338 lines
13 KiB
C++
338 lines
13 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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std::map<std::string, Weights> loadWeights(const std::string file) {
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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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IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
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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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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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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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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* convBnLeaky(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int p, std::string convname, std::string bnname, bool bias = false) {
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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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IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), bnname, 1e-4);
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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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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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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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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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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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conv1->setStrideNd(DimsHW{ stride, stride });
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conv3->setPaddingNd(DimsHW{ 0, 0 });
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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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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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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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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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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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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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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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conv1->setStrideNd(DimsHW{ stride, stride });
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conv1->setPaddingNd(DimsHW{ 0, 0 });
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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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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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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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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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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* netAddUpsample(INetworkDefinition* network, ITensor* input, int inputChannels, int stride){
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nvinfer1::Dims inpDims = input->getDimensions();
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assert(inpDims.nbDims == 3); // chw
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assert(inpDims.d[1] == inpDims.d[2]);
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int h = inpDims.d[1];
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int w = inpDims.d[2];
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// add pre multiply matrix as a constant
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/*
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kSPATIA Elements correspond to different spatial data.
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kCHANNEL Elements correspond to different channels.
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*/
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nvinfer1::Dims preDims{ 3,
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{1, stride * h, w},
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{nvinfer1::DimensionType::kCHANNEL,
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nvinfer1::DimensionType::kSPATIAL,
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nvinfer1::DimensionType::kSPATIAL} };
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int size = stride * h * w;
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nvinfer1::Weights preMul{ nvinfer1::DataType::kFLOAT, nullptr, size };
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float* preWt = new float[size];
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/* (2*h * w)
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[ [1, 0, ..., 0],
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[1, 0, ..., 0],
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[0, 1, ..., 0],
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[0, 1, ..., 0],
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...,
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...,
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[0, 0, ..., 1],
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[0, 0, ..., 1] ]
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*/
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for (int i = 0, idx = 0; i < h; ++i)
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{
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for (int s = 0; s < stride; ++s)
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{
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for (int j = 0; j < w; ++j, ++idx)
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{
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preWt[idx] = (i == j) ? 1.0 : 0.0;
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}
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}
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}
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preMul.values = preWt;
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nvinfer1::IConstantLayer* preM = network->addConstant(preDims, preMul);
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assert(preM != nullptr);
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//std::string preLayerName = "preMul_" + std::to_string(layerIdx);
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//preM->setName(preLayerName.c_str());
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// add post multiply matrix as a constant
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nvinfer1::Dims postDims{ 3,
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{1, h, stride * w},
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{nvinfer1::DimensionType::kCHANNEL,
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nvinfer1::DimensionType::kSPATIAL,
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nvinfer1::DimensionType::kSPATIAL} };
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size = stride * h * w;
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nvinfer1::Weights postMul{ nvinfer1::DataType::kFLOAT, nullptr, size };
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float* postWt = new float[size];
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/* (h * 2*w)
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[ [1, 1, 0, 0, ..., 0, 0],
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[0, 0, 1, 1, ..., 0, 0],
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...,
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...,
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[0, 0, 0, 0, ..., 1, 1] ]
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*/
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for (int i = 0, idx = 0; i < h; ++i)
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{
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for (int j = 0; j < stride * w; ++j, ++idx)
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{
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postWt[idx] = (j / stride == i) ? 1.0 : 0.0;
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}
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}
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postMul.values = postWt;
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nvinfer1::IConstantLayer* post_m = network->addConstant(postDims, postMul);
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assert(post_m != nullptr);
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// add matrix multiply layers for upsampling
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nvinfer1::IMatrixMultiplyLayer* mm1
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= network->addMatrixMultiply(*preM->getOutput(0),
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nvinfer1::MatrixOperation::kNONE, *input,
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nvinfer1::MatrixOperation::kNONE);
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assert(mm1 != nullptr);
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nvinfer1::IMatrixMultiplyLayer* mm2
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= network->addMatrixMultiply(*mm1->getOutput(0),
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nvinfer1::MatrixOperation::kNONE,
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*post_m->getOutput(0),
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nvinfer1::MatrixOperation::kNONE);
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assert(mm2 != nullptr);
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return mm2;
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}
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