Saves model checkpoint every epoch

Changes model checkpoint saving frequency to every epoch,
allowing for more frequent progress tracking and easier
recovery of potentially better model states.

fast_pointnet_class.py

@@ -316,16 +316,15 @@ def train_pointnet(dataset_dir: str, model_save_path: str, epochs: int = 100, ba
         
         scheduler.step()
 
-        # Save model checkpoint every 10 epochs
-        if (epoch + 1) % 10 == 0:
-            checkpoint_path = model_save_path.replace('.pth', f'_epoch_{epoch+1}.pth')
-            torch.save({
-                'model_state_dict': model.state_dict(),
-                'optimizer_state_dict': optimizer.state_dict(),
-                'epoch': epoch + 1,
-                'loss': avg_loss,
-                'accuracy': accuracy,
-            }, checkpoint_path)
+        # Save model checkpoint every epoch
+        checkpoint_path = model_save_path.replace('.pth', f'_epoch_{epoch+1}.pth')
+        torch.save({
+            'model_state_dict': model.state_dict(),
+            'optimizer_state_dict': optimizer.state_dict(),
+            'epoch': epoch + 1,
+            'loss': avg_loss,
+            'accuracy': accuracy,
+        }, checkpoint_path)
     
     # Save the trained model
     torch.save({