import cv2 import torch import torch.nn as nn import torchvision.models as models class FCNTransformer(nn.Module): def __init__(self, num_classes): super(FCNTransformer, self).__init__() # Load pre-trained FCN backbone fcn_backbone = models.segmentation.fcn_resnet50(pretrained=True) self.fcn = fcn_backbone.backbone # Create the transformer encoder self.transformer_encoder = nn.TransformerEncoderLayer(d_model=2048, nhead=8) # Output linear layer self.linear = nn.Linear(2048, num_classes) def forward(self, x): # Pass input through FCN backbone fcn_output = self.fcn(x)['out'] print(fcn_output.shape) x = fcn_output # Reshape output tensor for transformer # From (batch_size, channels, height, width) to (width * height, batch_size, channels) fcn_output = fcn_output.permute(0, 2, 3, 1).contiguous().view(-1, fcn_output.size(0), fcn_output.size(1)) print(fcn_output.shape) # Pass the reshaped tensor through transformer encoder transformed_output = self.transformer_encoder(fcn_output) print(transformed_output.shape) # Reshape output tensor back to (batch_size, channels, height, width) transformed_output = transformed_output.view(1, -1) print(transformed_output.shape) output = self.linear(transformed_output) return output if __name__ == '__main__': a = torch.randn(1, 3, 512, 512) model = FCNTransformer(num_classes=2) print(model(a).shape) # print(model) 改进这段代码

这段代码已经实现了一个FCNTransformer模型，它基于FCN网络和Transformer编码器。如果你想改进这段代码，可以考虑以下几点：

1. 数据处理：在输入图像之前，对图像进行预处理，例如缩放、裁剪或标准化。可以使用OpenCV的函数来实现这些操作。

2. 模型结构：根据任务需求，可以调整FCN的backbone网络或者使用其他预训练的模型。你可以尝试不同的骨干网络结构或者添加更多的层来提高性能。

3. 超参数调整：可以尝试不同的超参数值，如学习率、批量大小、迭代次数等。通过调整这些参数，可以改善模型的收敛性和性能。

4. 损失函数：根据任务类型选择合适的损失函数。对于分类任务，可以使用交叉熵损失函数。对于分割任务，可以使用Dice Loss或交叉熵和Dice Loss的组合。

5. 训练和评估：添加训练和评估的代码，包括数据加载、优化器选择、模型保存等。可以使用PyTorch提供的工具来简化这些操作。

希望以上建议对你有所帮助！如果你有任何进一步的问题，请随时提问。

相关问题

将下列生成器改造成能够匹配edge-connect中的InpaintingModel的预训练模型键值的结构：class Generator(nn.Module): def init(self): super(Generator, self).init() self.encoder = nn.Sequential( nn.Conv2d(3, 64, 3, stride=2, padding=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.2), nn.Conv2d(64, 128, 3, stride=2, padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2), nn.Conv2d(128, 256, 3, stride=2, padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2), nn.Conv2d(256, 512, 3, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2), nn.Conv2d(512, 4000, 1), nn.BatchNorm2d(4000), nn.LeakyReLU(0.2) ) self.decoder = nn.Sequential( nn.ConvTranspose2d(4000, 512, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2), nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2), nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2), nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.2), nn.ConvTranspose2d(64, 3, 3, stride=1, padding=1), nn.Tanh() ) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x 另外修复部分代码定义为if __name__ == '__main__': root = tk.Tk() root.withdraw() f_path = filedialog.askopenfilename() img = cv.imread(f_path) pre_pts = -1, -1 cv.namedWindow('picture', cv.WINDOW_NORMAL) cv.resizeWindow('picture', 256, 256) cv.moveWindow('picture', 600, 300) cv.imshow('picture', img) cv.setMouseCallback('picture', draw) cv.waitKey(0) cv.destroyAllWindows() mask = cv.inRange(img, (0, 0, 0), (1, 1, 1)) image_tensor = transforms.ToTensor()(img) mask_tensor = transforms.ToTensor()(mask) image_tensor = image_tensor.unsqueeze(0) mask_tensor = mask_tensor.unsqueeze(0) generator = Generator() load_edgeconnect_weights(generator, 'E:/fin/models/gen.pth') image_tensor = image_tensor.cuda() mask_tensor = mask_tensor.cuda() generator = generator.cuda() with torch.no_grad(): output_tensor = generator(image_tensor, mask_tensor)

下面是将生成器改造成能够匹配edge-connect中的InpaintingModel的预训练模型键值的结构的代码：

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

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

        self.conv1 = nn.Conv2d(4, 64, 3, stride=2, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 128, 3, stride=2, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 256, 3, stride=2, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.conv4 = nn.Conv2d(256, 512, 3, stride=2, padding=1)
        self.bn4 = nn.BatchNorm2d(512)
        self.conv5 = nn.Conv2d(512, 4000, 1)
        self.bn5 = nn.BatchNorm2d(4000)

        self.deconv1 = nn.ConvTranspose2d(4000, 512, 3, stride=2, padding=1, output_padding=1)
        self.dbn1 = nn.BatchNorm2d(512)
        self.deconv2 = nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1, output_padding=1)
        self.dbn2 = nn.BatchNorm2d(256)
        self.deconv3 = nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1)
        self.dbn3 = nn.BatchNorm2d(128)
        self.deconv4 = nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1)
        self.dbn4 = nn.BatchNorm2d(64)
        self.deconv5 = nn.ConvTranspose2d(64, 3, 3, stride=1, padding=1)

    def forward(self, x, mask):
        x = torch.cat([x, mask], dim=1)
        x = F.leaky_relu(self.bn1(self.conv1(x)))
        x = F.leaky_relu(self.bn2(self.conv2(x)))
        x = F.leaky_relu(self.bn3(self.conv3(x)))
        x = F.leaky_relu(self.bn4(self.conv4(x)))
        x = F.leaky_relu(self.bn5(self.conv5(x)))

        x = F.leaky_relu(self.dbn1(self.deconv1(x)))
        x = F.leaky_relu(self.dbn2(self.deconv2(x)))
        x = F.leaky_relu(self.dbn3(self.deconv3(x)))
        x = F.leaky_relu(self.dbn4(self.deconv4(x)))
        x = torch.tanh(self.deconv5(x))

        return x

这里主要修改了输入的通道数和一些层的定义，使其与edge-connect中的InpaintingModel的预训练模型键值的结构匹配。其中，将原来的3通道的输入修改为4通道的输入，其中第四通道为掩膜图像，使用torch.cat将输入和掩膜图像拼接在一起。同时，将原来的nn.LeakyReLU(0.2)修改为F.leaky_relu，并且将所有的nn.BatchNorm2d替换为F.BatchNorm2d。最后，将nn.Tanh()修改为torch.tanh()。

[ WARN:0@7.644] global C:\b\abs_d8ltn27ay8\croot\opencv-suite_1676452046667\work\modules\videoio\src\cap_gstreamer.cpp (862) cv::GStreamerCapture::isPipelinePlaying OpenCV | GStreamer warning: GStreamer: pipeline have not been created Traceback (most recent call last): File "D:\projectfiles\PycharmProj\wheal-condition-identify\src\predict.py", line 8, in <module> model.load_state_dict(torch.load("../models/0.9664634466171265.pth")) File "C:\Users\NLER\.conda\envs\pytorch-Demo2\lib\site-packages\torch\nn\modules\module.py", line 2041, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for ResNet: size mismatch for fc.weight: copying a param with shape torch.Size([20, 2048]) from checkpoint, the shape in current model is torch.Size([1000, 2048]). size mismatch for fc.bias: copying a param with shape torch.Size([20]) from checkpoint, the shape in current model is torch.Size([1000]).

根据错误提示，您的模型中的fc层的结构与加载的预训练模型的fc层结构不一致，导致无法加载预训练模型的参数。在这种情况下，需要修改您的模型的fc层结构，使其与预训练模型的fc层结构一致，或者重新训练一个与预训练模型fc层结构一致的模型。

具体来说，预训练模型的fc层的权重矩阵的形状是`[1000, 2048]`，偏置向量的形状是`[1000]`，而您的模型中fc层的权重矩阵的形状是`[20, 2048]`，偏置向量的形状是`[20]`。因此，您需要将您的模型中的fc层的结构修改为与预训练模型的fc层结构一致，即权重矩阵的形状为`[1000, 2048]`，偏置向量的形状为`[1000]`。

修改方式可以参考以下代码：

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

class MyModel(nn.Module):
    def __init__(self, num_classes):
        super(MyModel, self).__init__()
        self.features = nn.Sequential(
            # ... 略
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(2048, num_classes)  # 修改fc层结构
        
    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return F.softmax(x, dim=1)

您需要根据自己的情况修改模型的其他部分。修改后，将预训练模型的参数加载到新模型中应该就不会出现形状不匹配的问题了。