376 lines
14 KiB
C++
376 lines
14 KiB
C++
#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 <iostream>
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#include <map>
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#include <sstream>
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#include <vector>
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#include <chrono>
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#include <cmath>
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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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// stuff we know about the network and the input/output blobs
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static const int INPUT_H = 224;
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static const int INPUT_W = 224;
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static const int OUTPUT_SIZE = 1000;
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const char* INPUT_BLOB_NAME = "data";
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const char* OUTPUT_BLOB_NAME = "prob";
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using namespace nvinfer1;
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static Logger gLogger;
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// Load weights from files shared with TensorRT samples.
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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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{
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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* seLayer(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int c, int w, std::string lname) {
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IPoolingLayer* l1 = network->addPoolingNd(input, PoolingType::kAVERAGE, DimsHW(w, w));
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assert(l1);
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l1->setStrideNd(DimsHW{w, w});
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IFullyConnectedLayer* l2 = network->addFullyConnected(*l1->getOutput(0), c / 16, weightMap[lname + "fc.0.weight"], weightMap[lname+"fc.0.bias"]);
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IActivationLayer* relu1 = network->addActivation(*l2->getOutput(0), ActivationType::kRELU);
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IFullyConnectedLayer* l4 = network->addFullyConnected(*relu1->getOutput(0), c, weightMap[lname+"fc.2.weight"],weightMap[lname+"fc.2.bias"]);
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IActivationLayer* l5 = network->addActivation(*l4->getOutput(0), ActivationType::kSIGMOID);
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ILayer* se = network->addElementWise(input, *l5->getOutput(0), ElementWiseOperation::kPROD);
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assert(se);
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return se;
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}
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IActivationLayer* bottleneck(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int inch, int outch, int stride, std::string lname, int w) {
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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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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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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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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IConvolutionLayer* conv3 = network->addConvolutionNd(*relu2->getOutput(0), outch * 4, DimsHW{1, 1}, weightMap[lname + "conv3.weight"], emptywts);
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assert(conv3);
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IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + "bn3", 1e-5);
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ILayer *se = seLayer(network, weightMap, *bn3->getOutput(0), outch * 4, w, lname + "se.");
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IElementWiseLayer* ew1;
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if (stride != 1 || inch != outch * 4) {
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IConvolutionLayer* conv4 = network->addConvolutionNd(input, outch * 4, 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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IScaleLayer* bn4 = addBatchNorm2d(network, weightMap, *conv4->getOutput(0), lname + "downsample.1", 1e-5);
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ew1 = network->addElementWise(*bn4->getOutput(0), *se->getOutput(0), ElementWiseOperation::kSUM);
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} else {
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ew1 = network->addElementWise(input, *se->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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// Creat the engine using only the API and not any parser.
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ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt)
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{
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INetworkDefinition* network = builder->createNetworkV2(0U);
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// Create input tensor of shape { 3, INPUT_H, INPUT_W } with name INPUT_BLOB_NAME
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ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{3, INPUT_H, INPUT_W});
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assert(data);
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std::map<std::string, Weights> weightMap = loadWeights("../se_resnet50.wts");
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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IConvolutionLayer* conv1 = network->addConvolutionNd(*data, 64, DimsHW{7, 7}, weightMap["conv1.weight"], emptywts);
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assert(conv1);
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conv1->setStrideNd(DimsHW{2, 2});
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conv1->setPaddingNd(DimsHW{3, 3});
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IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "bn1", 1e-5);
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// Add activation layer using the ReLU algorithm.
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IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
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assert(relu1);
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IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});
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assert(pool1);
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pool1->setStrideNd(DimsHW{2, 2});
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pool1->setPaddingNd(DimsHW{1, 1});
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IActivationLayer* x = bottleneck(network, weightMap, *pool1->getOutput(0), 64, 64, 1, "layer1.0.", 56);
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x = bottleneck(network, weightMap, *x->getOutput(0), 256, 64, 1, "layer1.1.", 56);
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x = bottleneck(network, weightMap, *x->getOutput(0), 256, 64, 1, "layer1.2.", 56);
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x = bottleneck(network, weightMap, *x->getOutput(0), 256, 128, 2, "layer2.0.", 28);
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x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.1.", 28);
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x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.2.", 28);
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x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.3.", 28);
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x = bottleneck(network, weightMap, *x->getOutput(0), 512, 256, 2, "layer3.0.", 14);
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x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.1.", 14);
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x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.2.", 14);
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x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.3.", 14);
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x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.4.", 14);
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x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.5.", 14);
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x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 512, 2, "layer4.0.", 7);
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x = bottleneck(network, weightMap, *x->getOutput(0), 2048, 512, 1, "layer4.1.", 7);
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x = bottleneck(network, weightMap, *x->getOutput(0), 2048, 512, 1, "layer4.2.", 7);
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IPoolingLayer* pool2 = network->addPoolingNd(*x->getOutput(0), PoolingType::kAVERAGE, DimsHW{7, 7});
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assert(pool2);
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pool2->setStrideNd(DimsHW{1, 1});
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IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool2->getOutput(0), 1000, weightMap["fc.weight"], weightMap["fc.bias"]);
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assert(fc1);
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fc1->getOutput(0)->setName(OUTPUT_BLOB_NAME);
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std::cout << "set name out" << std::endl;
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network->markOutput(*fc1->getOutput(0));
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// Build engine
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builder->setMaxBatchSize(maxBatchSize);
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config->setMaxWorkspaceSize(1 << 20);
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ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
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std::cout << "build out" << 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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{
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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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{
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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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config->destroy();
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}
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void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
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{
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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), 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, 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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{
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if (argc != 2) {
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std::cerr << "arguments not right!" << std::endl;
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std::cerr << "./se_resnet -s // serialize model to plan file" << std::endl;
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std::cerr << "./se_resnet -d // deserialize plan file and run inference" << std::endl;
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return -1;
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}
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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 (std::string(argv[1]) == "-s") {
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IHostMemory* modelStream{nullptr};
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APIToModel(1, &modelStream);
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assert(modelStream != nullptr);
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std::ofstream p("se_resnet50.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 1;
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} else if (std::string(argv[1]) == "-d") {
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std::ifstream file("se_resnet50.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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return -1;
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}
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static float data[3 * INPUT_H * INPUT_W];
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for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
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data[i] = 1.0;
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IRuntime* runtime = createInferRuntime(gLogger);
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assert(runtime != nullptr);
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ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
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assert(engine != nullptr);
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IExecutionContext* context = engine->createExecutionContext();
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assert(context != nullptr);
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delete[] trtModelStream;
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// Run inference
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static float prob[OUTPUT_SIZE];
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for (int i = 0; i < 10; i++) {
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auto start = std::chrono::system_clock::now();
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doInference(*context, data, prob, 1);
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auto end = std::chrono::system_clock::now();
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std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
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}
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// Destroy the engine
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context->destroy();
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engine->destroy();
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runtime->destroy();
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// Print histogram of the output distribution
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std::cout << "\nOutput:\n\n";
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for (unsigned int i = 0; i < OUTPUT_SIZE; i++)
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{
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std::cout << prob[i] << ", ";
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if (i % 10 == 0) std::cout << std::endl;
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}
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std::cout << std::endl;
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return 0;
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}
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