def run_method_1(): # config discriminator_config = gan.config_discriminator generator_config = gan.config_generator num_col = 10 num_generation = 20 batchsize = 2 * num_generation base_z = gan.sample_z(batchsize) mix_z = np.zeros((num_col * num_generation, generator_config.ndim_input), dtype=np.float32) for g in xrange(num_generation): for i in xrange(num_col): mix_z[g * num_col + i] = base_z[2 * g] * (i / float(num_col)) + base_z[2 * g + 1] * (1 - i / float(num_col)) x_negative = gan.generate_x_from_z(mix_z, test=True, as_numpy=True) x_negative = (x_negative + 1.0) / 2.0 # optimize z # xp = gan.xp # x_fake = gan.generate_x_from_z(mix_z, test=True, as_numpy=False) # x_fake.unchain_backward() # for n in xrange(500): # discrimination_fake, _ = gan.discriminate(x_fake, test=True) # opt = F.sum(discrimination_fake) # print opt.data # opt.backward() # # gan.backprop_generator(-F.sum(discrimination_fake)) # x_fake = gan.to_variable(xp.clip(x_fake.data + x_fake.grad * 0.01, -1, 1)) # x_negative = gan.to_numpy(x_fake) # x_negative = (x_negative + 1.0) / 2.0 plot.tile_rgb_images(x_negative.transpose(0, 2, 3, 1), dir=args.plot_dir, filename="analogy_1", row=num_generation, col=num_col)
def run_method_1(): # config discriminator_config = gan.config_discriminator generator_config = gan.config_generator num_col = 20 num_generation = 20 batchsize = 2 * num_generation base_z = gan.sample_z(batchsize) mix_z = np.zeros((num_col * num_generation, generator_config.ndim_input), dtype=np.float32) for g in xrange(num_generation): for i in xrange(num_col): mix_z[g * num_col + i] = base_z[2 * g] * (i / float(num_col)) + base_z[2 * g + 1] * (1 - i / float(num_col)) x_negative = gan.generate_x_from_z(mix_z, test=True, as_numpy=True) plot.tile_binary_images(x_negative.reshape((-1, 28, 28)), dir=args.plot_dir, filename="analogy_1", row=num_generation, col=num_col)
def run_method_1(): # config discriminator_config = gan.config_discriminator generator_config = gan.config_generator num_col = 10 num_generation = 20 batchsize = 2 * num_generation base_z = gan.to_variable(gan.sample_z(batchsize)) # optimize z class_true = gan.to_variable(np.zeros(batchsize, dtype=np.int32)) for n in xrange(5): x_fake = gan.generate_x_from_z(base_z, test=True, as_numpy=False) discrimination_fake, _ = gan.discriminate(x_fake, apply_softmax=False, test=True) cross_entropy = F.softmax_cross_entropy(discrimination_fake, class_true) gan.backprop_generator(cross_entropy) base_z = gan.to_variable(base_z.data + base_z.grad * 0.01) base_z = gan.to_numpy(base_z) sum_z = np.sum(base_z) if sum_z != sum_z: raise Exception("NaN") mix_z = np.zeros((num_col * num_generation, generator_config.ndim_input), dtype=np.float32) for g in xrange(num_generation): for i in xrange(num_col): mix_z[g * num_col + i] = base_z[2 * g] * (i / float(num_col)) + base_z[ 2 * g + 1] * (1 - i / float(num_col)) x_negative = gan.generate_x_from_z(mix_z, test=True, as_numpy=True) x_negative = (x_negative + 1.0) / 2.0 visualizer.tile_rgb_images(x_negative.transpose(0, 2, 3, 1), dir=args.plot_dir, filename="analogy_1", row=num_generation, col=num_col)
def main(): images = load_rgb_images(args.image_dir) # config discriminator_config = gan.config_discriminator generator_config = gan.config_generator # settings max_epoch = 1000 n_trains_per_epoch = 500 batchsize_true = 128 batchsize_fake = 128 plot_interval = 5 # seed np.random.seed(args.seed) if args.gpu_device != -1: cuda.cupy.random.seed(args.seed) # init weightnorm layers if discriminator_config.use_weightnorm: print "initializing weight normalization layers of the discriminator ..." x_true = sample_from_data(images, batchsize_true) gan.discriminate(x_true) if generator_config.use_weightnorm: print "initializing weight normalization layers of the generator ..." gan.generate_x(batchsize_fake) # classification # 0 -> true sample # 1 -> generated sample class_true = gan.to_variable(np.zeros(batchsize_true, dtype=np.int32)) class_fake = gan.to_variable(np.ones(batchsize_fake, dtype=np.int32)) # training progress = Progress() for epoch in xrange(1, max_epoch): progress.start_epoch(epoch, max_epoch) sum_loss_discriminator = 0 sum_loss_generator = 0 sum_loss_vat = 0 for t in xrange(n_trains_per_epoch): # sample data x_true = sample_from_data(images, batchsize_true) x_fake = gan.generate_x(batchsize_fake).data # unchain # train discriminator discrimination_true, activations_true = gan.discriminate( x_true, apply_softmax=False) discrimination_fake, _ = gan.discriminate(x_fake, apply_softmax=False) loss_discriminator = F.softmax_cross_entropy( discrimination_true, class_true) + F.softmax_cross_entropy( discrimination_fake, class_fake) gan.backprop_discriminator(loss_discriminator) # virtual adversarial training loss_vat = 0 if discriminator_config.use_virtual_adversarial_training: z = gan.sample_z(batchsize_fake) loss_vat = -F.sum(gan.compute_lds(z)) / batchsize_fake gan.backprop_discriminator(loss_vat) sum_loss_vat += float(loss_vat.data) # train generator x_fake = gan.generate_x(batchsize_fake) discrimination_fake, activations_fake = gan.discriminate( x_fake, apply_softmax=False) loss_generator = F.softmax_cross_entropy(discrimination_fake, class_true) # feature matching if discriminator_config.use_feature_matching: features_true = activations_true[-1] features_fake = activations_fake[-1] loss_generator += F.mean_squared_error(features_true, features_fake) gan.backprop_generator(loss_generator) sum_loss_discriminator += float(loss_discriminator.data) sum_loss_generator += float(loss_generator.data) if t % 10 == 0: progress.show(t, n_trains_per_epoch, {}) progress.show( n_trains_per_epoch, n_trains_per_epoch, { "loss_d": sum_loss_discriminator / n_trains_per_epoch, "loss_g": sum_loss_generator / n_trains_per_epoch, "loss_vat": sum_loss_vat / n_trains_per_epoch, }) gan.save(args.model_dir) if epoch % plot_interval == 0 or epoch == 1: plot(filename="epoch_{}_time_{}min".format( epoch, progress.get_total_time()))