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main.py
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main.py
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"""
Recommendeds configs:
--model_type=GAN --learning_rate=0.0002
--model_type=WGAN --learning_rate=0.00005 --beta1=0.9
--model_type=WGAN_GP --learning_rate=0.0001 --beta1=0.5 --beta2=0.9
"""
import numpy as np
import os
import pprint
from model import UnifiedDCGAN
from utils import show_all_variables
import tensorflow as tf
flags = tf.app.flags
flags.DEFINE_string("model_type", "GAN", "Type of GAN model to use. [GAN]")
flags.DEFINE_float("learning_rate", 0.0002, "Learning rate of for Adam. [0.0002]")
flags.DEFINE_float("beta1", 0.5, "Momentum term of Adam. [0.5]")
flags.DEFINE_float("beta2", 0.9, "Momentum term of Adam. [0.9]")
flags.DEFINE_integer("max_iter", 10000, "Maximum number of training iterations. [10000]")
flags.DEFINE_integer("d_iter", 5, "Num. batches used for training D model in one iteration. [5]")
flags.DEFINE_integer("train_size", np.inf, "The size of train images. [np.inf]")
flags.DEFINE_integer("batch_size", 64, "The size of batch images. [64]")
flags.DEFINE_integer("input_height", 108, "The size of image to use (will be center cropped). [108]")
flags.DEFINE_integer("input_width", None, "The size of image to use (will be center cropped). "
"If None, same value as input_height [None]")
flags.DEFINE_integer("output_height", 64, "The size of the output images to produce [64]")
flags.DEFINE_integer("output_width", None,
"The size of the output images to produce. If None, same value as output_height [None]")
flags.DEFINE_string("dataset", "mnist", "The name of dataset [check folders in ./data]")
flags.DEFINE_string("input_fname_pattern", "*.jpg", "Glob pattern of filename of input images [*]")
flags.DEFINE_string("checkpoint_dir", "checkpoint", "Directory name to save the checkpoints [checkpoint]")
flags.DEFINE_string("sample_dir", "samples", "Directory name to save the image samples [samples]")
flags.DEFINE_boolean("train", False, "True for training, False for testing [False]")
flags.DEFINE_boolean("crop", False, "True for training, False for testing [False]")
FLAGS = flags.FLAGS
pp = pprint.PrettyPrinter()
def main(_):
pp.pprint(flags.FLAGS.__flags)
FLAGS.input_width = FLAGS.input_width or FLAGS.input_height
FLAGS.output_width = FLAGS.output_width or FLAGS.output_height
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)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
if FLAGS.dataset == 'mnist':
model = UnifiedDCGAN(
sess,
FLAGS.model_type,
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,
y_dim=10,
d_iter=FLAGS.d_iter,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
else:
model = UnifiedDCGAN(
sess,
FLAGS.model_type,
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,
d_iter=FLAGS.d_iter,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
show_all_variables()
if FLAGS.train:
model.train(FLAGS)
else:
if not model.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
if __name__ == '__main__':
tf.app.run()