f2521a5ffa
* rcnn upgrade to support TensorRT 8. * Update RpnDecodePlugin.h Remove Chinese * Update rcnn.cpp Remove Chinese * Update backbone.hpp Remove Chinese * Update backbone.hpp * Update rcnn.cpp * Update rcnn.cpp * Update rcnn.cpp * Update MaskRcnnInferencePlugin.h * rcnn upgrade to support TensorRT 8.x * rcnn upgrade to support TensorRT 8.x * Update macros.h * Update README.md --------- Co-authored-by: nengwp <nengwp@github.ai> Co-authored-by: Wang Xinyu <shaywxy@gmail.com>
110 lines
3.0 KiB
C++
Executable File
110 lines
3.0 KiB
C++
Executable File
#pragma once
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#include <NvInfer.h>
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#include <cuda_runtime_api.h>
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#include <assert.h>
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#include <dirent.h>
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#include <fstream>
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#include <sstream>
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#include <iostream>
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#include <string>
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#include <vector>
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#include <map>
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#include <algorithm>
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#include <opencv2/opencv.hpp>
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#include "./logging.h"
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#include "./cuda_utils.h"
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static Logger gLogger;
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using namespace nvinfer1;
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void loadWeights(const std::string file, std::map<std::string, Weights>& weightMap) {
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std::cout << "Loading weights: " << file << std::endl;
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// Open weights file
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std::ifstream input(file);
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assert(input.is_open() && "Unable to load weight file. please check if the .wts file path is right!!!!!!");
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// Read number of weight blobs
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int32_t count;
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input >> count;
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assert(count > 0 && "Invalid weight map file.");
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while (count--) {
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Weights wt{ DataType::kFLOAT, nullptr, 0 };
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uint32_t size;
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// Read name and type of blob
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std::string name;
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input >> name >> std::dec >> size;
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wt.type = DataType::kFLOAT;
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// Load blob
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uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
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for (uint32_t x = 0, y = size; x < y; ++x) {
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input >> std::hex >> val[x];
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}
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wt.values = val;
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wt.count = size;
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weightMap[name] = wt;
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}
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}
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static inline int read_files_in_dir(const char *p_dir_name, std::vector<std::string> &file_names) {
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DIR *p_dir = opendir(p_dir_name);
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if (p_dir == nullptr) {
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return -1;
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}
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struct dirent* p_file = nullptr;
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while ((p_file = readdir(p_dir)) != nullptr) {
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if (strcmp(p_file->d_name, ".") != 0 &&
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strcmp(p_file->d_name, "..") != 0) {
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// std::string cur_file_name(p_dir_name);
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// cur_file_name += "/";
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// cur_file_name += p_file->d_name;
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std::string cur_file_name(p_file->d_name);
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file_names.push_back(cur_file_name);
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}
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}
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closedir(p_dir);
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return 0;
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}
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static inline cv::Mat preprocessImg(cv::Mat& img, int input_w, int input_h, int& X_LEFT_PAD, int& X_RIGHT_PAD, int& Y_TOP_PAD, int& Y_BOTTOM_PAD) {
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int w, h;
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float x, y;
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float r_w = input_w / (img.cols*1.0);
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float r_h = input_h / (img.rows*1.0);
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// this code can also support left-right and top-bottom padding if you need
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if (r_h > r_w) {
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w = input_w;
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h = r_w * img.rows;
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x = 0.0;
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y = (input_h - h) / 2.f;
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} else {
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w = r_h * img.cols;
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h = input_h;
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x = (input_w - w) / 2.f;
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y = 0.0;
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}
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// support both odd and even cases
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X_LEFT_PAD = (int)(round(x - 0.1));
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X_RIGHT_PAD = (int)(round(x + 0.1));
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Y_TOP_PAD = (int)(round(y - 0.1));
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Y_BOTTOM_PAD = (int)(round(y + 0.1));
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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_LINEAR);
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cv::Mat out(input_h, input_w, CV_8UC3, cv::Scalar(128, 128, 128));
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re.copyTo(out(cv::Rect(X_LEFT_PAD, Y_TOP_PAD, re.cols, re.rows)));
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return out;
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} |