def _demo(): # Load checkpoint if not tf.gfile.IsDirectory(FLAGS.checkpoint_dir): raise FileNotFoundError("Could not find folder `%s'" % (FLAGS.checkpoint_dir,)) # Setup global tensorflow state sess, summary_writer = setup_tensorflow() # Prepare directories filenames = prepare_dirs(delete_train_dir=False) # Setup async input queues features, labels = srez_input.setup_inputs(sess, filenames) # Create and initialize model [gene_minput, gene_moutput, gene_output, gene_var_list, disc_real_output, disc_fake_output, disc_var_list] = \ srez_model.create_model(sess, features, labels) # Restore variables from checkpoint saver = tf.train.Saver() filename = 'checkpoint_new.txt' filename = os.path.join(FLAGS.checkpoint_dir, filename) saver.restore(sess, filename) # Execute demo srez_demo.demo1(sess)
def _demo(): # Load checkpoint if not tf.gfile.IsDirectory(FLAGS.checkpoint_dir): raise FileNotFoundError("Could not find folder `%s'" % (FLAGS.checkpoint_dir,)) # Setup global tensorflow state sess, summary_writer = setup_tensorflow() # Prepare directories filenames = prepare_dirs(delete_train_dir=False) # Setup async input queues features, labels = srez_input.setup_inputs(sess, filenames) # Create and initialize model [gene_minput, gene_moutput, gene_output, gene_var_list, disc_real_output, disc_fake_output, disc_var_list] = \ srez_model.create_model(sess, features, labels) # Restore variables from checkpoint saver = tf.train.Saver() filename = 'checkpoint_new.txt' filename = os.path.join(FLAGS.checkpoint_dir, filename) saver.restore(sess, filename) # Execute demo srez_demo.demo1(sess)
def main(argv=None): # Training or showing off? if tf.__version__ != '1.0.0': print("please use tensorflow version 1.0.0") exit(0) if FLAGS.run == 'demo': #_demo() srez_demo.demo1() elif FLAGS.run == 'train': _train()