def main(_):
pp.pprint(flags.FLAGS.__flags)
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = 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':
dcgan = DCGAN(
sess,
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,
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:
dcgan = DCGAN(
sess,
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,
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:
dcgan.train(FLAGS)
else:
if not dcgan.load(FLAGS.checkpoint_dir)[0]:
raise Exception("[!] Train a model first, then run test mode")
dcgan.vector_algebra(FLAGS, seed_one=FLAGS.seed_one, seed_two=FLAGS.seed_two)
dcgan.interpolate(FLAGS,seed_one=FLAGS.seed_one, seed_two=FLAGS.seed_two)
#!/usr/bin/env python3.4
#
# Irmak Sirer
# License: MIT
# 2016-09
import argparse
import os
import tensorflow as tf
from model import DCGAN
parser = argparse.ArgumentParser()
parser.add_argument('--imgSize', type=int, default=64)
parser.add_argument('--lam', type=float, default=0.1)
parser.add_argument('--checkpointDir', type=str, default='checkpoint')
parser.add_argument('--outDir', type=str, default='interpolation')
parser.add_argument('--vector1', type=str, default='')
parser.add_argument('--vector2', type=str, default='')
args = parser.parse_args()
assert(os.path.exists(args.checkpointDir))
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
dcgan = DCGAN(sess, image_size=args.imgSize,
checkpoint_dir=args.checkpointDir, lam=args.lam)
dcgan.interpolate(config=args)
