First commit.
This commit is contained in:
Nataniel Ruiz Gutierrez
+46
-90
@@ -384,66 +384,51 @@ class Solver(Utils):
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reference_expression_images[target_idx]))
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if mode == 'animate_image':
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black = np.zeros((1,3,128,128))
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black = torch.FloatTensor(black).to(self.device)
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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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min_dist = 0.0
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l0_error = 0.0
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perceptual_error = 0.0
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n_samples = 0
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l1_error, l2_error, min_dist, l0_error = 0.0, 0.0, 0.0, 0.0
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n_dist, n_samples = 0, 0
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pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device)
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images_to_animate_path = sorted(glob.glob(
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self.animation_images_dir + '/*'))
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x_advs = []
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for idx, image_path in enumerate(images_to_animate_path):
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image_to_animate = regular_image_transform(Image.open(image_path)).unsqueeze(0).cuda()
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all_images = torch.cat([regular_image_transform(Image.open(path)).unsqueeze(0) for path in images_to_animate_path], dim=0).cuda()
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for target_idx in range(targets.size(0)-1):
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print('image', idx, 'AU', target_idx)
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# Transfer to different images
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# if idx == 0:
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# for target_idx in range(targets.size(0)):
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# x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets[target_idx, :].unsqueeze(0).cuda())
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# x_advs.append((x_adv, perturb))
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for target_idx in range(targets.size(0)):
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# Transfer to different classes
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# if target_idx == 0:
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# img = regular_image_transform(Image.open(images_to_animate_path[idx])).unsqueeze(0).cuda()
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# Wrong Class
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# x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets[0, :].unsqueeze(0).cuda())
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# Joint Class Conditional
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# x_adv, perturb = pgd_attack.perturb_joint_class(image_to_animate, black, targets[:, :].cuda())
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# Iterative Class Conditional
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# x_adv, perturb = pgd_attack.perturb_iter_class(image_to_animate, black, targets[:, :].cuda())
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# Iterative Data
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# _, perturb = pgd_attack.perturb_iter_data(image_to_animate, all_images, black, targets[68, :].unsqueeze(0).cuda())
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targets_au = targets[target_idx, :].unsqueeze(0).cuda()
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with torch.no_grad():
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resulting_images_att_noattack, resulting_images_reg_noattack = self.G(
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image_to_animate, targets_au)
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resulting_image_noattack = self.imFromAttReg(
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resulting_images_att_noattack, resulting_images_reg_noattack, image_to_animate).cuda()
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# Transfer to different classes
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# if target_idx == 0:
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# Wrong Class
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# x_adv, perturb = pgd_attack.perturb(image_to_animate, image_to_animate, targets[0, :].unsqueeze(0).cuda())
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# Joint Class Conditional
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# x_adv, perturb = pgd_attack.perturb_joint_class(image_to_animate, image_to_animate, targets[:, :].cuda())
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# Iterative Class Conditional
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# x_adv, perturb = pgd_attack.perturb_iter_class(image_to_animate, image_to_animate, targets[:, :].cuda())
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# Iterative Data
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# _, perturb = pgd_attack.perturb_iter_data(image_to_animate, all_images, image_to_animate, targets[68, :].unsqueeze(0).cuda())
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# Normal Attack
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# x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets_au)
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x_adv, perturb = pgd_attack.perturb(image_to_animate, resulting_image_noattack, targets_au)
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# x_adv, perturb = x_advs[target_idx]
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# x_adv = image_to_animate + perturb
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# Use this line if transferring attacks
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x_adv = image_to_animate + perturb
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# No Attack
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x_adv = image_to_animate
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# print(image_to_animate.shape, x_adv.shape)
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# x_adv = image_to_animate
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with torch.no_grad():
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resulting_images_att, resulting_images_reg = self.G(
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@@ -451,12 +436,6 @@ class Solver(Utils):
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resulting_image = self.imFromAttReg(
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resulting_images_att, resulting_images_reg, x_adv).cuda()
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# with torch.no_grad():
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# resulting_images_att_noattack, resulting_images_reg_noattack = self.G(
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# image_to_animate, targets_au)
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# resulting_image_noattack = self.imFromAttReg(
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# resulting_images_att_noattack, resulting_images_reg_noattack, image_to_animate).cuda()
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save_image((resulting_image+1)/2, os.path.join(self.animation_results_dir,
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image_path.split('/')[-1].split('.')[0]
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+ '_' + reference_expression_images[target_idx]))
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@@ -465,50 +444,27 @@ class Solver(Utils):
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image_path.split('/')[-1].split('.')[0]
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+ '_ref.jpg'))
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# l1_error += F.l1_loss(resulting_image, resulting_image_noattack)
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# l2_error += F.mse_loss(resulting_image, resulting_image_noattack)
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# l0_error += (resulting_image - resulting_image_noattack).norm(0)
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# min_dist += (resulting_image - resulting_image_noattack).norm(float('-inf'))
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# Compare to ground-truth output
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l1_error += F.l1_loss(resulting_image, resulting_image_noattack)
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l2_error += F.mse_loss(resulting_image, resulting_image_noattack)
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l0_error += (resulting_image - resulting_image_noattack).norm(0)
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min_dist += (resulting_image - resulting_image_noattack).norm(float('-inf'))
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# Compare to input image
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l1_error += F.l1_loss(resulting_image, image_to_animate)
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l2_error += F.mse_loss(resulting_image, image_to_animate)
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l0_error += (resulting_image - image_to_animate).norm(0)
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min_dist += (resulting_image - image_to_animate).norm(float('-inf'))
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# l1_error += F.l1_loss(resulting_image, x_adv)
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# l2_error += F.mse_loss(resulting_image, x_adv)
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# l0_error += (resulting_image - x_adv).norm(0)
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# min_dist += (resulting_image - x_adv).norm(float('-inf'))
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if F.mse_loss(resulting_image, resulting_image_noattack) > 0.05:
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n_dist += 1
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n_samples += 1
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# Debug
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# x_adv, targets_au, resulting_image, resulting_images_att, resulting_images_reg = None, None, None, None, None
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image_to_animate = None
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# Print metrics
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print('{} images. L1 error: {}. L2 error: {}. L0 error: {}. L_-inf error: {}. Perceptual error: {}.'.format(n_samples,
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l1_error / n_samples, l2_error / n_samples, l0_error / n_samples, min_dist / n_samples, perceptual_error / n_samples))
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# """ Code to modify single Action Units """
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# Set data loader.
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# self.data_loader = self.data_loader
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# with torch.no_grad():
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# for i, (self.x_real, c_org) in enumerate(self.data_loader):
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# # Prepare input images and target domain labels.
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# self.x_real = self.x_real.to(self.device)
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# c_org = c_org.to(self.device)
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# # c_trg_list = self.create_labels(self.data_loader)
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# crit, cl_regression = self.D(self.x_real)
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# # print(crit)
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# print("ORIGINAL", c_org[0])
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# print("REGRESSION", cl_regression[0])
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# for au in range(17):
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# alpha = np.linspace(-0.3,0.3,10)
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# for j, a in enumerate(alpha):
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# new_emotion = c_org.clone()
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# new_emotion[:,au]=torch.clamp(new_emotion[:,au]+a, 0, 1)
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# attention, reg = self.G(self.x_real, new_emotion)
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# x_fake = self.imFromAttReg(attention, reg, self.x_real)
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# save_image((x_fake+1)/2, os.path.join(self.result_dir, '{}-{}-{}-images.jpg'.format(i,au,j)))
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# if i >= 3:
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# break
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print('{} images. L1 error: {}. L2 error: {}. prop_dist: {}. L0 error: {}. L_-inf error: {}.'.format(n_samples,
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l1_error / n_samples, l2_error / n_samples, float(n_dist) / float(n_samples), l0_error / n_samples, min_dist / n_samples))
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