本文整理汇总了Python中object_detection.protos.losses_pb2.Loss方法的典型用法代码示例。如果您正苦于以下问题：Python losses_pb2.Loss方法的具体用法？Python losses_pb2.Loss怎么用？Python losses_pb2.Loss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.protos.losses_pb2的用法示例。
在下文中一共展示了losses_pb2.Loss方法的15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: test_build_weighted_l2_localization_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_weighted_l2_localization_loss(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedL2LocalizationLoss)) 示例2: test_build_weighted_smooth_l1_localization_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_weighted_smooth_l1_localization_loss(self):
losses_text_proto = """
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedSmoothL1LocalizationLoss)) 示例3: test_anchorwise_output
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_anchorwise_output(self):
losses_text_proto = """
localization_loss {
weighted_smooth_l1 {
anchorwise_output: true
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedSmoothL1LocalizationLoss))
predictions = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]])
targets = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]])
weights = tf.constant([[1.0, 1.0]])
loss = localization_loss(predictions, targets, weights=weights)
self.assertEqual(loss.shape, [1, 2]) 示例4: test_build_weighted_sigmoid_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_weighted_sigmoid_classification_loss(self):
losses_text_proto = """
classification_loss {
weighted_sigmoid {
}
}
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(classification_loss,
losses.WeightedSigmoidClassificationLoss)) 示例5: test_build_weighted_softmax_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_weighted_softmax_classification_loss(self):
losses_text_proto = """
classification_loss {
weighted_softmax {
}
}
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss)) 示例6: test_build_bootstrapped_sigmoid_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_bootstrapped_sigmoid_classification_loss(self):
losses_text_proto = """
classification_loss {
bootstrapped_sigmoid {
alpha: 0.5
}
}
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(classification_loss,
losses.BootstrappedSigmoidClassificationLoss)) 示例7: test_build_hard_example_miner_for_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_hard_example_miner_for_classification_loss(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_softmax {
}
}
hard_example_miner {
loss_type: CLASSIFICATION
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, _, _, _, hard_example_miner = losses_builder.build(losses_proto)
self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
self.assertEqual(hard_example_miner._loss_type, 'cls') 示例8: test_build_hard_example_miner_for_localization_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_hard_example_miner_for_localization_loss(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_softmax {
}
}
hard_example_miner {
loss_type: LOCALIZATION
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, _, _, _, hard_example_miner = losses_builder.build(losses_proto)
self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
self.assertEqual(hard_example_miner._loss_type, 'loc') 示例9: test_build_weighted_l2_localization_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_weighted_l2_localization_loss(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedL2LocalizationLoss)) 示例10: test_build_weighted_smooth_l1_localization_loss_non_default_delta
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_weighted_smooth_l1_localization_loss_non_default_delta(self):
losses_text_proto = """
localization_loss {
weighted_smooth_l1 {
delta: 0.1
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedSmoothL1LocalizationLoss))
self.assertAlmostEqual(localization_loss._delta, 0.1) 示例11: test_build_weighted_iou_localization_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_weighted_iou_localization_loss(self):
losses_text_proto = """
localization_loss {
weighted_iou {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedIOULocalizationLoss)) 示例12: test_anchorwise_output
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_anchorwise_output(self):
losses_text_proto = """
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedSmoothL1LocalizationLoss))
predictions = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]])
targets = tf.constant([[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]])
weights = tf.constant([[1.0, 1.0]])
loss = localization_loss(predictions, targets, weights=weights)
self.assertEqual(loss.shape, [1, 2]) 示例13: test_build_weighted_sigmoid_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_weighted_sigmoid_classification_loss(self):
losses_text_proto = """
classification_loss {
weighted_sigmoid {
}
}
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(classification_loss,
losses.WeightedSigmoidClassificationLoss)) 示例14: test_build_weighted_sigmoid_focal_loss_non_default
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_weighted_sigmoid_focal_loss_non_default(self):
losses_text_proto = """
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 3.0
}
}
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(classification_loss,
losses.SigmoidFocalClassificationLoss))
self.assertAlmostEqual(classification_loss._alpha, 0.25)
self.assertAlmostEqual(classification_loss._gamma, 3.0) 示例15: test_build_weighted_softmax_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import Loss [as 别名]
def test_build_weighted_softmax_classification_loss(self):
losses_text_proto = """
classification_loss {
weighted_softmax {
}
}
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss))