477 lines
18 KiB
C++
477 lines
18 KiB
C++
#include <fstream>
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#include <iostream>
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#include <map>
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#include <sstream>
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#include <vector>
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#include <chrono>
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#include <opencv2/opencv.hpp>
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#include <dirent.h>
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#include "NvInfer.h"
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#include "cuda_runtime_api.h"
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#include "logging.h"
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#include "yololayer.h"
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#define CHECK(status) \
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do\
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{\
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auto ret = (status);\
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if (ret != 0)\
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{\
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std::cerr << "Cuda failure: " << ret << std::endl;\
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abort();\
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}\
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} while (0)
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#define USE_FP16 // comment out this if want to use FP32
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#define DEVICE 0 // GPU id
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#define NMS_THRESH 0.5
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#define BBOX_CONF_THRESH 0.4
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using namespace nvinfer1;
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// stuff we know about the network and the input/output blobs
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static const int INPUT_H = Yolo::INPUT_H;
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static const int INPUT_W = Yolo::INPUT_W;
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static const int OUTPUT_SIZE = 1000 * 7 + 1; // we assume the yololayer outputs no more than 1000 boxes that conf >= 0.1
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const char* INPUT_BLOB_NAME = "data";
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const char* OUTPUT_BLOB_NAME = "prob";
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static Logger gLogger;
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cv::Mat preprocess_img(cv::Mat& img) {
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int w, h, x, y;
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float r_w = INPUT_W / (img.cols*1.0);
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float r_h = INPUT_H / (img.rows*1.0);
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if (r_h > r_w) {
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w = INPUT_W;
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h = r_w * img.rows;
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x = 0;
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y = (INPUT_H - h) / 2;
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} else {
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w = r_h* img.cols;
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h = INPUT_H;
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x = (INPUT_W - w) / 2;
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y = 0;
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}
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cv::Mat re(h, w, CV_8UC3);
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cv::resize(img, re, re.size(), 0, 0, cv::INTER_CUBIC);
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cv::Mat out(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(128, 128, 128));
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re.copyTo(out(cv::Rect(x, y, re.cols, re.rows)));
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return out;
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}
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cv::Rect get_rect(cv::Mat& img, float bbox[4]) {
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int l, r, t, b;
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float r_w = INPUT_W / (img.cols * 1.0);
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float r_h = INPUT_H / (img.rows * 1.0);
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if (r_h > r_w) {
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l = bbox[0] - bbox[2]/2.f;
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r = bbox[0] + bbox[2]/2.f;
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t = bbox[1] - bbox[3]/2.f - (INPUT_H - r_w * img.rows) / 2;
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b = bbox[1] + bbox[3]/2.f - (INPUT_H - r_w * img.rows) / 2;
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l = l / r_w;
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r = r / r_w;
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t = t / r_w;
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b = b / r_w;
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} else {
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l = bbox[0] - bbox[2]/2.f - (INPUT_W - r_h * img.cols) / 2;
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r = bbox[0] + bbox[2]/2.f - (INPUT_W - r_h * img.cols) / 2;
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t = bbox[1] - bbox[3]/2.f;
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b = bbox[1] + bbox[3]/2.f;
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l = l / r_h;
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r = r / r_h;
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t = t / r_h;
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b = b / r_h;
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}
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return cv::Rect(l, t, r-l, b-t);
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}
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float iou(float lbox[4], float rbox[4]) {
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float interBox[] = {
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std::max(lbox[0] - lbox[2]/2.f , rbox[0] - rbox[2]/2.f), //left
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std::min(lbox[0] + lbox[2]/2.f , rbox[0] + rbox[2]/2.f), //right
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std::max(lbox[1] - lbox[3]/2.f , rbox[1] - rbox[3]/2.f), //top
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std::min(lbox[1] + lbox[3]/2.f , rbox[1] + rbox[3]/2.f), //bottom
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};
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if(interBox[2] > interBox[3] || interBox[0] > interBox[1])
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return 0.0f;
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float interBoxS =(interBox[1]-interBox[0])*(interBox[3]-interBox[2]);
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return interBoxS/(lbox[2]*lbox[3] + rbox[2]*rbox[3] -interBoxS);
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}
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bool cmp(const Yolo::Detection& a, const Yolo::Detection& b) {
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return a.det_confidence > b.det_confidence;
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}
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void nms(std::vector<Yolo::Detection>& res, float *output, float nms_thresh = NMS_THRESH) {
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std::map<float, std::vector<Yolo::Detection>> m;
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for (int i = 0; i < output[0] && i < 1000; i++) {
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if (output[1 + 7 * i + 4] <= BBOX_CONF_THRESH) continue;
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Yolo::Detection det;
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memcpy(&det, &output[1 + 7 * i], 7 * sizeof(float));
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if (m.count(det.class_id) == 0) m.emplace(det.class_id, std::vector<Yolo::Detection>());
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m[det.class_id].push_back(det);
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}
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for (auto it = m.begin(); it != m.end(); it++) {
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//std::cout << it->second[0].class_id << " --- " << std::endl;
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auto& dets = it->second;
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std::sort(dets.begin(), dets.end(), cmp);
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for (size_t m = 0; m < dets.size(); ++m) {
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auto& item = dets[m];
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res.push_back(item);
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for (size_t n = m + 1; n < dets.size(); ++n) {
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if (iou(item.bbox, dets[n].bbox) > nms_thresh) {
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dets.erase(dets.begin()+n);
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--n;
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}
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}
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}
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}
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}
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// TensorRT weight files have a simple space delimited format:
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// [type] [size] <data x size in hex>
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std::map<std::string, Weights> loadWeights(const std::string file) {
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std::cout << "Loading weights: " << file << std::endl;
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std::map<std::string, Weights> weightMap;
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// Open weights file
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std::ifstream input(file);
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assert(input.is_open() && "Unable to load weight file.");
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// Read number of weight blobs
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int32_t count;
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input >> count;
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assert(count > 0 && "Invalid weight map file.");
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while (count--)
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{
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Weights wt{DataType::kFLOAT, nullptr, 0};
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uint32_t size;
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// Read name and type of blob
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std::string name;
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input >> name >> std::dec >> size;
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wt.type = DataType::kFLOAT;
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// Load blob
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uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
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for (uint32_t x = 0, y = size; x < y; ++x)
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{
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input >> std::hex >> val[x];
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}
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wt.values = val;
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wt.count = size;
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weightMap[name] = wt;
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}
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return weightMap;
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}
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IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
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float *gamma = (float*)weightMap[lname + ".weight"].values;
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float *beta = (float*)weightMap[lname + ".bias"].values;
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float *mean = (float*)weightMap[lname + ".running_mean"].values;
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float *var = (float*)weightMap[lname + ".running_var"].values;
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int len = weightMap[lname + ".running_var"].count;
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float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
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for (int i = 0; i < len; i++) {
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scval[i] = gamma[i] / sqrt(var[i] + eps);
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}
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Weights scale{DataType::kFLOAT, scval, len};
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float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
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for (int i = 0; i < len; i++) {
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shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
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}
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Weights shift{DataType::kFLOAT, shval, len};
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float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
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for (int i = 0; i < len; i++) {
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pval[i] = 1.0;
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}
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Weights power{DataType::kFLOAT, pval, len};
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weightMap[lname + ".scale"] = scale;
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weightMap[lname + ".shift"] = shift;
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weightMap[lname + ".power"] = power;
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IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
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assert(scale_1);
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return scale_1;
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}
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ILayer* convBnLeaky(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int p, int linx) {
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap["module_list." + std::to_string(linx) + ".Conv2d.weight"], emptywts);
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assert(conv1);
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conv1->setStrideNd(DimsHW{s, s});
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conv1->setPaddingNd(DimsHW{p, p});
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IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "module_list." + std::to_string(linx) + ".BatchNorm2d", 1e-4);
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auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
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lr->setAlpha(0.1);
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return lr;
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}
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// Creat the engine using only the API and not any parser.
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ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
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INetworkDefinition* network = builder->createNetworkV2(0U);
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// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
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ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
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assert(data);
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std::map<std::string, Weights> weightMap = loadWeights("../yolov3-tiny.wts");
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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auto lr0 = convBnLeaky(network, weightMap, *data, 16, 3, 1, 1, 0);
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auto pool1 = network->addPoolingNd(*lr0->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
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pool1->setStrideNd(DimsHW{2, 2});
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auto lr2 = convBnLeaky(network, weightMap, *pool1->getOutput(0), 32, 3, 1, 1, 2);
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auto pool3 = network->addPoolingNd(*lr2->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
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pool3->setStrideNd(DimsHW{2, 2});
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auto lr4 = convBnLeaky(network, weightMap, *pool3->getOutput(0), 64, 3, 1, 1, 4);
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auto pool5 = network->addPoolingNd(*lr4->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
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pool5->setStrideNd(DimsHW{2, 2});
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auto lr6 = convBnLeaky(network, weightMap, *pool5->getOutput(0), 128, 3, 1, 1, 6);
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auto pool7 = network->addPoolingNd(*lr6->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
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pool7->setStrideNd(DimsHW{2, 2});
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auto lr8 = convBnLeaky(network, weightMap, *pool7->getOutput(0), 256, 3, 1, 1, 8);
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auto pool9 = network->addPoolingNd(*lr8->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
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pool9->setStrideNd(DimsHW{2, 2});
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auto lr10 = convBnLeaky(network, weightMap, *pool9->getOutput(0), 512, 3, 1, 1, 10);
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auto pad11 = network->addPaddingNd(*lr10->getOutput(0), DimsHW{0, 0}, DimsHW{1, 1});
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auto pool11 = network->addPoolingNd(*pad11->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
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pool11->setStrideNd(DimsHW{1, 1});
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auto lr12 = convBnLeaky(network, weightMap, *pool11->getOutput(0), 1024, 3, 1, 1, 12);
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auto lr13 = convBnLeaky(network, weightMap, *lr12->getOutput(0), 256, 1, 1, 0, 13);
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auto lr14 = convBnLeaky(network, weightMap, *lr13->getOutput(0), 512, 3, 1, 1, 14);
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IConvolutionLayer* conv15 = network->addConvolutionNd(*lr14->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.15.Conv2d.weight"], weightMap["module_list.15.Conv2d.bias"]);
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// 16 is yolo
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auto l17 = lr13;
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auto lr18 = convBnLeaky(network, weightMap, *l17->getOutput(0), 128, 1, 1, 0, 18);
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float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * 128 * 2 * 2));
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for (int i = 0; i < 128 * 2 * 2; i++) {
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deval[i] = 1.0;
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}
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Weights deconvwts19{DataType::kFLOAT, deval, 128 * 2 * 2};
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IDeconvolutionLayer* deconv19 = network->addDeconvolutionNd(*lr18->getOutput(0), 128, DimsHW{2, 2}, deconvwts19, emptywts);
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assert(deconv19);
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deconv19->setStrideNd(DimsHW{2, 2});
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deconv19->setNbGroups(128);
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weightMap["deconv19"] = deconvwts19;
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ITensor* inputTensors[] = {deconv19->getOutput(0), lr8->getOutput(0)};
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auto cat20 = network->addConcatenation(inputTensors, 2);
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auto lr21 = convBnLeaky(network, weightMap, *cat20->getOutput(0), 256, 3, 1, 1, 21);
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IConvolutionLayer* conv22 = network->addConvolutionNd(*lr21->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.22.Conv2d.weight"], weightMap["module_list.22.Conv2d.bias"]);
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// 22 is yolo
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auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1");
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const PluginFieldCollection* pluginData = creator->getFieldNames();
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IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData);
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ITensor* inputTensors_yolo[] = {conv15->getOutput(0), conv22->getOutput(0)};
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auto yolo = network->addPluginV2(inputTensors_yolo, 2, *pluginObj);
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yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
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network->markOutput(*yolo->getOutput(0));
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// Build engine
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builder->setMaxBatchSize(maxBatchSize);
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config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
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#ifdef USE_FP16
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config->setFlag(BuilderFlag::kFP16);
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#endif
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std::cout << "Building engine, please wait for a while..." << std::endl;
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ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
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std::cout << "Build engine successfully!" << std::endl;
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// Don't need the network any more
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network->destroy();
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// Release host memory
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for (auto& mem : weightMap)
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{
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free((void*) (mem.second.values));
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}
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return engine;
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}
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void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream) {
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// Create builder
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IBuilder* builder = createInferBuilder(gLogger);
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IBuilderConfig* config = builder->createBuilderConfig();
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// Create model to populate the network, then set the outputs and create an engine
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ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT);
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assert(engine != nullptr);
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// Serialize the engine
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(*modelStream) = engine->serialize();
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// Close everything down
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engine->destroy();
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builder->destroy();
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}
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void doInference(IExecutionContext& context, float* input, float* output, int batchSize) {
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const ICudaEngine& engine = context.getEngine();
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// Pointers to input and output device buffers to pass to engine.
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// Engine requires exactly IEngine::getNbBindings() number of buffers.
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assert(engine.getNbBindings() == 2);
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void* buffers[2];
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// In order to bind the buffers, we need to know the names of the input and output tensors.
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// Note that indices are guaranteed to be less than IEngine::getNbBindings()
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const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
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const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
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// Create GPU buffers on device
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CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
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CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
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// Create stream
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cudaStream_t stream;
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CHECK(cudaStreamCreate(&stream));
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// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
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CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
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context.enqueue(batchSize, buffers, stream, nullptr);
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CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
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cudaStreamSynchronize(stream);
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// Release stream and buffers
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cudaStreamDestroy(stream);
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CHECK(cudaFree(buffers[inputIndex]));
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CHECK(cudaFree(buffers[outputIndex]));
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}
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int read_files_in_dir(const char *p_dir_name, std::vector<std::string> &file_names) {
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DIR *p_dir = opendir(p_dir_name);
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if (p_dir == nullptr) {
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return -1;
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}
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struct dirent* p_file = nullptr;
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while ((p_file = readdir(p_dir)) != nullptr) {
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if (strcmp(p_file->d_name, ".") != 0 &&
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strcmp(p_file->d_name, "..") != 0) {
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//std::string cur_file_name(p_dir_name);
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//cur_file_name += "/";
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//cur_file_name += p_file->d_name;
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std::string cur_file_name(p_file->d_name);
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file_names.push_back(cur_file_name);
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}
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}
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closedir(p_dir);
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return 0;
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}
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int main(int argc, char** argv) {
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cudaSetDevice(DEVICE);
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// create a model using the API directly and serialize it to a stream
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char *trtModelStream{nullptr};
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size_t size{0};
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if (argc == 2 && std::string(argv[1]) == "-s") {
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IHostMemory* modelStream{nullptr};
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APIToModel(1, &modelStream);
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assert(modelStream != nullptr);
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std::ofstream p("yolov3-tiny.engine", std::ios::binary);
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if (!p) {
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std::cerr << "could not open plan output file" << std::endl;
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return -1;
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}
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p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
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modelStream->destroy();
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return 0;
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} else if (argc == 3 && std::string(argv[1]) == "-d") {
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std::ifstream file("yolov3-tiny.engine", std::ios::binary);
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if (file.good()) {
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file.seekg(0, file.end);
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size = file.tellg();
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|
file.seekg(0, file.beg);
|
|
trtModelStream = new char[size];
|
|
assert(trtModelStream);
|
|
file.read(trtModelStream, size);
|
|
file.close();
|
|
}
|
|
} else {
|
|
std::cerr << "arguments not right!" << std::endl;
|
|
std::cerr << "./yolov3-tiny -s // serialize model to plan file" << std::endl;
|
|
std::cerr << "./yolov3-tiny -d ../samples // deserialize plan file and run inference" << std::endl;
|
|
return -1;
|
|
}
|
|
|
|
std::vector<std::string> file_names;
|
|
if (read_files_in_dir(argv[2], file_names) < 0) {
|
|
std::cout << "read_files_in_dir failed." << std::endl;
|
|
return -1;
|
|
}
|
|
|
|
// prepare input data ---------------------------
|
|
static float data[3 * INPUT_H * INPUT_W];
|
|
//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
|
|
// data[i] = 1.0;
|
|
static float prob[OUTPUT_SIZE];
|
|
IRuntime* runtime = createInferRuntime(gLogger);
|
|
assert(runtime != nullptr);
|
|
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
|
|
assert(engine != nullptr);
|
|
IExecutionContext* context = engine->createExecutionContext();
|
|
assert(context != nullptr);
|
|
delete[] trtModelStream;
|
|
|
|
int fcount = 0;
|
|
for (auto f: file_names) {
|
|
fcount++;
|
|
std::cout << fcount << " " << f << std::endl;
|
|
cv::Mat img = cv::imread(std::string(argv[2]) + "/" + f);
|
|
if (img.empty()) continue;
|
|
cv::Mat pr_img = preprocess_img(img);
|
|
for (int i = 0; i < INPUT_H * INPUT_W; i++) {
|
|
data[i] = pr_img.at<cv::Vec3b>(i)[2] / 255.0;
|
|
data[i + INPUT_H * INPUT_W] = pr_img.at<cv::Vec3b>(i)[1] / 255.0;
|
|
data[i + 2 * INPUT_H * INPUT_W] = pr_img.at<cv::Vec3b>(i)[0] / 255.0;
|
|
}
|
|
|
|
// Run inference
|
|
auto start = std::chrono::system_clock::now();
|
|
doInference(*context, data, prob, 1);
|
|
auto end = std::chrono::system_clock::now();
|
|
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
|
|
std::vector<Yolo::Detection> res;
|
|
nms(res, prob);
|
|
for (int i=0; i<20; i++) {
|
|
std::cout << prob[i] << ",";
|
|
}
|
|
std::cout << res.size() << std::endl;
|
|
for (size_t j = 0; j < res.size(); j++) {
|
|
float *p = (float*)&res[j];
|
|
for (size_t k = 0; k < 7; k++) {
|
|
std::cout << p[k] << ", ";
|
|
}
|
|
std::cout << std::endl;
|
|
cv::Rect r = get_rect(img, res[j].bbox);
|
|
cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
|
|
cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2);
|
|
}
|
|
cv::imwrite("_" + f, img);
|
|
}
|
|
|
|
// Destroy the engine
|
|
context->destroy();
|
|
engine->destroy();
|
|
runtime->destroy();
|
|
return 0;
|
|
}
|