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trainer.py
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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 time
import os
from six.moves import xrange
import tensorflow as tf
from input_ops import create_input_ops
from config import argparser
from util import log
class Trainer(object):
def __init__(self, config, model, dataset, dataset_test):
self.config = config
self.model = model
hyper_parameter_str = 'bs_{}_lr_{}'.format(
config.batch_size,
config.learning_rate,
)
self.train_dir = './train_dir/%s-%s-%s-%s' % (
config.prefix,
config.dataset,
hyper_parameter_str,
time.strftime("%Y%m%d-%H%M%S")
)
if not os.path.exists(self.train_dir): 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)
self.learning_rate = config.learning_rate
self.check_op = tf.no_op()
# --- checkpoint and monitoring ---
all_var = tf.trainable_variables()
tf.contrib.slim.model_analyzer.analyze_vars(all_var, print_info=True)
self.optimizer = tf.train.AdamOptimizer(
self.learning_rate
).minimize(self.model.loss, global_step=self.global_step,
var_list=all_var, name='optimizer_loss')
self.train_summary_op = tf.summary.merge_all(key='train')
self.test_summary_op = tf.summary.merge_all(key='test')
self.saver = tf.train.Saver(max_to_keep=100)
self.pretrain_saver = tf.train.Saver(var_list=all_var, max_to_keep=1)
self.summary_writer = tf.summary.FileWriter(self.train_dir)
self.max_steps = self.config.max_steps
self.ckpt_save_step = self.config.ckpt_save_step
self.log_step = self.config.log_step
self.test_sample_step = self.config.test_sample_step
self.write_summary_step = self.config.write_summary_step
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)
self.ckpt_path = config.checkpoint
if self.ckpt_path is not None:
log.info("Checkpoint path: %s", self.ckpt_path)
self.pretrain_saver.restore(self.session, self.ckpt_path, )
log.info("Loaded the pretrain parameters from the provided checkpoint path")
def train(self):
log.infov("Training Starts!")
print(self.batch_train)
max_steps = self.max_steps
ckpt_save_step = self.ckpt_save_step
log_step = self.log_step
test_sample_step = self.test_sample_step
write_summary_step = self.write_summary_step
for s in xrange(max_steps):
# periodic inference
if s % test_sample_step == 0:
step, test_summary, loss, output, step_time = \
self.run_test(self.batch_test, step=s, is_train=False)
self.log_step_message(step, loss, step_time, is_train=False)
self.summary_writer.add_summary(test_summary, global_step=step)
step, train_summary, loss, output, step_time = \
self.run_single_step(self.batch_train, step=s, is_train=True)
if s % log_step == 0:
self.log_step_message(step, loss, step_time)
if s % write_summary_step == 0:
self.summary_writer.add_summary(train_summary, global_step=step)
if s % 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=step)
def run_single_step(self, batch, step=None, opt_gan=False, is_train=True):
_start_time = time.time()
batch_chunk = self.session.run(batch)
fetch = [self.global_step, self.train_summary_op, self.model.output,
self.model.loss, self.check_op, self.optimizer]
fetch_values = self.session.run(
fetch,
feed_dict=self.model.get_feed_dict(batch_chunk, step=step)
)
[step, summary, output, loss] = fetch_values[:4]
_end_time = time.time()
return step, summary, loss, output, (_end_time - _start_time)
def run_test(self, batch, step, is_train=False):
_start_time = time.time()
batch_chunk = self.session.run(batch)
step, summary, loss, output = self.session.run(
[self.global_step, self.test_summary_op,
self.model.loss, self.model.output],
feed_dict=self.model.get_feed_dict(batch_chunk, step=step, is_training=False)
)
_end_time = time.time()
return step, summary, loss, output, (_end_time - _start_time)
def log_step_message(self, step, 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}] " +
"Loss: {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,
loss=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=False)
trainer = Trainer(config, model, dataset_train, dataset_test)
log.warning("dataset: %s", config.dataset)
trainer.train()
if __name__ == '__main__':
main()