| /* | |
| * Copyright (c) 2018-2020 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); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType:F32 */ | |
| RelativeTolerance<half> rel_tolerance_f16(half(0.2)); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType:F16 */ | |
| constexpr float abs_tolerance_f16(0.02f); /**< Absolute tolerance value for comparing reference's output against implementation's output for DataType:F16 */ | |
| } // 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), // Wrong data type | |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Wrong input size | |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong weights size | |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong recurrent weights size | |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong bias size | |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong output size | |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong hidden output size | |
| }), | |
| framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32), | |
| TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), | |
| })), | |
| framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), | |
| })), | |
| framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(30U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U), 1, DataType::F32), | |
| })), | |
| framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| })), | |
| framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), | |
| TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32), | |
| })), | |
| 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, rel_tolerance_f16, 0.f, abs_tolerance_f16); | |
| } | |
| TEST_SUITE_END() // FP16 | |
| TEST_SUITE_END() // RNNLayer | |
| TEST_SUITE_END() // CL | |
| } // namespace validation | |
| } // namespace test | |
| } // namespace arm_compute |