127 lines
4.1 KiB
Python
127 lines
4.1 KiB
Python
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
||
|
|
#
|
||
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
|
# you may not use this file except in compliance with the License.
|
||
|
|
# You may obtain a copy of the License at
|
||
|
|
#
|
||
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
#
|
||
|
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
|
# See the License for the specific language governing permissions and
|
||
|
|
# limitations under the License.
|
||
|
|
# ==============================================================================
|
||
|
|
"""Contains Gradient functions for image ops."""
|
||
|
|
|
||
|
|
from __future__ import absolute_import
|
||
|
|
from __future__ import division
|
||
|
|
from __future__ import print_function
|
||
|
|
|
||
|
|
from tensorflow.python.framework import dtypes
|
||
|
|
from tensorflow.python.framework import ops
|
||
|
|
from tensorflow.python.ops import array_ops
|
||
|
|
from tensorflow.python.ops import gen_image_ops
|
||
|
|
|
||
|
|
|
||
|
|
@ops.RegisterGradient("ResizeNearestNeighbor")
|
||
|
|
def _ResizeNearestNeighborGrad(op, grad):
|
||
|
|
"""The derivatives for nearest neighbor resizing.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
op: The ResizeNearestNeighbor op.
|
||
|
|
grad: The tensor representing the gradient w.r.t. the output.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The gradients w.r.t. the input and the output.
|
||
|
|
"""
|
||
|
|
image = op.inputs[0]
|
||
|
|
if image.get_shape()[1:3].is_fully_defined():
|
||
|
|
image_shape = image.get_shape()[1:3]
|
||
|
|
else:
|
||
|
|
image_shape = array_ops.shape(image)[1:3]
|
||
|
|
|
||
|
|
grads = gen_image_ops.resize_nearest_neighbor_grad(
|
||
|
|
grad,
|
||
|
|
image_shape,
|
||
|
|
align_corners=op.get_attr("align_corners"))
|
||
|
|
return [grads, None]
|
||
|
|
|
||
|
|
|
||
|
|
@ops.RegisterGradient("ResizeBilinear")
|
||
|
|
def _ResizeBilinearGrad(op, grad):
|
||
|
|
"""The derivatives for bilinear resizing.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
op: The ResizeBilinear op.
|
||
|
|
grad: The tensor representing the gradient w.r.t. the output.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The gradients w.r.t. the input.
|
||
|
|
"""
|
||
|
|
grad0 = gen_image_ops.resize_bilinear_grad(
|
||
|
|
grad, op.inputs[0], align_corners=op.get_attr("align_corners"))
|
||
|
|
return [grad0, None]
|
||
|
|
|
||
|
|
|
||
|
|
@ops.RegisterGradient("ResizeBicubic")
|
||
|
|
def _ResizeBicubicGrad(op, grad):
|
||
|
|
"""The derivatives for bicubic resizing.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
op: The ResizeBicubic op.
|
||
|
|
grad: The tensor representing the gradient w.r.t. the output.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The gradients w.r.t. the input.
|
||
|
|
"""
|
||
|
|
allowed_types = [dtypes.float32, dtypes.float64]
|
||
|
|
grad0 = None
|
||
|
|
if op.inputs[0].dtype in allowed_types:
|
||
|
|
grad0 = gen_image_ops.resize_bicubic_grad(
|
||
|
|
grad, op.inputs[0], align_corners=op.get_attr("align_corners"))
|
||
|
|
return [grad0, None]
|
||
|
|
|
||
|
|
|
||
|
|
@ops.RegisterGradient("CropAndResize")
|
||
|
|
def _CropAndResizeGrad(op, grad):
|
||
|
|
"""The derivatives for crop_and_resize.
|
||
|
|
|
||
|
|
We back-propagate to the image only when the input image tensor has floating
|
||
|
|
point dtype but we always back-propagate to the input boxes tensor.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
op: The CropAndResize op.
|
||
|
|
grad: The tensor representing the gradient w.r.t. the output.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The gradients w.r.t. the input image, boxes, as well as the always-None
|
||
|
|
gradients w.r.t. box_ind and crop_size.
|
||
|
|
"""
|
||
|
|
image = op.inputs[0]
|
||
|
|
if image.get_shape().is_fully_defined():
|
||
|
|
image_shape = image.get_shape().as_list()
|
||
|
|
else:
|
||
|
|
image_shape = array_ops.shape(image)
|
||
|
|
|
||
|
|
allowed_types = [dtypes.float16, dtypes.float32, dtypes.float64]
|
||
|
|
if op.inputs[0].dtype in allowed_types:
|
||
|
|
# pylint: disable=protected-access
|
||
|
|
grad0 = gen_image_ops.crop_and_resize_grad_image(
|
||
|
|
grad, op.inputs[1], op.inputs[2], image_shape, T=op.get_attr("T"),
|
||
|
|
method=op.get_attr("method"))
|
||
|
|
# pylint: enable=protected-access
|
||
|
|
else:
|
||
|
|
grad0 = None
|
||
|
|
|
||
|
|
# `grad0` is the gradient to the input image pixels and it
|
||
|
|
# has been implemented for nearest neighbor and bilinear sampling
|
||
|
|
# respectively. `grad1` is the gradient to the input crop boxes' coordinates.
|
||
|
|
# When using nearest neighbor sampling, the gradient to crop boxes'
|
||
|
|
# coordinates are not well defined. In practice, we still approximate
|
||
|
|
# grad1 using the gradient derived from bilinear sampling.
|
||
|
|
grad1 = gen_image_ops.crop_and_resize_grad_boxes(
|
||
|
|
grad, op.inputs[0], op.inputs[1], op.inputs[2])
|
||
|
|
|
||
|
|
return [grad0, grad1, None, None]
|