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Python evaluator.evaluate方法代码示例

本文整理汇总了Python中object_detection.evaluator.evaluate方法的典型用法代码示例。如果您正苦于以下问题：Python evaluator.evaluate方法的具体用法？Python evaluator.evaluate怎么用？Python evaluator.evaluate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.evaluator的用法示例。


在下文中一共展示了evaluator.evaluate方法的11个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: main

# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import evaluate [as 别名]
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
开发者ID:ringringyi，项目名称:DOTA_models，代码行数:26，代码来源:eval.py

示例2: evaluate

# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import evaluate [as 别名]
def evaluate(self, eval_pipeline_file, model_dir, eval_dir):
        configs = self._get_configs_from_pipeline_file(eval_pipeline_file)
        model_config = configs['model']
        eval_config = configs['eval_config']
        input_config = configs['eval_input_config']
        model_fn = functools.partial(
            model_builder.build,
            model_config=model_config,
            is_training=True)
        create_input_dict_fn = functools.partial(self.get_next, input_config)
        label_map = label_map_util.load_labelmap(input_config.label_map_path)
        max_num_classes = max([item.id for item in label_map.item])
        categories = label_map_util.convert_label_map_to_categories(
                        label_map, max_num_classes)
        evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                        model_dir, eval_dir)
开发者ID:autoai-org，项目名称:CVTron，代码行数:18，代码来源:object_detection_trainer.py

示例3: main

# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import evaluate [as 别名]
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  model_config, train_config, input_config, eval_config = get_configs_from_pipeline_file()

  model_fn = functools.partial(
      build_man_model,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
开发者ID:xiaobai1217，项目名称:MBMD，代码行数:23，代码来源:eval.py

示例4: main

# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import evaluate [as 别名]
def main(unused_argv):
  os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
  if FLAGS.clear:
    if os.path.exists(FLAGS.eval_dir):
      shutil.rmtree(FLAGS.eval_dir)

  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
开发者ID:simonmeister，项目名称:motion-rcnn，代码行数:31，代码来源:eval.py

示例5: main

# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import evaluate [as 别名]
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    tf.gfile.Copy(FLAGS.pipeline_config_path,
                  os.path.join(FLAGS.eval_dir, 'pipeline.config'),
                  overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        eval_config_path=FLAGS.eval_config_path,
        eval_input_config_path=FLAGS.input_config_path)
    for name, config in [('model.config', FLAGS.model_config_path),
                         ('eval.config', FLAGS.eval_config_path),
                         ('input.config', FLAGS.input_config_path)]:
      tf.gfile.Copy(config,
                    os.path.join(FLAGS.eval_dir, name),
                    overwrite=True)

  model_config = configs['model']
  eval_config = configs['eval_config']
  input_config = configs['eval_input_config']
  if FLAGS.eval_training_data:
    input_config = configs['train_input_config']

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  def get_next(config):
    return dataset_util.make_initializable_iterator(
        dataset_builder.build(config)).get_next()

  create_input_dict_fn = functools.partial(get_next, input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  if FLAGS.run_once:
    eval_config.max_evals = 1

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
开发者ID:cagbal，项目名称:ros_people_object_detection_tensorflow，代码行数:51，代码来源:eval.py

示例6: main

# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import evaluate [as 别名]
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    tf.gfile.Copy(FLAGS.pipeline_config_path,
                  os.path.join(FLAGS.eval_dir, 'pipeline.config'),
                  overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        eval_config_path=FLAGS.eval_config_path,
        eval_input_config_path=FLAGS.input_config_path)
    for name, config in [('model.config', FLAGS.model_config_path),
                         ('eval.config', FLAGS.eval_config_path),
                         ('input.config', FLAGS.input_config_path)]:
      tf.gfile.Copy(config,
                    os.path.join(FLAGS.eval_dir, name),
                    overwrite=True)

  model_config = configs['model']
  eval_config = configs['eval_config']
  input_config = configs['eval_input_config']
  if FLAGS.eval_training_data:
    input_config = configs['train_input_config']

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  def get_next(config):
    return dataset_util.make_initializable_iterator(
        dataset_builder.build(config)).get_next()

  create_input_dict_fn = functools.partial(get_next, input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  if FLAGS.run_once:
    eval_config.max_evals = 1

  graph_rewriter_fn = None
  if 'graph_rewriter_config' in configs:
    graph_rewriter_fn = graph_rewriter_builder.build(
        configs['graph_rewriter_config'], is_training=False)

  evaluator.evaluate(
      create_input_dict_fn,
      model_fn,
      eval_config,
      categories,
      FLAGS.checkpoint_dir,
      FLAGS.eval_dir,
      graph_hook_fn=graph_rewriter_fn)
开发者ID:ambakick，项目名称:Person-Detection-and-Tracking，代码行数:62，代码来源:eval.py

示例7: main

# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import evaluate [as 别名]
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    tf.gfile.Copy(FLAGS.pipeline_config_path,
                  os.path.join(FLAGS.eval_dir, 'pipeline.config'),
                  overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        eval_config_path=FLAGS.eval_config_path,
        eval_input_config_path=FLAGS.input_config_path)
    for name, config in [('model.config', FLAGS.model_config_path),
                         ('eval.config', FLAGS.eval_config_path),
                         ('input.config', FLAGS.input_config_path)]:
      tf.gfile.Copy(config,
                    os.path.join(FLAGS.eval_dir, name),
                    overwrite=True)

  model_config = configs['model']
  eval_config = configs['eval_config']
  if FLAGS.eval_training_data:
    input_config = configs['train_input_config']
  else:
    input_config = configs['eval_input_config']

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  if FLAGS.run_once:
    eval_config.max_evals = 1

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
开发者ID:rky0930，项目名称:yolo_v2，代码行数:50，代码来源:eval.py

示例8: main

# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import evaluate [as 别名]
def main(unused_argv):
    assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
    assert FLAGS.eval_dir, '`eval_dir` is missing.'
    tf.gfile.MakeDirs(FLAGS.eval_dir)
    if FLAGS.pipeline_config_path:
        configs = config_util.get_configs_from_pipeline_file(
            FLAGS.pipeline_config_path)
        tf.gfile.Copy(FLAGS.pipeline_config_path,
                      os.path.join(FLAGS.eval_dir, 'pipeline.config'),
                      overwrite=True)
    else:
        configs = config_util.get_configs_from_multiple_files(
            model_config_path=FLAGS.model_config_path,
            eval_config_path=FLAGS.eval_config_path,
            eval_input_config_path=FLAGS.input_config_path)
        for name, config in [('model.config', FLAGS.model_config_path),
                             ('eval.config', FLAGS.eval_config_path),
                             ('input.config', FLAGS.input_config_path)]:
            tf.gfile.Copy(config,
                          os.path.join(FLAGS.eval_dir, name),
                          overwrite=True)

    model_config = configs['model']
    eval_config = configs['eval_config']
    input_config = configs['eval_input_config']
    if FLAGS.eval_training_data:
        input_config = configs['train_input_config']

    model_fn = functools.partial(
        model_builder.build,
        model_config=model_config,
        is_training=False)

    def get_next(config):
        return dataset_util.make_initializable_iterator(
            dataset_builder.build(config)).get_next()

    create_input_dict_fn = functools.partial(get_next, input_config)

    label_map = label_map_util.load_labelmap(input_config.label_map_path)
    max_num_classes = max([item.id for item in label_map.item])
    categories = label_map_util.convert_label_map_to_categories(
        label_map, max_num_classes)

    if FLAGS.run_once:
        eval_config.max_evals = 1

    evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                       FLAGS.checkpoint_dir, FLAGS.eval_dir)
开发者ID:scorelab，项目名称:Elphas，代码行数:51，代码来源:eval.py

示例9: main

# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import evaluate [as 别名]
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    tf.gfile.Copy(FLAGS.pipeline_config_path,
                  os.path.join(FLAGS.eval_dir, 'pipeline.config'),
                  overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        eval_config_path=FLAGS.eval_config_path,
        eval_input_config_path=FLAGS.input_config_path)
    for name, config in [('model.config', FLAGS.model_config_path),
                         ('eval.config', FLAGS.eval_config_path),
                         ('input.config', FLAGS.input_config_path)]:
      tf.gfile.Copy(config,
                    os.path.join(FLAGS.eval_dir, name),
                    overwrite=True)

  model_config = configs['model']
  eval_config = configs['eval_config']
  input_config = configs['eval_input_config']
  if FLAGS.eval_training_data:
    input_config = configs['train_input_config']

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  def get_next(config):
    return dataset_builder.make_initializable_iterator(
        dataset_builder.build(config)).get_next()

  create_input_dict_fn = functools.partial(get_next, input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  if FLAGS.run_once:
    eval_config.max_evals = 1

  graph_rewriter_fn = None
  if 'graph_rewriter_config' in configs:
    graph_rewriter_fn = graph_rewriter_builder.build(
        configs['graph_rewriter_config'], is_training=False)

  evaluator.evaluate(
      create_input_dict_fn,
      model_fn,
      eval_config,
      categories,
      FLAGS.checkpoint_dir,
      FLAGS.eval_dir,
      graph_hook_fn=graph_rewriter_fn)
开发者ID:vsmolyakov，项目名称:cv，代码行数:62，代码来源:eval.py

示例10: main

# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import evaluate [as 别名]
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    tf.gfile.Copy(FLAGS.pipeline_config_path,
                  os.path.join(FLAGS.eval_dir, 'pipeline.config'),
                  overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        eval_config_path=FLAGS.eval_config_path,
        eval_input_config_path=FLAGS.input_config_path)
    for name, config in [('model.config', FLAGS.model_config_path),
                         ('eval.config', FLAGS.eval_config_path),
                         ('input.config', FLAGS.input_config_path)]:
      tf.gfile.Copy(config,
                    os.path.join(FLAGS.eval_dir, name),
                    overwrite=True)

  model_config = configs['model']
  eval_config = configs['eval_config']
  input_config = configs['eval_input_config']

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  if FLAGS.run_once:
    eval_config.max_evals = 1

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
开发者ID:scotthuang1989，项目名称:object_detection_with_tensorflow，代码行数:47，代码来源:eval.py

示例11: eval_main

# 需要导入模块: from object_detection import evaluator [as 别名]
# 或者: from object_detection.evaluator import evaluate [as 别名]
def eval_main(max_number_of_evaluations=None):
  if FLAGS.eval_label:
    if FLAGS.pipeline_config_path == '':
      FLAGS.pipeline_config_path = '../configs/test/' + FLAGS.eval_label + '.config'
    if FLAGS.checkpoint_dir == '':
      FLAGS.checkpoint_dir = '../checkpoints/train/' + FLAGS.eval_label
    FLAGS.eval_dir = '../checkpoints/eval/' + FLAGS.eval_label
    FLAGS.eval_tag = FLAGS.eval_label

  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'

  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  elif FLAGS.eval_config_path:
    model_config, eval_config, input_config = get_configs_from_multiple_files()
  else:
    model_config, eval_config, input_config = get_configs_from_checkpoint_dir()

  if not FLAGS.eval_dir:
    if not FLAGS.eval_tag:
      FLAGS.eval_tag = time.strftime("%Y%m%d-%H%M%S")
    FLAGS.eval_dir = utils.mkdir_p(FLAGS.checkpoint_dir + '_eval_' + FLAGS.eval_tag)

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  input_path = input_config.tf_record_input_reader.input_path
  num_examples = sum(1 for _ in tf.python_io.tf_record_iterator(input_path))

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(
    create_input_dict_fn, model_fn, eval_config, categories,
    FLAGS.checkpoint_dir, FLAGS.eval_dir, num_examples,
    FLAGS.gpu_fraction, max_number_of_evaluations)
开发者ID:wonheeML，项目名称:mtl-ssl，代码行数:46，代码来源:eval.py


注：本文中的object_detection.evaluator.evaluate方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。