tf.raw_ops.CropAndResizeGradImage

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

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Compat aliases for migration

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tf.compat.v1.raw_ops.CropAndResizeGradImage

tf.raw_ops.CropAndResizeGradImage(
    grads, boxes, box_ind, image_size, T, method='bilinear', name=None
)

Args
grads	A Tensor of type float32. A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].
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_valueto extrapolate the input image values. </td> </tr><tr> <td>box_ind</td> <td> ATensorof typeint32. A 1-D tensor of shape[num_boxes]with int32 values in[0, batch). The value ofbox_ind[i]specifies the image that thei-th box refers to. </td> </tr><tr> <td>image_size</td> <td> ATensorof typeint32. A 1-D tensor with value[batch, image_height, image_width, depth]containing the original image size. Bothimage_heightandimage_widthneed to be positive. </td> </tr><tr> <td>T</td> <td> A <a href="../../tf/dtypes/DType"><code>tf.DType</code></a> from:tf.float32, tf.half, tf.float64. </td> </tr><tr> <td>method</td> <td> An optionalstringfrom:"bilinear", "nearest". Defaults to"bilinear". A string specifying the interpolation method. Only 'bilinear' is supported for now. </td> </tr><tr> <td>name`	A name for the operation (optional).

Returns
A Tensor of type T.