diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index 205b2aa779e6..26955d0a3d34 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -832,11 +832,19 @@ def adaptive_avg_pool_3d(self, inputs, input_types):
         output_size = inputs[1]
         return _op.nn.adaptive_avg_pool3d(data, output_size=output_size)
 
+    @staticmethod
+    def convert_const_list(data):
+        if isinstance(data, list):
+            for i, _ in enumerate(data):
+                if isinstance(data[i], _expr.Expr):
+                    data[i] = int(_infer_value_simulated(data[i], {}).asnumpy())
+        return data
+
     def maxpool_2d(self, inputs, input_types):
         data = inputs[0]
 
-        pool_size = inputs[1]
-        strides = inputs[2] if inputs[2] else pool_size
+        pool_size = self.convert_const_list(inputs[1])
+        strides = self.convert_const_list(inputs[2] if inputs[2] else pool_size)
         padding = inputs[3]
         dilation = inputs[4]
         ceil_mode = int(inputs[5])
@@ -1316,8 +1324,8 @@ def softplus(self, inputs, input_types):
     def avg_pool2d(self, inputs, input_types):
         data = inputs[0]
 
-        pool_size = inputs[1]
-        strides = inputs[2] if inputs[2] else pool_size
+        pool_size = self.convert_const_list(inputs[1])
+        strides = self.convert_const_list(inputs[2] if inputs[2] else pool_size)
         padding = inputs[3]
         ceil_mode = int(inputs[4])
         count_include_pad = int(inputs[5])
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index aa42b0fb84e4..9846e6a53a8f 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -736,7 +736,16 @@ def forward(self, *args):
             output, indices = self.pool(args[0])
             return output
 
+    class MaxPool2DWithIntStrides(Module):
+        def forward(self, *args):
+            # Makes kernel_size and strides a Relay expr to test converting back to int
+            x_shape = args[0].shape
+            kernel_size = [torch.tensor(x_shape[1]).int(), torch.tensor(x_shape[1]).int()]
+            strides = [torch.tensor(x_shape[0]).int(), torch.tensor(x_shape[0]).int()]
+            return torch.nn.functional.max_pool2d(args[0], kernel_size=[4, 4], stride=strides)
+
     verify_model(MaxPool2DWithIndices().float().eval(), input_data=input_data)
+    verify_model(MaxPool2DWithIntStrides().float().eval(), input_data=input_data)
 
 
 @tvm.testing.uses_gpu