[Test] add Brevitas act quant test

tests/brevitas/test_brevitas_act_export.py

+import numpy as np
+import torch
+import brevitas.onnx as bo
+from brevitas.nn import QuantHardTanh
+from brevitas.core.restrict_val import RestrictValueType
+from brevitas.core.scaling import ScalingImplType
+from models.common import get_quant_type
+import pytest
+from finn.core.modelwrapper import ModelWrapper
+import finn.core.onnx_exec as oxe
+from finn.transformation.infer_shapes import InferShapes
+
+export_onnx_path = "test_act.onnx"
+
+
+@pytest.mark.parametrize("abits", [1, 2, 4, 8])
+@pytest.mark.parametrize("narrow_range", [False, True])
+@pytest.mark.parametrize("max_val", [1.0, 1 - 2 ** (-7)])
+def test_brevitas_act_export(abits, narrow_range, max_val):
+    act_quant_type = get_quant_type(abits)
+    min_val = -1.0
+    ishape = (1, 10)
+    b_act = QuantHardTanh(
+        bit_width=abits,
+        quant_type=act_quant_type,
+        max_val=max_val,
+        min_val=min_val,
+        restrict_scaling_type=RestrictValueType.LOG_FP,
+        scaling_impl_type=ScalingImplType.CONST,
+        narrow_range=narrow_range,
+    )
+    bo.export_finn_onnx(b_act, ishape, export_onnx_path)
+    model = ModelWrapper(export_onnx_path)
+    model = model.transform(InferShapes())
+    inp_tensor = np.random.uniform(low=min_val, high=max_val, size=ishape).astype(
+        np.float32
+    )
+    idict = {model.graph.input[0].name: inp_tensor}
+    odict = oxe.execute_onnx(model, idict, True)
+    produced = odict[model.graph.output[0].name]
+    inp_tensor = torch.from_numpy(inp_tensor).float()
+    expected = b_act.forward(inp_tensor).detach().numpy()
+    assert np.isclose(produced, expected, atol=1e-3).all()