我在使用Pytorch Lightning进行U-Net模型的训练。模型训练成功，但在训练后尝试从检查点加载模型时，遇到了以下错误：
完整的错误追踪信息如下：
Traceback (most recent call last): File "src/train.py", line 269, in <module> main(sys.argv[1:]) File "src/train.py", line 263, in main model = Unet.load_from_checkpoint(checkpoint_callback.best_model_path) File "/home/africa_wikilimo/miniconda3/envs/xarray_test/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 153, in load_from_checkpoint model = cls._load_model_state(checkpoint, *args, strict=strict, **kwargs) File "/home/africa_wikilimo/miniconda3/envs/xarray_test/lib/python3.8/site-packages/pytorch_lightning/core/saving.py", line 190, in _load_model_state model = cls(*cls_args, **cls_kwargs) File "src/train.py", line 162, in __init__ self.inc = double_conv(self.n_channels, 64) File "src/train.py", line 122, in double_conv nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), File "/home/africa_wikilimo/miniconda3/envs/xarray_test/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 406, in __init__ super(Conv2d, self).__init__( File "/home/africa_wikilimo/miniconda3/envs/xarray_test/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 50, in __init__ if in_channels % groups != 0:TypeError: unsupported operand type(s) for %: 'dict' and 'int'我尝试查看了GitHub的问题和论坛，但无法找出问题所在。请帮助我解决这个问题。
以下是我的模型代码和加载检查点的步骤：
模型：
class Unet(pl.LightningModule): def __init__(self, n_channels, n_classes=5): super(Unet, self).__init__() # self.hparams = hparams self.n_channels = n_channels self.n_classes = n_classes self.bilinear = True self.logger = WandbLogger(name="Adam", project="pytorchlightning") def double_conv(in_channels, out_channels): return nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), ) def down(in_channels, out_channels): return nn.Sequential( nn.MaxPool2d(2), double_conv(in_channels, out_channels) ) class up(nn.Module): def __init__(self, in_channels, out_channels, bilinear=False): super().__init__() if bilinear: self.up = nn.Upsample( scale_factor=2, mode="bilinear", align_corners=True ) else: self.up = nn.ConvTranspose2d( in_channels // 2, in_channels // 2, kernel_size=2, stride=2 ) self.conv = double_conv(in_channels, out_channels) def forward(self, x1, x2): x1 = self.up(x1) # [?, C, H, W] diffY = x2.size()[2] - x1.size()[2] diffX = x2.size()[3] - x1.size()[3] x1 = F.pad( x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2] ) x = torch.cat([x2, x1], dim=1) return self.conv(x) self.inc = double_conv(self.n_channels, 64) self.down1 = down(64, 128) self.down2 = down(128, 256) self.down3 = down(256, 512) self.down4 = down(512, 512) self.up1 = up(1024, 256) self.up2 = up(512, 128) self.up3 = up(256, 64) self.up4 = up(128, 64) self.out = nn.Conv2d(64, self.n_classes, kernel_size=1) def forward(self, x): x1 = self.inc(x) x2 = self.down1(x1) x3 = self.down2(x2) x4 = self.down3(x3) x5 = self.down4(x4) x = self.up1(x5, x4) x = self.up2(x, x3) x = self.up3(x, x2) x = self.up4(x, x1) return self.out(x) def training_step(self, batch, batch_nb): x, y = batch y_hat = self.forward(x) loss = self.MSE(y_hat, y) # wandb_logger.log_metrics({"loss":loss}) return {"loss": loss} def training_epoch_end(self, outputs): avg_train_loss = torch.stack([x["loss"] for x in outputs]).mean() self.logger.log_metrics({"train_loss": avg_train_loss}) return {"average_loss": avg_train_loss} def test_step(self, batch, batch_nb): x, y = batch y_hat = self.forward(x) loss = self.MSE(y_hat, y) return {"test_loss": loss, "pred": y_hat} def test_end(self, outputs): avg_loss = torch.stack([x["test_loss"] for x in outputs]).mean() return {"avg_test_loss": avg_loss} def MSE(self, logits, labels): return torch.mean((logits - labels) ** 2) def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0.1, weight_decay=1e-8)主函数：
def main(expconfig): # 定义检查点回调 checkpoint_callback = ModelCheckpoint( filepath="/home/africa_wikilimo/data/model_checkpoint/", save_top_k=1, verbose=True, monitor="loss", mode="min", prefix="", ) # 初始化数据集 print("正在初始化气候数据集....") clima_train = Clima_Dataset(expconfig[0]) # 初始化数据加载器 print("正在初始化训练加载器....") train_dataloader = DataLoader(clima_train, batch_size=2, num_workers=4) # 初始化模型和训练器 print("正在初始化模型...") model = Unet(n_channels=9, n_classes=5) print("正在初始化训练器....") if torch.cuda.is_available(): model.cuda() trainer = pl.Trainer( max_epochs=1, gpus=1, checkpoint_callback=checkpoint_callback, early_stop_callback=None, ) else: trainer = pl.Trainer(max_epochs=1, checkpoint_callback=checkpoint_callback) trainer.fit(model, train_dataloader=train_dataloader) print(checkpoint_callback.best_model_path) model = Unet.load_from_checkpoint(checkpoint_callback.best_model_path)回答：
原因
出现这个问题是因为您的模型无法从检查点中加载超参数（n_channels, n_classes=5），因为您没有明确保存这些参数。
解决方法
您可以通过在Unet类的init方法中使用self.save_hyperparameters('n_channels', 'n_classes')方法来解决这个问题。有关此方法的更多详细信息，请参考PyTorch Lightning超参数文档。使用save_hyperparameters方法可以将选定的参数保存到hparams.yaml文件中，并与检查点一起保存。
感谢PyTorch Lightning核心贡献者团队的@Adrian Wälchli(awaelchli)提出了这个解决方案，当我遇到相同问题时，他提供了帮助。
