updated memory fix

src/aiunn/finetune/memory_trainer.py

@@ -241,6 +241,7 @@ class MemoryOptimizedTrainer(aiuNNTrainer):
                                 total=len(train_batches),
                                 desc=f"Epoch {epoch + 1}/{epochs}")
 
+
             for batch_idx, (low_res, high_res) in progress_bar:
                 # Move data to device
                 low_res = low_res.to(self.device, non_blocking=True).to(memory_format=torch.channels_last)
@@ -251,8 +252,10 @@ class MemoryOptimizedTrainer(aiuNNTrainer):
                     if hasattr(self, 'use_checkpointing') and self.use_checkpointing:
                         low_res.requires_grad_()
                         outputs = checkpoint(self.model, low_res)
+                        outputs = outputs.clone()  # <-- Clone added here
                     else:
                         outputs = self.model(low_res)
+                        outputs = outputs.clone()  # <-- Clone added here
                     loss = self.criterion(outputs, high_res)
                     
                     # Scale loss for gradient accumulation
@@ -348,6 +351,16 @@ class MemoryOptimizedTrainer(aiuNNTrainer):
         
         return self.best_loss
 
+        
+        # Stop memory monitoring
+        if self.use_memory_profiling:
+            self.stop_monitoring = True
+            if self.memory_monitor_thread:
+                self.memory_monitor_thread.join(timeout=1)
+            print(f"Training completed. Peak GPU memory usage: {self.peak_memory:.2f}GB")
+        
+        return self.best_loss
+
     def get_memory_summary(self):
         """Get a summary of memory usage during training"""
         if not self.memory_stats: