OHEM的pytorch代码实现细节

详细解读一下OHEM的实现代码：

def ohem_loss(
    batch_size, cls_pred, cls_target, loc_pred, loc_target, smooth_l1_sigma=1.0
):
    """
    Arguments:
        batch_size (int): number of sampled rois for bbox head training
        loc_pred (FloatTensor): [R, 4], location of positive rois
        loc_target (FloatTensor): [R, 4], location of positive rois
        pos_mask (FloatTensor): [R], binary mask for sampled positive rois
        cls_pred (FloatTensor): [R, C]
        cls_target (LongTensor): [R]

    Returns:
        cls_loss, loc_loss (FloatTensor)
    """
    ohem_cls_loss = F.cross_entropy(cls_pred, cls_target, reduction='none', ignore_index=-1)
    ohem_loc_loss = smooth_l1_loss(loc_pred, loc_target, sigma=smooth_l1_sigma, reduce=False)
    #这里先暂存下正常的分类loss和回归loss
    loss = ohem_cls_loss + ohem_loc_loss
    #然后对分类和回归loss求和

  
    sorted_ohem_loss, idx = torch.sort(loss, descending=True)
    #再对loss进行降序排列
    keep_num = min(sorted_o