Python tensor_shape.unknown_shape方法代码示例

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def fix_image_flip_shape(image, result):
    """Set the shape to 3 dimensional if we don't know anything else.
    Args:
      image: original image size
      result: flipped or transformed image
    Returns:
      An image whose shape is at least None,None,None.
    """
    image_shape = image.get_shape()
    if image_shape == tensor_shape.unknown_shape():
        result.set_shape([None, None, None])
    else:
        result.set_shape(image_shape)
    return result


# =========================================================================== #
# Image + BBoxes methods: cropping, resizing, flipping, ...
# =========================================================================== # 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def __init__(self, dtype, shape, accumulator_ref):
    """Creates a new ConditionalAccumulator.

    Args:
      dtype: Datatype of the accumulated gradients.
      shape: Shape of the accumulated gradients.
      accumulator_ref: A handle to the conditional accumulator, created by sub-
        classes
    """
    self._dtype = dtype
    if shape is not None:
      self._shape = tensor_shape.TensorShape(shape)
    else:
      self._shape = tensor_shape.unknown_shape()
    self._accumulator_ref = accumulator_ref
    self._name = self._accumulator_ref.op.name.split("/")[-1] 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def _TileGradShape(op):
  """Shape function for the TileGrad op."""
  multiples_shape = op.inputs[1].get_shape().with_rank(1)
  input_shape = op.inputs[0].get_shape().with_rank(multiples_shape[0])
  # NOTE(mrry): Represent `multiples` as a `TensorShape` because (i)
  # it is a vector of non-negative integers, and (ii) doing so allows
  # us to handle partially-known multiples.
  multiples = tensor_util.constant_value_as_shape(op.inputs[1]).with_rank(
      input_shape.ndims)
  if multiples.ndims is None:
    return [tensor_shape.unknown_shape()]
  else:
    output_dims = []
    for dim, multiple in zip(input_shape.dims, multiples.dims):
      output_dims.append(dim // multiple)
    return [tensor_shape.TensorShape(output_dims)] 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def fix_image_flip_shape(image, result):
  """Set the shape to 3 dimensional if we don't know anything else.

  Args:
    image: original image size
    result: flipped or transformed image

  Returns:
    An image whose shape is at least None,None,None.
  """

  image_shape = image.get_shape()
  if image_shape == tensor_shape.unknown_shape():
    result.set_shape([None, None, None])
  else:
    result.set_shape(image_shape)
  return result 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def __init__(self, op, value_index, dtype):
    """Creates a new `Tensor`.

    Args:
      op: An `Operation`. `Operation` that computes this tensor.
      value_index: An `int`. Index of the operation's endpoint that produces
        this tensor.
      dtype: A `DType`. Type of elements stored in this tensor.

    Raises:
      TypeError: If the op is not an `Operation`.
    """
    if not isinstance(op, Operation):
      raise TypeError("op needs to be an Operation: %s" % op)
    self._op = op
    self._value_index = value_index
    self._dtype = dtypes.as_dtype(dtype)
    self._shape = tensor_shape.unknown_shape()
    # List of operations that use this Tensor as input.  We maintain this list
    # to easily navigate a computation graph.
    self._consumers = []

    # Attributes used for C++ shape inference. Not inspected, only forwarded.
    self._handle_shape = tensor_shape_pb2.TensorShapeProto()
    self._handle_dtype = types_pb2.DT_INVALID 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def testSplitShape(self):
    with self.test_session():
      ta = tensor_array_ops.TensorArray(
          dtype=tf.float32, tensor_array_name="foo",
          size=0, dynamic_size=True, infer_shape=True)
      value = tf.constant([[1.0, -1.0], [2.0, -2.0], [3.0, -3.0]])
      w0 = ta.split(value, [1, 1, 1])
      r0 = w0.read(0)
      self.assertAllEqual((1, 2), r0.get_shape())

      ta1 = tensor_array_ops.TensorArray(
          dtype=tf.float32, tensor_array_name="foo1",
          size=0, dynamic_size=True, infer_shape=True)
      w0 = ta1.split(value, [1, 2])
      r0 = w0.read(0)
      self.assertAllEqual(r0.get_shape(), tensor_shape.unknown_shape()) 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def testWhile_5(self):
    with self.test_session():

      def compute(i, c, o):
        c = tf.slice(x, tf.expand_dims(i, 0), [1])
        o = tf.concat(0, [o, c])
        i = tf.add(i, 1)
        return [i, c, o]

      i = tf.convert_to_tensor(0)
      c = tf.convert_to_tensor([0])
      o = tf.convert_to_tensor([0])
      x = tf.convert_to_tensor([1, 2, 3, 4, 5, 6])
      s = tf.size(x)
      r = tf.while_loop(
          lambda i, c, o: tf.less(i, s), compute, [i, c, o],
          [i.get_shape(), c.get_shape(), tensor_shape.unknown_shape()])
      result = r[2].eval()
    self.assertTrue(check_op_order(i.graph))
    self.assertAllEqual(np.array([0, 1, 2, 3, 4, 5, 6]), result) 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def testWhileFuncBasic(self):
    @function.Defun(tf.float32)
    def func(x):
      return tf.square(tf.square(x))

    with self.test_session():
      x = tf.constant(2.0, tf.float32)
      r = tf.while_loop(
          lambda i, v: i < 2,
          lambda i, v: [i + 1, func(v)],
          [tf.constant(0), x],
          [tensor_shape.unknown_shape(), tensor_shape.unknown_shape()])
      self.assertEqual(r[1].eval(), 65536.0)

      r = tf.gradients(r, x)[0]
      self.assertEqual(r.eval(), 524288.0)
      self.assertEqual(len([op for op in x.graph.get_operations()
                            if op.type == "Stack"]),
                       1) 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def _testStackWhileSwap(self, use_gpu):
    with self.test_session(use_gpu=use_gpu):
      n = tf.constant(0)
      h = gen_data_flow_ops._stack(tf.float32, stack_name="foo")

      def c(x):
        return tf.less(x, 10)
      def b(x):
        with tf.control_dependencies([x]):
          a = tf.constant(np.ones(2000), dtype=tf.float32)
          v = gen_data_flow_ops._stack_push(h, a, swap_memory=True)
        with tf.control_dependencies([v]):
          return tf.add(x, 1)
      r = tf.while_loop(c, b, [n])

      v = tf.constant(np.zeros(2000), dtype=tf.float32)
      def c1(x, y):
        return tf.greater(x, 0)
      def b1(x, y):
        nx = tf.sub(x, 1)
        ny = y + gen_data_flow_ops._stack_pop(h, tf.float32)
        return [nx, ny]
      rx, ry = tf.while_loop(c1, b1, [r, v],
                             [r.get_shape(), tensor_shape.unknown_shape()])
      self.assertAllClose(np.ones(2000) * 10.0, ry.eval()) 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def _DynamicPartitionShape(op):
  """Shape function for data_flow_ops.dynamic_partition."""
  data_shape = op.inputs[0].get_shape()
  partitions_shape = op.inputs[1].get_shape()
  # If we don't know the rank of partitions, we don't know anything
  mid = partitions_shape.ndims
  if mid is None:
    result_shape = tensor_shape.unknown_shape()
  else:
    # data_shape must start with partitions_shape
    partitions_shape.assert_is_compatible_with(data_shape[:mid])
    # The partition shape is dynamic in the 0th dimension, and matches
    # data_shape in the remaining dimensions.
    result_shape = tensor_shape.TensorShape([None]).concatenate(
        data_shape[mid:])
  return [result_shape] * op.get_attr("num_partitions") 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def variable_op(shape, dtype, name="Variable", set_shape=True, container="",
                shared_name=""):
  """Deprecated. Used variable_op_v2 instead."""
  if not set_shape:
    shape = tensor_shape.unknown_shape()
  ret = gen_state_ops._variable(shape=shape, dtype=dtype, name=name,
                                container=container, shared_name=shared_name)
  # TODO(mrry): Move this to where it is used, so we can get rid of this op
  #   wrapper?
  if set_shape:
    ret.set_shape(shape)
  return ret 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def _shape_common(s1, s2):
  """The greatest lower bound (ordered by specificity) TensorShape."""
  s1 = tensor_shape.TensorShape(s1)
  s2 = tensor_shape.TensorShape(s2)
  if s1.ndims is None or s2.ndims is None or s1.ndims != s2.ndims:
    return tensor_shape.unknown_shape()
  d = [
      d1 if d1 is not None and d1 == d2 else None
      for (d1, d2) in zip(s1.as_list(), s2.as_list())]
  return tensor_shape.TensorShape(d)


# pylint: disable=protected-access 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def _AccumulatorShape(inputs):
  shape = tensor_shape.unknown_shape()
  for i in inputs:
    if isinstance(i, ops.Tensor):
      shape = shape.merge_with(i.get_shape())
  return shape 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def scatter(self, indices, value, name=None):
    """Scatter the values of a `Tensor` in specific indices of a `TensorArray`.

    Args:
      indices: A `1-D` `Tensor` taking values in `[0, max_value)`.  If
        the `TensorArray` is not dynamic, `max_value=size()`.
      value: (N+1)-D.  Tensor of type `dtype`.  The Tensor to unpack.
      name: A name for the operation (optional).

    Returns:
      A new TensorArray object with flow that ensures the scatter occurs.
      Use this object all for subsequent operations.

    Raises:
      ValueError: if the shape inference fails.
    """
    with ops.name_scope(name, "TensorArrayScatter",
                        [self._handle, value, indices]):
      value = ops.convert_to_tensor(value, name="value")
      with self._maybe_colocate_with(value):
        flow_out = gen_data_flow_ops._tensor_array_scatter_v3(
            handle=self._handle,
            indices=indices,
            value=value,
            flow_in=self._flow,
            name=name)
      ta = TensorArray(
          dtype=self._dtype, handle=self._handle, flow=flow_out,
          colocate_with_first_write_call=self._colocate_with_first_write_call)
      ta._infer_shape = self._infer_shape
      ta._element_shape = self._element_shape
      ta._colocate_with = self._colocate_with
      if ta._infer_shape:
        val_shape = flow_out.op.inputs[2].get_shape()
        element_shape = tensor_shape.unknown_shape()
        if val_shape.dims is not None:
          element_shape = tensor_shape.TensorShape(val_shape.dims[1:])
        ta._merge_element_shape(element_shape)
      return ta 

# 需要导入模块: from tensorflow.python.framework import tensor_shape [as 别名]
# 或者: from tensorflow.python.framework.tensor_shape import unknown_shape [as 别名]
def scatter(self, indices, value, name=None):
    """Scatter the values of a `Tensor` in specific indices of a `TensorArray`.

    Args:
      indices: A `1-D` `Tensor` taking values in `[0, max_value)`.  If
        the `TensorArray` is not dynamic, `max_value=size()`.
      value: (N+1)-D.  Tensor of type `dtype`.  The Tensor to unpack.
      name: A name for the operation (optional).

    Returns:
      A new TensorArray object with flow that ensures the scatter occurs.
      Use this object all for subsequent operations.

    Raises:
      ValueError: if the shape inference fails.
    """
    with ops.name_scope(name, "TensorArrayScatter",
                        [self._handle, value, indices]):
      value = ops.convert_to_tensor(value, name="value")
      _maybe_set_device(self._handle.op, value)
      with ops.colocate_with(self._handle):
        flow_out = gen_data_flow_ops._tensor_array_scatter_v3(
            handle=self._handle,
            indices=indices,
            value=value,
            flow_in=self._flow,
            name=name)
      ta = TensorArray(dtype=self._dtype, handle=self._handle, flow=flow_out)
      ta._infer_shape = self._infer_shape
      ta._element_shape = self._element_shape
      if ta._infer_shape:
        val_shape = flow_out.op.inputs[2].get_shape()
        element_shape = tensor_shape.unknown_shape()
        if val_shape.dims is not None:
          element_shape = tensor_shape.TensorShape(val_shape.dims[1:])
        ta._merge_element_shape(element_shape)
      return ta