diff --git a/docs/tutorials/pytorch2torchscript.md b/docs/tutorials/pytorch2torchscript.md
index d9f110575..13ea2ccf5 100644
--- a/docs/tutorials/pytorch2torchscript.md
+++ b/docs/tutorials/pytorch2torchscript.md
@@ -24,7 +24,7 @@ python tools/deployment/pytorch2torchscript.py \
     --verify \
 ```
 
-### Description of all arguments:
+### Description of all arguments
 
 - `config` : The path of a model config file.
 - `--checkpoint` : The path of a model checkpoint file.
@@ -48,6 +48,7 @@ Notes:
 
 ## Reminders
 
+- For torch.jit.is_tracing() is only supported after v1.6. For users with pytorch v1.3-v1.5, we suggest early returning tensors manually.
 - If you meet any problem with the models in this repo, please create an issue and it would be taken care of soon.
 
 ## FAQs
diff --git a/mmcls/models/heads/cls_head.py b/mmcls/models/heads/cls_head.py
index c4ac12963..5a5457e9f 100644
--- a/mmcls/models/heads/cls_head.py
+++ b/mmcls/models/heads/cls_head.py
@@ -62,7 +62,9 @@ class ClsHead(BaseHead):
         if isinstance(cls_score, list):
             cls_score = sum(cls_score) / float(len(cls_score))
         pred = F.softmax(cls_score, dim=1) if cls_score is not None else None
-        if torch.onnx.is_in_onnx_export() or torch.jit.is_tracing():
+
+        on_trace = hasattr(torch.jit, 'is_tracing') and torch.jit.is_tracing()
+        if torch.onnx.is_in_onnx_export() or on_trace:
             return pred
         pred = list(pred.detach().cpu().numpy())
         return pred
diff --git a/mmcls/models/heads/linear_head.py b/mmcls/models/heads/linear_head.py
index 1be0991ae..7672f7cbd 100644
--- a/mmcls/models/heads/linear_head.py
+++ b/mmcls/models/heads/linear_head.py
@@ -43,7 +43,9 @@ class LinearClsHead(ClsHead):
         if isinstance(cls_score, list):
             cls_score = sum(cls_score) / float(len(cls_score))
         pred = F.softmax(cls_score, dim=1) if cls_score is not None else None
-        if torch.onnx.is_in_onnx_export() or torch.jit.is_tracing():
+
+        on_trace = hasattr(torch.jit, 'is_tracing') and torch.jit.is_tracing()
+        if torch.onnx.is_in_onnx_export() or on_trace:
             return pred
         pred = list(pred.detach().cpu().numpy())
         return pred
diff --git a/mmcls/models/heads/multi_label_head.py b/mmcls/models/heads/multi_label_head.py
index 3e087c6a9..3635d0d27 100644
--- a/mmcls/models/heads/multi_label_head.py
+++ b/mmcls/models/heads/multi_label_head.py
@@ -47,7 +47,9 @@ class MultiLabelClsHead(BaseHead):
         if isinstance(cls_score, list):
             cls_score = sum(cls_score) / float(len(cls_score))
         pred = F.sigmoid(cls_score) if cls_score is not None else None
-        if torch.onnx.is_in_onnx_export() or torch.jit.is_tracing():
+
+        on_trace = hasattr(torch.jit, 'is_tracing') and torch.jit.is_tracing()
+        if torch.onnx.is_in_onnx_export() or on_trace:
             return pred
         pred = list(pred.detach().cpu().numpy())
         return pred
diff --git a/mmcls/models/heads/multi_label_linear_head.py b/mmcls/models/heads/multi_label_linear_head.py
index 07d23703b..118fe747b 100644
--- a/mmcls/models/heads/multi_label_linear_head.py
+++ b/mmcls/models/heads/multi_label_linear_head.py
@@ -56,7 +56,9 @@ class MultiLabelLinearClsHead(MultiLabelClsHead):
         if isinstance(cls_score, list):
             cls_score = sum(cls_score) / float(len(cls_score))
         pred = F.sigmoid(cls_score) if cls_score is not None else None
-        if torch.onnx.is_in_onnx_export() or torch.jit.is_tracing():
+
+        on_trace = hasattr(torch.jit, 'is_tracing') and torch.jit.is_tracing()
+        if torch.onnx.is_in_onnx_export() or on_trace:
             return pred
         pred = list(pred.detach().cpu().numpy())
         return pred
diff --git a/mmcls/models/heads/vision_transformer_head.py b/mmcls/models/heads/vision_transformer_head.py
index 8b5dfc37a..606217d4f 100644
--- a/mmcls/models/heads/vision_transformer_head.py
+++ b/mmcls/models/heads/vision_transformer_head.py
@@ -68,7 +68,9 @@ class VisionTransformerClsHead(ClsHead):
         if isinstance(cls_score, list):
             cls_score = sum(cls_score) / float(len(cls_score))
         pred = F.softmax(cls_score, dim=1) if cls_score is not None else None
-        if torch.onnx.is_in_onnx_export() or torch.jit.is_tracing():
+
+        on_trace = hasattr(torch.jit, 'is_tracing') and torch.jit.is_tracing()
+        if torch.onnx.is_in_onnx_export() or on_trace:
             return pred
         pred = list(pred.detach().cpu().numpy())
         return pred
diff --git a/tools/deployment/pytorch2torchscript.py b/tools/deployment/pytorch2torchscript.py
index d1764a22b..edaca2d2d 100644
--- a/tools/deployment/pytorch2torchscript.py
+++ b/tools/deployment/pytorch2torchscript.py
@@ -1,5 +1,5 @@
-import os
 import argparse
+import os
 import os.path as osp
 from functools import partial