tf.raw_ops.CropAndResizeGradBoxes

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

tf.raw_ops.CropAndResizeGradBoxes(
    grads, image, boxes, box_ind, method='bilinear', name=None
)

Args:

  • grads: A Tensor of type float32. A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].
  • image: A Tensor. Must be one of the following types: uint8, uint16, int8, int16, int32, int64, half, float32, float64. A 4-D tensor of shape [batch, image_height, image_width, depth]. Both image_height and image_width need to be positive.
  • boxes: A Tensor of type float32. 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 useextrapolation_value` to extrapolate the input image values.
  • box_ind: A Tensor of type int32. 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.
  • method: An optional string from: "bilinear". Defaults to "bilinear". A string specifying the interpolation method. Only 'bilinear' is supported for now.
  • name: A name for the operation (optional).

Returns:

A Tensor of type float32.