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| author | Sang-Hoon Park <sang-hoon.park@arm.com> | 2020-07-08 11:06:30 +0100 |
|---|---|---|
| committer | Sang-Hoon Park <sang-hoon.park@arm.com> | 2020-07-13 08:03:32 +0000 |
| commit | 38515425707b239a1d02d3a4f480a9d97efbb9ba (patch) | |
| tree | 1e68a4035e934d3e5ab829c0124bf09757e64a38 /tests | |
| parent | 3ef9b5fb7c3f393a32977250ce0c4cb5d45ae555 (diff) | |
| download | ComputeLibrary-38515425707b239a1d02d3a4f480a9d97efbb9ba.tar.gz | |
COMPMID-3531: fix index offset overflows in NEDirectConvolutionLayerKernel
When a large input and kernel is used, the computation of "max_offset"
variable can overflow. Adjust types of the variable as well as
the variable compared with for consistency.
The test spotted the overflow is added to nightly suite.
Change-Id: I2f114e4b49167889a6d3729c71823c089d6f42e3
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3527
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Manuel Bottini <manuel.bottini@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'tests')
| -rw-r--r-- | tests/validation/NEON/DirectConvolutionLayer.cpp | 27 |
1 files changed, 27 insertions, 0 deletions
diff --git a/tests/validation/NEON/DirectConvolutionLayer.cpp b/tests/validation/NEON/DirectConvolutionLayer.cpp index fe61a9d98a..88578ca586 100644 --- a/tests/validation/NEON/DirectConvolutionLayer.cpp +++ b/tests/validation/NEON/DirectConvolutionLayer.cpp @@ -98,6 +98,25 @@ const auto data_f16_nightly = combine(data_f16, framework::dataset::make("NumKer const auto data_precommit = combine(data, framework::dataset::make("NumKernels", { 1 })); const auto data_precommit9x9 = combine(data9x9, framework::dataset::make("NumKernels", { 4 })); +/* The following tests is from real use-case that made DirectConvolution + * overflows in terms of its tensor indexing. This test case is using + * a separate tolerance due to the following reason. + * - It has shown that it requires generally larger absolute tolerance + * for large numbers or larger relative tolerance for small numbers. + * - With the first reason, since it is mainly testing index overflow, + * a value with a margin is used to avoid uninteded test failures + * during nightly. + */ +constexpr AbsoluteTolerance<float> usecase_tolerance_fp32(0.05f); + +const auto data_nightly_usecase = combine(framework::dataset::make("InputShape", { TensorShape{ 3U, 800U, 800U } }), + combine(framework::dataset::make("StrideX", { 1 }), + combine(framework::dataset::make("StrideY", { 1 }), + combine(framework::dataset::make("PadX", { 4 }), + combine(framework::dataset::make("PadY", { 4 }), + combine(framework::dataset::make("KernelSize", 9), + framework::dataset::make("NumKernels", { 16 }))))))); + /** Activation function Dataset*/ const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", { @@ -227,6 +246,14 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDirectConvolutionLayerFixture<float>, framewo // Validate output validate(Accessor(_target), _reference, tolerance_fp32); } +FIXTURE_DATA_TEST_CASE(RunLargeUsecase, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly_usecase, framework::dataset::make("DataType", + DataType::F32)), + framework::dataset::make("ActivationInfo", { ActivationLayerInfo() })), + framework::dataset::make("DataLayout", { DataLayout::NHWC }))) +{ + // Validate output + validate(Accessor(_target), _reference, usecase_tolerance_fp32); +} TEST_SUITE_END() // FP32 TEST_SUITE_END() // Float TEST_SUITE_END() // DirectConvolutionLayer |