本文整理汇总了Python中object_detection.utils.test_utils.MockBoxCoder方法的典型用法代码示例。如果您正苦于以下问题:Python test_utils.MockBoxCoder方法的具体用法?Python test_utils.MockBoxCoder怎么用?Python test_utils.MockBoxCoder使用的例子?那么, 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.utils.test_utils
的用法示例。
在下文中一共展示了test_utils.MockBoxCoder方法的4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: setUp
# 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 MockBoxCoder [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)
示例2: _create_model
# 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 MockBoxCoder [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
示例3: _create_model
# 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 MockBoxCoder [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
示例4: _create_model
# 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 MockBoxCoder [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