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duan8/yolov3-spp/yolov3-spp.cpp
wang-xinyu 53709d2ef6 fix opencv/dnn .h
2020-11-19 15:59:16 +08:00

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#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#include <vector>
#include <chrono>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn/dnn.hpp>
#include <dirent.h>
#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "logging.h"
#include "yololayer.h"
#define CHECK(status) \
do\
{\
auto ret = (status);\
if (ret != 0)\
{\
std::cerr << "Cuda failure: " << ret << std::endl;\
abort();\
}\
} while (0)
#define USE_FP16 // comment out this if want to use FP32
#define DEVICE 0 // GPU id
#define NMS_THRESH 0.4
#define BBOX_CONF_THRESH 0.5
using namespace nvinfer1;
// stuff we know about the network and the input/output blobs
static const int MAX_INPUT_SIZE = 608;
static const int MIN_INPUT_SIZE = 128;
static const int OPT_INPUT_W = 608;
static const int OPT_INPUT_H = 608;
static const int DET_LEN = sizeof(Yolo::Detection) / sizeof(float);
static const int OUTPUT_SIZE = Yolo::MAX_OUTPUT_BBOX_COUNT * DET_LEN + 1; // we limit the yololayer to output no more than MAX_OUTPUT_BBOX_COUNT bboxes
const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
static Logger gLogger;
cv::Mat letterbox(cv::Mat& img) {
float r = std::min(MAX_INPUT_SIZE / (img.cols*1.0), MAX_INPUT_SIZE / (img.rows*1.0));
r = std::min(r, 1.0f);
int unpad_w = r * img.cols;
int unpad_h = r * img.rows;
int dw = (MAX_INPUT_SIZE - unpad_w) % 32;
int dh = (MAX_INPUT_SIZE - unpad_h) % 32;
cv::Mat re(unpad_h, unpad_w, CV_8UC3);
cv::resize(img, re, re.size());
cv::Mat out(unpad_h + dh, unpad_w + dw, CV_8UC3, cv::Scalar(128, 128, 128));
re.copyTo(out(cv::Rect(dw / 2, dh / 2, re.cols, re.rows)));
return out;
}
cv::Rect get_rect(cv::Size src_shape, cv::Size pre_shape, float bbox[4]) {
float ra = std::min(MAX_INPUT_SIZE / (src_shape.width * 1.0), MAX_INPUT_SIZE / (src_shape.height * 1.0));
ra = std::min(ra, 1.0f);
int unpad_w = ra * src_shape.width;
int unpad_h = ra * src_shape.height;
int dw = (MAX_INPUT_SIZE - unpad_w) % 32;
int dh = (MAX_INPUT_SIZE - unpad_h) % 32;
int l = bbox[0] - bbox[2]/2.f - dw / 2;
int r = bbox[0] + bbox[2]/2.f - dw / 2;
int t = bbox[1] - bbox[3]/2.f - dh / 2;
int b = bbox[1] + bbox[3]/2.f - dh / 2;
l /= ra;
r /= ra;
t /= ra;
b /= ra;
return cv::Rect(l, t, r-l, b-t);
}
float iou(float lbox[4], float rbox[4]) {
float interBox[] = {
std::max(lbox[0] - lbox[2]/2.f , rbox[0] - rbox[2]/2.f), //left
std::min(lbox[0] + lbox[2]/2.f , rbox[0] + rbox[2]/2.f), //right
std::max(lbox[1] - lbox[3]/2.f , rbox[1] - rbox[3]/2.f), //top
std::min(lbox[1] + lbox[3]/2.f , rbox[1] + rbox[3]/2.f), //bottom
};
if(interBox[2] > interBox[3] || interBox[0] > interBox[1])
return 0.0f;
float interBoxS =(interBox[1]-interBox[0])*(interBox[3]-interBox[2]);
return interBoxS/(lbox[2]*lbox[3] + rbox[2]*rbox[3] -interBoxS);
}
bool cmp(const Yolo::Detection& a, const Yolo::Detection& b) {
return a.det_confidence > b.det_confidence;
}
void nms(std::vector<Yolo::Detection>& res, float *output, float nms_thresh = NMS_THRESH) {
std::map<float, std::vector<Yolo::Detection>> m;
for (int i = 0; i < output[0] && i < Yolo::MAX_OUTPUT_BBOX_COUNT; i++) {
if (output[1 + DET_LEN * i + 4] <= BBOX_CONF_THRESH) continue;
Yolo::Detection det;
memcpy(&det, &output[1 + DET_LEN * i], DET_LEN * sizeof(float));
if (m.count(det.class_id) == 0) m.emplace(det.class_id, std::vector<Yolo::Detection>());
m[det.class_id].push_back(det);
}
for (auto it = m.begin(); it != m.end(); it++) {
//std::cout << it->second[0].class_id << " --- " << std::endl;
auto& dets = it->second;
std::sort(dets.begin(), dets.end(), cmp);
for (size_t m = 0; m < dets.size(); ++m) {
auto& item = dets[m];
res.push_back(item);
for (size_t n = m + 1; n < dets.size(); ++n) {
if (iou(item.bbox, dets[n].bbox) > nms_thresh) {
dets.erase(dets.begin()+n);
--n;
}
}
}
}
}
// TensorRT weight files have a simple space delimited format:
// [type] [size] <data x size in hex>
std::map<std::string, Weights> loadWeights(const std::string file) {
std::cout << "Loading weights: " << file << std::endl;
std::map<std::string, Weights> weightMap;
// Open weights file
std::ifstream input(file);
assert(input.is_open() && "Unable to load weight file.");
// Read number of weight blobs
int32_t count;
input >> count;
assert(count > 0 && "Invalid weight map file.");
while (count--)
{
Weights wt{DataType::kFLOAT, nullptr, 0};
uint32_t size;
// Read name and type of blob
std::string name;
input >> name >> std::dec >> size;
wt.type = DataType::kFLOAT;
// Load blob
uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
for (uint32_t x = 0, y = size; x < y; ++x)
{
input >> std::hex >> val[x];
}
wt.values = val;
wt.count = size;
weightMap[name] = wt;
}
return weightMap;
}
IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
float *gamma = (float*)weightMap[lname + ".weight"].values;
float *beta = (float*)weightMap[lname + ".bias"].values;
float *mean = (float*)weightMap[lname + ".running_mean"].values;
float *var = (float*)weightMap[lname + ".running_var"].values;
int len = weightMap[lname + ".running_var"].count;
float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
scval[i] = gamma[i] / sqrt(var[i] + eps);
}
Weights scale{DataType::kFLOAT, scval, len};
float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
}
Weights shift{DataType::kFLOAT, shval, len};
float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
pval[i] = 1.0;
}
Weights power{DataType::kFLOAT, pval, len};
weightMap[lname + ".scale"] = scale;
weightMap[lname + ".shift"] = shift;
weightMap[lname + ".power"] = power;
IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
assert(scale_1);
return scale_1;
}
ILayer* convBnLeaky(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int p, int linx) {
Weights emptywts{DataType::kFLOAT, nullptr, 0};
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap["module_list." + std::to_string(linx) + ".Conv2d.weight"], emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{s, s});
conv1->setPaddingNd(DimsHW{p, p});
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "module_list." + std::to_string(linx) + ".BatchNorm2d", 1e-5);
auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
lr->setAlpha(0.1);
return lr;
}
// Creat the engine using only the API and not any parser.
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
const auto explicitBatch = 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
auto network = builder->createNetworkV2(explicitBatch);
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims4{1, 3, -1, -1});
assert(data);
std::map<std::string, Weights> weightMap = loadWeights("../yolov3-spp_ultralytics68.wts");
Weights emptywts{DataType::kFLOAT, nullptr, 0};
// Yeah I am stupid, I just want to expand the complete arch of darknet..
auto lr0 = convBnLeaky(network, weightMap, *data, 32, 3, 1, 1, 0);
auto lr1 = convBnLeaky(network, weightMap, *lr0->getOutput(0), 64, 3, 2, 1, 1);
auto lr2 = convBnLeaky(network, weightMap, *lr1->getOutput(0), 32, 1, 1, 0, 2);
auto lr3 = convBnLeaky(network, weightMap, *lr2->getOutput(0), 64, 3, 1, 1, 3);
auto ew4 = network->addElementWise(*lr3->getOutput(0), *lr1->getOutput(0), ElementWiseOperation::kSUM);
auto lr5 = convBnLeaky(network, weightMap, *ew4->getOutput(0), 128, 3, 2, 1, 5);
auto lr6 = convBnLeaky(network, weightMap, *lr5->getOutput(0), 64, 1, 1, 0, 6);
auto lr7 = convBnLeaky(network, weightMap, *lr6->getOutput(0), 128, 3, 1, 1, 7);
auto ew8 = network->addElementWise(*lr7->getOutput(0), *lr5->getOutput(0), ElementWiseOperation::kSUM);
auto lr9 = convBnLeaky(network, weightMap, *ew8->getOutput(0), 64, 1, 1, 0, 9);
auto lr10 = convBnLeaky(network, weightMap, *lr9->getOutput(0), 128, 3, 1, 1, 10);
auto ew11 = network->addElementWise(*lr10->getOutput(0), *ew8->getOutput(0), ElementWiseOperation::kSUM);
auto lr12 = convBnLeaky(network, weightMap, *ew11->getOutput(0), 256, 3, 2, 1, 12);
auto lr13 = convBnLeaky(network, weightMap, *lr12->getOutput(0), 128, 1, 1, 0, 13);
auto lr14 = convBnLeaky(network, weightMap, *lr13->getOutput(0), 256, 3, 1, 1, 14);
auto ew15 = network->addElementWise(*lr14->getOutput(0), *lr12->getOutput(0), ElementWiseOperation::kSUM);
auto lr16 = convBnLeaky(network, weightMap, *ew15->getOutput(0), 128, 1, 1, 0, 16);
auto lr17 = convBnLeaky(network, weightMap, *lr16->getOutput(0), 256, 3, 1, 1, 17);
auto ew18 = network->addElementWise(*lr17->getOutput(0), *ew15->getOutput(0), ElementWiseOperation::kSUM);
auto lr19 = convBnLeaky(network, weightMap, *ew18->getOutput(0), 128, 1, 1, 0, 19);
auto lr20 = convBnLeaky(network, weightMap, *lr19->getOutput(0), 256, 3, 1, 1, 20);
auto ew21 = network->addElementWise(*lr20->getOutput(0), *ew18->getOutput(0), ElementWiseOperation::kSUM);
auto lr22 = convBnLeaky(network, weightMap, *ew21->getOutput(0), 128, 1, 1, 0, 22);
auto lr23 = convBnLeaky(network, weightMap, *lr22->getOutput(0), 256, 3, 1, 1, 23);
auto ew24 = network->addElementWise(*lr23->getOutput(0), *ew21->getOutput(0), ElementWiseOperation::kSUM);
auto lr25 = convBnLeaky(network, weightMap, *ew24->getOutput(0), 128, 1, 1, 0, 25);
auto lr26 = convBnLeaky(network, weightMap, *lr25->getOutput(0), 256, 3, 1, 1, 26);
auto ew27 = network->addElementWise(*lr26->getOutput(0), *ew24->getOutput(0), ElementWiseOperation::kSUM);
auto lr28 = convBnLeaky(network, weightMap, *ew27->getOutput(0), 128, 1, 1, 0, 28);
auto lr29 = convBnLeaky(network, weightMap, *lr28->getOutput(0), 256, 3, 1, 1, 29);
auto ew30 = network->addElementWise(*lr29->getOutput(0), *ew27->getOutput(0), ElementWiseOperation::kSUM);
auto lr31 = convBnLeaky(network, weightMap, *ew30->getOutput(0), 128, 1, 1, 0, 31);
auto lr32 = convBnLeaky(network, weightMap, *lr31->getOutput(0), 256, 3, 1, 1, 32);
auto ew33 = network->addElementWise(*lr32->getOutput(0), *ew30->getOutput(0), ElementWiseOperation::kSUM);
auto lr34 = convBnLeaky(network, weightMap, *ew33->getOutput(0), 128, 1, 1, 0, 34);
auto lr35 = convBnLeaky(network, weightMap, *lr34->getOutput(0), 256, 3, 1, 1, 35);
auto ew36 = network->addElementWise(*lr35->getOutput(0), *ew33->getOutput(0), ElementWiseOperation::kSUM);
auto lr37 = convBnLeaky(network, weightMap, *ew36->getOutput(0), 512, 3, 2, 1, 37);
auto lr38 = convBnLeaky(network, weightMap, *lr37->getOutput(0), 256, 1, 1, 0, 38);
auto lr39 = convBnLeaky(network, weightMap, *lr38->getOutput(0), 512, 3, 1, 1, 39);
auto ew40 = network->addElementWise(*lr39->getOutput(0), *lr37->getOutput(0), ElementWiseOperation::kSUM);
auto lr41 = convBnLeaky(network, weightMap, *ew40->getOutput(0), 256, 1, 1, 0, 41);
auto lr42 = convBnLeaky(network, weightMap, *lr41->getOutput(0), 512, 3, 1, 1, 42);
auto ew43 = network->addElementWise(*lr42->getOutput(0), *ew40->getOutput(0), ElementWiseOperation::kSUM);
auto lr44 = convBnLeaky(network, weightMap, *ew43->getOutput(0), 256, 1, 1, 0, 44);
auto lr45 = convBnLeaky(network, weightMap, *lr44->getOutput(0), 512, 3, 1, 1, 45);
auto ew46 = network->addElementWise(*lr45->getOutput(0), *ew43->getOutput(0), ElementWiseOperation::kSUM);
auto lr47 = convBnLeaky(network, weightMap, *ew46->getOutput(0), 256, 1, 1, 0, 47);
auto lr48 = convBnLeaky(network, weightMap, *lr47->getOutput(0), 512, 3, 1, 1, 48);
auto ew49 = network->addElementWise(*lr48->getOutput(0), *ew46->getOutput(0), ElementWiseOperation::kSUM);
auto lr50 = convBnLeaky(network, weightMap, *ew49->getOutput(0), 256, 1, 1, 0, 50);
auto lr51 = convBnLeaky(network, weightMap, *lr50->getOutput(0), 512, 3, 1, 1, 51);
auto ew52 = network->addElementWise(*lr51->getOutput(0), *ew49->getOutput(0), ElementWiseOperation::kSUM);
auto lr53 = convBnLeaky(network, weightMap, *ew52->getOutput(0), 256, 1, 1, 0, 53);
auto lr54 = convBnLeaky(network, weightMap, *lr53->getOutput(0), 512, 3, 1, 1, 54);
auto ew55 = network->addElementWise(*lr54->getOutput(0), *ew52->getOutput(0), ElementWiseOperation::kSUM);
auto lr56 = convBnLeaky(network, weightMap, *ew55->getOutput(0), 256, 1, 1, 0, 56);
auto lr57 = convBnLeaky(network, weightMap, *lr56->getOutput(0), 512, 3, 1, 1, 57);
auto ew58 = network->addElementWise(*lr57->getOutput(0), *ew55->getOutput(0), ElementWiseOperation::kSUM);
auto lr59 = convBnLeaky(network, weightMap, *ew58->getOutput(0), 256, 1, 1, 0, 59);
auto lr60 = convBnLeaky(network, weightMap, *lr59->getOutput(0), 512, 3, 1, 1, 60);
auto ew61 = network->addElementWise(*lr60->getOutput(0), *ew58->getOutput(0), ElementWiseOperation::kSUM);
auto lr62 = convBnLeaky(network, weightMap, *ew61->getOutput(0), 1024, 3, 2, 1, 62);
auto lr63 = convBnLeaky(network, weightMap, *lr62->getOutput(0), 512, 1, 1, 0, 63);
auto lr64 = convBnLeaky(network, weightMap, *lr63->getOutput(0), 1024, 3, 1, 1, 64);
auto ew65 = network->addElementWise(*lr64->getOutput(0), *lr62->getOutput(0), ElementWiseOperation::kSUM);
auto lr66 = convBnLeaky(network, weightMap, *ew65->getOutput(0), 512, 1, 1, 0, 66);
auto lr67 = convBnLeaky(network, weightMap, *lr66->getOutput(0), 1024, 3, 1, 1, 67);
auto ew68 = network->addElementWise(*lr67->getOutput(0), *ew65->getOutput(0), ElementWiseOperation::kSUM);
auto lr69 = convBnLeaky(network, weightMap, *ew68->getOutput(0), 512, 1, 1, 0, 69);
auto lr70 = convBnLeaky(network, weightMap, *lr69->getOutput(0), 1024, 3, 1, 1, 70);
auto ew71 = network->addElementWise(*lr70->getOutput(0), *ew68->getOutput(0), ElementWiseOperation::kSUM);
auto lr72 = convBnLeaky(network, weightMap, *ew71->getOutput(0), 512, 1, 1, 0, 72);
auto lr73 = convBnLeaky(network, weightMap, *lr72->getOutput(0), 1024, 3, 1, 1, 73);
auto ew74 = network->addElementWise(*lr73->getOutput(0), *ew71->getOutput(0), ElementWiseOperation::kSUM);
auto lr75 = convBnLeaky(network, weightMap, *ew74->getOutput(0), 512, 1, 1, 0, 75);
auto lr76 = convBnLeaky(network, weightMap, *lr75->getOutput(0), 1024, 3, 1, 1, 76);
auto lr77 = convBnLeaky(network, weightMap, *lr76->getOutput(0), 512, 1, 1, 0, 77);
auto pool78 = network->addPoolingNd(*lr77->getOutput(0), PoolingType::kMAX, DimsHW{5,5});
pool78->setPaddingNd(DimsHW{2, 2});
pool78->setStrideNd(DimsHW{1, 1});
auto pool80 = network->addPoolingNd(*lr77->getOutput(0), PoolingType::kMAX, DimsHW{9,9});
pool80->setPaddingNd(DimsHW{4, 4});
pool80->setStrideNd(DimsHW{1, 1});
auto pool82 = network->addPoolingNd(*lr77->getOutput(0), PoolingType::kMAX, DimsHW{13,13});
pool82->setPaddingNd(DimsHW{6, 6});
pool82->setStrideNd(DimsHW{1, 1});
ITensor* inputTensors83[] = {pool82->getOutput(0), pool80->getOutput(0), pool78->getOutput(0), lr77->getOutput(0)};
auto cat83 = network->addConcatenation(inputTensors83, 4);
auto lr84 = convBnLeaky(network, weightMap, *cat83->getOutput(0), 512, 1, 1, 0, 84);
auto lr85 = convBnLeaky(network, weightMap, *lr84->getOutput(0), 1024, 3, 1, 1, 85);
auto lr86 = convBnLeaky(network, weightMap, *lr85->getOutput(0), 512, 1, 1, 0, 86);
auto lr87 = convBnLeaky(network, weightMap, *lr86->getOutput(0), 1024, 3, 1, 1, 87);
IConvolutionLayer* conv88 = network->addConvolutionNd(*lr87->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.88.Conv2d.weight"], weightMap["module_list.88.Conv2d.bias"]);
assert(conv88);
auto lr91 = convBnLeaky(network, weightMap, *lr86->getOutput(0), 256, 1, 1, 0, 91);
float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * 256 * 2 * 2));
for (int i = 0; i < 256 * 2 * 2; i++) {
deval[i] = 1.0;
}
Weights deconvwts92{DataType::kFLOAT, deval, 256 * 2 * 2};
IDeconvolutionLayer* deconv92 = network->addDeconvolutionNd(*lr91->getOutput(0), 256, DimsHW{2, 2}, deconvwts92, emptywts);
assert(deconv92);
deconv92->setStrideNd(DimsHW{2, 2});
deconv92->setNbGroups(256);
weightMap["deconv92"] = deconvwts92;
ITensor* inputTensors[] = {deconv92->getOutput(0), ew61->getOutput(0)};
auto cat93 = network->addConcatenation(inputTensors, 2);
auto lr94 = convBnLeaky(network, weightMap, *cat93->getOutput(0), 256, 1, 1, 0, 94);
auto lr95 = convBnLeaky(network, weightMap, *lr94->getOutput(0), 512, 3, 1, 1, 95);
auto lr96 = convBnLeaky(network, weightMap, *lr95->getOutput(0), 256, 1, 1, 0, 96);
auto lr97 = convBnLeaky(network, weightMap, *lr96->getOutput(0), 512, 3, 1, 1, 97);
auto lr98 = convBnLeaky(network, weightMap, *lr97->getOutput(0), 256, 1, 1, 0, 98);
auto lr99 = convBnLeaky(network, weightMap, *lr98->getOutput(0), 512, 3, 1, 1, 99);
IConvolutionLayer* conv100 = network->addConvolutionNd(*lr99->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.100.Conv2d.weight"], weightMap["module_list.100.Conv2d.bias"]);
assert(conv100);
auto lr103 = convBnLeaky(network, weightMap, *lr98->getOutput(0), 128, 1, 1, 0, 103);
Weights deconvwts104{DataType::kFLOAT, deval, 128 * 2 * 2};
IDeconvolutionLayer* deconv104 = network->addDeconvolutionNd(*lr103->getOutput(0), 128, DimsHW{2, 2}, deconvwts104, emptywts);
assert(deconv104);
deconv104->setStrideNd(DimsHW{2, 2});
deconv104->setNbGroups(128);
ITensor* inputTensors1[] = {deconv104->getOutput(0), ew36->getOutput(0)};
auto cat105 = network->addConcatenation(inputTensors1, 2);
auto lr106 = convBnLeaky(network, weightMap, *cat105->getOutput(0), 128, 1, 1, 0, 106);
auto lr107 = convBnLeaky(network, weightMap, *lr106->getOutput(0), 256, 3, 1, 1, 107);
auto lr108 = convBnLeaky(network, weightMap, *lr107->getOutput(0), 128, 1, 1, 0, 108);
auto lr109 = convBnLeaky(network, weightMap, *lr108->getOutput(0), 256, 3, 1, 1, 109);
auto lr110 = convBnLeaky(network, weightMap, *lr109->getOutput(0), 128, 1, 1, 0, 110);
auto lr111 = convBnLeaky(network, weightMap, *lr110->getOutput(0), 256, 3, 1, 1, 111);
IConvolutionLayer* conv112 = network->addConvolutionNd(*lr111->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.112.Conv2d.weight"], weightMap["module_list.112.Conv2d.bias"]);
assert(conv112);
auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1");
const PluginFieldCollection* pluginData = creator->getFieldNames();
IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData);
ITensor* inputTensors_yolo[] = {conv88->getOutput(0), conv100->getOutput(0), conv112->getOutput(0)};
auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj);
auto dim = yolo->getOutput(0)->getDimensions();
std::cout << "yololayer output shape: ";
for (int i = 0; i < dim.nbDims; i++) {
std::cout << dim.d[i] << " ";
}
std::cout << std::endl;
yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
network->markOutput(*yolo->getOutput(0));
IOptimizationProfile* profile = builder->createOptimizationProfile();
profile->setDimensions(INPUT_BLOB_NAME, OptProfileSelector::kMIN, Dims4(1, 3, MIN_INPUT_SIZE, MIN_INPUT_SIZE));
profile->setDimensions(INPUT_BLOB_NAME, OptProfileSelector::kOPT, Dims4(1, 3, OPT_INPUT_H, OPT_INPUT_W));
profile->setDimensions(INPUT_BLOB_NAME, OptProfileSelector::kMAX, Dims4(1, 3, MAX_INPUT_SIZE, MAX_INPUT_SIZE));
config->addOptimizationProfile(profile);
// Build engine
config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
#ifdef USE_FP16
config->setFlag(BuilderFlag::kFP16);
#endif
std::cout << "Building engine, please wait for a while..." << std::endl;
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
std::cout << "Build engine successfully!" << std::endl;
// Don't need the network any more
network->destroy();
// Release host memory
for (auto& mem : weightMap)
{
free((void*) (mem.second.values));
}
return engine;
}
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream) {
// Create builder
IBuilder* builder = createInferBuilder(gLogger);
IBuilderConfig* config = builder->createBuilderConfig();
// Create model to populate the network, then set the outputs and create an engine
ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT);
assert(engine != nullptr);
// Serialize the engine
(*modelStream) = engine->serialize();
// Close everything down
engine->destroy();
builder->destroy();
}
void doInference(IExecutionContext& context, float* input, float* output, cv::Size input_shape) {
const ICudaEngine& engine = context.getEngine();
// Pointers to input and output device buffers to pass to engine.
// Engine requires exactly IEngine::getNbBindings() number of buffers.
assert(engine.getNbBindings() == 2);
void* buffers[2];
// In order to bind the buffers, we need to know the names of the input and output tensors.
// Note that indices are guaranteed to be less than IEngine::getNbBindings()
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
context.setBindingDimensions(inputIndex, Dims4(1, 3, input_shape.height, input_shape.width));
// Create GPU buffers on device
CHECK(cudaMalloc(&buffers[inputIndex], 3 * input_shape.height * input_shape.width * sizeof(float)));
CHECK(cudaMalloc(&buffers[outputIndex], OUTPUT_SIZE * sizeof(float)));
// Create stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, 3 * input_shape.height * input_shape.width * sizeof(float), cudaMemcpyHostToDevice, stream));
context.enqueueV2(buffers, stream, nullptr);
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
cudaStreamSynchronize(stream);
// Release stream and buffers
cudaStreamDestroy(stream);
CHECK(cudaFree(buffers[inputIndex]));
CHECK(cudaFree(buffers[outputIndex]));
}
int read_files_in_dir(const char *p_dir_name, std::vector<std::string> &file_names) {
DIR *p_dir = opendir(p_dir_name);
if (p_dir == nullptr) {
return -1;
}
struct dirent* p_file = nullptr;
while ((p_file = readdir(p_dir)) != nullptr) {
if (strcmp(p_file->d_name, ".") != 0 &&
strcmp(p_file->d_name, "..") != 0) {
//std::string cur_file_name(p_dir_name);
//cur_file_name += "/";
//cur_file_name += p_file->d_name;
std::string cur_file_name(p_file->d_name);
file_names.push_back(cur_file_name);
}
}
closedir(p_dir);
return 0;
}
int main(int argc, char** argv) {
cudaSetDevice(DEVICE);
// create a model using the API directly and serialize it to a stream
char *trtModelStream{nullptr};
size_t size{0};
if (argc == 2 && std::string(argv[1]) == "-s") {
IHostMemory* modelStream{nullptr};
APIToModel(1, &modelStream);
assert(modelStream != nullptr);
std::ofstream p("yolov3-spp.engine", std::ios::binary);
if (!p) {
std::cerr << "could not open plan output file" << std::endl;
return -1;
}
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
modelStream->destroy();
return 0;
} else if (argc == 3 && std::string(argv[1]) == "-d") {
std::ifstream file("yolov3-spp.engine", std::ios::binary);
if (file.good()) {
file.seekg(0, file.end);
size = file.tellg();
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-spp -s // serialize model to plan file" << std::endl;
std::cerr << "./yolov3-spp -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;
}
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;
context->setOptimizationProfile(0);
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 = letterbox(img);
std::cout << "letterbox shape: " << pr_img.cols << ", " << pr_img.rows << std::endl;
if (pr_img.cols < MIN_INPUT_SIZE || pr_img.rows < MIN_INPUT_SIZE) continue;
cv::Mat blob = cv::dnn::blobFromImage(pr_img, 1.0 / 255.0, pr_img.size(), cv::Scalar(0, 0, 0), true, false);
// Run inference
auto start = std::chrono::system_clock::now();
doInference(*context, blob.ptr<float>(0), prob, pr_img.size());
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);
std::cout << "num of bbox: " << res.size() << std::endl;
for (size_t j = 0; j < res.size(); j++) {
cv::Rect r = get_rect(img.size(), pr_img.size(), 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;
}
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