Python losses.WeightedSoftmaxClassificationAgainstLogitsLoss方法代碼示例

本文整理匯總了Python中object_detection.core.losses.WeightedSoftmaxClassificationAgainstLogitsLoss方法的典型用法代碼示例。如果您正苦於以下問題：Python losses.WeightedSoftmaxClassificationAgainstLogitsLoss方法的具體用法？Python losses.WeightedSoftmaxClassificationAgainstLogitsLoss怎麽用？Python losses.WeightedSoftmaxClassificationAgainstLogitsLoss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.WeightedSoftmaxClassificationAgainstLogitsLoss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_weighted_logits_softmax_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 WeightedSoftmaxClassificationAgainstLogitsLoss [as 別名]
def test_build_weighted_logits_softmax_classification_loss(self):
    losses_text_proto = """
      classification_loss {
        weighted_logits_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.WeightedSoftmaxClassificationAgainstLogitsLoss))

開發者ID:ShivangShekhar，項目名稱:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代碼行數:19，代碼來源:losses_builder_test.py

示例2: testReturnsCorrectLoss

# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationAgainstLogitsLoss [as 別名]
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([[[-100, 100, -100],
                                  [100, -100, -100],
                                  [100, -100, -100],
                                  [-100, -100, 100]],
                                 [[-100, -100, 100],
                                  [-100, 100, -100],
                                  [-100, 100, -100],
                                  [100, -100, -100]]], tf.float32)
    weights = tf.constant([[1, 1, .5, 1],
                           [1, 1, 1, 1]], tf.float32)
    weights_shape = tf.shape(weights)
    weights_multiple = tf.concat(
        [tf.ones_like(weights_shape), tf.constant([3])],
        axis=0)
    weights = tf.tile(tf.expand_dims(weights, 2), weights_multiple)
    loss_op = losses.WeightedSoftmaxClassificationAgainstLogitsLoss()
    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)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:35，代碼來源:losses_test.py

示例3: testReturnsCorrectAnchorWiseLoss

# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationAgainstLogitsLoss [as 別名]
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([[[-100, 100, -100],
                                  [100, -100, -100],
                                  [100, -100, -100],
                                  [-100, -100, 100]],
                                 [[-100, -100, 100],
                                  [-100, 100, -100],
                                  [-100, 100, -100],
                                  [100, -100, -100]]], tf.float32)
    weights = tf.constant([[1, 1, .5, 1],
                           [1, 1, 1, 0]], tf.float32)
    weights_shape = tf.shape(weights)
    weights_multiple = tf.concat(
        [tf.ones_like(weights_shape), tf.constant([3])],
        axis=0)
    weights = tf.tile(tf.expand_dims(weights, 2), weights_multiple)
    loss_op = losses.WeightedSoftmaxClassificationAgainstLogitsLoss()
    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)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:34，代碼來源:losses_test.py

示例4: build_faster_rcnn_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 WeightedSoftmaxClassificationAgainstLogitsLoss [as 別名]
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)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:43，代碼來源:losses_builder.py

示例5: test_build_logits_softmax_loss

# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationAgainstLogitsLoss [as 別名]
def test_build_logits_softmax_loss(self):
    losses_text_proto = """
      weighted_logits_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.WeightedSoftmaxClassificationAgainstLogitsLoss))

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:14，代碼來源:losses_builder_test.py

示例6: testReturnsCorrectLoss

# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationAgainstLogitsLoss [as 別名]
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([[[-100, 100, -100],
                                  [100, -100, -100],
                                  [100, -100, -100],
                                  [-100, -100, 100]],
                                 [[-100, -100, 100],
                                  [-100, 100, -100],
                                  [-100, 100, -100],
                                  [100, -100, -100]]], tf.float32)
    weights = tf.constant([[1, 1, .5, 1],
                           [1, 1, 1, 1]], tf.float32)
    loss_op = losses.WeightedSoftmaxClassificationAgainstLogitsLoss()
    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)

開發者ID:ambakick，項目名稱:Person-Detection-and-Tracking，代碼行數:30，代碼來源:losses_test.py

示例7: testReturnsCorrectAnchorWiseLoss

# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationAgainstLogitsLoss [as 別名]
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([[[-100, 100, -100],
                                  [100, -100, -100],
                                  [100, -100, -100],
                                  [-100, -100, 100]],
                                 [[-100, -100, 100],
                                  [-100, 100, -100],
                                  [-100, 100, -100],
                                  [100, -100, -100]]], tf.float32)
    weights = tf.constant([[1, 1, .5, 1],
                           [1, 1, 1, 0]], tf.float32)
    loss_op = losses.WeightedSoftmaxClassificationAgainstLogitsLoss()
    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)

開發者ID:ambakick，項目名稱:Person-Detection-and-Tracking，代碼行數:29，代碼來源:losses_test.py

示例8: build_faster_rcnn_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 WeightedSoftmaxClassificationAgainstLogitsLoss [as 別名]
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)

  # 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)

開發者ID:ambakick，項目名稱:Person-Detection-and-Tracking，代碼行數:35，代碼來源:losses_builder.py

示例9: testReturnsCorrectLoss

# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationAgainstLogitsLoss [as 別名]
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([[[-100, 100, -100],
                                      [100, -100, -100],
                                      [100, -100, -100],
                                      [-100, -100, 100]],
                                     [[-100, -100, 100],
                                      [-100, 100, -100],
                                      [-100, 100, -100],
                                      [100, -100, -100]]], tf.float32)
        weights = tf.constant([[1, 1, .5, 1],
                               [1, 1, 1, 1]], tf.float32)
        loss_op = losses.WeightedSoftmaxClassificationAgainstLogitsLoss()
        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)

開發者ID:kujason，項目名稱:monopsr，代碼行數:30，代碼來源:losses_test.py

示例10: testReturnsCorrectAnchorWiseLoss

# 需要導入模塊: from object_detection.core import losses [as 別名]
# 或者: from object_detection.core.losses import WeightedSoftmaxClassificationAgainstLogitsLoss [as 別名]
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([[[-100, 100, -100],
                                      [100, -100, -100],
                                      [100, -100, -100],
                                      [-100, -100, 100]],
                                     [[-100, -100, 100],
                                      [-100, 100, -100],
                                      [-100, 100, -100],
                                      [100, -100, -100]]], tf.float32)
        weights = tf.constant([[1, 1, .5, 1],
                               [1, 1, 1, 0]], tf.float32)
        loss_op = losses.WeightedSoftmaxClassificationAgainstLogitsLoss()
        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)

開發者ID:kujason，項目名稱:monopsr，代碼行數:29，代碼來源:losses_test.py

注：本文中的object_detection.core.losses.WeightedSoftmaxClassificationAgainstLogitsLoss方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。