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| author | Ioan-Cristian Szabo <ioan-cristian.szabo@arm.com> | 2017-11-27 16:31:10 +0000 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:41:58 +0000 |
| commit | 303be90ee1f03f75309b421297ba16428ea98ea5 (patch) | |
| tree | 132ea8efd1a8fc40552f2361bdf72fb47d2ece3d /tests/validation/NEON/BatchNormalizationLayer.cpp | |
| parent | 236bfe7033a313ab98ff436d85f38a58b0738ed1 (diff) | |
| download | ComputeLibrary-303be90ee1f03f75309b421297ba16428ea98ea5.tar.gz | |
COMPMID-617: Add validate support for NEON BatchNormalizationLayer.
Change-Id: I037ec6df7eee06bdd1381e908677803426fa614c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110788
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'tests/validation/NEON/BatchNormalizationLayer.cpp')
| -rw-r--r-- | tests/validation/NEON/BatchNormalizationLayer.cpp | 46 |
1 files changed, 46 insertions, 0 deletions
diff --git a/tests/validation/NEON/BatchNormalizationLayer.cpp b/tests/validation/NEON/BatchNormalizationLayer.cpp index a1421d0b17..806d3b3f20 100644 --- a/tests/validation/NEON/BatchNormalizationLayer.cpp +++ b/tests/validation/NEON/BatchNormalizationLayer.cpp @@ -80,6 +80,52 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::Ran validate(dst.info()->valid_region(), valid_region); } +// *INDENT-OFF* +// clang-format off +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( + framework::dataset::make("InputInfo", { TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Mismatching data types + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Mismatching data types + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Invalid mean/var/beta/gamma shape + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2), // Mismatching fixed point position + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2), + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2), + }), + framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F16), + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 3), + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2), + TensorInfo(), + })), + framework::dataset::make("MVBGInfo",{ TensorInfo(TensorShape(2U), 1, DataType::F32), + TensorInfo(TensorShape(2U), 1, DataType::F32), + TensorInfo(TensorShape(2U), 1, DataType::F16), + TensorInfo(TensorShape(2U), 1, DataType::F32), + TensorInfo(TensorShape(5U), 1, DataType::F32), + TensorInfo(TensorShape(2U), 1, DataType::QS8, 2), + TensorInfo(TensorShape(2U), 1, DataType::QS8, 2), + TensorInfo(TensorShape(2U), 1, DataType::QS8, 2), + })), + framework::dataset::make("Expected", { false, true, true, true, true, true, false, false})), + input_info, output_info, mvbg_info, expected) +{ + const auto &mean_info = mvbg_info; + const auto &var_info = mvbg_info; + const auto &beta_info = mvbg_info; + const auto &gamma_info = mvbg_info; + bool has_error = bool(NEBatchNormalizationLayer::validate( + &input_info.clone()->set_is_resizable(false), output_info.total_size() ? &output_info.clone()->set_is_resizable(false) : nullptr, + &mean_info.clone()->set_is_resizable(false), &var_info.clone()->set_is_resizable(false), + &beta_info.clone()->set_is_resizable(false), &gamma_info.clone()->set_is_resizable(false), 1.f)); + ARM_COMPUTE_EXPECT(has_error == expected, framework::LogLevel::ERRORS); +} +// clang-format on +// *INDENT-ON* + TEST_SUITE(Float) FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::RandomBatchNormalizationLayerDataset(), framework::dataset::make("DataType", DataType::F32))) |