diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index 1f23fe4a2c83..1ef8b6faee62 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -1107,6 +1107,10 @@ def hard_swish(self, inputs, input_types):
     def adaptive_avg_pool(self, op, inputs, input_types):
         data = inputs[0]
         output_size = inputs[1]
+        for i, item in enumerate(output_size):
+            if isinstance(item, tvm.relay.expr.Constant):
+                # convert Constant to int
+                output_size[i] = item.data.numpy()[()]
 
         def func(x):
             return op(x, output_size=output_size)
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index b2d0bf3a2edf..ffa37af3315a 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -873,6 +873,9 @@ def test_forward_adaptive_avgpool():
     verify_model(torch.nn.AdaptiveAvgPool1d([1]).eval(), input_data=input_data)
     verify_model(torch.nn.AdaptiveAvgPool1d([5]).eval(), input_data=input_data)
 
+    input_data = torch.rand([1, 3, 5, 6]).float()
+    verify_model(torch.nn.AdaptiveAvgPool2d([3, None]).eval(), input_data=input_data)
+
 
 @tvm.testing.uses_gpu
 def test_forward_adaptive_maxpool():