Python test_utils.MockMatcher方法代码示例

# 需要导入模块: from object_detection.utils import test_utils [as 别名]
# 或者: from object_detection.utils.test_utils import MockMatcher [as 别名]
def setUp(self):
    """Set up mock SSD model.

    Here we set up a simple mock SSD model that will always predict 4
    detections that happen to always be exactly the anchors that are set up
    in the above MockAnchorGenerator.  Because we let max_detections=5,
    we will also always end up with an extra padded row in the detection
    results.
    """
    is_training = False
    self._num_classes = 1
    mock_anchor_generator = MockAnchorGenerator2x2()
    mock_box_predictor = test_utils.MockBoxPredictor(
        is_training, self._num_classes)
    mock_box_coder = test_utils.MockBoxCoder()
    fake_feature_extractor = FakeSSDFeatureExtractor()
    mock_matcher = test_utils.MockMatcher()
    region_similarity_calculator = sim_calc.IouSimilarity()

    def image_resizer_fn(image):
      return tf.identity(image)

    classification_loss = losses.WeightedSigmoidClassificationLoss(
        anchorwise_output=True)
    localization_loss = losses.WeightedSmoothL1LocalizationLoss(
        anchorwise_output=True)
    non_max_suppression_fn = functools.partial(
        post_processing.batch_multiclass_non_max_suppression,
        score_thresh=-20.0,
        iou_thresh=1.0,
        max_size_per_class=5,
        max_total_size=5)
    classification_loss_weight = 1.0
    localization_loss_weight = 1.0
    normalize_loss_by_num_matches = False

    # This hard example miner is expected to be a no-op.
    hard_example_miner = losses.HardExampleMiner(
        num_hard_examples=None,
        iou_threshold=1.0)

    self._num_anchors = 4
    self._code_size = 4
    self._model = ssd_meta_arch.SSDMetaArch(
        is_training, mock_anchor_generator, mock_box_predictor, mock_box_coder,
        fake_feature_extractor, mock_matcher, region_similarity_calculator,
        image_resizer_fn, non_max_suppression_fn, tf.identity,
        classification_loss, localization_loss, classification_loss_weight,
        localization_loss_weight, normalize_loss_by_num_matches,
        hard_example_miner) 

# 需要导入模块: from object_detection.utils import test_utils [as 别名]
# 或者: from object_detection.utils.test_utils import MockMatcher [as 别名]
def _create_model(self, apply_hard_mining=True,
                    normalize_loc_loss_by_codesize=False):
    is_training = False
    num_classes = 1
    mock_anchor_generator = MockAnchorGenerator2x2()
    mock_box_predictor = test_utils.MockBoxPredictor(
        is_training, num_classes)
    mock_box_coder = test_utils.MockBoxCoder()
    fake_feature_extractor = FakeSSDFeatureExtractor()
    mock_matcher = test_utils.MockMatcher()
    region_similarity_calculator = sim_calc.IouSimilarity()
    encode_background_as_zeros = False
    def image_resizer_fn(image):
      return [tf.identity(image), tf.shape(image)]

    classification_loss = losses.WeightedSigmoidClassificationLoss()
    localization_loss = losses.WeightedSmoothL1LocalizationLoss()
    non_max_suppression_fn = functools.partial(
        post_processing.batch_multiclass_non_max_suppression,
        score_thresh=-20.0,
        iou_thresh=1.0,
        max_size_per_class=5,
        max_total_size=5)
    classification_loss_weight = 1.0
    localization_loss_weight = 1.0
    negative_class_weight = 1.0
    normalize_loss_by_num_matches = False

    hard_example_miner = None
    if apply_hard_mining:
      # This hard example miner is expected to be a no-op.
      hard_example_miner = losses.HardExampleMiner(
          num_hard_examples=None,
          iou_threshold=1.0)

    code_size = 4
    model = ssd_meta_arch.SSDMetaArch(
        is_training, mock_anchor_generator, mock_box_predictor, mock_box_coder,
        fake_feature_extractor, mock_matcher, region_similarity_calculator,
        encode_background_as_zeros, negative_class_weight, image_resizer_fn,
        non_max_suppression_fn, tf.identity, classification_loss,
        localization_loss, classification_loss_weight, localization_loss_weight,
        normalize_loss_by_num_matches, hard_example_miner, add_summaries=False,
        normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize)
    return model, num_classes, mock_anchor_generator.num_anchors(), code_size 

# 需要导入模块: from object_detection.utils import test_utils [as 别名]
# 或者: from object_detection.utils.test_utils import MockMatcher [as 别名]
def _create_model(self, apply_hard_mining=True):
    is_training = False
    num_classes = 1
    mock_anchor_generator = MockAnchorGenerator2x2()
    mock_box_predictor = test_utils.MockBoxPredictor(
        is_training, num_classes)
    mock_box_coder = test_utils.MockBoxCoder()
    fake_feature_extractor = FakeSSDFeatureExtractor()
    mock_matcher = test_utils.MockMatcher()
    region_similarity_calculator = sim_calc.IouSimilarity()

    def image_resizer_fn(image):
      return [tf.identity(image), tf.shape(image)]

    classification_loss = losses.WeightedSigmoidClassificationLoss()
    localization_loss = losses.WeightedSmoothL1LocalizationLoss()
    non_max_suppression_fn = functools.partial(
        post_processing.batch_multiclass_non_max_suppression,
        score_thresh=-20.0,
        iou_thresh=1.0,
        max_size_per_class=5,
        max_total_size=5)
    classification_loss_weight = 1.0
    localization_loss_weight = 1.0
    normalize_loss_by_num_matches = False

    hard_example_miner = None
    if apply_hard_mining:
      # This hard example miner is expected to be a no-op.
      hard_example_miner = losses.HardExampleMiner(
          num_hard_examples=None,
          iou_threshold=1.0)

    code_size = 4
    model = ssd_meta_arch.SSDMetaArch(
        is_training, mock_anchor_generator, mock_box_predictor, mock_box_coder,
        fake_feature_extractor, mock_matcher, region_similarity_calculator,
        image_resizer_fn, non_max_suppression_fn, tf.identity,
        classification_loss, localization_loss, classification_loss_weight,
        localization_loss_weight, normalize_loss_by_num_matches,
        hard_example_miner, add_summaries=False)
    return model, num_classes, mock_anchor_generator.num_anchors(), code_size 

# 需要导入模块: from object_detection.utils import test_utils [as 别名]
# 或者: from object_detection.utils.test_utils import MockMatcher [as 别名]
def _create_model(self, apply_hard_mining=True):
    is_training = False
    num_classes = 1
    mock_anchor_generator = MockAnchorGenerator2x2()
    mock_box_predictor = test_utils.MockBoxPredictor(
        is_training, num_classes)
    mock_box_coder = test_utils.MockBoxCoder()
    fake_feature_extractor = FakeSSDFeatureExtractor()
    mock_matcher = test_utils.MockMatcher()
    region_similarity_calculator = sim_calc.IouSimilarity()
    encode_background_as_zeros = False
    def image_resizer_fn(image):
      return [tf.identity(image), tf.shape(image)]

    classification_loss = losses.WeightedSigmoidClassificationLoss()
    localization_loss = losses.WeightedSmoothL1LocalizationLoss()
    non_max_suppression_fn = functools.partial(
        post_processing.batch_multiclass_non_max_suppression,
        score_thresh=-20.0,
        iou_thresh=1.0,
        max_size_per_class=5,
        max_total_size=5)
    classification_loss_weight = 1.0
    localization_loss_weight = 1.0
    normalize_loss_by_num_matches = False

    hard_example_miner = None
    if apply_hard_mining:
      # This hard example miner is expected to be a no-op.
      hard_example_miner = losses.HardExampleMiner(
          num_hard_examples=None,
          iou_threshold=1.0)

    code_size = 4
    model = ssd_meta_arch.SSDMetaArch(
        is_training, mock_anchor_generator, mock_box_predictor, mock_box_coder,
        fake_feature_extractor, mock_matcher, region_similarity_calculator,
        encode_background_as_zeros, image_resizer_fn, non_max_suppression_fn,
        tf.identity, classification_loss, localization_loss,
        classification_loss_weight, localization_loss_weight,
        normalize_loss_by_num_matches, hard_example_miner, add_summaries=False)
    return model, num_classes, mock_anchor_generator.num_anchors(), code_size