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/*
* 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/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "tests/Globals.h"
#include "tests/Utils.h"
#include "tests/framework/Fixture.h"
namespace arm_compute
{
namespace test
{
namespace benchmark
{
template <typename TensorType,
typename HOGType,
typename MultiHOGType,
typename DetectionWindowArrayType,
typename DetectionWindowStrideType,
typename Function,
typename Accessor,
typename HOGAccessorType,
typename Size2DArrayAccessorType>
class HOGMultiDetectionFixture : 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);
std::mt19937 generator(library->seed());
std::uniform_int_distribution<uint8_t> distribution_u8(0, 255);
uint8_t constant_border_value = static_cast<uint8_t>(distribution_u8(generator));
// Load the image (cached by the library if loaded before)
const RawTensor &raw = library->get(image, format);
// Initialize descriptors vector
std::vector<std::vector<float>> descriptors(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);
// Create tensors
src = create_tensor<TensorType>(raw.shape(), format);
// Use default values for threshold and min_distance
const float threshold = 0.f;
const float min_distance = 1.f;
hog_multi_detection_func.configure(&src,
&multi_hog,
&detection_windows,
&detection_window_strides,
border_mode,
constant_border_value,
threshold,
non_maxima_suppression,
min_distance);
// Reset detection windows
detection_windows.clear();
// Allocate tensor
src.allocator()->allocate();
library->fill(Accessor(src), raw);
}
void run()
{
hog_multi_detection_func.run();
}
void sync()
{
sync_if_necessary<TensorType>();
}
private:
void initialize_batch(const std::vector<HOGInfo> &models, MultiHOGType &multi_hog,
std::vector<std::vector<float>> &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(float));
}
// Initialize detection window stride
Size2DArrayAccessorType accessor(detection_window_strides);
accessor.at(i) = models[i].block_stride();
}
}
private:
static const unsigned int model_size = 4;
static const unsigned int max_num_detection_windows = 100000;
MultiHOGType multi_hog{ model_size };
DetectionWindowStrideType detection_window_strides{ model_size };
DetectionWindowArrayType detection_windows{ max_num_detection_windows };
TensorType src{};
Function hog_multi_detection_func{};
};
} // namespace benchmark
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_HOG_MULTI_DETECTION_FIXTURE */
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