diff --git a/onnxruntime/python/tools/quantization/onnx_quantizer.py b/onnxruntime/python/tools/quantization/onnx_quantizer.py
index d54e0be71b..8514058ff5 100644
--- a/onnxruntime/python/tools/quantization/onnx_quantizer.py
+++ b/onnxruntime/python/tools/quantization/onnx_quantizer.py
@@ -93,7 +93,7 @@ class ONNXQuantizer:
         self.op_types_to_quantize = op_types_to_quantize
         self.new_nodes = []
 
-        self.check_opset_version()
+        self.opset_version = self.check_opset_version()
 
         if not self.mode in quantization_modes:
             raise ValueError('unsupported quantization mode {}'.format(self.mode))
@@ -124,7 +124,7 @@ class ONNXQuantizer:
             print(
                 "Warning: The original model opset version is {}, which does not support node fusions. Please update the model to opset >= 11 for better performance."
                 .format(opset_version))
-            return
+            return 10
 
         if opset_version < 10:
             print(
@@ -132,8 +132,10 @@ class ONNXQuantizer:
                 .format(opset_version))
             self.model.model.opset_import.remove(ai_onnx_domain[0])
             self.model.model.opset_import.extend([onnx.helper.make_opsetid("", 11)])
+            opset_version = 11
         
         self.fuse_dynamic_quant = True
+        return opset_version
 
     def replace_gemm_with_matmul(self):
         nodes_to_remove = []
diff --git a/onnxruntime/python/tools/quantization/operators/maxpool.py b/onnxruntime/python/tools/quantization/operators/maxpool.py
index 9ba993f2a2..8cc1b7da7a 100644
--- a/onnxruntime/python/tools/quantization/operators/maxpool.py
+++ b/onnxruntime/python/tools/quantization/operators/maxpool.py
@@ -12,6 +12,10 @@ class QMaxPool(QuantOperatorBase):
         node = self.node
         assert (node.op_type == "MaxPool")
 
+        if self.quantizer.opset_version < 12:
+            super().quantize()
+            return
+
         # When mode is QLinearOps, the output quantization params are calculated based on outputs from
         # activation nodes, therefore these nodes can be removed from the graph if they follow a quantized op.
         # If input to this node is not quantized then keep this node