def test_interpolation(self): """ Test interpolation. """ interpolations = None perm = numpy.random.permutation( numpy.array(range(self.pred_codes.shape[0]))) for i in range(50): first = self.pred_codes[i] second = self.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]) batch_codes = common.torch.as_variable( interpolations[b_start:b_end], self.args.use_gpu) # To get the correct images! 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)
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 test(self): """ Test the model. """ self.model.eval() assert self.model.training is False log('[Training] %d set classifier to eval' % self.epoch) loss = error = 0 num_batches = int( math.ceil(self.args.test_samples / self.args.batch_size)) for b in range(num_batches): perm = numpy.take(range(self.args.test_samples), range(b * self.args.batch_size, (b + 1) * self.args.batch_size), mode='clip') batch_images = common.torch.as_variable(self.test_images[perm], self.args.use_gpu) batch_classes = common.torch.as_variable(self.test_codes[perm], self.args.use_gpu) batch_images = batch_images.permute(0, 3, 1, 2) output_classes = self.model(batch_images) e = self.loss(batch_classes, output_classes) loss += e.item() a = self.error(batch_classes, output_classes) error += a.item() perturbation_loss = perturbation_error = success = iterations = norm = 0 num_batches = int( math.ceil(self.args.attack_samples / self.args.batch_size)) assert self.args.attack_samples > 0 and self.args.attack_samples <= self.test_images.shape[ 0] for b in range(num_batches): perm = numpy.take(range(self.args.attack_samples), range(b * self.args.batch_size, (b + 1) * self.args.batch_size), mode='clip') batch_images = common.torch.as_variable(self.test_images[perm], self.args.use_gpu) batch_classes = common.torch.as_variable(self.test_codes[perm], self.args.use_gpu) batch_images = batch_images.permute(0, 3, 1, 2) objective = self.objective_class() attack = self.setup_attack(self.model, batch_images, batch_classes) s, p, _, _, _ = attack.run(objective, False) batch_images = batch_images + common.torch.as_variable( p.astype(numpy.float32), self.args.use_gpu) output_classes = self.model(batch_images) e = self.loss(batch_classes, output_classes) perturbation_loss += e.item() e = self.error(batch_classes, output_classes) perturbation_error += e.item() iterations += numpy.mean( s[s >= 0]) if numpy.sum(s >= 0) > 0 else -1 norm += numpy.mean( numpy.linalg.norm(p.reshape(p.shape[0], -1), axis=1, ord=self.norm)) success += numpy.sum(s >= 0) / self.args.batch_size decoder_perturbation_loss = decoder_perturbation_error = decoder_success = decoder_iterations = decoder_norm = 0 num_batches = int( math.ceil(self.args.attack_samples / self.args.batch_size)) assert self.args.attack_samples > 0 and self.args.attack_samples <= self.test_images.shape[ 0] for b in range(num_batches): perm = numpy.take(range(self.args.attack_samples), range(b * self.args.batch_size, (b + 1) * self.args.batch_size), mode='clip') batch_theta = common.torch.as_variable(self.test_theta[perm], self.args.use_gpu) batch_classes = common.torch.as_variable(self.test_codes[perm], self.args.use_gpu) objective = self.objective_class() if isinstance(self.decoder, models.SelectiveDecoder): self.decoder.set_code(batch_classes) attack = self.setup_decoder_attack(self.decoder_classifier, batch_theta, batch_classes) attack.set_bound(torch.from_numpy(self.min_bound), torch.from_numpy(self.max_bound)) s, p, _, _, _ = attack.run(objective, False) perturbations = common.torch.as_variable(p, self.args.use_gpu) batch_perturbed_theta = batch_theta + perturbations batch_perturbed_images = self.decoder(batch_perturbed_theta) output_classes = self.model(batch_perturbed_images) e = self.loss(batch_classes, output_classes) perturbation_loss += e.item() a = self.error(batch_classes, output_classes) perturbation_error += a.item() decoder_iterations += numpy.mean( s[s >= 0]) if numpy.sum(s >= 0) > 0 else -1 decoder_norm += numpy.mean( numpy.linalg.norm(p.reshape(p.shape[0], -1), axis=1, ord=self.norm)) decoder_success += numpy.sum(s >= 0) / self.args.batch_size loss /= num_batches error /= num_batches perturbation_loss /= num_batches perturbation_error /= num_batches success /= num_batches iterations /= num_batches norm /= num_batches decoder_perturbation_loss /= num_batches decoder_perturbation_error /= num_batches decoder_success /= num_batches decoder_iterations /= num_batches decoder_norm /= num_batches log('[Training] %d: test %g (%g) %g (%g) %g (%g)' % (self.epoch, loss, error, perturbation_loss, perturbation_error, decoder_perturbation_loss, decoder_perturbation_error)) log('[Training] %d: test %g (%g, %g) %g (%g, %g)' % (self.epoch, success, iterations, norm, decoder_success, decoder_iterations, decoder_norm)) num_batches = int( math.ceil(self.train_images.shape[0] / self.args.batch_size)) iteration = self.epoch * num_batches self.test_statistics = numpy.vstack(( self.test_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, ]])))
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 test(self): """ Test the model. """ self.model.eval() assert self.model.training is False log('[Training] %d set classifier to eval' % self.epoch) loss = error = perturbation_loss = perturbation_error = 0 num_batches = int(math.ceil(self.args.test_samples/self.args.batch_size)) for b in range(num_batches): perm = numpy.take(range(self.args.test_samples), range(b*self.args.batch_size, (b+1)*self.args.batch_size), mode='clip') batch_images = common.torch.as_variable(self.test_images[perm], self.args.use_gpu) batch_classes = common.torch.as_variable(self.test_codes[perm], self.args.use_gpu) batch_theta = common.torch.as_variable(self.test_theta[perm], self.args.use_gpu) batch_images = batch_images.permute(0, 3, 1, 2) output_classes = self.model(batch_images) e = self.loss(batch_classes, output_classes) loss += e.item() a = self.error(batch_classes, output_classes) error += a.item() if self.args.strong_variant: random = common.numpy.truncated_normal(batch_theta.size(), lower=-self.args.bound, upper=self.args.bound) batch_perturbed_theta = common.torch.as_variable(random.astype(numpy.float32), self.args.use_gpu) if isinstance(self.decoder, models.SelectiveDecoder): self.decoder.set_code(batch_classes) batch_perturbed_images = self.decoder(batch_perturbed_theta) else: random = common.numpy.uniform_ball(batch_theta.size(0), batch_theta.size(1), epsilon=self.args.epsilon, ord=self.norm) batch_perturbed_theta = batch_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) if isinstance(self.decoder, models.SelectiveDecoder): self.decoder.set_code(batch_classes) batch_perturbed_images = self.decoder(batch_perturbed_theta) output_classes = self.model(batch_perturbed_images) e = self.loss(batch_classes, output_classes) perturbation_loss += e.item() e = self.error(batch_classes, output_classes) perturbation_error += e.item() loss /= num_batches error /= num_batches perturbation_loss /= num_batches perturbation_error /= num_batches log('[Training] %d: test %g (%g) %g (%g)' % (self.epoch, loss, error, perturbation_loss, perturbation_error)) num_batches = int(math.ceil(self.train_images.shape[0]/self.args.batch_size)) iteration = self.epoch * num_batches self.test_statistics = numpy.vstack((self.test_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 ]])))
def test(self): """ Test the model. """ self.model.eval() log('[Training] %d set classifier to eval' % self.epoch) assert self.model.training is False loss = error = perturbation_loss = perturbation_error = success = iterations = norm = 0 num_batches = int(math.ceil(self.args.test_samples/self.args.batch_size)) for b in range(num_batches): perm = numpy.take(range(self.args.test_samples), range(b*self.args.batch_size, (b+1)*self.args.batch_size), mode='clip') batch_images = common.torch.as_variable(self.test_images[perm], self.args.use_gpu) batch_classes = common.torch.as_variable(self.test_codes[perm], self.args.use_gpu) batch_images = batch_images.permute(0, 3, 1, 2) output_classes = self.model(batch_images) e = self.loss(batch_classes, output_classes) loss += e.data # 0-dim tensor a = self.error(batch_classes, output_classes) error += a.data loss /= num_batches error /= num_batches num_batches = int(math.ceil(self.args.attack_samples/self.args.batch_size)) assert self.args.attack_samples > 0 and self.args.attack_samples <= self.test_images.shape[0] for b in range(num_batches): perm = numpy.take(range(self.args.attack_samples), range(b*self.args.batch_size, (b+1)*self.args.batch_size), mode='clip') batch_images = common.torch.as_variable(self.test_images[perm], self.args.use_gpu) batch_classes = common.torch.as_variable(self.test_codes[perm], self.args.use_gpu) batch_images = batch_images.permute(0, 3, 1, 2) objective = self.objective_class() attack = self.setup_attack(self.model, batch_images, batch_classes) s, p, _, _, _ = attack.run(objective, False) batch_images = batch_images + common.torch.as_variable(p.astype(numpy.float32), self.args.use_gpu) output_classes = self.model(batch_images) e = self.loss(batch_classes, output_classes) perturbation_loss += e.item() e = self.error(batch_classes, output_classes) perturbation_error += e.item() iterations += numpy.mean(s[s >= 0]) if numpy.sum(s >= 0) > 0 else -1 norm += numpy.mean(numpy.linalg.norm(p.reshape(p.shape[0], -1), axis=1, ord=self.norm)) success += numpy.sum(s >= 0)/self.args.batch_size perturbation_error /= num_batches perturbation_loss /= num_batches success /= num_batches iterations /= num_batches norm /= num_batches log('[Training] %d: test %g (%g) %g (%g)' % (self.epoch, loss, error, perturbation_loss, perturbation_error)) log('[Training] %d: test %g (%g, %g)' % (self.epoch, success, iterations, norm)) num_batches = int(math.ceil(self.train_images.shape[0]/self.args.batch_size)) iteration = self.epoch*num_batches self.test_statistics = numpy.vstack((self.test_statistics, numpy.array([[ iteration, iteration * self.args.batch_size, min(num_batches, iteration) * self.args.batch_size, loss, error, perturbation_loss, perturbation_error, success, iterations, norm ]])))
def attack(self): """ Test the model. """ assert self.model is not None assert self.model.training is False assert self.test_images.shape[0] == self.test_codes.shape[0], 'number of samples has to match' concatenate_axis = -1 if os.path.exists(self.args.perturbations_file) and os.path.exists(self.args.success_file): self.original_perturbations = utils.read_hdf5(self.args.perturbations_file) if self.test_images.shape[3] > 1: assert len(self.original_perturbations.shape) == 5 else: assert len(self.original_perturbations.shape) == 4 log('[Attack] read %s' % self.args.perturbations_file) self.original_success = utils.read_hdf5(self.args.success_file) log('[Attack] read %s' % self.args.success_file) assert self.original_perturbations.shape[0] == self.original_success.shape[0] assert self.original_perturbations.shape[1] == self.original_success.shape[1] assert self.original_perturbations.shape[2] == self.test_images.shape[1] assert self.original_perturbations.shape[3] == self.test_images.shape[2]# if self.original_perturbations.shape[1] >= self.args.max_samples and self.original_perturbations.shape[0] >= self.args.max_attempts: log('[Attack] found %d attempts, %d samples, requested no more' % (self.original_perturbations.shape[0], self.original_perturbations.shape[1])) return elif self.original_perturbations.shape[0] == self.args.max_attempts or self.original_perturbations.shape[1] == self.args.max_samples: if self.original_perturbations.shape[0] == self.args.max_attempts: self.test_images = self.test_images[self.original_perturbations.shape[1]:] self.test_codes = self.test_codes[self.original_perturbations.shape[1]:] self.args.max_samples = self.args.max_samples - self.original_perturbations.shape[1] concatenate_axis = 1 log('[Attack] found %d attempts with %d perturbations, computing %d more perturbations' % (self.original_perturbations.shape[0], self.original_perturbations.shape[1], self.args.max_samples)) elif self.original_perturbations.shape[1] == self.args.max_samples: self.args.max_attempts = self.args.max_attempts - self.original_perturbations.shape[0] concatenate_axis = 0 log('[Attack] found %d attempts with %d perturbations, computing %d more attempts' % (self.original_perturbations.shape[0], self.original_perturbations.shape[1], self.args.max_attempts)) # can't squeeze here! if self.test_images.shape[3] > 1: self.perturbations = numpy.zeros((self.args.max_attempts, self.args.max_samples, self.test_images.shape[1], self.test_images.shape[2], self.test_images.shape[3])) else: self.perturbations = numpy.zeros((self.args.max_attempts, self.args.max_samples, self.test_images.shape[1], self.test_images.shape[2])) self.success = numpy.ones((self.args.max_attempts, self.args.max_samples), dtype=int) * -1 if self.args.attack.find('Batch') >= 0: batch_size = min(self.args.batch_size, self.args.max_samples) else: batch_size = 1 objective = self.objective_class() num_batches = int(math.ceil(self.args.max_samples/batch_size)) for i in range(num_batches): # self.test_images.shape[0] if i*batch_size == self.args.max_samples: break i_start = i*batch_size i_end = min((i+1)*batch_size, self.args.max_samples) batch_images = common.torch.as_variable(self.test_images[i_start: i_end], self.args.use_gpu) batch_classes = common.torch.as_variable(numpy.array(self.test_codes[i_start: i_end]), self.args.use_gpu) batch_images = batch_images.permute(0, 3, 1, 2) t = 0 while t < self.args.max_attempts: attack = self.setup_attack(batch_images, batch_classes) success, perturbations, probabilities, norm, _ = attack.run(objective) assert not numpy.any(perturbations != perturbations), perturbations # Note that we save the perturbed image, not only the perturbation! self.perturbations[t][i_start: i_end] = numpy.squeeze(numpy.transpose(perturbations + batch_images.cpu().numpy(), (0, 2, 3, 1))) self.success[t][i_start: i_end] = success # IMPORTANT: The adversarial examples are not considering whether the classifier is # actually correct to start with. t += 1 log('[Attack] %d: completed' % i) if concatenate_axis >= 0: if self.perturbations.shape[0] == self.args.max_attempts: self.perturbations = numpy.concatenate((self.original_perturbations, self.perturbations), axis=concatenate_axis) self.success = numpy.concatenate((self.original_success, self.success), axis=concatenate_axis) log('[Attack] concatenated') utils.write_hdf5(self.args.perturbations_file, self.perturbations) log('[Attack] wrote %s' % self.args.perturbations_file) utils.write_hdf5(self.args.success_file, self.success) log('[Attack] wrote %s' % self.args.success_file)
def load_data(self): """ Load data. """ assert self.args.batch_size % 4 == 0 self.train_images = utils.read_hdf5( self.args.train_images_file).astype(numpy.float32) log('[Training] read %s' % self.args.train_images_file) self.test_images = utils.read_hdf5(self.args.test_images_file).astype( numpy.float32) log('[Training] read %s' % self.args.test_images_file) # For handling both color and gray images. if len(self.train_images.shape) < 4: self.train_images = numpy.expand_dims(self.train_images, axis=3) self.test_images = numpy.expand_dims(self.test_images, axis=3) log('[Training] no color images, adjusted size') self.resolution = self.test_images.shape[2] log('[Training] resolution %d' % self.resolution) self.train_codes = utils.read_hdf5(self.args.train_codes_file).astype( numpy.int) assert self.train_codes.shape[1] >= self.args.label_index + 1 self.train_codes = self.train_codes[:, self.args.label_index] log('[Training] read %s' % self.args.train_codes_file) self.N_class = numpy.max(self.train_codes) + 1 self.test_codes = utils.read_hdf5(self.args.test_codes_file).astype( numpy.int) assert self.test_codes.shape[1] >= self.args.label_index + 1 self.test_codes = self.test_codes[:, self.args.label_index] log('[Training] read %s' % self.args.test_codes_file) self.train_theta = utils.read_hdf5(self.args.train_theta_file).astype( numpy.float32) log('[Training] read %s' % self.args.train_theta_file) assert self.test_images.shape[0] == self.test_codes.shape[0] self.min_bound = numpy.min(self.train_theta, axis=0) self.max_bound = numpy.max(self.train_theta, axis=0) log('[Training] min bound: %s' % ' '.join( ['%g' % self.min_bound[i] for i in range(self.min_bound.shape[0])])) log('[Training] max bound: %s' % ' '.join( ['%g' % self.max_bound[i] for i in range(self.max_bound.shape[0])])) self.test_theta = utils.read_hdf5(self.args.test_theta_file).astype( numpy.float32) log('[Training] read %s' % self.args.test_theta_file) assert self.train_codes.shape[0] == self.train_images.shape[0] assert self.test_codes.shape[0] == self.test_images.shape[0] assert self.train_theta.shape[ 0] == self.train_images.shape[0], '%s != %s' % ('x'.join( list(map(str, self.train_theta.shape))), 'x'.join( list(map(str, self.train_images.shape)))) assert self.test_theta.shape[0] == self.test_images.shape[0] # Select subset of samples if self.args.training_samples < 0: self.args.training_samples = self.train_images.shape[0] else: self.args.training_samples = min(self.args.training_samples, self.train_images.shape[0]) log('[Training] using %d training samples' % self.args.training_samples) if self.args.test_samples < 0: self.args.test_samples = self.test_images.shape[0] else: self.args.test_samples = min(self.args.test_samples, self.test_images.shape[0]) if self.args.early_stopping: assert self.args.validation_samples > 0 assert self.args.training_samples + self.args.validation_samples <= self.train_images.shape[ 0] self.val_images = self.train_images[self.train_images.shape[0] - self.args.validation_samples:] self.val_codes = self.train_codes[self.train_codes.shape[0] - self.args.validation_samples:] self.train_images = self.train_images[:self.train_images.shape[0] - self.args.validation_samples] self.train_codes = self.train_codeſ[:self.train_codes.shape[0] - self.args.validation_samples] assert self.val_images.shape[ 0] == self.args.validation_samples and self.val_codes.shape[ 0] == self.args.validation_samples if self.args.random_samples: perm = numpy.random.permutation(self.train_images.shape[0] // 10) perm = perm[:self.args.training_samples // 10] perm = numpy.repeat(perm, self.N_class, axis=0) * 10 + numpy.tile( numpy.array(range(self.N_class)), (perm.shape[0])) self.train_images = self.train_images[perm] self.train_codes = self.train_codes[perm] self.train_theta = self.train_theta[perm] else: self.train_images = self.train_images[:self.args.training_samples] self.train_codes = self.train_codes[:self.args.training_samples] self.train_theta = self.train_theta[:self.args.training_samples] self.train_valid = (numpy.max(numpy.abs(self.train_theta), axis=1) <= self.args.bound).astype(int) self.test_valid = (numpy.max(numpy.abs(self.test_theta), axis=1) <= self.args.bound).astype(int) # Check that the dataset is balanced. number_samples = self.train_codes.shape[0] // self.N_class for c in range(self.N_class): number_samples_ = numpy.sum(self.train_codes == c) if number_samples_ != number_samples: log( '[Training] dataset not balanced, class %d should have %d samples but has %d' % (c, number_samples, number_samples_), LogLevel.WARNING)
def load_data(self): """ Load data. """ assert self.args.batch_size % 4 == 0 self.database = utils.read_hdf5(self.args.database_file).astype( numpy.float32) log('[Training] read %s' % self.args.database_file) self.N_font = self.database.shape[0] self.N_class = self.database.shape[1] self.database = self.database.reshape( (self.database.shape[0] * self.database.shape[1], self.database.shape[2], self.database.shape[3])) self.database = torch.from_numpy(self.database) if self.args.use_gpu: self.database = self.database.cuda() self.database = torch.autograd.Variable(self.database, False) self.train_images = utils.read_hdf5( self.args.train_images_file).astype(numpy.float32) log('[Training] read %s' % self.args.train_images_file) self.test_images = utils.read_hdf5(self.args.test_images_file).astype( numpy.float32) log('[Training] read %s' % self.args.test_images_file) # For handling both color and gray images. if len(self.train_images.shape) < 4: self.train_images = numpy.expand_dims(self.train_images, axis=3) self.test_images = numpy.expand_dims(self.test_images, axis=3) log('[Training] no color images, adjusted size') self.resolution = self.train_images.shape[2] log('[Training] resolution %d' % self.resolution) self.train_codes = utils.read_hdf5(self.args.train_codes_file).astype( numpy.int) assert self.train_codes.shape[1] == 3 log('[Training] read %s' % self.args.train_codes_file) self.test_codes = utils.read_hdf5(self.args.test_codes_file).astype( numpy.int) assert self.test_codes.shape[1] == 3 log('[Training] read %s' % self.args.test_codes_file) self.train_theta = utils.read_hdf5(self.args.train_theta_file).astype( numpy.float32) log('[Training] read %s' % self.args.train_theta_file) self.min_bound = numpy.min(self.train_theta, axis=0) self.max_bound = numpy.max(self.train_theta, axis=0) self.test_theta = utils.read_hdf5(self.args.test_theta_file).astype( numpy.float32) log('[Training] read %s' % self.args.test_theta_file) assert self.train_codes.shape[0] == self.train_images.shape[0] assert self.test_codes.shape[0] == self.test_images.shape[0] assert self.train_theta.shape[0] == self.train_images.shape[0] assert self.test_theta.shape[0] == self.test_images.shape[0] # Select subset of samples if self.args.training_samples < 0: self.args.training_samples = self.train_images.shape[0] else: self.args.training_samples = min(self.args.training_samples, self.train_images.shape[0]) log('[Training] found %d classes' % self.N_class) log('[Training] using %d training samples' % self.args.training_samples) if self.args.test_samples < 0: self.args.test_samples = self.test_images.shape[0] else: self.args.test_samples = min(self.args.test_samples, self.test_images.shape[0]) if self.args.early_stopping: assert self.args.validation_samples > 0 assert self.args.training_samples + self.args.validation_samples <= self.train_images.shape[ 0] self.val_images = self.train_images[self.train_images.shape[0] - self.args.validation_samples:] self.val_codes = self.train_codes[self.train_codes.shape[0] - self.args.validation_samples:, self.args.label_index] self.train_images = self.train_images[:self.train_images.shape[0] - self.args.validation_samples] self.train_codes = self.train_codes[:self.train_codes.shape[0] - self.args.validation_samples] assert self.val_images.shape[ 0] == self.args.validation_samples and self.val_codes.shape[ 0] == self.args.validation_samples if self.args.random_samples: perm = numpy.random.permutation(self.train_images.shape[0] // 10) perm = perm[:self.args.training_samples // 10] perm = numpy.repeat(perm, self.N_class, axis=0) * 10 + numpy.tile( numpy.array(range(self.N_class)), (perm.shape[0])) self.train_images = self.train_images[perm] self.train_codes = self.train_codes[perm] self.train_theta = self.train_theta[perm] else: self.train_images = self.train_images[:self.args.training_samples] self.train_codes = self.train_codes[:self.args.training_samples] self.train_theta = self.train_theta[:self.args.training_samples] # Check that the dataset is balanced. number_samples = self.train_codes.shape[0] // self.N_class for c in range(self.N_class): number_samples_ = numpy.sum( self.train_codes[:, self.args.label_index] == c) if number_samples_ != number_samples: log( '[Training] dataset not balanced, class %d should have %d samples but has %d' % (c, number_samples, number_samples_), LogLevel.WARNING)
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 test(self): """ Test the model. """ self.model.eval() log('[Training] %d set classifier to eval' % self.epoch) loss = error = perturbation_loss = perturbation_error = 0 num_batches = int( math.ceil(self.args.test_samples / self.args.batch_size)) assert self.model.training is False for b in range(num_batches): perm = numpy.take(range(self.args.test_samples), range(b * self.args.batch_size, (b + 1) * self.args.batch_size), mode='clip') batch_images = common.torch.as_variable(self.test_images[perm], self.args.use_gpu) batch_theta = common.torch.as_variable(self.test_theta[perm], self.args.use_gpu) batch_images = batch_images.permute(0, 3, 1, 2) batch_fonts = self.test_codes[perm, 1] batch_classes = self.test_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) output_classes = self.model(batch_images) e = self.loss(batch_classes, output_classes) loss += e.item() a = self.error(batch_classes, output_classes) error += a.item() if self.args.strong_variant: min_bound = numpy.repeat(self.min_bound.reshape(1, -1), batch_theta.size(0), axis=0) max_bound = numpy.repeat(self.max_bound.reshape(1, -1), batch_theta.size(0), axis=0) random = numpy.random.uniform( min_bound, max_bound, (batch_theta.size(0), batch_theta.size(1))) batch_perturbed_theta = common.torch.as_variable( random.astype(numpy.float32), self.args.use_gpu) self.decoder.set_code(batch_code) batch_perturbed_images = self.decoder(batch_perturbed_theta) else: random = common.numpy.uniform_ball(batch_theta.size(0), batch_theta.size(1), epsilon=self.args.epsilon, ord=self.norm) batch_perturbed_theta = batch_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_code(batch_code) batch_perturbed_images = self.decoder(batch_perturbed_theta) output_classes = self.model(batch_perturbed_images) l = self.loss(batch_classes, output_classes) perturbation_loss += l.item() e = self.error(batch_classes, output_classes) perturbation_error += e.item() loss /= num_batches error /= num_batches perturbation_loss /= num_batches perturbation_error /= num_batches log('[Training] %d: test %g (%g) %g (%g)' % (self.epoch, loss, error, perturbation_loss, perturbation_error)) num_batches = int( math.ceil(self.train_images.shape[0] / self.args.batch_size)) iteration = self.epoch * num_batches self.test_statistics = numpy.vstack(( self.test_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 ]])))
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_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) loss = error = gradient = 0 if self.args.full_variant: for t in range(self.args.max_iterations): if self.args.strong_variant: # Here, we want to uniformly sample all allowed transformations, so that's OK. 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, (batch_theta.size(0), batch_theta.size(1))) batch_perturbed_theta = common.torch.as_variable( random.astype(numpy.float32), self.args.use_gpu) self.decoder.set_code(batch_code) batch_perturbed_images = self.decoder( batch_perturbed_theta) else: random = common.numpy.uniform_ball( batch_theta.size(0), batch_theta.size(1), epsilon=self.args.epsilon, ord=self.norm) batch_perturbed_theta = batch_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_code(batch_code) 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: # Again, sampling all possible transformations. 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, batch_theta.size(1))) batch_perturbed_theta = common.torch.as_variable( random.astype(numpy.float32), self.args.use_gpu) self.decoder.set_code(batch_code[split:]) batch_perturbed_images = self.decoder( batch_perturbed_theta) else: random = common.numpy.uniform_ball( split, batch_theta.size(1), epsilon=self.args.epsilon, ord=self.norm) batch_perturbed_theta = batch_theta[ split:] + 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_code(batch_code[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')
up = np.zeros(len(instance.nodes) * 3) bc_dofs = [] for bc in boundary_conditions: set_nodes = [] if bc.type == 'surface': base = results_odb.rootAssembly.surfaces[bc.set_name] elif bc.type == 'node_set': base = results_odb.rootAssembly.nodeSets[bc.set_name] idx = base.instances.index(instance) nodes = base.nodes[idx] print(len(nodes), "in set", bc.set_name) for n in nodes: set_nodes.append(3 * (n.label - 1) + bc.component - 1) bc_dofs.append(3 * (n.label - 1) + bc.component - 1) bc_dofs = np.unique(np.array(bc_dofs)) with open('up.pkl') as pickle_handle: up_red = pickle.load(pickle_handle) bc_set = set(bc_dofs) j = 0 for i in range(up.shape[0]): if i not in bc_set: up[i] = up_red[j] j += 1 idx = np.argmin(up[1::3]) print(idx) for i, n in enumerate(node_labels): permanent_deformation[i, :] = up[3 * (n - 1):3 * n] results_odb.close()
def train(self): """ Train with fair data augmentation. """ self.model.train() assert self.model.training is True assert self.decoder.training is False 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_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: # Here we want to sample form a truncated Gaussian random = common.numpy.truncated_normal(batch_theta.size(), lower=-self.args.bound, upper=self.args.bound) batch_perturbed_theta = common.torch.as_variable(random.astype(numpy.float32), self.args.use_gpu) if isinstance(self.decoder, models.SelectiveDecoder): self.decoder.set_code(batch_classes) batch_perturbed_images = self.decoder(batch_perturbed_theta) else: random = common.numpy.uniform_ball(batch_theta.size(0), batch_theta.size(1), epsilon=self.args.epsilon, ord=self.norm) batch_perturbed_theta = batch_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) if isinstance(self.decoder, models.SelectiveDecoder): self.decoder.set_code(batch_classes) 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: # Here we want to sample form a truncated Gaussian random = common.numpy.truncated_normal([split, batch_theta.size(1)], lower=-self.args.bound, upper=self.args.bound) batch_perturbed_theta = common.torch.as_variable(random.astype(numpy.float32), self.args.use_gpu) if isinstance(self.decoder, models.SelectiveDecoder): self.decoder.set_code(batch_classes[split:]) batch_perturbed_images = self.decoder(batch_perturbed_theta) else: random = common.numpy.uniform_ball(split, batch_theta.size(1), epsilon=self.args.epsilon, ord=self.norm) batch_perturbed_theta = batch_theta[split:] + 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) if isinstance(self.decoder, models.SelectiveDecoder): self.decoder.set_code(batch_classes[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 load_model(self): """ Load the decoder. """ assert self.args.N_theta > 0 and self.args.N_theta <= 9 min_translation_x, max_translation_x = map( float, self.args.translation_x.split(',')) min_translation_y, max_translation_y = map( float, self.args.translation_y.split(',')) min_shear_x, max_shear_x = map(float, self.args.shear_x.split(',')) min_shear_y, max_shear_y = map(float, self.args.shear_y.split(',')) min_scale, max_scale = map(float, self.args.scale.split(',')) min_rotation, max_rotation = map(float, self.args.rotation.split(',')) min_color, max_color = self.args.color, 1 self.min_bound = numpy.array([ min_translation_x, min_translation_y, min_shear_x, min_shear_y, min_scale, min_rotation, min_color, min_color, min_color, ]) self.max_bound = numpy.array([ max_translation_x, max_translation_y, max_shear_x, max_shear_y, max_scale, max_rotation, max_color, max_color, max_color ]) self.min_bound = self.min_bound[:self.args.N_theta].astype( numpy.float32) self.max_bound = self.max_bound[:self.args.N_theta].astype( numpy.float32) decoder = models.STNDecoder(self.args.N_theta) log('[Attack] set up STN decoder') classifier = models.Classifier( self.N_class, resolution=(self.test_images.shape[3], self.test_images.shape[1], self.test_images.shape[2]), architecture='standard', activation=self.args.network_activation, batch_normalization=not self.args.network_no_batch_normalization, start_channels=self.args.network_channels, dropout=self.args.network_dropout) assert os.path.exists( self.args.classifier_file ), 'state file %s not found' % self.args.classifier_file state = State.load(self.args.classifier_file) log('[Attack] read %s' % self.args.classifier_file) classifier.load_state_dict(state.model) if self.args.use_gpu and not cuda.is_cuda(classifier): log('[Attack] classifier is not CUDA') classifier = classifier.cuda() classifier.eval() log('[Attack] loaded classifier') self.model = models.DecoderClassifier(decoder, classifier) log('[Training] set up decoder classifier')