tensorflow::ops::CropAndResizeGradImage

Computes the gradient of the crop_and_resize op wrt the input image tensor.

Summary

Arguments:

• scope: A Scope object
• grads: A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].
• boxes: A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is mapped to `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the [0, 1] range are allowed, in which case we use extrapolation_value to extrapolate the input image values.
• box_ind: A 1-D tensor of shape [num_boxes] with int32 values in [0, batch). The value of box_ind[i] specifies the image that the i-th box refers to.
• image_size: A 1-D tensor with value [batch, image_height, image_width, depth] containing the original image size. Both image_height and image_width need to be positive.

Optional attributes (see Attrs):

• method: A string specifying the interpolation method. Only 'bilinear' is supported for now.

Returns:

• Output: A 4-D tensor of shape [batch, image_height, image_width, depth].

Public attributes

Operation operation

::tensorflow::Output output

 CropAndResizeGradImage(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input grads,
  ::tensorflow::Input boxes,
  ::tensorflow::Input box_ind,
  ::tensorflow::Input image_size,
  DataType T
)

 CropAndResizeGradImage(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input grads,
  ::tensorflow::Input boxes,
  ::tensorflow::Input box_ind,
  ::tensorflow::Input image_size,
  DataType T,
  const CropAndResizeGradImage::Attrs & attrs
)

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const 

Attrs Method(
  StringPiece x
)