Enable QONNX ingestion for test_brevitas_act_export_relu_imagenet test.

tests/brevitas/test_brevitas_relu_act_export.py

@@ -114,7 +114,10 @@ scaling_impl.learned_value": torch.tensor(
 @pytest.mark.parametrize("abits", [2, 4, 8])
 @pytest.mark.parametrize("max_val", [1.0, 1.5, 1 - 2 ** (-7)])
 @pytest.mark.parametrize("scaling_per_channel", [True, False])
-def test_brevitas_act_export_relu_imagenet(abits, max_val, scaling_per_channel):
+@pytest.mark.parametrize("QONNX_export", [False, True])
+def test_brevitas_act_export_relu_imagenet(
+    abits, max_val, scaling_per_channel, QONNX_export
+):
     out_channels = 32
     ishape = (1, out_channels, 1, 1)
     min_val = -1.0
@@ -126,7 +129,7 @@ def test_brevitas_act_export_relu_imagenet(abits, max_val, scaling_per_channel):
         restrict_scaling_type=RestrictValueType.LOG_FP,
         scaling_min_val=2e-16,
         max_val=6.0,
-        return_quant_tensor=True,
+        return_quant_tensor=False,
         per_channel_broadcastable_shape=(1, out_channels, 1, 1),
     )
     if scaling_per_channel is True:
@@ -140,7 +143,15 @@ scaling_impl.learned_value": rand_tensor.type(
         )
     }
     b_act.load_state_dict(checkpoint)
-    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(
@@ -151,7 +162,7 @@ scaling_impl.learned_value": rand_tensor.type(
     produced = odict[model.graph.output[0].name]
     inp_tensor = torch.from_numpy(inp_tensor).float()
     b_act.eval()
-    expected = b_act.forward(inp_tensor).tensor.detach().numpy()
+    expected = b_act.forward(inp_tensor).detach().numpy()
     if not np.isclose(produced, expected, atol=1e-3).all():
         print(abits, max_val)
         print("scale: ", b_act.quant_act_scale().type(torch.FloatTensor).detach())