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/*
* Copyright (c) 2016, 2017 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.
*/
#include "arm_compute/core/NEON/kernels/NEHOGDetectorKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/HOGInfo.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/Validate.h"
#include <arm_neon.h>
using namespace arm_compute;
NEHOGDetectorKernel::NEHOGDetectorKernel()
: _input(nullptr), _detection_windows(), _hog_descriptor(nullptr), _bias(0.0f), _threshold(0.0f), _idx_class(0), _num_bins_per_descriptor_x(0), _num_blocks_per_descriptor_y(0), _block_stride_width(0),
_block_stride_height(0), _detection_window_width(0), _detection_window_height(0), _max_num_detection_windows(0), _mutex()
{
}
void NEHOGDetectorKernel::configure(const ITensor *input, const IHOG *hog, IDetectionWindowArray *detection_windows, const Size2D &detection_window_stride, float threshold, uint16_t idx_class)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_NOT_IN(input, DataType::F32);
ARM_COMPUTE_ERROR_ON(hog == nullptr);
ARM_COMPUTE_ERROR_ON(detection_windows == nullptr);
ARM_COMPUTE_ERROR_ON((detection_window_stride.width % hog->info()->block_stride().width) != 0);
ARM_COMPUTE_ERROR_ON((detection_window_stride.height % hog->info()->block_stride().height) != 0);
const Size2D &detection_window_size = hog->info()->detection_window_size();
const Size2D &block_size = hog->info()->block_size();
const Size2D &block_stride = hog->info()->block_stride();
_input = input;
_detection_windows = detection_windows;
_threshold = threshold;
_idx_class = idx_class;
_hog_descriptor = hog->descriptor();
_bias = _hog_descriptor[hog->info()->descriptor_size() - 1];
_num_bins_per_descriptor_x = ((detection_window_size.width - block_size.width) / block_stride.width + 1) * input->info()->num_channels();
_num_blocks_per_descriptor_y = (detection_window_size.height - block_size.height) / block_stride.height + 1;
_block_stride_width = block_stride.width;
_block_stride_height = block_stride.height;
_detection_window_width = detection_window_size.width;
_detection_window_height = detection_window_size.height;
_max_num_detection_windows = detection_windows->max_num_values();
ARM_COMPUTE_ERROR_ON((_num_bins_per_descriptor_x * _num_blocks_per_descriptor_y + 1) != hog->info()->descriptor_size());
// Get the number of blocks along the x and y directions of the input tensor
const ValidRegion &valid_region = input->info()->valid_region();
const size_t num_blocks_x = valid_region.shape[0];
const size_t num_blocks_y = valid_region.shape[1];
// Get the number of blocks along the x and y directions of the detection window
const size_t num_blocks_per_detection_window_x = detection_window_size.width / block_stride.width;
const size_t num_blocks_per_detection_window_y = detection_window_size.height / block_stride.height;
const size_t window_step_x = detection_window_stride.width / block_stride.width;
const size_t window_step_y = detection_window_stride.height / block_stride.height;
// Configure kernel window
Window win;
win.set(Window::DimX, Window::Dimension(0, floor_to_multiple(num_blocks_x - num_blocks_per_detection_window_x, window_step_x), window_step_x));
win.set(Window::DimY, Window::Dimension(0, floor_to_multiple(num_blocks_y - num_blocks_per_detection_window_y, window_step_y), window_step_y));
constexpr unsigned int num_elems_read_per_iteration = 1;
const unsigned int num_rows_read_per_iteration = _num_blocks_per_descriptor_y;
update_window_and_padding(win, AccessWindowRectangle(input->info(), 0, 0, num_elems_read_per_iteration, num_rows_read_per_iteration));
INEKernel::configure(win);
}
void NEHOGDetectorKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
ARM_COMPUTE_ERROR_ON(_hog_descriptor == nullptr);
const size_t in_step_y = _input->info()->strides_in_bytes()[Window::DimY] / data_size_from_type(_input->info()->data_type());
Iterator in(_input, window);
execute_window_loop(window, [&](const Coordinates & id)
{
const auto *in_row_ptr = reinterpret_cast<const float *>(in.ptr());
// Init score_f32 with 0
float32x4_t score_f32 = vdupq_n_f32(0.0f);
// Init score with bias
float score = _bias;
// Compute Linear SVM
for(size_t yb = 0; yb < _num_blocks_per_descriptor_y; ++yb, in_row_ptr += in_step_y)
{
int32_t xb = 0;
const int32_t offset_y = yb * _num_bins_per_descriptor_x;
for(; xb < static_cast<int32_t>(_num_bins_per_descriptor_x) - 16; xb += 16)
{
// Load descriptor values
const float32x4x4_t a_f32 =
{
{
vld1q_f32(&in_row_ptr[xb + 0]),
vld1q_f32(&in_row_ptr[xb + 4]),
vld1q_f32(&in_row_ptr[xb + 8]),
vld1q_f32(&in_row_ptr[xb + 12])
}
};
// Load detector values
const float32x4x4_t b_f32 =
{
{
vld1q_f32(&_hog_descriptor[xb + 0 + offset_y]),
vld1q_f32(&_hog_descriptor[xb + 4 + offset_y]),
vld1q_f32(&_hog_descriptor[xb + 8 + offset_y]),
vld1q_f32(&_hog_descriptor[xb + 12 + offset_y])
}
};
// Multiply accumulate
score_f32 = vmlaq_f32(score_f32, a_f32.val[0], b_f32.val[0]);
score_f32 = vmlaq_f32(score_f32, a_f32.val[1], b_f32.val[1]);
score_f32 = vmlaq_f32(score_f32, a_f32.val[2], b_f32.val[2]);
score_f32 = vmlaq_f32(score_f32, a_f32.val[3], b_f32.val[3]);
}
for(; xb < static_cast<int32_t>(_num_bins_per_descriptor_x); ++xb)
{
const float a = in_row_ptr[xb];
const float b = _hog_descriptor[xb + offset_y];
score += a * b;
}
}
score += vgetq_lane_f32(score_f32, 0);
score += vgetq_lane_f32(score_f32, 1);
score += vgetq_lane_f32(score_f32, 2);
score += vgetq_lane_f32(score_f32, 3);
if(score > _threshold)
{
if(_detection_windows->num_values() < _max_num_detection_windows)
{
DetectionWindow win;
win.x = (id.x() * _block_stride_width);
win.y = (id.y() * _block_stride_height);
win.width = _detection_window_width;
win.height = _detection_window_height;
win.idx_class = _idx_class;
win.score = score;
std::unique_lock<arm_compute::Mutex> lock(_mutex);
_detection_windows->push_back(win);
lock.unlock();
}
}
},
in);
}