Cleaned code
Esse commit está contido em:
alexbrx
+1
-7
@@ -109,12 +109,6 @@ class AffectNet(data.Dataset):
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split = line.split()
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filename, v, a = split
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self.test_dataset.append([filename, [float(v), float(a)]])
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elif self.affectnet_emo_descr in ['va-reg', 'va-cls']:
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for mode, dataset in zip(['train_', 'test_'], [self.train_dataset, self.test_dataset]):
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filenames = [line.rstrip() for line in open(os.path.join(self.image_dir, mode + 'images.txt'), 'r')]
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labels = [[int(line.rstrip())] for line in open(os.path.join(self.image_dir, mode + 'labels.txt'), 'r')]
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predictions = [[float(x) for x in line.rstrip().split()] for line in open(os.path.join(self.image_dir, mode + 'predictions.txt'), 'r')]
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dataset += list(zip(filenames, labels, predictions))
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elif self.affectnet_emo_descr == '64d_cls':
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for mode, dataset in zip(['train', 'test'], [self.train_dataset, self.test_dataset]):
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filenames = [line.rstrip() for line in open(os.path.join(self.image_dir, self.affectnet_emo_descr, mode, 'images.txt'), 'r')]
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@@ -142,7 +136,7 @@ class AffectNet(data.Dataset):
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filename, label = dataset[index]
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image = Image.open(os.path.join(self.image_dir, 'validation', filename))
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return self.transform(image), torch.tensor(label)
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elif self.affectnet_emo_descr in ['va-reg', 'va-cls', '64d_reg', '64d_cls']:
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elif self.affectnet_emo_descr in ['64d_reg', '64d_cls']:
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if self.mode == 'train':
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dataset = self.train_dataset
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filename, label, prediction = dataset[index]
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+4
-4
@@ -86,16 +86,16 @@ if __name__ == '__main__':
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parser.add_argument('--selected_attrs', '--list', nargs='+', help='selected attributes for the CelebA dataset',
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default=['Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Male', 'Young'])
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#
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parser.add_argument('--affectnet_emo_descr', type=str, default='emotiw', help='emotiw for categorical emotions, va for valence-arousal')
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parser.add_argument('--affectnet_emo_descr', type=str, default='emotiw', choices=['emotiw', 'va', '64d_reg', '64d_cls'], help='emotiw for categorical emotions, va for valence-arousal')
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parser.add_argument('--use_ccc', type=bool, default=False, help='use ccc loss instead of mse')
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parser.add_argument('--depth_concat', type=bool, default=True, help='perform depthwise concatenation in G')
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parser.add_argument('--d_loss_cls_type', type=str, default='actv', help='chosen terms for classification loss')
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parser.add_argument('--d_loss_cls_type', type=str, default='actv', choices=['actv', 'pred', 'both'], help='chosen terms for classification loss')
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# Test configuration.
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parser.add_argument('--test_iters', type=int, default=200000, help='test model from this step')
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#
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parser.add_argument('--pca_n_components', type=int, default=0.99, help='PCA visualization number of components kept')
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parser.add_argument('--pca_variant', type=str, default='quantiles', help='PCA visualization variant')
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parser.add_argument('--pca_n_components', type=int, default=3, help='PCA visualization number of components kept')
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parser.add_argument('--pca_variant', type=str, default='quantiles', choices=['quantiles', 'projections'], help='PCA visualization variant')
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# Miscellaneous.
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parser.add_argument('--num_workers', type=int, default=1)
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parser.add_argument('--mode', type=str, default='train', choices=['train', 'test'])
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+9
-73
@@ -23,8 +23,7 @@ class LabelTransform(object):
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if self.affectnet_emo_descr in ['64d_reg', '64d_cls']:
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actvs_train = np.array([[float(x) for x in line.rstrip().split()] for line in open(os.path.join(self.image_dir, self.affectnet_emo_descr, 'train', 'predictions.txt'), 'r')])
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elif self.affectnet_emo_descr in ['va-reg', 'va-cls']:
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actvs_train = np.array([[float(x) for x in line.rstrip().split()] for line in open(os.path.join(self.image_dir,'train_predictions.txt'), 'r')])
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actvs_latent_train = self.pca.fit_transform(self.ss.fit_transform(actvs_train))
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self.stats = pd.DataFrame(actvs_latent_train).describe(percentiles=[0.1, 0.5, 0.9]).T.drop(columns=['count']) #components in rows
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@@ -35,13 +34,6 @@ class LabelTransform(object):
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if self.pca_variant == 'quantiles':
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for i in range(self.pca.n_components_):
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# transformed_labels_lo, transformed_labels_hi = c_org_latent, c_org_latent
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# transformed_labels_lo[:,i] = self.stats.loc[i,'10%']
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# c_trg_list.append(torch.FloatTensor(self.ss.inverse_transform(self.pca.inverse_transform(transformed_labels_lo))).to(self.device))
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# transformed_labels_lo[:,i] = self.stats.loc[i,'50%']
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# c_trg_list.append(torch.FloatTensor(self.ss.inverse_transform(self.pca.inverse_transform(transformed_labels_lo))).to(self.device))
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# transformed_labels_hi[:,i] = self.stats.loc[i,'90%']
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# c_trg_list.append(torch.FloatTensor(self.ss.inverse_transform(self.pca.inverse_transform(transformed_labels_hi))).to(self.device))
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transformed_labels = c_org_latent
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transformed_labels[:,i] = self.stats.loc[i,'10%']
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c_trg_list.append(torch.FloatTensor(self.ss.inverse_transform(self.pca.inverse_transform(transformed_labels))).to(self.device))
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@@ -52,23 +44,6 @@ class LabelTransform(object):
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transformed_labels[:,i] = self.stats.loc[i,'90%']
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c_trg_list.append(torch.FloatTensor(self.ss.inverse_transform(self.pca.inverse_transform(transformed_labels))).to(self.device))
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# transformed_labels = c_org_latent
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# transformed_labels[:,0] = self.stats.loc[i,'10%']
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# transformed_labels[:,1] = self.stats.loc[i,'10%']
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# c_trg_list.append(torch.FloatTensor(self.ss.inverse_transform(self.pca.inverse_transform(transformed_labels))).to(self.device))
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# transformed_labels = c_org_latent
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# transformed_labels[:,0] = self.stats.loc[i,'90%']
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# transformed_labels[:,1] = self.stats.loc[i,'90%']
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# c_trg_list.append(torch.FloatTensor(self.ss.inverse_transform(self.pca.inverse_transform(transformed_labels))).to(self.device))
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# transformed_labels = c_org_latent
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# transformed_labels[:,0] = self.stats.loc[i,'10%']
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# transformed_labels[:,1] = self.stats.loc[i,'90%']
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# c_trg_list.append(torch.FloatTensor(self.ss.inverse_transform(self.pca.inverse_transform(transformed_labels))).to(self.device))
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# transformed_labels = c_org_latent
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# transformed_labels[:,0] = self.stats.loc[i,'90%']
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# transformed_labels[:,1] = self.stats.loc[i,'10%']
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# c_trg_list.append(torch.FloatTensor(self.ss.inverse_transform(self.pca.inverse_transform(transformed_labels))).to(self.device))
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else:
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for i in range(1, self.pca.n_components_):
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transformed_labels = c_org_latent
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@@ -122,7 +97,7 @@ class Solver(object):
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# Miscellaneous.
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self.use_tensorboard = config.use_tensorboard
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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if self.affectnet_emo_descr in ['64d_reg', '64d_cls', 'va-reg', 'va-cls']:
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if self.affectnet_emo_descr in ['64d_reg', '64d_cls']:
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self.label_transform = LabelTransform(config)
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# Directories.
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@@ -148,15 +123,6 @@ class Solver(object):
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self.G = Generator(self.g_conv_dim, self.c_dim, self.g_repeat_num, self.depth_concat)
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self.D = Discriminator(self.image_size, self.d_conv_dim, self.c_dim, self.d_repeat_num, self.affectnet_emo_descr, self.d_loss_cls_type)
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# if self.affectnet_emo_descr == '64d_cls' and (self.d_loss_cls_type in ['both', 'pred']):
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# self.D.fc = torch.nn.Linear(self.c_dim, 7)
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# elif self.affectnet_emo_descr == '64d_reg' and (self.d_loss_cls_type in ['both', 'pred']):
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# self.D.fc = torch.nn.Linear(self.c_dim, 2)
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# elif self.dataset in ['Both']:
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# self.G = Generator(self.g_conv_dim, self.c_dim+self.c2_dim+2, self.g_repeat_num) # 2 for mask vector.
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# self.D = Discriminator(self.image_size, self.d_conv_dim, self.c_dim+self.c2_dim, self.d_repeat_num)
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self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr, [self.beta1, self.beta2])
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self.d_optimizer = torch.optim.Adam(self.D.parameters(), self.d_lr, [self.beta1, self.beta2])
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self.print_network(self.G, 'G')
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@@ -262,7 +228,7 @@ class Solver(object):
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for a in [-1., -0.5, 0., 0.5, 1.]:
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c_trg = torch.Tensor([v, a]).repeat(c_org.size(0), 1)
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c_trg_list.append(c_trg.to(self.device))
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elif self.affectnet_emo_descr in ['va-reg', 'va-cls', '64d_cls']:
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elif self.affectnet_emo_descr in ['64d_cls']:
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# c_trg_list = np.loadtxt('/vol/bitbucket/apg416/affectnet/vgg2pcs.txt')
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# c_trg_list = torch.FloatTensor(c_trg_list).to(self.device)
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# c_trg_list = [label_vec.repeat(c_org.size(0), 1) for label_vec in torch.unbind(c_trg_list)]
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@@ -288,9 +254,9 @@ class Solver(object):
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elif dataset == 'RaFD':
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return F.cross_entropy(logit, target)
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elif dataset == 'AffectNet':
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if self.affectnet_emo_descr in ['emotiw', 'va-cls']:
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if self.affectnet_emo_descr in ['emotiw']:
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return F.cross_entropy(logit, target)
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elif self.affectnet_emo_descr in ['va', 'va-reg']:
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elif self.affectnet_emo_descr in ['va']:
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if self.use_ccc:
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return self.ccc_loss(logit, target)
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else:
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@@ -349,18 +315,6 @@ class Solver(object):
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start_iters = self.resume_iters
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self.restore_model(self.resume_iters)
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# # --- converting
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# if self.affectnet_emo_descr == '64d_cls' and (self.d_loss_cls_type in ['both', 'pred']):
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# self.D.fc = torch.nn.Linear(self.c_dim, 7)
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# elif self.affectnet_emo_descr == '64d_reg' and (self.d_loss_cls_type in ['both', 'pred']):
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# self.D.fc = torch.nn.Linear(self.c_dim, 2)
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# G_path = os.path.join(self.model_save_dir, '{}-G.ckpt'.format(self.resume_iters))
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# D_path = os.path.join(self.model_save_dir, '{}-D.ckpt'.format(self.resume_iters))
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# torch.save(self.G.state_dict(), G_path)
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# torch.save(self.D.state_dict(), D_path)
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# return 0
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# # ---
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# Start training.
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print('Start training...')
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start_time = time.time()
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@@ -402,18 +356,6 @@ class Solver(object):
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c_trg = label_trg.clone().to(self.device)
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label_org = label_org.to(self.device) # Labels for computing classification loss.
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label_trg = label_trg.to(self.device) # Labels for computing classification loss.
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elif self.affectnet_emo_descr == 'va-reg':
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label_org = label_org[:,1:].to(self.device)
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label_trg = label_trg[:,1:].to(self.device)
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c_org = label_org.clone().to(self.device)
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c_trg = label_trg.clone().to(self.device)
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elif self.affectnet_emo_descr == 'va-cls':
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pred_org = label_org[:,0].clone().long().to(self.device)
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pred_trg = label_trg[:,0].clone().long().to(self.device)
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label_org = label_org[:,1:].to(self.device)
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label_trg = label_trg[:,1:].to(self.device)
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c_org = label_org.clone().to(self.device)
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c_trg = label_trg.clone().to(self.device)
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elif self.affectnet_emo_descr == '64d_cls':
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pred_org = label_org[:,0].clone().long().to(self.device)
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pred_trg = label_trg[:,0].clone().long().to(self.device)
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@@ -440,9 +382,8 @@ class Solver(object):
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# Compute loss with real images.
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out_src, out_cls = self.D(x_real)
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d_loss_real = - torch.mean(out_src)
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if self.affectnet_emo_descr == 'va-cls':
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d_loss_cls = self.classification_loss(out_cls, pred_org, self.dataset)
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elif self.affectnet_emo_descr in ['64d_reg', '64d_cls']:
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if self.affectnet_emo_descr in ['64d_reg', '64d_cls']:
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if self.d_loss_cls_type == 'actv':
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d_loss_cls = self.classification_loss((None, out_cls), (None, actv_org), self.dataset)
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elif self.d_loss_cls_type == 'pred':
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@@ -485,13 +426,8 @@ class Solver(object):
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x_fake = self.G(x_real, c_trg)
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out_src, out_cls = self.D(x_fake)
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g_loss_fake = - torch.mean(out_src)
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# if self.affectnet_emo_descr == 'va-cls':
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# g_loss_cls = self.classification_loss(out_cls, pred_trg, self.dataset)
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# else:
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# g_loss_cls = self.classification_loss(out_cls, label_trg, self.dataset)
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if self.affectnet_emo_descr == 'va-cls':
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g_loss_cls = self.classification_loss(out_cls, pred_trg, self.dataset)
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elif self.affectnet_emo_descr in ['64d_reg', '64d_cls']:
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if self.affectnet_emo_descr in ['64d_reg', '64d_cls']:
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if self.d_loss_cls_type == 'actv':
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g_loss_cls = self.classification_loss((None, out_cls), (None, actv_trg), self.dataset)
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elif self.d_loss_cls_type == 'pred':
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