rpn：def concat_box_prediction_layers

#因为特征图大小不一样，生成的prediction_layers维度？也不一样，所以需要进行调整！！
def concat_box_prediction_layers(box_cls: List[Tensor], box_regression: List[Tensor]) -> Tuple[Tensor, Tensor]:
    box_cls_flattened = []
    box_regression_flattened = []
    #意思就是lable是对整体的feature-concat的特征负责，所以需要使得不同feature的输出，对objectness and the box_regression有相同的表示，直接看代码吧
    # for each feature level, permute（排列） the outputs to make them be in the
    # same format as the labels. Note that the labels are computed for
    # all feature levels concatenated, so we keep the same representation
    # for the objectness and the box_regression
    for box_cls_per_level, box_regression_per_level in zip(box_cls, box_regression):
        N, AxC, H, W = box_cls_per_level.shape
        Ax4 = box_regression_per_level.shape[1]
        A = Ax4 // 4
        C = AxC // A
        box_cls_per_level = permute_and_flatten(box_cls_per_level, N, A, C, H, W)
        box_cls_flattened.append(box_cls_per_level)

        box_regression_per_level = permute_and_flatten(box_regression_per_level, N, A, 4, H, W)
        box_regression_flattened.append(box_regression_per_level)
    # concatenate on the first dimension (representing the feature levels), to
    # take into account the way the labels were generated (with all feature maps
    # being concatenated as well)
# 前面还好，后面关于torch.cat按指定维度拼接这里有点晕。难道其它维度相同吗都？？
    box_cls = torch.cat(box_cls_flattened, dim=1).flatten(0, -2)
    box_regression = torch.cat(box_regression_flattened, dim=1).reshape(-1, 4)
    return box_cls, box_regression