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  1. completion done in 3017.6958396434784s
  2. #FalledGAN/README.MD
  3.  
  4. FalledGAN (Future AI learning learn lair Dimooon GAN)
  5. Self-supervised image by phrase generation called FalledGAN.
  6.  
  7.  
  8. requirements:
  9. torch
  10. numpy
  11. ftfy
  12. regex
  13. tqdm
  14. torchvision
  15.  
  16. Usage:
  17. To train model really fast:
  18. python train.py --batch_size 1 --image_size 512 --save_every 100 --dir ./trained_model --epochs 2000
  19.  
  20.  
  21. Its create a dirrectory ./trained model wich constains model files.
  22.  
  23. Full usage of train.py:
  24. --batch-size=<size> Batch size [default: 1]
  25. --image_size=<size> Size if image in pixels [default:512]
  26. --save_every=<steps> Autosave model every n steps [default: 100]
  27. --dir=<dirrectory> Dirrectory were model was saving [default:./trained_model]
  28. --epochs=<epochs> Number of epochs to train [default:2000]
  29. --lr=<learning_rate> Learning Rate [default:1e-4]
  30. --optimizer=<optimizer> Oprimizer for the nn, recomended MADGRAD form facebook research [default:MADGRAD]
  31.  
  32. To sample image run:
  33. python main.py --generate --image_size 512 --batch_size 1 --dir ./trained_model --phrase "a big apple inside a wall"
  34.  
  35. Its was generate image in folder with file main.py
  36.  
  37. Full usage of main.py:
  38. --batch-size=<size> Batch size [default: 1]
  39. --image_size=<size> Size if image in pixels [default:512]
  40. --phrase=<phrase> FalledGAn will generate image by that phrase [default: "a big watch"]
  41. --dir=<dirrectory> Dirrectory were model was saving [default:./trained_model]
  42. --num_samples<num> Number of image samples [default:1]
  43.  
  44. #FalledGAN/train.py
  45.  
  46. import torch
  47. import torch.autograd as autograd
  48. import torch.nn as nn
  49. import torch.utils.data as data
  50. import torchvision.transforms as transforms
  51. import torch.nn.functional as F
  52. import json
  53. import cv2
  54. import os
  55. import time
  56. from img_utils import get_img_from_json
  57. import sys
  58.  
  59. from tqdm import tqdm
  60. from models import SegmentationModel
  61. from utils.torch_utils import tate
  62.  
  63. def main():
  64. start_time = time.time()
  65.  
  66. images_train, categories = get_training_data()
  67. train_data = tate.dataset(images_train, categories, batch_size=32)
  68. segments = get_classes(categories)
  69.  
  70. model = dein(train_data, segments, begin=1)
  71.  
  72. mean, std = size_normalization(images_train) # normalization
  73. image_size = 512
  74. input_images = train_data.transform("per_image", mean=True, std=std, log=True).format_img(
  75. input_images).unsqueeze_(0)
  76. input_image = torch.unsqueeze(input_images, 0) # (B, H, W) -> (B, H, 1, W)
  77.  
  78. optimizer = torch.optim.Adam(
  79. filter(lambda a: a.requires_grad, torch.parameters(model)),
  80. lr=0.01,
  81. memory=False
  82. )
  83.  
  84. num_samples = 1
  85.  
  86. loss = autograd.Function([image_size, image_size], 0.0) # B
  87. loss_m = autograd.Function([image_size // 2, image_size // 2], 0.0) # B
  88.  
  89. image_str = " ".join(get_img_from_json(torch.randn(image_size // 2, self.B * 3))).tolist()
  90.  
  91. # print(image_str)
  92. for epoch in range(251):
  93. train = torch.ByteTensor(list(tqdm(train_data, desc="EPOCH {}, generate_seq, generate_img, save".format(epoch + 1)))) # B, C #N, H, W, 1
  94. optimizer.zero_grad()
  95. for i, (edges, edge_name_n_bottom) in enumerate(tqdm(train, desc="")):
  96. if num_samples!= 0:
  97. v = torch.randn(num_samples, self.batch_size) # T # 8 # C=8-> (T, C, 8)
  98. max_output = torch.randn_like(min_input_features) # T # 1 # 8
  99. max_output_0 = torch.randn(num_samples, self.batch_size) # T # 1 # 8
  100. pc = 0
  101. else:
  102. v = torch.zeros(num_samples) # B # T # 8
  103. max_output = torch.zeros(num_samples) # B # T # 8
  104. max_output_0 = torch.zeros(num_samples) # B # T # 8
  105.  
  106. # model.train()
  107. image_str = get_resegmented_image(input_image, v, max_output_0, edges, edge_name_n_bottom) # T # C, 1
  108. output = model(v, max_output_0) # T # C // 2
  109.  
  110. # image_str = torch.tensor(input_image, dtype=torch.IntTensor, device='cuda') # C // 2 # C
  111.  
  112. loss_m.backward(input_image, output) # T # I=1 # C, C // 2
  113. loss_m.backward(v, output) # T # I=1 # C, C // 2
  114.  
  115. loss.backward() # B # I=1 # C, C // 2
  116.  
  117. output.copy_(max_output_0.max(2).float()) # B # T # 8
  118.  
  119. targ_b = torch.miss(max_output_0) # B # T # 8
  120. targ_m = torch.miss(max_output) # T # C, 1: C
  121.  
  122. avg = torch.mean(targ_m, -1, keepdim=True) # T
  123.  
  124. loss.backward()
  125. optimize_op = autograd.Variable([max_output_0, avg])
  126. optimize_val0, optimize_op0 = autograd.Variable([input_image, avg]), autograd.Variable([max_output_0, avg])
  127. optimize_b = autograd.Variable([input_image, avg])
  128.  
  129. optimize_op.data.copy_(optimize_val0.add(optimal=True).div_(optimal=True))
  130. optimize_op.data.copy_(optimize_op0.add(optimal=True).div_(optimal=True))
  131. predict_op = optimize_op.cuda
  132. predict_val = predict_op.data.copy_(input)
  133.  
  134. predict_val_0, predictor_val0 = predictor_op, predictor_val
  135. minimize_op = minimize_val_(data)
  136. minimize_nb = torch.data.to_function(data).size
  137. std = 2 / self.autominute translate
  138. c_autominute_op = tate('\'')
  139. c_autominute_autocom = tate(')')
  140. autodoc_text = tate('")''')
  141. image_path = torchvision.transforms
  142. autograd. training begin
  143. end_time = time.time()
  144. start_time = time.time()
  145.  
  146. if otherwise_data_path:os.stat
  147. time = torch.time as time
  148.  
  149. system = system
  150. if verbose_or_error_path:
  151. local_path = os.path.expand(torch.true), local_path # os.path -> file
  152. local_path = os.path.expand(local_path + ":\\expand:\\win:\\expand:\\ctrwin#7##-#16>#%cmp16/cmp16", "slowness/slowness", "road", "speed", "my add real")
  153. if weiwin_path == 'w' and not changing_path =='w', '$w1', '$.$', '$o1', '$o2', '$o3', '$o4', '$d1', '$d1', '$d1', '$d1', '$d2', '$d2', '$d2', '$d2', '$d2', '%d3', '%s', '%s', '%s', '%s', '%s' ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~ ~ ~
  154. f = tate.IDDynamicModel.train_ID(data=update_seg_data() / 4, visual_id=torch.rand(1, 5, size=image_size) * 2) * (image_size // 2)
  155. f.id_input=input_image
  156. f.id_output=output.squeeze(2)
  157.  
  158. loss.d_weight = 0.4
  159. loss.d_weight = 0.5
  160. else:
  161.  
  162. image_str = torch.nn.utils.normalized_input
  163. image_str = get_resegmented_images(
  164. self.B // 2 * self.B, #A
  165. v,
  166. torch.randn(num_samples, self.B) * 3 # B [?] [ N, H, W, 1 ] [H, T, I // 2] [I // 2] #B [H, T, I // 2] [I // 2] #B [H, T, I // 2] [I // 4] #H [C] #H) [I] // H #T # 8 1. (tuples=8, name_n_bottom=1, edge_name_bottom=1) = train visualize edges shape ground_truth_0, ground_adv_b, ground_adv_m // 8 for bottom resegmented only / 8 m
  167. ground_adv_m.add_(loop=True)
  168. ground_adv_m.add_(loop=True)
  169.  
  170. if autograd:
  171. self.loss_m, self.vectors_m, self.generate_out_m
  172. self.loss_m.mutable_batch(v)
  173. v.copy_from_(input)
  174. else:
  175. scale_b = torch.miss(max_output_0 / torch.miss(avg)) # B # T # 8
  176. target_b = torch.miss(max_output_0) # B
  177. loss_m.backward(v)
  178. optimize_op = autograd.Variable([max_output_0, avg])
  179. q2 = model(v)[1 // C]
  180. if q2.var[1] == torch.IntTensor:
  181. avg = torch.mean(q2, -1, keepdim=True) # B # T # I
  182. optimize_op0.data.copy_(optimize_val0.div_(optimprice=falseoptimal=True))
  183. optimize_b.smote(optimal=True, label=avg, optimal=False).data.zero_
  184. f1, f2, q_e = autograd.Variable([input_image, avg]),optimize_op0,autograd.Variable([max_output_0, avg])
  185. optimize_op0.data.abs_(t:=b,res=be)*res
  186. optimizing_op->optimize(val0, optimize_b)
  187. @optimize_op
  188. @optimize_op2
  189. print(opr.data)+a/a+res
  190. print(b/a)
  191. res
  192. label = res
  193.  
  194. data = optimize_op0.div_(optimal=True)
  195. array_ = copy(d, res)
  196. regular
  197. optimize_op.
  198. m
  199. encapsulate(v)
  200. add(v, res)
  201. res
  202. optimizer.
  203. m
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