From 7e86bdbf8c5e81b800f1f74c90d37b59cc1e5a28 Mon Sep 17 00:00:00 2001
From: Tobi-Alonso <tobi.alonso@gmail.com>
Date: Fri, 8 May 2020 13:50:59 +0100
Subject: [PATCH] Add test for export of QuantReLu
---
.../brevitas/test_brevitas_relu_act_export.py | 67 +++++++++++++++++++
1 file changed, 67 insertions(+)
create mode 100644 tests/brevitas/test_brevitas_relu_act_export.py
diff --git a/tests/brevitas/test_brevitas_relu_act_export.py b/tests/brevitas/test_brevitas_relu_act_export.py
new file mode 100644
index 000000000..8a71ea54c
--- /dev/null
+++ b/tests/brevitas/test_brevitas_relu_act_export.py
@@ -0,0 +1,67 @@
+import onnx # noqa
+import numpy as np
+import torch
+import brevitas.onnx as bo
+from brevitas.nn import QuantReLU
+from brevitas.core.quant import QuantType
+from brevitas.core.restrict_val import RestrictValueType
+from brevitas.core.scaling import ScalingImplType
+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("max_val", [1.0, 1.5, 1 - 2 ** (-7)])
+@pytest.mark.parametrize(
+ "scaling_impl_type", [ScalingImplType.CONST, ScalingImplType.PARAMETER]
+)
+def test_brevitas_relu_act_export(abits, max_val, scaling_impl_type):
+ min_val = -1.0
+ ishape = (1, 15)
+ b_act = QuantReLU(
+ bit_width=abits,
+ max_val=max_val,
+ scaling_impl_type=scaling_impl_type,
+ restrict_scaling_type=RestrictValueType.LOG_FP,
+ quant_type=QuantType.INT,
+ )
+ if scaling_impl_type == ScalingImplType.PARAMETER:
+ checkpoint = {
+ "act_quant_proxy.fused_activation_quant_proxy.tensor_quant.\
+scaling_impl.learned_value": torch.tensor(
+ 0.49
+ ).type(
+ torch.FloatTensor
+ )
+ }
+ b_act.load_state_dict(checkpoint)
+
+ 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()
+ b_act.eval()
+ expected = b_act.forward(inp_tensor).detach().numpy()
+ if not np.isclose(produced, expected, atol=1e-3).all():
+ print(abits, max_val, scaling_impl_type)
+ print("scale: ", b_act.quant_act_scale().type(torch.FloatTensor).detach())
+ if abits < 5:
+ print(
+ "thres:",
+ ", ".join(["{:8.4f}".format(x) for x in b_act.export_thres[0]]),
+ )
+ print("input:", ", ".join(["{:8.4f}".format(x) for x in inp_tensor[0]]))
+ print("prod :", ", ".join(["{:8.4f}".format(x) for x in produced[0]]))
+ print("expec:", ", ".join(["{:8.4f}".format(x) for x in expected[0]]))
+
+ assert np.isclose(produced, expected, atol=1e-3).all()
--
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