def main(_): pp.pprint(FLAGS.__flags) # training/inference with tf.Session() as sess: dcgan = DCGAN(sess, FLAGS) # path checks if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists( os.path.join(FLAGS.log_dir, dcgan.get_model_dir())): os.makedirs(os.path.join(FLAGS.log_dir, dcgan.get_model_dir())) # load checkpoint if found if dcgan.checkpoint_exists(): print("Loading checkpoints...") if dcgan.load(): print "success!" else: raise IOError("Could not read checkpoints from {0}!".format( FLAGS.checkpoint_dir)) else: print "No checkpoints found. Training from scratch." dcgan.load() # train DCGAN if FLAGS.train: train(dcgan) else: dcgan.load()
def main(_): pp.pprint(FLAGS.__flags) with tf.Session() as sess: dcgan = DCGAN(sess, FLAGS) if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists( os.path.join(FLAGS.sample_dir, dcgan.get_model_dir())): os.makedirs(os.path.join(FLAGS.sample_dir, dcgan.get_model_dir())) if not os.path.exists( os.path.join(FLAGS.log_dir, dcgan.get_model_dir())): os.makedirs(os.path.join(FLAGS.log_dir, dcgan.get_model_dir())) if dcgan.checkpoint_exists(): print "Loading checkpoints" if dcgan.load(): print "Success" else: raise IOError("Could not read checkpoints from {}".format( FLAGS.checkpoint_dir)) else: if not FLAGS.train: raise IOError("No checkpoints found") print "No checkpoints found. Training from scratch" dcgan.load() if FLAGS.train: train(dcgan) print "Generating samples..." inference.sample_images(dcgan) inference.visualize_z(dcgan)
def main(_): pp.pprint(FLAGS.__flags) # training/inference with tf.Session() as sess: dcgan = DCGAN(sess, FLAGS) # path checks if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists( os.path.join(FLAGS.log_dir, dcgan.get_model_dir())): os.makedirs(os.path.join(FLAGS.log_dir, dcgan.get_model_dir())) if not os.path.exists( os.path.join(FLAGS.sample_dir, dcgan.get_model_dir())): os.makedirs(os.path.join(FLAGS.sample_dir, dcgan.get_model_dir())) # load checkpoint if found if dcgan.checkpoint_exists(): print("Loading checkpoints...") if dcgan.load(): print "success!" else: raise IOError("Could not read checkpoints from {0}!".format( FLAGS.checkpoint_dir)) else: if not FLAGS.train: raise IOError("No checkpoints found but need for sampling!") print "No checkpoints found. Training from scratch." dcgan.load() # train DCGAN if FLAGS.train: train(dcgan) # inference/visualization code goes here print "Generating samples..." inference.sample_images(dcgan) print "Generating visualizations of z..." inference.visualize_z(dcgan)