2e53f0cae7
There are some problems with the preprocessing on LPRnet when infer, which will cause the predicted output results to be inconsistent with the pytorch version; as shown, the preprocessing has been updated here
462 lines
20 KiB
C++
462 lines
20 KiB
C++
#include <iostream>
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#include <chrono>
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#include <map>
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#include <opencv2/opencv.hpp>
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#include "NvInfer.h"
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#include "cuda_runtime_api.h"
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#include "logging.h"
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#include <fstream>
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#include <map>
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#include <sstream>
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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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//#define USE_FP16 // comment out this if want to use FP32
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#define DEVICE 0 // GPU id
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#define BATCH_SIZE 1
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// stuff we know about the network and the input/output blobs
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static const int INPUT_H = 24;
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static const int INPUT_W = 94;
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static const int OUTPUT_SIZE = 18 * 68;
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const char *INPUT_BLOB_NAME = "data";
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const char *OUTPUT_BLOB_NAME = "prob";
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static Logger gLogger;
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using namespace nvinfer1;
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const std::string alphabet[] = {"京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑",
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"苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤",
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"桂", "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁",
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"新",
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"0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
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"A", "B", "C", "D", "E", "F", "G", "H", "J", "K",
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"L", "M", "N", "P", "Q", "R", "S", "T", "U", "V",
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"W", "X", "Y", "Z", "I", "O", "-"
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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. 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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return weightMap;
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}
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IScaleLayer *addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights> &weightMap, ITensor &input,
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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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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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IConvolutionLayer *
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small_basic_block(INetworkDefinition *network, std::map<std::string, Weights> &weightMap, ITensor &input,
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int nbOutputMaps, std::string lname) {
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IConvolutionLayer *conv = network->addConvolutionNd(input, nbOutputMaps / 4, DimsHW{1, 1},
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weightMap[lname + ".block.0.weight"],
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weightMap[lname + ".block.0.bias"]);
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auto relu = network->addActivation(*conv->getOutput(0), ActivationType::kRELU);
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IConvolutionLayer *conv2 = network->addConvolutionNd(*relu->getOutput(0), nbOutputMaps / 4, DimsHW{3, 1},
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weightMap[lname + ".block.2.weight"],
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weightMap[lname + ".block.2.bias"]);
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conv2->setPaddingNd(DimsHW{1, 0});
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auto relu2 = network->addActivation(*conv2->getOutput(0), ActivationType::kRELU);
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IConvolutionLayer *conv3 = network->addConvolutionNd(*relu2->getOutput(0), nbOutputMaps / 4, DimsHW{1, 3},
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weightMap[lname + ".block.4.weight"],
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weightMap[lname + ".block.4.bias"]);
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conv3->setPaddingNd(DimsHW{0, 1});
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auto relu3 = network->addActivation(*conv3->getOutput(0), ActivationType::kRELU);
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IConvolutionLayer *conv4 = network->addConvolutionNd(*relu3->getOutput(0), nbOutputMaps, DimsHW{1, 1},
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weightMap[lname + ".block.6.weight"],
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weightMap[lname + ".block.6.bias"]);
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return conv4;
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}
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ICudaEngine *createEngine(unsigned int maxBatchSize, IBuilder *builder, IBuilderConfig *config, DataType dt) {
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INetworkDefinition *network = builder->createNetworkV2(0U);
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// Create input tensor of shape {C, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
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ITensor *data = network->addInput(INPUT_BLOB_NAME, dt, Dims4{1, 3, INPUT_H, INPUT_W});
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assert(data);
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std::map<std::string, Weights> weightMap = loadWeights("../LPRNet.wts");
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//LPRnet
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IConvolutionLayer *conv = network->addConvolutionNd(*data, 64, DimsHW{3, 3}, weightMap["backbone.0.weight"],weightMap["backbone.0.bias"]);
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assert(conv);
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ILayer *tmp = addBatchNorm2d(network, weightMap, *conv->getOutput(0), "backbone.1", 1e-5);
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auto relu = network->addActivation(*tmp->getOutput(0), ActivationType::kRELU);
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//f0
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auto f0 = network->addPoolingNd(*relu->getOutput(0), PoolingType::kAVERAGE, DimsHW{5, 5});
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f0->setStrideNd(DimsHW{5, 5});
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auto p = network->addPoolingNd(*relu->getOutput(0), PoolingType::kMAX, Dims3{1, 3, 3});
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p->setStrideNd(Dims3{1, 1, 1});
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auto small = small_basic_block(network, weightMap, *p->getOutput(0), 128, "backbone.4");
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ILayer *tmp2 = addBatchNorm2d(network, weightMap, *small->getOutput(0), "backbone.5", 1e-5);
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auto relu2 = network->addActivation(*tmp2->getOutput(0), ActivationType::kRELU);
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auto f1 = network->addPoolingNd(*relu2->getOutput(0), PoolingType::kAVERAGE, DimsHW{5, 5});
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f1->setStrideNd(DimsHW{5, 5});
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auto p2 = network->addPoolingNd(*relu2->getOutput(0), PoolingType::kMAX, Dims3{1, 3, 3});
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p2->setStrideNd(Dims3{2, 1, 2});
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auto small2 = small_basic_block(network, weightMap, *p2->getOutput(0), 256, "backbone.8");
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ILayer *tmp3 = addBatchNorm2d(network, weightMap, *small2->getOutput(0), "backbone.9", 1e-5);
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auto relu3 = network->addActivation(*tmp3->getOutput(0), ActivationType::kRELU);
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auto small3 = small_basic_block(network, weightMap, *relu3->getOutput(0), 256, "backbone.11");
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ILayer *tmp4 = addBatchNorm2d(network, weightMap, *small3->getOutput(0), "backbone.12", 1e-5);
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auto relu4 = network->addActivation(*tmp4->getOutput(0), ActivationType::kRELU);
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auto f2 = network->addPoolingNd(*relu4->getOutput(0), PoolingType::kAVERAGE, DimsHW{4, 10});
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f2->setStrideNd(DimsHW{4, 2});
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auto p3 = network->addPoolingNd(*relu4->getOutput(0), PoolingType::kMAX, Dims3{1, 3, 3});
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p3->setStrideNd(Dims3{4, 1, 2});
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Dims pf3 = p3->getOutput(0)->getDimensions();
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IConvolutionLayer *conv2 = network->addConvolutionNd(*p3->getOutput(0), 256, DimsHW{1, 4},
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weightMap["backbone.16.weight"],
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weightMap["backbone.16.bias"]);
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ILayer *tmp5 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), "backbone.17", 1e-5);
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auto relu5 = network->addActivation(*tmp5->getOutput(0), ActivationType::kRELU);
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IConvolutionLayer *conv3 = network->addConvolutionNd(*relu5->getOutput(0), 68, DimsHW{13, 1},
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weightMap["backbone.20.weight"],
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weightMap["backbone.20.bias"]);
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ILayer *tmp6 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), "backbone.21", 1e-5);
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auto backbone = network->addActivation(*tmp6->getOutput(0), ActivationType::kRELU);
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float *deval = reinterpret_cast<float *>(malloc(sizeof(float) * 64 * 4 * 18));
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for (int i = 0; i < 64 * 4 * 18; i++) {
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deval[i] = 2.0;
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}
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Weights deconvwts11{DataType::kFLOAT, deval, 64 * 4 * 18};
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IConstantLayer *d = network->addConstant(Dims4{1, 64, 4, 18}, deconvwts11);
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IElementWiseLayer *f_pow = network->addElementWise(*f0->getOutput(0), *d->getOutput(0), ElementWiseOperation::kPOW);
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Dims pf0 = f0->getOutput(0)->getDimensions();
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Dims pD = d->getOutput(0)->getDimensions();
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Dims pf_pow = f_pow->getOutput(0)->getDimensions();
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auto f_mean = network->addReduce(*f_pow->getOutput(0), ReduceOperation::kAVG, 0XF, true);
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Dims pf_mean = f_mean->getOutput(0)->getDimensions();
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IElementWiseLayer *f_div = network->addElementWise(*f0->getOutput(0), *f_mean->getOutput(0),
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ElementWiseOperation::kDIV);
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Dims pf_div = f_div->getOutput(0)->getDimensions();
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float *deval2 = reinterpret_cast<float *>(malloc(sizeof(float) * 1 * 128 * 4 * 18));
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for (int i = 0; i < 128 * 4 * 18 * 1; i++) {
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deval2[i] = 2.0;
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}
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Weights deconvwts22{DataType::kFLOAT, deval2, 128 * 4 * 18 * 1};
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IConstantLayer *d2 = network->addConstant(Dims4{1, 128, 4, 18}, deconvwts22);
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IElementWiseLayer *f_pow2 = network->addElementWise(*f1->getOutput(0), *d2->getOutput(0),
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ElementWiseOperation::kPOW);
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auto f_mean2 = network->addReduce(*f_pow2->getOutput(0), ReduceOperation::kAVG, 0XF, true);
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IElementWiseLayer *f_div2 = network->addElementWise(*f1->getOutput(0), *f_mean2->getOutput(0),
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ElementWiseOperation::kDIV);
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float *deval3 = reinterpret_cast<float *>(malloc(sizeof(float) * 256 * 4 * 18 * 1));
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for (int i = 0; i < 256 * 4 * 18 * 1; i++) {
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deval3[i] = 2.0;
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}
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Weights deconvwts33{DataType::kFLOAT, deval3, 256 * 4 * 18 * 1};
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IConstantLayer *d3 = network->addConstant(Dims4{1, 256, 4, 18}, deconvwts33);
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IElementWiseLayer *f_pow3 = network->addElementWise(*f2->getOutput(0), *d3->getOutput(0),
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ElementWiseOperation::kPOW);
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auto f_mean3 = network->addReduce(*f_pow3->getOutput(0), ReduceOperation::kAVG, 0XF, true);
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IElementWiseLayer *f_div3 = network->addElementWise(*f2->getOutput(0), *f_mean3->getOutput(0),
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ElementWiseOperation::kDIV);
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float *deval4 = reinterpret_cast<float *>(malloc(sizeof(float) * 68 * 4 * 18 * 1));
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for (int i = 0; i < 68 * 4 * 18 * 1; i++) {
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deval4[i] = 2.0;
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}
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Weights deconvwts44{DataType::kFLOAT, deval4, 68 * 4 * 18 * 1};
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IConstantLayer *d4 = network->addConstant(Dims4{1, 68, 4, 18}, deconvwts44);
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IElementWiseLayer *f_pow4 = network->addElementWise(*backbone->getOutput(0), *d4->getOutput(0),
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ElementWiseOperation::kPOW);
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auto f_mean4 = network->addReduce(*f_pow4->getOutput(0), ReduceOperation::kAVG, 0XF, true);
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IElementWiseLayer *f_div4 = network->addElementWise(*backbone->getOutput(0), *f_mean4->getOutput(0),
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ElementWiseOperation::kDIV);
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ITensor *inputTensors[] = {f_div->getOutput(0), f_div2->getOutput(0), f_div3->getOutput(0), f_div4->getOutput(0)};
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auto f_divdims = f_div->getOutput(0)->getDimensions();
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auto f_div2dims = f_div2->getOutput(0)->getDimensions();
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auto f_div3dims = f_div3->getOutput(0)->getDimensions();
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auto backbonedims = backbone->getOutput(0)->getDimensions();
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auto cat = network->addConcatenation(inputTensors, 4);
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Dims pcat = cat->getOutput(0)->getDimensions();
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IConvolutionLayer *container = network->addConvolutionNd(*cat->getOutput(0), 68, DimsHW{1, 1},
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weightMap["container.0.weight"],
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weightMap["container.0.bias"]);
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auto logits = network->addReduce(*container->getOutput(0), ReduceOperation::kAVG, 0X04, false);
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Dims dims = logits->getOutput(0)->getDimensions();
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std::cout << "logits shape " << dims.d[0] << " " << dims.d[1] << " " << dims.d[2] << std::endl;
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logits->getOutput(0)->setName(OUTPUT_BLOB_NAME);
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network->markOutput(*logits->getOutput(0));
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// Build engine
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builder->setMaxBatchSize(maxBatchSize);
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config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
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#ifdef USE_FP16
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config->setFlag(BuilderFlag::kFP16);
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#endif
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std::cout << "Building engine, please wait for a while..." << std::endl;
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ICudaEngine *engine = builder->buildEngineWithConfig(*network, *config);
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std::cout << "Build engine successfully!" << std::endl;
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// Don't need the network any more
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network->destroy();
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// Release host memory
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for (auto &mem : weightMap) {
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free((void *) (mem.second.values));
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}
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return engine;
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}
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void APIToModel(unsigned int maxBatchSize, IHostMemory **modelStream) {
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// Create builder
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IBuilder *builder = createInferBuilder(gLogger);
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IBuilderConfig *config = builder->createBuilderConfig();
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// Create model to populate the network, then set the outputs and create an engine
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ICudaEngine *engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT);
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assert(engine != nullptr);
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// Serialize the engine
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(*modelStream) = engine->serialize();
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// Close everything down
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engine->destroy();
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builder->destroy();
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}
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void doInference(IExecutionContext &context, float *input, float *output, int batchSize) {
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const ICudaEngine &engine = context.getEngine();
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// Pointers to input and output device buffers to pass to engine.
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// Engine requires exactly IEngine::getNbBindings() number of buffers.
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assert(engine.getNbBindings() == 2);
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void *buffers[2];
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// In order to bind the buffers, we need to know the names of the input and output tensors.
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// Note that indices are guaranteed to be less than IEngine::getNbBindings()
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const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
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const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
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// Create GPU buffers on device
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CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
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CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
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// Create stream
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cudaStream_t stream;
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CHECK(cudaStreamCreate(&stream));
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// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
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CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float),
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cudaMemcpyHostToDevice, stream));
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context.enqueue(batchSize, buffers, stream, nullptr);
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CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost,
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stream));
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cudaStreamSynchronize(stream);
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// Release stream and buffers
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cudaStreamDestroy(stream);
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CHECK(cudaFree(buffers[inputIndex]));
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CHECK(cudaFree(buffers[outputIndex]));
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}
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int main(int argc, char **argv) {
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cudaSetDevice(DEVICE);
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// create a model using the API directly and serialize it to a stream
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char *trtModelStream{nullptr};
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size_t size{0};
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if (argc == 2 && std::string(argv[1]) == "-s") {
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IHostMemory *modelStream{nullptr};
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APIToModel(BATCH_SIZE, &modelStream);
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assert(modelStream != nullptr);
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std::ofstream p("LPRnet.engine", std::ios::binary);
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if (!p) {
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std::cerr << "could not open plan output file" << std::endl;
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return -1;
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}
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p.write(reinterpret_cast<const char *>(modelStream->data()), modelStream->size());
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modelStream->destroy();
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return 0;
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} else if (argc == 2 && std::string(argv[1]) == "-d") {
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std::ifstream file("LPRnet.engine", std::ios::binary);
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if (file.good()) {
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file.seekg(0, file.end);
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size = file.tellg();
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file.seekg(0, file.beg);
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trtModelStream = new char[size];
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assert(trtModelStream);
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file.read(trtModelStream, size);
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file.close();
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}
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} else {
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std::cerr << "arguments not right!" << std::endl;
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std::cerr << "./LPRnet -s // serialize model to plan file" << std::endl;
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std::cerr << "./LPRnet -d ../samples // deserialize plan file and run inference" << std::endl;
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return -1;
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}
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|
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|
// prepare input data ---------------------------
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static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
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|
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cv::Mat img = cv::imread("../1.jpg");
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cv::Mat pr_img;
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cv::resize(img, pr_img, cv::Size(INPUT_W, INPUT_H), 0, 0, cv::INTER_CUBIC);
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// For multi-batch, I feed the same image multiple times.
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|
// If you want to process different images in a batch, you need adapt it.
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|
//cv::Mat blob = cv::dnn::blobFromImage(pr_img, 0.0078125, pr_img.size(), cv::Scalar(127.5, 127.5, 127.5), true,
|
|
//false);
|
|
int i = 0;
|
|
for (int row = 0; row < INPUT_H; ++row) {
|
|
uchar* uc_pixel = pr_img.data + row * pr_img.step;
|
|
for (int col = 0; col < INPUT_W; ++col) {
|
|
data[i + 2 * INPUT_H * INPUT_W] = ((float)uc_pixel[2] - 127.5)*0.0078125;
|
|
data[i + INPUT_H * INPUT_W] = ((float)uc_pixel[1]-127.5)*0.0078125;
|
|
data[i] = ((float)uc_pixel[0]-127.5)*0.0078125;
|
|
uc_pixel += 3;
|
|
++i;
|
|
}
|
|
}
|
|
|
|
IRuntime *runtime = createInferRuntime(gLogger);
|
|
assert(runtime != nullptr);
|
|
ICudaEngine *engine = runtime->deserializeCudaEngine(trtModelStream, size);
|
|
//ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
|
|
assert(engine != nullptr);
|
|
IExecutionContext *context = engine->createExecutionContext();
|
|
assert(context != nullptr);
|
|
|
|
// Run inference
|
|
static float prob[BATCH_SIZE * OUTPUT_SIZE];
|
|
auto start = std::chrono::system_clock::now();
|
|
doInference(*context, data, prob, BATCH_SIZE);
|
|
auto end = std::chrono::system_clock::now();
|
|
std::cout << std::chrono::duration_cast<std::chrono::microseconds>(end - start).count() << "us" << std::endl;
|
|
std::vector<int> preds;
|
|
std::cout << std::endl;
|
|
for (int i = 0; i < 18; i++) {
|
|
int maxj = 0;
|
|
for (int j = 0; j < 68; j++) {
|
|
if (prob[i + 18 * j] > prob[i + 18 * maxj]) maxj = j;
|
|
}
|
|
preds.push_back(maxj);
|
|
}
|
|
int pre_c = preds[0];
|
|
std::vector<int> no_repeat_blank_label;
|
|
for (auto c: preds) {
|
|
if (c == pre_c || c == 68 - 1) {
|
|
if (c == 68 - 1) pre_c = c;
|
|
continue;
|
|
}
|
|
no_repeat_blank_label.push_back(c);
|
|
pre_c = c;
|
|
}
|
|
std::string str;
|
|
for (auto v: no_repeat_blank_label) {
|
|
str += alphabet[v];
|
|
}
|
|
std::cout<<"result:"<<str<<std::endl;
|
|
// Destroy the engine
|
|
context->destroy();
|
|
engine->destroy();
|
|
runtime->destroy();
|
|
|
|
|
|
return 0;
|
|
}
|