Python object_detection.utils.shape_utils.combined_static_and_dynamic_shape() Examples

def _flatten_first_two_dimensions(self, inputs):
    """Flattens `K-d` tensor along batch dimension to be a `(K-1)-d` tensor.

    Converts `inputs` with shape [A, B, ..., depth] into a tensor of shape
    [A * B, ..., depth].

    Args:
      inputs: A float tensor with shape [A, B, ..., depth].  Note that the first
        two and last dimensions must be statically defined.
    Returns:
      A float tensor with shape [A * B, ..., depth] (where the first and last
        dimension are statically defined.
    """
    combined_shape = shape_utils.combined_static_and_dynamic_shape(inputs)
    flattened_shape = tf.stack([combined_shape[0] * combined_shape[1]] +
                               combined_shape[2:])
    return tf.reshape(inputs, flattened_shape) 

def matmul_gather_on_zeroth_axis(params, indices, scope=None):
  """Matrix multiplication based implementation of tf.gather on zeroth axis.

  TODO(rathodv, jonathanhuang): enable sparse matmul option.

  Args:
    params: A float32 Tensor. The tensor from which to gather values.
      Must be at least rank 1.
    indices: A Tensor. Must be one of the following types: int32, int64.
      Must be in range [0, params.shape[0])
    scope: A name for the operation (optional).

  Returns:
    A Tensor. Has the same type as params. Values from params gathered
    from indices given by indices, with shape indices.shape + params.shape[1:].
  """
  with tf.name_scope(scope, 'MatMulGather'):
    params_shape = shape_utils.combined_static_and_dynamic_shape(params)
    indices_shape = shape_utils.combined_static_and_dynamic_shape(indices)
    params2d = tf.reshape(params, [params_shape[0], -1])
    indicator_matrix = tf.one_hot(indices, params_shape[0])
    gathered_result_flattened = tf.matmul(indicator_matrix, params2d)
    return tf.reshape(gathered_result_flattened,
                      tf.stack(indices_shape + params_shape[1:])) 

def _flatten_first_two_dimensions(self, inputs):
    """Flattens `K-d` tensor along batch dimension to be a `(K-1)-d` tensor.

    Converts `inputs` with shape [A, B, ..., depth] into a tensor of shape
    [A * B, ..., depth].

    Args:
      inputs: A float tensor with shape [A, B, ..., depth].  Note that the first
        two and last dimensions must be statically defined.
    Returns:
      A float tensor with shape [A * B, ..., depth] (where the first and last
        dimension are statically defined.
    """
    combined_shape = shape_utils.combined_static_and_dynamic_shape(inputs)
    flattened_shape = tf.stack([combined_shape[0] * combined_shape[1]] +
                               combined_shape[2:])
    return tf.reshape(inputs, flattened_shape) 

def nearest_neighbor_upsampling(input_tensor, scale):
  """Nearest neighbor upsampling implementation.

  Nearest neighbor upsampling function that maps input tensor with shape
  [batch_size, height, width, channels] to [batch_size, height * scale
  , width * scale, channels]. This implementation only uses reshape and
  broadcasting to make it TPU compatible.

  Args:
    input_tensor: A float32 tensor of size [batch, height_in, width_in,
      channels].
    scale: An integer multiple to scale resolution of input data.
  Returns:
    data_up: A float32 tensor of size
      [batch, height_in*scale, width_in*scale, channels].
  """
  with tf.name_scope('nearest_neighbor_upsampling'):
    (batch_size, height, width,
     channels) = shape_utils.combined_static_and_dynamic_shape(input_tensor)
    output_tensor = tf.reshape(
        input_tensor, [batch_size, height, 1, width, 1, channels]) * tf.ones(
            [1, 1, scale, 1, scale, 1], dtype=input_tensor.dtype)
    return tf.reshape(output_tensor,
                      [batch_size, height * scale, width * scale, channels]) 

def matmul_gather_on_zeroth_axis(params, indices, scope=None):
  """Matrix multiplication based implementation of tf.gather on zeroth axis.

  TODO(rathodv, jonathanhuang): enable sparse matmul option.

  Args:
    params: A float32 Tensor. The tensor from which to gather values.
      Must be at least rank 1.
    indices: A Tensor. Must be one of the following types: int32, int64.
      Must be in range [0, params.shape[0])
    scope: A name for the operation (optional).

  Returns:
    A Tensor. Has the same type as params. Values from params gathered
    from indices given by indices, with shape indices.shape + params.shape[1:].
  """
  with tf.name_scope(scope, 'MatMulGather'):
    params_shape = shape_utils.combined_static_and_dynamic_shape(params)
    indices_shape = shape_utils.combined_static_and_dynamic_shape(indices)
    params2d = tf.reshape(params, [params_shape[0], -1])
    indicator_matrix = tf.one_hot(indices, params_shape[0])
    gathered_result_flattened = tf.matmul(indicator_matrix, params2d)
    return tf.reshape(gathered_result_flattened,
                      tf.stack(indices_shape + params_shape[1:])) 

def nearest_neighbor_upsampling(input_tensor, scale):
  """Nearest neighbor upsampling implementation.

  Nearest neighbor upsampling function that maps input tensor with shape
  [batch_size, height, width, channels] to [batch_size, height * scale
  , width * scale, channels]. This implementation only uses reshape and tile to
  make it compatible with certain hardware.

  Args:
    input_tensor: A float32 tensor of size [batch, height_in, width_in,
      channels].
    scale: An integer multiple to scale resolution of input data.
  Returns:
    data_up: A float32 tensor of size
      [batch, height_in*scale, width_in*scale, channels].
  """
  shape = shape_utils.combined_static_and_dynamic_shape(input_tensor)
  shape_before_tile = [shape[0], shape[1], 1, shape[2], 1, shape[3]]
  shape_after_tile = [shape[0], shape[1] * scale, shape[2] * scale, shape[3]]
  data_reshaped = tf.reshape(input_tensor, shape_before_tile)
  resized_tensor = tf.tile(data_reshaped, [1, 1, scale, 1, scale, 1])
  resized_tensor = tf.reshape(resized_tensor, shape_after_tile)
  return resized_tensor 

def matmul_gather_on_zeroth_axis(params, indices, scope=None):
  """Matrix multiplication based implementation of tf.gather on zeroth axis.

  TODO(rathodv, jonathanhuang): enable sparse matmul option.

  Args:
    params: A float32 Tensor. The tensor from which to gather values.
      Must be at least rank 1.
    indices: A Tensor. Must be one of the following types: int32, int64.
      Must be in range [0, params.shape[0])
    scope: A name for the operation (optional).

  Returns:
    A Tensor. Has the same type as params. Values from params gathered
    from indices given by indices, with shape indices.shape + params.shape[1:].
  """
  with tf.name_scope(scope, 'MatMulGather'):
    params_shape = shape_utils.combined_static_and_dynamic_shape(params)
    indices_shape = shape_utils.combined_static_and_dynamic_shape(indices)
    params2d = tf.reshape(params, [params_shape[0], -1])
    indicator_matrix = tf.one_hot(indices, params_shape[0])
    gathered_result_flattened = tf.matmul(indicator_matrix, params2d)
    return tf.reshape(gathered_result_flattened,
                      tf.stack(indices_shape + params_shape[1:])) 

def _flatten_first_two_dimensions(self, inputs):
    """Flattens `K-d` tensor along batch dimension to be a `(K-1)-d` tensor.

    Converts `inputs` with shape [A, B, ..., depth] into a tensor of shape
    [A * B, ..., depth].

    Args:
      inputs: A float tensor with shape [A, B, ..., depth].  Note that the first
        two and last dimensions must be statically defined.
    Returns:
      A float tensor with shape [A * B, ..., depth] (where the first and last
        dimension are statically defined.
    """
    combined_shape = shape_utils.combined_static_and_dynamic_shape(inputs)
    flattened_shape = tf.stack([combined_shape[0] * combined_shape[1]] +
                               combined_shape[2:])
    return tf.reshape(inputs, flattened_shape) 

def _flatten_first_two_dimensions(self, inputs):
    """Flattens `K-d` tensor along batch dimension to be a `(K-1)-d` tensor.

    Converts `inputs` with shape [A, B, ..., depth] into a tensor of shape
    [A * B, ..., depth].

    Args:
      inputs: A float tensor with shape [A, B, ..., depth].  Note that the first
        two and last dimensions must be statically defined.
    Returns:
      A float tensor with shape [A * B, ..., depth] (where the first and last
        dimension are statically defined.
    """
    combined_shape = shape_utils.combined_static_and_dynamic_shape(inputs)
    flattened_shape = tf.stack([combined_shape[0] * combined_shape[1]] +
                               combined_shape[2:])
    return tf.reshape(inputs, flattened_shape) 

def _batch_decode(self, box_encodings):
    """Decodes a batch of box encodings with respect to the anchors.

    Args:
      box_encodings: A float32 tensor of shape
        [batch_size, num_anchors, box_code_size] containing box encodings.

    Returns:
      decoded_boxes: A float32 tensor of shape
        [batch_size, num_anchors, 4] containing the decoded boxes.
    """
    combined_shape = shape_utils.combined_static_and_dynamic_shape(
        box_encodings)
    batch_size = combined_shape[0]
    tiled_anchor_boxes = tf.tile(
        tf.expand_dims(self.anchors.get(), 0), [batch_size, 1, 1])
    tiled_anchors_boxlist = box_list.BoxList(
        tf.reshape(tiled_anchor_boxes, [-1, self._box_coder.code_size]))
    decoded_boxes = self._box_coder.decode(
        tf.reshape(box_encodings, [-1, self._box_coder.code_size]),
        tiled_anchors_boxlist)
    return tf.reshape(decoded_boxes.get(),
                      tf.stack([combined_shape[0], combined_shape[1],
                                4])) 

def _flatten_first_two_dimensions(self, inputs):
    """Flattens `K-d` tensor along batch dimension to be a `(K-1)-d` tensor.

    Converts `inputs` with shape [A, B, ..., depth] into a tensor of shape
    [A * B, ..., depth].

    Args:
      inputs: A float tensor with shape [A, B, ..., depth].  Note that the first
        two and last dimensions must be statically defined.
    Returns:
      A float tensor with shape [A * B, ..., depth] (where the first and last
        dimension are statically defined.
    """
    combined_shape = shape_utils.combined_static_and_dynamic_shape(inputs)
    flattened_shape = tf.stack([combined_shape[0] * combined_shape[1]] +
                               combined_shape[2:])
    return tf.reshape(inputs, flattened_shape) 

def _flatten_first_two_dimensions(self, inputs):
    """Flattens `K-d` tensor along batch dimension to be a `(K-1)-d` tensor.

    Converts `inputs` with shape [A, B, ..., depth] into a tensor of shape
    [A * B, ..., depth].

    Args:
      inputs: A float tensor with shape [A, B, ..., depth].  Note that the first
        two and last dimensions must be statically defined.
    Returns:
      A float tensor with shape [A * B, ..., depth] (where the first and last
        dimension are statically defined.
    """
    combined_shape = shape_utils.combined_static_and_dynamic_shape(inputs)
    flattened_shape = tf.stack([combined_shape[0] * combined_shape[1]] +
                               combined_shape[2:])
    return tf.reshape(inputs, flattened_shape) 

def nearest_neighbor_upsampling(input_tensor, scale):
  """Nearest neighbor upsampling implementation.

  Nearest neighbor upsampling function that maps input tensor with shape
  [batch_size, height, width, channels] to [batch_size, height * scale
  , width * scale, channels]. This implementation only uses reshape and tile to
  make it compatible with certain hardware.

  Args:
    input_tensor: A float32 tensor of size [batch, height_in, width_in,
      channels].
    scale: An integer multiple to scale resolution of input data.
  Returns:
    data_up: A float32 tensor of size
      [batch, height_in*scale, width_in*scale, channels].
  """
  shape = shape_utils.combined_static_and_dynamic_shape(input_tensor)
  shape_before_tile = [shape[0], shape[1], 1, shape[2], 1, shape[3]]
  shape_after_tile = [shape[0], shape[1] * scale, shape[2] * scale, shape[3]]
  data_reshaped = tf.reshape(input_tensor, shape_before_tile)
  resized_tensor = tf.tile(data_reshaped, [1, 1, scale, 1, scale, 1])
  resized_tensor = tf.reshape(resized_tensor, shape_after_tile)
  return resized_tensor 

def nearest_neighbor_upsampling(input_tensor, scale):
  """Nearest neighbor upsampling implementation.

  Nearest neighbor upsampling function that maps input tensor with shape
  [batch_size, height, width, channels] to [batch_size, height * scale
  , width * scale, channels]. This implementation only uses reshape and
  broadcasting to make it TPU compatible.

  Args:
    input_tensor: A float32 tensor of size [batch, height_in, width_in,
      channels].
    scale: An integer multiple to scale resolution of input data.
  Returns:
    data_up: A float32 tensor of size
      [batch, height_in*scale, width_in*scale, channels].
  """
  with tf.name_scope('nearest_neighbor_upsampling'):
    (batch_size, height, width,
     channels) = shape_utils.combined_static_and_dynamic_shape(input_tensor)
    output_tensor = tf.reshape(
        input_tensor, [batch_size, height, 1, width, 1, channels]) * tf.ones(
            [1, 1, scale, 1, scale, 1], dtype=input_tensor.dtype)
    return tf.reshape(output_tensor,
                      [batch_size, height * scale, width * scale, channels]) 

def matmul_gather_on_zeroth_axis(params, indices, scope=None):
  """Matrix multiplication based implementation of tf.gather on zeroth axis.

  TODO(rathodv, jonathanhuang): enable sparse matmul option.

  Args:
    params: A float32 Tensor. The tensor from which to gather values.
      Must be at least rank 1.
    indices: A Tensor. Must be one of the following types: int32, int64.
      Must be in range [0, params.shape[0])
    scope: A name for the operation (optional).

  Returns:
    A Tensor. Has the same type as params. Values from params gathered
    from indices given by indices, with shape indices.shape + params.shape[1:].
  """
  with tf.name_scope(scope, 'MatMulGather'):
    params_shape = shape_utils.combined_static_and_dynamic_shape(params)
    indices_shape = shape_utils.combined_static_and_dynamic_shape(indices)
    params2d = tf.reshape(params, [params_shape[0], -1])
    indicator_matrix = tf.one_hot(indices, params_shape[0])
    gathered_result_flattened = tf.matmul(indicator_matrix, params2d)
    return tf.reshape(gathered_result_flattened,
                      tf.stack(indices_shape + params_shape[1:])) 

def select_random_box(boxlist,
                      default_box=None,
                      seed=None,
                      scope=None):
  """Selects a random bounding box from a `BoxList`.

  Args:
    boxlist: A BoxList.
    default_box: A [1, 4] float32 tensor. If no boxes are present in `boxlist`,
      this default box will be returned. If None, will use a default box of
      [[-1., -1., -1., -1.]].
    seed: Random seed.
    scope: Name scope.

  Returns:
    bbox: A [1, 4] tensor with a random bounding box.
    valid: A bool tensor indicating whether a valid bounding box is returned
      (True) or whether the default box is returned (False).
  """
  with tf.name_scope(scope, 'SelectRandomBox'):
    bboxes = boxlist.get()
    combined_shape = shape_utils.combined_static_and_dynamic_shape(bboxes)
    number_of_boxes = combined_shape[0]
    default_box = default_box or tf.constant([[-1., -1., -1., -1.]])

    def select_box():
      random_index = tf.random_uniform([],
                                       maxval=number_of_boxes,
                                       dtype=tf.int32,
                                       seed=seed)
      return tf.expand_dims(bboxes[random_index], axis=0), tf.constant(True)

  return tf.cond(
      tf.greater_equal(number_of_boxes, 1),
      true_fn=select_box,
      false_fn=lambda: (default_box, tf.constant(False))) 

def _batch_decode_boxes(self, box_encodings, anchor_boxes):
    """Decodes box encodings with respect to the anchor boxes.

    Args:
      box_encodings: a 4-D tensor with shape
        [batch_size, num_anchors, num_classes, self._box_coder.code_size]
        representing box encodings.
      anchor_boxes: [batch_size, num_anchors, self._box_coder.code_size]
        representing decoded bounding boxes. If using a shared box across
        classes the shape will instead be
        [total_num_proposals, 1, self._box_coder.code_size].

    Returns:
      decoded_boxes: a
        [batch_size, num_anchors, num_classes, self._box_coder.code_size]
        float tensor representing bounding box predictions (for each image in
        batch, proposal and class). If using a shared box across classes the
        shape will instead be
        [batch_size, num_anchors, 1, self._box_coder.code_size].
    """
    combined_shape = shape_utils.combined_static_and_dynamic_shape(
        box_encodings)
    num_classes = combined_shape[2]
    tiled_anchor_boxes = tf.tile(
        tf.expand_dims(anchor_boxes, 2), [1, 1, num_classes, 1])
    tiled_anchors_boxlist = box_list.BoxList(
        tf.reshape(tiled_anchor_boxes, [-1, 4]))
    decoded_boxes = self._box_coder.decode(
        tf.reshape(box_encodings, [-1, self._box_coder.code_size]),
        tiled_anchors_boxlist)
    return tf.reshape(decoded_boxes.get(),
                      tf.stack([combined_shape[0], combined_shape[1],
                                num_classes, 4])) 

def test_unequal_static_shape_along_first_dim_raises_exception(self):
    shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
    shape_b = tf.constant(np.zeros([6, 2, 3, 1]))
    with self.assertRaisesRegexp(
        ValueError, 'Unequal first dimension'):
      shape_utils.assert_shape_equal_along_first_dimension(
          shape_utils.combined_static_and_dynamic_shape(shape_a),
          shape_utils.combined_static_and_dynamic_shape(shape_b)) 

def _compute_clip_window(self, preprocessed_images, true_image_shapes):
    """Computes clip window to use during post_processing.

    Computes a new clip window to use during post-processing based on
    `resized_image_shapes` and `true_image_shapes` only if `preprocess` method
    has been called. Otherwise returns a default clip window of [0, 0, 1, 1].

    Args:
      preprocessed_images: the [batch, height, width, channels] image
          tensor.
      true_image_shapes: int32 tensor of shape [batch, 3] where each row is
        of the form [height, width, channels] indicating the shapes
        of true images in the resized images, as resized images can be padded
        with zeros. Or None if the clip window should cover the full image.

    Returns:
      a 2-D float32 tensor of the form [batch_size, 4] containing the clip
      window for each image in the batch in normalized coordinates (relative to
      the resized dimensions) where each clip window is of the form [ymin, xmin,
      ymax, xmax] or a default clip window of [0, 0, 1, 1].

    """
    if true_image_shapes is None:
      return tf.constant([0, 0, 1, 1], dtype=tf.float32)

    resized_inputs_shape = shape_utils.combined_static_and_dynamic_shape(
        preprocessed_images)
    true_heights, true_widths, _ = tf.unstack(
        tf.to_float(true_image_shapes), axis=1)
    padded_height = tf.to_float(resized_inputs_shape[1])
    padded_width = tf.to_float(resized_inputs_shape[2])
    return tf.stack(
        [
            tf.zeros_like(true_heights),
            tf.zeros_like(true_widths), true_heights / padded_height,
            true_widths / padded_width
        ],
        axis=1) 

def test_equal_dynamic_shape_along_first_dim_succeeds(self):
    tensor_a = tf.placeholder(tf.float32, shape=[None, None, None, 3])
    tensor_b = tf.placeholder(tf.float32, shape=[None])
    op = shape_utils.assert_shape_equal_along_first_dimension(
        shape_utils.combined_static_and_dynamic_shape(tensor_a),
        shape_utils.combined_static_and_dynamic_shape(tensor_b))
    with self.test_session() as sess:
      sess.run(op, feed_dict={tensor_a: np.zeros([5, 2, 2, 3]),
                              tensor_b: np.zeros([5])}) 

def test_unequal_dynamic_shape_along_first_dim_raises_tf_assert(self):
    tensor_a = tf.placeholder(tf.float32, shape=[None, None, None, 3])
    tensor_b = tf.placeholder(tf.float32, shape=[None, None, 3])
    op = shape_utils.assert_shape_equal_along_first_dimension(
        shape_utils.combined_static_and_dynamic_shape(tensor_a),
        shape_utils.combined_static_and_dynamic_shape(tensor_b))
    with self.test_session() as sess:
      with self.assertRaises(tf.errors.InvalidArgumentError):
        sess.run(op, feed_dict={tensor_a: np.zeros([1, 2, 2, 3]),
                                tensor_b: np.zeros([2, 4, 3])}) 

def _get_feature_map_spatial_dims(self, feature_maps):
    """Return list of spatial dimensions for each feature map in a list.

    Args:
      feature_maps: a list of tensors where the ith tensor has shape
          [batch, height_i, width_i, depth_i].

    Returns:
      a list of pairs (height, width) for each feature map in feature_maps
    """
    feature_map_shapes = [
        shape_utils.combined_static_and_dynamic_shape(
            feature_map) for feature_map in feature_maps
    ]
    return [(shape[1], shape[2]) for shape in feature_map_shapes] 

def test_combines_static_dynamic_shape(self):
    tensor = tf.placeholder(tf.float32, shape=(None, 2, 3))
    combined_shape = shape_utils.combined_static_and_dynamic_shape(
        tensor)
    self.assertTrue(tf.contrib.framework.is_tensor(combined_shape[0]))
    self.assertListEqual(combined_shape[1:], [2, 3]) 

def _predict(self, image_features, num_predictions_per_location):
    combined_feature_shape = shape_utils.combined_static_and_dynamic_shape(
        image_features)
    batch_size = combined_feature_shape[0]
    num_anchors = (combined_feature_shape[1] * combined_feature_shape[2])
    code_size = 4
    zero = tf.reduce_sum(0 * image_features)
    box_encodings = zero + tf.zeros(
        (batch_size, num_anchors, 1, code_size), dtype=tf.float32)
    class_predictions_with_background = zero + tf.zeros(
        (batch_size, num_anchors, self.num_classes + 1), dtype=tf.float32)
    return {box_predictor.BOX_ENCODINGS: box_encodings,
            box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
            class_predictions_with_background} 

def _get_feature_map_spatial_dims(self, feature_maps):
    """Return list of spatial dimensions for each feature map in a list.

    Args:
      feature_maps: a list of tensors where the ith tensor has shape
          [batch, height_i, width_i, depth_i].

    Returns:
      a list of pairs (height, width) for each feature map in feature_maps
    """
    feature_map_shapes = [
        shape_utils.combined_static_and_dynamic_shape(
            feature_map) for feature_map in feature_maps
    ]
    return [(shape[1], shape[2]) for shape in feature_map_shapes] 

def test_equal_dynamic_shape_succeeds(self):
    tensor_a = tf.placeholder(tf.float32, shape=[1, None, None, 3])
    tensor_b = tf.placeholder(tf.float32, shape=[1, None, None, 3])
    op = shape_utils.assert_shape_equal(
        shape_utils.combined_static_and_dynamic_shape(tensor_a),
        shape_utils.combined_static_and_dynamic_shape(tensor_b))
    with self.test_session() as sess:
      sess.run(op, feed_dict={tensor_a: np.zeros([1, 2, 2, 3]),
                              tensor_b: np.zeros([1, 2, 2, 3])}) 

def test_equal_static_shape_succeeds(self):
    shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
    shape_b = tf.constant(np.zeros([4, 2, 2, 1]))
    with self.test_session() as sess:
      op = shape_utils.assert_shape_equal(
          shape_utils.combined_static_and_dynamic_shape(shape_a),
          shape_utils.combined_static_and_dynamic_shape(shape_b))
      sess.run(op) 

def test_combines_static_dynamic_shape(self):
    tensor = tf.placeholder(tf.float32, shape=(None, 2, 3))
    combined_shape = shape_utils.combined_static_and_dynamic_shape(
        tensor)
    self.assertTrue(tf.contrib.framework.is_tensor(combined_shape[0]))
    self.assertListEqual(combined_shape[1:], [2, 3]) 

def _batch_decode(self, box_encodings):
    """Decodes a batch of box encodings with respect to the anchors.

    Args:
      box_encodings: A float32 tensor of shape
        [batch_size, num_anchors, box_code_size] containing box encodings.

    Returns:
      decoded_boxes: A float32 tensor of shape
        [batch_size, num_anchors, 4] containing the decoded boxes.
      decoded_keypoints: A float32 tensor of shape
        [batch_size, num_anchors, num_keypoints, 2] containing the decoded
        keypoints if present in the input `box_encodings`, None otherwise.
    """
    combined_shape = shape_utils.combined_static_and_dynamic_shape(
        box_encodings)
    batch_size = combined_shape[0]
    tiled_anchor_boxes = tf.tile(
        tf.expand_dims(self.anchors.get(), 0), [batch_size, 1, 1])
    tiled_anchors_boxlist = box_list.BoxList(
        tf.reshape(tiled_anchor_boxes, [-1, 4]))
    decoded_boxes = self._box_coder.decode(
        tf.reshape(box_encodings, [-1, self._box_coder.code_size]),
        tiled_anchors_boxlist)
    decoded_keypoints = None
    if decoded_boxes.has_field(fields.BoxListFields.keypoints):
      decoded_keypoints = decoded_boxes.get_field(
          fields.BoxListFields.keypoints)
      num_keypoints = decoded_keypoints.get_shape()[1]
      decoded_keypoints = tf.reshape(
          decoded_keypoints,
          tf.stack([combined_shape[0], combined_shape[1], num_keypoints, 2]))
    decoded_boxes = tf.reshape(decoded_boxes.get(), tf.stack(
        [combined_shape[0], combined_shape[1], 4]))
    return decoded_boxes, decoded_keypoints 

def _predict(self, image_features, num_predictions_per_location):
    combined_feature_shape = shape_utils.combined_static_and_dynamic_shape(
        image_features)
    batch_size = combined_feature_shape[0]
    num_anchors = (combined_feature_shape[1] * combined_feature_shape[2])
    code_size = 4
    zero = tf.reduce_sum(0 * image_features)
    box_encodings = zero + tf.zeros(
        (batch_size, num_anchors, 1, code_size), dtype=tf.float32)
    class_predictions_with_background = zero + tf.zeros(
        (batch_size, num_anchors, self.num_classes + 1), dtype=tf.float32)
    return {box_predictor.BOX_ENCODINGS: box_encodings,
            box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
            class_predictions_with_background}