ZHENG

out0_branch = self.last_layer0\[:5](x0) 表示make_last_layers的前五层,图片中红框内容 out0= self.last_layer0\[5:](out0_branch) 表示make_last_layers最后两层，图片中蓝框内容  ```python def make_last_layers(filters_list, in_filters, out_filter): m = nn.Sequential( conv2d(in_filters, filters_list[0], 1), conv2d(filters_list[0], filters_list[1], 3), conv2d(filters_list[1], filters_list[0], 1), conv2d(filters_list[0], filters_list[1], 3), conv2d(filters_list[1], filters_list[0],...

@fangwei123456 您好，我看了您推荐的论文和代码。 论文“Training Full Spike Neural Networks via Auxiliary Accumulation Pathway”中提到snn的理论计算功耗范围为 $\mathrm E(\mathcal F)\in[T\cdot E_{m a c}\cdot O_{m a c},T\cdot(E_{a c}\cdot O_{a c}+E_{m a c}\cdot O_{m a c})] \quad(15)$ ,...