Python eval_util.get_evaluators方法代码示例

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import get_evaluators [as 别名]
def test_get_evaluator_with_evaluator_options(self):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'precision_at_recall_detection_metrics'])
    eval_config.include_metrics_per_category = True
    eval_config.recall_lower_bound = 0.2
    eval_config.recall_upper_bound = 0.6
    categories = self._get_categories_list()

    evaluator_options = eval_util.evaluator_options_from_eval_config(
        eval_config)
    evaluator = eval_util.get_evaluators(eval_config, categories,
                                         evaluator_options)

    self.assertTrue(evaluator[0]._include_metrics_per_category)
    self.assertAlmostEqual(evaluator[1]._recall_lower_bound,
                           eval_config.recall_lower_bound)
    self.assertAlmostEqual(evaluator[1]._recall_upper_bound,
                           eval_config.recall_upper_bound) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import get_evaluators [as 别名]
def test_get_evaluator_with_no_evaluator_options(self):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(
        ['coco_detection_metrics', 'precision_at_recall_detection_metrics'])
    eval_config.include_metrics_per_category = True
    eval_config.recall_lower_bound = 0.2
    eval_config.recall_upper_bound = 0.6
    categories = self._get_categories_list()

    evaluator = eval_util.get_evaluators(
        eval_config, categories, evaluator_options=None)

    # Even though we are setting eval_config.include_metrics_per_category = True
    # and bounds on recall, these options are never passed into the
    # DetectionEvaluator constructor (via `evaluator_options`).
    self.assertFalse(evaluator[0]._include_metrics_per_category)
    self.assertAlmostEqual(evaluator[1]._recall_lower_bound, 0.0)
    self.assertAlmostEqual(evaluator[1]._recall_upper_bound, 1.0) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import get_evaluators [as 别名]
def test_get_evaluator_with_evaluator_options(self):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(['coco_detection_metrics'])
    eval_config.include_metrics_per_category = True
    categories = self._get_categories_list()

    evaluator_options = eval_util.evaluator_options_from_eval_config(
        eval_config)
    evaluator = eval_util.get_evaluators(
        eval_config, categories, evaluator_options)

    self.assertTrue(evaluator[0]._include_metrics_per_category) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import get_evaluators [as 别名]
def test_get_evaluator_with_no_evaluator_options(self):
    eval_config = eval_pb2.EvalConfig()
    eval_config.metrics_set.extend(['coco_detection_metrics'])
    eval_config.include_metrics_per_category = True
    categories = self._get_categories_list()

    evaluator = eval_util.get_evaluators(
        eval_config, categories, evaluator_options=None)

    # Even though we are setting eval_config.include_metrics_per_category = True
    # this option is never passed into the DetectionEvaluator constructor (via
    # `evaluator_options`).
    self.assertFalse(evaluator[0]._include_metrics_per_category) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import get_evaluators [as 别名]
def test_get_evaluator_with_keypoint_metrics(self):
    eval_config = eval_pb2.EvalConfig()
    person_keypoints_metric = eval_config.parameterized_metric.add()
    person_keypoints_metric.coco_keypoint_metrics.class_label = 'person'
    person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
        'left_eye'] = 0.1
    person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
        'right_eye'] = 0.2
    dog_keypoints_metric = eval_config.parameterized_metric.add()
    dog_keypoints_metric.coco_keypoint_metrics.class_label = 'dog'
    dog_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
        'tail_start'] = 0.3
    dog_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
        'mouth'] = 0.4
    categories = self._get_categories_list_with_keypoints()

    evaluator = eval_util.get_evaluators(
        eval_config, categories, evaluator_options=None)

    # Verify keypoint evaluator class variables.
    self.assertLen(evaluator, 3)
    self.assertFalse(evaluator[0]._include_metrics_per_category)
    self.assertEqual(evaluator[1]._category_name, 'person')
    self.assertEqual(evaluator[2]._category_name, 'dog')
    self.assertAllEqual(evaluator[1]._keypoint_ids, [0, 3])
    self.assertAllEqual(evaluator[2]._keypoint_ids, [1, 2])
    self.assertAllClose([0.1, 0.2], evaluator[1]._oks_sigmas)
    self.assertAllClose([0.3, 0.4], evaluator[2]._oks_sigmas) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import get_evaluators [as 别名]
def test_get_evaluator_with_unmatched_label(self):
    eval_config = eval_pb2.EvalConfig()
    person_keypoints_metric = eval_config.parameterized_metric.add()
    person_keypoints_metric.coco_keypoint_metrics.class_label = 'unmatched'
    person_keypoints_metric.coco_keypoint_metrics.keypoint_label_to_sigmas[
        'kpt'] = 0.1
    categories = self._get_categories_list_with_keypoints()

    evaluator = eval_util.get_evaluators(
        eval_config, categories, evaluator_options=None)
    self.assertLen(evaluator, 1)
    self.assertNotIsInstance(
        evaluator[0], coco_evaluation.CocoKeypointEvaluator) 

# 需要导入模块: from object_detection import eval_util [as 别名]
# 或者: from object_detection.eval_util import get_evaluators [as 别名]
def read_data_and_evaluate(input_config, eval_config):
  """Reads pre-computed object detections and groundtruth from tf_record.

  Args:
    input_config: input config proto of type
      object_detection.protos.InputReader.
    eval_config: evaluation config proto of type
      object_detection.protos.EvalConfig.

  Returns:
    Evaluated detections metrics.

  Raises:
    ValueError: if input_reader type is not supported or metric type is unknown.
  """
  if input_config.WhichOneof('input_reader') == 'tf_record_input_reader':
    input_paths = input_config.tf_record_input_reader.input_path

    categories = label_map_util.create_categories_from_labelmap(
        input_config.label_map_path)

    object_detection_evaluators = eval_util.get_evaluators(
        eval_config, categories)
    # Support a single evaluator
    object_detection_evaluator = object_detection_evaluators[0]

    skipped_images = 0
    processed_images = 0
    for input_path in _generate_filenames(input_paths):
      tf.logging.info('Processing file: {0}'.format(input_path))

      record_iterator = tf.python_io.tf_record_iterator(path=input_path)
      data_parser = tf_example_parser.TfExampleDetectionAndGTParser()

      for string_record in record_iterator:
        tf.logging.log_every_n(tf.logging.INFO, 'Processed %d images...', 1000,
                               processed_images)
        processed_images += 1

        example = tf.train.Example()
        example.ParseFromString(string_record)
        decoded_dict = data_parser.parse(example)

        if decoded_dict:
          object_detection_evaluator.add_single_ground_truth_image_info(
              decoded_dict[standard_fields.DetectionResultFields.key],
              decoded_dict)
          object_detection_evaluator.add_single_detected_image_info(
              decoded_dict[standard_fields.DetectionResultFields.key],
              decoded_dict)
        else:
          skipped_images += 1
          tf.logging.info('Skipped images: {0}'.format(skipped_images))

    return object_detection_evaluator.evaluate()

  raise ValueError('Unsupported input_reader_config.')