fixed training model error, was a extra argument passed into the callback class

data/training/train.py

@@ -31,29 +31,24 @@ import mobilenetv3
 from lightning.pytorch.callbacks import TQDMProgressBar
 
 class EpochOnlyProgressBar(TQDMProgressBar):
-    def on_train_start(self, trainer, pl_module):
+    def __init__(self):
-        super().on_train_start(trainer, pl_module)
+        super().__init__()
-    
+        self.enable = True
-    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+        
-        # Disable batch progress updates
+    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
+        # Skip batch-level updates
         pass
-
+        
     def on_train_epoch_end(self, trainer, pl_module):
-        # Update and close progress bar at epoch end
+        epoch = trainer.current_epoch + 1
-        if self.main_progress_bar is not None:
-            self.main_progress_bar.update(self.main_progress_bar.total - self.main_progress_bar.n)
-            self.main_progress_bar.close()
-
-        epoch = trainer.current_epoch + 1  # zero-based to one-based
-
         train_loss = trainer.callback_metrics.get("train_loss")
-        val_loss = trainer.callback_metrics.get("val_loss")
+        val_loss = trainer.callback_metrics.get("val_loss") 
         val_acc = trainer.callback_metrics.get("val_acc")
-
+        
-        print(f"Epoch {epoch} Summary:")
+        print(f"\nEpoch {epoch}:")
-        print(f"  Train Loss: {train_loss:.4f}" if train_loss is not None else "  Train Loss: N/A")
+        if train_loss: print(f"  Train Loss: {train_loss:.4f}")
-        print(f"  Val Loss:   {val_loss:.4f}" if val_loss is not None else "  Val Loss:   N/A")
+        if val_loss: print(f"  Val Loss: {val_loss:.4f}")
-        print(f"  Val Acc:    {val_acc:.4f}" if val_acc is not None else "  Val Acc:    N/A")
+        if val_acc: print(f"  Val Acc: {val_acc:.4f}")
         print("-" * 30)
 
 
@@ -173,7 +168,7 @@ def train_model():
         enable_version_counter=False,
     )
     
-    progress_bar = EpochOnlyProgressBar(refresh_rate=1)
+    progress_bar = EpochOnlyProgressBar()
     
     trainer = L.Trainer(
         max_epochs=epochs,