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| r"""Evaluation executable for detection models.
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| This executable is used to evaluate DetectionModels. 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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| """
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| import functools
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| import os
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| import tensorflow.compat.v1 as tf
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| from google.protobuf import text_format
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| from lstm_object_detection import evaluator
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| from lstm_object_detection import model_builder
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| from lstm_object_detection.inputs import seq_dataset_builder
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| from lstm_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('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('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_boolean('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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| 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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| 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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| 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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| pipeline_proto = config_util.create_pipeline_proto_from_configs(configs)
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| config_text = text_format.MessageToString(pipeline_proto)
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| tf.gfile.MakeDirs(FLAGS.eval_dir)
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| with tf.gfile.Open(os.path.join(FLAGS.eval_dir, 'pipeline.config'),
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| 'wb') as f:
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| f.write(config_text)
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| model_config = configs['model']
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| lstm_config = configs['lstm_model']
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| eval_config = configs['eval_config']
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| input_config = configs['eval_input_config']
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|
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| if FLAGS.eval_training_data:
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| input_config.external_input_reader.CopyFrom(
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| configs['train_input_config'].external_input_reader)
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| lstm_config.eval_unroll_length = lstm_config.train_unroll_length
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|
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| model_fn = functools.partial(
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| model_builder.build,
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| model_config=model_config,
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| lstm_config=lstm_config,
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| is_training=False)
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|
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| def get_next(config, model_config, lstm_config, unroll_length):
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| return seq_dataset_builder.build(config, model_config, lstm_config,
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| unroll_length)
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| create_input_dict_fn = functools.partial(get_next, input_config, model_config,
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| lstm_config,
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| lstm_config.eval_unroll_length)
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| label_map = label_map_util.load_labelmap(input_config.label_map_path)
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| max_num_classes = max([item.id for item in label_map.item])
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| categories = label_map_util.convert_label_map_to_categories(
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| label_map, max_num_classes)
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|
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| if FLAGS.run_once:
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| eval_config.max_evals = 1
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|
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| evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
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| FLAGS.checkpoint_dir, FLAGS.eval_dir)
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|
|
| if __name__ == '__main__':
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| tf.app.run()
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|
|
