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image_grad.py
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153 lines (127 loc) · 4.85 KB
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# 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.framework import tensor_shape
from tensorflow.python.framework import tensor_util
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.
"""
# pylint: disable=protected-access
grads = gen_image_ops._resize_nearest_neighbor_grad(
grad,
op.inputs[0].get_shape()[1:3],
align_corners=op.get_attr("align_corners"))
# pylint: enable=protected-access
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.
"""
allowed_types = [dtypes.float32, dtypes.float64]
grad0 = None
if op.inputs[0].dtype in allowed_types:
# pylint: disable=protected-access
grad0 = gen_image_ops._resize_bilinear_grad(
grad,
op.inputs[0],
align_corners=op.get_attr("align_corners"))
# pylint: enable=protected-access
return [grad0, None]
@ops.RegisterShape("ResizeNearestNeighborGrad")
def _ResizeShape(op):
"""Shape function for the resize grad ops."""
input_shape = op.inputs[0].get_shape().with_rank(4)
size = tensor_util.constant_value(op.inputs[1])
if size is not None:
height = size[0]
width = size[1]
else:
height = None
width = None
return [
tensor_shape.TensorShape([input_shape[0], height, width, input_shape[3]])
]
@ops.RegisterShape("ResizeBilinearGrad")
def _ResizeBilinearGradShape(op):
"""Shape function for ResizeBilinearGrad."""
return [op.inputs[1].get_shape()]
@ops.RegisterShape("CropAndResizeGradImage")
def _CropAndResizeGradImageShape(op):
"""Shape function for CropAndResizeGradImage."""
image_size = tensor_util.constant_value(op.inputs[3])
if image_size is not None:
batch = image_size[0]
height = image_size[1]
width = image_size[2]
depth = image_size[3]
else:
batch = None
height = None
width = None
depth = None
return [tensor_shape.TensorShape([batch, height, width, depth])]
@ops.RegisterShape("CropAndResizeGradBoxes")
def _CropAndResizeGradBoxesShape(op):
"""Shape function for CropAndResizeGradBoxes."""
return [op.inputs[2].get_shape()]
@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"))
# pylint: enable=protected-access
else:
grad0 = None
grad1 = gen_image_ops.crop_and_resize_grad_boxes(grad, op.inputs[0],
op.inputs[1], op.inputs[2])
return [grad0, grad1, None, None]