diff options
Diffstat (limited to 'tests/validation_new')
-rw-r--r-- | tests/validation_new/CPP/NormalizationLayer.cpp | 274 | ||||
-rw-r--r-- | tests/validation_new/CPP/NormalizationLayer.h | 47 | ||||
-rw-r--r-- | tests/validation_new/NEON/NormalizationLayer.cpp | 125 | ||||
-rw-r--r-- | tests/validation_new/fixtures/NormalizationLayerFixture.h | 133 |
4 files changed, 579 insertions, 0 deletions
diff --git a/tests/validation_new/CPP/NormalizationLayer.cpp b/tests/validation_new/CPP/NormalizationLayer.cpp new file mode 100644 index 0000000000..72f49007cc --- /dev/null +++ b/tests/validation_new/CPP/NormalizationLayer.cpp @@ -0,0 +1,274 @@ +/* + * Copyright (c) 2017 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 "NormalizationLayer.h" + +#include "tests/validation_new/FixedPoint.h" +#include "tests/validation_new/half.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type> +SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info) +{ + // Create reference + SimpleTensor<T> dst{ src.shape(), src.data_type(), 1, src.fixed_point_position() }; + + // Compute reference + const uint32_t norm_size = info.norm_size(); + NormType type = info.type(); + float beta = info.beta(); + uint32_t kappa = info.kappa(); + + const int cols = src.shape()[0]; + const int rows = src.shape()[1]; + const int depth = src.shape()[2]; + int upper_dims = src.shape().total_size() / (cols * rows); + + float coeff = info.scale_coeff(); + int radius_cols = norm_size / 2; + + // IN_MAP_1D and CROSS_MAP normalize over a single axis only + int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0; + + if(type == NormType::CROSS_MAP) + { + // Remove also depth from upper dimensions since it is the dimension we + // want to use for normalization + upper_dims /= depth; + + for(int r = 0; r < upper_dims; ++r) + { + for(int i = 0; i < rows; ++i) + { + for(int k = 0; k < cols; ++k) + { + for(int l = 0; l < depth; ++l) + { + float accumulated_scale = 0.f; + + for(int j = -radius_cols; j <= radius_cols; ++j) + { + const int z = l + j; + + if(z >= 0 && z < depth) + { + const T value = src[k + i * cols + z * rows * cols + r * cols * rows * depth]; + accumulated_scale += value * value; + } + } + + dst[k + i * cols + l * rows * cols + r * cols * rows * depth] = kappa + accumulated_scale * coeff; + } + } + } + } + } + else + { + for(int r = 0; r < upper_dims; ++r) + { + for(int i = 0; i < rows; ++i) + { + for(int k = 0; k < cols; ++k) + { + float accumulated_scale = 0.f; + + for(int j = -radius_rows; j <= radius_rows; ++j) + { + const int y = i + j; + for(int l = -radius_cols; l <= radius_cols; ++l) + { + const int x = k + l; + + if((x >= 0 && y >= 0) && (x < cols && y < rows)) + { + const T value = src[x + y * cols + r * cols * rows]; + accumulated_scale += value * value; + } + } + } + + dst[k + i * cols + r * cols * rows] = kappa + accumulated_scale * coeff; + } + } + } + } + + if(beta == 1.f) + { + for(int i = 0; i < dst.num_elements(); ++i) + { + dst[i] = src[i] / dst[i]; + } + } + else if(beta == 0.5f) + { + for(int i = 0; i < dst.num_elements(); ++i) + { + dst[i] = src[i] / std::sqrt(dst[i]); + } + } + else + { + for(int i = 0; i < dst.num_elements(); ++i) + { + dst[i] = src[i] * std::exp(std::log(dst[i]) * -beta); + } + } + + return dst; +} + +template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type> +SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info) +{ + using namespace fixed_point_arithmetic; + + // Create reference + SimpleTensor<T> dst{ src.shape(), src.data_type(), 1, src.fixed_point_position() }; + + // Compute reference + const int fixed_point_position = src.fixed_point_position(); + + const uint32_t norm_size = info.norm_size(); + NormType type = info.type(); + fixed_point<T> beta(info.beta(), fixed_point_position); + fixed_point<T> kappa(info.kappa(), fixed_point_position); + + const int cols = src.shape()[0]; + const int rows = src.shape()[1]; + const int depth = src.shape()[2]; + int upper_dims = src.shape().total_size() / (cols * rows); + + fixed_point<T> coeff(info.scale_coeff(), fixed_point_position); + int radius_cols = norm_size / 2; + + // IN_MAP_1D and CROSS_MAP normalize over a single axis only + int radius_rows = (NormType::IN_MAP_2D == type) ? norm_size / 2 : 0; + + if(type == NormType::CROSS_MAP) + { + // Remove also depth from upper dimensions since it is the dimension we + // want to use for normalization + upper_dims /= depth; + + for(int r = 0; r < upper_dims; ++r) + { + for(int i = 0; i < rows; ++i) + { + for(int k = 0; k < cols; ++k) + { + for(int l = 0; l < depth; ++l) + { + fixed_point<T> accumulated_scale(0.f, fixed_point_position); + + for(int j = -radius_cols; j <= radius_cols; ++j) + { + const int z = l + j; + + if(z >= 0 && z < depth) + { + const T value = src[k + i * cols + z * rows * cols + r * cols * rows * depth]; + const fixed_point<T> fp_value(value, fixed_point_position, true); + accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value)); + } + } + + accumulated_scale = add(kappa, mul(accumulated_scale, coeff)); + dst[k + i * cols + l * rows * cols + r * cols * rows * depth] = accumulated_scale.raw(); + } + } + } + } + } + else + { + for(int r = 0; r < upper_dims; ++r) + { + for(int i = 0; i < rows; ++i) + { + for(int k = 0; k < cols; ++k) + { + fixed_point<T> accumulated_scale(0.f, fixed_point_position); + + for(int j = -radius_rows; j <= radius_rows; ++j) + { + const int y = i + j; + + for(int l = -radius_cols; l <= radius_cols; ++l) + { + const int x = k + l; + + if((x >= 0 && y >= 0) && (x < cols && y < rows)) + { + const T value = src[x + y * cols + r * cols * rows]; + const fixed_point<T> fp_value(value, fixed_point_position, true); + accumulated_scale = add(accumulated_scale, mul(fp_value, fp_value)); + } + } + } + + accumulated_scale = add(kappa, mul(accumulated_scale, coeff)); + dst[k + i * cols + r * cols * rows] = accumulated_scale.raw(); + } + } + } + } + + if(info.beta() == 1.f) + { + for(int i = 0; i < dst.num_elements(); ++i) + { + fixed_point<T> res = div(fixed_point<T>(src[i], fixed_point_position, true), fixed_point<T>(dst[i], fixed_point_position, true)); + dst[i] = res.raw(); + } + } + else + { + const fixed_point<T> beta(info.beta(), fixed_point_position); + + for(int i = 0; i < dst.num_elements(); ++i) + { + fixed_point<T> res = pow(fixed_point<T>(dst[i], fixed_point_position, true), beta); + res = div(fixed_point<T>(src[i], fixed_point_position, true), res); + dst[i] = res.raw(); + } + } + + return dst; +} + +template SimpleTensor<float> normalization_layer(const SimpleTensor<float> &src, NormalizationLayerInfo info); +template SimpleTensor<half_float::half> normalization_layer(const SimpleTensor<half_float::half> &src, NormalizationLayerInfo info); +template SimpleTensor<qint8_t> normalization_layer(const SimpleTensor<qint8_t> &src, NormalizationLayerInfo info); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation_new/CPP/NormalizationLayer.h b/tests/validation_new/CPP/NormalizationLayer.h new file mode 100644 index 0000000000..54284b1d50 --- /dev/null +++ b/tests/validation_new/CPP/NormalizationLayer.h @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2017 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. + */ +#ifndef __ARM_COMPUTE_TEST_NORMALIZATION_LAYER_H__ +#define __ARM_COMPUTE_TEST_NORMALIZATION_LAYER_H__ + +#include "tests/validation_new/Helpers.h" +#include "tests/validation_new/SimpleTensor.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0> +SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info); + +template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0> +SimpleTensor<T> normalization_layer(const SimpleTensor<T> &src, NormalizationLayerInfo info); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* __ARM_COMPUTE_TEST_NORMALIZATION_LAYER_H__ */ diff --git a/tests/validation_new/NEON/NormalizationLayer.cpp b/tests/validation_new/NEON/NormalizationLayer.cpp new file mode 100644 index 0000000000..f364975332 --- /dev/null +++ b/tests/validation_new/NEON/NormalizationLayer.cpp @@ -0,0 +1,125 @@ +/* + * Copyright (c) 2017 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/core/Types.h" +#include "arm_compute/runtime/NEON/functions/NENormalizationLayer.h" +#include "arm_compute/runtime/Tensor.h" +#include "arm_compute/runtime/TensorAllocator.h" +#include "framework/Asserts.h" +#include "framework/Macros.h" +#include "framework/datasets/Datasets.h" +#include "tests/NEON/Accessor.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets_new/NormalizationTypesDataset.h" +#include "tests/datasets_new/ShapeDatasets.h" +#include "tests/validation_new/Validation.h" +#include "tests/validation_new/fixtures/NormalizationLayerFixture.h" +#include "tests/validation_new/half.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +/** Tolerance for float operations */ +#ifdef ARM_COMPUTE_ENABLE_FP16 +constexpr float tolerance_f16 = 0.001f; +#endif /* ARM_COMPUTE_ENABLE_FP16 */ +constexpr float tolerance_f32 = 0.00001f; +/** Tolerance for fixed point operations */ +constexpr int8_t tolerance_qs8 = 2; + +/** Input data set. */ +const auto NormalizationDataset = combine(combine(combine(datasets::SmallShapes(), datasets::NormalizationTypes()), framework::dataset::make("NormalizationSize", 3, 9, 2)), + framework::dataset::make("Beta", { 0.5f, 1.f, 2.f })); +} // namespace + +TEST_SUITE(NEON) +TEST_SUITE(NormalizationLayer) + +//TODO(COMPMID-415): Missing configuration? + +template <typename T> +using NENormalizationLayerFixture = NormalizationValidationFixture<Tensor, Accessor, NENormalizationLayer, T>; + +TEST_SUITE(Float) +#ifdef ARM_COMPUTE_ENABLE_FP16 +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, NENormalizationLayerFixture<half_float::half>, framework::DatasetMode::PRECOMMIT, combine(NormalizationDataset, framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NENormalizationLayerFixture<half_float::half>, framework::DatasetMode::NIGHTLY, combine(NormalizationDataset, framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f16); +} +TEST_SUITE_END() +#endif /* ARM_COMPUTE_ENABLE_FP16 */ + +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmall, NENormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(NormalizationDataset, framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NENormalizationLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(NormalizationDataset, framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() +TEST_SUITE_END() + +template <typename T> +using NENormalizationLayerFixedPointFixture = NormalizationValidationFixedPointFixture<Tensor, Accessor, NENormalizationLayer, T>; + +TEST_SUITE(Quantized) +TEST_SUITE(QS8) +// Testing for fixed point position [1,6) as reciprocal limits the maximum fixed point position to 5 +FIXTURE_DATA_TEST_CASE(RunSmall, NENormalizationLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(NormalizationDataset, framework::dataset::make("DataType", + DataType::QS8)), + framework::dataset::make("FractionalBits", 1, 6))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qs8); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NENormalizationLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(NormalizationDataset, framework::dataset::make("DataType", + DataType::QS8)), + framework::dataset::make("FractionalBits", 1, 6))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qs8); +} +TEST_SUITE_END() +TEST_SUITE_END() + +TEST_SUITE_END() +TEST_SUITE_END() +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation_new/fixtures/NormalizationLayerFixture.h b/tests/validation_new/fixtures/NormalizationLayerFixture.h new file mode 100644 index 0000000000..044405473b --- /dev/null +++ b/tests/validation_new/fixtures/NormalizationLayerFixture.h @@ -0,0 +1,133 @@ +/* + * Copyright (c) 2017 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. + */ +#ifndef ARM_COMPUTE_TEST_NORMALIZATION_LAYER_FIXTURE +#define ARM_COMPUTE_TEST_NORMALIZATION_LAYER_FIXTURE + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/Tensor.h" +#include "framework/Asserts.h" +#include "framework/Fixture.h" +#include "tests/AssetsLibrary.h" +#include "tests/Globals.h" +#include "tests/IAccessor.h" +#include "tests/validation_new/CPP/NormalizationLayer.h" + +#include <random> + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class NormalizationValidationFixedPointFixture : public framework::Fixture +{ +public: + template <typename...> + void setup(TensorShape shape, NormType norm_type, int norm_size, float beta, DataType data_type, int fractional_bits) + { + _fractional_bits = fractional_bits; + NormalizationLayerInfo info(norm_type, norm_size, 5, beta); + + _target = compute_target(shape, info, data_type, fractional_bits); + _reference = compute_reference(shape, info, data_type, fractional_bits); + } + +protected: + template <typename U> + void fill(U &&tensor) + { + if(_fractional_bits == 0) + { + library->fill_tensor_uniform(tensor, 0); + } + else + { + const int one_fixed = 1 << _fractional_bits; + std::uniform_int_distribution<> distribution(-one_fixed, one_fixed); + library->fill(tensor, distribution, 0); + } + } + + TensorType compute_target(const TensorShape &shape, NormalizationLayerInfo info, DataType data_type, int fixed_point_position = 0) + { + // Create tensors + TensorType src = create_tensor<TensorType>(shape, data_type, 1, fixed_point_position); + TensorType dst = create_tensor<TensorType>(shape, data_type, 1, fixed_point_position); + + // Create and configure function + FunctionType norm_layer; + norm_layer.configure(&src, &dst, info); + + ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + src.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(src)); + + // Compute function + norm_layer.run(); + + return dst; + } + + SimpleTensor<T> compute_reference(const TensorShape &shape, NormalizationLayerInfo info, DataType data_type, int fixed_point_position = 0) + { + // Create reference + SimpleTensor<T> src{ shape, data_type, 1, fixed_point_position }; + + // Fill reference + fill(src); + + return reference::normalization_layer<T>(src, info); + } + + TensorType _target{}; + SimpleTensor<T> _reference{}; + int _fractional_bits{}; +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class NormalizationValidationFixture : public NormalizationValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + template <typename...> + void setup(TensorShape shape, NormType norm_type, int norm_size, float beta, DataType data_type) + { + NormalizationValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, norm_type, norm_size, beta, data_type, 0); + } +}; +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_NORMALIZATION_LAYER_FIXTURE */ |