start_time = time.time()

with torch.no_grad():

    best_network = Network()
    best_network.cuda()
    best_network.load_state_dict(torch.load('../moth_landmarks.pth'))
    best_network.eval()

    batch = next(iter(train_loader))
    images, landmarks = batch['image'], batch['landmarks']
    landmarks = landmarks.view(landmarks.size(0),-1).cuda()


    for i in range(8):
        if(i%2==0):
            landmarks[:,i] = landmarks[:,i]/800
        else:
            landmarks[:,i] = landmarks[:,i]/600
    landmarks [landmarks != landmarks] = 0
    #landmarks = landmarks.unsqueeze_(0)

    images = images.cuda()

    norm_image = transforms.Normalize(0.3812, 0.1123)
    print('images shape: ', images.shape)
    for image in images:
        image = image.unsqueeze_(1)

        #images = torch.cat((images,images,images),1)
        image = image.float()
        ##image = to_tensor(image) #TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
        image = norm_image(image)
    landmarks = (landmarks + 0.5) * 224 #??

    ##[8, 600, 800] --> [8,3,600,800]
    images = images.unsqueeze(1)
    images = torch.cat((images, images, images), 1)

    predictions = (best_network(images).cpu() + 0.5) * 224
    predictions = predictions.view(-1,4,2)

    plt.figure(figsize=(10,40))
    landmarks = landmarks.cpu()
    print(type(landmarks), landmarks.shape)
    for img_num in range(8):
        plt.subplot(8,1,img_num+1)
        plt.imshow(images[img_num].cpu().numpy().transpose(1,2,0).squeeze(), cmap='gray')
        plt.scatter(predictions[img_num,:,0], predictions[img_num,:,1], c = 'r')
        plt.scatter(landmarks[img_num,:,0], landmarks[img_num,:,1], c = 'g')

print('Total number of test images: {}'.format(len(test_dataset)))

end_time = time.time()
print("Elapsed Time : {}".format(end_time - start_time))