reid任务中在resnet50backbone后加入pcb模块提升性能的具体代码

以下是在ResNet50 backbone后加入PCB模块的PyTorch代码示例：

import torch.nn as nn
import torchvision.models as models

class ResNet50_PCB(nn.Module):
    def __init__(self, num_classes):
        super(ResNet50_PCB, self).__init__()
        self.backbone = models.resnet50(pretrained=True)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.num_classes = num_classes
        self.parts = nn.ModuleList([nn.Sequential(
            nn.Conv2d(2048, 256, kernel_size=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 512, kernel_size=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
        ) for i in range(6)])
        self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.classifier = nn.Linear(512*6, num_classes)

    def forward(self, x):
        features = self.backbone.conv1(x)
        features = self.backbone.bn1(features)
        features = self.backbone.relu(features)
        features = self.backbone.maxpool(features)

        features = self.backbone.layer1(features)
        features = self.backbone.layer2(features)
        features = self.backbone.layer3(features)
        features = self.backbone.layer4(features)

        pcbs = []
        for i in range(6):
            part = self.parts[i](features)
            part = self.avgpool(part)
            pcbs.append(part)

        global_feat = self.global_pool(features)
        global_feat = global_feat.view(global_feat.shape[0], -1)

        pcb_feat = torch.cat(pcbs, dim=1)
        pcb_feat = pcb_feat.view(pcb_feat.shape[0], -1)

        feat = torch.cat([global_feat, pcb_feat], dim=1)
        logits = self.classifier(feat)

        return logits

在这个模型中，我们通过`nn.ModuleList`定义了6个PCB模块，每个模块包含3个卷积层和1个自适应平均池化层。我们将每个PCB模块的输出通过平均池化层转换为256维向量，并将这6个向量拼接在一起，得到一个1536维的向量。同时，我们也计算出了整张图像的全局特征，通过自适应平均池化层和线性层将其转换为512维向量。最后，将全局特征和每个PCB模块的特征拼接在一起，通过线性层输出分类结果。