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train.py
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train.py
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# Copyright 2018 The Defense-GAN Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
"""The main class for training GANs."""
import argparse
import sys
import tensorflow as tf
from models.gan import MnistDefenseGAN, FmnistDefenseDefenseGAN, \
CelebADefenseGAN
from utils.config import load_config
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', required=True, help='Config file')
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args, _ = parser.parse_known_args()
return args
def main(cfg, *args):
FLAGS = tf.app.flags.FLAGS
ds_gan = {
'mnist': MnistDefenseGAN, 'f-mnist': FmnistDefenseDefenseGAN,
'celeba': CelebADefenseGAN,
}
GAN = ds_gan[FLAGS.dataset_name]
gan = GAN(cfg=cfg, test_mode=not FLAGS.is_train)
if FLAGS.is_train:
gan.train()
if FLAGS.train_encoder:
gan.load(checkpoint_dir=FLAGS.init_path)
gan.train(phase='just_enc')
if FLAGS.save_recs:
gan.reconstruct_dataset(ckpt_path=FLAGS.init_path,
max_num=FLAGS.max_num)
if FLAGS.test_generator:
gan.load_generator(ckpt_path=FLAGS.init_path)
gan.sess.run(gan.global_step.initializer)
gan.generate_image(iteration=0)
if FLAGS.test_batch:
gan.test_batch()
if FLAGS.save_ds:
gan.save_ds()
if __name__ == '__main__':
args = parse_args()
# Note: The load_config() call will convert all the parameters that are defined in
# experiments/config files into FLAGS.param_name and can be passed in from command line.
# arguments : python train.py --cfg <config_path> --<param_name> <param_value>
cfg = load_config(args.cfg)
flags = tf.app.flags
flags.DEFINE_boolean("is_train", False,
"True for training, False for testing. [False]")
flags.DEFINE_boolean("save_recs", False,
"True for saving reconstructions. [False]")
flags.DEFINE_boolean("debug", False,
"True for debug. [False]")
flags.DEFINE_boolean("test_generator", False,
"True for generator samples. [False]")
flags.DEFINE_boolean("test_decoder", False,
"True for decoder samples. [False]")
flags.DEFINE_boolean("test_again", False,
"True for not using cache. [False]")
flags.DEFINE_boolean("test_batch", False,
"True for visualizing the batches and labels. [False]")
flags.DEFINE_boolean("save_ds", False,
"True for saving the dataset in a pickle file. ["
"False]")
flags.DEFINE_boolean("tensorboard_log", True, "True for saving "
"tensorboard logs. [True]")
flags.DEFINE_boolean("train_encoder", False,
"Add an encoder to a pretrained model. ["
"False]")
flags.DEFINE_boolean("init_with_enc", False,
"Initializes the z with an encoder, must run "
"--train_encoder first. [False]")
flags.DEFINE_integer("max_num", -1,
"True for saving the dataset in a pickle file ["
"False]")
flags.DEFINE_string("init_path", None, "Checkpoint path. [None]")
main_cfg = lambda x: main(cfg, x)
tf.app.run(main=main_cfg)