Python shape_utils.combined_static_and_dynamic_shape方法代码示例

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
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:])) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
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) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
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]) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
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 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
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])) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
def nearest_neighbor_upsampling(input_tensor, scale=None, height_scale=None,
                                width_scale=None):
  """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 in both height
      and width dimensions.
    height_scale: An integer multiple to scale the height of input image. This
      option when provided overrides `scale` option.
    width_scale: An integer multiple to scale the width of input image. This
      option when provided overrides `scale` option.
  Returns:
    data_up: A float32 tensor of size
      [batch, height_in*scale, width_in*scale, channels].

  Raises:
    ValueError: If both scale and height_scale or if both scale and width_scale
      are None.
  """
  if not scale and (height_scale is None or width_scale is None):
    raise ValueError('Provide either `scale` or `height_scale` and'
                     ' `width_scale`.')
  with tf.name_scope('nearest_neighbor_upsampling'):
    h_scale = scale if height_scale is None else height_scale
    w_scale = scale if width_scale is None else width_scale
    (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, h_scale, 1, w_scale, 1], dtype=input_tensor.dtype)
    return tf.reshape(output_tensor,
                      [batch_size, height * h_scale, width * w_scale, channels]) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
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]) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
def test_unequal_static_shape_raises_exception(self):
    shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
    shape_b = tf.constant(np.zeros([4, 2, 3, 1]))
    with self.assertRaisesRegexp(
        ValueError, 'Unequal shapes'):
      shape_utils.assert_shape_equal(
          shape_utils.combined_static_and_dynamic_shape(shape_a),
          shape_utils.combined_static_and_dynamic_shape(shape_b)) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
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) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
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])}) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
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)) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
def test_equal_static_shape_along_first_dim_succeeds(self):
    shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
    shape_b = tf.constant(np.zeros([4, 7, 2]))
    with self.test_session() as sess:
      op = 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))
      sess.run(op) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
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])}) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
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])}) 

# 需要导入模块: from object_detection.utils import shape_utils [as 别名]
# 或者: from object_detection.utils.shape_utils import combined_static_and_dynamic_shape [as 别名]
def _predict(self, image_features, **kwargs):
    image_feature = image_features[0]
    combined_feature_shape = shape_utils.combined_static_and_dynamic_shape(
        image_feature)
    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_feature)
    num_class_slots = self.num_classes
    if self._add_background_class:
      num_class_slots = num_class_slots + 1
    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, num_class_slots), dtype=tf.float32)
    masks = zero + tf.zeros(
        (batch_size, num_anchors, self.num_classes, DEFAULT_MASK_SIZE,
         DEFAULT_MASK_SIZE),
        dtype=tf.float32)
    predictions_dict = {
        box_predictor.BOX_ENCODINGS:
            box_encodings,
        box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
            class_predictions_with_background
    }
    if self._predict_mask:
      predictions_dict[box_predictor.MASK_PREDICTIONS] = masks
    return predictions_dict