加载InpaintingModel_gen.pth预训练模型时出现:RuntimeError: Error(s) in loading state_dict for ContextEncoder: Missing key(s) in state_dict: "encoder.0.weight", "encoder.0.bias", "encoder.2.weight", "encoder.2.bias", "encoder.3.weight", "encoder.3.bias", "encoder.3.running_mean", "encoder.3.running_var", "encoder.5.weight", "encoder.5.bias", "encoder.6.weight", "encoder.6.bias", "encoder.6.running_mean", "encoder.6.running_var",...并且载入的模型为:class ContextEncoder(nn.Module): def init(self): super(ContextEncoder, self).init() # 编码器 self.encoder = nn.Sequential( nn.Conv2d(4, 64, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), ) # 解码器 self.decoder = nn.Sequential( nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(256), nn.ReLU(inplace=True), nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1), nn.Sigmoid(), ) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x 要怎么改
时间: 2023-06-16 12:04:01 浏览: 245
根据报错信息可以看到,预训练模型中缺少了一些键对应的值,这些键是`self.encoder`中的权重和偏置参数。因此,我们需要在模型的`__init__`方法中对`self.encoder`的权重和偏置进行初始化,这样就可以让预训练模型的键和模型的键对应上。
具体来说,可以在`__init__`方法中添加如下代码:
```
self.encoder[0].weight = nn.Parameter(torch.zeros_like(self.encoder[0].weight))
self.encoder[0].bias = nn.Parameter(torch.zeros_like(self.encoder[0].bias))
self.encoder[2].weight = nn.Parameter(torch.zeros_like(self.encoder[2].weight))
self.encoder[2].bias = nn.Parameter(torch.zeros_like(self.encoder[2].bias))
self.encoder[3].weight = nn.Parameter(torch.zeros_like(self.encoder[3].weight))
self.encoder[3].bias = nn.Parameter(torch.zeros_like(self.encoder[3].bias))
self.encoder[5].weight = nn.Parameter(torch.zeros_like(self.encoder[5].weight))
self.encoder[5].bias = nn.Parameter(torch.zeros_like(self.encoder[5].bias))
self.encoder[6].weight = nn.Parameter(torch.zeros_like(self.encoder[6].weight))
self.encoder[6].bias = nn.Parameter(torch.zeros_like(self.encoder[6].bias))
self.encoder[6].running_mean = nn.Parameter(torch.zeros_like(self.encoder[6].running_mean))
self.encoder[6].running_var = nn.Parameter(torch.zeros_like(self.encoder[6].running_var))
```
这段代码中,我们对`self.encoder`的前7个卷积层的权重和偏置进行了初始化,并对第7个卷积层的`running_mean`和`running_var`也进行了初始化。这样,在加载预训练模型时,就可以正确地对应到模型的键,避免了报错。