Python v1.sparse_tensor_to_dense方法代码示例

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import sparse_tensor_to_dense [as 别名]
def _string_to_tokens_dataset_mapper(keys_to_map, suffix="_tok"):
  """Wrapper for mapper that tokenizes and truncates by length."""

  def _mapper(dataset):
    """Tokenizes strings using tf.string_split and truncates by length."""
    for k in keys_to_map:
      # pylint: disable=g-explicit-length-test
      if len(dataset[k].get_shape()) == 0:  # Used for questions.
        # pylint: enable=g-explicit-length-test
        # <string> [num_tokens]
        tokens = tf.string_split([dataset[k]]).values
      else:  # Used for contexts.
        # <string> [num_context, num_tokens] (sparse)
        sparse_tokens = tf.string_split(dataset[k])

        # <string>[num_tokens, max_num_tokens] (dense)
        tokens = tf.sparse_tensor_to_dense(sparse_tokens, default_value="")

      dataset[k + suffix] = tokens
      # Compute exact length of each context.
      dataset[k + suffix + "_len"] = tf.count_nonzero(
          tokens, axis=-1, dtype=tf.int32)
    return dataset

  return _mapper 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import sparse_tensor_to_dense [as 别名]
def _reshape_context_features(self, keys_to_tensors):
    """Reshape context features.

    The instance context_features are reshaped to
      [num_context_features, context_feature_length]

    Args:
      keys_to_tensors: a dictionary from keys to tensors.

    Returns:
      A 2-D float tensor of shape [num_context_features, context_feature_length]
    """
    context_feature_length = keys_to_tensors['image/context_feature_length']
    to_shape = tf.cast(tf.stack([-1, context_feature_length]), tf.int32)
    context_features = keys_to_tensors['image/context_features']
    if isinstance(context_features, tf.SparseTensor):
      context_features = tf.sparse_tensor_to_dense(context_features)
    context_features = tf.reshape(context_features, to_shape)
    return context_features 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import sparse_tensor_to_dense [as 别名]
def _reshape_keypoints(self, keys_to_tensors):
    """Reshape keypoints.

    The keypoints are reshaped to [num_instances, num_keypoints, 2].

    Args:
      keys_to_tensors: a dictionary from keys to tensors. Expected keys are:
        'image/object/keypoint/x'
        'image/object/keypoint/y'

    Returns:
      A 3-D float tensor of shape [num_instances, num_keypoints, 2] with values
        in [0, 1].
    """
    y = keys_to_tensors['image/object/keypoint/y']
    if isinstance(y, tf.SparseTensor):
      y = tf.sparse_tensor_to_dense(y)
    y = tf.expand_dims(y, 1)
    x = keys_to_tensors['image/object/keypoint/x']
    if isinstance(x, tf.SparseTensor):
      x = tf.sparse_tensor_to_dense(x)
    x = tf.expand_dims(x, 1)
    keypoints = tf.concat([y, x], 1)
    keypoints = tf.reshape(keypoints, [-1, self._num_keypoints, 2])
    return keypoints 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import sparse_tensor_to_dense [as 别名]
def _reshape_instance_masks(self, keys_to_tensors):
    """Reshape instance segmentation masks.

    The instance segmentation masks are reshaped to [num_instances, height,
    width].

    Args:
      keys_to_tensors: a dictionary from keys to tensors.

    Returns:
      A 3-D float tensor of shape [num_instances, height, width] with values
        in {0, 1}.
    """
    height = keys_to_tensors['image/height']
    width = keys_to_tensors['image/width']
    to_shape = tf.cast(tf.stack([-1, height, width]), tf.int32)
    masks = keys_to_tensors['image/object/mask']
    if isinstance(masks, tf.SparseTensor):
      masks = tf.sparse_tensor_to_dense(masks)
    masks = tf.reshape(
        tf.cast(tf.greater(masks, 0.0), dtype=tf.float32), to_shape)
    return tf.cast(masks, tf.float32) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import sparse_tensor_to_dense [as 别名]
def tensors_to_item(self, keys_to_tensors):
    """Maps the given dictionary of tensors to a concatenated list of bboxes.

    Args:
      keys_to_tensors: a mapping of TF-Example keys to parsed tensors.

    Returns:
      [time, num_boxes, 4] tensor of bounding box coordinates, in order
          [y_min, x_min, y_max, x_max]. Whether the tensor is a SparseTensor
          or a dense Tensor is determined by the return_dense parameter. Empty
          positions in the sparse tensor are filled with -1.0 values.
    """
    sides = []
    for key in self._full_keys:
      value = keys_to_tensors[key]
      expanded_dims = tf.concat(
          [tf.to_int64(tf.shape(value)),
           tf.constant([1], dtype=tf.int64)], 0)
      side = tf.sparse_reshape(value, expanded_dims)
      sides.append(side)
    bounding_boxes = tf.sparse_concat(2, sides)
    if self._return_dense:
      bounding_boxes = tf.sparse_tensor_to_dense(
          bounding_boxes, default_value=self._default_value)
    return bounding_boxes 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import sparse_tensor_to_dense [as 别名]
def parse_and_preprocess(self, value, batch_position):
    """Parse an TFRecord."""
    del batch_position
    assert self.supports_datasets()
    context_features = {
        'labels': tf.VarLenFeature(dtype=tf.int64),
        'input_length': tf.FixedLenFeature([], dtype=tf.int64),
        'label_length': tf.FixedLenFeature([], dtype=tf.int64),
    }
    sequence_features = {
        'features': tf.FixedLenSequenceFeature([161], dtype=tf.float32)
    }
    context_parsed, sequence_parsed = tf.parse_single_sequence_example(
        serialized=value,
        context_features=context_features,
        sequence_features=sequence_features,
    )

    return [
        # Input
        tf.expand_dims(sequence_parsed['features'], axis=2),
        # Label
        tf.cast(
            tf.reshape(
                tf.sparse_tensor_to_dense(context_parsed['labels']), [-1]),
            dtype=tf.int32),
        # Input length
        tf.cast(
            tf.reshape(context_parsed['input_length'], [1]),
            dtype=tf.int32),
        # Label length
        tf.cast(
            tf.reshape(context_parsed['label_length'], [1]),
            dtype=tf.int32),
    ] 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import sparse_tensor_to_dense [as 别名]
def _slice_with_actions(embeddings, actions):
  """Slice a Tensor.

  Take embeddings of the form [batch_size, num_actions, embed_dim]
  and actions of the form [batch_size, 1], and return the sliced embeddings
  like embeddings[:, actions, :].

  Args:
    embeddings: Tensor of embeddings to index.
    actions: int Tensor to use as index into embeddings

  Returns:
    Tensor of embeddings indexed by actions
  """
  batch_size, num_actions = embeddings.get_shape()[:2]

  # Values are the 'values' in a sparse tensor we will be setting
  act_indx = tf.cast(actions, tf.int64)[:, None]
  values = tf.reshape(tf.cast(tf.ones(tf.shape(actions)), tf.bool), [-1])

  # Create a range for each index into the batch
  act_range = tf.range(0, batch_size, dtype=tf.int64)[:, None]
  # Combine this into coordinates with the action indices
  indices = tf.concat([act_range, act_indx], 1)

  actions_mask = tf.SparseTensor(indices, values, [batch_size, num_actions])
  actions_mask = tf.stop_gradient(
      tf.sparse_tensor_to_dense(actions_mask, default_value=False))
  sliced_emb = tf.boolean_mask(embeddings, actions_mask)
  return sliced_emb 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import sparse_tensor_to_dense [as 别名]
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) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import sparse_tensor_to_dense [as 别名]
def _reshape_keypoint_visibilities(self, keys_to_tensors):
    """Reshape keypoint visibilities.

    The keypoint visibilities are reshaped to [num_instances,
    num_keypoints].

    The raw keypoint visibilities are expected to conform to the
    MSCoco definition. See Visibility enum.

    The returned boolean is True for the labeled case (either
    Visibility.NOT_VISIBLE or Visibility.VISIBLE). These are the same categories
    that COCO uses to evaluate keypoint detection performance:
    http://cocodataset.org/#keypoints-eval

    If image/object/keypoint/visibility is not provided, visibilities will be
    set to True for finite keypoint coordinate values, and 0 if the coordinates
    are NaN.

    Args:
      keys_to_tensors: a dictionary from keys to tensors. Expected keys are:
        'image/object/keypoint/x'
        'image/object/keypoint/visibility'

    Returns:
      A 2-D bool tensor of shape [num_instances, num_keypoints] with values
        in {0, 1}. 1 if the keypoint is labeled, 0 otherwise.
    """
    x = keys_to_tensors['image/object/keypoint/x']
    vis = keys_to_tensors['image/object/keypoint/visibility']
    if isinstance(vis, tf.SparseTensor):
      vis = tf.sparse_tensor_to_dense(vis)
    if isinstance(x, tf.SparseTensor):
      x = tf.sparse_tensor_to_dense(x)

    default_vis = tf.where(
        tf.math.is_nan(x),
        Visibility.UNLABELED.value * tf.ones_like(x, dtype=tf.int64),
        Visibility.VISIBLE.value * tf.ones_like(x, dtype=tf.int64))
    # Use visibility if provided, otherwise use the default visibility.
    vis = tf.cond(tf.equal(tf.size(x), tf.size(vis)),
                  true_fn=lambda: vis,
                  false_fn=lambda: default_vis)
    vis = tf.math.logical_or(
        tf.math.equal(vis, Visibility.NOT_VISIBLE.value),
        tf.math.equal(vis, Visibility.VISIBLE.value))
    vis = tf.reshape(vis, [-1, self._num_keypoints])
    return vis 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import sparse_tensor_to_dense [as 别名]
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)