246 lines
9.1 KiB
C++
246 lines
9.1 KiB
C++
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// Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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#include <stdio.h>
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#include <stdlib.h>
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#include <vector>
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#include <string>
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#include <algorithm>
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#include <regex>
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#include <sstream>
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#include <MNN/ImageProcess.hpp>
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#include <MNN/expr/Module.hpp>
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#include <MNN/expr/Executor.hpp>
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#include <MNN/expr/ExprCreator.hpp>
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#include <cv/cv.hpp>
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using namespace MNN;
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using namespace MNN::Express;
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using namespace MNN::CV;
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class Inference {
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public:
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// Load model: Create runtime, set cache if needed, and load the model file.
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bool loadModel(const std::string &modelPath,
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int forwardType = MNN_FORWARD_CPU,
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int precision = 0,
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int thread = 4) {
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MNN::ScheduleConfig sConfig;
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sConfig.type = static_cast<MNNForwardType>(forwardType);
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sConfig.numThread = thread;
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BackendConfig bConfig;
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bConfig.precision = static_cast<BackendConfig::PrecisionMode>(precision);
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sConfig.backendConfig = &bConfig;
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std::shared_ptr<Executor::RuntimeManager> rtmgr(
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Executor::RuntimeManager::createRuntimeManager(sConfig)
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);
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if (rtmgr == nullptr) {
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MNN_ERROR("Empty RuntimeManager\n");
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return false;
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}
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rtmgr->setCache(".cachefile");
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net = std::shared_ptr<Module>(Module::load(std::vector<std::string>{},
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std::vector<std::string>{}, modelPath.c_str(), rtmgr));
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if (net == nullptr) {
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return false;
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}
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runtimeManager = rtmgr;
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const Module::Info* info = net->getInfo();
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if (info == nullptr) {
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MNN_ERROR("Empty Module Info\n");
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return false;
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}
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// Parse bizCode to extract class names.
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if (info->bizCode.empty()) {
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MNN_ERROR("Empty bizCode\n");
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classNames.clear();
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return false;
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}
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// Get imgsz from bizCode.
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auto imgsz_start = info->bizCode.find("\"imgsz\": [");
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if (imgsz_start == std::string::npos) {
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MNN_PRINT("No imgsz found in bizCode, setting classNames empty.\n");
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} else {
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auto imgsz_end = info->bizCode.find("]", imgsz_start);
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if (imgsz_end == std::string::npos) {
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MNN_PRINT("No closing bracket for imgsz in bizCode, setting classNames empty.\n");
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} else {
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std::string imgszText = info->bizCode.substr(imgsz_start + 10, imgsz_end - imgsz_start - 10);
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std::vector<std::string> imgszVec;
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std::stringstream ss(imgszText);
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std::string item;
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while (std::getline(ss, item, ',')) {
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imgszVec.push_back(item);
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}
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}
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}
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// Get names from bizCode.
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auto names_start = info->bizCode.find("\"names\": {");
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if (names_start == std::string::npos) {
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MNN_PRINT("No names found in bizCode, setting classNames empty.\n");
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classNames.clear();
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} else {
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auto names_end = info->bizCode.find("}", names_start);
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if (names_end == std::string::npos) {
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MNN_PRINT("No closing brace for names in bizCode, setting classNames empty.\n");
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classNames.clear();
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} else {
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std::string namesDict = info->bizCode.substr(names_start + 10, names_end - names_start - 10);
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parseClassNamesFromBizCode(namesDict);
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}
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}
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return true;
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}
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void parseImgszFromBizCode(const std::string& bizText) {
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std::regex rgx("\"imgsz\":\\s*\\[(\\d+),\\s*(\\d+)\\]");
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std::smatch match;
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if (std::regex_search(bizText, match, rgx)) {
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int ih = std::stoi(match[1].str());
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int iw = std::stoi(match[2].str());
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MNN_PRINT("Input size: %d x %d\n", iw, ih);
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} else {
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MNN_PRINT("No imgsz found in bizCode.\n");
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}
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}
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void parseClassNamesFromBizCode(const std::string& bizText) {
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std::regex rgx("\"(\\d+)\"\\s*:\\s*\"([^\"]+)\"");
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std::smatch match;
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std::string s = bizText;
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classNames.clear();
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while (std::regex_search(s, match, rgx)) {
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int index = std::stoi(match[1].str());
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std::string name = match[2].str();
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if (classNames.size() <= static_cast<size_t>(index)) {
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classNames.resize(index + 1);
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}
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classNames[index] = name;
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s = match.suffix().str();
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}
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}
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VARP preprocess(VARP &originalImage, float &scale) {
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auto dims = originalImage->getInfo()->dim;
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int ih = dims[0];
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int iw = dims[1];
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int targetWidth = std::stoi(imgszVec[0]);
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int targetHeight = std::stoi(imgszVec[1]);
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int len = ih > iw ? ih : iw;
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scale = static_cast<float>(len) / std::max(targetWidth, targetHeight);
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std::vector<int> padvals { 0, len - ih, 0, len - iw, 0, 0 };
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auto pads = _Const(static_cast<void*>(padvals.data()), {3, 2}, NCHW, halide_type_of<int>());
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auto image = _Pad(originalImage, pads, CONSTANT);
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image = resize(image, Size(targetWidth, targetHeight), 0, 0, INTER_LINEAR, -1, {0., 0., 0.}, {1./255., 1./255., 1./255.});
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auto input = _Unsqueeze(image, {0});
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input = _Convert(input, NC4HW4);
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return input;
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}
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void runInference(VARP input) {
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std::vector<VARP> outputs = net->onForward({input});
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mOutput = outputs[0];
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}
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void postprocess(float scale, VARP originalImage, float iouThreshold = 0.45, float scoreThreshold = 0.25) {
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auto output = _Convert(mOutput, NCHW);
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output = _Squeeze(output);
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// Expected output shape: [84, 8400]
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auto cx = _Gather(output, _Scalar<int>(0));
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auto cy = _Gather(output, _Scalar<int>(1));
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auto w = _Gather(output, _Scalar<int>(2));
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auto h = _Gather(output, _Scalar<int>(3));
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std::vector<int> startvals { 4, 0 };
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auto start = _Const(static_cast<void*>(startvals.data()), {2}, NCHW, halide_type_of<int>());
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std::vector<int> sizevals { -1, -1 };
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auto size = _Const(static_cast<void*>(sizevals.data()), {2}, NCHW, halide_type_of<int>());
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auto probs = _Slice(output, start, size);
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// [cx, cy, w, h] -> [x1, y1, x2, y2]
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auto x1 = cx - w * _Const(0.5);
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auto y1 = cy - h * _Const(0.5);
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auto x2 = cx + w * _Const(0.5);
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auto y2 = cy + h * _Const(0.5);
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auto boxes = _Stack({x1, y1, x2, y2}, 1);
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auto scores = _ReduceMax(probs, {0});
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auto ids = _ArgMax(probs, 0);
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auto result_ids = _Nms(boxes, scores, 100, 0.45, 0.25);
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auto result_ptr = result_ids->readMap<int>();
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auto box_ptr = boxes->readMap<float>();
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auto ids_ptr = ids->readMap<int>();
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auto score_ptr = scores->readMap<float>();
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for (int i = 0; i < 100; i++) {
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auto idx = result_ptr[i];
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if (idx < 0) break;
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auto x1 = box_ptr[idx * 4 + 0] * scale;
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auto y1 = box_ptr[idx * 4 + 1] * scale;
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auto x2 = box_ptr[idx * 4 + 2] * scale;
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auto y2 = box_ptr[idx * 4 + 3] * scale;
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auto class_idx = ids_ptr[idx];
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auto score = score_ptr[idx];
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printf("Detection: box = {%.2f, %.2f, %.2f, %.2f}, class = %s, score = %.2f\n",
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x1, y1, x2, y2, classNames[class_idx].c_str(), score);
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rectangle(originalImage, { x1, y1 }, { x2, y2 }, { 0, 255, 0 }, 2);
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}
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if (imwrite("mnn_yolov8_cpp.jpg", originalImage)) {
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MNN_PRINT("Result image write to `mnn_yolov8_cpp.jpg`.\n");
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}
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}
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// Update runtime cache.
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void updateCache() {
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if (runtimeManager)
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runtimeManager->updateCache();
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}
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private:
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std::shared_ptr<Module> net;
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VARP mOutput;
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std::shared_ptr<Executor::RuntimeManager> runtimeManager;
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std::vector<std::string> classNames;
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std::vector<std::string> imgszVec { "640", "640" };
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};
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int main(int argc, const char* argv[]) {
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if (argc < 3) {
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MNN_PRINT("Usage: ./main yolov8n.mnn bus.jpg [forwardType] [precision] [thread]\n");
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return 0;
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}
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int thread = 4;
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int precision = 0;
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int forwardType = MNN_FORWARD_CPU;
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if (argc >= 4) {
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forwardType = atoi(argv[3]);
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}
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if (argc >= 5) {
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precision = atoi(argv[4]);
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}
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if (argc >= 6) {
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thread = atoi(argv[5]);
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}
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Inference infer;
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if (!infer.loadModel(argv[1], forwardType, precision, thread))
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return 1;
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const clock_t t0 = clock();
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float scale = 1.0f;
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VARP originalImage = imread(argv[2]);
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VARP input = infer.preprocess(originalImage, scale);
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double preprocess_time = 1000.0 * (clock() - t0) / CLOCKS_PER_SEC;
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const clock_t t1 = clock();
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infer.runInference(input);
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double inference_time = 1000.0 * (clock() - t1) / CLOCKS_PER_SEC;
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const clock_t t2 = clock();
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infer.postprocess(scale, originalImage);
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double postprocess_time = 1000.0 * (clock() - t2) / CLOCKS_PER_SEC;
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printf("Speed: %.1fms preprocess, %.1fms inference, %.1fms postprocess\n",
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preprocess_time, inference_time, postprocess_time);
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infer.updateCache();
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return 0;
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}
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