MSDN.py 文件源码

def build_loss_objectiveness(self, region_objectiveness, targets):
        loss_objectiveness = F.cross_entropy(region_objectiveness, targets)
        maxv, predict = region_objectiveness.data.max(1)
        labels = targets.squeeze()
        fg_cnt = torch.sum(labels.data.ne(0))
        bg_cnt = labels.data.numel() - fg_cnt
        if fg_cnt > 0:
            self.tp_reg = torch.sum(predict[:fg_cnt].eq(labels.data[:fg_cnt]))
        else:
            self.tp_reg = 0.
        if bg_cnt > 0:
            self.tf_reg = torch.sum(predict[fg_cnt:].eq(labels.data[fg_cnt:]))
        else:
            self.tp_reg = 0.
        self.fg_cnt_reg = fg_cnt
        self.bg_cnt_reg = bg_cnt
        return loss_objectiveness