| /* |
| * 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_DEQUANTIZATION_LAYER_FIXTURE |
| #define ARM_COMPUTE_TEST_DEQUANTIZATION_LAYER_FIXTURE |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.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/CPP/DequantizationLayer.h" |
| |
| #include <random> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DequantizationValidationFixedPointFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, DataType data_type) |
| { |
| _target = compute_target(shape, data_type); |
| _reference = compute_reference(shape, data_type); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor) |
| { |
| library->fill_tensor_uniform(tensor, 0); |
| } |
| |
| template <typename U> |
| void fill_min_max(U &&tensor) |
| { |
| std::mt19937 gen(library->seed()); |
| std::uniform_real_distribution<float> distribution(-1.0f, 1.0f); |
| |
| Window window; |
| |
| window.set(0, Window::Dimension(0, tensor.shape()[0], 2)); |
| |
| for(unsigned int d = 1; d < tensor.shape().num_dimensions(); ++d) |
| { |
| window.set(d, Window::Dimension(0, tensor.shape()[d], 1)); |
| } |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const float n1 = distribution(gen); |
| const float n2 = distribution(gen); |
| |
| float min = 0.0f; |
| float max = 0.0f; |
| |
| if(n1 < n2) |
| { |
| min = n1; |
| max = n2; |
| } |
| else |
| { |
| min = n2; |
| max = n1; |
| } |
| |
| auto out_ptr = reinterpret_cast<float *>(tensor(id)); |
| out_ptr[0] = min; |
| out_ptr[1] = max; |
| }); |
| } |
| |
| TensorType compute_target(const TensorShape &shape, DataType data_type) |
| { |
| TensorShape shape_min_max = shape; |
| shape_min_max.set(Window::DimX, 2); |
| |
| // Remove Y and Z dimensions and keep the batches |
| shape_min_max.remove_dimension(1); |
| shape_min_max.remove_dimension(1); |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(shape, data_type); |
| TensorType dst = create_tensor<TensorType>(shape, DataType::F32); |
| TensorType min_max = create_tensor<TensorType>(shape_min_max, DataType::F32); |
| |
| // Create and configure function |
| FunctionType dequantization_layer; |
| dequantization_layer.configure(&src, &dst, &min_max); |
| |
| ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(min_max.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| min_max.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(!min_max.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(src)); |
| fill_min_max(AccessorType(min_max)); |
| |
| // Compute function |
| dequantization_layer.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<float> compute_reference(const TensorShape &shape, DataType data_type) |
| { |
| TensorShape shape_min_max = shape; |
| shape_min_max.set(Window::DimX, 2); |
| |
| // Remove Y and Z dimensions and keep the batches |
| shape_min_max.remove_dimension(1); |
| shape_min_max.remove_dimension(1); |
| |
| // Create reference |
| SimpleTensor<T> src{ shape, data_type }; |
| SimpleTensor<float> min_max{ shape_min_max, data_type }; |
| |
| // Fill reference |
| fill(src); |
| fill_min_max(min_max); |
| |
| return reference::dequantization_layer<T>(src, min_max); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<float> _reference{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DequantizationValidationFixture : public DequantizationValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape shape, DataType data_type) |
| { |
| DequantizationValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, data_type); |
| } |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_DEQUANTIZATION_LAYER_FIXTURE */ |