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| # Copyright 2017 Google Inc. 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. | |
| # ============================================================================== | |
| """Evaluates text classification model.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import math | |
| import time | |
| # Dependency imports | |
| import tensorflow as tf | |
| import graphs | |
| flags = tf.app.flags | |
| FLAGS = flags.FLAGS | |
| flags.DEFINE_string('master', '', | |
| 'BNS name prefix of the Tensorflow eval master, ' | |
| 'or "local".') | |
| flags.DEFINE_string('eval_dir', '/tmp/text_eval', | |
| 'Directory where to write event logs.') | |
| flags.DEFINE_string('eval_data', 'test', 'Specify which dataset is used. ' | |
| '("train", "valid", "test") ') | |
| flags.DEFINE_string('checkpoint_dir', '/tmp/text_train', | |
| 'Directory where to read model checkpoints.') | |
| flags.DEFINE_integer('eval_interval_secs', 60, 'How often to run the eval.') | |
| flags.DEFINE_integer('num_examples', 32, 'Number of examples to run.') | |
| flags.DEFINE_bool('run_once', False, 'Whether to run eval only once.') | |
| def restore_from_checkpoint(sess, saver): | |
| """Restore model from checkpoint. | |
| Args: | |
| sess: Session. | |
| saver: Saver for restoring the checkpoint. | |
| Returns: | |
| bool: Whether the checkpoint was found and restored | |
| """ | |
| ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir) | |
| if not ckpt or not ckpt.model_checkpoint_path: | |
| tf.logging.info('No checkpoint found at %s', FLAGS.checkpoint_dir) | |
| return False | |
| saver.restore(sess, ckpt.model_checkpoint_path) | |
| return True | |
| def run_eval(eval_ops, summary_writer, saver): | |
| """Runs evaluation over FLAGS.num_examples examples. | |
| Args: | |
| eval_ops: dict<metric name, tuple(value, update_op)> | |
| summary_writer: Summary writer. | |
| saver: Saver. | |
| Returns: | |
| dict<metric name, value>, with value being the average over all examples. | |
| """ | |
| sv = tf.train.Supervisor( | |
| logdir=FLAGS.eval_dir, saver=None, summary_op=None, summary_writer=None) | |
| with sv.managed_session( | |
| master=FLAGS.master, start_standard_services=False) as sess: | |
| if not restore_from_checkpoint(sess, saver): | |
| return | |
| sv.start_queue_runners(sess) | |
| metric_names, ops = zip(*eval_ops.items()) | |
| value_ops, update_ops = zip(*ops) | |
| value_ops_dict = dict(zip(metric_names, value_ops)) | |
| # Run update ops | |
| num_batches = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size)) | |
| tf.logging.info('Running %d batches for evaluation.', num_batches) | |
| for i in range(num_batches): | |
| if (i + 1) % 10 == 0: | |
| tf.logging.info('Running batch %d/%d...', i + 1, num_batches) | |
| if (i + 1) % 50 == 0: | |
| _log_values(sess, value_ops_dict) | |
| sess.run(update_ops) | |
| _log_values(sess, value_ops_dict, summary_writer=summary_writer) | |
| def _log_values(sess, value_ops, summary_writer=None): | |
| """Evaluate, log, and write summaries of the eval metrics in value_ops.""" | |
| metric_names, value_ops = zip(*value_ops.items()) | |
| values = sess.run(value_ops) | |
| tf.logging.info('Eval metric values:') | |
| summary = tf.summary.Summary() | |
| for name, val in zip(metric_names, values): | |
| summary.value.add(tag=name, simple_value=val) | |
| tf.logging.info('%s = %.3f', name, val) | |
| if summary_writer is not None: | |
| global_step_val = sess.run(tf.train.get_global_step()) | |
| tf.logging.info('Finished eval for step ' + str(global_step_val)) | |
| summary_writer.add_summary(summary, global_step_val) | |
| def main(_): | |
| tf.logging.set_verbosity(tf.logging.INFO) | |
| tf.gfile.MakeDirs(FLAGS.eval_dir) | |
| tf.logging.info('Building eval graph...') | |
| output = graphs.get_model().eval_graph(FLAGS.eval_data) | |
| eval_ops, moving_averaged_variables = output | |
| saver = tf.train.Saver(moving_averaged_variables) | |
| summary_writer = tf.summary.FileWriter( | |
| FLAGS.eval_dir, graph=tf.get_default_graph()) | |
| while True: | |
| run_eval(eval_ops, summary_writer, saver) | |
| if FLAGS.run_once: | |
| break | |
| time.sleep(FLAGS.eval_interval_secs) | |
| if __name__ == '__main__': | |
| tf.app.run() | |