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) #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=FLAGS.input_width, input_height=FLAGS.input_height, output_width=FLAGS.output_width, output_height=FLAGS.output_height, batch_size=FLAGS.batch_size, sample_num=FLAGS.batch_size, dataset_name=FLAGS.dataset, input_fname_pattern=FLAGS.input_fname_pattern, crop=FLAGS.crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir, data_path=FLAGS.data_dir, training_subset=FLAGS.training_subset) show_all_variables() if FLAGS.train: dcgan.train(FLAGS) else: if not dcgan.load(FLAGS.checkpoint_dir)[0]: raise Exception("[!] Train a model first, then run test mode") dcgan.predict(FLAGS) # 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 = 1
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) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu_frac) config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess: dcgan = DCGAN(sess) dcgan.predict()