import torch import torch.nn as nn import torch.nn.functional as F import torchvision.models as models import os class FCNTransformerNet(nn.Module): def __init__(self, num_classes): super(FCNTransformerNet, self).__init__() self.fcn_backbone = models.segmentation.fcn_resnet50(pretrained=True).backbone self.fcn_backbone.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.transformer_layers = nn.TransformerEncoderLayer(d_model=2048, nhead=8) self.transformer_encoder = nn.TransformerEncoder(self.transformer_layers, num_layers=6) self.classification_head = nn.Sequential( nn.Linear(2048, 512), nn.ReLU(), nn.Linear(512, num_classes) ) def forward(self, x): fcn_output = self.fcn_backbone(x)['out'] fcn_output = fcn_output.view(fcn_output.size(0), fcn_output.size(1), -1) fcn_output = fcn_output.permute(2, 0, 1) transformer_output = self.transformer_encoder(fcn_output) transformer_output = transformer_output.permute(1, 2, 0) transformer_output = transformer_output.contiguous().view(transformer_output.size(0), -1, 1, 1) output = self.classification_head(transformer_output) return output FCNTransformerNet net = FCNTransformerNet(num_classes=2) input_batch = torch.randn(4, 3, 512, 512) output_batch = net(input_batch) print(output_batch.size()) # Should print: torch.Size([4, 2, 512, 512]) 运行这段代码，并改错

在这段代码中，有几个问题需要进行修正。

首先，你定义了一个名为 `FCNTransformerNet` 的类，但是没有在代码末尾实例化这个类。你需要添加 `()` 来实例化该类，将其赋值给变量 `net`。

其次，你需要将 `FCNTransformerNet` 类的定义放在 `if __name__ == "__main__":` 条件语句内部，以确保它只在主程序中运行，而不是在模块导入时运行。

最后，你的代码中缺少了一些必要的导入语句。你需要导入 `torch`, `torch.nn` 和 `torchvision.models`。

下面是修改后的代码：

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

class FCNTransformerNet(nn.Module):
    def __init__(self, num_classes):
        super(FCNTransformerNet, self).__init__()

        self.fcn_backbone = models.segmentation.fcn_resnet50(pretrained=True).backbone
        self.fcn_backbone.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)

        self.transformer_layers = nn.TransformerEncoderLayer(d_model=2048, nhead=8)
        self.transformer_encoder = nn.TransformerEncoder(self.transformer_layers, num_layers=6)

        self.classification_head = nn.Sequential(
            nn.Linear(2048, 512),
            nn.ReLU(),
            nn.Linear(512, num_classes)
        )

    def forward(self, x):
        fcn_output = self.fcn_backbone(x)['out']
        fcn_output = fcn_output.view(fcn_output.size(0), fcn_output.size(1), -1)
        fcn_output = fcn_output.permute(2, 0, 1)
        
        transformer_output = self.transformer_encoder(fcn_output)
        
        transformer_output = transformer_output.permute(1, 2, 0)
        transformer_output = transformer_output.contiguous().view(transformer_output.size(0), -1, 1, 1)

        output = self.classification_head(transformer_output)
       
        return output

if __name__ == "__main__":
    net = FCNTransformerNet(num_classes=2)

    input_batch = torch.randn(4, 3, 512, 512)
    output_batch = net(input_batch)
    print(output_batch.size())  # Should print: torch.Size([4, 2, 512, 512])

请注意，这段代码假设你已经正确安装了 `torch` 和 `torchvision` 库。如果出现任何错误，你可能需要检查这些库的安装情况。