def generate_sample(self, counter): n = int(np.random.random() * (len(self.x_data) - self.sampler_batch_size)) x_batch = self.x_data[n:n + self.sampler_batch_size, :, :, :] z_batch = self.sess.run(self.sample_z_batch_op) sampler = [ ops.inverse_transform(self.G), ops.inverse_transform(self.X_recons) ] g, x_recons = self.sess.run(sampler, feed_dict={ self.X: x_batch, self.Z: z_batch }) filename = '{}.png'.format(counter) recons_path = os.path.join(self.samples_path, 'recons') utils.make_sure_path_exits(recons_path) utils.save_images_h(os.path.join(recons_path, filename), np.concatenate([x_batch, x_recons], 0), self.sampler_batch_size) gens_path = os.path.join(self.samples_path, 'gens') utils.make_sure_path_exits(gens_path) utils.save_images_h(os.path.join(gens_path, filename), g, int(np.sqrt(self.sampler_batch_size))) self.inc_scores.append(self.get_inception_score()) self.diff_scores.append(self.get_inception_score_diff()) self._save_score() return [gens_path, recons_path]
def _post_train(self): imgs = [] for path in self.sample_imgs: img = cv2.imread(path) imgs.append(cv2.cvtColor(img, cv2.COLOR_RGB2BGR)) imgs = np.array(imgs) samplefile_path = os.path.join(self.samples_path, 'combined.png') utils.save_images_h(samplefile_path, imgs, 10)
def generate_sample(self, counter): z = self.sess.run(self.sample_z_batch_op) samples = self.sess.run(ops.inverse_transform(self.G), feed_dict={self.Z: z}) utils.make_sure_path_exits(self.samples_path) samplefile_path = os.path.join(self.samples_path, '{}.png'.format(counter)) utils.save_images_h(path=samplefile_path, images=samples, imagesPerRow=self.sampler_interpolations) return [samplefile_path]
def generate_sample(self, counter): z_batch = self.sess.run(self.sample_z_batch_op) g = self.sess.run(ops.inverse_transform(self.G), feed_dict={self.Z: z_batch}) # img_array = tf.transpose(samples, [1, 0, 2, 3, 4]) utils.make_sure_path_exits(self.samples_path) samplefile_path = os.path.join(self.samples_path, '{}.png'.format(counter)) utils.save_images_h(samplefile_path, g, int(np.sqrt(self.sampler_batch_size))) self.inc_scores.append(self.get_inception_score()) self._save_score() return [samplefile_path]
def generate_sample(self, counter): x_batch, z_batch = self.sess.run( [self.sample_x_batch_op, self.sample_z_batch_op]) sampler = [ ops.inverse_transform(self.G), ops.inverse_transform(self.X_recons) ] g, x_recons = self.sess.run(sampler, feed_dict={ self.X: x_batch, self.Z: z_batch }) filename = '{}.png'.format(counter) recons_path = os.path.join(self.samples_path, 'train/recons') utils.make_sure_path_exits(recons_path) utils.save_images_h(os.path.join(recons_path, filename), np.concatenate([x_batch, x_recons], 0), self.sampler_batch_size) gens_path = os.path.join(self.samples_path, 'train/gens') utils.make_sure_path_exits(gens_path) utils.save_images_h(os.path.join(gens_path, filename), g, int(np.sqrt(self.sampler_batch_size))) # Test set x_batch = self.sess.run(self.test_x_batch_op) g, x_recons = self.sess.run(sampler, feed_dict={ self.X: x_batch, self.Z: self.test_z_batch }) filename = '{}.png'.format(counter) recons_path = os.path.join(self.samples_path, 'test/recons') utils.make_sure_path_exits(recons_path) utils.save_images_h(os.path.join(recons_path, filename), np.concatenate([x_batch, x_recons], 0), self.sampler_batch_size) gens_path = os.path.join(self.samples_path, 'test/gens') utils.make_sure_path_exits(gens_path) utils.save_images_h(os.path.join(gens_path, filename), g, int(np.sqrt(self.sampler_batch_size))) return [gens_path, recons_path]