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trainer.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from six.moves import xrange
from pprint import pprint
import tensorflow as tf
import tensorflow.contrib.slim as slim
from input_ops import create_input_ops
from util import log
from config import argparser
class Trainer(object):
def __init__(self, config, model, dataset, dataset_test):
self.config = config
self.model = model
learning_hyperparameter_str = '{}_{}_bs_{}_lr_g_{}_lr_d_{}_update_G{}D{}'.format(
config.dataset, config.gan_type, config.batch_size,
config.learning_rate_g, config.learning_rate_d,
config.update_g, config.update_d)
model_hyperparameter_str = 'G_deconv_{}_dis_conv_{}_{}_{}_norm'.format(
config.deconv_type, config.num_dis_conv,
config.g_norm_type, config.d_norm_type)
self.train_dir = './train_dir/%s-%s-%s' % (
config.prefix,
learning_hyperparameter_str + '_' + model_hyperparameter_str,
time.strftime("%Y%m%d-%H%M%S")
)
os.makedirs(self.train_dir)
log.infov("Train Dir: %s", self.train_dir)
# --- input ops ---
self.batch_size = config.batch_size
_, self.batch_train = create_input_ops(
dataset, self.batch_size, is_training=True)
_, self.batch_test = create_input_ops(
dataset_test, self.batch_size, is_training=False)
# --- optimizer ---
self.global_step = tf.contrib.framework.get_or_create_global_step(graph=None)
# --- checkpoint and monitoring ---
all_var = tf.trainable_variables()
d_var = [v for v in all_var if v.name.startswith('Discriminator')]
log.warn("********* d_var ********** ")
slim.model_analyzer.analyze_vars(d_var, print_info=True)
g_var = [v for v in all_var if v.name.startswith(('Generator'))]
log.warn("********* g_var ********** ")
slim.model_analyzer.analyze_vars(g_var, print_info=True)
rem_var = (set(all_var) - set(d_var) - set(g_var))
print([v.name for v in rem_var])
assert not rem_var
self.g_optimizer = tf.train.AdamOptimizer(
self.config.learning_rate_g,
beta1=self.config.adam_beta1, beta2=self.config.adam_beta2
).minimize(self.model.g_loss, var_list=g_var,
name='g_optimize_loss', global_step=self.global_step)
self.d_optimizer = tf.train.AdamOptimizer(
self.config.learning_rate_d,
beta1=self.config.adam_beta1, beta2=self.config.adam_beta2
).minimize(self.model.d_loss, var_list=d_var,
name='d_optimize_loss')
self.summary_op = tf.summary.merge_all()
self.saver = tf.train.Saver(max_to_keep=1000)
pretrain_saver = tf.train.Saver(var_list=all_var, max_to_keep=1)
pretrain_saver_g = tf.train.Saver(var_list=g_var, max_to_keep=1)
pretrain_saver_d = tf.train.Saver(var_list=d_var, max_to_keep=1)
self.summary_writer = tf.summary.FileWriter(self.train_dir)
self.supervisor = tf.train.Supervisor(
logdir=self.train_dir,
is_chief=True,
saver=None,
summary_op=None,
summary_writer=self.summary_writer,
save_summaries_secs=300,
save_model_secs=600,
global_step=self.global_step,
)
session_config = tf.ConfigProto(
allow_soft_placement=True,
gpu_options=tf.GPUOptions(allow_growth=True),
device_count={'GPU': 1},
)
self.session = self.supervisor.prepare_or_wait_for_session(config=session_config)
def load_checkpoint(ckpt_path, saver, name=None):
if ckpt_path is not None:
log.info("Checkpoint path for {}: {}".format(name, ckpt_path))
saver.restore(self.session, ckpt_path)
log.info("Loaded the pretrain parameters " +
"from the provided checkpoint path.")
load_checkpoint(
config.checkpoint_g, pretrain_saver_g, name='Generator')
load_checkpoint(
config.checkpoint_d, pretrain_saver_d, name='Discriminator')
load_checkpoint(
config.checkpoint, pretrain_saver, name='All vars')
def train(self):
log.infov("Training Starts!")
pprint(self.batch_train)
step = self.session.run(self.global_step)
for s in xrange(self.config.max_training_steps):
if s % self.config.ckpt_save_step == 0:
log.infov("Saved checkpoint at %d", s)
self.saver.save(self.session, os.path.join(
self.train_dir, 'model'), global_step=s)
step, summary, d_loss, g_loss, step_time = \
self.run_single_step(self.batch_train, step=s, is_train=True)
if s % self.config.log_step == 0:
self.log_step_message(step, d_loss, g_loss, step_time)
if s % self.config.write_summary_step == 0:
self.summary_writer.add_summary(summary, global_step=step)
def run_single_step(self, batch, step=None, is_train=True):
_start_time = time.time()
batch_chunk = self.session.run(batch)
fetch = [self.global_step, self.summary_op,
self.model.d_loss, self.model.g_loss,
self.g_optimizer, self.d_optimizer]
fetch_values = self.session.run(
fetch,
feed_dict=self.model.get_feed_dict(batch_chunk)
)
[step, summary, d_loss, g_loss] = fetch_values[:4]
for t in range(self.config.update_g - 1):
self.session.run(self.g_optimizer,
feed_dict=self.model.get_feed_dict(batch_chunk))
for t in range(self.config.update_d - 1):
batch_chunk = self.session.run(batch)
self.session.run(self.d_optimizer,
feed_dict=self.model.get_feed_dict(batch_chunk))
_end_time = time.time()
return step, summary, d_loss, g_loss, (_end_time - _start_time)
def log_step_message(self, step, d_loss, g_loss, step_time, is_train=True):
if step_time == 0: step_time = 0.001
log_fn = (is_train and log.info or log.infov)
log_fn((
" [{split_mode:5s} step {step:4d}] " +
"D loss: {d_loss:.5f} G loss: {g_loss:.5f} " +
"({sec_per_batch:.3f} sec/batch, {instance_per_sec:.3f} instances/sec) "
).format(split_mode=(is_train and 'train' or 'val'),
step=step, d_loss=d_loss, g_loss=g_loss,
sec_per_batch=step_time,
instance_per_sec=self.batch_size / step_time))
def main():
config, model, dataset_train, dataset_test = argparser(is_train=True)
trainer = Trainer(config, model, dataset_train, dataset_test)
log.warning("dataset: %s, learning_rate_g: %f, learning_rate_d: %f",
config.dataset, config.learning_rate_g, config.learning_rate_d)
trainer.train()
if __name__ == '__main__':
main()