Python shape_utils.pad_or_clip_nd方法代碼示例

# 需要導入模塊: from object_detection.utils import shape_utils [as 別名]
# 或者: from object_detection.utils.shape_utils import pad_or_clip_nd [as 別名]
def test_pad_or_clip_nd_tensor(self):
    tensor_placeholder = tf.placeholder(tf.float32, [None, 5, 4, 7])
    output_tensor = shape_utils.pad_or_clip_nd(
        tensor_placeholder, [None, 3, 5, tf.constant(6)])

    self.assertAllEqual(output_tensor.shape.as_list(), [None, 3, 5, None])

    with self.test_session() as sess:
      output_tensor_np = sess.run(
          output_tensor,
          feed_dict={
              tensor_placeholder: np.random.rand(2, 5, 4, 7),
          })

    self.assertAllEqual(output_tensor_np.shape, [2, 3, 5, 6]) 

# 需要導入模塊: from object_detection.utils import shape_utils [as 別名]
# 或者: from object_detection.utils.shape_utils import pad_or_clip_nd [as 別名]
def _dense_pose_part_indices(self, keys_to_tensors):
    """Creates a tensor that contains part indices for each DensePose point.

    Args:
      keys_to_tensors: a dictionary from keys to tensors.

    Returns:
      A 2-D int32 tensor of shape [num_instances, num_points] where each element
      contains the DensePose part index (0-23). The value `num_points`
      corresponds to the maximum number of sampled points across all instances
      in the image. Note that instances with less sampled points will be padded
      with zeros in the last dimension.
    """
    num_points_per_instances = keys_to_tensors['image/object/densepose/num']
    part_index = keys_to_tensors['image/object/densepose/part_index']
    if isinstance(num_points_per_instances, tf.SparseTensor):
      num_points_per_instances = tf.sparse_tensor_to_dense(
          num_points_per_instances)
    if isinstance(part_index, tf.SparseTensor):
      part_index = tf.sparse_tensor_to_dense(part_index)
    part_index = tf.cast(part_index, dtype=tf.int32)
    max_points_per_instance = tf.cast(
        tf.math.reduce_max(num_points_per_instances), dtype=tf.int32)
    num_points_cumulative = tf.concat([
        [0], tf.math.cumsum(num_points_per_instances)], axis=0)

    def pad_parts_tensor(instance_ind):
      points_range_start = num_points_cumulative[instance_ind]
      points_range_end = num_points_cumulative[instance_ind + 1]
      part_inds = part_index[points_range_start:points_range_end]
      return shape_utils.pad_or_clip_nd(part_inds,
                                        output_shape=[max_points_per_instance])

    return tf.map_fn(pad_parts_tensor,
                     tf.range(tf.size(num_points_per_instances)),
                     dtype=tf.int32) 

# 需要導入模塊: from object_detection.utils import shape_utils [as 別名]
# 或者: from object_detection.utils.shape_utils import pad_or_clip_nd [as 別名]
def test_pad_or_clip_nd_tensor(self):

    def graph_fn(input_tensor):
      output_tensor = shape_utils.pad_or_clip_nd(
          input_tensor, [None, 3, 5, tf.constant(6)])

      return output_tensor

    for n in [2, 3, 4, 5]:
      input_np = np.zeros((n, 5, 4, 7))
      output_tensor_np = self.execute(graph_fn, [input_np])
      self.assertAllEqual(output_tensor_np.shape[1:], [3, 5, 6]) 

# 需要導入模塊: from object_detection.utils import shape_utils [as 別名]
# 或者: from object_detection.utils.shape_utils import pad_or_clip_nd [as 別名]
def _dense_pose_surface_coordinates(self, keys_to_tensors):
    """Creates a tensor that contains surface coords for each DensePose point.

    Args:
      keys_to_tensors: a dictionary from keys to tensors.

    Returns:
      A 3-D float32 tensor of shape [num_instances, num_points, 4] where each
      point contains (y, x, v, u) data for each sampled DensePose point. The
      (y, x) coordinate has normalized image locations for the point, and (v, u)
      contains the surface coordinate (also normalized) for the part. The value
      `num_points` corresponds to the maximum number of sampled points across
      all instances in the image. Note that instances with less sampled points
      will be padded with zeros in dim=1.
    """
    num_points_per_instances = keys_to_tensors['image/object/densepose/num']
    dp_y = keys_to_tensors['image/object/densepose/y']
    dp_x = keys_to_tensors['image/object/densepose/x']
    dp_v = keys_to_tensors['image/object/densepose/v']
    dp_u = keys_to_tensors['image/object/densepose/u']
    if isinstance(num_points_per_instances, tf.SparseTensor):
      num_points_per_instances = tf.sparse_tensor_to_dense(
          num_points_per_instances)
    if isinstance(dp_y, tf.SparseTensor):
      dp_y = tf.sparse_tensor_to_dense(dp_y)
    if isinstance(dp_x, tf.SparseTensor):
      dp_x = tf.sparse_tensor_to_dense(dp_x)
    if isinstance(dp_v, tf.SparseTensor):
      dp_v = tf.sparse_tensor_to_dense(dp_v)
    if isinstance(dp_u, tf.SparseTensor):
      dp_u = tf.sparse_tensor_to_dense(dp_u)
    max_points_per_instance = tf.cast(
        tf.math.reduce_max(num_points_per_instances), dtype=tf.int32)
    num_points_cumulative = tf.concat([
        [0], tf.math.cumsum(num_points_per_instances)], axis=0)

    def pad_surface_coordinates_tensor(instance_ind):
      """Pads DensePose surface coordinates for each instance."""
      points_range_start = num_points_cumulative[instance_ind]
      points_range_end = num_points_cumulative[instance_ind + 1]
      y = dp_y[points_range_start:points_range_end]
      x = dp_x[points_range_start:points_range_end]
      v = dp_v[points_range_start:points_range_end]
      u = dp_u[points_range_start:points_range_end]
      # Create [num_points_i, 4] tensor, where num_points_i is the number of
      # sampled points for instance i.
      unpadded_tensor = tf.stack([y, x, v, u], axis=1)
      return shape_utils.pad_or_clip_nd(
          unpadded_tensor, output_shape=[max_points_per_instance, 4])

    return tf.map_fn(pad_surface_coordinates_tensor,
                     tf.range(tf.size(num_points_per_instances)),
                     dtype=tf.float32)