[Test] Add test for streaming dwc

tests/fpgadataflow/test_fpgadataflow_dwc.py

+import pytest
+
+from onnx import TensorProto, helper
+
+from finn.core.datatype import DataType
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.fpgadataflow.codegen_ipgen import CodeGen_ipgen
+from finn.transformation.fpgadataflow.hlssynth_ipgen import HLSSynth_IPGen
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+from finn.transformation.general import GiveUniqueNodeNames
+from finn.util.basic import gen_finn_dt_tensor
+import finn.core.onnx_exec as oxe
+
+
+def make_single_dwc_modelwrapper(Shape, INWidth, OUTWidth, finn_dtype):
+
+    inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, Shape)
+    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, Shape)
+
+    DWC_node = helper.make_node(
+        "StreamingDataWidthConverter_Batch",
+        ["inp"],
+        ["outp"],
+        domain="finn",
+        backend="fpgadataflow",
+        shape=Shape,
+        inWidth=INWidth,
+        outWidth=OUTWidth,
+        dataType=str(finn_dtype.name),
+    )
+
+    graph = helper.make_graph(
+        nodes=[DWC_node], name="dwc_graph", inputs=[inp], outputs=[outp]
+    )
+
+    model = helper.make_model(graph, producer_name="dwc-model")
+    model = ModelWrapper(model)
+
+    model.set_tensor_datatype("inp", finn_dtype)
+    model.set_tensor_datatype("outp", finn_dtype)
+
+    return model
+
+
+def prepare_inputs(input_tensor, dt):
+    return {"inp": input_tensor}
+
+
+# shape
+@pytest.mark.parametrize("Shape", [[1, 4], [1, 2, 8]])
+# inWidth
+@pytest.mark.parametrize("INWidth", [2, 4])
+# outWidth
+@pytest.mark.parametrize("OUTWidth", [2, 4])
+# finn_dtype
+@pytest.mark.parametrize("finn_dtype", [DataType.BIPOLAR, DataType.INT2])
+def test_fpgadataflow_dwc_rtlsim(Shape, INWidth, OUTWidth, finn_dtype):
+
+    # generate input data
+    x = gen_finn_dt_tensor(finn_dtype, Shape)
+    input_dict = prepare_inputs(x, finn_dtype)
+
+    model = make_single_dwc_modelwrapper(Shape, INWidth, OUTWidth, finn_dtype)
+
+    model = model.transform(SetExecMode("rtlsim"))
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(CodeGen_ipgen("xc7z020clg400-1", 5))
+    model = model.transform(HLSSynth_IPGen())
+    y = oxe.execute_onnx(model, input_dict)["outp"]
+
+    assert (
+        y == x
+    ).all(), """The output values are not the same as the
+        input values anymore."""
+    assert y.shape == tuple(Shape), """The output shape is incorrect."""