我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用utils.visualize()。
def main(_): pp.pprint(flags.FLAGS.__flags) if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) with tf.Session() as sess: if FLAGS.dataset == 'mnist': dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, y_dim=10, dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir) else: dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir) if FLAGS.is_train: dcgan.train(FLAGS) else: if FLAGS.is_single: dcgan.single_test(FLAGS.checkpoint_dir, FLAGS.file_name) elif FLAGS.is_small: dcgan.batch_test2(FLAGS.checkpoint_dir) else: dcgan.batch_test(FLAGS.checkpoint_dir, FLAGS.file_name) # dcgan.load(FLAGS.checkpoint_dir) # dcgan.single_test(FLAGS.checkpoint_dir) # dcgan.batch_test(FLAGS.checkpoint_dir) """ if FLAGS.visualize: to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0], [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1], [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2], [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3], [dcgan.h4_w, dcgan.h4_b, None]) # Below is codes for visualization OPTION = 2 visualize(sess, dcgan, FLAGS, OPTION) """
def main(_): loader = Loader(FLAGS.data_dir, FLAGS.data, FLAGS.batch_size) print("# of data: {}".format(loader.data_num)) with tf.Session() as sess: lsgan = LSGAN([FLAGS.batch_size, 112, 112, 3]) sess.run(tf.global_variables_initializer()) for epoch in range(10000): loader.reset() for step in range(int(loader.batch_num/FLAGS.d)): if (step == 0 and epoch % 1 == 100): utils.visualize(sess.run(lsgan.gen_img), epoch) for _ in range(FLAGS.d): batch = np.asarray(loader.next_batch(), dtype=np.float32) batch = (batch-127.5) / 127.5 #print("{}".format(batch.shape)) feed={lsgan.X: batch} _ = sess.run(lsgan.d_train_op, feed_dict=feed) #utils.visualize(batch, (epoch+1)*100) #cv2.namedWindow("window") #cv2.imshow("window", cv2.cvtColor(batch[0], cv2.COLOR_RGB2BGR)) #cv2.waitKey(0) #cv2.destroyAllWindows() _ = sess.run(lsgan.g_train_op)
def main(_): pp.pprint(flags.FLAGS.__flags) sample_dir_ = os.path.join(FLAGS.sample_dir, FLAGS.name) checkpoint_dir_ = os.path.join(FLAGS.checkpoint_dir, FLAGS.name) log_dir_ = os.path.join(FLAGS.log_dir, FLAGS.name) if not os.path.exists(checkpoint_dir_): os.makedirs(checkpoint_dir_) if not os.path.exists(sample_dir_): os.makedirs(sample_dir_) if not os.path.exists(log_dir_): os.makedirs(log_dir_) with tf.Session() as sess: if FLAGS.dataset == 'mnist': dcgan = DCGAN(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=28, c_dim=1, dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=checkpoint_dir_, sample_dir=sample_dir_, log_dir=log_dir_) else: dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=FLAGS.c_dim, dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir) if FLAGS.is_train: dcgan.train(FLAGS) else: dcgan.sampling(FLAGS) if FLAGS.visualize: to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0], [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1], [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2], [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3], [dcgan.h4_w, dcgan.h4_b, None]) # Below is codes for visualization OPTION = 2 visualize(sess, dcgan, FLAGS, OPTION)
def main(_): pp.pprint(flags.FLAGS.__flags) sample_dir_ = os.path.join(FLAGS.sample_dir, FLAGS.name) checkpoint_dir_ = os.path.join(FLAGS.checkpoint_dir, FLAGS.name) log_dir_ = os.path.join(FLAGS.log_dir, FLAGS.name) if not os.path.exists(checkpoint_dir_): os.makedirs(checkpoint_dir_) if not os.path.exists(sample_dir_): os.makedirs(sample_dir_) if not os.path.exists(log_dir_): os.makedirs(log_dir_) with tf.Session() as sess: if FLAGS.dataset == 'mnist': dcgan = DCGAN(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=28, c_dim=1, dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=checkpoint_dir_, sample_dir=sample_dir_, log_dir=log_dir_) else: dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=FLAGS.c_dim, dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir) if FLAGS.is_train: dcgan.train(FLAGS) else: dcgan.sampling(FLAGS) #dcgan.load(FLAGS.checkpoint_dir) if FLAGS.visualize: to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0], [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1], [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2], [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3], [dcgan.h4_w, dcgan.h4_b, None]) # Below is codes for visualization OPTION = 2 visualize(sess, dcgan, FLAGS, OPTION)
def main(_): pp.pprint(flags.FLAGS.__flags) if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) with tf.Session() as sess: dcgan = DCGAN(sess, image_size = FLAGS.image_size, output_size = FLAGS.output_size, batch_size=FLAGS.batch_size, sample_size = FLAGS.sample_size) if FLAGS.is_train: dcgan.train(FLAGS) else: dcgan.load(FLAGS.checkpoint_dir) if FLAGS.visualize: # to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0], # [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1], # [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2], # [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3], # [dcgan.h4_w, dcgan.h4_b, None]) # Below is codes for visualization OPTION = 2 visualize(sess, dcgan, FLAGS, OPTION)
def main(_): pp.pprint(flags.FLAGS.__flags) if FLAGS.input_width is None: FLAGS.input_width = FLAGS.input_height if FLAGS.output_width is None: FLAGS.output_width = FLAGS.output_height if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) run_config = tf.ConfigProto() run_config.gpu_options.allow_growth=True with tf.Session(config=run_config) as sess: wgan = WGAN( sess, input_width=FLAGS.input_width, input_height=FLAGS.input_height, input_water_width=FLAGS.input_water_width, input_water_height=FLAGS.input_water_height, output_width=FLAGS.output_width, output_height=FLAGS.output_height, batch_size=FLAGS.batch_size, c_dim=FLAGS.c_dim, max_depth = FLAGS.max_depth, save_epoch=FLAGS.save_epoch, water_dataset_name=FLAGS.water_dataset, air_dataset_name = FLAGS.air_dataset, depth_dataset_name = FLAGS.depth_dataset, input_fname_pattern=FLAGS.input_fname_pattern, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, results_dir = FLAGS.results_dir, sample_dir=FLAGS.sample_dir, num_samples = FLAGS.num_samples) if FLAGS.is_train: wgan.train(FLAGS) else: if not wgan.load(FLAGS.checkpoint_dir): raise Exception("[!] Train a model first, then run test mode") wgan.test(FLAGS) # to_json("./web/js/layers.js", [wgan.h0_w, wgan.h0_b, wgan.g_bn0], # [wgan.h1_w, wgan.h1_b, wgan.g_bn1], # [wgan.h2_w, wgan.h2_b, wgan.g_bn2], # [wgan.h3_w, wgan.h3_b, wgan.g_bn3], # [wgan.h4_w, wgan.h4_b, None]) # Below is codes for visualization #OPTION = 1 #visualize(sess, wgan, FLAGS, OPTION)
def train(self, config=None): #mnist = input_data.read_data_sets("/tmp/tensorflow/mnist/input_dat", one_hot=True) loader = Loader(config.data_dir, config.data, config.batch_size) loaded = False if not config.reset: loaded, global_step = self.restore(config.checkpoint_dir) if not loaded: tf.global_variables_initializer().run() global_step = 0 d_losses = [] g_losses = [] steps = [] gif = [] for epoch in range(config.epoch): loader.reset() #for idx in range(config.step): for idx in range(loader.batch_num): #batch_X, _ = mnist.train.next_batch(config.batch_size) #batch_X = batch_X.reshape([-1]+self.in_dim) batch_X = np.asarray(loader.next_batch(), dtype=np.float32) #batch_X = (batch_X*255.-127.5)/127.5 batch_X = (batch_X - 127.5)/127.5 batch_z = np.random.uniform(-1, 1, [config.batch_size, self.z_dim]) _, d_loss = self.sess.run([self.d_train_op, self.d_loss], feed_dict={self.X: batch_X, self.z: batch_z}) _, g_loss = self.sess.run([self.g_train_op, self.g_loss], feed_dict={self.z: batch_z}) d_losses.append(d_loss) g_losses.append(g_loss) steps.append(global_step) global_step += 1 print(" [Epoch {}] d_loss:{}, g_loss:{}".format(epoch, d_loss, g_loss)) batch_z = np.random.uniform(-1, 1, [config.batch_size, self.z_dim]) imgs = self.sess.run(self.sampler, feed_dict={self.z: batch_z}) gif.append(visualize(imgs, epoch, config.data)) self.save("{}_{}".format(config.checkpoint_dir, config.data), global_step, model_name="dcgan") plot({'d_loss':d_losses, 'g_loss':g_losses}, steps, title="DCGAN loss ({})".format(config.data), x_label="Step", y_label="Loss") save_gif(gif, "gen_img_{}".format(config.data))
def run(checkpoint_dir = 'checkpoints', batch_size = 64, input_height = 108, input_width = None, output_height = 64, output_width = None, dataset = 'celebA', input_fname_pattern = '*.jpg', output_dir = 'output', sample_dir = 'samples', crop=True): #pp.pprint(flags.FLAGS.__flags) if input_width is None: input_width = input_height if output_width is None: output_width = output_height #if not os.path.exists(checkpoint_dir): # os.makedirs(checkpoint_dir) #if not os.path.exists(output_dir): # os.makedirs(output_dir) #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333) run_config = tf.ConfigProto() run_config.gpu_options.allow_growth=True with tf.Session(config=run_config) as sess: dcgan = DCGAN( sess, input_width=input_width, input_height=input_height, output_width=output_width, output_height=output_height, batch_size=batch_size, sample_num=batch_size, dataset_name=dataset, input_fname_pattern=input_fname_pattern, crop=crop, checkpoint_dir=checkpoint_dir, sample_dir=sample_dir, output_dir=output_dir) show_all_variables() try: tf.global_variables_initializer().run() except: tf.initialize_all_variables().run() # Below is code for visualization visualize(sess, dcgan, batch_size = batch_size, input_height = input_height, input_width = input_width, output_dir = output_dir)
