Added QONNX_export test to test_brevitas_act_export_qhardtanh_nonscaled test.

tests/brevitas/test_brevitas_non_scaled_QuantHardTanh_export.py

@@ -36,11 +36,14 @@ import torch
 from brevitas.core.quant import QuantType
 from brevitas.core.restrict_val import RestrictValueType
 from brevitas.core.scaling import ScalingImplType
+from brevitas.export.onnx.generic.manager import BrevitasONNXManager
 from brevitas.nn import QuantHardTanh
+from qonnx.util.cleanup import cleanup as qonnx_cleanup
 
 import finn.core.onnx_exec as oxe
 from finn.core.modelwrapper import ModelWrapper
 from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.qonnx.convert_qonnx_to_finn import ConvertQONNXtoFINN
 
 export_onnx_path = "test_brevitas_non_scaled_QuantHardTanh_export.onnx"
 
@@ -48,7 +51,10 @@ export_onnx_path = "test_brevitas_non_scaled_QuantHardTanh_export.onnx"
 @pytest.mark.parametrize("abits", [1, 2, 4, 8])
 @pytest.mark.parametrize("narrow_range", [False, True])
 @pytest.mark.parametrize("max_val", [1.0, 1 - 2 ** (-7)])
-def test_brevitas_act_export_qhardtanh_nonscaled(abits, narrow_range, max_val):
+@pytest.mark.parametrize("QONNX_export", [False, True])
+def test_brevitas_act_export_qhardtanh_nonscaled(
+    abits, narrow_range, max_val, QONNX_export
+):
     def get_quant_type(bit_width):
         if bit_width is None:
             return QuantType.FP
@@ -69,7 +75,15 @@ def test_brevitas_act_export_qhardtanh_nonscaled(abits, narrow_range, max_val):
         scaling_impl_type=ScalingImplType.CONST,
         narrow_range=narrow_range,
     )
-    bo.export_finn_onnx(b_act, ishape, export_onnx_path)
+    if QONNX_export:
+        m_path = export_onnx_path
+        BrevitasONNXManager.export(b_act, ishape, m_path)
+        qonnx_cleanup(m_path, out_file=m_path)
+        model = ModelWrapper(m_path)
+        model = model.transform(ConvertQONNXtoFINN())
+        model.save(m_path)
+    else:
+        bo.export_finn_onnx(b_act, ishape, export_onnx_path)
     model = ModelWrapper(export_onnx_path)
     model = model.transform(InferShapes())
     inp_tensor = np.random.uniform(low=min_val, high=max_val, size=ishape).astype(