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train_imagenet.py
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train_imagenet.py
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# Copyright 2018 Google LLC
#
# 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
#
# https://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.
# ==============================================================================
"""Train Imagenet."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import flags
import tensorflow as tf
import utils_ori as utils
import model
tfgan = tf.contrib.gan
gfile = tf.gfile
flags.DEFINE_string('master', 'local',
'BNS name of the TensorFlow master to use. [local]')
# flags.DEFINE_string('checkpoint_dir', '/usr/local/google/home/zhanghan/Documents/Research/model_output',
# 'Directory name to save the checkpoints. [checkpoint]')
flags.DEFINE_string('checkpoint_dir', 'checkpoint',
'Directory name to save the checkpoints. [checkpoint]')
flags.DEFINE_integer('batch_size', 64, 'Number of images in input batch. [64]') # ori 16
flags.DEFINE_integer('shuffle_buffer_size', 100000, 'Number of records to load '
'before shuffling and yielding for consumption. [100000]')
flags.DEFINE_integer('save_summaries_steps', 200, 'Number of seconds between '
'saving summary statistics. [1]') # default 300
flags.DEFINE_integer('save_checkpoint_secs', 1200, 'Number of seconds between '
'saving checkpoints of model. [1200]')
flags.DEFINE_boolean('is_train', True, 'True for training. [default: True]')
flags.DEFINE_boolean('is_gd_equal', True, 'True for 1:1, False for 1:5')
# TODO(olganw) Find the best way to clean up these flags for eval and train.
flags.DEFINE_integer('task', 0, 'The task id of the current worker. [0]')
flags.DEFINE_integer('ps_tasks', 0, 'The number of ps tasks. [0]')
flags.DEFINE_integer('num_workers', 1, 'The number of worker tasks. [1]')
flags.DEFINE_integer('replicas_to_aggregate', 1, 'The number of replicas '
'to aggregate for synchronous optimization [1]')
flags.DEFINE_boolean('sync_replicas', True, 'Whether to sync replicas. [True]')
flags.DEFINE_integer('num_towers', 4, 'The number of GPUs to use per task. [1]')
flags.DEFINE_integer('d_step', 1, 'The number of D_step')
flags.DEFINE_integer('g_step', 1, 'The number of G_step')
# flags.DEFINE_integer('z_dim', 128, 'The dimension of z')
FLAGS = flags.FLAGS
def main(_, is_test=False):
print('d_learning_rate', FLAGS.discriminator_learning_rate)
print('g_learning_rate', FLAGS.generator_learning_rate)
print('data_dir', FLAGS.data_dir)
print(FLAGS.loss_type, FLAGS.batch_size, FLAGS.beta1)
print('gf_df_dim', FLAGS.gf_dim, FLAGS.df_dim)
print('Starting the program..')
gfile.MakeDirs(FLAGS.checkpoint_dir)
model_dir = '%s_%s' % ('imagenet', FLAGS.batch_size)
logdir = os.path.join(FLAGS.checkpoint_dir, model_dir)
gfile.MakeDirs(logdir)
graph = tf.Graph()
with graph.as_default():
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
# Instantiate global_step.
global_step = tf.train.create_global_step()
# Create model with FLAGS, global_step, and devices.
devices = ['/gpu:{}'.format(tower) for tower in range(FLAGS.num_towers)]
# Create noise tensors
zs = utils.make_z_normal(
FLAGS.num_towers, FLAGS.batch_size, FLAGS.z_dim)
print('save_summaries_steps', FLAGS.save_summaries_steps)
dcgan = model.SNGAN(
zs=zs,
config=FLAGS,
global_step=global_step,
devices=devices)
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
# Create sync_hooks when needed.
if FLAGS.sync_replicas and FLAGS.num_workers > 1:
print('condition 1')
sync_hooks = [
dcgan.d_opt.make_session_run_hook(FLAGS.task == 0),
dcgan.g_opt.make_session_run_hook(FLAGS.task == 0)
]
else:
print('condition 2')
sync_hooks = []
train_ops = tfgan.GANTrainOps(
generator_train_op=dcgan.g_optim,
discriminator_train_op=dcgan.d_optim,
global_step_inc_op=dcgan.increment_global_step)
# We set allow_soft_placement to be True because Saver for the DCGAN model
# gets misplaced on the GPU.
session_config = tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False)
if is_test:
return graph
print("G step: ", FLAGS.g_step)
print("D_step: ", FLAGS.d_step)
train_steps = tfgan.GANTrainSteps(FLAGS.g_step, FLAGS.d_step)
tfgan.gan_train(
train_ops,
get_hooks_fn=tfgan.get_sequential_train_hooks(
train_steps=train_steps),
hooks=([tf.train.StopAtStepHook(num_steps=2000000)] + sync_hooks),
logdir=logdir,
# master=FLAGS.master,
# scaffold=scaffold, # load from google checkpoint
is_chief=(FLAGS.task == 0),
save_summaries_steps=FLAGS.save_summaries_steps,
save_checkpoint_secs=FLAGS.save_checkpoint_secs,
config=session_config)
if __name__ == '__main__':
tf.app.run()