From e5dcac88c4fdd26dd64bc4b5eba269c4d7d7682b Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Wed, 20 May 2020 13:56:26 +0100
Subject: [PATCH] [Test] Add test for QuantRelu:
per_channel_broadcastable_shape is not None but scaling_per_channel is False
---
.../brevitas/test_brevitas_relu_act_export.py | 50 ++++---------------
1 file changed, 9 insertions(+), 41 deletions(-)
diff --git a/tests/brevitas/test_brevitas_relu_act_export.py b/tests/brevitas/test_brevitas_relu_act_export.py
index 25c44f0cc..c5ddad12c 100644
--- a/tests/brevitas/test_brevitas_relu_act_export.py
+++ b/tests/brevitas/test_brevitas_relu_act_export.py
@@ -70,9 +70,10 @@ scaling_impl.learned_value": torch.tensor(
os.remove(export_onnx_path)
-@pytest.mark.parametrize("abits", [4])
+@pytest.mark.parametrize("abits", [1, 2, 4, 8])
@pytest.mark.parametrize("max_val", [1.0, 1.5, 1 - 2 ** (-7)])
-def test_brevitas_act_export_relu_imagenet(abits, max_val):
+@pytest.mark.parametrize("scaling_per_channel", [True, False])
+def test_brevitas_act_export_relu_imagenet(abits, max_val, scaling_per_channel):
out_channels = 32
ishape = (1, out_channels, 1, 1)
min_val = -1.0
@@ -80,53 +81,20 @@ def test_brevitas_act_export_relu_imagenet(abits, max_val):
bit_width=abits,
quant_type=QuantType.INT,
scaling_impl_type=ScalingImplType.PARAMETER,
- scaling_per_channel=True,
+ scaling_per_channel=scaling_per_channel,
restrict_scaling_type=RestrictValueType.LOG_FP,
scaling_min_val=2e-16,
max_val=6.0,
return_quant_tensor=True,
per_channel_broadcastable_shape=(1, out_channels, 1, 1),
)
+ if scaling_per_channel is True:
+ rand_tensor = (2) * torch.rand((1, out_channels, 1, 1))
+ else:
+ rand_tensor = torch.tensor(1.2398)
checkpoint = {
"act_quant_proxy.fused_activation_quant_proxy.tensor_quant.\
-scaling_impl.learned_value": torch.tensor(
- [
- [
- [[0.8520]],
- [[0.7784]],
- [[0.8643]],
- [[1.0945]],
- [[0.8520]],
- [[0.7239]],
- [[0.8520]],
- [[1.0609]],
- [[1.0299]],
- [[0.0121]],
- [[0.8520]],
- [[0.8489]],
- [[0.1376]],
- [[0.8520]],
- [[0.7096]],
- [[0.8520]],
- [[0.8520]],
- [[0.8608]],
- [[0.9484]],
- [[0.8520]],
- [[0.7237]],
- [[0.5425]],
- [[0.5774]],
- [[0.5975]],
- [[0.6685]],
- [[0.4472]],
- [[0.8520]],
- [[0.7879]],
- [[0.8520]],
- [[1.0322]],
- [[0.4550]],
- [[1.0612]],
- ]
- ]
- ).type(
+scaling_impl.learned_value": rand_tensor.type(
torch.FloatTensor
)
}
--
GitLab