303 lines
11 KiB
Python
303 lines
11 KiB
Python
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""ResNet Train/Eval module.
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"""
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import os
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import six
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import subprocess
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import sys
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import time
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import cifar_input
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import numpy as np
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import resnet_model
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import tensorflow as tf
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FLAGS = tf.app.flags.FLAGS
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tf.app.flags.DEFINE_string('dataset', 'cifar10', 'cifar10 or cifar100.')
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tf.app.flags.DEFINE_string('mode', 'train', 'train or eval.')
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tf.app.flags.DEFINE_string('model', '', 'model to train.')
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tf.app.flags.DEFINE_string('data_format', 'NHWC',
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"""Data layout to use: NHWC (TF native)
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or NCHW (cuDNN native).""")
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tf.app.flags.DEFINE_string('train_data_path', '',
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'Filepattern for training data.')
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tf.app.flags.DEFINE_string('eval_data_path', '',
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'Filepattern for eval data')
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tf.app.flags.DEFINE_integer('image_size', 32, 'Image side length.')
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tf.app.flags.DEFINE_string('train_dir', '',
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'Directory to keep training outputs.')
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tf.app.flags.DEFINE_string('eval_dir', '',
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'Directory to keep eval outputs.')
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tf.app.flags.DEFINE_integer('eval_batch_count', 50,
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'Number of batches to eval.')
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tf.app.flags.DEFINE_bool('eval_once', False,
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'Whether evaluate the model only once.')
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tf.app.flags.DEFINE_string('log_root', '',
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'Should be a parent directory of FLAGS.train_dir/eval_dir.')
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tf.app.flags.DEFINE_string('checkpoint_dir', '',
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'Directory to store the checkpoints')
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tf.app.flags.DEFINE_integer('num_gpus', 0,
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'Number of gpus used for training. (0 or 1)')
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tf.app.flags.DEFINE_bool('use_bottleneck', False,
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'Use bottleneck module or not.')
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tf.app.flags.DEFINE_bool('time_inference', False,
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'Time inference.')
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tf.app.flags.DEFINE_integer('batch_size', -1,
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'Batch size to use.')
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def train(hps):
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"""Training loop."""
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images, labels = cifar_input.build_input(
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FLAGS.dataset, FLAGS.train_data_path, hps.batch_size, FLAGS.mode, hps.data_format)
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model = resnet_model.ResNet(hps, images, labels, FLAGS.mode)
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model.build_graph()
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param_stats = tf.contrib.tfprof.model_analyzer.print_model_analysis(
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tf.get_default_graph(),
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tfprof_options=tf.contrib.tfprof.model_analyzer.
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TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
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sys.stdout.write('total_params: %d\n' % param_stats.total_parameters)
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tf.contrib.tfprof.model_analyzer.print_model_analysis(
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tf.get_default_graph(),
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tfprof_options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
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truth = tf.argmax(model.labels, axis=1)
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predictions = tf.argmax(model.predictions, axis=1)
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precision = tf.reduce_mean(tf.to_float(tf.equal(predictions, truth)))
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summary_hook = tf.train.SummarySaverHook(
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save_steps=100,
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output_dir=FLAGS.train_dir,
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summary_op=tf.summary.merge([model.summaries,
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tf.summary.scalar('Precision', precision)]))
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num_steps_per_epoch = 391 # TODO: Don't hardcode this.
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logging_hook = tf.train.LoggingTensorHook(
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tensors={'step': model.global_step,
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'loss': model.cost,
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'precision': precision},
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every_n_iter=100)
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class _LearningRateSetterHook(tf.train.SessionRunHook):
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"""Sets learning_rate based on global step."""
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def begin(self):
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self._lrn_rate = 0.01
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def before_run(self, run_context):
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return tf.train.SessionRunArgs(
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model.global_step, # Asks for global step value.
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feed_dict={model.lrn_rate: self._lrn_rate}) # Sets learning rate
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def after_run(self, run_context, run_values):
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train_step = run_values.results
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if train_step < num_steps_per_epoch:
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self._lrn_rate = 0.01
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elif train_step < (91 * num_steps_per_epoch):
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self._lrn_rate = 0.1
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elif train_step < (136 * num_steps_per_epoch):
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self._lrn_rate = 0.01
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elif train_step < (181 * num_steps_per_epoch):
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self._lrn_rate = 0.001
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else:
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self._lrn_rate = 0.0001
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class _SaverHook(tf.train.SessionRunHook):
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"""Sets learning_rate based on global step."""
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def begin(self):
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self.saver = tf.train.Saver(max_to_keep=10000)
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subprocess.call("rm -rf %s; mkdir -p %s" % (FLAGS.checkpoint_dir,
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FLAGS.checkpoint_dir), shell=True)
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self.f = open(os.path.join(FLAGS.checkpoint_dir, "times.log"), 'w')
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def after_create_session(self, sess, coord):
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self.sess = sess
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self.start_time = time.time()
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def before_run(self, run_context):
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return tf.train.SessionRunArgs(
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model.global_step # Asks for global step value.
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)
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def after_run(self, run_context, run_values):
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train_step = run_values.results
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epoch = train_step / num_steps_per_epoch
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if train_step % num_steps_per_epoch == 0:
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end_time = time.time()
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directory = os.path.join(FLAGS.checkpoint_dir, ("%5d" % epoch).replace(' ', '0'))
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subprocess.call("mkdir -p %s" % directory, shell=True)
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ckpt_name = 'model.ckpt'
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self.saver.save(self.sess, os.path.join(directory, ckpt_name),
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global_step=train_step)
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self.f.write("Step: %d\tTime: %s\n" % (train_step, end_time - self.start_time))
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print("Saved checkpoint after %d epoch(s) to %s..." % (epoch, directory))
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sys.stdout.flush()
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self.start_time = time.time()
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def end(self, sess):
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self.f.close()
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with tf.train.MonitoredTrainingSession(
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checkpoint_dir=FLAGS.log_root,
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hooks=[logging_hook, _LearningRateSetterHook()],
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chief_only_hooks=[summary_hook, _SaverHook()],
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save_checkpoint_secs=None,
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# Since we provide a SummarySaverHook, we need to disable default
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# SummarySaverHook. To do that we set save_summaries_steps to 0.
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save_summaries_steps=None,
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save_summaries_secs=None,
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config=tf.ConfigProto(allow_soft_placement=True)) as mon_sess:
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for i in range(num_steps_per_epoch * 181):
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mon_sess.run(model.train_op)
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def evaluate(hps):
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"""Eval loop."""
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images, labels = cifar_input.build_input(
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FLAGS.dataset, FLAGS.eval_data_path, hps.batch_size, FLAGS.mode, hps.data_format)
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model = resnet_model.ResNet(hps, images, labels, FLAGS.mode)
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model.build_graph()
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saver = tf.train.Saver()
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summary_writer = tf.summary.FileWriter(FLAGS.eval_dir)
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sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
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tf.train.start_queue_runners(sess)
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best_precision = 0.0
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while True:
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try:
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ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
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except tf.errors.OutOfRangeError as e:
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tf.logging.error('Cannot restore checkpoint: %s', e)
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continue
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if not (ckpt_state and ckpt_state.model_checkpoint_path):
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tf.logging.info('No model to eval yet at %s', FLAGS.log_root)
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break
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tf.logging.info('Loading checkpoint %s', ckpt_state.model_checkpoint_path)
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saver.restore(sess, ckpt_state.model_checkpoint_path)
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global_step = ckpt_state.model_checkpoint_path.split('/')[-1].split('-')[-1]
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if not global_step.isdigit():
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global_step = 0
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else:
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global_step = int(global_step)
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total_prediction, correct_prediction, correct_prediction_top5 = 0, 0, 0
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start_time = time.time()
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for _ in six.moves.range(FLAGS.eval_batch_count):
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(summaries, loss, predictions, truth, train_step) = sess.run(
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[model.summaries, model.cost, model.predictions,
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model.labels, model.global_step])
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if not FLAGS.time_inference:
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for (indiv_truth, indiv_prediction) in zip(truth, predictions):
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indiv_truth = np.argmax(indiv_truth)
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top5_prediction = np.argsort(indiv_prediction)[-5:]
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top1_prediction = np.argsort(indiv_prediction)[-1]
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correct_prediction += (indiv_truth == top1_prediction)
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if indiv_truth in top5_prediction:
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correct_prediction_top5 += 1
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total_prediction += 1
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if FLAGS.time_inference:
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print("Time for inference: %.4f" % (time.time() - start_time))
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else:
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precision = 1.0 * correct_prediction / total_prediction
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precision_top5 = 1.0 * correct_prediction_top5 / total_prediction
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best_precision = max(precision, best_precision)
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precision_summ = tf.Summary()
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precision_summ.value.add(
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tag='Precision', simple_value=precision)
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summary_writer.add_summary(precision_summ, train_step)
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best_precision_summ = tf.Summary()
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best_precision_summ.value.add(
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tag='Best Precision', simple_value=best_precision)
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summary_writer.add_summary(best_precision_summ, train_step)
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summary_writer.add_summary(summaries, train_step)
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print('Precision @ 1 = %.4f, Recall @ 5 = %.4f, Global step = %d' %
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(precision, precision_top5, global_step))
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summary_writer.flush()
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if FLAGS.eval_once:
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break
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time.sleep(60)
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def main(_):
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if FLAGS.model == '':
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raise Exception('--model must be specified.')
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if FLAGS.num_gpus == 0:
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dev = '/cpu:0'
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elif FLAGS.num_gpus == 1:
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dev = '/gpu:0'
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else:
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raise ValueError('Only support 0 or 1 gpu.')
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if FLAGS.batch_size == -1:
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if FLAGS.mode == 'train':
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batch_size = 128
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elif FLAGS.mode == 'eval':
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batch_size = 100
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else:
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batch_size = FLAGS.batch_size
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if FLAGS.dataset == 'cifar10':
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num_classes = 10
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elif FLAGS.dataset == 'cifar100':
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num_classes = 100
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if FLAGS.model == 'resnet20':
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num_residual_units = 3
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elif FLAGS.model == 'resnet56':
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num_residual_units = 9
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elif FLAGS.model == 'resnet164' and FLAGS.use_bottleneck:
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num_residual_units = 18
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elif FLAGS.model == 'resnet164' and not FLAGS.use_bottleneck:
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num_residual_units = 27
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else:
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raise Exception("Invalid model -- only resnet20, resnet56 and resnet164 supported")
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data_format = FLAGS.data_format
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hps = resnet_model.HParams(batch_size=batch_size,
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num_classes=num_classes,
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min_lrn_rate=0.0001,
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lrn_rate=0.1,
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num_residual_units=num_residual_units,
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use_bottleneck=FLAGS.use_bottleneck,
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weight_decay_rate=0.0005,
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relu_leakiness=0.1,
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optimizer='mom',
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data_format=data_format)
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with tf.device(dev):
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if FLAGS.mode == 'train':
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train(hps)
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elif FLAGS.mode == 'eval':
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evaluate(hps)
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if __name__ == '__main__':
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tf.logging.set_verbosity(tf.logging.INFO)
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tf.app.run()
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