/* * 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 class HOGMultiDetectionValidationFixture : public framework::Fixture { public: template void setup(std::string image, std::vector 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 int_dist(0, 255); const T constant_border_value = int_dist(gen); // Initialize descriptors vector std::vector> 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 void fill(V &&tensor, const std::string image, Format format) { library->fill(tensor, image, format); } void initialize_batch(const std::vector &models, MultiHOGType &multi_hog, std::vector> &descriptors, DetectionWindowStrideType &detection_window_strides) { for(unsigned i = 0; i < models.size(); ++i) { auto hog_model = reinterpret_cast(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 compute_target(const std::string image, Format &format, BorderMode &border_mode, T constant_border_value, const std::vector &models, std::vector> &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(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 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 compute_reference(const std::string image, Format format, BorderMode border_mode, T constant_border_value, const std::vector &models, const std::vector> &descriptors, unsigned int max_num_detection_windows, float threshold, bool non_max_suppression, float min_distance) { // Create reference SimpleTensor 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 _target{}; std::vector _reference{}; }; } // namespace validation } // namespace test } // namespace arm_compute #endif /* ARM_COMPUTE_TEST_HOG_MULTI_DETECTION_FIXTURE */