pytorch实现用shufflenetv2代替CSPdarknet53的代码

以下是将ShuffleNetV2替换为CSPDarknet53的代码示例：

首先，我们需要导入必要的库和模块：

import torch
import torch.nn as nn
import torch.nn.functional as F

然后，我们定义ShuffleNetV2模块：

class ShuffleNetV2Block(nn.Module):
    def __init__(self, input_channels, output_channels, mid_channels=None, ksize=3, stride=1, use_bn=True):
        super(ShuffleNetV2Block, self).__init__()
        if mid_channels is None:
            mid_channels = output_channels // 2
        self.stride = stride
        self.use_bn = use_bn

        if stride == 1:
            self.branch1 = nn.Sequential()
        else:
            self.branch1 = nn.Sequential(
                nn.AvgPool2d(kernel_size=3, stride=stride, padding=1),
                nn.Conv2d(input_channels, input_channels, kernel_size=1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(input_channels),
                nn.ReLU(inplace=True),
            )

        self.branch2 = nn.Sequential(
            nn.Conv2d(input_channels if stride > 1 else mid_channels, mid_channels, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(mid_channels, mid_channels, kernel_size=ksize, stride=stride, padding=ksize // 2, groups=mid_channels, bias=False),
            nn.BatchNorm2d(mid_channels),
            nn.Conv2d(mid_channels, output_channels, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(output_channels),
        )

    def forward(self, x):
        x1 = self.branch1(x)
        x2 = self.branch2(x if self.stride > 1 else F.relu(x1))
        out = torch.cat((x1, x2), dim=1)
        out = F.shuffle(out, 2) if self.stride == 2 else out
        return out

接下来，我们定义CSPDarknet53模块：

class CSPDarknet53(nn.Module):
    def __init__(self):
        super(CSPDarknet53, self).__init__()
        self.stem = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
        )
        self.layer1 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(64, 64, ksize=3, stride=1),
            nn.Conv2d(128, 64, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(64, 64, ksize=3, stride=1),
            nn.Conv2d(128, 64, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(64, 64, ksize=3, stride=1),
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(128, 128, ksize=3, stride=1),
            nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(128, 128, ksize=3, stride=1),
            nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(128, 128, ksize=3, stride=1),
            nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(128, 128, ksize=3, stride=1),
            nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(128, 128, ksize=3, stride=1),
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(256, 256, ksize=3, stride=1),
            nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(256, 256, ksize=3, stride=1),
            nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(256, 256, ksize=3, stride=1),
            nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(256, 256, ksize=3, stride=1),
            nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(256, 256, ksize=3, stride=1),
            nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(256, 256, ksize=3, stride=1),
        )
        self.layer4 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(512, 512, ksize=3, stride=1),
            nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(512, 512, ksize=3, stride=1),
            nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            ShuffleNetV2Block(512, 512, ksize=3, stride=1),
        )

    def forward(self, x):
        x = self.stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        return x

最后，我们可以使用CSPDarknet53替换任何需要使用Darknet53的模型。

例如，下面是一个RetinaNet模型，其中使用CSPDarknet53代替了标准的Darknet53：

class RetinaNet(nn.Module):
    def __init__(self, num_classes):
        super(RetinaNet, self).__init__()
        self.backbone = CSPDarknet53()
        self.fpn = FPN([512, 1024, 2048], 256)
        self.head = RetinaNetHead(num_classes)

    def forward(self, x):
        x = self.backbone(x)
        features = self.fpn(x)
        out = self.head(features)
        return out