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main.py
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main.py
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import os
import scipy.misc
import numpy as np
from model import LAYOUTGAN
from utils import pp, show_all_variables
import tensorflow as tf
flags = tf.app.flags
flags.DEFINE_integer("epoch", 50, "Epoch to train [25]")
flags.DEFINE_integer("batch_size", 64, "The size of batch images [64]")
flags.DEFINE_string("dataset", "mnist", "The name of dataset [mnist, doc, clipart, tangram]")
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 = flags.FLAGS
def main(_):
pp.pprint(flags.FLAGS.__flags)
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:
layoutgan = LAYOUTGAN(sess,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
dataset_name=FLAGS.dataset,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir)
show_all_variables()
if FLAGS.train:
layoutgan.train(FLAGS)
else:
if not layoutgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
if __name__ == '__main__':
tf.app.run()