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main.py
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import os
import scipy.misc
import numpy as np
import json
from model import DCGAN
from utils import pp, visualize, to_json, show_all_variables, expand_path, timestamp
import tensorflow as tf
flags = tf.app.flags
flags.DEFINE_integer ("epoch", 25, "Epoch to train [25]")
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 ("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", "celebA", "The name of dataset [celebA, mnist, lsun]")
flags.DEFINE_string ("input_fname_pattern", "*.jpg", "Glob pattern of filename of input images [*]")
flags.DEFINE_string ("data_dir", "./data", "path to datasets [e.g. $HOME/data]")
flags.DEFINE_string ("out_dir", "./out", "Root directory for outputs [e.g. $HOME/out]")
flags.DEFINE_string ("out_name", "", "Folder (under out_root_dir) for all outputs. Generated automatically if left blank []")
flags.DEFINE_string ("checkpoint_dir", "checkpoint", "Folder (under out_root_dir/out_name) to save checkpoints [checkpoint]")
flags.DEFINE_string ("sample_dir", "samples", "Folder (under out_root_dir/out_name) to save 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.DEFINE_boolean ("visualize", False, "True for visualizing, False for nothing [False]")
flags.DEFINE_boolean ("export", False, "True for exporting with new batch size")
flags.DEFINE_boolean ("freeze", False, "True for exporting with new batch size")
flags.DEFINE_integer ("max_to_keep", 1, "maximum number of checkpoints to keep")
flags.DEFINE_integer ("sample_freq", 200, "sample every this many iterations")
flags.DEFINE_integer ("ckpt_freq", 200, "save checkpoint every this many iterations")
flags.DEFINE_integer ("z_dim", 100, "dimensions of z")
flags.DEFINE_string ("z_dist", "uniform_signed", "'normal01' or 'uniform_unsigned' or uniform_signed")
flags.DEFINE_boolean ("G_img_sum", False, "Save generator image summaries in log")
#flags.DEFINE_integer ("generate_test_images", 100, "Number of images to generate during test. [100]")
FLAGS = flags.FLAGS
def main(_):
pp.pprint(flags.FLAGS.__flags)
# expand user name and environment variables
FLAGS.data_dir = expand_path(FLAGS.data_dir)
FLAGS.out_dir = expand_path(FLAGS.out_dir)
FLAGS.out_name = expand_path(FLAGS.out_name)
FLAGS.checkpoint_dir = expand_path(FLAGS.checkpoint_dir)
FLAGS.sample_dir = expand_path(FLAGS.sample_dir)
if FLAGS.output_height is None: FLAGS.output_height = FLAGS.input_height
if FLAGS.input_width is None: FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None: FLAGS.output_width = FLAGS.output_height
# output folders
if FLAGS.out_name == "":
FLAGS.out_name = '{} - {} - {}'.format(timestamp(), FLAGS.data_dir.split('/')[-1], FLAGS.dataset) # penultimate folder of path
if FLAGS.train:
FLAGS.out_name += ' - x{}.z{}.{}.y{}.b{}'.format(FLAGS.input_width, FLAGS.z_dim, FLAGS.z_dist, FLAGS.output_width, FLAGS.batch_size)
FLAGS.out_dir = os.path.join(FLAGS.out_dir, FLAGS.out_name)
FLAGS.checkpoint_dir = os.path.join(FLAGS.out_dir, FLAGS.checkpoint_dir)
FLAGS.sample_dir = os.path.join(FLAGS.out_dir, FLAGS.sample_dir)
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)
with open(os.path.join(FLAGS.out_dir, 'FLAGS.json'), 'w') as f:
flags_dict = {k:FLAGS[k].value for k in FLAGS}
json.dump(flags_dict, f, indent=4, sort_keys=True, ensure_ascii=False)
#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,
z_dim=FLAGS.z_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir,
out_dir=FLAGS.out_dir,
max_to_keep=FLAGS.max_to_keep)
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,
z_dim=FLAGS.z_dim,
dataset_name=FLAGS.dataset,
input_fname_pattern=FLAGS.input_fname_pattern,
crop=FLAGS.crop,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
data_dir=FLAGS.data_dir,
out_dir=FLAGS.out_dir,
max_to_keep=FLAGS.max_to_keep)
show_all_variables()
if FLAGS.train:
dcgan.train(FLAGS)
else:
load_success, load_counter = dcgan.load(FLAGS.checkpoint_dir)
if not load_success:
raise Exception("Checkpoint not found in " + FLAGS.checkpoint_dir)
# to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
# [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
# [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
# [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
# [dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
if FLAGS.export:
export_dir = os.path.join(FLAGS.checkpoint_dir, 'export_b'+str(FLAGS.batch_size))
dcgan.save(export_dir, load_counter, ckpt=True, frozen=False)
if FLAGS.freeze:
export_dir = os.path.join(FLAGS.checkpoint_dir, 'frozen_b'+str(FLAGS.batch_size))
dcgan.save(export_dir, load_counter, ckpt=False, frozen=True)
if FLAGS.visualize:
OPTION = 1
visualize(sess, dcgan, FLAGS, OPTION, FLAGS.sample_dir)
if __name__ == '__main__':
tf.app.run()