Python array_ops.space_to_batch_nd方法代码示例

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import space_to_batch_nd [as 别名]
def _BatchToSpaceNDGrad(op, grad):
  # Its gradient is the opposite op: SpaceToBatchND.
  return [array_ops.space_to_batch_nd(grad, op.inputs[1], op.inputs[2]),
          None, None] 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import space_to_batch_nd [as 别名]
def _test_space_to_batch_nd(input_shape, block_shape, paddings, dtype='int32'):
    data = np.random.uniform(0, 5, size=input_shape).astype(dtype)

    with tf.Graph().as_default():
        in_data = array_ops.placeholder(shape=input_shape, dtype=dtype)

        out = array_ops.space_to_batch_nd(in_data, block_shape, paddings)

        compare_tflite_with_tvm(data, 'Placeholder:0', [in_data], [out]) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import space_to_batch_nd [as 别名]
def test_forward_space_to_batch_nd():
    # test cases: https://www.tensorflow.org/api_docs/python/tf/space_to_batch_nd
    _test_space_to_batch_nd(
        input_shape=[1, 2, 2, 1],
        block_shape=[2, 2],
        paddings=[[0, 0], [0, 0]]
    )

    _test_space_to_batch_nd(
        input_shape=[1, 2, 2, 3],
        block_shape=[2, 2],
        paddings=[[0, 0], [0, 0]]
    )

    _test_space_to_batch_nd(
        input_shape=[1, 4, 4, 1],
        block_shape=[2, 2],
        paddings=[[0, 0], [0, 0]]
    )

    _test_space_to_batch_nd(
        input_shape=[2, 2, 4, 1],
        block_shape=[2, 2],
        paddings=[[0, 0], [2, 0]]
    )

#######################################################################
# Pooling
# ------- 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import space_to_batch_nd [as 别名]
def _with_space_to_batch_call(self, inp, filter):  # pylint: disable=redefined-builtin
    """Call functionality for with_space_to_batch."""
    # Handle input whose shape is unknown during graph creation.
    input_spatial_shape = None
    input_shape = self.input_shape
    spatial_dims = self.spatial_dims
    if input_shape.ndims is not None:
      input_shape_list = input_shape.as_list()
      input_spatial_shape = [input_shape_list[i] for i in spatial_dims]
    if input_spatial_shape is None or None in input_spatial_shape:
      input_shape_tensor = array_ops.shape(inp)
      input_spatial_shape = array_ops.stack(
          [input_shape_tensor[i] for i in spatial_dims])

    base_paddings = self.base_paddings
    if base_paddings is None:
      # base_paddings could not be computed at build time since static filter
      # shape was not fully defined.
      filter_shape = array_ops.shape(filter)
      base_paddings = _with_space_to_batch_base_paddings(
          filter_shape,
          self.num_spatial_dims,
          self.rate_or_const_rate)
    paddings, crops = array_ops.required_space_to_batch_paddings(
        input_shape=input_spatial_shape,
        base_paddings=base_paddings,
        block_shape=self.dilation_rate)

    dilation_rate = _with_space_to_batch_adjust(self.dilation_rate, 1,
                                                spatial_dims)
    paddings = _with_space_to_batch_adjust(paddings, 0, spatial_dims)
    crops = _with_space_to_batch_adjust(crops, 0, spatial_dims)
    input_converted = array_ops.space_to_batch_nd(
        input=inp,
        block_shape=dilation_rate,
        paddings=paddings)

    result = self.op(input_converted, filter)

    result_converted = array_ops.batch_to_space_nd(
        input=result, block_shape=dilation_rate, crops=crops)
    return result_converted