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Nataniel Ruiz
@@ -574,75 +574,76 @@ class Solver(object):
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layer_dict = {0: 2, 1: 5, 2: 8, 3: 9, 4: 10, 5: 11, 6: 12, 7: 13, 8: 14, 9: 17, 10: 20}
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for layer_num_orig in range(11):
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# Load the trained generator.
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self.restore_model(self.test_iters)
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# for layer_num_orig in range(11):
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# Load the trained generator.
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self.restore_model(self.test_iters)
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# Set data loader.
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if self.dataset == 'CelebA':
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data_loader = self.celeba_loader
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elif self.dataset == 'RaFD':
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data_loader = self.rafd_loader
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# Initialize Metrics
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l1_error = 0.0
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l2_error = 0.0
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perceptual_error = 0.0
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n_samples = 0
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# 11 layers
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# layer_num_orig = 1
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# print('Layer ', layer_num_orig)
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for i, (x_real, c_org) in enumerate(data_loader):
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# Black image
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black = np.zeros((1,3,256,256))
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black = torch.FloatTensor(black).to(self.device)
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# Prepare input images and target domain labels.
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x_real = x_real.to(self.device)
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c_trg_list = self.create_labels(c_org, self.c_dim, self.dataset, self.selected_attrs)
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# layer_num = layer_dict[layer_num_orig]
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layer_num = None
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pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device, feat=layer_num)
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# Translate images.
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x_fake_list = [x_real]
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# if i == 0:
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# x_adv, perturb = pgd_attack.perturb(x_real, x_real, c_trg_list[0])
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for c_trg in c_trg_list:
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# Attack
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x_adv, perturb = pgd_attack.perturb(x_real, black, c_trg)
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# x_adv = x_real + perturb
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# x_adv = self.blur_tensor(x_adv)
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# Metrics
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with torch.no_grad():
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# gen, preproc_x = self.G(x_adv, c_trg)
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gen, gen_feats = self.G(x_adv, c_trg)
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# Add to lists
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# x_fake_list.append(preproc_x)
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x_fake_list.append(gen)
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# No Attack
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# gen_noattack, _ = self.G(x_real, c_trg)
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gen_noattack, gen_noattack_feats = self.G(x_real, c_trg)
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l1_error += F.l1_loss(gen, gen_noattack)
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l2_error += F.mse_loss(gen, gen_noattack)
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n_samples += 1
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# Save the translated images.
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x_concat = torch.cat(x_fake_list, dim=3)
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result_path = os.path.join(self.result_dir, '{}-images.jpg'.format(i+1))
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save_image(self.denorm(x_concat.data.cpu()), result_path, nrow=1, padding=0)
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# print('Saved real and fake images into {}...'.format(result_path))
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# Set data loader.
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if self.dataset == 'CelebA':
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data_loader = self.celeba_loader
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elif self.dataset == 'RaFD':
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data_loader = self.rafd_loader
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# Initialize Metrics
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l1_error = 0.0
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l2_error = 0.0
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perceptual_error = 0.0
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n_samples = 0
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# 11 layers
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# layer_num_orig = 1
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print('Layer ', layer_num_orig)
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for i, (x_real, c_org) in enumerate(data_loader):
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# Black image
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black = np.zeros((1,3,256,256))
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black = torch.FloatTensor(black).to(self.device)
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# Prepare input images and target domain labels.
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x_real = x_real.to(self.device)
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c_trg_list = self.create_labels(c_org, self.c_dim, self.dataset, self.selected_attrs)
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layer_num = layer_dict[layer_num_orig]
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pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device, feat=layer_num)
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# Translate images.
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x_fake_list = [x_real]
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# if i == 0:
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# x_adv, perturb = pgd_attack.perturb(x_real, x_real, c_trg_list[0])
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for c_trg in c_trg_list:
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# Attack
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x_adv, perturb = pgd_attack.perturb(x_real, black, c_trg)
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# x_adv = x_real + perturb
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# x_adv = self.blur_tensor(x_adv)
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# Metrics
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with torch.no_grad():
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# gen, preproc_x = self.G(x_adv, c_trg)
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gen, gen_feats = self.G(x_adv, c_trg)
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# Add to lists
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# x_fake_list.append(preproc_x)
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x_fake_list.append(gen)
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# No Attack
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# gen_noattack, _ = self.G(x_real, c_trg)
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gen_noattack, gen_noattack_feats = self.G(x_real, c_trg)
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l1_error += F.l1_loss(gen, gen_noattack)
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l2_error += F.mse_loss(gen, gen_noattack)
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n_samples += 1
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# Save the translated images.
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x_concat = torch.cat(x_fake_list, dim=3)
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result_path = os.path.join(self.result_dir, '{}-images.jpg'.format(i+1))
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save_image(self.denorm(x_concat.data.cpu()), result_path, nrow=1, padding=0)
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# print('Saved real and fake images into {}...'.format(result_path))
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if i == 199:
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break
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if i == 199:
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break
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# Print metrics
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print('{} images. L1 error: {}. L2 error: {}. Perceptual error: {}.'.format(n_samples, l1_error / n_samples, l2_error / n_samples,
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