Python config_util.get_number_of_classes方法代码示例

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_number_of_classes [as 别名]
def testGetNumberOfClasses(self):
    """Tests that number of classes can be retrieved."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 20
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    number_of_classes = config_util.get_number_of_classes(configs["model"])
    self.assertEqual(20, number_of_classes) 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_number_of_classes [as 别名]
def testUpdateNumClasses(self):
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10

    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)

    self.assertEqual(config_util.get_number_of_classes(configs["model"]), 10)

    config_util.merge_external_params_with_configs(
        configs, kwargs_dict={"num_classes": 2})

    self.assertEqual(config_util.get_number_of_classes(configs["model"]), 2) 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_number_of_classes [as 别名]
def create_predict_input_fn(model_config, predict_input_config):
  """Creates a predict `input` function for `Estimator`.

  Args:
    model_config: A model_pb2.DetectionModel.
    predict_input_config: An input_reader_pb2.InputReader.

  Returns:
    `input_fn` for `Estimator` in PREDICT mode.
  """

  def _predict_input_fn(params=None):
    """Decodes serialized tf.Examples and returns `ServingInputReceiver`.

    Args:
      params: Parameter dictionary passed from the estimator.

    Returns:
      `ServingInputReceiver`.
    """
    del params
    example = tf.placeholder(dtype=tf.string, shape=[], name='tf_example')

    num_classes = config_util.get_number_of_classes(model_config)
    model = model_builder.build(model_config, is_training=False)
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    image_resizer_fn = image_resizer_builder.build(image_resizer_config)

    transform_fn = functools.partial(
        transform_input_data, model_preprocess_fn=model.preprocess,
        image_resizer_fn=image_resizer_fn,
        num_classes=num_classes,
        data_augmentation_fn=None)

    decoder = tf_example_decoder.TfExampleDecoder(
        load_instance_masks=False,
        num_additional_channels=predict_input_config.num_additional_channels)
    input_dict = transform_fn(decoder.decode(example))
    images = tf.to_float(input_dict[fields.InputDataFields.image])
    images = tf.expand_dims(images, axis=0)
    true_image_shape = tf.expand_dims(
        input_dict[fields.InputDataFields.true_image_shape], axis=0)

    return tf.estimator.export.ServingInputReceiver(
        features={
            fields.InputDataFields.image: images,
            fields.InputDataFields.true_image_shape: true_image_shape},
        receiver_tensors={SERVING_FED_EXAMPLE_KEY: example})

  return _predict_input_fn 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_number_of_classes [as 别名]
def create_predict_input_fn(model_config):
  """Creates a predict `input` function for `Estimator`.

  Args:
    model_config: A model_pb2.DetectionModel.

  Returns:
    `input_fn` for `Estimator` in PREDICT mode.
  """

  def _predict_input_fn(params=None):
    """Decodes serialized tf.Examples and returns `ServingInputReceiver`.

    Args:
      params: Parameter dictionary passed from the estimator.

    Returns:
      `ServingInputReceiver`.
    """
    del params
    example = tf.placeholder(dtype=tf.string, shape=[], name='input_feature')

    num_classes = config_util.get_number_of_classes(model_config)
    model = model_builder.build(model_config, is_training=False)
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    image_resizer_fn = image_resizer_builder.build(image_resizer_config)

    transform_fn = functools.partial(
        transform_input_data, model_preprocess_fn=model.preprocess,
        image_resizer_fn=image_resizer_fn,
        num_classes=num_classes,
        data_augmentation_fn=None)

    decoder = tf_example_decoder.TfExampleDecoder(load_instance_masks=False)
    input_dict = transform_fn(decoder.decode(example))
    images = tf.to_float(input_dict[fields.InputDataFields.image])
    images = tf.expand_dims(images, axis=0)
    true_image_shape = tf.expand_dims(
        input_dict[fields.InputDataFields.true_image_shape], axis=0)

    return tf.estimator.export.ServingInputReceiver(
        features={
            fields.InputDataFields.image: images,
            fields.InputDataFields.true_image_shape: true_image_shape},
        receiver_tensors={SERVING_FED_EXAMPLE_KEY: example})

  return _predict_input_fn 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_number_of_classes [as 别名]
def create_predict_input_fn(model_config):
  """Creates a predict `input` function for `Estimator`.

  Args:
    model_config: A model_pb2.DetectionModel.

  Returns:
    `input_fn` for `Estimator` in PREDICT mode.
  """

  def _predict_input_fn(params=None):
    """Decodes serialized tf.Examples and returns `ServingInputReceiver`.

    Args:
      params: Parameter dictionary passed from the estimator.

    Returns:
      `ServingInputReceiver`.
    """
    del params
    example = tf.placeholder(dtype=tf.string, shape=[], name='input_feature')

    num_classes = config_util.get_number_of_classes(model_config)
    model = model_builder.build(model_config, is_training=False)
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    image_resizer_fn = image_resizer_builder.build(image_resizer_config)

    transform_fn = functools.partial(
        transform_input_data, model_preprocess_fn=model.preprocess,
        image_resizer_fn=image_resizer_fn,
        num_classes=num_classes,
        data_augmentation_fn=None)

    decoder = tf_example_decoder.TfExampleDecoder(load_instance_masks=False)
    input_dict = transform_fn(decoder.decode(example))
    images = tf.to_float(input_dict[fields.InputDataFields.image])
    images = tf.expand_dims(images, axis=0)

    return tf.estimator.export.ServingInputReceiver(
        features={fields.InputDataFields.image: images},
        receiver_tensors={SERVING_FED_EXAMPLE_KEY: example})

  return _predict_input_fn 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_number_of_classes [as 别名]
def create_predict_input_fn(model_config):
    """Creates a predict `input` function for `Estimator`.

    Args:
      model_config: A model_pb2.DetectionModel.

    Returns:
      `input_fn` for `Estimator` in PREDICT mode.
    """

    def _predict_input_fn(params=None):
        """Decodes serialized tf.Examples and returns `ServingInputReceiver`.

        Args:
          params: Parameter dictionary passed from the estimator.

        Returns:
          `ServingInputReceiver`.
        """
        del params
        example = tf.placeholder(
            dtype=tf.string,
            shape=[],
            name='input_feature')

        num_classes = config_util.get_number_of_classes(model_config)
        model = model_builder.build(model_config, is_training=False)
        image_resizer_config = config_util.get_image_resizer_config(
            model_config)
        image_resizer_fn = image_resizer_builder.build(image_resizer_config)

        transform_fn = functools.partial(
            transform_input_data, model_preprocess_fn=model.preprocess,
            image_resizer_fn=image_resizer_fn,
            num_classes=num_classes,
            data_augmentation_fn=None)

        decoder = tf_example_decoder.TfExampleDecoder(
            load_instance_masks=False)
        input_dict = transform_fn(decoder.decode(example))
        images = tf.to_float(input_dict[fields.InputDataFields.image])
        images = tf.expand_dims(images, axis=0)

        return tf.estimator.export.ServingInputReceiver(
            features={fields.InputDataFields.image: images},
            receiver_tensors={SERVING_FED_EXAMPLE_KEY: example})

    return _predict_input_fn 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_number_of_classes [as 别名]
def create_predict_input_fn(model_config, predict_input_config):
  """Creates a predict `input` function for `Estimator`.

  Args:
    model_config: A model_pb2.DetectionModel.
    predict_input_config: An input_reader_pb2.InputReader.

  Returns:
    `input_fn` for `Estimator` in PREDICT mode.
  """

  def _predict_input_fn(params=None):
    """Decodes serialized tf.Examples and returns `ServingInputReceiver`.

    Args:
      params: Parameter dictionary passed from the estimator.

    Returns:
      `ServingInputReceiver`.
    """
    del params
    example = tf.placeholder(dtype=tf.string, shape=[], name='tf_example')

    num_classes = config_util.get_number_of_classes(model_config)
    model_preprocess_fn = INPUT_BUILDER_UTIL_MAP['model_build'](
        model_config, is_training=False).preprocess

    image_resizer_config = config_util.get_image_resizer_config(model_config)
    image_resizer_fn = image_resizer_builder.build(image_resizer_config)

    transform_fn = functools.partial(
        transform_input_data, model_preprocess_fn=model_preprocess_fn,
        image_resizer_fn=image_resizer_fn,
        num_classes=num_classes,
        data_augmentation_fn=None)

    decoder = tf_example_decoder.TfExampleDecoder(
        load_instance_masks=False,
        num_additional_channels=predict_input_config.num_additional_channels)
    input_dict = transform_fn(decoder.decode(example))
    images = tf.cast(input_dict[fields.InputDataFields.image], dtype=tf.float32)
    images = tf.expand_dims(images, axis=0)
    true_image_shape = tf.expand_dims(
        input_dict[fields.InputDataFields.true_image_shape], axis=0)

    return tf.estimator.export.ServingInputReceiver(
        features={
            fields.InputDataFields.image: images,
            fields.InputDataFields.true_image_shape: true_image_shape},
        receiver_tensors={SERVING_FED_EXAMPLE_KEY: example})

  return _predict_input_fn