fixed the training bar, so it only shows final epoch results.

data/training/train.py

@@ -2,7 +2,6 @@ import sys, os
 
 os.environ["NNPACK"] = "0"
 
-
 script_dir = os.path.dirname(os.path.abspath(__file__))
 data_dir = os.path.abspath(os.path.join(script_dir, ".."))
 project_root = os.path.abspath(os.path.join(script_dir, "../.."))
@@ -17,7 +16,7 @@ if project_root not in sys.path:
     sys.path.insert(0, project_root)
 
 import lightning as L
-from lightning.pytorch.callbacks import ModelCheckpoint
+from lightning.pytorch.callbacks import ModelCheckpoint, Callback
 
 import torch
 import torch.nn.functional as F
@@ -28,27 +27,25 @@ from modulation_dataset import ModulationH5Dataset
 
 import mobilenetv3
 
-from lightning.pytorch.callbacks import TQDMProgressBar
+class CleanProgressCallback(Callback):
-
+    """Clean progress callback that only shows epoch summaries"""
-class EpochOnlyProgressBar(TQDMProgressBar):
+    
-    def __init__(self):
-        super().__init__()
-        self.enable = True
-        
-    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):
         epoch = trainer.current_epoch + 1
+        
+        # Get metrics
         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"\nEpoch {epoch}:")
+        # Print clean output
-        if train_loss: print(f"  Train Loss: {train_loss:.4f}")
+        print(f"Epoch {epoch}:")
-        if val_loss: print(f"  Val Loss: {val_loss:.4f}")
+        if train_loss is not None:
-        if val_acc: print(f"  Val Acc: {val_acc:.4f}")
+            print(f"  Train Loss: {train_loss:.4f}")
+        if val_loss is not None:
+            print(f"  Val Loss: {val_loss:.4f}")
+        if val_acc is not None:
+            print(f"  Val Acc: {val_acc:.4f}")
         print("-" * 30)
 
 
@@ -64,12 +61,8 @@ def train_model():
     checkpoint_dir = training_cfg.checkpoint_dir
     checkpoint_filename = training_cfg.checkpoint_filename
 
-    train_data = (
+    train_data = f"{dataset_cfg.output_dir}/train.h5"
-        f"{dataset_cfg.output_dir}/train.h5"
+    val_data = f"{dataset_cfg.output_dir}/val.h5"
-    )
-    val_data = (
-        f"{dataset_cfg.output_dir}/val.h5"
-    )
 
     dataset_name = "Modulation Inference - Initial Model"
     metadata_names = "Modulation"
@@ -138,7 +131,7 @@ def train_model():
             x, y = batch
             y_hat = self(x)
             loss = F.cross_entropy(y_hat, y)
-            self.log("train_loss", loss, on_epoch=True, prog_bar=True)
+            self.log("train_loss", loss, on_epoch=True, prog_bar=False)
             return loss
 
         def validation_step(self, batch, batch_idx):
@@ -146,8 +139,8 @@ def train_model():
             y_hat = self(x)
             loss = F.cross_entropy(y_hat, y)
             self.accuracy(y_hat, y)
-            self.log("val_loss", loss, prog_bar=True)
+            self.log("val_loss", loss, prog_bar=False)
-            self.log("val_acc", self.accuracy, prog_bar=True)
+            self.log("val_acc", self.accuracy, prog_bar=False)
 
     model = RFClassifier(
         mobilenetv3.mobilenetv3(
@@ -161,24 +154,25 @@ def train_model():
     checkpoint_callback = ModelCheckpoint(
         dirpath=checkpoint_dir,
         filename=checkpoint_filename,
-        save_top_k=True,
+        save_top_k=1,
-        verbose=True,
+        verbose=False,  # Disable checkpoint verbose output
         monitor="val_acc",
         mode="max",
         enable_version_counter=False,
     )
     
-    progress_bar = EpochOnlyProgressBar()
+    clean_progress = CleanProgressCallback()
     
     trainer = L.Trainer(
         max_epochs=epochs,
-        callbacks=[checkpoint_callback,progress_bar],
+        callbacks=[checkpoint_callback, clean_progress],
         accelerator="gpu",
         devices=1,
         benchmark=True,
         precision="bf16-mixed",
         logger=False,
-        enable_progress_bar=True,
+        enable_progress_bar=False,  # Disable all progress bars
+        enable_model_summary=False,  # Disable model summary
     )
 
     if train_flag:
@@ -186,4 +180,4 @@ def train_model():
 
 
 if __name__ == "__main__":
-    train_model()
+    train_model()