update readme, yolov3-spp migrated to trt7
This commit is contained in:
wang-xinyu
parent
453c063639
commit
f4e9063b42
@ -78,6 +78,7 @@ Some tricky operations encountered in these models, already solved, but might ha
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|-|-|:-:|:-:|:-:|:-:|
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| YOLOv3(darknet53) | Xavier | 1 | FP16 | 320x320 | 55 |
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| YOLOv3-spp(darknet53) | Xeon E5-2620/GTX1080 | 1 | FP32 | 256x416 | 94 |
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| YOLOv3-spp(darknet53) | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 38.5 |
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| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 1 | FP16 | 608x608 | 35.7 |
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| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 4 | FP16 | 608x608 | 40.9 |
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| YOLOv4(CSPDarknet53) | Xeon E5-2620/GTX1080 | 8 | FP16 | 608x608 | 41.3 |
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@ -4,7 +4,7 @@ The mxnet implementation is from [deepinsight/insightface.](https://github.com/d
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The pretrained model is [LResNet50E-IR,ArcFace@ms1m-refine-v1.](https://github.com/deepinsight/insightface/wiki/Model-Zoo#32-lresnet50e-irarcfacems1m-refine-v1)
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The two images used in this project are joey0.ppm and joey1.ppm, download them from [Google Drive.](https://drive.google.com/drive/folders/1ctqpkRCRKyBZRCNwo9Uq4eUoMRLtFq1e)
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The two input images used in this project are joey0.ppm and joey1.ppm, download them from [Google Drive.](https://drive.google.com/drive/folders/1ctqpkRCRKyBZRCNwo9Uq4eUoMRLtFq1e). The input image is 112x112, and generated from `get_input()` in `insightface/deploy/face_model.py`, which is cropped and aligned face image.
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<p align="center">
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<img src="https://user-images.githubusercontent.com/15235574/83122953-f45f8d80-a106-11ea-84b0-4f6ff91b5924.jpg">
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@ -32,8 +32,8 @@ cuda_add_library(yololayer SHARED ${PROJECT_SOURCE_DIR}/yololayer.cu)
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find_package(OpenCV)
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include_directories(OpenCV_INCLUDE_DIRS)
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add_executable(yolov3-spp ${PROJECT_SOURCE_DIR}/plugin_factory.cpp ${PROJECT_SOURCE_DIR}/yolov3-spp.cpp)
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target_link_libraries(yolov3-spp nvinfer nvinfer_plugin)
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add_executable(yolov3-spp ${PROJECT_SOURCE_DIR}/yolov3-spp.cpp)
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target_link_libraries(yolov3-spp nvinfer)
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target_link_libraries(yolov3-spp cudart)
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target_link_libraries(yolov3-spp yololayer)
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target_link_libraries(yolov3-spp ${OpenCV_LIBS})
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@ -1,356 +0,0 @@
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#ifndef _TRT_COMMON_H_
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#define _TRT_COMMON_H_
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#include "NvInfer.h"
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#include "NvOnnxConfig.h"
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#include "NvOnnxParser.h"
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#include <cuda_runtime_api.h>
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#include <algorithm>
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#include <cassert>
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#include <fstream>
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#include <iostream>
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#include <iterator>
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#include <map>
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#include <memory>
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#include <numeric>
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#include <string>
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#include <vector>
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#include <cstring>
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#include <cmath>
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using namespace std;
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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::cout << "Cuda failure: " << ret; \
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abort(); \
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} \
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} while (0)
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constexpr long double operator"" _GB(long double val) { return val * (1 << 30); }
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constexpr long double operator"" _MB(long double val) { return val * (1 << 20); }
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constexpr long double operator"" _KB(long double val) { return val * (1 << 10); }
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// These is necessary if we want to be able to write 1_GB instead of 1.0_GB.
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// Since the return type is signed, -1_GB will work as expected.
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constexpr long long int operator"" _GB(long long unsigned int val) { return val * (1 << 30); }
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constexpr long long int operator"" _MB(long long unsigned int val) { return val * (1 << 20); }
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constexpr long long int operator"" _KB(long long unsigned int val) { return val * (1 << 10); }
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// Logger for TensorRT info/warning/errors
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class Logger : public nvinfer1::ILogger
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{
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public:
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Logger(): Logger(Severity::kWARNING) {}
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Logger(Severity severity): reportableSeverity(severity) {}
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void log(Severity severity, const char* msg) override
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{
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// suppress messages with severity enum value greater than the reportable
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if (severity > reportableSeverity) return;
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switch (severity)
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{
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case Severity::kINTERNAL_ERROR: std::cerr << "INTERNAL_ERROR: "; break;
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case Severity::kERROR: std::cerr << "ERROR: "; break;
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case Severity::kWARNING: std::cerr << "WARNING: "; break;
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case Severity::kINFO: std::cerr << "INFO: "; break;
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default: std::cerr << "UNKNOWN: "; break;
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}
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std::cerr << msg << std::endl;
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}
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Severity reportableSeverity{Severity::kWARNING};
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};
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// Locate path to file, given its filename or filepath suffix and possible dirs it might lie in
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// Function will also walk back MAX_DEPTH dirs from CWD to check for such a file path
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inline std::string locateFile(const std::string& filepathSuffix, const std::vector<std::string>& directories)
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{
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const int MAX_DEPTH{10};
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bool found{false};
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std::string filepath;
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for (auto& dir : directories)
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{
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filepath = dir + filepathSuffix;
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for (int i = 0; i < MAX_DEPTH && !found; i++)
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{
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std::ifstream checkFile(filepath);
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found = checkFile.is_open();
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if (found) break;
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filepath = "../" + filepath; // Try again in parent dir
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}
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if (found)
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{
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break;
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}
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filepath.clear();
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}
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if (filepath.empty()) {
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std::string directoryList = std::accumulate(directories.begin() + 1, directories.end(), directories.front(),
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[](const std::string& a, const std::string& b) { return a + "\n\t" + b; });
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throw std::runtime_error("Could not find " + filepathSuffix + " in data directories:\n\t" + directoryList);
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}
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return filepath;
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}
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inline void readPGMFile(const std::string& fileName, uint8_t* buffer, int inH, int inW)
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{
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std::ifstream infile(fileName, std::ifstream::binary);
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assert(infile.is_open() && "Attempting to read from a file that is not open.");
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std::string magic, h, w, max;
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infile >> magic >> h >> w >> max;
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infile.seekg(1, infile.cur);
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infile.read(reinterpret_cast<char*>(buffer), inH * inW);
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}
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namespace samples_common
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{
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inline void* safeCudaMalloc(size_t memSize)
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{
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void* deviceMem;
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CHECK(cudaMalloc(&deviceMem, memSize));
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if (deviceMem == nullptr)
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{
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std::cerr << "Out of memory" << std::endl;
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exit(1);
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}
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return deviceMem;
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}
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inline bool isDebug()
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{
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return (std::getenv("TENSORRT_DEBUG") ? true : false);
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}
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struct InferDeleter
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{
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template <typename T>
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void operator()(T* obj) const
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{
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if (obj) {
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obj->destroy();
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}
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}
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};
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template <typename T>
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inline std::shared_ptr<T> infer_object(T* obj)
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{
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if (!obj) {
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throw std::runtime_error("Failed to create object");
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}
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return std::shared_ptr<T>(obj, InferDeleter());
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}
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template <class Iter>
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inline std::vector<size_t> argsort(Iter begin, Iter end, bool reverse = false)
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{
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std::vector<size_t> inds(end - begin);
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std::iota(inds.begin(), inds.end(), 0);
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if (reverse) {
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std::sort(inds.begin(), inds.end(), [&begin](size_t i1, size_t i2) {
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return begin[i2] < begin[i1];
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});
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}
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else
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{
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std::sort(inds.begin(), inds.end(), [&begin](size_t i1, size_t i2) {
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return begin[i1] < begin[i2];
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});
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}
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return inds;
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}
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inline bool readReferenceFile(const std::string& fileName, std::vector<std::string>& refVector)
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{
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std::ifstream infile(fileName);
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if (!infile.is_open()) {
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cout << "ERROR: readReferenceFile: Attempting to read from a file that is not open." << endl;
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return false;
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}
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std::string line;
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while (std::getline(infile, line)) {
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if (line.empty()) continue;
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refVector.push_back(line);
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}
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infile.close();
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return true;
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}
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template <typename result_vector_t>
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inline std::vector<std::string> classify(const vector<string>& refVector, const result_vector_t& output, const size_t topK)
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{
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auto inds = samples_common::argsort(output.cbegin(), output.cend(), true);
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std::vector<std::string> result;
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for (size_t k = 0; k < topK; ++k) {
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result.push_back(refVector[inds[k]]);
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}
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return result;
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}
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//...LG returns top K indices, not values.
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template <typename T>
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inline vector<size_t> topK(const vector<T> inp, const size_t k)
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{
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vector<size_t> result;
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std::vector<size_t> inds = samples_common::argsort(inp.cbegin(), inp.cend(), true);
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result.assign(inds.begin(), inds.begin()+k);
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return result;
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}
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template <typename T>
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inline bool readASCIIFile(const string& fileName, const size_t size, vector<T>& out)
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{
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std::ifstream infile(fileName);
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if (!infile.is_open()) {
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cout << "ERROR readASCIIFile: Attempting to read from a file that is not open." << endl;
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return false;
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}
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out.clear();
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out.reserve(size);
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out.assign(std::istream_iterator<T>(infile), std::istream_iterator<T>());
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infile.close();
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return true;
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}
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template <typename T>
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inline bool writeASCIIFile(const string& fileName, const vector<T>& in)
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{
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std::ofstream outfile(fileName);
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if (!outfile.is_open()) {
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cout << "ERROR: writeASCIIFile: Attempting to write to a file that is not open." << endl;
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return false;
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}
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for (auto fn : in) {
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outfile << fn << " ";
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}
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outfile.close();
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return true;
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}
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inline void print_version()
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{
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//... This can be only done after statically linking this support into parserONNX.library
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#if 0
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std::cout << "Parser built against:" << std::endl;
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std::cout << " ONNX IR version: " << nvonnxparser::onnx_ir_version_string(onnx::IR_VERSION) << std::endl;
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#endif
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std::cout << " TensorRT version: "
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<< NV_TENSORRT_MAJOR << "."
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<< NV_TENSORRT_MINOR << "."
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<< NV_TENSORRT_PATCH << "."
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<< NV_TENSORRT_BUILD << std::endl;
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}
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inline string getFileType(const string& filepath)
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{
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return filepath.substr(filepath.find_last_of(".") + 1);
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}
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inline string toLower(const string& inp)
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{
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string out = inp;
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std::transform(out.begin(), out.end(), out.begin(), ::tolower);
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return out;
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}
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inline unsigned int getElementSize(nvinfer1::DataType t)
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{
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switch (t)
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{
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case nvinfer1::DataType::kINT32: return 4;
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case nvinfer1::DataType::kFLOAT: return 4;
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case nvinfer1::DataType::kHALF: return 2;
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case nvinfer1::DataType::kINT8: return 1;
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}
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throw std::runtime_error("Invalid DataType.");
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return 0;
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}
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inline int64_t volume(const nvinfer1::Dims& d)
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{
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return std::accumulate(d.d, d.d + d.nbDims, 1, std::multiplies<int64_t>());
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}
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// Struct to maintain command-line arguments.
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struct Args
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{
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bool runInInt8 = false;
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};
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// Populates the Args struct with the provided command-line parameters.
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inline void parseArgs(Args& args, int argc, char* argv[])
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{
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if (argc >= 1)
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{
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for (int i = 1; i < argc; ++i)
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{
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if (!strcmp(argv[i], "--int8")) args.runInInt8 = true;
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}
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}
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}
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template <int C, int H, int W>
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struct PPM
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{
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std::string magic, fileName;
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int h, w, max;
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uint8_t buffer[C * H * W];
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};
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struct BBox
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{
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float x1, y1, x2, y2;
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};
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template <int C, int H, int W>
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inline void writePPMFileWithBBox(const std::string& filename, PPM<C, H, W>& ppm, const BBox& bbox)
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{
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std::ofstream outfile("./" + filename, std::ofstream::binary);
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assert(!outfile.fail());
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outfile << "P6" << "\n" << ppm.w << " " << ppm.h << "\n" << ppm.max << "\n";
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auto round = [](float x) -> int { return int(std::floor(x + 0.5f)); };
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const int x1 = std::min(std::max(0, round(int(bbox.x1))), W - 1);
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const int x2 = std::min(std::max(0, round(int(bbox.x2))), W - 1);
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const int y1 = std::min(std::max(0, round(int(bbox.y1))), H - 1);
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const int y2 = std::min(std::max(0, round(int(bbox.y2))), H - 1);
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for (int x = x1; x <= x2; ++x)
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{
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// bbox top border
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ppm.buffer[(y1 * ppm.w + x) * 3] = 255;
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ppm.buffer[(y1 * ppm.w + x) * 3 + 1] = 0;
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ppm.buffer[(y1 * ppm.w + x) * 3 + 2] = 0;
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// bbox bottom border
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ppm.buffer[(y2 * ppm.w + x) * 3] = 255;
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ppm.buffer[(y2 * ppm.w + x) * 3 + 1] = 0;
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ppm.buffer[(y2 * ppm.w + x) * 3 + 2] = 0;
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}
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for (int y = y1; y <= y2; ++y)
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{
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// bbox left border
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ppm.buffer[(y * ppm.w + x1) * 3] = 255;
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ppm.buffer[(y * ppm.w + x1) * 3 + 1] = 0;
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ppm.buffer[(y * ppm.w + x1) * 3 + 2] = 0;
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// bbox right border
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ppm.buffer[(y * ppm.w + x2) * 3] = 255;
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ppm.buffer[(y * ppm.w + x2) * 3 + 1] = 0;
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ppm.buffer[(y * ppm.w + x2) * 3 + 2] = 0;
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}
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outfile.write(reinterpret_cast<char*>(ppm.buffer), ppm.w * ppm.h * 3);
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}
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} // namespace samples_common
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#endif // _TRT_COMMON_H_
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503
yolov3-spp/logging.h
Normal file
503
yolov3-spp/logging.h
Normal file
@ -0,0 +1,503 @@
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/*
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* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef TENSORRT_LOGGING_H
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#define TENSORRT_LOGGING_H
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#include "NvInferRuntimeCommon.h"
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#include <cassert>
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#include <ctime>
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#include <iomanip>
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#include <iostream>
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#include <ostream>
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#include <sstream>
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#include <string>
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using Severity = nvinfer1::ILogger::Severity;
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class LogStreamConsumerBuffer : public std::stringbuf
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{
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public:
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LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
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: mOutput(stream)
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, mPrefix(prefix)
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, mShouldLog(shouldLog)
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{
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}
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LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
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: mOutput(other.mOutput)
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{
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}
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~LogStreamConsumerBuffer()
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{
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// std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
|
||||
// std::streambuf::pptr() gives a pointer to the current position of the output sequence
|
||||
// if the pointer to the beginning is not equal to the pointer to the current position,
|
||||
// call putOutput() to log the output to the stream
|
||||
if (pbase() != pptr())
|
||||
{
|
||||
putOutput();
|
||||
}
|
||||
}
|
||||
|
||||
// synchronizes the stream buffer and returns 0 on success
|
||||
// synchronizing the stream buffer consists of inserting the buffer contents into the stream,
|
||||
// resetting the buffer and flushing the stream
|
||||
virtual int sync()
|
||||
{
|
||||
putOutput();
|
||||
return 0;
|
||||
}
|
||||
|
||||
void putOutput()
|
||||
{
|
||||
if (mShouldLog)
|
||||
{
|
||||
// prepend timestamp
|
||||
std::time_t timestamp = std::time(nullptr);
|
||||
tm* tm_local = std::localtime(×tamp);
|
||||
std::cout << "[";
|
||||
std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
|
||||
std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
|
||||
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
|
||||
// std::stringbuf::str() gets the string contents of the buffer
|
||||
// insert the buffer contents pre-appended by the appropriate prefix into the stream
|
||||
mOutput << mPrefix << str();
|
||||
// set the buffer to empty
|
||||
str("");
|
||||
// flush the stream
|
||||
mOutput.flush();
|
||||
}
|
||||
}
|
||||
|
||||
void setShouldLog(bool shouldLog)
|
||||
{
|
||||
mShouldLog = shouldLog;
|
||||
}
|
||||
|
||||
private:
|
||||
std::ostream& mOutput;
|
||||
std::string mPrefix;
|
||||
bool mShouldLog;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \class LogStreamConsumerBase
|
||||
//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
|
||||
//!
|
||||
class LogStreamConsumerBase
|
||||
{
|
||||
public:
|
||||
LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
|
||||
: mBuffer(stream, prefix, shouldLog)
|
||||
{
|
||||
}
|
||||
|
||||
protected:
|
||||
LogStreamConsumerBuffer mBuffer;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \class LogStreamConsumer
|
||||
//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
|
||||
//! Order of base classes is LogStreamConsumerBase and then std::ostream.
|
||||
//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
|
||||
//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
|
||||
//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
|
||||
//! Please do not change the order of the parent classes.
|
||||
//!
|
||||
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
|
||||
{
|
||||
public:
|
||||
//! \brief Creates a LogStreamConsumer which logs messages with level severity.
|
||||
//! Reportable severity determines if the messages are severe enough to be logged.
|
||||
LogStreamConsumer(Severity reportableSeverity, Severity severity)
|
||||
: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
|
||||
, std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
, mShouldLog(severity <= reportableSeverity)
|
||||
, mSeverity(severity)
|
||||
{
|
||||
}
|
||||
|
||||
LogStreamConsumer(LogStreamConsumer&& other)
|
||||
: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
|
||||
, std::ostream(&mBuffer) // links the stream buffer with the stream
|
||||
, mShouldLog(other.mShouldLog)
|
||||
, mSeverity(other.mSeverity)
|
||||
{
|
||||
}
|
||||
|
||||
void setReportableSeverity(Severity reportableSeverity)
|
||||
{
|
||||
mShouldLog = mSeverity <= reportableSeverity;
|
||||
mBuffer.setShouldLog(mShouldLog);
|
||||
}
|
||||
|
||||
private:
|
||||
static std::ostream& severityOstream(Severity severity)
|
||||
{
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
static std::string severityPrefix(Severity severity)
|
||||
{
|
||||
switch (severity)
|
||||
{
|
||||
case Severity::kINTERNAL_ERROR: return "[F] ";
|
||||
case Severity::kERROR: return "[E] ";
|
||||
case Severity::kWARNING: return "[W] ";
|
||||
case Severity::kINFO: return "[I] ";
|
||||
case Severity::kVERBOSE: return "[V] ";
|
||||
default: assert(0); return "";
|
||||
}
|
||||
}
|
||||
|
||||
bool mShouldLog;
|
||||
Severity mSeverity;
|
||||
};
|
||||
|
||||
//! \class Logger
|
||||
//!
|
||||
//! \brief Class which manages logging of TensorRT tools and samples
|
||||
//!
|
||||
//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
|
||||
//! and supports logging two types of messages:
|
||||
//!
|
||||
//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
|
||||
//! - Test pass/fail messages
|
||||
//!
|
||||
//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
|
||||
//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
|
||||
//!
|
||||
//! In the future, this class could be extended to support dumping test results to a file in some standard format
|
||||
//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
|
||||
//!
|
||||
//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
|
||||
//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
|
||||
//! library and messages coming from the sample.
|
||||
//!
|
||||
//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
|
||||
//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
|
||||
//! object.
|
||||
|
||||
class Logger : public nvinfer1::ILogger
|
||||
{
|
||||
public:
|
||||
Logger(Severity severity = Severity::kWARNING)
|
||||
: mReportableSeverity(severity)
|
||||
{
|
||||
}
|
||||
|
||||
//!
|
||||
//! \enum TestResult
|
||||
//! \brief Represents the state of a given test
|
||||
//!
|
||||
enum class TestResult
|
||||
{
|
||||
kRUNNING, //!< The test is running
|
||||
kPASSED, //!< The test passed
|
||||
kFAILED, //!< The test failed
|
||||
kWAIVED //!< The test was waived
|
||||
};
|
||||
|
||||
//!
|
||||
//! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
|
||||
//! \return The nvinfer1::ILogger associated with this Logger
|
||||
//!
|
||||
//! TODO Once all samples are updated to use this method to register the logger with TensorRT,
|
||||
//! we can eliminate the inheritance of Logger from ILogger
|
||||
//!
|
||||
nvinfer1::ILogger& getTRTLogger()
|
||||
{
|
||||
return *this;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
|
||||
//!
|
||||
//! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
|
||||
//! inheritance from nvinfer1::ILogger
|
||||
//!
|
||||
void log(Severity severity, const char* msg) override
|
||||
{
|
||||
LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Method for controlling the verbosity of logging output
|
||||
//!
|
||||
//! \param severity The logger will only emit messages that have severity of this level or higher.
|
||||
//!
|
||||
void setReportableSeverity(Severity severity)
|
||||
{
|
||||
mReportableSeverity = severity;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Opaque handle that holds logging information for a particular test
|
||||
//!
|
||||
//! This object is an opaque handle to information used by the Logger to print test results.
|
||||
//! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
|
||||
//! with Logger::reportTest{Start,End}().
|
||||
//!
|
||||
class TestAtom
|
||||
{
|
||||
public:
|
||||
TestAtom(TestAtom&&) = default;
|
||||
|
||||
private:
|
||||
friend class Logger;
|
||||
|
||||
TestAtom(bool started, const std::string& name, const std::string& cmdline)
|
||||
: mStarted(started)
|
||||
, mName(name)
|
||||
, mCmdline(cmdline)
|
||||
{
|
||||
}
|
||||
|
||||
bool mStarted;
|
||||
std::string mName;
|
||||
std::string mCmdline;
|
||||
};
|
||||
|
||||
//!
|
||||
//! \brief Define a test for logging
|
||||
//!
|
||||
//! \param[in] name The name of the test. This should be a string starting with
|
||||
//! "TensorRT" and containing dot-separated strings containing
|
||||
//! the characters [A-Za-z0-9_].
|
||||
//! For example, "TensorRT.sample_googlenet"
|
||||
//! \param[in] cmdline The command line used to reproduce the test
|
||||
//
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
//!
|
||||
static TestAtom defineTest(const std::string& name, const std::string& cmdline)
|
||||
{
|
||||
return TestAtom(false, name, cmdline);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
|
||||
//! as input
|
||||
//!
|
||||
//! \param[in] name The name of the test
|
||||
//! \param[in] argc The number of command-line arguments
|
||||
//! \param[in] argv The array of command-line arguments (given as C strings)
|
||||
//!
|
||||
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
|
||||
{
|
||||
auto cmdline = genCmdlineString(argc, argv);
|
||||
return defineTest(name, cmdline);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has started.
|
||||
//!
|
||||
//! \pre reportTestStart() has not been called yet for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has started
|
||||
//!
|
||||
static void reportTestStart(TestAtom& testAtom)
|
||||
{
|
||||
reportTestResult(testAtom, TestResult::kRUNNING);
|
||||
assert(!testAtom.mStarted);
|
||||
testAtom.mStarted = true;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has ended.
|
||||
//!
|
||||
//! \pre reportTestStart() has been called for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has ended
|
||||
//! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
|
||||
//! TestResult::kFAILED, TestResult::kWAIVED
|
||||
//!
|
||||
static void reportTestEnd(const TestAtom& testAtom, TestResult result)
|
||||
{
|
||||
assert(result != TestResult::kRUNNING);
|
||||
assert(testAtom.mStarted);
|
||||
reportTestResult(testAtom, result);
|
||||
}
|
||||
|
||||
static int reportPass(const TestAtom& testAtom)
|
||||
{
|
||||
reportTestEnd(testAtom, TestResult::kPASSED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportFail(const TestAtom& testAtom)
|
||||
{
|
||||
reportTestEnd(testAtom, TestResult::kFAILED);
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
static int reportWaive(const TestAtom& testAtom)
|
||||
{
|
||||
reportTestEnd(testAtom, TestResult::kWAIVED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportTest(const TestAtom& testAtom, bool pass)
|
||||
{
|
||||
return pass ? reportPass(testAtom) : reportFail(testAtom);
|
||||
}
|
||||
|
||||
Severity getReportableSeverity() const
|
||||
{
|
||||
return mReportableSeverity;
|
||||
}
|
||||
|
||||
private:
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a log message with the given severity
|
||||
//!
|
||||
static const char* severityPrefix(Severity severity)
|
||||
{
|
||||
switch (severity)
|
||||
{
|
||||
case Severity::kINTERNAL_ERROR: return "[F] ";
|
||||
case Severity::kERROR: return "[E] ";
|
||||
case Severity::kWARNING: return "[W] ";
|
||||
case Severity::kINFO: return "[I] ";
|
||||
case Severity::kVERBOSE: return "[V] ";
|
||||
default: assert(0); return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a test result message with the given result
|
||||
//!
|
||||
static const char* testResultString(TestResult result)
|
||||
{
|
||||
switch (result)
|
||||
{
|
||||
case TestResult::kRUNNING: return "RUNNING";
|
||||
case TestResult::kPASSED: return "PASSED";
|
||||
case TestResult::kFAILED: return "FAILED";
|
||||
case TestResult::kWAIVED: return "WAIVED";
|
||||
default: assert(0); return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
|
||||
//!
|
||||
static std::ostream& severityOstream(Severity severity)
|
||||
{
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief method that implements logging test results
|
||||
//!
|
||||
static void reportTestResult(const TestAtom& testAtom, TestResult result)
|
||||
{
|
||||
severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
|
||||
<< testAtom.mCmdline << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief generate a command line string from the given (argc, argv) values
|
||||
//!
|
||||
static std::string genCmdlineString(int argc, char const* const* argv)
|
||||
{
|
||||
std::stringstream ss;
|
||||
for (int i = 0; i < argc; i++)
|
||||
{
|
||||
if (i > 0)
|
||||
ss << " ";
|
||||
ss << argv[i];
|
||||
}
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
Severity mReportableSeverity;
|
||||
};
|
||||
|
||||
namespace
|
||||
{
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_INFO(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_INFO(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_WARN(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_WARN(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_ERROR(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_ERROR(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
|
||||
// ("fatal" severity)
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_FATAL(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_FATAL(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
#endif // TENSORRT_LOGGING_H
|
||||
@ -1,17 +0,0 @@
|
||||
#include "common.h"
|
||||
#include "plugin_factory.h"
|
||||
#include "NvInferPlugin.h"
|
||||
#include "yololayer.h"
|
||||
|
||||
using namespace nvinfer1;
|
||||
using nvinfer1::PluginFactory;
|
||||
|
||||
IPlugin* PluginFactory::createPlugin(const char* layerName, const void* serialData, size_t serialLength) {
|
||||
IPlugin *plugin = nullptr;
|
||||
if (strstr(layerName, "leaky") != NULL) {
|
||||
plugin = plugin::createPReLUPlugin(serialData, serialLength);
|
||||
} else if (strstr(layerName, "yolo") != NULL) {
|
||||
plugin = new YoloLayerPlugin(serialData, serialLength);
|
||||
}
|
||||
return plugin;
|
||||
}
|
||||
@ -1,12 +0,0 @@
|
||||
#ifndef MY_PLUGIN_FACTORY_H
|
||||
#define MY_PLUGIN_FACTORY_H
|
||||
#include <NvInfer.h>
|
||||
|
||||
namespace nvinfer1 {
|
||||
class PluginFactory : public IPluginFactory {
|
||||
public:
|
||||
IPlugin* createPlugin(const char* layerName, const void* serialData, size_t serialLength) override;
|
||||
};
|
||||
|
||||
}
|
||||
#endif
|
||||
@ -4,7 +4,7 @@ using namespace Yolo;
|
||||
|
||||
namespace nvinfer1
|
||||
{
|
||||
YoloLayerPlugin::YoloLayerPlugin(const int cudaThread /*= 512*/):mThreadCount(cudaThread)
|
||||
YoloLayerPlugin::YoloLayerPlugin()
|
||||
{
|
||||
mClassCount = CLASS_NUM;
|
||||
mYoloKernel.clear();
|
||||
@ -18,7 +18,7 @@ namespace nvinfer1
|
||||
YoloLayerPlugin::~YoloLayerPlugin()
|
||||
{
|
||||
}
|
||||
|
||||
|
||||
// create the plugin at runtime from a byte stream
|
||||
YoloLayerPlugin::YoloLayerPlugin(const void* data, size_t length)
|
||||
{
|
||||
@ -35,7 +35,7 @@ namespace nvinfer1
|
||||
assert(d == a + length);
|
||||
}
|
||||
|
||||
void YoloLayerPlugin::serialize(void* buffer)
|
||||
void YoloLayerPlugin::serialize(void* buffer) const
|
||||
{
|
||||
using namespace Tn;
|
||||
char* d = static_cast<char*>(buffer), *a = d;
|
||||
@ -49,34 +49,89 @@ namespace nvinfer1
|
||||
assert(d == a + getSerializationSize());
|
||||
}
|
||||
|
||||
size_t YoloLayerPlugin::getSerializationSize()
|
||||
size_t YoloLayerPlugin::getSerializationSize() const
|
||||
{
|
||||
return sizeof(mClassCount) + sizeof(mThreadCount) + sizeof(mKernelCount) + sizeof(Yolo::YoloKernel) * mYoloKernel.size();
|
||||
}
|
||||
|
||||
int YoloLayerPlugin::initialize()
|
||||
{
|
||||
int totalCount = 0;
|
||||
for(const auto& yolo : mYoloKernel)
|
||||
totalCount += (LOCATIONS + 1) * yolo.width*yolo.height * CHECK_COUNT;
|
||||
|
||||
totalCount = 0;//detection count
|
||||
for(const auto& yolo : mYoloKernel)
|
||||
totalCount += yolo.width*yolo.height * CHECK_COUNT;
|
||||
return 0;
|
||||
}
|
||||
|
||||
Dims YoloLayerPlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims)
|
||||
{
|
||||
//output the result to channel
|
||||
int totalCount = 0;
|
||||
for(const auto& yolo : mYoloKernel)
|
||||
totalCount += yolo.width*yolo.height * CHECK_COUNT * sizeof(Detection) / sizeof(float);
|
||||
int totalsize = MAX_OUTPUT_BBOX_COUNT * sizeof(Detection) / sizeof(float);
|
||||
|
||||
return Dims3(totalCount + 1, 1, 1);
|
||||
return Dims3(totalsize + 1, 1, 1);
|
||||
}
|
||||
|
||||
__device__ float Logist(float data){ return 1./(1. + exp(-data)); };
|
||||
// Set plugin namespace
|
||||
void YoloLayerPlugin::setPluginNamespace(const char* pluginNamespace)
|
||||
{
|
||||
mPluginNamespace = pluginNamespace;
|
||||
}
|
||||
|
||||
const char* YoloLayerPlugin::getPluginNamespace() const
|
||||
{
|
||||
return mPluginNamespace;
|
||||
}
|
||||
|
||||
// Return the DataType of the plugin output at the requested index
|
||||
DataType YoloLayerPlugin::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
|
||||
{
|
||||
return DataType::kFLOAT;
|
||||
}
|
||||
|
||||
// Return true if output tensor is broadcast across a batch.
|
||||
bool YoloLayerPlugin::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// Return true if plugin can use input that is broadcast across batch without replication.
|
||||
bool YoloLayerPlugin::canBroadcastInputAcrossBatch(int inputIndex) const
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
void YoloLayerPlugin::configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput)
|
||||
{
|
||||
}
|
||||
|
||||
// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
|
||||
void YoloLayerPlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator)
|
||||
{
|
||||
}
|
||||
|
||||
// Detach the plugin object from its execution context.
|
||||
void YoloLayerPlugin::detachFromContext() {}
|
||||
|
||||
const char* YoloLayerPlugin::getPluginType() const
|
||||
{
|
||||
return "YoloLayer_TRT";
|
||||
}
|
||||
|
||||
const char* YoloLayerPlugin::getPluginVersion() const
|
||||
{
|
||||
return "1";
|
||||
}
|
||||
|
||||
void YoloLayerPlugin::destroy()
|
||||
{
|
||||
delete this;
|
||||
}
|
||||
|
||||
// Clone the plugin
|
||||
IPluginV2IOExt* YoloLayerPlugin::clone() const
|
||||
{
|
||||
YoloLayerPlugin *p = new YoloLayerPlugin();
|
||||
p->setPluginNamespace(mPluginNamespace);
|
||||
return p;
|
||||
}
|
||||
|
||||
__device__ float Logist(float data){ return 1.0f / (1.0f + expf(-data)); };
|
||||
|
||||
__global__ void CalDetection(const float *input, float *output,int noElements,
|
||||
int yoloWidth,int yoloHeight,const float anchors[CHECK_COUNT*2],int classes,int outputElem) {
|
||||
@ -85,26 +140,27 @@ namespace nvinfer1
|
||||
if (idx >= noElements) return;
|
||||
|
||||
int total_grid = yoloWidth * yoloHeight;
|
||||
int bnIdx = idx / total_grid;
|
||||
idx = idx - total_grid*bnIdx;
|
||||
int info_len_i = 5 + classes;
|
||||
//int info_len_o = 7;
|
||||
int input_col = idx;
|
||||
//int out_row = input_col;
|
||||
const float* curInput = input + bnIdx * (info_len_i * total_grid * CHECK_COUNT);
|
||||
|
||||
for (int k = 0; k < 3; ++k) {
|
||||
int class_id = 0;
|
||||
float max_cls_prob = 0.0;
|
||||
for (int i = 5; i < info_len_i; ++i) {
|
||||
float p = Logist(input[input_col + k * info_len_i * total_grid + i * total_grid]);
|
||||
float p = Logist(curInput[idx + k * info_len_i * total_grid + i * total_grid]);
|
||||
if (p > max_cls_prob) {
|
||||
max_cls_prob = p;
|
||||
class_id = i - 5;
|
||||
}
|
||||
}
|
||||
float box_prob = Logist(input[input_col + k * info_len_i * total_grid + 4 * total_grid]);
|
||||
float box_prob = Logist(curInput[idx + k * info_len_i * total_grid + 4 * total_grid]);
|
||||
if (max_cls_prob < IGNORE_THRESH || box_prob < IGNORE_THRESH) continue;
|
||||
|
||||
float *res_count = output;
|
||||
float *res_count = output + bnIdx*outputElem;
|
||||
int count = (int)atomicAdd(res_count, 1);
|
||||
if (count >= MAX_OUTPUT_BBOX_COUNT) return;
|
||||
char* data = (char * )res_count + sizeof(float) + count*sizeof(Detection);
|
||||
Detection* det = (Detection*)(data);
|
||||
|
||||
@ -112,37 +168,32 @@ namespace nvinfer1
|
||||
int col = idx % yoloWidth;
|
||||
|
||||
//Location
|
||||
det->bbox[0] = (col + Logist(input[input_col + k * info_len_i * total_grid + 0 * total_grid])) * INPUT_W / yoloWidth;
|
||||
det->bbox[1] = (row + Logist(input[input_col + k * info_len_i * total_grid + 1 * total_grid])) * INPUT_H / yoloHeight;
|
||||
det->bbox[2] = exp(input[input_col + k * info_len_i * total_grid + 2 * total_grid]) * anchors[2*k];
|
||||
det->bbox[3] = exp(input[input_col + k * info_len_i * total_grid + 3 * total_grid]) * anchors[2*k + 1];
|
||||
det->bbox[0] = (col + Logist(curInput[idx + k * info_len_i * total_grid + 0 * total_grid])) * INPUT_W / yoloWidth;
|
||||
det->bbox[1] = (row + Logist(curInput[idx + k * info_len_i * total_grid + 1 * total_grid])) * INPUT_H / yoloHeight;
|
||||
det->bbox[2] = expf(curInput[idx + k * info_len_i * total_grid + 2 * total_grid]) * anchors[2*k];
|
||||
det->bbox[3] = expf(curInput[idx + k * info_len_i * total_grid + 3 * total_grid]) * anchors[2*k + 1];
|
||||
det->det_confidence = box_prob;
|
||||
det->class_id = class_id;
|
||||
det->class_confidence = max_cls_prob;
|
||||
}
|
||||
}
|
||||
|
||||
void YoloLayerPlugin::forwardGpu(const float *const * inputs,float * output,cudaStream_t stream,int batchSize) {
|
||||
|
||||
void YoloLayerPlugin::forwardGpu(const float *const * inputs, float* output, cudaStream_t stream, int batchSize) {
|
||||
void* devAnchor;
|
||||
size_t AnchorLen = sizeof(float)* CHECK_COUNT*2;
|
||||
CUDA_CHECK(cudaMalloc(&devAnchor,AnchorLen));
|
||||
|
||||
int outputElem = 1;
|
||||
for (unsigned int i = 0;i< mYoloKernel.size();++i)
|
||||
{
|
||||
const auto& yolo = mYoloKernel[i];
|
||||
outputElem += yolo.width*yolo.height * CHECK_COUNT * sizeof(Detection) / sizeof(float);
|
||||
}
|
||||
int outputElem = 1 + MAX_OUTPUT_BBOX_COUNT * sizeof(Detection) / sizeof(float);
|
||||
|
||||
for(int idx = 0 ;idx < batchSize;++idx)
|
||||
for(int idx = 0 ; idx < batchSize; ++idx) {
|
||||
CUDA_CHECK(cudaMemset(output + idx*outputElem, 0, sizeof(float)));
|
||||
|
||||
}
|
||||
int numElem = 0;
|
||||
for (unsigned int i = 0;i< mYoloKernel.size();++i)
|
||||
{
|
||||
const auto& yolo = mYoloKernel[i];
|
||||
numElem = yolo.width*yolo.height*batchSize;
|
||||
if (numElem < 256)
|
||||
if (numElem < mThreadCount)
|
||||
mThreadCount = numElem;
|
||||
CUDA_CHECK(cudaMemcpy(devAnchor, yolo.anchors, AnchorLen, cudaMemcpyHostToDevice));
|
||||
CalDetection<<< (yolo.width*yolo.height*batchSize + mThreadCount - 1) / mThreadCount, mThreadCount>>>
|
||||
@ -158,9 +209,51 @@ namespace nvinfer1
|
||||
//assert(batchSize == 1);
|
||||
//GPU
|
||||
//CUDA_CHECK(cudaStreamSynchronize(stream));
|
||||
forwardGpu((const float *const *)inputs,(float *)outputs[0],stream,batchSize);
|
||||
forwardGpu((const float *const *)inputs, (float*)outputs[0], stream, batchSize);
|
||||
|
||||
return 0;
|
||||
};
|
||||
}
|
||||
|
||||
PluginFieldCollection YoloPluginCreator::mFC{};
|
||||
std::vector<PluginField> YoloPluginCreator::mPluginAttributes;
|
||||
|
||||
YoloPluginCreator::YoloPluginCreator()
|
||||
{
|
||||
mPluginAttributes.clear();
|
||||
|
||||
mFC.nbFields = mPluginAttributes.size();
|
||||
mFC.fields = mPluginAttributes.data();
|
||||
}
|
||||
|
||||
const char* YoloPluginCreator::getPluginName() const
|
||||
{
|
||||
return "YoloLayer_TRT";
|
||||
}
|
||||
|
||||
const char* YoloPluginCreator::getPluginVersion() const
|
||||
{
|
||||
return "1";
|
||||
}
|
||||
|
||||
const PluginFieldCollection* YoloPluginCreator::getFieldNames()
|
||||
{
|
||||
return &mFC;
|
||||
}
|
||||
|
||||
IPluginV2IOExt* YoloPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
|
||||
{
|
||||
YoloLayerPlugin* obj = new YoloLayerPlugin();
|
||||
obj->setPluginNamespace(mNamespace.c_str());
|
||||
return obj;
|
||||
}
|
||||
|
||||
IPluginV2IOExt* YoloPluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength)
|
||||
{
|
||||
// This object will be deleted when the network is destroyed, which will
|
||||
// call MishPlugin::destroy()
|
||||
YoloLayerPlugin* obj = new YoloLayerPlugin(serialData, serialLength);
|
||||
obj->setPluginNamespace(mNamespace.c_str());
|
||||
return obj;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@ -4,7 +4,6 @@
|
||||
#include <assert.h>
|
||||
#include <cmath>
|
||||
#include <string.h>
|
||||
#include <cudnn.h>
|
||||
#include <cublas_v2.h>
|
||||
#include "NvInfer.h"
|
||||
#include "Utils.h"
|
||||
@ -14,9 +13,10 @@ namespace Yolo
|
||||
{
|
||||
static constexpr int CHECK_COUNT = 3;
|
||||
static constexpr float IGNORE_THRESH = 0.1f;
|
||||
static constexpr int MAX_OUTPUT_BBOX_COUNT = 1000;
|
||||
static constexpr int CLASS_NUM = 80;
|
||||
static constexpr int INPUT_H = 256;
|
||||
static constexpr int INPUT_W = 416;
|
||||
static constexpr int INPUT_H = 608;
|
||||
static constexpr int INPUT_W = 608;
|
||||
|
||||
struct YoloKernel
|
||||
{
|
||||
@ -25,17 +25,17 @@ namespace Yolo
|
||||
float anchors[CHECK_COUNT*2];
|
||||
};
|
||||
|
||||
static YoloKernel yolo1 = {
|
||||
static constexpr YoloKernel yolo1 = {
|
||||
INPUT_W / 32,
|
||||
INPUT_H / 32,
|
||||
{116,90, 156,198, 373,326}
|
||||
};
|
||||
static YoloKernel yolo2 = {
|
||||
static constexpr YoloKernel yolo2 = {
|
||||
INPUT_W / 16,
|
||||
INPUT_H / 16,
|
||||
{30,61, 62,45, 59,119}
|
||||
};
|
||||
static YoloKernel yolo3 = {
|
||||
static constexpr YoloKernel yolo3 = {
|
||||
INPUT_W / 8,
|
||||
INPUT_H / 8,
|
||||
{10,13, 16,30, 33,23}
|
||||
@ -54,48 +54,106 @@ namespace Yolo
|
||||
|
||||
namespace nvinfer1
|
||||
{
|
||||
class YoloLayerPlugin: public IPluginExt
|
||||
class YoloLayerPlugin: public IPluginV2IOExt
|
||||
{
|
||||
public:
|
||||
explicit YoloLayerPlugin(const int cudaThread = 256);
|
||||
YoloLayerPlugin(const void* data, size_t length);
|
||||
public:
|
||||
explicit YoloLayerPlugin();
|
||||
YoloLayerPlugin(const void* data, size_t length);
|
||||
|
||||
~YoloLayerPlugin();
|
||||
~YoloLayerPlugin();
|
||||
|
||||
int getNbOutputs() const override
|
||||
{
|
||||
return 1;
|
||||
}
|
||||
int getNbOutputs() const override
|
||||
{
|
||||
return 1;
|
||||
}
|
||||
|
||||
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override;
|
||||
Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override;
|
||||
|
||||
bool supportsFormat(DataType type, PluginFormat format) const override {
|
||||
return type == DataType::kFLOAT && format == PluginFormat::kNCHW;
|
||||
}
|
||||
int initialize() override;
|
||||
|
||||
void configureWithFormat(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, DataType type, PluginFormat format, int maxBatchSize) override {};
|
||||
virtual void terminate() override {};
|
||||
|
||||
int initialize() override;
|
||||
virtual size_t getWorkspaceSize(int maxBatchSize) const override { return 0;}
|
||||
|
||||
virtual void terminate() override {};
|
||||
virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override;
|
||||
|
||||
virtual size_t getWorkspaceSize(int maxBatchSize) const override { return 0;}
|
||||
virtual size_t getSerializationSize() const override;
|
||||
|
||||
virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override;
|
||||
virtual void serialize(void* buffer) const override;
|
||||
|
||||
virtual size_t getSerializationSize() override;
|
||||
bool supportsFormatCombination(int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) const override {
|
||||
return inOut[pos].format == TensorFormat::kLINEAR && inOut[pos].type == DataType::kFLOAT;
|
||||
}
|
||||
|
||||
virtual void serialize(void* buffer) override;
|
||||
const char* getPluginType() const override;
|
||||
|
||||
void forwardGpu(const float *const * inputs,float * output, cudaStream_t stream,int batchSize = 1);
|
||||
const char* getPluginVersion() const override;
|
||||
|
||||
private:
|
||||
int mClassCount;
|
||||
int mKernelCount;
|
||||
std::vector<Yolo::YoloKernel> mYoloKernel;
|
||||
int mThreadCount;
|
||||
//int mDetNum;
|
||||
void destroy() override;
|
||||
|
||||
IPluginV2IOExt* clone() const override;
|
||||
|
||||
void setPluginNamespace(const char* pluginNamespace) override;
|
||||
|
||||
const char* getPluginNamespace() const override;
|
||||
|
||||
DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override;
|
||||
|
||||
bool isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const override;
|
||||
|
||||
bool canBroadcastInputAcrossBatch(int inputIndex) const override;
|
||||
|
||||
void attachToContext(
|
||||
cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator) override;
|
||||
|
||||
void configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) override;
|
||||
|
||||
void detachFromContext() override;
|
||||
|
||||
private:
|
||||
void forwardGpu(const float *const * inputs,float * output, cudaStream_t stream,int batchSize = 1);
|
||||
int mClassCount;
|
||||
int mKernelCount;
|
||||
std::vector<Yolo::YoloKernel> mYoloKernel;
|
||||
int mThreadCount = 256;
|
||||
const char* mPluginNamespace;
|
||||
};
|
||||
|
||||
class YoloPluginCreator : public IPluginCreator
|
||||
{
|
||||
public:
|
||||
YoloPluginCreator();
|
||||
|
||||
~YoloPluginCreator() override = default;
|
||||
|
||||
const char* getPluginName() const override;
|
||||
|
||||
const char* getPluginVersion() const override;
|
||||
|
||||
const PluginFieldCollection* getFieldNames() override;
|
||||
|
||||
IPluginV2IOExt* createPlugin(const char* name, const PluginFieldCollection* fc) override;
|
||||
|
||||
IPluginV2IOExt* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override;
|
||||
|
||||
void setPluginNamespace(const char* libNamespace) override
|
||||
{
|
||||
mNamespace = libNamespace;
|
||||
}
|
||||
|
||||
const char* getPluginNamespace() const override
|
||||
{
|
||||
return mNamespace.c_str();
|
||||
}
|
||||
|
||||
private:
|
||||
std::string mNamespace;
|
||||
static PluginFieldCollection mFC;
|
||||
static std::vector<PluginField> mPluginAttributes;
|
||||
};
|
||||
|
||||
|
||||
|
||||
};
|
||||
|
||||
#endif
|
||||
|
||||
@ -1,19 +1,29 @@
|
||||
#include "NvInfer.h"
|
||||
#include "NvInferPlugin.h"
|
||||
#include "cuda_runtime_api.h"
|
||||
#include "common.h"
|
||||
#include <fstream>
|
||||
#include <iostream>
|
||||
#include <map>
|
||||
#include <sstream>
|
||||
#include <vector>
|
||||
#include <chrono>
|
||||
#include "plugin_factory.h"
|
||||
#include "yololayer.h"
|
||||
#include <opencv2/opencv.hpp>
|
||||
#include <dirent.h>
|
||||
#include "NvInfer.h"
|
||||
#include "cuda_runtime_api.h"
|
||||
#include "logging.h"
|
||||
#include "yololayer.h"
|
||||
|
||||
//#define USE_FP16 // comment out this if want to use FP32
|
||||
#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
|
||||
@ -27,6 +37,7 @@ static const int OUTPUT_SIZE = 1000 * 7 + 1; // we assume the yololayer outputs
|
||||
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;
|
||||
@ -78,10 +89,10 @@ cv::Rect get_rect(cv::Mat& img, float bbox[4]) {
|
||||
|
||||
float iou(float lbox[4], float rbox[4]) {
|
||||
float interBox[] = {
|
||||
max(lbox[0] - lbox[2]/2.f , rbox[0] - rbox[2]/2.f), //left
|
||||
min(lbox[0] + lbox[2]/2.f , rbox[0] + rbox[2]/2.f), //right
|
||||
max(lbox[1] - lbox[3]/2.f , rbox[1] - rbox[3]/2.f), //top
|
||||
min(lbox[1] + lbox[3]/2.f , rbox[1] + rbox[3]/2.f), //bottom
|
||||
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])
|
||||
@ -167,7 +178,6 @@ IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, W
|
||||
float *mean = (float*)weightMap[lname + ".running_mean"].values;
|
||||
float *var = (float*)weightMap[lname + ".running_var"].values;
|
||||
int len = weightMap[lname + ".running_var"].count;
|
||||
std::cout << "len " << len << std::endl;
|
||||
|
||||
float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
@ -196,29 +206,25 @@ IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, W
|
||||
}
|
||||
|
||||
ILayer* convBnLeaky(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int p, int linx) {
|
||||
std::cout << linx << std::endl;
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
IConvolutionLayer* conv1 = network->addConvolution(input, outch, DimsHW{ksize, ksize}, weightMap["module_list." + std::to_string(linx) + ".Conv2d.weight"], emptywts);
|
||||
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap["module_list." + std::to_string(linx) + ".Conv2d.weight"], emptywts);
|
||||
assert(conv1);
|
||||
conv1->setStride(DimsHW{s, s});
|
||||
conv1->setPadding(DimsHW{p, p});
|
||||
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);
|
||||
|
||||
ITensor* inputTensors[] = {bn1->getOutput(0)};
|
||||
//LeakyPlugin *lr = new LeakyPlugin();
|
||||
auto lr = plugin::createPReLUPlugin(0.1);
|
||||
auto lr1 = network->addPlugin(inputTensors, 1, *lr);
|
||||
assert(lr1);
|
||||
lr1->setName(("leaky" + std::to_string(linx)).c_str());
|
||||
return lr1;
|
||||
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, DataType dt) {
|
||||
INetworkDefinition* network = builder->createNetwork();
|
||||
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
|
||||
INetworkDefinition* network = builder->createNetworkV2(0U);
|
||||
|
||||
// Create input tensor of shape { 1, 1, 32, 32 } with name INPUT_BLOB_NAME
|
||||
// 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);
|
||||
|
||||
@ -305,15 +311,15 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType
|
||||
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->addPooling(*lr77->getOutput(0), PoolingType::kMAX, DimsHW{5,5});
|
||||
pool78->setPadding(DimsHW{2, 2});
|
||||
pool78->setStride(DimsHW{1, 1});
|
||||
auto pool80 = network->addPooling(*lr77->getOutput(0), PoolingType::kMAX, DimsHW{9,9});
|
||||
pool80->setPadding(DimsHW{4, 4});
|
||||
pool80->setStride(DimsHW{1, 1});
|
||||
auto pool82 = network->addPooling(*lr77->getOutput(0), PoolingType::kMAX, DimsHW{13,13});
|
||||
pool82->setPadding(DimsHW{6, 6});
|
||||
pool82->setStride(DimsHW{1, 1});
|
||||
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);
|
||||
@ -322,7 +328,7 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType
|
||||
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->addConvolution(*lr87->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.88.Conv2d.weight"], weightMap["module_list.88.Conv2d.bias"]);
|
||||
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);
|
||||
|
||||
@ -331,9 +337,9 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType
|
||||
deval[i] = 1.0;
|
||||
}
|
||||
Weights deconvwts92{DataType::kFLOAT, deval, 256 * 2 * 2};
|
||||
IDeconvolutionLayer* deconv92 = network->addDeconvolution(*lr91->getOutput(0), 256, DimsHW{2, 2}, deconvwts92, emptywts);
|
||||
IDeconvolutionLayer* deconv92 = network->addDeconvolutionNd(*lr91->getOutput(0), 256, DimsHW{2, 2}, deconvwts92, emptywts);
|
||||
assert(deconv92);
|
||||
deconv92->setStride(DimsHW{2, 2});
|
||||
deconv92->setStrideNd(DimsHW{2, 2});
|
||||
deconv92->setNbGroups(256);
|
||||
weightMap["deconv92"] = deconvwts92;
|
||||
|
||||
@ -345,13 +351,13 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType
|
||||
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->addConvolution(*lr99->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.100.Conv2d.weight"], weightMap["module_list.100.Conv2d.bias"]);
|
||||
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->addDeconvolution(*lr103->getOutput(0), 128, DimsHW{2, 2}, deconvwts104, emptywts);
|
||||
IDeconvolutionLayer* deconv104 = network->addDeconvolutionNd(*lr103->getOutput(0), 128, DimsHW{2, 2}, deconvwts104, emptywts);
|
||||
assert(deconv104);
|
||||
deconv104->setStride(DimsHW{2, 2});
|
||||
deconv104->setStrideNd(DimsHW{2, 2});
|
||||
deconv104->setNbGroups(128);
|
||||
ITensor* inputTensors1[] = {deconv104->getOutput(0), ew36->getOutput(0)};
|
||||
auto cat105 = network->addConcatenation(inputTensors1, 2);
|
||||
@ -361,26 +367,27 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType
|
||||
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->addConvolution(*lr111->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{1, 1}, weightMap["module_list.112.Conv2d.weight"], weightMap["module_list.112.Conv2d.bias"]);
|
||||
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 yolo = new YoloLayerPlugin();
|
||||
ITensor* inputTensors_yolo[] = {conv88->getOutput(0), conv100->getOutput(0), conv112->getOutput(0)};
|
||||
auto yolo113 = network->addPlugin(inputTensors_yolo, 3, *yolo);
|
||||
assert(yolo113);
|
||||
yolo113->setName("yolo113");
|
||||
|
||||
yolo113->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
std::cout << "set name out" << std::endl;
|
||||
network->markOutput(*yolo113->getOutput(0));
|
||||
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);
|
||||
|
||||
yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
network->markOutput(*yolo->getOutput(0));
|
||||
|
||||
// Build engine
|
||||
builder->setMaxBatchSize(maxBatchSize);
|
||||
builder->setMaxWorkspaceSize(1 << 20);
|
||||
config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
|
||||
#ifdef USE_FP16
|
||||
builder->setFp16Mode(true);
|
||||
config->setFlag(BuilderFlag::kFP16);
|
||||
#endif
|
||||
ICudaEngine* engine = builder->buildCudaEngine(*network);
|
||||
std::cout << "build out" << std::endl;
|
||||
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();
|
||||
@ -397,9 +404,10 @@ ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType
|
||||
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, DataType::kFLOAT);
|
||||
ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT);
|
||||
assert(engine != nullptr);
|
||||
|
||||
// Serialize the engine
|
||||
@ -475,7 +483,7 @@ int main(int argc, char** argv) {
|
||||
IHostMemory* modelStream{nullptr};
|
||||
APIToModel(1, &modelStream);
|
||||
assert(modelStream != nullptr);
|
||||
std::ofstream p("yolov3-spp.engine");
|
||||
std::ofstream p("yolov3-spp.engine", std::ios::binary);
|
||||
if (!p) {
|
||||
std::cerr << "could not open plan output file" << std::endl;
|
||||
return -1;
|
||||
@ -512,13 +520,13 @@ int main(int argc, char** argv) {
|
||||
//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
|
||||
// data[i] = 1.0;
|
||||
static float prob[OUTPUT_SIZE];
|
||||
PluginFactory pf;
|
||||
IRuntime* runtime = createInferRuntime(gLogger);
|
||||
assert(runtime != nullptr);
|
||||
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, &pf);
|
||||
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) {
|
||||
@ -536,10 +544,10 @@ int main(int argc, char** argv) {
|
||||
// Run inference
|
||||
auto start = std::chrono::system_clock::now();
|
||||
doInference(*context, data, prob, 1);
|
||||
std::vector<Yolo::Detection> res;
|
||||
nms(res, prob);
|
||||
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] << ",";
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user