无法从 Pytorch-Lightning 中的检查点加载模型
Unable to load model from checkpoint in Pytorch-Lightning
我正在 Pytorch Lightning 中使用 U-Net。我能够成功地训练模型但是在训练之后当我尝试从检查点加载模型时我得到这个错误：
完整的回溯：
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'
我尝试浏览 github 问题和论坛，但无法弄清楚问题是什么。请帮忙。
这是我的模型代码和检查点加载步骤：
型号：
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)
主要功能：
def main(expconfig):
# Define checkpoint callback
checkpoint_callback = ModelCheckpoint(
filepath="/home/africa_wikilimo/data/model_checkpoint/",
save_top_k=1,
verbose=True,
monitor="loss",
mode="min",
prefix="",
)
# Initialise datasets
print("Initializing Climate Dataset....")
clima_train = Clima_Dataset(expconfig[0])
# Initialise dataloaders
print("Initializing train_loader....")
train_dataloader = DataLoader(clima_train, batch_size=2, num_workers=4)
# Initialise model and trainer
print("Initializing model...")
model = Unet(n_channels=9, n_classes=5)
print("Initializing Trainer....")
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)
原因
发生这种情况是因为您的模型无法从检查点加载超参数（n_channels、n_classes=5），因为您没有明确保存它们。
修复
您可以在您的 Unet class 的 init 方法中使用 self.save_hyperparameters('n_channels', 'n_classes') 方法来解决它。
有关使用此方法的更多详细信息，请参阅 PyTorch Lightning hyperparams-docs。使用 save_hyperparameters 可以将选定的参数与检查点一起保存在 hparams.yaml 中。
谢谢@Adrian Wälchli
当我遇到同样的问题时，来自 PyTorch Lightning 核心贡献者团队的 (aaelchli) 建议了这个修复。
我正在 Pytorch Lightning 中使用 U-Net。我能够成功地训练模型但是在训练之后当我尝试从检查点加载模型时我得到这个错误：
完整的回溯：
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'
我尝试浏览 github 问题和论坛，但无法弄清楚问题是什么。请帮忙。
这是我的模型代码和检查点加载步骤：
型号：
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)
主要功能：
def main(expconfig):
# Define checkpoint callback
checkpoint_callback = ModelCheckpoint(
filepath="/home/africa_wikilimo/data/model_checkpoint/",
save_top_k=1,
verbose=True,
monitor="loss",
mode="min",
prefix="",
)
# Initialise datasets
print("Initializing Climate Dataset....")
clima_train = Clima_Dataset(expconfig[0])
# Initialise dataloaders
print("Initializing train_loader....")
train_dataloader = DataLoader(clima_train, batch_size=2, num_workers=4)
# Initialise model and trainer
print("Initializing model...")
model = Unet(n_channels=9, n_classes=5)
print("Initializing Trainer....")
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)
原因
发生这种情况是因为您的模型无法从检查点加载超参数（n_channels、n_classes=5），因为您没有明确保存它们。
修复
您可以在您的 Unet class 的 init 方法中使用 self.save_hyperparameters('n_channels', 'n_classes') 方法来解决它。
有关使用此方法的更多详细信息，请参阅 PyTorch Lightning hyperparams-docs。使用 save_hyperparameters 可以将选定的参数与检查点一起保存在 hparams.yaml 中。
谢谢@Adrian Wälchli 当我遇到同样的问题时，来自 PyTorch Lightning 核心贡献者团队的 (aaelchli) 建议了这个修复。