[Test] add a streamlining test for cnv-w1a1

tests/transformation/streamline/test_streamline_cnv.py

+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
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+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import brevitas.onnx as bo
+import numpy as np
+import pytest
+import pkg_resources as pk
+
+import finn.core.onnx_exec as oxe
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline import Streamline
+from finn.util.test import get_test_model_trained
+from finn.util.basic import make_build_dir
+
+export_onnx_path = make_build_dir("test_streamline_cnv_")
+
+# act bits
+@pytest.mark.parametrize("abits", [1])
+# weight bits
+@pytest.mark.parametrize("wbits", [1])
+# network topology / size
+@pytest.mark.parametrize("size", ["CNV"])
+def test_streamline_cnv(size, wbits, abits):
+    if wbits > abits:
+        pytest.skip("No wbits > abits cases at the moment")
+    nname = "%s_%dW%dA" % (size, wbits, abits)
+    finn_onnx = export_onnx_path + "/%s.onnx" % nname
+    fc = get_test_model_trained(size, wbits, abits)
+    bo.export_finn_onnx(fc, (1, 3, 32, 32), finn_onnx)
+    model = ModelWrapper(finn_onnx)
+    model = model.transform(InferShapes())
+    model = model.transform(FoldConstants())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+    # load one of the test vectors
+    fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
+    input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+    assert input_tensor.shape == (1, 3, 32, 32)
+    # run using FINN-based execution
+    input_dict = {"global_in": input_tensor}
+    expected_ctx = oxe.execute_onnx(model, input_dict, True)
+    expected = expected_ctx[model.graph.output[0].name]
+    model.save("orig_cnv.onnx")
+    model = model.transform(Streamline())
+    produced_ctx = oxe.execute_onnx(model, input_dict, True)
+    produced = produced_ctx[model.graph.output[0].name]
+    assert np.isclose(expected, produced, atol=1e-3).all()
+    assert model.graph.node[2].op_type == "MultiThreshold"
+    model.save("streamlined_cnv.onnx")