def main(_):
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
file_name = time.strftime("%Y_%m_%d_%H%M", time.localtime())
if FLAGS.unreg:
file_name += "_unreg_dcgan"
else:
file_name += "_regularized_dcgan_" + str(FLAGS.gamma) + "gamma"
if FLAGS.annealing:
file_name += "_annealing_" + str(FLAGS.decay_factor) + "decayfactor"
if FLAGS.rmsprop:
file_name += "_rmsprop"
else:
file_name += "_adam"
file_name += "_" + str(FLAGS.disc_update_steps) + "dsteps"
file_name += "_" + str(FLAGS.disc_learning_rate) + "dlnr"
file_name += "_" + str(FLAGS.gen_learning_rate) + "glnr"
file_name += "_" + str(FLAGS.epochs) + "epochs"
file_name += "_" + str(FLAGS.dataset)
log_dir = os.path.abspath(os.path.join(FLAGS.root_dir, file_name))
if not os.path.exists(log_dir):
os.makedirs(log_dir)
pp.pprint(flags.FLAGS.__flags)
with tf.Session() as sess:
dcgan = DCGAN(sess, log_dir, FLAGS)
show_all_variables()
if FLAGS.checkpoint_dir is not "None":
if not dcgan.load_ckpt()[0]:
raise Exception("[!] ERROR: provide valid checkpoint_dir")
else:
starttime = time.time()
dcgan.train(FLAGS)
endtime = time.time()
print('Total Train Time: {:.2f}'.format(endtime - starttime))
dcgan.generate(FLAGS, option=1)
file = open(os.path.join(log_dir, "flags.json"), 'a')
json.dump(vars(FLAGS), file)
file.close()
