Python object_detection.utils.config_util.get_image_resizer_config() Examples

The following are 26 code examples of object_detection.utils.config_util.get_image_resizer_config(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module object_detection.utils.config_util , or try the search function .
Example #1
Source File: config_util_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
def  testGetImageResizerConfig(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #2
Source File: config_util_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def  testGetImageResizerConfig(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #3
Source File: config_util_test.py    From models with Apache License 2.0 5 votes vote down vote up 
def  testGetImageResizerConfig(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #4
Source File: config_util_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def  testGetImageResizerConfig(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #5
Source File: config_util_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
def  testGetImageResizerConfig(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #6
Source File: config_util_test.py    From AniSeg with Apache License 2.0 5 votes vote down vote up 
def  test_get_image_resizer_config(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #7
Source File: config_util_test.py    From Elphas with Apache License 2.0 5 votes vote down vote up 
def  test_get_image_resizer_config(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #8
Source File: config_util_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
def  testGetImageResizerConfig(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #9
Source File: config_util_test.py    From ros_tensorflow with Apache License 2.0 5 votes vote down vote up 
def  test_get_image_resizer_config(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #10
Source File: config_util_test.py    From Person-Detection-and-Tracking with MIT License 5 votes vote down vote up 
def  test_get_image_resizer_config(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #11
Source File: config_util_test.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def  testGetImageResizerConfig(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #12
Source File: config_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def  test_get_image_resizer_config(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #13
Source File: config_util_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def  test_get_image_resizer_config(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #14
Source File: config_util_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
def  test_get_image_resizer_config(self):
    """Tests that number of classes can be retrieved."""
    model_config = model_pb2.DetectionModel()
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
    model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
    image_resizer_config = config_util.get_image_resizer_config(model_config)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
    self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
Example #15
Source File: inputs.py    From vehicle_counting_tensorflow with MIT License 4 votes vote down vote up 
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
Example #16
Source File: inputs.py    From Elphas with Apache License 2.0 4 votes vote down vote up 
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
Example #17
Source File: inputs.py    From Traffic-Rule-Violation-Detection-System with MIT License 4 votes vote down vote up 
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
Example #18
Source File: inputs.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 4 votes vote down vote up 
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
Example #19
Source File: inputs.py    From MAX-Object-Detector with Apache License 2.0 4 votes vote down vote up 
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
Example #20
Source File: inputs.py    From Person-Detection-and-Tracking with MIT License 4 votes vote down vote up 
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
Example #21
Source File: inputs.py    From g-tensorflow-models with Apache License 2.0 4 votes vote down vote up 
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
Example #22
Source File: inputs.py    From Gun-Detector with Apache License 2.0 4 votes vote down vote up 
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
Example #23
Source File: inputs.py    From models with Apache License 2.0 4 votes vote down vote up 
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
Example #24
Source File: inputs.py    From ros_people_object_detection_tensorflow with Apache License 2.0 4 votes vote down vote up 
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
Example #25
Source File: inputs.py    From multilabel-image-classification-tensorflow with MIT License 4 votes vote down vote up 
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
Example #26
Source File: inputs.py    From ros_tensorflow with Apache License 2.0 4 votes vote down vote up 
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