Python object_detection.builders.losses_builder.build() Examples

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') 

def test_anchorwise_output(self):
    losses_text_proto = """
      classification_loss {
        weighted_sigmoid {
          anchorwise_output: true
        }
      }
      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))
    predictions = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.5, 0.5]]])
    targets = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]])
    weights = tf.constant([[1.0, 1.0]])
    loss = classification_loss(predictions, targets, weights=weights)
    self.assertEqual(loss.shape, [1, 2]) 

def build(model_config, is_training, add_summaries=True):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.
    add_summaries: Whether to add tensorflow summaries in the model graph.
  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training, add_summaries)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
                                    add_summaries)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture)) 

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) 

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) 

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)) 

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)) 

def test_build_weighted_sigmoid_focal_classification_loss(self):
    losses_text_proto = """
      classification_loss {
        weighted_sigmoid_focal {
        }
      }
      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, None)
    self.assertAlmostEqual(classification_loss._gamma, 2.0) 

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) 

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)) 

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)) 

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)) 

def test_anchorwise_output(self):
    losses_text_proto = """
      classification_loss {
        weighted_sigmoid {
          anchorwise_output: true
        }
      }
      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))
    predictions = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.5, 0.5]]])
    targets = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]])
    weights = tf.constant([[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]])
    loss = classification_loss(predictions, targets, weights=weights)
    self.assertEqual(loss.shape, [1, 2, 3]) 

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) 

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)) 

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)) 

def test_raise_error_when_both_focal_loss_and_hard_example_miner(self):
    losses_text_proto = """
      localization_loss {
        weighted_l2 {
        }
      }
      classification_loss {
        weighted_sigmoid_focal {
        }
      }
      hard_example_miner {
      }
      classification_weight: 0.8
      localization_weight: 0.2
    """
    losses_proto = losses_pb2.Loss()
    text_format.Merge(losses_text_proto, losses_proto)
    with self.assertRaises(ValueError):
      losses_builder.build(losses_proto) 

def build(model_config, is_training, add_summaries=True):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.
    add_summaries: Whether to add tensorflow summaries in the model graph.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training, add_summaries)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
                                    add_summaries)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture)) 

def test_raise_error_when_both_focal_loss_and_hard_example_miner(self):
    losses_text_proto = """
      localization_loss {
        weighted_l2 {
        }
      }
      classification_loss {
        weighted_sigmoid_focal {
        }
      }
      hard_example_miner {
      }
      classification_weight: 0.8
      localization_weight: 0.2
    """
    losses_proto = losses_pb2.Loss()
    text_format.Merge(losses_text_proto, losses_proto)
    with self.assertRaises(ValueError):
      losses_builder.build(losses_proto) 

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)) 

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) 

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) 

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]) 

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)) 

def test_build_weighted_sigmoid_focal_classification_loss(self):
    losses_text_proto = """
      classification_loss {
        weighted_sigmoid_focal {
        }
      }
      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, None)
    self.assertAlmostEqual(classification_loss._gamma, 2.0) 

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) 

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)) 

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)) 

def test_anchorwise_output(self):
    losses_text_proto = """
      classification_loss {
        weighted_sigmoid {
          anchorwise_output: true
        }
      }
      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))
    predictions = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.5, 0.5]]])
    targets = tf.constant([[[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]])
    weights = tf.constant([[1.0, 1.0]])
    loss = classification_loss(predictions, targets, weights=weights)
    self.assertEqual(loss.shape, [1, 2, 3]) 

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')