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GANimation conditional attacks

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This commit is contained in:
Nataniel Ruiz
2020-01-09 16:07:30 -04:00
parent c960620cbf
commit 3d000626dc
1 changed files with 17 additions and 11 deletions
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ganimation/solver.py
+17 -11
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@@ -420,10 +420,10 @@ class Solver(Utils):
# img = regular_image_transform(Image.open(images_to_animate_path[idx])).unsqueeze(0).cuda()
# Wrong Class
# x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets[0, :].unsqueeze(0).cuda())
x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets[0, :].unsqueeze(0).cuda())
# Joint Class Conditional
x_adv, perturb = pgd_attack.perturb_joint_class(image_to_animate, black, targets[:, :].cuda())
# x_adv, perturb = pgd_attack.perturb_joint_class(image_to_animate, black, targets[:, :].cuda())
# Iterative Class Conditional
# x_adv, perturb = pgd_attack.perturb_iter_class(image_to_animate, black, targets[:, :].cuda())
@@ -451,11 +451,11 @@ class Solver(Utils):
resulting_image = self.imFromAttReg(
resulting_images_att, resulting_images_reg, x_adv).cuda()
with torch.no_grad():
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()
# with torch.no_grad():
# 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]
@@ -465,10 +465,16 @@ class Solver(Utils):
image_path.split('/')[-1].split('.')[0]
+ '_ref.jpg'))
l1_error += F.l1_loss(resulting_image, resulting_image_noattack)
l2_error += F.mse_loss(resulting_image, resulting_image_noattack)
l0_error += (resulting_image - resulting_image_noattack).norm(0)
min_dist += (resulting_image - resulting_image_noattack).norm(float('-inf'))
# l1_error += F.l1_loss(resulting_image, resulting_image_noattack)
# l2_error += F.mse_loss(resulting_image, resulting_image_noattack)
# l0_error += (resulting_image - resulting_image_noattack).norm(0)
# min_dist += (resulting_image - resulting_image_noattack).norm(float('-inf'))
# Compare to input image
l1_error += F.l1_loss(resulting_image, image_to_animate)
l2_error += F.mse_loss(resulting_image, image_to_animate)
l0_error += (resulting_image - image_to_animate).norm(0)
min_dist += (resulting_image - image_to_animate).norm(float('-inf'))
n_samples += 1
# Print metrics