Python losses.WeightedSoftmaxClassificationLoss方法代码示例

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [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)) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [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)) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
def test_build_weighted_softmax_classification_loss_with_logit_scale(self):
    losses_text_proto = """
      classification_loss {
        weighted_softmax {
          logit_scale: 2.0
        }
      }
      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.WeightedSoftmaxClassificationLoss)) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
def test_build_weighted_softmax_classification_loss_with_logit_scale(self):
    losses_text_proto = """
      classification_loss {
        weighted_softmax {
          logit_scale: 2.0
        }
      }
      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.WeightedSoftmaxClassificationLoss)) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
def testReturnsCorrectLoss(self):
    prediction_tensor = tf.constant([[[-100, 100, -100],
                                      [100, -100, -100],
                                      [0, 0, -100],
                                      [-100, -100, 100]],
                                     [[-100, 0, 0],
                                      [-100, 100, -100],
                                      [-100, 100, -100],
                                      [100, -100, -100]]], tf.float32)
    target_tensor = tf.constant([[[0, 1, 0],
                                  [1, 0, 0],
                                  [1, 0, 0],
                                  [0, 0, 1]],
                                 [[0, 0, 1],
                                  [0, 1, 0],
                                  [0, 1, 0],
                                  [1, 0, 0]]], tf.float32)
    weights = tf.constant([[1, 1, .5, 1],
                           [1, 1, 1, 0]], tf.float32)
    loss_op = losses.WeightedSoftmaxClassificationLoss()
    loss = loss_op(prediction_tensor, target_tensor, weights=weights)

    exp_loss = - 1.5 * math.log(.5)
    with self.test_session() as sess:
      loss_output = sess.run(loss)
      self.assertAllClose(loss_output, exp_loss) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
def testReturnsCorrectAnchorWiseLoss(self):
    prediction_tensor = tf.constant([[[-100, 100, -100],
                                      [100, -100, -100],
                                      [0, 0, -100],
                                      [-100, -100, 100]],
                                     [[-100, 0, 0],
                                      [-100, 100, -100],
                                      [-100, 100, -100],
                                      [100, -100, -100]]], tf.float32)
    target_tensor = tf.constant([[[0, 1, 0],
                                  [1, 0, 0],
                                  [1, 0, 0],
                                  [0, 0, 1]],
                                 [[0, 0, 1],
                                  [0, 1, 0],
                                  [0, 1, 0],
                                  [1, 0, 0]]], tf.float32)
    weights = tf.constant([[1, 1, .5, 1],
                           [1, 1, 1, 0]], tf.float32)
    loss_op = losses.WeightedSoftmaxClassificationLoss(True)
    loss = loss_op(prediction_tensor, target_tensor, weights=weights)

    exp_loss = np.matrix([[0, 0, - 0.5 * math.log(.5), 0],
                          [-math.log(.5), 0, 0, 0]])
    with self.test_session() as sess:
      loss_output = sess.run(loss)
      self.assertAllClose(loss_output, exp_loss) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
def _build_classification_loss(loss_config):
  """Builds a classification loss based on the loss config.

  Args:
    loss_config: A losses_pb2.ClassificationLoss object.

  Returns:
    Loss based on the config.

  Raises:
    ValueError: On invalid loss_config.
  """
  if not isinstance(loss_config, losses_pb2.ClassificationLoss):
    raise ValueError('loss_config not of type losses_pb2.ClassificationLoss.')

  loss_type = loss_config.WhichOneof('classification_loss')

  if loss_type == 'weighted_sigmoid':
    config = loss_config.weighted_sigmoid
    return losses.WeightedSigmoidClassificationLoss(
        anchorwise_output=config.anchorwise_output)

  if loss_type == 'weighted_softmax':
    config = loss_config.weighted_softmax
    return losses.WeightedSoftmaxClassificationLoss(
        anchorwise_output=config.anchorwise_output)

  if loss_type == 'bootstrapped_sigmoid':
    config = loss_config.bootstrapped_sigmoid
    return losses.BootstrappedSigmoidClassificationLoss(
        alpha=config.alpha,
        bootstrap_type=('hard' if config.hard_bootstrap else 'soft'),
        anchorwise_output=config.anchorwise_output)

  raise ValueError('Empty loss config.') 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
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) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
def testReturnsCorrectLoss(self):
    prediction_tensor = tf.constant([[[-100, 100, -100],
                                      [100, -100, -100],
                                      [0, 0, -100],
                                      [-100, -100, 100]],
                                     [[-100, 0, 0],
                                      [-100, 100, -100],
                                      [-100, 100, -100],
                                      [100, -100, -100]]], tf.float32)
    target_tensor = tf.constant([[[0, 1, 0],
                                  [1, 0, 0],
                                  [1, 0, 0],
                                  [0, 0, 1]],
                                 [[0, 0, 1],
                                  [0, 1, 0],
                                  [0, 1, 0],
                                  [1, 0, 0]]], tf.float32)
    weights = tf.constant([[[1, 1, 1],
                            [1, 1, 1],
                            [0.5, 0.5, 0.5],
                            [1, 1, 1]],
                           [[1, 1, 1],
                            [1, 1, 1],
                            [1, 1, 1],
                            [0, 0, 0]]], tf.float32)
    loss_op = losses.WeightedSoftmaxClassificationLoss()
    loss = loss_op(prediction_tensor, target_tensor, weights=weights)
    loss = tf.reduce_sum(loss)

    exp_loss = - 1.5 * math.log(.5)
    with self.test_session() as sess:
      loss_output = sess.run(loss)
      self.assertAllClose(loss_output, exp_loss) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
def testReturnsCorrectAnchorWiseLoss(self):
    prediction_tensor = tf.constant([[[-100, 100, -100],
                                      [100, -100, -100],
                                      [0, 0, -100],
                                      [-100, -100, 100]],
                                     [[-100, 0, 0],
                                      [-100, 100, -100],
                                      [-100, 100, -100],
                                      [100, -100, -100]]], tf.float32)
    target_tensor = tf.constant([[[0, 1, 0],
                                  [1, 0, 0],
                                  [1, 0, 0],
                                  [0, 0, 1]],
                                 [[0, 0, 1],
                                  [0, 1, 0],
                                  [0, 1, 0],
                                  [1, 0, 0]]], tf.float32)
    weights = tf.constant([[[1, 1, 1],
                            [1, 1, 1],
                            [0.5, 0.5, 0.5],
                            [1, 1, 1]],
                           [[1, 1, 1],
                            [1, 1, 1],
                            [1, 1, 1],
                            [0, 0, 0]]], tf.float32)
    loss_op = losses.WeightedSoftmaxClassificationLoss()
    loss = loss_op(prediction_tensor, target_tensor, weights=weights)

    exp_loss = np.matrix([[0, 0, - 0.5 * math.log(.5), 0],
                          [-math.log(.5), 0, 0, 0]])
    with self.test_session() as sess:
      loss_output = sess.run(loss)
      self.assertAllClose(loss_output, exp_loss) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
def testReturnsCorrectAnchorWiseLossWithHighLogitScaleSetting(self):
    """At very high logit_scale, all predictions will be ~0.33."""
    # TODO(yonib): Also test logit_scale with anchorwise=False.
    logit_scale = 10e16
    prediction_tensor = tf.constant([[[-100, 100, -100],
                                      [100, -100, -100],
                                      [0, 0, -100],
                                      [-100, -100, 100]],
                                     [[-100, 0, 0],
                                      [-100, 100, -100],
                                      [-100, 100, -100],
                                      [100, -100, -100]]], tf.float32)
    target_tensor = tf.constant([[[0, 1, 0],
                                  [1, 0, 0],
                                  [1, 0, 0],
                                  [0, 0, 1]],
                                 [[0, 0, 1],
                                  [0, 1, 0],
                                  [0, 1, 0],
                                  [1, 0, 0]]], tf.float32)
    weights = tf.constant([[[1, 1, 1],
                            [1, 1, 1],
                            [1, 1, 1],
                            [1, 1, 1]],
                           [[1, 1, 1],
                            [1, 1, 1],
                            [1, 1, 1],
                            [1, 1, 1]]], tf.float32)
    loss_op = losses.WeightedSoftmaxClassificationLoss(logit_scale=logit_scale)
    loss = loss_op(prediction_tensor, target_tensor, weights=weights)

    uniform_distribution_loss = - math.log(.33333333333)
    exp_loss = np.matrix([[uniform_distribution_loss] * 4,
                          [uniform_distribution_loss] * 4])
    with self.test_session() as sess:
      loss_output = sess.run(loss)
      self.assertAllClose(loss_output, exp_loss) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
def build_faster_rcnn_classification_loss(loss_config):
  """Builds a classification loss for Faster RCNN based on the loss config.

  Args:
    loss_config: A losses_pb2.ClassificationLoss object.

  Returns:
    Loss based on the config.

  Raises:
    ValueError: On invalid loss_config.
  """
  if not isinstance(loss_config, losses_pb2.ClassificationLoss):
    raise ValueError('loss_config not of type losses_pb2.ClassificationLoss.')

  loss_type = loss_config.WhichOneof('classification_loss')

  if loss_type == 'weighted_sigmoid':
    return losses.WeightedSigmoidClassificationLoss()
  if loss_type == 'weighted_softmax':
    config = loss_config.weighted_softmax
    return losses.WeightedSoftmaxClassificationLoss(
        logit_scale=config.logit_scale)
  if loss_type == 'weighted_logits_softmax':
    config = loss_config.weighted_logits_softmax
    return losses.WeightedSoftmaxClassificationAgainstLogitsLoss(
        logit_scale=config.logit_scale)
  if loss_type == 'weighted_sigmoid_focal':
    config = loss_config.weighted_sigmoid_focal
    alpha = None
    if config.HasField('alpha'):
      alpha = config.alpha
    return losses.SigmoidFocalClassificationLoss(
        gamma=config.gamma,
        alpha=alpha)

  # By default, Faster RCNN second stage classifier uses Softmax loss
  # with anchor-wise outputs.
  config = loss_config.weighted_softmax
  return losses.WeightedSoftmaxClassificationLoss(
      logit_scale=config.logit_scale) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
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) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
def test_build_softmax_loss(self):
    losses_text_proto = """
      weighted_softmax {
      }
    """
    losses_proto = losses_pb2.ClassificationLoss()
    text_format.Merge(losses_text_proto, losses_proto)
    classification_loss = losses_builder.build_faster_rcnn_classification_loss(
        losses_proto)
    self.assertTrue(isinstance(classification_loss,
                               losses.WeightedSoftmaxClassificationLoss)) 

# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationLoss [as 别名]
def test_build_softmax_loss_by_default(self):
    losses_text_proto = """
    """
    losses_proto = losses_pb2.ClassificationLoss()
    text_format.Merge(losses_text_proto, losses_proto)
    classification_loss = losses_builder.build_faster_rcnn_classification_loss(
        losses_proto)
    self.assertTrue(isinstance(classification_loss,
                               losses.WeightedSoftmaxClassificationLoss))