网络构架图：

 

代码实现：

import math
from collections import OrderedDict

import torch.nn as nn


#---------------------------------------------------------------------#
#   残差结构
#   利用一个1x1卷积下降通道数，然后利用一个3x3卷积提取特征并且上升通道数
#   最后接上一个残差边
#---------------------------------------------------------------------#
class BasicBlock(nn.Module):
    def __init__(self, inplanes, planes):
        super(BasicBlock, self).__init__()
        self.conv1  = nn.Conv2d(inplanes, planes[0], kernel_size=1, stride=1, padding=0, bias=False)
        self.bn1    = nn.BatchNorm2d(planes[0])
        self.relu1  = nn.LeakyReLU(0.1)
        
        self.conv2  = nn.Conv2d(planes[0], planes[1], kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2    = nn.BatchNorm2d(planes[1])
        self.relu2  = nn.LeakyReLU(0.1)

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu2(out)

        out += residual
        return out

class DarkNet(nn.Module):
    def __init__(self, layers):
        super(DarkNet, self).__init__()
        self.inplanes = 32
        # 416,416,3 -> 416,416,32
        self.conv1  = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1    = nn.BatchNorm2d(self.inplanes)
        self.relu1  = nn.LeakyReLU(0.1)

        # 416,416,32 -> 208,208,64
        self.layer1 = self._make_layer([32, 64], layers[0])
        # 208,208,64 -> 104,104,128
        self.layer2 = self._make_layer([64, 128], layers[1])
        # 104,104,128 -> 52,52,256
        self.layer3 = self._make_layer([128, 256], layers[2])
        # 52,52,256 -> 26,26,512
        self.layer4 = self._make_layer([256, 512], layers[3])
        # 26,26,512 -> 13,13,1024
        self.layer5 = self._make_layer([512, 1024], layers[4])

        self.layers_out_filters = [64, 128, 256, 512, 1024]

        # 进行权值初始化
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    #---------------------------------------------------------------------#
    #   在每一个layer里面，首先利用一个步长为2的3x3卷积进行下采样
    #   然后进行残差结构的堆叠
    #---------------------------------------------------------------------#
    def _make_layer(self, planes, blocks):
        layers = []
        # 下采样，步长为2，卷积核大小为3
        layers.append(("ds_conv", nn.Conv2d(self.inplanes, planes[1], kernel_size=3, stride=2, padding=1, bias=False)))
        layers.append(("ds_bn", nn.BatchNorm2d(planes[1])))
        layers.append(("ds_relu", nn.LeakyReLU(0.1)))
        # 加入残差结构
        self.inplanes = planes[1]
        for i in range(0, blocks):
            layers.append(("residual_{}".format(i), BasicBlock(self.inplanes, planes)))
        return nn.Sequential(OrderedDict(layers))

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)

        x = self.layer1(x)
        x = self.layer2(x)
        out3 = self.layer3(x)
        out4 = self.layer4(out3)
        out5 = self.layer5(out4)

        return out3, out4, out5

def darknet53():
    model = DarkNet([1, 2, 8, 8, 4])
    return model


from collections import OrderedDict

import torch
import torch.nn as nn

from nets.darknet import darknet53

def conv2d(filter_in, filter_out, kernel_size):
    pad = (kernel_size - 1) // 2 if kernel_size else 0
    return nn.Sequential(OrderedDict([
        ("conv", nn.Conv2d(filter_in, filter_out, kernel_size=kernel_size, stride=1, padding=pad, bias=False)),
        ("bn", nn.BatchNorm2d(filter_out)),
        ("relu", nn.LeakyReLU(0.1)),
    ]))

#------------------------------------------------------------------------#
#   make_last_layers里面一共有七个卷积，前五个用于提取特征。
#   后两个用于获得yolo网络的预测结果
#------------------------------------------------------------------------#
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

class YoloBody(nn.Module):
    def __init__(self, anchors_mask, num_classes):
        super(YoloBody, self).__init__()
        #---------------------------------------------------#   
        #   生成darknet53的主干模型
        #   获得三个有效特征层，他们的shape分别是：
        #   52,52,256
        #   26,26,512
        #   13,13,1024
        #---------------------------------------------------#
        self.backbone = darknet53()

        #---------------------------------------------------#
        #   out_filters : [64, 128, 256, 512, 1024]
        #---------------------------------------------------#
        out_filters = self.backbone.layers_out_filters

        #------------------------------------------------------------------------#
        #   计算yolo_head的输出通道数，对于voc数据集而言
        #   final_out_filter0 = final_out_filter1 = final_out_filter2 = 75
        #------------------------------------------------------------------------#
        self.last_layer0            = make_last_layers([512, 1024], out_filters[-1], len(anchors_mask[0]) * (num_classes + 5))

        self.last_layer1_conv       = conv2d(512, 256, 1)
        self.last_layer1_upsample   = nn.Upsample(scale_factor=2, mode='nearest')
        self.last_layer1            = make_last_layers([256, 512], out_filters[-2] + 256, len(anchors_mask[1]) * (num_classes + 5))

        self.last_layer2_conv       = conv2d(256, 128, 1)
        self.last_layer2_upsample   = nn.Upsample(scale_factor=2, mode='nearest')
        self.last_layer2            = make_last_layers([128, 256], out_filters[-3] + 128, len(anchors_mask[2]) * (num_classes + 5))

    def forward(self, x):
        #---------------------------------------------------#   
        #   获得三个有效特征层，他们的shape分别是：
        #   52,52,256；26,26,512；13,13,1024
        #---------------------------------------------------#
        x2, x1, x0 = self.backbone(x)

        #---------------------------------------------------#
        #   第一个特征层
        #   out0 = (batch_size,255,13,13)
        #---------------------------------------------------#
        # 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512 -> 13,13,1024 -> 13,13,512
        out0_branch = self.last_layer0[:5](x0)
        out0        = self.last_layer0[5:](out0_branch)

        # 13,13,512 -> 13,13,256 -> 26,26,256
        x1_in = self.last_layer1_conv(out0_branch)
        x1_in = self.last_layer1_upsample(x1_in)

        # 26,26,256 + 26,26,512 -> 26,26,768
        x1_in = torch.cat([x1_in, x1], 1)
        #---------------------------------------------------#
        #   第二个特征层
        #   out1 = (batch_size,255,26,26)
        #---------------------------------------------------#
        # 26,26,768 -> 26,26,256 -> 26,26,512 -> 26,26,256 -> 26,26,512 -> 26,26,256
        out1_branch = self.last_layer1[:5](x1_in)
        out1        = self.last_layer1[5:](out1_branch)

        # 26,26,256 -> 26,26,128 -> 52,52,128
        x2_in = self.last_layer2_conv(out1_branch)
        x2_in = self.last_layer2_upsample(x2_in)

        # 52,52,128 + 52,52,256 -> 52,52,384
        x2_in = torch.cat([x2_in, x2], 1)
        #---------------------------------------------------#
        #   第三个特征层
        #   out3 = (batch_size,255,52,52)
        #---------------------------------------------------#
        # 52,52,384 -> 52,52,128 -> 52,52,256 -> 52,52,128 -> 52,52,256 -> 52,52,128
        out2 = self.last_layer2(x2_in)
        return out0, out1, out2