357 lines
15 KiB
C++
357 lines
15 KiB
C++
#pragma once
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#include <fstream>
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#include <map>
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#include <sstream>
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#include <vector>
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#include <opencv2/opencv.hpp>
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#include "NvInfer.h"
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#include "yololayer.h"
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using namespace nvinfer1;
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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 = Yolo::INPUT_W / (img.cols * 1.0);
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float r_h = Yolo::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 - (Yolo::INPUT_H - r_w * img.rows) / 2;
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b = bbox[1] + bbox[3] / 2.f - (Yolo::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 - (Yolo::INPUT_W - r_h * img.cols) / 2;
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r = bbox[0] + bbox[2] / 2.f - (Yolo::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.conf > b.conf;
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}
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void nms(std::vector<Yolo::Detection>& res, float *output, float conf_thresh, float nms_thresh = 0.5) {
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int det_size = sizeof(Yolo::Detection) / sizeof(float);
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std::map<float, std::vector<Yolo::Detection>> m;
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for (int i = 0; i < output[0] && i < Yolo::MAX_OUTPUT_BBOX_COUNT; i++) {
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if (output[1 + det_size * i + 4] <= conf_thresh) continue;
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Yolo::Detection det;
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memcpy(&det, &output[1 + det_size * i], det_size * 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. please check if the .wts file path is right!!!!!!");
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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* convBlock(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname) {
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Weights emptywts{ DataType::kFLOAT, nullptr, 0 };
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int p = ksize / 2;
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IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ ksize, ksize }, weightMap[lname + ".conv.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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conv1->setNbGroups(g);
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IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".bn", 1e-3);
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// silu = x * sigmoid
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// auto sig = network->addActivation(*bn1->getOutput(0), ActivationType::kSIGMOID);
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// assert(sig);
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// auto ew = network->addElementWise(*bn1->getOutput(0), *sig->getOutput(0), ElementWiseOperation::kPROD);
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// assert(ew);
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// hard_swish = x * hard_sigmoid
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auto hsig = network->addActivation(*bn1->getOutput(0), ActivationType::kHARD_SIGMOID);
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assert(hsig);
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hsig->setAlpha(1.0 / 6.0);
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hsig->setBeta(0.5);
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auto ew = network->addElementWise(*bn1->getOutput(0), *hsig->getOutput(0), ElementWiseOperation::kPROD);
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assert(ew);
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return ew;
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}
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ILayer* focus(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int inch, int outch, int ksize, std::string lname) {
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ISliceLayer *s1 = network->addSlice(input, Dims3{ 0, 0, 0 }, Dims3{ inch, Yolo::INPUT_H / 2, Yolo::INPUT_W / 2 }, Dims3{ 1, 2, 2 });
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ISliceLayer *s2 = network->addSlice(input, Dims3{ 0, 1, 0 }, Dims3{ inch, Yolo::INPUT_H / 2, Yolo::INPUT_W / 2 }, Dims3{ 1, 2, 2 });
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ISliceLayer *s3 = network->addSlice(input, Dims3{ 0, 0, 1 }, Dims3{ inch, Yolo::INPUT_H / 2, Yolo::INPUT_W / 2 }, Dims3{ 1, 2, 2 });
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ISliceLayer *s4 = network->addSlice(input, Dims3{ 0, 1, 1 }, Dims3{ inch, Yolo::INPUT_H / 2, Yolo::INPUT_W / 2 }, Dims3{ 1, 2, 2 });
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ITensor* inputTensors[] = { s1->getOutput(0), s2->getOutput(0), s3->getOutput(0), s4->getOutput(0) };
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auto cat = network->addConcatenation(inputTensors, 4);
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auto conv = convBlock(network, weightMap, *cat->getOutput(0), outch, ksize, 1, 1, lname + ".conv");
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return conv;
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}
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ILayer* bottleneck(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int c1, int c2, bool shortcut, int g, float e, std::string lname) {
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auto cv1 = convBlock(network, weightMap, input, (int)((float)c2 * e), 1, 1, 1, lname + ".cv1");
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auto cv2 = convBlock(network, weightMap, *cv1->getOutput(0), c2, 3, 1, g, lname + ".cv2");
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if (shortcut && c1 == c2) {
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auto ew = network->addElementWise(input, *cv2->getOutput(0), ElementWiseOperation::kSUM);
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return ew;
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}
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return cv2;
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}
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ILayer* bottleneckCSP(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int c1, int c2, int n, bool shortcut, int g, float e, std::string lname) {
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Weights emptywts{ DataType::kFLOAT, nullptr, 0 };
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int c_ = (int)((float)c2 * e);
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auto cv1 = convBlock(network, weightMap, input, c_, 1, 1, 1, lname + ".cv1");
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auto cv2 = network->addConvolutionNd(input, c_, DimsHW{ 1, 1 }, weightMap[lname + ".cv2.weight"], emptywts);
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ITensor *y1 = cv1->getOutput(0);
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for (int i = 0; i < n; i++) {
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auto b = bottleneck(network, weightMap, *y1, c_, c_, shortcut, g, 1.0, lname + ".m." + std::to_string(i));
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y1 = b->getOutput(0);
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}
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auto cv3 = network->addConvolutionNd(*y1, c_, DimsHW{ 1, 1 }, weightMap[lname + ".cv3.weight"], emptywts);
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ITensor* inputTensors[] = { cv3->getOutput(0), cv2->getOutput(0) };
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auto cat = network->addConcatenation(inputTensors, 2);
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IScaleLayer* bn = addBatchNorm2d(network, weightMap, *cat->getOutput(0), lname + ".bn", 1e-4);
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auto lr = network->addActivation(*bn->getOutput(0), ActivationType::kLEAKY_RELU);
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lr->setAlpha(0.1);
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auto cv4 = convBlock(network, weightMap, *lr->getOutput(0), c2, 1, 1, 1, lname + ".cv4");
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return cv4;
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}
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ILayer* C3(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int c1, int c2, int n, bool shortcut, int g, float e, std::string lname) {
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int c_ = (int)((float)c2 * e);
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auto cv1 = convBlock(network, weightMap, input, c_, 1, 1, 1, lname + ".cv1");
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auto cv2 = convBlock(network, weightMap, input, c_, 1, 1, 1, lname + ".cv2");
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ITensor *y1 = cv1->getOutput(0);
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for (int i = 0; i < n; i++) {
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auto b = bottleneck(network, weightMap, *y1, c_, c_, shortcut, g, 1.0, lname + ".m." + std::to_string(i));
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y1 = b->getOutput(0);
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}
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ITensor* inputTensors[] = { y1, cv2->getOutput(0) };
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auto cat = network->addConcatenation(inputTensors, 2);
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auto cv3 = convBlock(network, weightMap, *cat->getOutput(0), c2, 1, 1, 1, lname + ".cv3");
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return cv3;
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}
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ILayer* SPP(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int c1, int c2, int k1, int k2, int k3, std::string lname) {
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int c_ = c1 / 2;
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auto cv1 = convBlock(network, weightMap, input, c_, 1, 1, 1, lname + ".cv1");
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auto pool1 = network->addPoolingNd(*cv1->getOutput(0), PoolingType::kMAX, DimsHW{ k1, k1 });
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pool1->setPaddingNd(DimsHW{ k1 / 2, k1 / 2 });
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pool1->setStrideNd(DimsHW{ 1, 1 });
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auto pool2 = network->addPoolingNd(*cv1->getOutput(0), PoolingType::kMAX, DimsHW{ k2, k2 });
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pool2->setPaddingNd(DimsHW{ k2 / 2, k2 / 2 });
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pool2->setStrideNd(DimsHW{ 1, 1 });
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auto pool3 = network->addPoolingNd(*cv1->getOutput(0), PoolingType::kMAX, DimsHW{ k3, k3 });
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pool3->setPaddingNd(DimsHW{ k3 / 2, k3 / 2 });
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pool3->setStrideNd(DimsHW{ 1, 1 });
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ITensor* inputTensors[] = { cv1->getOutput(0), pool1->getOutput(0), pool2->getOutput(0), pool3->getOutput(0) };
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auto cat = network->addConcatenation(inputTensors, 4);
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auto cv2 = convBlock(network, weightMap, *cat->getOutput(0), c2, 1, 1, 1, lname + ".cv2");
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return cv2;
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}
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ILayer* preprocess_layer(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input) {
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// rescale
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auto rescale = network->addResize(input);
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rescale->setOutputDimensions(Dims3{ 3, Yolo::IMG_H, Yolo::IMG_W });
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rescale->setResizeMode(ResizeMode::kLINEAR);
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// normalize
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// long len = 3 * Yolo::IMG_H * Yolo::IMG_W;
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// float *normval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
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// for (size_t i = 0; i < len; ++i) {
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// normval[i] = 255.0;
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// }
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// Weights norm{ DataType::kFLOAT, normval, len };
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// weightMap["prep.norm"] = norm;
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// auto constant = network->addConstant(Dims3{ 3, Yolo::IMG_H, Yolo::IMG_W }, norm);
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// auto normalize = network->addElementWise(*rescale->getOutput(0), *constant->getOutput(0), ElementWiseOperation::kDIV);
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//paddng
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auto padding = network->addPaddingNd(*rescale->getOutput(0),
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DimsHW{ (Yolo::INPUT_H - Yolo::IMG_H) / 2, (Yolo::INPUT_W - Yolo::IMG_W) / 2 },
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DimsHW{ (Yolo::INPUT_H - Yolo::IMG_H) / 2, (Yolo::INPUT_W - Yolo::IMG_W) / 2 });
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assert(padding);
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return padding;
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}
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std::vector<float> getAnchors(std::map<std::string, Weights>& weightMap)
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{
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std::vector<float> anchors_yolo;
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Weights Yolo_Anchors = weightMap["model.24.anchor_grid"];
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assert(Yolo_Anchors.count == 18);
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int each_yololayer_anchorsnum = Yolo_Anchors.count / 3;
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const float* tempAnchors = (const float*)(Yolo_Anchors.values);
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for (int i = 0; i < Yolo_Anchors.count; i++)
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{
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if (i < each_yololayer_anchorsnum)
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{
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anchors_yolo.push_back(const_cast<float*>(tempAnchors)[i]);
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}
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if ((i >= each_yololayer_anchorsnum) && (i < (2 * each_yololayer_anchorsnum)))
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{
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anchors_yolo.push_back(const_cast<float*>(tempAnchors)[i]);
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}
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if (i >= (2 * each_yololayer_anchorsnum))
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{
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anchors_yolo.push_back(const_cast<float*>(tempAnchors)[i]);
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}
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}
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return anchors_yolo;
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}
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IPluginV2Layer* addYoLoLayer(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, IConvolutionLayer* det0, IConvolutionLayer* det1, IConvolutionLayer* det2)
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{
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auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1");
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std::vector<float> anchors_yolo = getAnchors(weightMap);
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PluginField pluginMultidata[4];
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int NetData[4];
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NetData[0] = Yolo::CLASS_NUM;
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NetData[1] = Yolo::INPUT_W;
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NetData[2] = Yolo::INPUT_H;
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NetData[3] = Yolo::MAX_OUTPUT_BBOX_COUNT;
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pluginMultidata[0].data = NetData;
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pluginMultidata[0].length = 3;
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pluginMultidata[0].name = "netdata";
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pluginMultidata[0].type = PluginFieldType::kFLOAT32;
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int scale[3] = { 8, 16, 32 };
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int plugindata[3][8];
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std::string names[3];
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for (int k = 1; k < 4; k++)
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{
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plugindata[k - 1][0] = Yolo::INPUT_W / scale[k - 1];
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plugindata[k - 1][1] = Yolo::INPUT_H / scale[k - 1];
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for (int i = 2; i < 8; i++)
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{
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plugindata[k - 1][i] = int(anchors_yolo[(k - 1) * 6 + i - 2]);
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}
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pluginMultidata[k].data = plugindata[k - 1];
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pluginMultidata[k].length = 8;
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names[k - 1] = "yolodata" + std::to_string(k);
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pluginMultidata[k].name = names[k - 1].c_str();
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pluginMultidata[k].type = PluginFieldType::kFLOAT32;
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}
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PluginFieldCollection pluginData;
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pluginData.nbFields = 4;
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pluginData.fields = pluginMultidata;
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IPluginV2 *pluginObj = creator->createPlugin("yololayer", &pluginData);
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ITensor* inputTensors_yolo[] = { det2->getOutput(0), det1->getOutput(0), det0->getOutput(0) };
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auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj);
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return yolo;
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}
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