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, output_size=28, c_dim=1, dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_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.load(FLAGS.checkpoint_dir) print(dcgan.evaluate(6400)) 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(_): if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.result_dir): os.makedirs(FLAGS.result_dir) dataset = FlowersData(subset=FLAGS.subset) assert dataset.data_files() dcgan = DCGAN(z_dim=200, dataset=dataset) dcgan.evaluate() if FLAGS.visualize: # Below is codes for visualization OPTION = 2 visualize(dcgan, FLAGS, OPTION)
def main(_): if FLAGS.output_height is None: FLAGS.output_height = FLAGS.input_height if FLAGS.input_width is None: FLAGS.input_width = FLAGS.input_height if FLAGS.output_width is None: FLAGS.output_width = FLAGS.output_height FLAGS.out_name = '{}-{}x{}-bn{}'.format(FLAGS.dataset, FLAGS.output_height, FLAGS.output_width, FLAGS.batch_size) # FLAGS.out_name = '{}-{}x{}'.format(FLAGS.dataset, FLAGS.output_height,FLAGS.output_width) FLAGS.out_dir = os.path.join(FLAGS.out_dir, FLAGS.out_name) FLAGS.checkpoint_dir = os.path.join(FLAGS.out_dir, FLAGS.checkpoint_dir) FLAGS.sample_dir = os.path.join(FLAGS.out_dir, FLAGS.sample_dir) 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: 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, z_dim=FLAGS.z_dim, dataset_name=FLAGS.dataset, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir, data_dir=FLAGS.data_dir, out_dir=FLAGS.out_dir, max_to_keep=FLAGS.max_to_keep) if FLAGS.train: dcgan.train(FLAGS) else: # dcgan.genPics(FLAGS) dcgan.evaluate(FLAGS)