| /* |
| * Copyright (c) 2017-2020 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. |
| */ |
| #ifndef ARM_COMPUTE_TEST_POOLING_LAYER_FIXTURE |
| #define ARM_COMPUTE_TEST_POOLING_LAYER_FIXTURE |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/Tensor.h" |
| #include "tests/AssetsLibrary.h" |
| #include "tests/Globals.h" |
| #include "tests/IAccessor.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Fixture.h" |
| #include "tests/validation/reference/PoolingLayer.h" |
| #include <random> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class PoolingLayerValidationGenericFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, PoolingLayerInfo pool_info, DataType data_type, DataLayout data_layout, bool indices = false) |
| { |
| std::mt19937 gen(library->seed()); |
| std::uniform_int_distribution<> offset_dis(0, 20); |
| const float scale = data_type == DataType::QASYMM8_SIGNED ? 1.f / 127.f : 1.f / 255.f; |
| const int scale_in = data_type == DataType::QASYMM8_SIGNED ? -offset_dis(gen) : offset_dis(gen); |
| const int scale_out = data_type == DataType::QASYMM8_SIGNED ? -offset_dis(gen) : offset_dis(gen); |
| const QuantizationInfo input_qinfo(scale, scale_in); |
| const QuantizationInfo output_qinfo(scale, scale_out); |
| |
| _pool_info = pool_info; |
| _target = compute_target(shape, pool_info, data_type, data_layout, input_qinfo, output_qinfo, indices); |
| _reference = compute_reference(shape, pool_info, data_type, input_qinfo, output_qinfo, indices); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor) |
| { |
| if(!is_data_type_quantized(tensor.data_type())) |
| { |
| std::uniform_real_distribution<> distribution(-1.f, 1.f); |
| library->fill(tensor, distribution, 0); |
| } |
| else // data type is quantized_asymmetric |
| { |
| library->fill_tensor_uniform(tensor, 0); |
| } |
| } |
| |
| TensorType compute_target(TensorShape shape, PoolingLayerInfo info, |
| DataType data_type, DataLayout data_layout, |
| QuantizationInfo input_qinfo, QuantizationInfo output_qinfo, |
| bool indices) |
| { |
| // Change shape in case of NHWC. |
| if(data_layout == DataLayout::NHWC) |
| { |
| permute(shape, PermutationVector(2U, 0U, 1U)); |
| } |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(shape, data_type, 1, input_qinfo, data_layout); |
| const TensorShape dst_shape = misc::shape_calculator::compute_pool_shape(*(src.info()), info); |
| TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, output_qinfo, data_layout); |
| _target_indices = create_tensor<TensorType>(dst_shape, DataType::U32, 1); |
| |
| // Create and configure function |
| FunctionType pool_layer; |
| pool_layer.configure(&src, &dst, info, (indices) ? &_target_indices : nullptr); |
| |
| ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(_target_indices.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| _target_indices.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!_target_indices.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(src)); |
| |
| // Compute function |
| pool_layer.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<T> compute_reference(const TensorShape &shape, PoolingLayerInfo info, DataType data_type, |
| QuantizationInfo input_qinfo, QuantizationInfo output_qinfo, bool indices) |
| { |
| // Create reference |
| SimpleTensor<T> src{ shape, data_type, 1, input_qinfo }; |
| // Fill reference |
| fill(src); |
| |
| return reference::pooling_layer<T>(src, info, output_qinfo, indices ? &_ref_indices : nullptr); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| PoolingLayerInfo _pool_info{}; |
| TensorType _target_indices{}; |
| SimpleTensor<uint32_t> _ref_indices{}; |
| }; |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class PoolingLayerIndicesValidationFixture : public PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, DataLayout data_layout) |
| { |
| PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, data_layout, pad_stride_info, exclude_padding), |
| data_type, data_layout, true); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class PoolingLayerValidationFixture : public PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, DataLayout data_layout) |
| { |
| PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, data_layout, pad_stride_info, exclude_padding), |
| data_type, data_layout); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class PoolingLayerValidationMixedPrecisionFixture : public PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, DataLayout data_layout, bool fp_mixed_precision = false) |
| { |
| PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, data_layout, pad_stride_info, exclude_padding, fp_mixed_precision), |
| data_type, data_layout); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class PoolingLayerValidationQuantizedFixture : public PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, PoolingType pool_type, Size2D pool_size, PadStrideInfo pad_stride_info, bool exclude_padding, DataType data_type, DataLayout data_layout = DataLayout::NCHW) |
| { |
| PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, pool_size, data_layout, pad_stride_info, exclude_padding), |
| data_type, data_layout); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class SpecialPoolingLayerValidationFixture : public PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape src_shape, PoolingLayerInfo pool_info, DataType data_type) |
| { |
| PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, pool_info, data_type, DataLayout::NCHW); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class GlobalPoolingLayerValidationFixture : public PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, PoolingType pool_type, DataType data_type, DataLayout data_layout = DataLayout::NCHW) |
| { |
| PoolingLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, PoolingLayerInfo(pool_type, data_layout), data_type, data_layout); |
| } |
| }; |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_POOLING_LAYER_FIXTURE */ |