本文整理匯總了Python中object_detection.core.losses.HardExampleMiner方法的典型用法代碼示例。如果您正苦於以下問題：Python losses.HardExampleMiner方法的具體用法？Python losses.HardExampleMiner怎麽用？Python losses.HardExampleMiner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.HardExampleMiner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: test_build_hard_example_miner_for_classification_loss
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import HardExampleMiner [as 別名]
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2: test_build_hard_example_miner_for_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 HardExampleMiner [as 別名]
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3: test_build_hard_example_miner_for_classification_loss
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import HardExampleMiner [as 別名]
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4: test_build_hard_example_miner_for_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 HardExampleMiner [as 別名]
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5: testHardMiningWithSingleLossType
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import HardExampleMiner [as 別名]
def testHardMiningWithSingleLossType(self):
location_losses = tf.constant([[100, 90, 80, 0],
[0, 1, 2, 3]], tf.float32)
cls_losses = tf.constant([[0, 10, 50, 110],
[9, 6, 3, 0]], tf.float32)
box_corners = tf.constant([[0.1, 0.1, 0.9, 0.9],
[0.1, 0.1, 0.9, 0.9],
[0.1, 0.1, 0.9, 0.9],
[0.1, 0.1, 0.9, 0.9]], tf.float32)
decoded_boxlist_list = []
decoded_boxlist_list.append(box_list.BoxList(box_corners))
decoded_boxlist_list.append(box_list.BoxList(box_corners))
# Uses only location loss to select hard examples
loss_op = losses.HardExampleMiner(num_hard_examples=1,
iou_threshold=0.0,
loss_type='loc',
cls_loss_weight=1,
loc_loss_weight=1)
(loc_loss, cls_loss) = loss_op(location_losses, cls_losses,
decoded_boxlist_list)
exp_loc_loss = 100 + 3
exp_cls_loss = 0 + 0
with self.test_session() as sess:
loc_loss_output = sess.run(loc_loss)
self.assertAllClose(loc_loss_output, exp_loc_loss)
cls_loss_output = sess.run(cls_loss)
self.assertAllClose(cls_loss_output, exp_cls_loss) 示例6: testHardMiningWithBothLossType
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import HardExampleMiner [as 別名]
def testHardMiningWithBothLossType(self):
location_losses = tf.constant([[100, 90, 80, 0],
[0, 1, 2, 3]], tf.float32)
cls_losses = tf.constant([[0, 10, 50, 110],
[9, 6, 3, 0]], tf.float32)
box_corners = tf.constant([[0.1, 0.1, 0.9, 0.9],
[0.1, 0.1, 0.9, 0.9],
[0.1, 0.1, 0.9, 0.9],
[0.1, 0.1, 0.9, 0.9]], tf.float32)
decoded_boxlist_list = []
decoded_boxlist_list.append(box_list.BoxList(box_corners))
decoded_boxlist_list.append(box_list.BoxList(box_corners))
loss_op = losses.HardExampleMiner(num_hard_examples=1,
iou_threshold=0.0,
loss_type='both',
cls_loss_weight=1,
loc_loss_weight=1)
(loc_loss, cls_loss) = loss_op(location_losses, cls_losses,
decoded_boxlist_list)
exp_loc_loss = 80 + 0
exp_cls_loss = 50 + 9
with self.test_session() as sess:
loc_loss_output = sess.run(loc_loss)
self.assertAllClose(loc_loss_output, exp_loc_loss)
cls_loss_output = sess.run(cls_loss)
self.assertAllClose(cls_loss_output, exp_cls_loss) 示例7: testHardMiningNMS
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import HardExampleMiner [as 別名]
def testHardMiningNMS(self):
location_losses = tf.constant([[100, 90, 80, 0],
[0, 1, 2, 3]], tf.float32)
cls_losses = tf.constant([[0, 10, 50, 110],
[9, 6, 3, 0]], tf.float32)
box_corners = tf.constant([[0.1, 0.1, 0.9, 0.9],
[0.9, 0.9, 0.99, 0.99],
[0.1, 0.1, 0.9, 0.9],
[0.1, 0.1, 0.9, 0.9]], tf.float32)
decoded_boxlist_list = []
decoded_boxlist_list.append(box_list.BoxList(box_corners))
decoded_boxlist_list.append(box_list.BoxList(box_corners))
loss_op = losses.HardExampleMiner(num_hard_examples=2,
iou_threshold=0.5,
loss_type='cls',
cls_loss_weight=1,
loc_loss_weight=1)
(loc_loss, cls_loss) = loss_op(location_losses, cls_losses,
decoded_boxlist_list)
exp_loc_loss = 0 + 90 + 0 + 1
exp_cls_loss = 110 + 10 + 9 + 6
with self.test_session() as sess:
loc_loss_output = sess.run(loc_loss)
self.assertAllClose(loc_loss_output, exp_loc_loss)
cls_loss_output = sess.run(cls_loss)
self.assertAllClose(cls_loss_output, exp_cls_loss) 示例8: build_hard_example_miner
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import HardExampleMiner [as 別名]
def build_hard_example_miner(config,
classification_weight,
localization_weight):
"""Builds hard example miner based on the config.
Args:
config: A losses_pb2.HardExampleMiner object.
classification_weight: Classification loss weight.
localization_weight: Localization loss weight.
Returns:
Hard example miner.
"""
loss_type = None
if config.loss_type == losses_pb2.HardExampleMiner.BOTH:
loss_type = 'both'
if config.loss_type == losses_pb2.HardExampleMiner.CLASSIFICATION:
loss_type = 'cls'
if config.loss_type == losses_pb2.HardExampleMiner.LOCALIZATION:
loss_type = 'loc'
max_negatives_per_positive = None
num_hard_examples = None
if config.max_negatives_per_positive > 0:
max_negatives_per_positive = config.max_negatives_per_positive
if config.num_hard_examples > 0:
num_hard_examples = config.num_hard_examples
hard_example_miner = losses.HardExampleMiner(
num_hard_examples=num_hard_examples,
iou_threshold=config.iou_threshold,
loss_type=loss_type,
cls_loss_weight=classification_weight,
loc_loss_weight=localization_weight,
max_negatives_per_positive=max_negatives_per_positive,
min_negatives_per_image=config.min_negatives_per_image)
return hard_example_miner 示例9: test_build_hard_example_miner_with_non_default_values
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import HardExampleMiner [as 別名]
def test_build_hard_example_miner_with_non_default_values(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_softmax {
}
}
hard_example_miner {
num_hard_examples: 32
iou_threshold: 0.5
loss_type: LOCALIZATION
max_negatives_per_positive: 10
min_negatives_per_image: 3
}
"""
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._num_hard_examples, 32)
self.assertAlmostEqual(hard_example_miner._iou_threshold, 0.5)
self.assertEqual(hard_example_miner._max_negatives_per_positive, 10)
self.assertEqual(hard_example_miner._min_negatives_per_image, 3) 示例10: test_build_all_loss_parameters
# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import HardExampleMiner [as 別名]
def test_build_all_loss_parameters(self):
losses_text_proto = """
localization_loss {
weighted_l2 {
}
}
classification_loss {
weighted_softmax {
}
}
hard_example_miner {
}
classification_weight: 0.8
localization_weight: 0.2
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
(classification_loss, localization_loss,
classification_weight, localization_weight,
hard_example_miner) = losses_builder.build(losses_proto)
self.assertTrue(isinstance(hard_example_miner, losses.HardExampleMiner))
self.assertTrue(isinstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss))
self.assertTrue(isinstance(localization_loss,
losses.WeightedL2LocalizationLoss))
self.assertAlmostEqual(classification_weight, 0.8)
self.assertAlmostEqual(localization_weight, 0.2)