def bbox_transform(boxes, deltas, weights=(1.0, 1.0, 1.0, 1.0)):
    """Forward transform that maps proposal boxes to predicted ground-truth
    boxes using bounding-box regression deltas. See bbox_transform_inv for a
    description of the weights argument.
    """
    if boxes.shape[0] == 0:
        return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)

    boxes = boxes.astype(deltas.dtype, copy=False)

    widths = boxes[:, 2] - boxes[:, 0] + 1.0
    heights = boxes[:, 3] - boxes[:, 1] + 1.0
    ctr_x = boxes[:, 0] + 0.5 * widths
    ctr_y = boxes[:, 1] + 0.5 * heights

    wx, wy, ww, wh = weights
    dx = deltas[:, 0::4] / wx
    dy = deltas[:, 1::4] / wy
    dw = deltas[:, 2::4] / ww
    dh = deltas[:, 3::4] / wh

    # Prevent sending too large values into np.exp()
    dw = np.minimum(dw, cfg.BBOX_XFORM_CLIP)
    dh = np.minimum(dh, cfg.BBOX_XFORM_CLIP)

    pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
    pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
    pred_w = np.exp(dw) * widths[:, np.newaxis]
    pred_h = np.exp(dh) * heights[:, np.newaxis]

    pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
    # x1
    pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
    # y1
    pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
    # x2 (note: "- 1" is correct; don't be fooled by the asymmetry)
    pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - 1
    # y2 (note: "- 1" is correct; don't be fooled by the asymmetry)
    pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - 1

    return pred_boxes
Forward transform that maps proposal boxes to predicted ground-truth
    boxes using bounding-box regression deltas. See bbox_transform_inv for a
    description of the weights argument.
    
detectron/utils/boxes.py