tests/validation/fixtures/HOGMultiDetectionFixture.h - ml/ComputeLibrary - Gitiles

 /*
  * 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 */
	/*
	* 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 */