CalvinBackup/disrupting-deepfakes
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This commit is contained in:
Nataniel Ruiz
2019-12-26 11:55:52 -04:00
parent 130e63f375
commit 3859120822
1 changed files with 30 additions and 7 deletions
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ganimation/solver.py
+30 -7
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@@ -388,6 +388,14 @@ class Solver(Utils):
black = np.zeros((1,3,128,128))
black = torch.FloatTensor(black).to(self.device)
# Initialize Metrics
l1_error = 0.0
l2_error = 0.0
min_dist = 0.0
l0_error = 0.0
perceptual_error = 0.0
n_samples = 0
pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device)
images_to_animate_path = sorted(glob.glob(
@@ -400,21 +408,26 @@ class Solver(Utils):
all_images = torch.cat([regular_image_transform(Image.open(path)).unsqueeze(0) for path in images_to_animate_path], dim=0).cuda()
for target_idx in range(targets.size(0)):
if target_idx == 0:
img = regular_image_transform(Image.open(images_to_animate_path[idx])).unsqueeze(0).cuda()
# x_adv, perturb = pgd_attack.perturb(img, black, targets[0, :].unsqueeze(0).cuda())
x_adv, perturb = pgd_attack.perturb_iter_class(image_to_animate, black, targets[:, :].cuda())
# _, perturb = pgd_attack.perturb_iter_data(image_to_animate, all_images, black, targets[68, :].unsqueeze(0).cuda())
# if target_idx == 0:
# img = regular_image_transform(Image.open(images_to_animate_path[idx])).unsqueeze(0).cuda()
# # x_adv, perturb = pgd_attack.perturb(img, black, targets[0, :].unsqueeze(0).cuda())
# x_adv, perturb = pgd_attack.perturb_iter_class(image_to_animate, black, targets[:, :].cuda())
# # _, perturb = pgd_attack.perturb_iter_data(image_to_animate, all_images, black, targets[68, :].unsqueeze(0).cuda())
targets_au = targets[target_idx, :].unsqueeze(0).cuda()
# x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets_au)
x_adv = image_to_animate
x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets_au)
# x_adv = image_to_animate
# print(image_to_animate.shape, x_adv.shape)
resulting_images_att, resulting_images_reg = self.G(
x_adv, targets_au)
resulting_image = self.imFromAttReg(
resulting_images_att, resulting_images_reg, x_adv).cuda()
resulting_images_att_noattack, resulting_images_reg_noattack = self.G(
image_to_animate, targets_au)
resulting_image_noattack = self.imFromAttReg(
resulting_images_att_noattack, resulting_images_reg_noattack, image_to_animate).cuda()
save_image((resulting_image+1)/2, os.path.join(self.animation_results_dir,
image_path.split('/')[-1].split('.')[0]
+ '_' + reference_expression_images[target_idx]))
@@ -423,6 +436,16 @@ class Solver(Utils):
image_path.split('/')[-1].split('.')[0]
+ '_ref.jpg'))
l1_error += F.l1_loss(resulting_image, gen_noattack)
l2_error += F.mse_loss(resulting_image, gen_noattack)
l0_error += (resulting_image - gen_noattack).norm(0)
min_dist += (resulting_image - gen_noattack).norm(float('-inf'))
n_samples += 1
# Print metrics
print('{} images. L1 error: {}. L2 error: {}. L0 error: {}. L_-inf error: {}. Perceptual error: {}.'.format(n_samples,
l1_error / n_samples, l2_error / n_samples, l0_error / n_samples, min_dist / n_samples, perceptual_error / n_samples))
# """ Code to modify single Action Units """
# Set data loader.