,
- 0.0985],
- [ 0.5466, -0.4080, 1.0668, -1.1764, -0.6289, -0.7617, 0.4855,
- 0.3007],
- [ 0.6174, -0.4490, 1.8885, -0.0996, -0.4845, -0.3691, 0.9814,
- 0.1715],
- [ 0.5791, -0.4289, 1.7694, 0.0379, -0.5923, -0.4927, 0.4126,
- 0.2107],
- [ 0.6059, -0.4080, 1.5594, -1.2928, -0.0861, -1.0542, 0.9485,
- 0.3157]]], device='cuda:0')
- loss_train_step before backward: tensor(0.0084, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train_step after backward: tensor(0.0084, device='cuda:0', grad_fn=<MseLossBackward>)
- loss_train: 1.3864263747818768
- step: 90
- running loss: 0.015404737497576409
- Valid Steps: 10/10 Loss: nan 6.8743
- --------------------------------------------------
- Epoch: 10 Train Loss: 0.0154 Valid Loss: nan
- --------------------------------------------------
- Training Complete
- Total Elapsed Time : 463.1469588279724 s
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement