Yolo head （YoloBody forward 部分）代码求解释

[S1monXuan]

您好，我想问一下YoloBody中Forward代码中的
out0_branch = self.last_layer0[:5](x0) out0= self.last_layer0[5:](out0_branch)
该怎么解释。其中5代表的是什么？是先验框的5个条件吗？
谢谢各位大佬

[ZHENG2049]

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

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], 1),

        conv2d(filters_list[0], filters_list[1], 3),
        nn.Conv2d(filters_list[1], out_filter, kernel_size=1, stride=1, padding=0, bias=True)
    )
    return m

您好，我想问一下YoloBody中Forward代码中的Hello, I would like to ask Yolobody in the Forward code out0_branch = self.last_layer0[:5](x0) out0= self.last_layer0[5:](out0_branch) 该怎么解释。其中5代表的是什么？是先验框的5个条件吗？How to explain. What does five of them stand for? Are the five conditions of a priori box? 谢谢各位大佬 Thank you