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_valueto extrapolate the input image values.
</td>
</tr><tr>
<td>box_ind<a id="box_ind"></a>
</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>method<a id="method"></a>
</td>
<td>
An optionalstringfrom:"bilinear". 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 float32.
|
