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| # Copyright 2017 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. | |
| # ============================================================================== | |
| """Script to evaluate a trained Attention OCR model. | |
| A simple usage example: | |
| python eval.py | |
| """ | |
| import tensorflow as tf | |
| from tensorflow.contrib import slim | |
| from tensorflow import app | |
| from tensorflow.python.platform import flags | |
| import data_provider | |
| import common_flags | |
| FLAGS = flags.FLAGS | |
| common_flags.define() | |
| # yapf: disable | |
| flags.DEFINE_integer('num_batches', 100, | |
| 'Number of batches to run eval for.') | |
| flags.DEFINE_string('eval_log_dir', '/tmp/attention_ocr/eval', | |
| 'Directory where the evaluation results are saved to.') | |
| flags.DEFINE_integer('eval_interval_secs', 60, | |
| 'Frequency in seconds to run evaluations.') | |
| flags.DEFINE_integer('number_of_steps', None, | |
| 'Number of times to run evaluation.') | |
| # yapf: enable | |
| def main(_): | |
| if not tf.gfile.Exists(FLAGS.eval_log_dir): | |
| tf.gfile.MakeDirs(FLAGS.eval_log_dir) | |
| dataset = common_flags.create_dataset(split_name=FLAGS.split_name) | |
| model = common_flags.create_model(dataset.num_char_classes, | |
| dataset.max_sequence_length, | |
| dataset.num_of_views, dataset.null_code) | |
| data = data_provider.get_data( | |
| dataset, | |
| FLAGS.batch_size, | |
| augment=False, | |
| central_crop_size=common_flags.get_crop_size()) | |
| endpoints = model.create_base(data.images, labels_one_hot=None) | |
| model.create_loss(data, endpoints) | |
| eval_ops = model.create_summaries( | |
| data, endpoints, dataset.charset, is_training=False) | |
| slim.get_or_create_global_step() | |
| session_config = tf.ConfigProto(device_count={"GPU": 0}) | |
| slim.evaluation.evaluation_loop( | |
| master=FLAGS.master, | |
| checkpoint_dir=FLAGS.train_log_dir, | |
| logdir=FLAGS.eval_log_dir, | |
| eval_op=eval_ops, | |
| num_evals=FLAGS.num_batches, | |
| eval_interval_secs=FLAGS.eval_interval_secs, | |
| max_number_of_evaluations=FLAGS.number_of_steps, | |
| session_config=session_config) | |
| if __name__ == '__main__': | |
| app.run() | |