Improve yolov8 post-processing efficiency.

[whyb]

Using OpenMP to improve yolov8 post-processing efficiency.

#if NCNN_SIMPLEOMP
#include "simpleomp.h"
#else
#include <omp.h>
#endif

...

static void parse_yolov8_detections(
    float* inputs, float confidence_threshold,
    int num_channels, int num_anchors, int num_labels,
    int infer_img_width, int infer_img_height,
    std::vector<Object>& objects)
{
    std::vector<Object> detections;
    cv::Mat output = cv::Mat((int)num_channels, (int)num_anchors, CV_32F, inputs).t();

    const size_t stride = num_anchors;
    const size_t num_threads = omp_get_max_threads();
    const size_t chunk_size = stride / num_threads;
    #pragma omp parallel shared(detections)
    {
        const size_t thread_id = omp_get_thread_num();
        const size_t start_idx = thread_id * chunk_size;
        const size_t end_idx = (thread_id == num_threads - 1) ? stride : (start_idx + chunk_size);
        for (int i = start_idx; i < end_idx; i++)
        {
            const float* row_ptr = output.row(i).ptr<float>();
            const float* bboxes_ptr = row_ptr;
            const float* scores_ptr = row_ptr + 4;
            const float* max_s_ptr = std::max_element(scores_ptr, scores_ptr + num_labels);
            float score = *max_s_ptr;
            if (score > confidence_threshold)
            {
                float x = *bboxes_ptr++;
                float y = *bboxes_ptr++;
                float w = *bboxes_ptr++;
                float h = *bboxes_ptr;

                float x0 = clampf((x - 0.5f * w), 0.f, (float)infer_img_width);
                float y0 = clampf((y - 0.5f * h), 0.f, (float)infer_img_height);
                float x1 = clampf((x + 0.5f * w), 0.f, (float)infer_img_width);
                float y1 = clampf((y + 0.5f * h), 0.f, (float)infer_img_height);

                cv::Rect_<float> bbox;
                bbox.x = x0;
                bbox.y = y0;
                bbox.width = x1 - x0;
                bbox.height = y1 - y0;
                Object object;
                object.label = max_s_ptr - scores_ptr;
                object.prob = score;
                object.rect = bbox;
                #pragma omp critical
                {
                    detections.push_back(object);
                }
            }
        }
    }
    objects = detections;
}

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[whyb]

Hello @nihui,

I found the some ncnn's example usage of OpenMP parallel sections ( #pragma omp parallel sections ) in the following files:

examples/yolov7.cpp
examples/scrfd_crowdhuman.cpp
examples/fasterrcnn.cpp
examples/yolov5.cpp
examples/yolox.cpp
examples/scrfd.cpp
examples/rfcn.cpp
examples/nanodet.cpp
examples/retinaface.cpp

But I checked the implementation of src/simpleomp.cpp and I did not find the necessary functions for sections:

GOMP_parallel_sections_start()
GOMP_parallel_sections()
GOMP_sections_start()
GOMP_sections_next()
GOMP_sections_end()
GOMP_sections_end_cancel()
GOMP_sections_end_nowait()

Does this mean that simpleomp will not support OpenMP sections feature for a long time?

If we follow the same standard, since the example previously allowed the use of features that simpleomp does not support, the OpenMP shared clause feature should be allowed to be added.🙈

[nihui]

hi, yolov8 examples are updated with full support for detection, segmentation, classification, pose estimation and obb https://github.com/Tencent/ncnn/tree/master/examples

android demo https://github.com/nihui/ncnn-android-yolov8

detailed instruction (zh) https://zhuanlan.zhihu.com/p/16030630352