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]
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]
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]
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]