[Test] add test_xnorpopcountmatmul

tests/test_xnorpopcountmatmul.py

+import numpy as np
+import onnx.helper as helper
+from onnx import TensorProto
+
+import finn.core.onnx_exec as oxe
+from finn.core.datatype import DataType
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_shapes import InferShapes
+
+
+def test_xnorpopcountmatmul():
+    M = 1
+    K = 3
+    N = 3
+    x = helper.make_tensor_value_info("x", TensorProto.FLOAT, [M, K])
+    W = helper.make_tensor_value_info("W", TensorProto.FLOAT, [K, N])
+    out = helper.make_tensor_value_info("out", TensorProto.FLOAT, ["x", "y"])
+    node_def = helper.make_node(
+        "XnorPopcountMatMul", ["x", "W"], ["out"], domain="finn"
+    )
+    modelproto = helper.make_model(
+        helper.make_graph([node_def], "test_model", [x], [out], value_info=[W])
+    )
+    model = ModelWrapper(modelproto)
+    model.set_tensor_datatype("x", DataType.BINARY)
+    model.set_tensor_datatype("W", DataType.BINARY)
+    W_data = np.asarray([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.float32)
+    model.set_initializer("W", W_data)
+    # test shape inference
+    model = model.transform(InferShapes())
+    assert model.get_tensor_shape("out") == [M, N]
+    # test datatype inference
+    assert model.get_tensor_datatype("out") is DataType.FLOAT32
+    model = model.transform(InferDataTypes())
+    assert model.get_tensor_datatype("out") is DataType.UINT32
+    # test execution
+    x_data = np.asarray([[1, 0, 0]], dtype=np.float32)
+    inp_dict = {"x": x_data}
+    out_dict = oxe.execute_onnx(model, inp_dict)
+    Wb = 2 * W_data - 1
+    xb = 2 * x_data - 1
+    rb = np.matmul(xb, Wb)
+    assert (2 * out_dict["out"] - K == rb).all()