diff --git a/PyTorch/built-in/nlp/LSTM_ID0468_for_PyTorch/timit/steps/train_ctc.py b/PyTorch/built-in/nlp/LSTM_ID0468_for_PyTorch/timit/steps/train_ctc.py
index d3508aaf1350407914e7f4e078482e9cc7e15329..3417344713a3d69303df8b7c0aea03aa27a6df3b 100644
--- a/PyTorch/built-in/nlp/LSTM_ID0468_for_PyTorch/timit/steps/train_ctc.py
+++ b/PyTorch/built-in/nlp/LSTM_ID0468_for_PyTorch/timit/steps/train_ctc.py
@@ -86,8 +86,8 @@ def run_epoch(epoch_id, model, data_iter, loss_fn, device, optimizer=None, print
         input_sizes = (input_sizes * out_len).long()
         loss = loss_fn(out, targets, input_sizes, target_sizes)
         loss /= batch_size
-        cur_loss += loss.item()
-        total_loss += loss.item()
+        cur_loss += loss.detach()
+        total_loss += loss.detach()
         prob, index = torch.max(out, dim=-1)
         batch_errs, batch_tokens = model.compute_wer(index.transpose(0,1).cpu().numpy(), input_sizes.cpu().numpy(), targets.cpu().numpy(), target_sizes.cpu().numpy())
         total_errs += batch_errs
diff --git a/PyTorch/dev/cv/image_classification/FasterRCNN_ID0100_for_PyTorch/detectron2/engine/train_loop.py b/PyTorch/dev/cv/image_classification/FasterRCNN_ID0100_for_PyTorch/detectron2/engine/train_loop.py
index e40ba95b01aac527683e0fcb5ee0d534606ce81b..3d8228f21af267c830d1b5a8db545f47b542b400 100644
--- a/PyTorch/dev/cv/image_classification/FasterRCNN_ID0100_for_PyTorch/detectron2/engine/train_loop.py
+++ b/PyTorch/dev/cv/image_classification/FasterRCNN_ID0100_for_PyTorch/detectron2/engine/train_loop.py
@@ -293,7 +293,7 @@ class SimpleTrainer(TrainerBase):
             metrics_dict (dict): dict of scalar metrics
         """
         metrics_dict = {
-            k: v.detach().cpu().item() if isinstance(v, torch.Tensor) else float(v)
+            k: v.detach()if isinstance(v, torch.Tensor) else float(v)
             for k, v in metrics_dict.items()
         }
         # gather metrics among all workers for logging
diff --git a/PyTorch/dev/cv/image_classification/MaskRCNN_ID0101_for_PyTorch/detectron2/modeling/proposal_generator/rpn.py b/PyTorch/dev/cv/image_classification/MaskRCNN_ID0101_for_PyTorch/detectron2/modeling/proposal_generator/rpn.py
index cbb097537550077a9af168f796e5aab1323f7431..c1e264fe65e953efc5a5ee554170ce2d412a34fd 100644
--- a/PyTorch/dev/cv/image_classification/MaskRCNN_ID0101_for_PyTorch/detectron2/modeling/proposal_generator/rpn.py
+++ b/PyTorch/dev/cv/image_classification/MaskRCNN_ID0101_for_PyTorch/detectron2/modeling/proposal_generator/rpn.py
@@ -384,8 +384,8 @@ class RPN(nn.Module):
 
         # Log the number of positive/negative anchors per-image that's used in training
         pos_mask = gt_labels == 1
-        num_pos_anchors = pos_mask.sum().item()
-        num_neg_anchors = (gt_labels == 0).sum().item()
+        num_pos_anchors = pos_mask.sum().detach()
+        num_neg_anchors = (gt_labels == 0).sum().detach()
         storage = get_event_storage()
         storage.put_scalar("rpn/num_pos_anchors", num_pos_anchors / num_images)
         storage.put_scalar("rpn/num_neg_anchors", num_neg_anchors / num_images)