| /* |
| * 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. |
| */ |
| #ifndef ARM_COMPUTE_TEST_HOG_MULTI_DETECTION_FIXTURE |
| #define ARM_COMPUTE_TEST_HOG_MULTI_DETECTION_FIXTURE |
| |
| #include "arm_compute/core/HOGInfo.h" |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "tests/AssetsLibrary.h" |
| #include "tests/Globals.h" |
| #include "tests/IAccessor.h" |
| #include "tests/IHOGAccessor.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Fixture.h" |
| #include "tests/validation/reference/HOGMultiDetection.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, |
| typename HOGType, |
| typename MultiHOGType, |
| typename DetectionWindowArrayType, |
| typename DetectionWindowStrideType, |
| typename AccessorType, |
| typename Size2DArrayAccessorType, |
| typename DetectionWindowArrayAccessorType, |
| typename HOGAccessorType, |
| typename FunctionType, |
| typename T, |
| typename U> |
| class HOGMultiDetectionValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(std::string image, std::vector<HOGInfo> models, Format format, BorderMode border_mode, bool non_maxima_suppression) |
| { |
| // Only defined borders supported |
| ARM_COMPUTE_ERROR_ON(border_mode == BorderMode::UNDEFINED); |
| |
| // Generate a random constant value |
| std::mt19937 gen(library->seed()); |
| std::uniform_int_distribution<T> int_dist(0, 255); |
| const T constant_border_value = int_dist(gen); |
| |
| // Initialize descriptors vector |
| std::vector<std::vector<U>> descriptors(models.size()); |
| |
| // Use default values for threshold and min_distance |
| const float threshold = 0.f; |
| const float min_distance = 1.f; |
| |
| // Maximum number of detection windows per batch |
| const unsigned int max_num_detection_windows = 100000; |
| |
| _target = compute_target(image, format, border_mode, constant_border_value, models, descriptors, max_num_detection_windows, threshold, non_maxima_suppression, min_distance); |
| _reference = compute_reference(image, format, border_mode, constant_border_value, models, descriptors, max_num_detection_windows, threshold, non_maxima_suppression, min_distance); |
| } |
| |
| protected: |
| template <typename V> |
| void fill(V &&tensor, const std::string image, Format format) |
| { |
| library->fill(tensor, image, format); |
| } |
| |
| void initialize_batch(const std::vector<HOGInfo> &models, MultiHOGType &multi_hog, |
| std::vector<std::vector<U>> &descriptors, DetectionWindowStrideType &detection_window_strides) |
| { |
| for(unsigned i = 0; i < models.size(); ++i) |
| { |
| auto hog_model = reinterpret_cast<HOGType *>(multi_hog.model(i)); |
| hog_model->init(models[i]); |
| |
| // Initialise descriptor (linear SVM coefficients). |
| std::random_device::result_type seed = 0; |
| descriptors.at(i) = generate_random_real(models[i].descriptor_size(), -0.505f, 0.495f, seed); |
| |
| // Copy HOG descriptor values to HOG memory |
| { |
| HOGAccessorType hog_accessor(*hog_model); |
| std::memcpy(hog_accessor.descriptor(), descriptors.at(i).data(), descriptors.at(i).size() * sizeof(U)); |
| } |
| |
| // Initialize detection window stride |
| Size2DArrayAccessorType accessor(detection_window_strides); |
| accessor.at(i) = models[i].block_stride(); |
| } |
| } |
| |
| std::vector<DetectionWindow> compute_target(const std::string image, Format &format, BorderMode &border_mode, T constant_border_value, |
| const std::vector<HOGInfo> &models, std::vector<std::vector<U>> &descriptors, unsigned int max_num_detection_windows, |
| float threshold, bool non_max_suppression, float min_distance) |
| { |
| MultiHOGType multi_hog(models.size()); |
| DetectionWindowArrayType detection_windows(max_num_detection_windows); |
| DetectionWindowStrideType detection_window_strides(models.size()); |
| |
| // Resize detection window_strides for index access |
| detection_window_strides.resize(models.size()); |
| |
| // Initialiize MultiHOG and detection windows |
| initialize_batch(models, multi_hog, descriptors, detection_window_strides); |
| |
| // Get image shape for src tensor |
| TensorShape shape = library->get_image_shape(image); |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(shape, data_type_from_format(format)); |
| ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Create and configure function |
| FunctionType hog_multi_detection; |
| hog_multi_detection.configure(&src, &multi_hog, &detection_windows, &detection_window_strides, border_mode, constant_border_value, threshold, non_max_suppression, min_distance); |
| |
| // Reset detection windows |
| detection_windows.clear(); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(src), image, format); |
| |
| // Compute function |
| hog_multi_detection.run(); |
| |
| // Copy detection windows |
| std::vector<DetectionWindow> windows; |
| DetectionWindowArrayAccessorType accessor(detection_windows); |
| |
| for(size_t i = 0; i < accessor.num_values(); i++) |
| { |
| DetectionWindow win; |
| win.x = accessor.at(i).x; |
| win.y = accessor.at(i).y; |
| win.width = accessor.at(i).width; |
| win.height = accessor.at(i).height; |
| win.idx_class = accessor.at(i).idx_class; |
| win.score = accessor.at(i).score; |
| |
| windows.push_back(win); |
| } |
| |
| return windows; |
| } |
| |
| std::vector<DetectionWindow> compute_reference(const std::string image, Format format, BorderMode border_mode, T constant_border_value, |
| const std::vector<HOGInfo> &models, const std::vector<std::vector<U>> &descriptors, unsigned int max_num_detection_windows, |
| float threshold, bool non_max_suppression, float min_distance) |
| { |
| // Create reference |
| SimpleTensor<T> src{ library->get_image_shape(image), data_type_from_format(format) }; |
| |
| // Fill reference |
| fill(src, image, format); |
| |
| // NOTE: Detection window stride fixed to block stride |
| return reference::hog_multi_detection(src, border_mode, constant_border_value, models, descriptors, max_num_detection_windows, threshold, non_max_suppression, min_distance); |
| } |
| |
| std::vector<DetectionWindow> _target{}; |
| std::vector<DetectionWindow> _reference{}; |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_HOG_MULTI_DETECTION_FIXTURE */ |