The first program, I think there is a connection between the top and bottom. `import torch import torch.nn as nn import pytorch_lightning as pl class LightningSegformerForSemanticSegmentation(pl.LightningModule): def init(self, segformer): super().init() self.segformer = segformer # 初始化時儲存一個 Segformer 模型 self.criterion = nn.CrossEntropyLoss() # 定義損失函數為交叉熵損失

def forward(self, x):
    return self.segformer(x) 
def training_step(self, batch, batch_idx):
    x, y = batch  # 從 batch 中讀取圖像和標籤
    out = self.segformer(x)  # 使用 Segformer 模型進行預測
    loss = self.criterion(out, y)  # 計算預測結果和標籤之間的損失
    self.log('train_loss', loss)  # 使用 pl.Logger 記錄訓練集損失
    return loss`

def validation_step(self, batch, batch_idx):
    x, y = batch  # 從 batch 中讀取圖像和標籤
    out = self.segformer(x)  # 使用 Segformer 模型進行預測
    loss = self.criterion(out, y)  # 計算預測結果和標籤之間的損失
    self.log('val_loss', loss)  # 使用 pl.Logger 記錄驗證集損失

def configure_optimizers(self):
    optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)  # 定義優化器為 Adam，學習率為 1e-3
    return optimizer`

`early_stop_callback = EarlyStopping( monitor="val_loss", min_delta=0.00, patience=10, verbose=False, mode="min", )

checkpoint_callback = ModelCheckpoint(save_top_k=1, monitor="val_loss")

trainer = pl.Trainer(

gpus='1',

accelerator='auto',
callbacks=[early_stop_callback, checkpoint_callback],
max_epochs=500,
val_check_interval=len(train_dataloader),

) trainer.fit(segformer_finetuner)`

error message `INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs

TypeError Traceback (most recent call last) in <cell line: 18>() 16 val_check_interval=len(train_dataloader), 17 ) ---> 18 trainer.fit(segformer_finetuner)

1 frames /usr/local/lib/python3.9/dist-packages/pytorch_lightning/utilities/compile.py in _maybe_unwrap_optimized(model) 123 if isinstance(model, pl.LightningModule): 124 return model --> 125 raise TypeError( 126 f"model must be a LightningModule or torch._dynamo.OptimizedModule, got {type(model).__qualname__}" 127 )

TypeError: model must be a LightningModule or torch._dynamo.OptimizedModule, got `SegformerForSemanticSegmentation`` [https://roboflow.com/models/semantic-segmentation] I referred to this URL to do semantic segmentation. Please can the original author answer my question? Thank you.

NVlabs / SegFormer

TypeError: `model` must be a `LightningModule` or `torch._dynamo.OptimizedModule`, got `SegformerForSemanticSegmentation`ON CLOAB #124

gpus='1',