diff --git a/tests/transformation/test_qonnx_to_finn.py b/tests/transformation/test_qonnx_to_finn.py
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+++ b/tests/transformation/test_qonnx_to_finn.py
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+# Copyright (c) 2021, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+import pkg_resources as pk
+
+import pytest
+
+import brevitas.onnx as bo
+import numpy as np
+import onnx
+import onnx.numpy_helper as nph
+from brevitas.export.onnx.generic.manager import BrevitasONNXManager
+from pkgutil import get_data
+from qonnx.util.cleanup import cleanup
+from tempfile import TemporaryDirectory
+
+import finn.core.onnx_exec as oxe
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.convert_qonnx_to_finn import ConvertQONNXtoFINN
+from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.general import GiveUniqueNodeNames, RemoveStaticGraphInputs
+from finn.transformation.infer_shapes import InferShapes
+from finn.util.test import get_test_model_trained
+
+
+def get_brev_model_and_sample_inputs(model_name, wbits, abits):
+ brev_model = get_test_model_trained(model_name, wbits, abits)
+ if "FC" in model_name:
+ in_shape = (1, 1, 28, 28)
+ raw_i = get_data("finn.data", "onnx/mnist-conv/test_data_set_0/input_0.pb")
+ input_tensor = onnx.load_tensor_from_string(raw_i)
+ input_tensor = nph.to_array(input_tensor)
+ elif model_name == "CNV":
+ in_shape = (1, 3, 32, 32)
+ fn = pk.resource_filename(
+ "finn.qnn-data", "cifar10/cifar10-test-data-class3.npz"
+ )
+ input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
+ else:
+ raise RuntimeError(f"The model with the name {model_name} is not supported.")
+
+ return brev_model, in_shape, input_tensor
+
+
+# ToDo: Add KWS networks, when they are ready to be added to finn-base.
+# ToDo: Add RadioML_VGG10, if possible
+@pytest.mark.parametrize("abits", [1, 2])
+@pytest.mark.parametrize("wbits", [1, 2])
+@pytest.mark.parametrize("model_name", ["TFC", "SFC", "LFC", "CNV"])
+def test_QONNX_to_FINN(model_name, wbits, abits):
+ if wbits > abits:
+ pytest.skip("No wbits > abits cases at the moment")
+ if model_name == "LFC" and wbits == 2 and abits == 2:
+ pytest.skip("No LFC-w2a2 present at the moment")
+
+ # ToDo: Remove the following restriction when QONNX supports binary operations.
+ if wbits == 1 or abits == 1:
+ pytest.skip("wbits == 1 or abits == 1 is currently not supported by QONNX.")
+
+ brev_model, in_shape, input_tensor = get_brev_model_and_sample_inputs(
+ model_name, wbits, abits
+ )
+
+ temp_dir = TemporaryDirectory()
+ qonnx_base_path = temp_dir.name + "/qonnx_{}.onnx"
+ finn_base_path = temp_dir.name + "/finn_{}.onnx"
+
+ # Get "clean" FINN model and it's output
+ _ = bo.export_finn_onnx(brev_model, in_shape, finn_base_path.format("raw"))
+ model = ModelWrapper(finn_base_path.format("raw"))
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(InferShapes())
+ model = model.transform(FoldConstants())
+ model = model.transform(RemoveStaticGraphInputs())
+ model.save(finn_base_path.format("clean"))
+
+ model = ModelWrapper(finn_base_path.format("clean"))
+ input_dict = {model.graph.input[0].name: input_tensor}
+ output_dict = oxe.execute_onnx(model, input_dict, False)
+ finn_export_output = output_dict[model.graph.output[0].name]
+
+ # Get the equivalent QONNX model
+ _ = BrevitasONNXManager.export(brev_model, in_shape, qonnx_base_path.format("raw"))
+ cleanup(qonnx_base_path.format("raw"), out_file=qonnx_base_path.format("clean"))
+
+ # Compare output
+ model = ModelWrapper(qonnx_base_path.format("clean"))
+ input_dict = {model.graph.input[0].name: input_tensor}
+ output_dict = oxe.execute_onnx(model, input_dict, False)
+ qonnx_export_output = output_dict[model.graph.output[0].name]
+
+ assert np.isclose(
+ qonnx_export_output, finn_export_output
+ ).all(), "The output of the FINN model and the QONNX model should match."
+
+ # Run QONNX to FINN conversion
+ model = ModelWrapper(qonnx_base_path.format("clean"))
+ model = model.transform(ConvertQONNXtoFINN())
+ model.save(qonnx_base_path.format("whole_trafo"))
+
+ # Compare output
+ model = ModelWrapper(qonnx_base_path.format("whole_trafo"))
+ input_dict = {model.graph.input[0].name: input_tensor}
+ output_dict = oxe.execute_onnx(model, input_dict, False)
+ test_output = output_dict[model.graph.output[0].name]
+
+ assert np.isclose(test_output, finn_export_output).all(), (
+ "The output of the FINN model "
+ "and the QONNX -> FINN converted model should match."
+ )
+
+ temp_dir.cleanup()