1f7672ce5a
* create psenet create psenet with weight from tensorflow * delete some useless code * repalce tab with 4 blanks * fix network bug, rewrite post-processing pse algorithm * update readme * update readme * add RepVGG * fix typo * add hrnetseg w18 w32 w48 * add hrnetseg with ocr w18 w32 w48 * merge hrnet and small, add hrnet_ocr * fix warning * change project name
399 lines
16 KiB
C++
399 lines
16 KiB
C++
#pragma once
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#include <fstream>
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#include <map>
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#include <sstream>
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#include <vector>
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#include <opencv2/opencv.hpp>
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#include <dirent.h>
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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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using namespace nvinfer1;
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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::cerr << "Cuda failure: " << ret << std::endl; \
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abort(); \
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} \
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} while (0)
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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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// TensorRT weight files have a simple space delimited format:
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// [type] [size] <data x size in hex>
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void debug_print(ITensor *input_tensor, std::string head)
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{
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std::cout << head << " : ";
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for (int i = 0; i < input_tensor->getDimensions().nbDims; i++)
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{
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std::cout << input_tensor->getDimensions().d[i] << " ";
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}
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std::cout << std::endl;
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}
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std::map<std::string, Weights> loadWeights(const std::string file)
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{
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std::cout << "Loading weights: " << file << std::endl;
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std::map<std::string, Weights> weightMap;
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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.");
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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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{
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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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{
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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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return weightMap;
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}
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cv::Mat createLTU(int len)
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{
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cv::Mat lookUpTable(1, 256, CV_8U);
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uchar *p = lookUpTable.data;
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for (int j = 0; j < 256; ++j)
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{
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p[j] = (j * (256 / len) > 255) ? uchar(255) : (uchar)(j * (256 / len));
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}
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return lookUpTable;
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}
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ITensor *MeanStd(INetworkDefinition *network, ITensor *input, float *mean, float *std, bool div255)
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{
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if (div255)
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{
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Weights Div_225{DataType::kFLOAT, nullptr, 3};
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float *wgt = reinterpret_cast<float *>(malloc(sizeof(float) * 3));
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for (int i = 0; i < 3; ++i)
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{
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wgt[i] = 255.0f;
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}
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Div_225.values = wgt;
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IConstantLayer *d = network->addConstant(Dims3{3, 1, 1}, Div_225);
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input = network->addElementWise(*input, *d->getOutput(0), ElementWiseOperation::kDIV)->getOutput(0);
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}
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Weights Mean{DataType::kFLOAT, nullptr, 3};
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Mean.values = mean;
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IConstantLayer *m = network->addConstant(Dims3{3, 1, 1}, Mean);
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IElementWiseLayer *sub_mean = network->addElementWise(*input, *m->getOutput(0), ElementWiseOperation::kSUB);
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if (std != nullptr)
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{
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Weights Std{DataType::kFLOAT, nullptr, 3};
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Std.values = std;
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IConstantLayer *s = network->addConstant(Dims3{3, 1, 1}, Std);
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IElementWiseLayer *std_mean = network->addElementWise(*sub_mean->getOutput(0), *s->getOutput(0), ElementWiseOperation::kDIV);
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return std_mean->getOutput(0);
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}
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else
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{
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return sub_mean->getOutput(0);
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}
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}
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IScaleLayer *addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights> &weightMap, ITensor &input, std::string lname, float eps)
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{
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float *gamma = (float *)weightMap[lname + ".weight"].values;
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float *beta = (float *)weightMap[lname + ".bias"].values;
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float *mean = (float *)weightMap[lname + ".running_mean"].values;
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float *var = (float *)weightMap[lname + ".running_var"].values;
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int len = weightMap[lname + ".running_var"].count;
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//std::cout << "len " << len << std::endl;
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float *scval = reinterpret_cast<float *>(malloc(sizeof(float) * len));
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for (int i = 0; i < len; i++)
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{
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scval[i] = gamma[i] / sqrt(var[i] + eps);
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}
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Weights scale{DataType::kFLOAT, scval, len};
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float *shval = reinterpret_cast<float *>(malloc(sizeof(float) * len));
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for (int i = 0; i < len; i++)
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{
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shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
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}
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Weights shift{DataType::kFLOAT, shval, len};
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float *pval = reinterpret_cast<float *>(malloc(sizeof(float) * len));
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for (int i = 0; i < len; i++)
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{
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pval[i] = 1.0;
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}
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Weights power{DataType::kFLOAT, pval, len};
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weightMap[lname + ".scale"] = scale;
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weightMap[lname + ".shift"] = shift;
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weightMap[lname + ".power"] = power;
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IScaleLayer *scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
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assert(scale_1);
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return scale_1;
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}
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ILayer *convBnRelu(INetworkDefinition *network,
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std::map<std::string, Weights> &weightMap,
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ITensor &input, int outch, int ksize, int s, int p,
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std::string convname, std::string bnname,
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bool relu = true,
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bool bias = false)
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{
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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IConvolutionLayer *conv1;
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//Dims dim;
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if (!bias)
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{
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conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[convname + ".weight"], emptywts);
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}
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else
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{
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conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[convname + ".weight"], weightMap[convname + ".bias"]);
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}
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assert(conv1);
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conv1->setStrideNd(DimsHW{s, s});
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conv1->setPaddingNd(DimsHW{p, p});
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debug_print(conv1->getOutput(0), convname);
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IScaleLayer *bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), bnname, 1e-5);
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debug_print(bn1->getOutput(0), bnname);
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if (relu)
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{
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auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
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return lr;
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}
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return bn1;
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}
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IActivationLayer *ResBlock2Conv(INetworkDefinition *network, std::map<std::string, Weights> &weightMap, ITensor &input, int inch, int outch, int stride, std::string lname)
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{
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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IConvolutionLayer *conv1 = network->addConvolutionNd(input, inch, DimsHW{1, 1}, weightMap[lname + ".conv1.weight"], emptywts);
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assert(conv1);
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conv1->setStrideNd(DimsHW{stride, stride});
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conv1->setPaddingNd(DimsHW{0, 0});
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debug_print(conv1->getOutput(0), lname + "_1");
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IScaleLayer *bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".bn1", 1e-5);
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IActivationLayer *relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
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assert(relu1);
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///
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IConvolutionLayer *conv2 = network->addConvolutionNd(*relu1->getOutput(0), inch, DimsHW{3, 3}, weightMap[lname + ".conv2.weight"], emptywts);
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assert(conv2);
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conv2->setStrideNd(DimsHW{stride, stride});
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conv2->setPaddingNd(DimsHW{1, 1});
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debug_print(conv2->getOutput(0), lname + "_2");
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IScaleLayer *bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + ".bn2", 1e-5);
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IActivationLayer *relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);
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assert(relu2);
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//////
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IConvolutionLayer *conv3 = network->addConvolutionNd(*relu2->getOutput(0), outch, DimsHW{1, 1}, weightMap[lname + ".conv3.weight"], emptywts);
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assert(conv3);
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conv3->setStrideNd(DimsHW{stride, stride});
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conv3->setPaddingNd(DimsHW{0, 0});
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debug_print(conv3->getOutput(0), lname + "_3");
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IScaleLayer *bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + ".bn3", 1e-5);
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IElementWiseLayer *ew1;
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if (inch != outch)
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{
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IConvolutionLayer *conv4 = network->addConvolutionNd(input, outch, DimsHW{1, 1}, weightMap[lname + ".downsample.0.weight"], emptywts);
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assert(conv4);
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conv4->setStrideNd(DimsHW{stride, stride});
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conv4->setPaddingNd(DimsHW{0, 0});
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debug_print(conv4->getOutput(0), lname + "_4");
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IScaleLayer *bn4 = addBatchNorm2d(network, weightMap, *conv4->getOutput(0), lname + ".downsample.1", 1e-5);
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ew1 = network->addElementWise(*bn4->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM);
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}
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else
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{
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ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM);
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}
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IActivationLayer *relu3 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
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assert(relu3);
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return relu3;
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}
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IActivationLayer *ResBlock(INetworkDefinition *network, std::map<std::string, Weights> &weightMap, ITensor &input, int inch, int outch, int stride, std::string lname)
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{
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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// in 256 out 64
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IConvolutionLayer *conv1 = network->addConvolutionNd(input, outch, DimsHW{1, 1}, weightMap[lname + ".conv1.weight"], emptywts);
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assert(conv1);
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conv1->setStrideNd(DimsHW{stride, stride});
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conv1->setPaddingNd(DimsHW{0, 0});
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debug_print(conv1->getOutput(0), lname + "_1");
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IScaleLayer *bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".bn1", 1e-5);
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IActivationLayer *relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
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assert(relu1);
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///
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IConvolutionLayer *conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + ".conv2.weight"], emptywts);
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assert(conv2);
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conv2->setStrideNd(DimsHW{stride, stride});
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conv2->setPaddingNd(DimsHW{1, 1});
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debug_print(conv2->getOutput(0), lname + "_2");
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IScaleLayer *bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + ".bn2", 1e-5);
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IActivationLayer *relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);
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assert(relu2);
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//////
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IConvolutionLayer *conv3 = network->addConvolutionNd(*relu2->getOutput(0), inch, DimsHW{1, 1}, weightMap[lname + ".conv3.weight"], emptywts);
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assert(conv3);
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conv3->setStrideNd(DimsHW{stride, stride});
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conv3->setPaddingNd(DimsHW{0, 0});
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debug_print(conv3->getOutput(0), lname + "_3");
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IScaleLayer *bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + ".bn3", 1e-5);
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IElementWiseLayer *ew1;
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ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM);
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IActivationLayer *relu3 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
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assert(relu3);
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return relu3;
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}
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IActivationLayer *liteResBlock(INetworkDefinition *network, std::map<std::string, Weights> &weightMap, ITensor &input, int outch, std::string lname)
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{
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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// in 256 out 64
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IConvolutionLayer *conv1 = network->addConvolutionNd(input, outch, DimsHW{3, 3}, weightMap[lname + ".conv1.weight"], emptywts);
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assert(conv1);
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conv1->setStrideNd(DimsHW{1, 1});
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conv1->setPaddingNd(DimsHW{1, 1});
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debug_print(conv1->getOutput(0), lname + "_1");
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IScaleLayer *bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".bn1", 1e-5);
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IActivationLayer *relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
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assert(relu1);
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///
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IConvolutionLayer *conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + ".conv2.weight"], emptywts);
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assert(conv2);
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conv2->setStrideNd(DimsHW{1, 1});
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conv2->setPaddingNd(DimsHW{1, 1});
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debug_print(conv2->getOutput(0), lname + "_2");
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IScaleLayer *bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + ".bn2", 1e-5);
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IElementWiseLayer *ew1;
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ew1 = network->addElementWise(input, *bn2->getOutput(0), ElementWiseOperation::kSUM);
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debug_print(ew1->getOutput(0), lname + "_add");
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IActivationLayer *relu3 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
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assert(relu3);
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return relu3;
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}
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ILayer *convBnAddRelu(INetworkDefinition *network, std::map<std::string, Weights> &weightMap, ITensor &input, ITensor &addinput, int outch, int ksize, int s, int p, std::string convname, std::string bnname, bool bias = false)
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{
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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IConvolutionLayer *conv1;
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//Dims dim;
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if (!bias)
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{
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conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[convname + ".weight"], emptywts);
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}
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else
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{
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conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[convname + ".weight"], weightMap[convname + ".bias"]);
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}
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assert(conv1);
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conv1->setStrideNd(DimsHW{s, s});
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conv1->setPaddingNd(DimsHW{p, p});
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debug_print(conv1->getOutput(0), convname);
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IScaleLayer *bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), bnname, 1e-5);
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auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
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debug_print(lr->getOutput(0), convname + "_add");
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return lr;
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}
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ILayer *netAddUpsampleBi(INetworkDefinition *network, ITensor *input, Dims outdims)
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{
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// Bi + True
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IResizeLayer *upSample = network->addResize(*input);
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upSample->setResizeMode(ResizeMode::kLINEAR);
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upSample->setOutputDimensions(outdims);
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upSample->setAlignCorners(true); // tips!
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return upSample;
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}
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IElementWiseLayer *convBnUpAdd(INetworkDefinition *network,
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std::map<std::string, Weights> &weightMap,
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ITensor &input, ITensor &addinput,
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int outch, int ksize, int s, int p,
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std::string convname,
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std::string bnname, bool upsample, bool bias = false)
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{
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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IConvolutionLayer *conv1;
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if (!bias)
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{
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conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[convname + ".weight"], emptywts);
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}
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else
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{
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conv1 = network->addConvolutionNd(input, outch, DimsHW{ksize, ksize}, weightMap[convname + ".weight"], weightMap[convname + ".bias"]);
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}
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assert(conv1);
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conv1->setStrideNd(DimsHW{s, s});
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conv1->setPaddingNd(DimsHW{p, p});
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debug_print(conv1->getOutput(0), convname + "_1");
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IScaleLayer *bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), bnname, 1e-5);
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if (!upsample)
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{
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IElementWiseLayer *add = network->addElementWise(*bn1->getOutput(0), addinput, ElementWiseOperation::kSUM);
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debug_print(add->getOutput(0), convname + "_add");
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return add;
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}
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else
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{
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nvinfer1::Dims dim = addinput.getDimensions();
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ILayer *up = netAddUpsampleBi(network, bn1->getOutput(0), dim);
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IElementWiseLayer *add = network->addElementWise(*up->getOutput(0), addinput, ElementWiseOperation::kSUM);
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debug_print(conv1->getOutput(0), convname + "_1");
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//auto lr = network->addActivation(*add->getOutput(0), ActivationType::kRELU);
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return add;
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}
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}
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