ncnn vulkan 以类的方式推理示例

#include "yolov5gpu.h" #include <iostream> Yolov5GPU::Yolov5GPU() { ncnn::create_gpu_instance(); use_gpu_ = true; RGB_ = 0; // 默认opencv加载 使用bgr 顺序 ncnn::Option opt; opt.lightmode = true; opt.num_threads = 4; opt.blob_allocator = &(g_blob_pool_allocator); opt.workspace_allocator = &(g_workspace_pool_allocator); opt.use_packing_layout = true; if (ncnn::get_gpu_count() != 0) opt.use_vulkan_compute = use_gpu_; yolov5_.opt = opt; std::cout << "get_gpu_count():" << ncnn::get_gpu_count() << std::endl; } Yolov5GPU::~Yolov5GPU() { g_blob_pool_allocator.clear(); g_workspace_pool_allocator.clear(); yolov5_.clear(); ncnn::destroy_gpu_instance(); } int Yolov5GPU::load_model() { // init params int ret = yolov5_.load_param("yolov5s_6.2.param"); if (ret != 0) { // error std::cout << "load_param error" << std::endl; return -1; } // init bin ret = yolov5_.load_model("yolov5s_6.2.bin"); if (ret != 0) { // error std::cout << "load_model error" << std::endl; return -1; } return 0; } int Yolov5GPU::inference(const cv::Mat &bgr, std::vector<Object> &objects) { const int target_size = 640; const float prob_threshold = 0.25f; const float nms_threshold = 0.45f; int img_w = bgr.cols; int img_h = bgr.rows; // letterbox pad to multiple of 32 int w = img_w; int h = img_h; float scale = 1.f; if (w > h) { scale = (float)target_size / w; w = target_size; h = h * scale; } else { scale = (float)target_size / h; h = target_size; w = w * scale; } // w h , is original size now. // img_w img_h,is target size now. ncnn::Mat in; if (RGB_ == 0) { in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h); } else { in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_RGB, img_w, img_h, w, h); } // pad to target_size rectangle // yolov5/utils/datasets.py letterbox int wpad = (w + 31) / 32 * 32 - w; int hpad = (h + 31) / 32 * 32 - h; ncnn::Mat in_pad; ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f); // yolov5 // std::vector<Object> objects; { const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f}; in_pad.substract_mean_normalize(0, norm_vals); ncnn::Extractor ex = yolov5_.create_extractor(); ex.set_vulkan_compute(use_gpu_); ex.input("images", in_pad); std::vector<Object> proposals; // anchor setting from yolov5/models/yolov5s.yaml // stride 8 { ncnn::Mat out; ex.extract("output", out); ncnn::Mat anchors(6); anchors[0] = 10.f; anchors[1] = 13.f; anchors[2] = 16.f; anchors[3] = 30.f; anchors[4] = 33.f; anchors[5] = 23.f; std::vector<Object> objects8; generate_proposals(anchors, 8, in_pad, out, prob_threshold, objects8); proposals.insert(proposals.end(), objects8.begin(), objects8.end()); } // stride 16 { ncnn::Mat out; ex.extract("353", out); ncnn::Mat anchors(6); anchors[0] = 30.f; anchors[1] = 61.f; anchors[2] = 62.f; anchors[3] = 45.f; anchors[4] = 59.f; anchors[5] = 119.f; std::vector<Object> objects16; generate_proposals(anchors, 16, in_pad, out, prob_threshold, objects16); proposals.insert(proposals.end(), objects16.begin(), objects16.end()); } // stride 32 { ncnn::Mat out; ex.extract("367", out); ncnn::Mat anchors(6); anchors[0] = 116.f; anchors[1] = 90.f; anchors[2] = 156.f; anchors[3] = 198.f; anchors[4] = 373.f; anchors[5] = 326.f; std::vector<Object> objects32; generate_proposals(anchors, 32, in_pad, out, prob_threshold, objects32); proposals.insert(proposals.end(), objects32.begin(), objects32.end()); } // sort all proposals by score from highest to lowest qsort_descent_inplace(proposals); // apply nms with nms_threshold std::vector<int> picked; nms_sorted_bboxes(proposals, picked, nms_threshold); int count = picked.size(); objects.resize(count); std::cout << "count:" << count << std::endl; for (int i = 0; i < count; i++) { objects[i] = proposals[picked[i]]; // adjust offset to original unpadded float x0 = (objects[i].rect.x - (wpad / 2)) / scale; float y0 = (objects[i].rect.y - (hpad / 2)) / scale; float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale; float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale; // clip x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f); y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f); x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f); y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f); objects[i].rect.x = x0; objects[i].rect.y = y0; objects[i].rect.width = x1 - x0; objects[i].rect.height = y1 - y0; } } return 0; } void Yolov5GPU::draw_objects(const cv::Mat &bgr, const std::vector<Object> &objects) { for (size_t i = 0; i < objects.size(); i++) { const Object &obj = objects[i]; fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob, obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height); cv::rectangle(bgr, obj.rect, cv::Scalar(255, 0, 0), 1); } return; } inline float Yolov5GPU::intersection_area(const Object &a, const Object &b) { cv::Rect_<float> inter = a.rect & b.rect; return inter.area(); } void Yolov5GPU::qsort_descent_inplace(std::vector<Object> &faceobjects, int left, int right) { int i = left; int j = right; float p = faceobjects[(left + right) / 2].prob; while (i <= j) { while (faceobjects[i].prob > p) i++; while (faceobjects[j].prob < p) j--; if (i <= j) { // swap std::swap(faceobjects[i], faceobjects[j]); i++; j--; } } #pragma omp parallel sections { #pragma omp section { if (left < j) qsort_descent_inplace(faceobjects, left, j); } #pragma omp section { if (i < right) qsort_descent_inplace(faceobjects, i, right); } } } void Yolov5GPU::qsort_descent_inplace(std::vector<Object> &faceobjects) { if (faceobjects.empty()) return; qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1); } void Yolov5GPU::nms_sorted_bboxes(const std::vector<Object> &faceobjects, std::vector<int> &picked, float nms_threshold) { picked.clear(); const int n = faceobjects.size(); std::vector<float> areas(n); for (int i = 0; i < n; i++) { areas[i] = faceobjects[i].rect.area(); } for (int i = 0; i < n; i++) { const Object &a = faceobjects[i]; int keep = 1; for (int j = 0; j < (int)picked.size(); j++) { const Object &b = faceobjects[picked[j]]; // if (!agnostic && a.label != b.label) // continue; // intersection over union float inter_area = intersection_area(a,