[Test] add test_convert_bipolar_matmul_to_xnorpopcountmatmul

tests/test_xnorpopcountmatmul.py

+import os
+from pkgutil import get_data
+
+import brevitas.onnx as bo
 import numpy as np
+import onnx
 import onnx.helper as helper
+import onnx.numpy_helper as nph
+import torch
+from models.LFC import LFC
 from onnx import TensorProto
 
 import finn.core.onnx_exec as oxe
+import finn.transformation.streamline as sl
 from finn.core.datatype import DataType
 from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
 from finn.transformation.infer_datatypes import InferDataTypes
 from finn.transformation.infer_shapes import InferShapes
 
+export_onnx_path = "test_output_lfc.onnx"
+# TODO get from config instead, hardcoded to Docker path for now
+trained_lfc_checkpoint = (
+    "/workspace/brevitas_cnv_lfc/pretrained_models/LFC_1W1A/checkpoints/best.tar"
+)
+
 
 def test_xnorpopcountmatmul():
     M = 1
@@ -42,3 +59,28 @@ def test_xnorpopcountmatmul():
     xb = 2 * x_data - 1
     rb = np.matmul(xb, Wb)
     assert (2 * out_dict["out"] - K == rb).all()
+
+
+def test_convert_bipolar_matmul_to_xnorpopcountmatmul():
+    lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
+    checkpoint = torch.load(trained_lfc_checkpoint, map_location="cpu")
+    lfc.load_state_dict(checkpoint["state_dict"])
+    bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
+    model = ModelWrapper(export_onnx_path)
+    model = model.transform(InferShapes())
+    model = model.transform(FoldConstants())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+    model = model.transform(sl.ConvertSignToThres())
+    # load one of the test vectors
+    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)
+    # run using FINN-based execution
+    input_dict = {"global_in": nph.to_array(input_tensor)}
+    expected_ctx = oxe.execute_onnx(model, input_dict, True)
+    expected = expected_ctx[model.graph.output[0].name]
+    model = model.transform(sl.ConvertBipolarMatMulToXnorPopcount())
+    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()
+    os.remove(export_onnx_path)