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resnet_main.py
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resnet_main.py
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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
#
# http://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.
# ==============================================================================
"""ResNet Train/Eval module.
"""
import sys
import time
import cifar_input
import numpy as np
import resnet_model
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('dataset', 'cifar10', 'cifar10 or cifar100.')
tf.app.flags.DEFINE_string('mode', 'train', 'train or eval.')
tf.app.flags.DEFINE_string('train_data_path', '', 'Filename for training data.')
tf.app.flags.DEFINE_string('eval_data_path', '', 'Filename for eval data')
tf.app.flags.DEFINE_integer('image_size', 32, 'Image side length.')
tf.app.flags.DEFINE_string('train_dir', '',
'Directory to keep training outputs.')
tf.app.flags.DEFINE_string('eval_dir', '',
'Directory to keep eval outputs.')
tf.app.flags.DEFINE_integer('eval_batch_count', 50,
'Number of batches to eval.')
tf.app.flags.DEFINE_bool('eval_once', False,
'Whether evaluate the model only once.')
tf.app.flags.DEFINE_string('log_root', '',
'Directory to keep the checkpoints. Should be a '
'parent directory of FLAGS.train_dir/eval_dir.')
tf.app.flags.DEFINE_integer('num_gpus', 0,
'Number of gpus used for training. (0 or 1)')
def train(hps):
"""Training loop."""
images, labels = cifar_input.build_input(
FLAGS.dataset, FLAGS.train_data_path, hps.batch_size, FLAGS.mode)
model = resnet_model.ResNet(hps, images, labels, FLAGS.mode)
model.build_graph()
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir)
sv = tf.train.Supervisor(logdir=FLAGS.log_root,
is_chief=True,
summary_op=None,
save_summaries_secs=60,
save_model_secs=300,
global_step=model.global_step)
sess = sv.prepare_or_wait_for_session()
step = 0
lrn_rate = 0.1
while not sv.should_stop():
(_, summaries, loss, predictions, truth, train_step) = sess.run(
[model.train_op, model.summaries, model.cost, model.predictions,
model.labels, model.global_step],
feed_dict={model.lrn_rate: lrn_rate})
if train_step < 40000:
lrn_rate = 0.1
elif train_step < 60000:
lrn_rate = 0.01
elif train_step < 80000:
lrn_rate = 0.001
else:
lrn_rate = 0.0001
truth = np.argmax(truth, axis=1)
predictions = np.argmax(predictions, axis=1)
precision = np.mean(truth == predictions)
step += 1
if step % 100 == 0:
precision_summ = tf.Summary()
precision_summ.value.add(
tag='Precision', simple_value=precision)
summary_writer.add_summary(precision_summ, train_step)
summary_writer.add_summary(summaries, train_step)
tf.logging.info('loss: %.3f, precision: %.3f\n' % (loss, precision))
summary_writer.flush()
sv.Stop()
def evaluate(hps):
"""Eval loop."""
images, labels = cifar_input.build_input(
FLAGS.dataset, FLAGS.eval_data_path, hps.batch_size, FLAGS.mode)
model = resnet_model.ResNet(hps, images, labels, FLAGS.mode)
model.build_graph()
saver = tf.train.Saver()
summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
tf.train.start_queue_runners(sess)
best_precision = 0.0
while True:
time.sleep(60)
try:
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
except tf.errors.OutOfRangeError as e:
tf.logging.error('Cannot restore checkpoint: %s', e)
continue
if not (ckpt_state and ckpt_state.model_checkpoint_path):
tf.logging.info('No model to eval yet at %s', FLAGS.log_root)
continue
tf.logging.info('Loading checkpoint %s', ckpt_state.model_checkpoint_path)
saver.restore(sess, ckpt_state.model_checkpoint_path)
total_prediction, correct_prediction = 0, 0
for _ in xrange(FLAGS.eval_batch_count):
(summaries, loss, predictions, truth, train_step) = sess.run(
[model.summaries, model.cost, model.predictions,
model.labels, model.global_step])
truth = np.argmax(truth, axis=1)
predictions = np.argmax(predictions, axis=1)
correct_prediction += np.sum(truth == predictions)
total_prediction += predictions.shape[0]
precision = 1.0 * correct_prediction / total_prediction
best_precision = max(precision, best_precision)
precision_summ = tf.Summary()
precision_summ.value.add(
tag='Precision', simple_value=precision)
summary_writer.add_summary(precision_summ, train_step)
best_precision_summ = tf.Summary()
best_precision_summ.value.add(
tag='Best Precision', simple_value=best_precision)
summary_writer.add_summary(best_precision_summ, train_step)
summary_writer.add_summary(summaries, train_step)
tf.logging.info('loss: %.3f, precision: %.3f, best precision: %.3f\n' %
(loss, precision, best_precision))
summary_writer.flush()
if FLAGS.eval_once:
break
def main(_):
if FLAGS.num_gpus == 0:
dev = '/cpu:0'
elif FLAGS.num_gpus == 1:
dev = '/gpu:0'
else:
raise ValueError('Only support 0 or 1 gpu.')
if FLAGS.mode == 'train':
batch_size = 128
elif FLAGS.mode == 'eval':
batch_size = 100
if FLAGS.dataset == 'cifar10':
num_classes = 10
elif FLAGS.dataset == 'cifar100':
num_classes = 100
hps = resnet_model.HParams(batch_size=batch_size,
num_classes=num_classes,
min_lrn_rate=0.0001,
lrn_rate=0.1,
num_residual_units=5,
use_bottleneck=False,
weight_decay_rate=0.0002,
relu_leakiness=0.1,
optimizer='mom')
with tf.device(dev):
if FLAGS.mode == 'train':
train(hps)
elif FLAGS.mode == 'eval':
evaluate(hps)
if __name__ == '__main__':
tf.app.run()