use new export_finn_onnx function from Brevitas

tests/test_brevitas_export.py

@@ -74,29 +74,28 @@ def test_brevitas_to_onnx_export():
     export_onnx_path = "test_output_lfc.onnx"
     with torch.no_grad():
         lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
-        lfc = lfc.eval()
-        bo.prepare_for_onnx_export(lfc, True)
-        torch.onnx.export(
-            lfc, torch.empty(784, dtype=torch.float), export_onnx_path, verbose=True
-        )
+        bo.export_finn_onnx(lfc, (1, 784), export_onnx_path)
         model = onnx.load(export_onnx_path)
         # TODO the following way of testing is highly sensitive to small changes
         # in PyTorch ONNX export: the order, names, count... of nodes could
         # easily change between different versions, and break this test.
-        assert len(model.graph.input) == 32
-        assert len(model.graph.node) == 33
+        assert len(model.graph.input) == 21
+        assert len(model.graph.node) == 25
         assert len(model.graph.output) == 1
         assert model.graph.output[0].type.tensor_type.shape.dim[1].dim_value == 10
-        assert model.graph.node[12].op_type == "QuantizedHardTanh"
-        assert model.graph.node[13].op_type == "Constant"
-        assert model.graph.node[14].op_type == "MatMul"
-        assert model.graph.node[12].output[0] == model.graph.node[14].input[1]
-        assert model.graph.node[13].output[0] == model.graph.node[14].input[0]
+        act_node = model.graph.node[8]
+        assert act_node.op_type == "QuantizedHardTanh"
+        assert act_node.attribute[0].name == "activation_qnt"
+        assert act_node.attribute[0].s.decode("utf-8") == "BIPOLAR"
+        matmul_node = model.graph.node[9]
+        assert matmul_node.op_type == "MatMul"
+        assert matmul_node.attribute[1].name == "weight_qnt"
+        assert matmul_node.attribute[1].s.decode("utf-8") == "BIPOLAR"
+        assert act_node.output[0] == matmul_node.input[1]
+        inits = [x.name for x in model.graph.initializer]
+        assert matmul_node.input[0] in inits
+        init_ind = inits.index(matmul_node.input[0])
         int_weights_pytorch = lfc.features[2].int_weight.detach().numpy()
-        int_weights_onnx = nph.to_array(model.graph.node[13].attribute[0].t)
+        int_weights_onnx = nph.to_array(model.graph.initializer[init_ind])
         assert (int_weights_onnx == int_weights_pytorch).all()
-        assert model.graph.node[12].attribute[0].name == "activation_qnt"
-        assert model.graph.node[12].attribute[0].s.decode("utf-8") == "BIPOLAR"
-        assert model.graph.node[14].attribute[1].name == "weight_qnt"
-        assert model.graph.node[14].attribute[1].s.decode("utf-8") == "BIPOLAR"
         os.remove(export_onnx_path)