将numpy代码转换为pytorch removing for循环

Question

我的理解是，在python中使用for循环会使我们的代码变慢。因此，应该尽可能地避免它们。我有一段代码，我想把它转换成PyTorch。有没有一种聪明的方法来编写代码，这样我们就不会有for循环在里面了？

def nms(dets, scores, thresh):
    '''
    dets is a numpy array : num_dets, 6
    scores ia  nump array : num_dets,
    '''
    x1 = dets[:, 0]
    y1 = dets[:, 1]
    z1 = dets[:, 2]
    x2 = dets[:, 3]
    y2 = dets[:, 4]
    z2 = dets[:, 5]

    volume = (x2 - x1 + 1) * (y2 - y1 + 1) * (z2 - z1 + 1)
    order = scores.argsort()[::-1]  # get boxes with more ious first

    keep = []
    while order.size > 0:
        i = order[0]  # pick maxmum iou box
        keep.append(i)
        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        zz1 = np.maximum(z1[i], z1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])
        zz2 = np.minimum(z2[i], z2[order[1:]])

        w = np.maximum(0.0, xx2 - xx1 + 1)  # maximum width
        h = np.maximum(0.0, yy2 - yy1 + 1)  # maxiumum height
        l = np.maximum(0.0, zz2 - zz1 + 1)  # maxiumum length
        inter = w * h * l
        ovr = inter / (volume[i] + volume[order[1:]] - inter)

        inds = np.where(ovr <= thresh)[0]
        order = order[inds + 1]

    return keep

如果我们可以使用索引来以某种方式替换for循环，我将非常感激。

Answer 1

这个算法是连续的，因为下一次迭代都依赖于前一次迭代。所以，在这里你不能从向量并行化中获益。