# 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 time import six import sys 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', '', 'Filepattern for training data.') tf.app.flags.DEFINE_string('eval_data_path', '', 'Filepattern 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() param_stats = tf.contrib.tfprof.model_analyzer.print_model_analysis( tf.get_default_graph(), tfprof_options=tf.contrib.tfprof.model_analyzer. TRAINABLE_VARS_PARAMS_STAT_OPTIONS) sys.stdout.write('total_params: %d\n' % param_stats.total_parameters) tf.contrib.tfprof.model_analyzer.print_model_analysis( tf.get_default_graph(), tfprof_options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS) truth = tf.argmax(model.labels, axis=1) predictions = tf.argmax(model.predictions, axis=1) precision = tf.reduce_mean(tf.to_float(tf.equal(predictions, truth))) summary_hook = tf.train.SummarySaverHook( save_steps=100, output_dir=FLAGS.train_dir, summary_op=tf.summary.merge([model.summaries, tf.summary.scalar('Precision', precision)])) logging_hook = tf.train.LoggingTensorHook( tensors={'step': model.global_step, 'loss': model.cost, 'precision': precision}, every_n_iter=100) class _LearningRateSetterHook(tf.train.SessionRunHook): """Sets learning_rate based on global step.""" def begin(self): self._lrn_rate = 0.1 def before_run(self, run_context): return tf.train.SessionRunArgs( model.global_step, # Asks for global step value. feed_dict={model.lrn_rate: self._lrn_rate}) # Sets learning rate def after_run(self, run_context, run_values): train_step = run_values.results if train_step < 40000: self._lrn_rate = 0.1 elif train_step < 60000: self._lrn_rate = 0.01 elif train_step < 80000: self._lrn_rate = 0.001 else: self._lrn_rate = 0.0001 with tf.train.MonitoredTrainingSession( checkpoint_dir=FLAGS.log_root, hooks=[logging_hook, _LearningRateSetterHook()], chief_only_hooks=[summary_hook], # Since we provide a SummarySaverHook, we need to disable default # SummarySaverHook. To do that we set save_summaries_steps to 0. save_summaries_steps=0, config=tf.ConfigProto(allow_soft_placement=True)) as mon_sess: while not mon_sess.should_stop(): mon_sess.run(model.train_op) 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.summary.FileWriter(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: 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 six.moves.range(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' % (loss, precision, best_precision)) summary_writer.flush() if FLAGS.eval_once: break time.sleep(60) 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.logging.set_verbosity(tf.logging.INFO) tf.app.run()