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 reconstruction_loss(self, batch_images, output_images):
"""
Reconstruction loss.
:param batch_images: original images
:type batch_images: torch.autograd.Variable
:param output_images: output images
:type output_images: torch.autograd.Variable
:return: error
:rtype: torch.autograd.Variable
"""
if self.args.absolute_error:
return torch.sum(torch.abs(batch_images - output_images))
else:
return torch.sum(torch.mul(batch_images - output_images, batch_images - output_images))
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)))