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Diffstat (limited to 'tests/validation/CL/RNNLayer.cpp')
-rw-r--r-- | tests/validation/CL/RNNLayer.cpp | 138 |
1 files changed, 138 insertions, 0 deletions
diff --git a/tests/validation/CL/RNNLayer.cpp b/tests/validation/CL/RNNLayer.cpp new file mode 100644 index 0000000000..0af6f8ea00 --- /dev/null +++ b/tests/validation/CL/RNNLayer.cpp @@ -0,0 +1,138 @@ +/* + * Copyright (c) 2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/runtime/CL/functions/CLRNNLayer.h" +#include "tests/CL/CLAccessor.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets/RNNLayerDataset.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "tests/validation/Validation.h" +#include "tests/validation/fixtures/RNNLayerFixture.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +RelativeTolerance<float> tolerance_f32(0.001f); +RelativeTolerance<half> tolerance_f16(half(0.1)); +} // namespace + +TEST_SUITE(CL) +TEST_SUITE(RNNLayer) + +// *INDENT-OFF* +// clang-format off +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( + framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8, 0), // Wrong data type + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Wrong input size + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong weights size + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong recurrent weights size + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong bias size + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong output size + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32, 0), // Wrong hidden output size + }), + framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32, 0), + TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(27U, 11U), 1, DataType::F32, 0), + })), + framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 11U), 1, DataType::F32, 0), + })), + framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(30U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U), 1, DataType::F32, 0), + })), + framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + })), + framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32, 0), + })), + framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + })), + framework::dataset::make("Expected", { false, false, false, false, false, false, false })), + input_info, weights_info, recurrent_weights_info, bias_info, output_info, hidden_output_info, info, expected) +{ + ARM_COMPUTE_EXPECT(bool(CLRNNLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), &hidden_output_info.clone()->set_is_resizable(false), info)) == expected, framework::LogLevel::ERRORS); +} +// clang-format on +// *INDENT-ON* + +template <typename T> +using CLRNNLayerFixture = RNNLayerValidationFixture<CLTensor, CLAccessor, CLRNNLayer, T>; + +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmall, CLRNNLayerFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() // FP32 + +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, CLRNNLayerFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16); +} +TEST_SUITE_END() // FP16 +TEST_SUITE_END() // RNNLayer +TEST_SUITE_END() // CL +} // namespace validation +} // namespace test +} // namespace arm_compute |