add retinaface, unfinished
This commit is contained in:
wang-xinyu
parent
1f73c63ef3
commit
004e2d5a5b
34
retinaface/CMakeLists.txt
Normal file
34
retinaface/CMakeLists.txt
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@ -0,0 +1,34 @@
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cmake_minimum_required(VERSION 2.6)
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project(retinaface)
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add_definitions(-std=c++11)
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option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
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set(CMAKE_CXX_STANDARD 11)
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set(CMAKE_BUILD_TYPE Debug)
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find_package(CUDA REQUIRED)
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set(CUDA_NVCC_PLAGS ${CUDA_NVCC_PLAGS};-std=c++11;-g;-G;-gencode;arch=compute_30;code=sm_30)
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include_directories(${PROJECT_SOURCE_DIR}/include)
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include_directories(/usr/local/cuda-9.0/targets/aarch64-linux/include)
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link_directories(/usr/local/cuda-9.0/targets/aarch64-linux/lib)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED")
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#cuda_add_library(leaky ${PROJECT_SOURCE_DIR}/leaky.cu)
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#cuda_add_library(yololayer ${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(retina_50 ${PROJECT_SOURCE_DIR}/retina_r50.cpp)
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target_link_libraries(retina_50 nvinfer nvinfer_plugin)
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target_link_libraries(retina_50 cudart)
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#target_link_libraries(retina yololayer)
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target_link_libraries(retina_50 ${OpenCV_LIBRARIES})
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add_definitions(-O2 -pthread)
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356
retinaface/common.h
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356
retinaface/common.h
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@ -0,0 +1,356 @@
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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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483
retinaface/retina_r50.cpp
Normal file
483
retinaface/retina_r50.cpp
Normal file
@ -0,0 +1,483 @@
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#include "NvInfer.h"
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#include "NvInferPlugin.h"
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#include "cuda_runtime_api.h"
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#include "common.h"
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#include <fstream>
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#include <iostream>
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#include <map>
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#include <sstream>
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#include <vector>
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#include <chrono>
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//#include "plugin_factory.h"
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//#include "yololayer.h"
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#include <opencv2/opencv.hpp>
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#define USE_FP16 // comment out this if want to use FP32
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// stuff we know about the network and the input/output blobs
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static const int INPUT_H = 360;
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static const int INPUT_W = 640;
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static const int OUTPUT_SIZE = 2048 * 12 * 20;
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const char* INPUT_BLOB_NAME = "data";
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const char* OUTPUT_BLOB_NAME = "prob";
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using namespace nvinfer1;
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static Logger gLogger;
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typedef struct {
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float bbox[4];
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float det_confidence;
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float class_id;
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float class_confidence;
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} Detection;
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cv::Mat preprocess_img(cv::Mat& img, int input_dim) {
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int w, h, x, y;
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if (img.cols > img.rows) {
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w = input_dim;
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h = input_dim * img.rows / img.cols;
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x = 0;
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y = (input_dim - h) / 2;
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} else {
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w = input_dim * img.cols / img.rows;
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h = input_dim;
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x = (input_dim - w) / 2;
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y = 0;
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}
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cv::Mat re(h, w, CV_8UC3);
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cv::resize(img, re, re.size(), 0, 0, cv::INTER_CUBIC);
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cv::Mat out(input_dim, input_dim, CV_8UC3, cv::Scalar(128, 128, 128));
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re.copyTo(out(cv::Rect(x, y, re.cols, re.rows)));
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return out;
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}
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cv::Rect get_rect(cv::Mat& img, int input_dim, float bbox[4]) {
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int l, r, t, b;
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if (img.cols > img.rows) {
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l = bbox[0] - bbox[2]/2.f;
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r = bbox[0] + bbox[2]/2.f;
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||||
t = bbox[1] - bbox[3]/2.f - (input_dim - input_dim * img.rows / img.cols) / 2;
|
||||
b = bbox[1] + bbox[3]/2.f - (input_dim - input_dim * img.rows / img.cols) / 2;
|
||||
l = l * img.cols / input_dim;
|
||||
r = r * img.cols / input_dim;
|
||||
t = t * img.cols / input_dim;
|
||||
b = b * img.cols / input_dim;
|
||||
} else {
|
||||
l = bbox[0] - bbox[2]/2.f - (input_dim - input_dim * img.cols / img.rows) / 2;
|
||||
r = bbox[0] + bbox[2]/2.f - (input_dim - input_dim * img.cols / img.rows) / 2;
|
||||
t = bbox[1] - bbox[3]/2.f;
|
||||
b = bbox[1] + bbox[3]/2.f;
|
||||
l = l * img.rows / input_dim;
|
||||
r = r * img.rows / input_dim;
|
||||
t = t * img.rows / input_dim;
|
||||
b = b * img.rows / input_dim;
|
||||
}
|
||||
return cv::Rect(l, t, r-l, b-t);
|
||||
}
|
||||
|
||||
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
|
||||
};
|
||||
|
||||
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(Detection& a, Detection& b) {
|
||||
return a.det_confidence > b.det_confidence;
|
||||
}
|
||||
|
||||
void nms(std::vector<Detection>& res, float *output, float nms_thresh = 0.4) {
|
||||
std::map<float, std::vector<Detection>> m;
|
||||
for (int i = 0; i < OUTPUT_SIZE / 7; i++) {
|
||||
if (output[7 * i + 4] <= 0.5) continue;
|
||||
Detection det;
|
||||
memcpy(&det, &output[7 * i], 7 * sizeof(float));
|
||||
if (m.count(det.class_id) == 0) m.emplace(det.class_id, std::vector<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;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Load weights from files shared with TensorRT samples.
|
||||
// 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;
|
||||
std::cout << "len " << len << std::endl;
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
IActivationLayer* bottleneck(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int inch, int outch, int stride, std::string lname) {
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
IConvolutionLayer* conv1 = network->addConvolution(input, outch, DimsHW{1, 1}, weightMap[lname + "conv1.weight"], emptywts);
|
||||
assert(conv1);
|
||||
|
||||
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "bn1", 1e-5);
|
||||
|
||||
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu1);
|
||||
|
||||
IConvolutionLayer* conv2 = network->addConvolution(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + "conv2.weight"], emptywts);
|
||||
assert(conv2);
|
||||
conv2->setStride(DimsHW{stride, stride});
|
||||
conv2->setPadding(DimsHW{1, 1});
|
||||
|
||||
IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + "bn2", 1e-5);
|
||||
|
||||
IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu2);
|
||||
|
||||
IConvolutionLayer* conv3 = network->addConvolution(*relu2->getOutput(0), outch * 4, DimsHW{1, 1}, weightMap[lname + "conv3.weight"], emptywts);
|
||||
assert(conv3);
|
||||
|
||||
IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + "bn3", 1e-5);
|
||||
|
||||
IElementWiseLayer* ew1;
|
||||
if (stride != 1 || inch != outch * 4) {
|
||||
IConvolutionLayer* conv4 = network->addConvolution(input, outch * 4, DimsHW{1, 1}, weightMap[lname + "downsample.0.weight"], emptywts);
|
||||
assert(conv4);
|
||||
conv4->setStride(DimsHW{stride, stride});
|
||||
|
||||
IScaleLayer* bn4 = addBatchNorm2d(network, weightMap, *conv4->getOutput(0), lname + "downsample.1", 1e-5);
|
||||
ew1 = network->addElementWise(*bn4->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM);
|
||||
} else {
|
||||
ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM);
|
||||
}
|
||||
IActivationLayer* relu3 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu3);
|
||||
return relu3;
|
||||
}
|
||||
|
||||
// Creat the engine using only the API and not any parser.
|
||||
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType dt)
|
||||
{
|
||||
INetworkDefinition* network = builder->createNetwork();
|
||||
|
||||
// Create input tensor of shape { 1, 1, 32, 32 } 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("../retinaface.wts");
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
// ------------- backbone resnet50 ---------------
|
||||
IConvolutionLayer* conv1 = network->addConvolution(*data, 64, DimsHW{7, 7}, weightMap["body.conv1.weight"], emptywts);
|
||||
assert(conv1);
|
||||
conv1->setStride(DimsHW{2, 2});
|
||||
conv1->setPadding(DimsHW{3, 3});
|
||||
|
||||
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "body.bn1", 1e-5);
|
||||
|
||||
// Add activation layer using the ReLU algorithm.
|
||||
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu1);
|
||||
|
||||
// Add max pooling layer with stride of 2x2 and kernel size of 2x2.
|
||||
IPoolingLayer* pool1 = network->addPooling(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});
|
||||
assert(pool1);
|
||||
pool1->setStride(DimsHW{2, 2});
|
||||
pool1->setPadding(DimsHW{1, 1});
|
||||
|
||||
IActivationLayer* x = bottleneck(network, weightMap, *pool1->getOutput(0), 64, 64, 1, "body.layer1.0.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 256, 64, 1, "body.layer1.1.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 256, 64, 1, "body.layer1.2.");
|
||||
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 256, 128, 2, "body.layer2.0.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "body.layer2.1.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "body.layer2.2.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "body.layer2.3.");
|
||||
IActivationLayer* layer2 = x;
|
||||
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 256, 2, "body.layer3.0.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "body.layer3.1.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "body.layer3.2.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "body.layer3.3.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "body.layer3.4.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "body.layer3.5.");
|
||||
IActivationLayer* layer3 = x;
|
||||
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 512, 2, "body.layer4.0.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 2048, 512, 1, "body.layer4.1.");
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 2048, 512, 1, "body.layer4.2.");
|
||||
IActivationLayer* layer4 = x;
|
||||
|
||||
//IPoolingLayer* pool2 = network->addPooling(*x->getOutput(0), PoolingType::kAVERAGE, DimsHW{7, 7});
|
||||
//assert(pool2);
|
||||
//pool2->setStride(DimsHW{1, 1});
|
||||
//
|
||||
//IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool2->getOutput(0), 1000, weightMap["fc.weight"], weightMap["fc.bias"]);
|
||||
//assert(fc1);
|
||||
|
||||
layer4->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
std::cout << "set name out" << std::endl;
|
||||
network->markOutput(*layer4->getOutput(0));
|
||||
|
||||
// Build engine
|
||||
builder->setMaxBatchSize(maxBatchSize);
|
||||
builder->setMaxWorkspaceSize(1 << 20);
|
||||
#ifdef USE_FP16
|
||||
builder->setFp16Mode(true);
|
||||
#endif
|
||||
ICudaEngine* engine = builder->buildCudaEngine(*network);
|
||||
std::cout << "build out" << 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);
|
||||
|
||||
// Create model to populate the network, then set the outputs and create an engine
|
||||
ICudaEngine* engine = createEngine(maxBatchSize, builder, 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 main(int argc, char** argv)
|
||||
{
|
||||
std::cout << "beginning" << std::endl;
|
||||
if (argc != 2) {
|
||||
std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./retina_r50 -s // serialize model to plan file" << std::endl;
|
||||
std::cerr << "./retina_r50 -d // deserialize plan file and run inference" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
// create a model using the API directly and serialize it to a stream
|
||||
char *trtModelStream{nullptr};
|
||||
size_t size{0};
|
||||
|
||||
if (std::string(argv[1]) == "-s") {
|
||||
IHostMemory* modelStream{nullptr};
|
||||
APIToModel(1, &modelStream);
|
||||
assert(modelStream != nullptr);
|
||||
|
||||
std::ofstream p("retina_r50.engine");
|
||||
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 1;
|
||||
} else if (std::string(argv[1]) == "-d") {
|
||||
std::ifstream file("retina_r50.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 {
|
||||
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;
|
||||
|
||||
//cv::Mat img = cv::imread("../dog.jpg");
|
||||
//cv::Mat pr_img = preprocess_img(img, INPUT_H);
|
||||
//cv::imwrite("123.jpg", pr_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;
|
||||
//}
|
||||
|
||||
//PluginFactory pf;
|
||||
IRuntime* runtime = createInferRuntime(gLogger);
|
||||
assert(runtime != nullptr);
|
||||
//ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, &pf);
|
||||
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
|
||||
assert(engine != nullptr);
|
||||
IExecutionContext* context = engine->createExecutionContext();
|
||||
assert(context != nullptr);
|
||||
|
||||
// Run inference
|
||||
static float prob[OUTPUT_SIZE];
|
||||
for (int i = 0; i < 10; i++) {
|
||||
auto start = std::chrono::system_clock::now();
|
||||
doInference(*context, data, prob, 1);
|
||||
//std::vector<Detection> res;
|
||||
//nms(res, prob);
|
||||
//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, INPUT_W, 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);
|
||||
//}
|
||||
auto end = std::chrono::system_clock::now();
|
||||
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
|
||||
//cv::imwrite("res.jpg", 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 << i / 10 << std::endl;
|
||||
}
|
||||
std::cout << std::endl;
|
||||
|
||||
return 0;
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user