From c641ed7abcdc258d6769745f30c001ab2de1efdd Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Wed, 20 May 2020 11:29:20 +0100
Subject: [PATCH] [Test] Add comparison of pytorch execution an finn onnx
execution of new quant relu node
---
.../brevitas/test_brevitas_relu_act_export.py | 36 ++++++++++++++++---
1 file changed, 31 insertions(+), 5 deletions(-)
diff --git a/tests/brevitas/test_brevitas_relu_act_export.py b/tests/brevitas/test_brevitas_relu_act_export.py
index eb56abc28..25c44f0cc 100644
--- a/tests/brevitas/test_brevitas_relu_act_export.py
+++ b/tests/brevitas/test_brevitas_relu_act_export.py
@@ -71,10 +71,11 @@ scaling_impl.learned_value": torch.tensor(
@pytest.mark.parametrize("abits", [4])
-@pytest.mark.parametrize("out_channels", [32])
-def test_brevitas_act_export_relu_imagenet(abits, out_channels):
- ishape = (32, 15)
-
+@pytest.mark.parametrize("max_val", [1.0, 1.5, 1 - 2 ** (-7)])
+def test_brevitas_act_export_relu_imagenet(abits, max_val):
+ out_channels = 32
+ ishape = (1, out_channels, 1, 1)
+ min_val = -1.0
b_act = QuantReLU(
bit_width=abits,
quant_type=QuantType.INT,
@@ -83,7 +84,7 @@ def test_brevitas_act_export_relu_imagenet(abits, out_channels):
restrict_scaling_type=RestrictValueType.LOG_FP,
scaling_min_val=2e-16,
max_val=6.0,
- return_quant_tensor=False,
+ return_quant_tensor=True,
per_channel_broadcastable_shape=(1, out_channels, 1, 1),
)
checkpoint = {
@@ -131,3 +132,28 @@ scaling_impl.learned_value": torch.tensor(
}
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).tensor.detach().numpy()
+ if not np.isclose(produced, expected, atol=1e-3).all():
+ print(abits, max_val)
+ 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()
+ os.remove(export_onnx_path)
--
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