diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index b256faa5d6f9..ca1d31cb9fe8 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -458,7 +458,10 @@ def _impl(inputs, input_types):
         data = inputs[0]
 
         pool_size = _infer_shape(inputs[1])
-        strides = _infer_shape(inputs[2])
+        if inputs[2]:
+            strides = _infer_shape(inputs[2])
+        else:
+            strides = pool_size
         padding = _infer_shape(inputs[3])
 
         ceil_mode = int(inputs[4])
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index c2ff94de546f..e60c1fd88183 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -375,8 +375,13 @@ class AvgPool2D1(Module):
         def forward(self, *args):
             return torch.nn.AvgPool2d(kernel_size=[10, 10])(args[0])
 
+    class AvgPool2D2(Module):
+        def forward(self, *args):
+            return torch.nn.functional.avg_pool2d(args[0], kernel_size=[10, 10])
+
     input_data = torch.rand(input_shape).float()
     verify_model(AvgPool2D1().float().eval(), input_data=input_data)
+    verify_model(AvgPool2D2().float().eval(), input_data=input_data)
 
 def test_forward_hardtanh():
     torch.set_grad_enabled(False)