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Diffstat (limited to 'tests/validation/fixtures/HOGMultiDetectionFixture.h')
| -rw-r--r-- | tests/validation/fixtures/HOGMultiDetectionFixture.h | 193 |
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diff --git a/tests/validation/fixtures/HOGMultiDetectionFixture.h b/tests/validation/fixtures/HOGMultiDetectionFixture.h new file mode 100644 index 0000000000..039f3f4b74 --- /dev/null +++ b/tests/validation/fixtures/HOGMultiDetectionFixture.h @@ -0,0 +1,193 @@ +/* + * 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 */ |