add yolov5s
This commit is contained in:
wang-xinyu
parent
00fc8e5d73
commit
37ce374b42
549
yolov5/yolov5s.cpp
Normal file
549
yolov5/yolov5s.cpp
Normal file
@ -0,0 +1,549 @@
|
||||
#include <fstream>
|
||||
#include <iostream>
|
||||
#include <map>
|
||||
#include <sstream>
|
||||
#include <vector>
|
||||
#include <chrono>
|
||||
#include <opencv2/opencv.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 0.5
|
||||
#define BBOX_CONF_THRESH 0.4
|
||||
|
||||
using namespace nvinfer1;
|
||||
|
||||
// stuff we know about the network and the input/output blobs
|
||||
static const int INPUT_H = Yolo::INPUT_H;
|
||||
static const int INPUT_W = Yolo::INPUT_W;
|
||||
static const int OUTPUT_SIZE = 1000 * 7 + 1; // we assume the yololayer outputs no more than 1000 boxes that conf >= 0.1
|
||||
const char* INPUT_BLOB_NAME = "data";
|
||||
const char* OUTPUT_BLOB_NAME = "prob";
|
||||
static Logger gLogger;
|
||||
REGISTER_TENSORRT_PLUGIN(YoloPluginCreator);
|
||||
|
||||
cv::Mat preprocess_img(cv::Mat& img) {
|
||||
int w, h, x, y;
|
||||
float r_w = INPUT_W / (img.cols*1.0);
|
||||
float r_h = INPUT_H / (img.rows*1.0);
|
||||
if (r_h > r_w) {
|
||||
w = INPUT_W;
|
||||
h = r_w * img.rows;
|
||||
x = 0;
|
||||
y = (INPUT_H - h) / 2;
|
||||
} else {
|
||||
w = r_h* img.cols;
|
||||
h = INPUT_H;
|
||||
x = (INPUT_W - w) / 2;
|
||||
y = 0;
|
||||
}
|
||||
cv::Mat re(h, w, CV_8UC3);
|
||||
cv::resize(img, re, re.size(), 0, 0, cv::INTER_CUBIC);
|
||||
cv::Mat out(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(128, 128, 128));
|
||||
re.copyTo(out(cv::Rect(x, y, re.cols, re.rows)));
|
||||
return out;
|
||||
}
|
||||
|
||||
cv::Rect get_rect(cv::Mat& img, float bbox[4]) {
|
||||
int l, r, t, b;
|
||||
float r_w = INPUT_W / (img.cols * 1.0);
|
||||
float r_h = INPUT_H / (img.rows * 1.0);
|
||||
if (r_h > r_w) {
|
||||
l = bbox[0] - bbox[2]/2.f;
|
||||
r = bbox[0] + bbox[2]/2.f;
|
||||
t = bbox[1] - bbox[3]/2.f - (INPUT_H - r_w * img.rows) / 2;
|
||||
b = bbox[1] + bbox[3]/2.f - (INPUT_H - r_w * img.rows) / 2;
|
||||
l = l / r_w;
|
||||
r = r / r_w;
|
||||
t = t / r_w;
|
||||
b = b / r_w;
|
||||
} else {
|
||||
l = bbox[0] - bbox[2]/2.f - (INPUT_W - r_h * img.cols) / 2;
|
||||
r = bbox[0] + bbox[2]/2.f - (INPUT_W - r_h * img.cols) / 2;
|
||||
t = bbox[1] - bbox[3]/2.f;
|
||||
b = bbox[1] + bbox[3]/2.f;
|
||||
l = l / r_h;
|
||||
r = r / r_h;
|
||||
t = t / r_h;
|
||||
b = b / r_h;
|
||||
}
|
||||
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(Yolo::Detection& a, Yolo::Detection& b) {
|
||||
return a.det_confidence * a.class_confidence > b.det_confidence * b.class_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 < 1000; i++) {
|
||||
if (output[1 + 7 * i + 4] * output[1 + 7 * i + 6] <= BBOX_CONF_THRESH) continue;
|
||||
Yolo::Detection det;
|
||||
memcpy(&det, &output[1 + 7 * i], 7 * 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 g, std::string lname) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
int p = ksize / 2;
|
||||
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[lname + ".conv.weight"], emptywts);
|
||||
assert(conv1);
|
||||
conv1->setStrideNd(DimsHW{s, s});
|
||||
conv1->setPaddingNd(DimsHW{p, p});
|
||||
conv1->setNbGroups(g);
|
||||
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".bn", 1e-4);
|
||||
auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
|
||||
lr->setAlpha(0.1);
|
||||
return lr;
|
||||
}
|
||||
|
||||
ILayer* focus(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int inch, int outch, int ksize, std::string lname) {
|
||||
ISliceLayer *s1 = network->addSlice(input, Dims3{0, 0, 0}, Dims3{inch, INPUT_H / 2, INPUT_W / 2}, Dims3{1, 2, 2});
|
||||
ISliceLayer *s2 = network->addSlice(input, Dims3{0, 1, 0}, Dims3{inch, INPUT_H / 2, INPUT_W / 2}, Dims3{1, 2, 2});
|
||||
ISliceLayer *s3 = network->addSlice(input, Dims3{0, 0, 1}, Dims3{inch, INPUT_H / 2, INPUT_W / 2}, Dims3{1, 2, 2});
|
||||
ISliceLayer *s4 = network->addSlice(input, Dims3{0, 1, 1}, Dims3{inch, INPUT_H / 2, INPUT_W / 2}, Dims3{1, 2, 2});
|
||||
ITensor* inputTensors[] = {s1->getOutput(0), s2->getOutput(0), s3->getOutput(0), s4->getOutput(0)};
|
||||
auto cat = network->addConcatenation(inputTensors, 4);
|
||||
auto conv = convBnLeaky(network, weightMap, *cat->getOutput(0), outch, ksize, 1, 1, lname + ".conv");
|
||||
return conv;
|
||||
}
|
||||
|
||||
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) {
|
||||
auto cv1 = convBnLeaky(network, weightMap, input, (int)((float)c2 * e), 1, 1, 1, lname + ".cv1");
|
||||
auto cv2 = convBnLeaky(network, weightMap, *cv1->getOutput(0), c2, 3, 1, g, lname + ".cv2");
|
||||
if (shortcut && c1 == c2) {
|
||||
auto ew = network->addElementWise(input, *cv2->getOutput(0), ElementWiseOperation::kSUM);
|
||||
return ew;
|
||||
}
|
||||
return cv2;
|
||||
}
|
||||
|
||||
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) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
int c_ = (int)((float)c2 * e);
|
||||
auto cv1 = convBnLeaky(network, weightMap, input, c_, 1, 1, 1, lname + ".cv1");
|
||||
auto cv2 = network->addConvolutionNd(input, c_, DimsHW{1, 1}, weightMap[lname + ".cv2.weight"], emptywts);
|
||||
ITensor *y1 = cv1->getOutput(0);
|
||||
for (int i = 0; i < n; i++) {
|
||||
auto b = bottleneck(network, weightMap, *y1, c_, c_, shortcut, g, 1.0, lname + ".m." + std::to_string(i));
|
||||
y1 = b->getOutput(0);
|
||||
}
|
||||
auto cv3 = network->addConvolutionNd(*y1, c_, DimsHW{1, 1}, weightMap[lname + ".cv3.weight"], emptywts);
|
||||
|
||||
ITensor* inputTensors[] = {cv3->getOutput(0), cv2->getOutput(0)};
|
||||
auto cat = network->addConcatenation(inputTensors, 2);
|
||||
|
||||
IScaleLayer* bn = addBatchNorm2d(network, weightMap, *cat->getOutput(0), lname + ".bn", 1e-4);
|
||||
auto lr = network->addActivation(*bn->getOutput(0), ActivationType::kLEAKY_RELU);
|
||||
lr->setAlpha(0.1);
|
||||
|
||||
auto cv4 = convBnLeaky(network, weightMap, *lr->getOutput(0), c2, 1, 1, 1, lname + ".cv4");
|
||||
return cv4;
|
||||
}
|
||||
|
||||
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) {
|
||||
int c_ = c1 / 2;
|
||||
auto cv1 = convBnLeaky(network, weightMap, input, c_, 1, 1, 1, lname + ".cv1");
|
||||
|
||||
auto pool1 = network->addPoolingNd(*cv1->getOutput(0), PoolingType::kMAX, DimsHW{k1, k1});
|
||||
pool1->setPaddingNd(DimsHW{k1 / 2, k1 / 2});
|
||||
pool1->setStrideNd(DimsHW{1, 1});
|
||||
auto pool2 = network->addPoolingNd(*cv1->getOutput(0), PoolingType::kMAX, DimsHW{k2, k2});
|
||||
pool2->setPaddingNd(DimsHW{k2 / 2, k2 / 2});
|
||||
pool2->setStrideNd(DimsHW{1, 1});
|
||||
auto pool3 = network->addPoolingNd(*cv1->getOutput(0), PoolingType::kMAX, DimsHW{k3, k3});
|
||||
pool3->setPaddingNd(DimsHW{k3 / 2, k3 / 2});
|
||||
pool3->setStrideNd(DimsHW{1, 1});
|
||||
|
||||
ITensor* inputTensors[] = {cv1->getOutput(0), pool1->getOutput(0), pool2->getOutput(0), pool3->getOutput(0)};
|
||||
auto cat = network->addConcatenation(inputTensors, 4);
|
||||
|
||||
auto cv2 = convBnLeaky(network, weightMap, *cat->getOutput(0), c2, 1, 1, 1, lname + ".cv2");
|
||||
return cv2;
|
||||
}
|
||||
|
||||
// Creat the engine using only the API and not any parser.
|
||||
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
|
||||
INetworkDefinition* network = builder->createNetworkV2(0U);
|
||||
|
||||
// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
|
||||
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
|
||||
assert(data);
|
||||
|
||||
std::map<std::string, Weights> weightMap = loadWeights("../yolov5s.wts");
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
// yolov5 backbone
|
||||
auto focus0 = focus(network, weightMap, *data, 3, 32, 3, "model.0");
|
||||
auto conv1 = convBnLeaky(network, weightMap, *focus0->getOutput(0), 64, 3, 2, 1, "model.1");
|
||||
auto bottleneck2 = bottleneck(network, weightMap, *conv1->getOutput(0), 64, 64, true, 1, 0.5, "model.2");
|
||||
auto conv3 = convBnLeaky(network, weightMap, *bottleneck2->getOutput(0), 128, 3, 2, 1, "model.3");
|
||||
auto bottleneck_csp4 = bottleneckCSP(network, weightMap, *conv3->getOutput(0), 128, 128, 3, true, 1, 0.5, "model.4");
|
||||
auto conv5 = convBnLeaky(network, weightMap, *bottleneck_csp4->getOutput(0), 256, 3, 2, 1, "model.5");
|
||||
auto bottleneck_csp6 = bottleneckCSP(network, weightMap, *conv5->getOutput(0), 256, 256, 3, true, 1, 0.5, "model.6");
|
||||
auto conv7 = convBnLeaky(network, weightMap, *bottleneck_csp6->getOutput(0), 512, 3, 2, 1, "model.7");
|
||||
auto spp8 = SPP(network, weightMap, *conv7->getOutput(0), 512, 512, 5, 9, 13, "model.8");
|
||||
auto bottleneck_csp9 = bottleneckCSP(network, weightMap, *spp8->getOutput(0), 512, 512, 2, true, 1, 0.5, "model.9");
|
||||
// yolov5 head
|
||||
auto bottleneck_csp10 = bottleneckCSP(network, weightMap, *bottleneck_csp9->getOutput(0), 512, 512, 1, false, 1, 0.5, "model.10");
|
||||
IConvolutionLayer* conv11 = network->addConvolutionNd(*bottleneck_csp10->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["model.11.weight"], weightMap["model.11.bias"]);
|
||||
|
||||
float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * 512 * 2 * 2));
|
||||
for (int i = 0; i < 512 * 2 * 2; i++) {
|
||||
deval[i] = 1.0;
|
||||
}
|
||||
Weights deconvwts12{DataType::kFLOAT, deval, 512 * 2 * 2};
|
||||
IDeconvolutionLayer* deconv12 = network->addDeconvolutionNd(*bottleneck_csp10->getOutput(0), 512, DimsHW{2, 2}, deconvwts12, emptywts);
|
||||
deconv12->setStrideNd(DimsHW{2, 2});
|
||||
deconv12->setNbGroups(512);
|
||||
weightMap["deconv12"] = deconvwts12;
|
||||
|
||||
ITensor* inputTensors13[] = {deconv12->getOutput(0), bottleneck_csp6->getOutput(0)};
|
||||
auto cat13 = network->addConcatenation(inputTensors13, 2);
|
||||
auto conv14 = convBnLeaky(network, weightMap, *cat13->getOutput(0), 256, 1, 1, 1, "model.14");
|
||||
auto bottleneck_csp15 = bottleneckCSP(network, weightMap, *conv14->getOutput(0), 256, 256, 1, false, 1, 0.5, "model.15");
|
||||
IConvolutionLayer* conv16 = network->addConvolutionNd(*bottleneck_csp15->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["model.16.weight"], weightMap["model.16.bias"]);
|
||||
|
||||
Weights deconvwts17{DataType::kFLOAT, deval, 256 * 2 * 2};
|
||||
IDeconvolutionLayer* deconv17 = network->addDeconvolutionNd(*bottleneck_csp15->getOutput(0), 256, DimsHW{2, 2}, deconvwts17, emptywts);
|
||||
deconv17->setStrideNd(DimsHW{2, 2});
|
||||
deconv17->setNbGroups(256);
|
||||
ITensor* inputTensors18[] = {deconv17->getOutput(0), bottleneck_csp4->getOutput(0)};
|
||||
auto cat18 = network->addConcatenation(inputTensors18, 2);
|
||||
auto conv19 = convBnLeaky(network, weightMap, *cat18->getOutput(0), 128, 1, 1, 1, "model.19");
|
||||
auto bottleneck_csp20 = bottleneckCSP(network, weightMap, *conv19->getOutput(0), 128, 128, 1, false, 1, 0.5, "model.20");
|
||||
IConvolutionLayer* conv21 = network->addConvolutionNd(*bottleneck_csp20->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["model.21.weight"], weightMap["model.21.bias"]);
|
||||
|
||||
auto creator = getPluginRegistry()->getPluginCreator("YoloLayer_TRT", "1");
|
||||
const PluginFieldCollection* pluginData = creator->getFieldNames();
|
||||
IPluginV2 *pluginObj = creator->createPlugin("yololayer", pluginData);
|
||||
ITensor* inputTensors_yolo[] = {conv11->getOutput(0), conv16->getOutput(0), conv21->getOutput(0)};
|
||||
auto yolo = network->addPluginV2(inputTensors_yolo, 3, *pluginObj);
|
||||
|
||||
yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
network->markOutput(*yolo->getOutput(0));
|
||||
|
||||
// Build engine
|
||||
builder->setMaxBatchSize(maxBatchSize);
|
||||
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, int batchSize) {
|
||||
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);
|
||||
|
||||
// Create GPU buffers on device
|
||||
CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
|
||||
CHECK(cudaMalloc(&buffers[outputIndex], batchSize * 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, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
|
||||
context.enqueue(batchSize, buffers, stream, nullptr);
|
||||
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * 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("yolov5s.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("yolov5s.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 << "./yolov5s -s // serialize model to plan file" << std::endl;
|
||||
std::cerr << "./yolov5s -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 ---------------------------
|
||||
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();
|
||||
|
||||
// Print histogram of the output distribution
|
||||
//std::cout << "\nOutput:\n\n";
|
||||
//for (unsigned int i = 0; i < OUTPUT_SIZE; i++)
|
||||
//{
|
||||
// std::cout << prob[i] << ", ";
|
||||
// if (i % 10 == 0) std::cout << std::endl;
|
||||
//}
|
||||
//std::cout << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user