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Introduce relaxed accuracy thresholds for CL target in some dnn tests.

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Partially addresses #9821
This commit is contained in:
Aaron Greig
2021-03-24 16:14:23 +00:00
parent 3df6bc58e9
commit f59917bea1
3 changed files with 46 additions and 11 deletions
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modules/dnn/test/test_torch_importer.cpp
+15 -3
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@@ -218,9 +218,21 @@ TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
runTorchNet("net_conv_gemm_lrn", "", false, true, true,
target == DNN_TARGET_OPENCL_FP16 ? 0.046 : 0.0,
target == DNN_TARGET_OPENCL_FP16 ? 0.023 : 0.0);
double l1 = 0.0, lInf = 0.0;
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.046;
lInf = 0.023;
}
// The OpenCL kernels use the native_ math functions which have
// implementation defined accuracy, so we use relaxed thresholds. See
// https://github.com/opencv/opencv/issues/9821 for more details.
else if (target == DNN_TARGET_OPENCL)
{
l1 = 0.02;
lInf = 0.02;
}
runTorchNet("net_conv_gemm_lrn", "", false, true, true, l1, lInf);
}
TEST_P(Test_Torch_layers, net_inception_block)