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142 lines
5.3 KiB
142 lines
5.3 KiB
# Copyright 2017 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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r"""Evaluation executable for detection models.
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This executable is used to evaluate DetectionModels. There are two ways of
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configuring the eval job.
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1) A single pipeline_pb2.TrainEvalPipelineConfig file maybe specified instead.
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In this mode, the --eval_training_data flag may be given to force the pipeline
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to evaluate on training data instead.
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Example usage:
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./eval \
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--logtostderr \
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--checkpoint_dir=path/to/checkpoint_dir \
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--eval_dir=path/to/eval_dir \
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--pipeline_config_path=pipeline_config.pbtxt
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2) Three configuration files may be provided: a model_pb2.DetectionModel
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configuration file to define what type of DetectionModel is being evaluated, an
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input_reader_pb2.InputReader file to specify what data the model is evaluating
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and an eval_pb2.EvalConfig file to configure evaluation parameters.
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Example usage:
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./eval \
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--logtostderr \
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--checkpoint_dir=path/to/checkpoint_dir \
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--eval_dir=path/to/eval_dir \
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--eval_config_path=eval_config.pbtxt \
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--model_config_path=model_config.pbtxt \
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--input_config_path=eval_input_config.pbtxt
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"""
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import functools
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import os
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import tensorflow as tf
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from object_detection.builders import dataset_builder
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from object_detection.builders import graph_rewriter_builder
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from object_detection.builders import model_builder
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from object_detection.legacy import evaluator
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from object_detection.utils import config_util
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from object_detection.utils import label_map_util
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tf.logging.set_verbosity(tf.logging.INFO)
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flags = tf.app.flags
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flags.DEFINE_boolean('eval_training_data', False,
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'If training data should be evaluated for this job.')
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flags.DEFINE_string(
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'checkpoint_dir', '',
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'Directory containing checkpoints to evaluate, typically '
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'set to `train_dir` used in the training job.')
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flags.DEFINE_string('eval_dir', '', 'Directory to write eval summaries to.')
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flags.DEFINE_string(
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'pipeline_config_path', '',
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'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
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'file. If provided, other configs are ignored')
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flags.DEFINE_string('eval_config_path', '',
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'Path to an eval_pb2.EvalConfig config file.')
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flags.DEFINE_string('input_config_path', '',
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'Path to an input_reader_pb2.InputReader config file.')
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flags.DEFINE_string('model_config_path', '',
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'Path to a model_pb2.DetectionModel config file.')
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flags.DEFINE_boolean(
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'run_once', False, 'Option to only run a single pass of '
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'evaluation. Overrides the `max_evals` parameter in the '
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'provided config.')
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FLAGS = flags.FLAGS
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@tf.contrib.framework.deprecated(None, 'Use object_detection/model_main.py.')
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def main(unused_argv):
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assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
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assert FLAGS.eval_dir, '`eval_dir` is missing.'
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tf.gfile.MakeDirs(FLAGS.eval_dir)
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if FLAGS.pipeline_config_path:
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configs = config_util.get_configs_from_pipeline_file(
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FLAGS.pipeline_config_path)
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tf.gfile.Copy(
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FLAGS.pipeline_config_path,
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os.path.join(FLAGS.eval_dir, 'pipeline.config'),
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overwrite=True)
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else:
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configs = config_util.get_configs_from_multiple_files(
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model_config_path=FLAGS.model_config_path,
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eval_config_path=FLAGS.eval_config_path,
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eval_input_config_path=FLAGS.input_config_path)
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for name, config in [('model.config', FLAGS.model_config_path),
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('eval.config', FLAGS.eval_config_path),
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('input.config', FLAGS.input_config_path)]:
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tf.gfile.Copy(config, os.path.join(FLAGS.eval_dir, name), overwrite=True)
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model_config = configs['model']
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eval_config = configs['eval_config']
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input_config = configs['eval_input_config']
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if FLAGS.eval_training_data:
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input_config = configs['train_input_config']
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model_fn = functools.partial(
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model_builder.build, model_config=model_config, is_training=False)
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def get_next(config):
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return dataset_builder.make_initializable_iterator(
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dataset_builder.build(config)).get_next()
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create_input_dict_fn = functools.partial(get_next, input_config)
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categories = label_map_util.create_categories_from_labelmap(
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input_config.label_map_path)
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if FLAGS.run_once:
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eval_config.max_evals = 1
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graph_rewriter_fn = None
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if 'graph_rewriter_config' in configs:
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graph_rewriter_fn = graph_rewriter_builder.build(
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configs['graph_rewriter_config'], is_training=False)
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evaluator.evaluate(
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create_input_dict_fn,
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model_fn,
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eval_config,
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categories,
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FLAGS.checkpoint_dir,
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FLAGS.eval_dir,
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graph_hook_fn=graph_rewriter_fn)
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if __name__ == '__main__':
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tf.app.run()
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