本文整理匯總了Python中object_detection.core.losses.WeightedSmoothL1LocalizationLoss方法的典型用法代碼示例。如果您正苦於以下問題：Python losses.WeightedSmoothL1LocalizationLoss方法的具體用法？Python losses.WeightedSmoothL1LocalizationLoss怎麽用？Python losses.WeightedSmoothL1LocalizationLoss使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.core.losses的用法示例。
在下文中一共展示了losses.WeightedSmoothL1LocalizationLoss方法的10個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: testReturnsCorrectLoss
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSmoothL1LocalizationLoss [as 別名]
def testReturnsCorrectLoss(self):
batch_size = 2
num_anchors = 3
code_size = 4
prediction_tensor = tf.constant([[[2.5, 0, .4, 0],
[0, 0, 0, 0],
[0, 2.5, 0, .4]],
[[3.5, 0, 0, 0],
[0, .4, 0, .9],
[0, 0, 1.5, 0]]], tf.float32)
target_tensor = tf.zeros([batch_size, num_anchors, code_size])
weights = tf.constant([[2, 1, 1],
[0, 3, 0]], tf.float32)
loss_op = losses.WeightedSmoothL1LocalizationLoss()
loss = loss_op(prediction_tensor, target_tensor, weights=weights)
exp_loss = 7.695
with self.test_session() as sess:
loss_output = sess.run(loss)
self.assertAllClose(loss_output, exp_loss) 示例2: test_build_weighted_smooth_l1_localization_loss
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSmoothL1LocalizationLoss [as 別名]
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: testReturnsCorrectLoss
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSmoothL1LocalizationLoss [as 別名]
def testReturnsCorrectLoss(self):
batch_size = 2
num_anchors = 3
code_size = 4
prediction_tensor = tf.constant([[[2.5, 0, .4, 0],
[0, 0, 0, 0],
[0, 2.5, 0, .4]],
[[3.5, 0, 0, 0],
[0, .4, 0, .9],
[0, 0, 1.5, 0]]], tf.float32)
target_tensor = tf.zeros([batch_size, num_anchors, code_size])
weights = tf.constant([[2, 1, 1],
[0, 3, 0]], tf.float32)
loss_op = losses.WeightedSmoothL1LocalizationLoss()
loss = loss_op(prediction_tensor, target_tensor, weights=weights)
loss = tf.reduce_sum(loss)
exp_loss = 7.695
with self.test_session() as sess:
loss_output = sess.run(loss)
self.assertAllClose(loss_output, exp_loss) 示例4: test_build_weighted_smooth_l1_localization_loss_default_delta
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSmoothL1LocalizationLoss [as 別名]
def test_build_weighted_smooth_l1_localization_loss_default_delta(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))
self.assertAlmostEqual(localization_loss._delta, 1.0) 示例5: test_anchorwise_output
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSmoothL1LocalizationLoss [as 別名]
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6: test_build_weighted_smooth_l1_localization_loss_default_delta
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSmoothL1LocalizationLoss [as 別名]
def test_build_weighted_smooth_l1_localization_loss_default_delta(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))
self.assertAlmostEqual(localization_loss._delta, 1.0) 示例7: test_anchorwise_output
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSmoothL1LocalizationLoss [as 別名]
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8: test_build_weighted_smooth_l1_localization_loss_non_default_delta
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSmoothL1LocalizationLoss [as 別名]
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9: test_anchorwise_output
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSmoothL1LocalizationLoss [as 別名]
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]) 開發者ID:PacktPublishing，項目名稱:Hands-On-Machine-Learning-with-OpenCV-4，代碼行數:24，代碼來源:losses_builder_test.py
示例10: test_build_weighted_smooth_l1_localization_loss_non_default_delta
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSmoothL1LocalizationLoss [as 別名]
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)