def reconstruction_error(self, batch_images, output_images):
"""
Reconstruction loss.
:param batch_images: target images
:type batch_images: torch.autograd.Variable
:param output_images: predicted images
:type output_images: torch.autograd.Variable
:return: error
:rtype: torch.autograd.Variable
"""
return torch.mean(torch.mul(batch_images - output_images, batch_images - output_images))
def train(self):
"""
Train with fair data augmentation.
"""
self.model.train()
assert self.model.training is True
split = self.args.batch_size // 2
num_batches = int(
math.ceil(self.train_images.shape[0] / self.args.batch_size))
permutation = numpy.random.permutation(self.train_images.shape[0])
for b in range(num_batches):
self.scheduler.update(self.epoch, float(b) / num_batches)
perm = numpy.take(permutation,
range(b * self.args.batch_size,
(b + 1) * self.args.batch_size),
mode='wrap')
batch_images = common.torch.as_variable(self.train_images[perm],
self.args.use_gpu)
batch_classes = common.torch.as_variable(self.train_codes[perm],
self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
loss = error = gradient = 0
if self.args.full_variant:
for t in range(self.args.max_iterations):
if self.args.strong_variant:
min_bound = numpy.repeat(self.min_bound.reshape(1, -1),
self.args.batch_size,
axis=0)
max_bound = numpy.repeat(self.max_bound.reshape(1, -1),
self.args.batch_size,
axis=0)
random = numpy.random.uniform(
min_bound, max_bound,
(self.args.batch_size, self.args.N_theta))
batch_perturbed_theta = common.torch.as_variable(
random.astype(numpy.float32), self.args.use_gpu)
self.decoder.set_image(batch_images)
batch_perturbed_images = self.decoder(
batch_perturbed_theta)
else:
random = common.numpy.uniform_ball(
self.args.batch_size,
self.args.N_theta,
epsilon=self.args.epsilon,
ord=self.norm)
batch_perturbed_theta = common.torch.as_variable(
random.astype(numpy.float32), self.args.use_gpu)
batch_perturbed_theta = torch.min(
common.torch.as_variable(self.max_bound,
self.args.use_gpu),
batch_perturbed_theta)
batch_perturbed_theta = torch.max(
common.torch.as_variable(self.min_bound,
self.args.use_gpu),
batch_perturbed_theta)
self.decoder.set_image(batch_images)
batch_perturbed_images = self.decoder(
batch_perturbed_theta)
output_classes = self.model(batch_perturbed_images)
self.scheduler.optimizer.zero_grad()
l = self.loss(batch_classes, output_classes)
l.backward()
self.scheduler.optimizer.step()
loss += l.item()
g = torch.mean(
torch.abs(list(self.model.parameters())[0].grad))
gradient += g.item()
e = self.error(batch_classes, output_classes)
error += e.item()
batch_perturbations = batch_perturbed_images - batch_images
gradient /= self.args.max_iterations
loss /= self.args.max_iterations
error /= self.args.max_iterations
perturbation_loss = loss
perturbation_error = error
else:
output_classes = self.model(batch_images[:split])
self.scheduler.optimizer.zero_grad()
l = self.loss(batch_classes[:split], output_classes)
l.backward()
self.scheduler.optimizer.step()
loss = l.item()
gradient = torch.mean(
torch.abs(list(self.model.parameters())[0].grad))
gradient = gradient.item()
e = self.error(batch_classes[:split], output_classes)
error = e.item()
perturbation_loss = perturbation_error = 0
for t in range(self.args.max_iterations):
if self.args.strong_variant:
min_bound = numpy.repeat(self.min_bound.reshape(1, -1),
split,
axis=0)
max_bound = numpy.repeat(self.max_bound.reshape(1, -1),
split,
axis=0)
random = numpy.random.uniform(
min_bound, max_bound, (split, self.args.N_theta))
batch_perturbed_theta = common.torch.as_variable(
random.astype(numpy.float32), self.args.use_gpu)
self.decoder.set_image(batch_images[split:])
batch_perturbed_images = self.decoder(
batch_perturbed_theta)
else:
random = common.numpy.uniform_ball(
split,
self.args.N_theta,
epsilon=self.args.epsilon,
ord=self.norm)
batch_perturbed_theta = common.torch.as_variable(
random.astype(numpy.float32), self.args.use_gpu)
batch_perturbed_theta = torch.min(
common.torch.as_variable(self.max_bound,
self.args.use_gpu),
batch_perturbed_theta)
batch_perturbed_theta = torch.max(
common.torch.as_variable(self.min_bound,
self.args.use_gpu),
batch_perturbed_theta)
self.decoder.set_image(batch_images[split:])
batch_perturbed_images = self.decoder(
batch_perturbed_theta)
output_classes = self.model(batch_perturbed_images)
self.scheduler.optimizer.zero_grad()
l = self.loss(batch_classes[split:], output_classes)
l.backward()
self.scheduler.optimizer.step()
perturbation_loss += l.item()
g = torch.mean(
torch.abs(list(self.model.parameters())[0].grad))
gradient += g.item()
e = self.error(batch_classes[split:], output_classes)
perturbation_error += e.item()
batch_perturbations = batch_perturbed_images - batch_images[
split:]
gradient /= self.args.max_iterations + 1
perturbation_loss /= self.args.max_iterations
perturbation_error /= self.args.max_iterations
iteration = self.epoch * num_batches + b + 1
self.train_statistics = numpy.vstack((
self.train_statistics,
numpy.array([[
iteration, # iterations
iteration * (1 + self.args.max_iterations) *
self.args.batch_size, # samples seen
min(num_batches, iteration) * self.args.batch_size +
iteration * self.args.max_iterations *
self.args.batch_size, # unique samples seen
loss,
error,
perturbation_loss,
perturbation_error,
gradient
]])))
if b % self.args.skip == self.args.skip // 2:
log('[Training] %d | %d: %g (%g) %g (%g) [%g]' % (
self.epoch,
b,
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 3]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 4]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 5]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 6]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, -1]),
))
self.debug('clean.%d.png' % self.epoch,
batch_images.permute(0, 2, 3, 1))
self.debug('perturbed.%d.png' % self.epoch,
batch_perturbed_images.permute(0, 2, 3, 1))
self.debug('perturbation.%d.png' % self.epoch,
batch_perturbations.permute(0, 2, 3, 1),
cmap='seismic')
def train(self):
"""
Train for one epoch.
"""
self.model.train()
log('[Training] %d set classifier to train' % self.epoch)
assert self.model.training is True
num_batches = int(
math.ceil(self.train_images.shape[0] / self.args.batch_size))
permutation = numpy.random.permutation(self.train_images.shape[0])
for b in range(num_batches):
self.scheduler.update(self.epoch, float(b) / num_batches)
perm = numpy.take(permutation,
range(b * self.args.batch_size,
(b + 1) * self.args.batch_size),
mode='wrap')
assert perm.shape[0] == self.args.batch_size
batch_images = common.torch.as_variable(self.train_images[perm],
self.args.use_gpu)
batch_true_classes = common.torch.as_variable(
self.train_codes[perm], self.args.use_gpu)
batch_training_classes = common.torch.as_variable(
self.train_codes[perm], self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
output_classes = self.model(batch_images)
self.scheduler.optimizer.zero_grad()
loss = self.loss(batch_training_classes, output_classes)
loss.backward()
self.scheduler.optimizer.step()
loss = loss.item()
gradient = torch.mean(
torch.abs(list(self.model.parameters())[0].grad))
gradient = gradient.item()
error = self.error(batch_true_classes, output_classes)
error = error.item()
iteration = self.epoch * num_batches + b + 1
self.train_statistics = numpy.vstack(
(self.train_statistics,
numpy.array([
iteration, iteration * self.args.batch_size,
min(num_batches, iteration) * self.args.batch_size, loss,
error, gradient
])))
if b % self.args.skip == self.args.skip // 2:
log('[Training] %d | %d: %g (%g) [%g]' % (
self.epoch,
b,
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 3]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 4]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, -1]),
))
# Only debug last iterations for efficiency!
self.debug('clean.png', batch_images.permute(0, 2, 3, 1))
def test_test(self):
"""
Test on testing set.
"""
num_batches = int(
math.ceil(self.test_images.shape[0] / self.args.batch_size))
for b in range(num_batches):
b_start = b * self.args.batch_size
b_end = min((b + 1) * self.args.batch_size,
self.test_images.shape[0])
batch_images = common.torch.as_variable(
self.test_images[b_start:b_end], self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
# Important to get the correct codes!
output_codes, output_logvar = self.encoder(batch_images)
output_images = self.decoder(output_codes)
e = self.reconstruction_loss(batch_images, output_images)
self.reconstruction_error += e.data
self.code_mean += torch.mean(output_codes).item()
self.code_var += torch.var(output_codes).item()
output_images = numpy.squeeze(
numpy.transpose(output_images.cpu().detach().numpy(),
(0, 2, 3, 1)))
self.pred_images = common.numpy.concatenate(
self.pred_images, output_images)
output_codes = output_codes.cpu().detach().numpy()
self.pred_codes = common.numpy.concatenate(self.pred_codes,
output_codes)
if b % 100 == 50:
log('[Testing] %d' % b)
assert self.pred_images.shape[0] == self.test_images.shape[
0], 'computed invalid number of test images'
if self.args.reconstruction_file:
utils.write_hdf5(self.args.reconstruction_file, self.pred_images)
log('[Testing] wrote %s' % self.args.reconstruction_file)
if self.args.test_theta_file:
assert self.pred_codes.shape[0] == self.test_images.shape[
0], 'computed invalid number of test codes'
utils.write_hdf5(self.args.test_theta_file, self.pred_codes)
log('[Testing] wrote %s' % self.args.test_theta_file)
threshold = 0.9
percentage = 0
# values = numpy.linalg.norm(pred_codes, ord=2, axis=1)
values = numpy.max(numpy.abs(self.pred_codes), axis=1)
while percentage < 0.9:
threshold += 0.1
percentage = numpy.sum(values <= threshold) / float(
values.shape[0])
log('[Testing] threshold %g percentage %g' %
(threshold, percentage))
log('[Testing] taking threshold %g with percentage %g' %
(threshold, percentage))
if self.args.output_directory and utils.display():
# fit = 10
# plot_file = os.path.join(self.args.output_directory, 'test_codes')
# plot.manifold(plot_file, pred_codes[::fit], None, None, 'tsne', None, title='t-SNE of Test Codes')
# log('[Testing] wrote %s' % plot_file)
for d in range(1, self.pred_codes.shape[1]):
plot_file = os.path.join(self.args.output_directory,
'test_codes_%s' % d)
plot.scatter(
plot_file,
self.pred_codes[:, 0],
self.pred_codes[:, d], (values <= threshold).astype(int),
['greater %g' % threshold,
'smaller %g' % threshold],
title='Dimensions 0 and %d of Test Codes' % d)
log('[Testing] wrote %s' % plot_file)
self.reconstruction_error /= num_batches
log('[Testing] reconstruction error %g' % self.reconstruction_error)
def test(self, epoch):
"""
Test the model.
:param epoch: current epoch
:type epoch: int
"""
self.encoder.eval()
log('[Training] %d set encoder to eval' % epoch)
self.decoder.eval()
log('[Training] %d set decoder to eval' % epoch)
self.classifier.eval()
log('[Training] %d set classifier to eval' % epoch)
latent_loss = 0
reconstruction_loss = 0
reconstruction_error = 0
decoder_loss = 0
discriminator_loss = 0
mean = 0
var = 0
logvar = 0
pred_images = None
pred_codes = None
num_batches = int(
math.ceil(self.test_images.shape[0] / self.args.batch_size))
assert self.encoder.training is False
for b in range(num_batches):
b_start = b * self.args.batch_size
b_end = min((b + 1) * self.args.batch_size,
self.test_images.shape[0])
batch_images = common.torch.as_variable(
self.test_images[b_start:b_end], self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
output_mu, output_logvar = self.encoder(batch_images)
output_images = self.decoder(output_mu)
output_real_classes = self.classifier(batch_images)
output_reconstructed_classes = self.classifier(output_images)
# Latent loss.
e = self.latent_loss(output_mu, output_logvar)
latent_loss += e.item()
# Reconstruction loss.
e = self.reconstruction_loss(batch_images, output_images)
reconstruction_loss += e.item()
# Reconstruction error.
e = self.reconstruction_error(batch_images, output_images)
reconstruction_error += e.item()
e = self.decoder_loss(output_reconstructed_classes)
decoder_loss += e.item()
# Adversarial loss.
e = self.discriminator_loss(output_real_classes,
output_reconstructed_classes)
discriminator_loss += e.item()
mean += torch.mean(output_mu).item()
var += torch.var(output_mu).item()
logvar += torch.mean(output_logvar).item()
output_images = numpy.squeeze(
numpy.transpose(output_images.cpu().detach().numpy(),
(0, 2, 3, 1)))
pred_images = common.numpy.concatenate(pred_images, output_images)
output_codes = output_mu.cpu().detach().numpy()
pred_codes = common.numpy.concatenate(pred_codes, output_codes)
utils.write_hdf5(self.args.reconstruction_file, pred_images)
log('[Training] %d: wrote %s' % (epoch, self.args.reconstruction_file))
if utils.display():
png_file = self.args.reconstruction_file + '.%d.png' % epoch
if epoch == 0:
vis.mosaic(png_file, self.test_images[:225], 15, 5, 'gray', 0,
1)
else:
vis.mosaic(png_file, pred_images[:225], 15, 5, 'gray', 0, 1)
log('[Training] %d: wrote %s' % (epoch, png_file))
latent_loss /= num_batches
reconstruction_loss /= num_batches
reconstruction_error /= num_batches
decoder_loss /= num_batches
discriminator_loss /= num_batches
mean /= num_batches
var /= num_batches
logvar /= num_batches
log('[Training] %d: test %g (%g) %g (%g, %g, %g)' %
(epoch, reconstruction_loss, reconstruction_error, latent_loss,
mean, var, logvar))
log('[Training] %d: test %g %g' %
(epoch, decoder_loss, discriminator_loss))
num_batches = int(
math.ceil(self.train_images.shape[0] / self.args.batch_size))
iteration = epoch * num_batches
self.test_statistics = numpy.vstack(
(self.test_statistics,
numpy.array([
iteration, iteration * self.args.batch_size,
min(num_batches, iteration),
min(num_batches, iteration) * self.args.batch_size,
reconstruction_loss, reconstruction_error, latent_loss, mean,
var, logvar, decoder_loss, discriminator_loss
])))
pred_images = None
if self.random_codes is None:
self.random_codes = common.numpy.truncated_normal(
(1000, self.args.latent_space_size)).astype(numpy.float32)
num_batches = int(
math.ceil(self.random_codes.shape[0] / self.args.batch_size))
for b in range(num_batches):
b_start = b * self.args.batch_size
b_end = min((b + 1) * self.args.batch_size,
self.test_images.shape[0])
if b_start >= b_end: break
batch_codes = common.torch.as_variable(
self.random_codes[b_start:b_end], self.args.use_gpu)
output_images = self.decoder(batch_codes)
output_images = numpy.squeeze(
numpy.transpose(output_images.cpu().detach().numpy(),
(0, 2, 3, 1)))
pred_images = common.numpy.concatenate(pred_images, output_images)
utils.write_hdf5(self.args.random_file, pred_images)
log('[Training] %d: wrote %s' % (epoch, self.args.random_file))
if utils.display() and epoch > 0:
png_file = self.args.random_file + '.%d.png' % epoch
vis.mosaic(png_file, pred_images[:225], 15, 5, 'gray', 0, 1)
log('[Training] %d: wrote %s' % (epoch, png_file))
interpolations = None
perm = numpy.random.permutation(numpy.array(range(
pred_codes.shape[0])))
for i in range(50):
first = pred_codes[i]
second = pred_codes[perm[i]]
linfit = scipy.interpolate.interp1d([0, 1],
numpy.vstack([first, second]),
axis=0)
interpolations = common.numpy.concatenate(
interpolations, linfit(numpy.linspace(0, 1, 10)))
pred_images = None
num_batches = int(
math.ceil(interpolations.shape[0] / self.args.batch_size))
interpolations = interpolations.astype(numpy.float32)
for b in range(num_batches):
b_start = b * self.args.batch_size
b_end = min((b + 1) * self.args.batch_size,
self.test_images.shape[0])
if b_start >= b_end: break
batch_codes = common.torch.as_variable(
interpolations[b_start:b_end], self.args.use_gpu)
output_images = self.decoder(batch_codes)
output_images = numpy.squeeze(
numpy.transpose(output_images.cpu().detach().numpy(),
(0, 2, 3, 1)))
pred_images = common.numpy.concatenate(pred_images, output_images)
if b % 100 == 50:
log('[Testing] %d' % b)
utils.write_hdf5(self.args.interpolation_file, pred_images)
log('[Testing] wrote %s' % self.args.interpolation_file)
if utils.display() and epoch > 0:
png_file = self.args.interpolation_file + '.%d.png' % epoch
vis.mosaic(png_file, pred_images[:100], 10, 5, 'gray', 0, 1)
log('[Training] %d: wrote %s' % (epoch, png_file))
def train(self, epoch):
"""
Train for one epoch.
:param epoch: current epoch
:type epoch: int
"""
self.encoder.train()
log('[Training] %d set encoder to train' % epoch)
self.decoder.train()
log('[Training] %d set decoder to train' % epoch)
self.classifier.train()
log('[Training] %d set classifier to train' % epoch)
num_batches = int(
math.ceil(self.train_images.shape[0] / self.args.batch_size))
assert self.encoder.training is True
permutation = numpy.random.permutation(self.train_images.shape[0])
permutation = numpy.concatenate(
(permutation, permutation[:self.args.batch_size]), axis=0)
for b in range(num_batches):
self.encoder_scheduler.update(epoch, float(b) / num_batches)
self.decoder_scheduler.update(epoch, float(b) / num_batches)
self.classifier_scheduler.update(epoch, float(b) / num_batches)
perm = permutation[b * self.args.batch_size:(b + 1) *
self.args.batch_size]
batch_images = common.torch.as_variable(self.train_images[perm],
self.args.use_gpu, True)
batch_images = batch_images.permute(0, 3, 1, 2)
output_mu, output_logvar = self.encoder(batch_images)
output_codes = self.reparameterize(output_mu, output_logvar)
output_images = self.decoder(output_codes)
output_real_classes = self.classifier(batch_images)
output_reconstructed_classes = self.classifier(output_images)
latent_loss = self.latent_loss(output_mu, output_logvar)
reconstruction_loss = self.reconstruction_loss(
batch_images, output_images)
decoder_loss = self.decoder_loss(output_reconstructed_classes)
discriminator_loss = self.discriminator_loss(
output_real_classes, output_reconstructed_classes)
self.encoder_scheduler.optimizer.zero_grad()
loss = latent_loss + self.args.beta * reconstruction_loss + self.args.gamma * decoder_loss + self.args.eta * torch.sum(
torch.abs(output_logvar))
loss.backward(retain_graph=True)
self.encoder_scheduler.optimizer.step()
self.decoder_scheduler.optimizer.zero_grad()
loss = self.args.beta * reconstruction_loss + self.args.gamma * decoder_loss
loss.backward(retain_graph=True)
self.decoder_scheduler.optimizer.step()
self.classifier_scheduler.optimizer.zero_grad()
loss = self.args.gamma * discriminator_loss
loss.backward()
self.classifier_scheduler.optimizer.step()
reconstruction_error = self.reconstruction_error(
batch_images, output_images)
iteration = epoch * num_batches + b + 1
self.train_statistics = numpy.vstack(
(self.train_statistics,
numpy.array([
iteration, iteration * self.args.batch_size,
min(num_batches, iteration),
min(num_batches, iteration) * self.args.batch_size,
reconstruction_loss.data, reconstruction_error.data,
latent_loss.data,
torch.mean(output_mu).item(),
torch.var(output_mu).item(),
torch.mean(output_logvar).item(),
decoder_loss.item(),
discriminator_loss.item(),
torch.mean(
torch.abs(list(
self.encoder.parameters())[0].grad)).item(),
torch.mean(
torch.abs(list(
self.decoder.parameters())[0].grad)).item(),
torch.mean(
torch.abs(list(
self.classifier.parameters())[0].grad)).item()
])))
skip = 10
if b % skip == skip // 2:
log('[Training] %d | %d: %g (%g) %g (%g, %g, %g)' % (
epoch,
b,
numpy.mean(self.train_statistics[max(0, iteration -
skip):iteration, 4]),
numpy.mean(self.train_statistics[max(0, iteration -
skip):iteration, 5]),
numpy.mean(self.train_statistics[max(0, iteration -
skip):iteration, 6]),
numpy.mean(self.train_statistics[max(0, iteration -
skip):iteration, 7]),
numpy.mean(self.train_statistics[max(0, iteration -
skip):iteration, 8]),
numpy.mean(self.train_statistics[max(0, iteration -
skip):iteration, 9]),
))
log('[Training] %d | %d: %g %g (%g, %g, %g)' % (
epoch,
b,
numpy.mean(self.train_statistics[max(0, iteration -
skip):iteration, 10]),
numpy.mean(self.train_statistics[max(0, iteration -
skip):iteration, 11]),
numpy.mean(self.train_statistics[max(0, iteration -
skip):iteration, 12]),
numpy.mean(self.train_statistics[max(0, iteration -
skip):iteration, 13]),
numpy.mean(self.train_statistics[max(0, iteration -
skip):iteration, 14]),
))
def train(self):
"""
Train adversarially.
"""
num_batches = int(
math.ceil(self.train_images.shape[0] / self.args.batch_size))
permutation = numpy.random.permutation(self.train_images.shape[0])
perturbation_permutation = numpy.random.permutation(
self.train_images.shape[0])
if self.args.safe:
perturbation_permutation = perturbation_permutation[
self.train_valid == 1]
else:
perturbation_permuation = permutation
for b in range(num_batches):
self.scheduler.update(self.epoch, float(b) / num_batches)
self.model.eval()
assert self.model.training is False
objective = self.objective_class()
split = self.args.batch_size // 2
if self.args.full_variant:
perm = numpy.concatenate(
(numpy.take(permutation,
range(b * self.args.batch_size,
b * self.args.batch_size + split),
mode='wrap'),
numpy.take(perturbation_permutation,
range(b * self.args.batch_size + split,
(b + 1) * self.args.batch_size),
mode='wrap')),
axis=0)
batch_images = common.torch.as_variable(
self.train_images[perm], self.args.use_gpu)
batch_classes = common.torch.as_variable(
self.train_codes[perm], self.args.use_gpu)
batch_theta = common.torch.as_variable(self.train_theta[perm],
self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
attack = self.setup_attack(self.model, batch_images[:split],
batch_classes[:split])
success, perturbations, _, _, _ = attack.run(
objective, self.args.verbose)
batch_perturbations1 = common.torch.as_variable(
perturbations.astype(numpy.float32), self.args.use_gpu)
batch_perturbed_images1 = batch_images[:split] + batch_perturbations1
if isinstance(self.decoder, models.SelectiveDecoder):
self.decoder.set_code(batch_classes[split:])
attack = self.setup_decoder_attack(self.decoder_classifier,
batch_theta[split:],
batch_classes[split:])
attack.set_bound(torch.from_numpy(self.min_bound),
torch.from_numpy(self.max_bound))
decoder_success, decoder_perturbations, probabilities, norm, _ = attack.run(
objective, self.args.verbose)
batch_perturbed_theta = batch_theta[
split:] + common.torch.as_variable(decoder_perturbations,
self.args.use_gpu)
batch_perturbed_images2 = self.decoder(batch_perturbed_theta)
batch_perturbations2 = batch_perturbed_images2 - batch_images[
split:]
batch_input_images = torch.cat(
(batch_perturbed_images1, batch_perturbed_images2), dim=0)
self.model.train()
assert self.model.training is True
output_classes = self.model(batch_input_images)
self.scheduler.optimizer.zero_grad()
perturbation_loss = self.loss(batch_classes[:split],
output_classes[:split])
decoder_perturbation_loss = self.loss(batch_classes[split:],
output_classes[split:])
loss = (perturbation_loss + decoder_perturbation_loss) / 2
loss.backward()
self.scheduler.optimizer.step()
loss = loss.item()
perturbation_loss = perturbation_loss.item()
decoder_perturbation_loss = decoder_perturbation_loss.item()
gradient = torch.mean(
torch.abs(list(self.model.parameters())[0].grad))
gradient = gradient.item()
perturbation_error = self.error(batch_classes[:split],
output_classes[:split])
perturbation_error = perturbation_error.item()
decoder_perturbation_error = self.error(
batch_classes[split:], output_classes[split:])
decoder_perturbation_error = decoder_perturbation_error.item()
error = (perturbation_error + decoder_perturbation_error) / 2
else:
perm = numpy.concatenate((
numpy.take(
perturbation_permutation,
range(b * self.args.batch_size + split + split // 2,
(b + 1) * self.args.batch_size),
mode='wrap'),
numpy.take(
permutation,
range(b * self.args.batch_size,
b * self.args.batch_size + split + split // 2),
mode='wrap'),
),
axis=0)
batch_images = common.torch.as_variable(
self.train_images[perm], self.args.use_gpu)
batch_classes = common.torch.as_variable(
self.train_codes[perm], self.args.use_gpu)
batch_theta = common.torch.as_variable(self.train_theta[perm],
self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
attack = self.setup_attack(self.model,
batch_images[split // 2:split],
batch_classes[split // 2:split])
success, perturbations, _, _, _ = attack.run(
objective, self.args.verbose)
batch_perturbations1 = common.torch.as_variable(
perturbations.astype(numpy.float32), self.args.use_gpu)
batch_perturbed_images1 = batch_images[
split // 2:split] + batch_perturbations1
if isinstance(self.decoder, models.SelectiveDecoder):
self.decoder.set_code(batch_classes[:split // 2])
attack = self.setup_decoder_attack(self.decoder_classifier,
batch_theta[:split // 2],
batch_classes[:split // 2])
attack.set_bound(torch.from_numpy(self.min_bound),
torch.from_numpy(self.max_bound))
decoder_success, decoder_perturbations, probabilities, norm, _ = attack.run(
objective, self.args.verbose)
batch_perturbed_theta = batch_theta[:split //
2] + common.torch.as_variable(
decoder_perturbations,
self.args.use_gpu)
batch_perturbed_images2 = self.decoder(batch_perturbed_theta)
batch_perturbations2 = batch_perturbed_images2 - batch_images[:split
//
2]
batch_input_images = torch.cat(
(batch_perturbed_images2, batch_perturbed_images1,
batch_images[split:]),
dim=0)
self.model.train()
assert self.model.training is True
output_classes = self.model(batch_input_images)
self.scheduler.optimizer.zero_grad()
loss = self.loss(batch_classes[split:], output_classes[split:])
perturbation_loss = self.loss(batch_classes[split // 2:split],
output_classes[split // 2:split])
decoder_perturbation_loss = self.loss(
batch_classes[:split // 2], output_classes[:split // 2])
l = (loss + perturbation_loss + decoder_perturbation_loss) / 3
l.backward()
self.scheduler.optimizer.step()
loss = loss.item()
perturbation_loss = perturbation_loss.item()
decoder_perturbation_loss = decoder_perturbation_loss.item()
gradient = torch.mean(
torch.abs(list(self.model.parameters())[0].grad))
gradient = gradient.item()
error = self.error(batch_classes[split:],
output_classes[split:])
error = error.item()
perturbation_error = self.error(
batch_classes[split // 2:split],
output_classes[split // 2:split])
perturbation_error = perturbation_error.item()
decoder_perturbation_error = self.error(
batch_classes[:split // 2], output_classes[:split // 2])
decoder_perturbation_error = decoder_perturbation_error.item()
iterations = numpy.mean(
success[success >= 0]) if numpy.sum(success >= 0) > 0 else -1
norm = numpy.mean(
numpy.linalg.norm(perturbations.reshape(
perturbations.shape[0], -1),
axis=1,
ord=self.norm))
success = numpy.sum(success >= 0) / self.args.batch_size
decoder_iterations = numpy.mean(
decoder_success[decoder_success >= 0]) if numpy.sum(
decoder_success >= 0) > 0 else -1
decoder_norm = numpy.mean(
numpy.linalg.norm(decoder_perturbations, axis=1,
ord=self.norm))
decoder_success = numpy.sum(
decoder_success >= 0) / self.args.batch_size
iteration = self.epoch * num_batches + b + 1
self.train_statistics = numpy.vstack((
self.train_statistics,
numpy.array([[
iteration, # iterations
iteration * (1 + self.args.max_iterations) *
self.args.batch_size, # samples seen
min(num_batches, iteration) * self.args.batch_size +
iteration * self.args.max_iterations *
self.args.batch_size, # unique samples seen
loss,
error,
perturbation_loss,
perturbation_error,
decoder_perturbation_loss,
decoder_perturbation_error,
success,
iterations,
norm,
decoder_success,
decoder_iterations,
decoder_norm,
gradient
]])))
if b % self.args.skip == self.args.skip // 2:
log('[Training] %d | %d: %g (%g) %g (%g) %g (%g) [%g]' % (
self.epoch,
b,
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 3]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 4]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 5]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 6]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 7]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 8]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, -1]),
))
log('[Training] %d | %d: %g (%g, %g) %g (%g, %g)' % (
self.epoch,
b,
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 9]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 10]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 11]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 12]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 13]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 14]),
))
self.debug('clean.%d.png' % self.epoch,
batch_images.permute(0, 2, 3, 1))
self.debug('perturbed.%d.png' % self.epoch,
batch_perturbed_images1.permute(0, 2, 3, 1))
self.debug('perturbed2.%d.png' % self.epoch,
batch_perturbed_images2.permute(0, 2, 3, 1))
self.debug('perturbation.%d.png' % self.epoch,
batch_perturbations1.permute(0, 2, 3, 1),
cmap='seismic')
self.debug('perturbation2.%d.png' % self.epoch,
batch_perturbations2.permute(0, 2, 3, 1),
cmap='seismic')
def train(self, epoch):
"""
Train for one epoch.
:param epoch: current epoch
:type epoch: int
"""
assert self.encoder is not None and self.decoder is not None
assert self.scheduler is not None
self.auto_encoder.train()
log('[Training] %d set auto encoder to train' % epoch)
self.encoder.train()
log('[Training] %d set encoder to train' % epoch)
self.decoder.train()
log('[Training] %d set decoder to train' % epoch)
num_batches = int(math.ceil(self.train_images.shape[0]/self.args.batch_size))
assert self.encoder.training is True
permutation = numpy.random.permutation(self.train_images.shape[0])
permutation = numpy.concatenate((permutation, permutation[:self.args.batch_size]), axis=0)
for b in range(num_batches):
self.scheduler.update(epoch, float(b)/num_batches)
perm = permutation[b * self.args.batch_size: (b + 1) * self.args.batch_size]
batch_images = common.torch.as_variable(self.train_images[perm], self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
output_images, output_mu, output_logvar = self.auto_encoder(batch_images)
reconstruction_loss = self.reconstruction_loss(batch_images, output_images)
self.scheduler.optimizer.zero_grad()
latent_loss = self.latent_loss(output_mu, output_logvar)
loss = self.args.beta*reconstruction_loss + latent_loss
loss.backward()
self.scheduler.optimizer.step()
reconstruction_loss = reconstruction_loss.item()
latent_loss = latent_loss.item()
reconstruction_error = self.reconstruction_error(batch_images, output_images)
reconstruction_error = reconstruction_error.item()
iteration = epoch*num_batches + b + 1
self.train_statistics = numpy.vstack((self.train_statistics, numpy.array([
iteration,
iteration * self.args.batch_size,
min(num_batches, iteration),
min(num_batches, iteration) * self.args.batch_size,
reconstruction_loss,
reconstruction_error,
latent_loss,
torch.mean(output_mu).item(),
torch.var(output_mu).item(),
torch.mean(output_logvar).item(),
])))
skip = 10
if b%skip == skip//2:
log('[Training] %d | %d: %g (%g) %g %g %g %g' % (
epoch,
b,
numpy.mean(self.train_statistics[max(0, iteration-skip):iteration, 4]),
numpy.mean(self.train_statistics[max(0, iteration-skip):iteration, 5]),
numpy.mean(self.train_statistics[max(0, iteration-skip):iteration, 6]),
numpy.mean(self.train_statistics[max(0, iteration-skip):iteration, 7]),
numpy.mean(self.train_statistics[max(0, iteration-skip):iteration, 8]),
numpy.mean(self.train_statistics[max(0, iteration-skip):iteration, 9]),
))
def train(self):
"""
Train with fair data augmentation.
"""
self.model.train()
log('[Training] %d set classifier to train' % self.epoch)
assert self.model.training is True
split = self.args.batch_size // 2
num_batches = int(
math.ceil(self.train_images.shape[0] / self.args.batch_size))
permutation = numpy.random.permutation(self.train_images.shape[0])
for b in range(num_batches):
self.scheduler.update(self.epoch, float(b) / num_batches)
perm = numpy.take(permutation,
range(b * self.args.batch_size,
(b + 1) * self.args.batch_size),
mode='wrap')
batch_images = common.torch.as_variable(self.train_images[perm],
self.args.use_gpu)
batch_classes = common.torch.as_variable(self.train_codes[perm],
self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
if self.args.full_variant:
loss = error = gradient = 0
for t in range(self.args.max_iterations):
size = batch_images.size()
batch_perturbations = common.numpy.uniform_ball(
size[0],
numpy.prod(size[1:]),
epsilon=self.args.epsilon,
ord=self.norm)
batch_perturbations = common.torch.as_variable(
batch_perturbations.reshape(size).astype(
numpy.float32), self.args.use_gpu)
batch_perturbations = torch.min(
torch.ones_like(batch_images) - batch_images,
batch_perturbations)
batch_perturbations = torch.max(
torch.zeros_like(batch_images) - batch_images,
batch_perturbations)
batch_perturbed_images = batch_images + batch_perturbations
output_perturbed_classes = self.model(
batch_perturbed_images)
self.scheduler.optimizer.zero_grad()
l = self.loss(batch_classes, output_perturbed_classes)
l.backward()
self.scheduler.optimizer.step()
loss += l.item()
g = torch.mean(
torch.abs(list(self.model.parameters())[0].grad))
gradient += g.item()
e = self.error(batch_classes, output_perturbed_classes)
error += e.item()
gradient /= self.args.max_iterations
loss /= self.args.max_iterations
error /= self.args.max_iterations
perturbation_loss = loss
perturbation_error = error
elif self.args.strong_variant:
raise NotImplementedError('strong_variant not implemented yet')
else:
output_classes = self.model(batch_images[:split])
self.scheduler.optimizer.zero_grad()
l = self.loss(batch_classes[:split], output_classes)
l.backward()
self.scheduler.optimizer.step()
loss = l.item()
gradient = torch.mean(
torch.abs(list(self.model.parameters())[0].grad))
gradient = gradient.item()
e = self.error(batch_classes[:split], output_classes)
error = e.item()
perturbation_loss = perturbation_error = 0
for t in range(self.args.max_iterations):
size = batch_images.size()
batch_perturbations = common.numpy.uniform_ball(
split,
numpy.prod(size[1:]),
epsilon=self.args.epsilon,
ord=self.norm)
batch_perturbations = common.torch.as_variable(
batch_perturbations.reshape(
split, size[1], size[2],
size[3]).astype(numpy.float32), self.args.use_gpu)
batch_perturbations = torch.min(
torch.ones_like(batch_images[split:]) -
batch_images[split:], batch_perturbations)
batch_perturbations = torch.max(
torch.zeros_like(batch_images[split:]) -
batch_images[split:], batch_perturbations)
batch_perturbed_images = batch_images[
split:] + batch_perturbations
output_perturbed_classes = self.model(
batch_perturbed_images)
self.scheduler.optimizer.zero_grad()
l = self.loss(batch_classes[split:],
output_perturbed_classes)
l.backward()
self.scheduler.optimizer.step()
perturbation_loss += l.item()
g = torch.mean(
torch.abs(list(self.model.parameters())[0].grad))
gradient += g.item()
e = self.error(batch_classes[split:],
output_perturbed_classes)
perturbation_error += e.item()
gradient /= self.args.max_iterations
perturbation_loss /= self.args.max_iterations
perturbation_error /= self.args.max_iterations
iteration = self.epoch * num_batches + b + 1
self.train_statistics = numpy.vstack((
self.train_statistics,
numpy.array([[
iteration, # iterations
iteration * (1 + self.args.max_iterations) *
self.args.batch_size, # samples seen
min(num_batches, iteration) * self.args.batch_size +
iteration * self.args.max_iterations *
self.args.batch_size, # unique samples seen
loss, # clean loss
error, # clean error (1-accuracy)
perturbation_loss, # perturbation loss
perturbation_error, # perturbation error (1-accuracy)
gradient
]])))
if b % self.args.skip == self.args.skip // 2:
log('[Training] %d | %d: %g (%g) %g (%g) [%g]' % (
self.epoch,
b,
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 3]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 4]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 5]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 6]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, -1]),
))
self.debug('clean.%d.png' % self.epoch,
batch_images.permute(0, 2, 3, 1))
self.debug('perturbed.%d.png' % self.epoch,
batch_perturbed_images.permute(0, 2, 3, 1))
self.debug('perturbation.%d.png' % self.epoch,
batch_perturbations.permute(0, 2, 3, 1),
cmap='seismic')
def train(self):
"""
Train adversarially.
"""
split = self.args.batch_size // 2
num_batches = int(
math.ceil(self.train_images.shape[0] / self.args.batch_size))
permutation = numpy.random.permutation(self.train_images.shape[0])
for b in range(num_batches):
self.scheduler.update(self.epoch, float(b) / num_batches)
perm = numpy.take(permutation,
range(b * self.args.batch_size,
(b + 1) * self.args.batch_size),
mode='wrap')
batch_images = common.torch.as_variable(self.train_images[perm],
self.args.use_gpu)
batch_theta = common.torch.as_variable(self.train_theta[perm],
self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
batch_fonts = self.train_codes[perm, 1]
batch_classes = self.train_codes[perm, self.args.label_index]
batch_code = numpy.concatenate(
(common.numpy.one_hot(batch_fonts, self.N_font),
common.numpy.one_hot(batch_classes, self.N_class)),
axis=1).astype(numpy.float32)
batch_code = common.torch.as_variable(batch_code,
self.args.use_gpu)
batch_classes = common.torch.as_variable(batch_classes,
self.args.use_gpu)
self.model.eval()
assert self.model.training is False
if self.args.full_variant:
objective = self.objective_class()
self.decoder.set_code(batch_code)
attack = self.setup_attack(self.decoder_classifier,
batch_theta, batch_classes)
attack.set_bound(torch.from_numpy(self.min_bound),
torch.from_numpy(self.max_bound))
success, perturbations, probabilities, norm, _ = attack.run(
objective, self.args.verbose)
batch_perturbed_theta = batch_theta + common.torch.as_variable(
perturbations, self.args.use_gpu)
batch_perturbed_images = self.decoder(batch_perturbed_theta)
batch_perturbations = batch_perturbed_images - batch_images
self.model.train()
assert self.model.training is True
output_classes = self.model(batch_perturbed_images)
self.scheduler.optimizer.zero_grad()
loss = self.loss(batch_classes, output_classes)
loss.backward()
self.scheduler.optimizer.step()
loss = perturbation_loss = loss.item()
gradient = torch.mean(
torch.abs(list(self.model.parameters())[0].grad))
gradient = gradient.item()
error = self.error(batch_classes, output_classes)
error = perturbation_error = error.item()
else:
objective = self.objective_class()
self.decoder.set_code(batch_code[split:])
attack = self.setup_attack(self.decoder_classifier,
batch_theta[split:],
batch_classes[split:])
attack.set_bound(torch.from_numpy(self.min_bound),
torch.from_numpy(self.max_bound))
success, perturbations, probabilities, norm, _ = attack.run(
objective, self.args.verbose)
batch_perturbed_theta = batch_theta[
split:] + common.torch.as_variable(perturbations,
self.args.use_gpu)
batch_perturbed_images = self.decoder(batch_perturbed_theta)
batch_perturbations = batch_perturbed_images - batch_images[
split:]
self.model.train()
assert self.model.training is True
batch_input_images = torch.cat(
(batch_images[:split], batch_perturbed_images), dim=0)
output_classes = self.model(batch_input_images)
self.scheduler.optimizer.zero_grad()
loss = self.loss(batch_classes[:split], output_classes[:split])
perturbation_loss = self.loss(batch_classes[split:],
output_classes[split:])
l = (loss + perturbation_loss) / 2
l.backward()
self.scheduler.optimizer.step()
loss = loss.item()
perturbation_loss = perturbation_loss.item()
gradient = torch.mean(
torch.abs(list(self.model.parameters())[0].grad))
gradient = gradient.item()
error = self.error(batch_classes[:split],
output_classes[:split])
error = error.item()
perturbation_error = self.error(batch_classes[split:],
output_classes[split:])
perturbation_error = perturbation_error.item()
iterations = numpy.mean(
success[success >= 0]) if numpy.sum(success >= 0) > 0 else -1
norm = numpy.mean(
numpy.linalg.norm(perturbations.reshape(
perturbations.shape[0], -1),
axis=1,
ord=self.norm))
success = numpy.sum(success >= 0) / (self.args.batch_size // 2)
iteration = self.epoch * num_batches + b + 1
self.train_statistics = numpy.vstack((
self.train_statistics,
numpy.array([[
iteration, # iterations
iteration * (1 + self.args.max_iterations) *
self.args.batch_size, # samples seen
min(num_batches, iteration) * self.args.batch_size +
iteration * self.args.max_iterations *
self.args.batch_size, # unique samples seen
loss,
error,
perturbation_loss,
perturbation_error,
success,
iterations,
norm,
gradient
]])))
if b % self.args.skip == self.args.skip // 2:
log('[Training] %d | %d: %g (%g) %g (%g) [%g]' % (
self.epoch,
b,
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 3]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 4]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 5]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 6]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, -1]),
))
log('[Training] %d | %d: %g (%g, %g)' % (
self.epoch,
b,
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 7]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 8]),
numpy.mean(self.train_statistics[
max(0, iteration - self.args.skip):iteration, 9]),
))
self.debug('clean.%d.png' % self.epoch,
batch_images.permute(0, 2, 3, 1))
self.debug('perturbed.%d.png' % self.epoch,
batch_perturbed_images.permute(0, 2, 3, 1))
self.debug('perturbation.%d.png' % self.epoch,
batch_perturbations.permute(0, 2, 3, 1),
cmap='seismic')
def compute_appr(self):
"""
Compute approximate.
"""
assert self.test_codes is not None
num_batches = int(math.ceil(self.perturbations.shape[0] / self.args.batch_size))
for b in range(num_batches):
b_start = b * self.args.batch_size
b_end = min((b + 1) * self.args.batch_size, self.perturbations.shape[0])
batch_classes = common.torch.as_variable(self.test_codes[b_start: b_end], self.args.use_gpu)
batch_theta = common.torch.as_variable(self.test_theta[b_start: b_end].astype(numpy.float32), self.args.use_gpu, True)
batch_perturbation = common.torch.as_variable(self.perturbations[b_start: b_end].astype(numpy.float32), self.args.use_gpu)
if isinstance(self.model, models.SelectiveDecoder):
self.model.set_code(batch_classes)
batch_theta = torch.nn.Parameter(batch_theta)
optimizer = torch.optim.Adam([batch_theta], lr=0.1)
log('[Detection] %d: start' % b)
for t in range(100):
optimizer.zero_grad()
output_perturbation = self.model.forward(batch_theta)
error = torch.mean(torch.mul(output_perturbation - batch_perturbation, output_perturbation - batch_perturbation))
error.backward()
optimizer.step()
log('[Detection] %d: %d = %g' % (b, t, error.item()))
output_perturbation = numpy.squeeze(output_perturbation.cpu().detach().numpy())
self.projected_perturbations = common.numpy.concatenate(self.projected_perturbations, output_perturbation)
batch_theta = common.torch.as_variable(self.test_theta[b_start: b_end].astype(numpy.float32), self.args.use_gpu, True)
batch_images = common.torch.as_variable(self.test_images[b_start: b_end].astype(numpy.float32), self.args.use_gpu)
batch_theta = torch.nn.Parameter(batch_theta)
optimizer = torch.optim.Adam([batch_theta], lr=0.5)
log('[Detection] %d: start' % b)
for t in range(100):
optimizer.zero_grad()
output_images = self.model.forward(batch_theta)
error = torch.mean(torch.mul(output_images - batch_images, output_images - batch_images))
error.backward()
optimizer.step()
log('[Detection] %d: %d = %g' % (b, t, error.item()))
output_images = numpy.squeeze(output_images.cpu().detach().numpy())
self.projected_test_images = common.numpy.concatenate(self.projected_test_images, output_images)
projected_perturbations = self.projected_perturbations.reshape((self.projected_perturbations.shape[0], -1))
projected_test_images = self.projected_test_images.reshape((self.projected_test_images.shape[0], -1))
perturbations = self.perturbations.reshape((self.perturbations.shape[0], -1))
test_images = self.test_images.reshape((self.test_images.shape[0], -1))
success = numpy.logical_and(self.success >= 0, self.accuracy)
log('[Detection] %d valid attacked samples' % numpy.sum(success))
self.distances['true'] = numpy.linalg.norm(perturbations - projected_perturbations, ord=2, axis=1)
self.angles['true'] = numpy.rad2deg(common.numpy.angles(perturbations.T, projected_perturbations.T))
self.distances['true'] = self.distances['true'][success]
self.angles['true'] = self.angles['true'][success]
self.distances['test'] = numpy.linalg.norm(test_images - projected_test_images, ord=2, axis=1)
self.angles['test'] = numpy.rad2deg(common.numpy.angles(test_images.T, projected_test_images.T))
self.distances['test'] = self.distances['test'][success]
self.angles['test'] = self.angles['test'][success]
def compute_true(self):
"""
Compute true.
"""
assert self.test_codes is not None
num_batches = int(math.ceil(self.perturbations.shape[0] / self.args.batch_size))
params = {
'lr': 0.09,
'lr_decay': 0.95,
'lr_min': 0.0000001,
'weight_decay': 0,
}
for b in range(num_batches):
b_start = b * self.args.batch_size
b_end = min((b + 1) * self.args.batch_size, self.perturbations.shape[0])
batch_fonts = self.test_codes[b_start: b_end, 1]
batch_classes = self.test_codes[b_start: b_end, 2]
batch_code = numpy.concatenate((common.numpy.one_hot(batch_fonts, self.N_font), common.numpy.one_hot(batch_classes, self.N_class)), axis=1).astype( numpy.float32)
batch_code = common.torch.as_variable(batch_code, self.args.use_gpu)
batch_images = common.torch.as_variable(self.test_images[b_start: b_end], self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
batch_theta = common.torch.as_variable(self.test_theta[b_start: b_end].astype(numpy.float32), self.args.use_gpu, True)
batch_perturbation = common.torch.as_variable(self.perturbations[b_start: b_end].astype(numpy.float32), self.args.use_gpu)
self.model.set_code(batch_code)
#output_images = self.model.forward(batch_theta)
#test_error = torch.mean(torch.mul(output_images - batch_images, output_images - batch_images))
#print(test_error.item())
#vis.mosaic('true.png', batch_images.cpu().detach().numpy()[:, 0, :, :])
#vis.mosaic('output.png', output_images.cpu().detach().numpy()[:, 0, :, :])
# print(batch_images.cpu().detach().numpy()[0])
# print(output_images.cpu().detach().numpy()[0, 0])
#_batch_images = batch_images.cpu().detach().numpy()
#_output_images = output_images.cpu().detach().numpy()[:, 0, :, :]
#test_error = numpy.max(numpy.abs(_batch_images.reshape(_batch_images.shape[0], -1) - _output_images.reshape(_output_images.shape[0], -1)), axis=1)
#print(test_error)
#test_error = numpy.mean(numpy.multiply(_batch_images - _output_images, _batch_images - _output_images), axis=1)
#print(test_error)
batch_theta = torch.nn.Parameter(batch_theta)
scheduler = ADAMScheduler([batch_theta], **params)
log('[Detection] %d: start' % b)
for t in range(100):
scheduler.update(t//10, float(t)/10)
scheduler.optimizer.zero_grad()
output_perturbation = self.model.forward(batch_theta)
error = torch.mean(torch.mul(output_perturbation - batch_perturbation, output_perturbation - batch_perturbation))
test_error = torch.mean(torch.mul(output_perturbation - batch_images, output_perturbation - batch_images))
#error.backward()
#scheduler.optimizer.step()
log('[Detection] %d: %d = %g, %g' % (b, t, error.item(), test_error.item()))
output_perturbation = numpy.squeeze(numpy.transpose(output_perturbation.cpu().detach().numpy(), (0, 2, 3, 1)))
self.projected_perturbations = common.numpy.concatenate(self.projected_perturbations, output_perturbation)
projected_perturbations = self.projected_perturbations.reshape((self.projected_perturbations.shape[0], -1))
perturbations = self.perturbations.reshape((self.perturbations.shape[0], -1))
success = numpy.logical_and(self.success >= 0, self.accuracy)
log('[Detection] %d valid attacked samples' % numpy.sum(success))
self.distances['true'] = numpy.linalg.norm(perturbations - projected_perturbations, ord=2, axis=1)
self.angles['true'] = numpy.rad2deg(common.numpy.angles(perturbations.T, projected_perturbations.T))
self.distances['true'] = self.distances['true'][success]
self.angles['true'] = self.angles['true'][success]
self.distances['test'] = numpy.zeros((numpy.sum(success)))
self.angles['test'] = numpy.zeros((numpy.sum(success)))
