314 lines
12 KiB
C++
314 lines
12 KiB
C++
#include "NvInfer.h"
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#include "cuda_runtime_api.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 "common.h"
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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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// Creat the engine using only the API and not any parser.
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ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType dt)
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{
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INetworkDefinition* network = builder->createNetwork();
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// Create input tensor of shape { 1, 1, 32, 32 } 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("../vgg.wts");
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Weights emptywts{DataType::kFLOAT, nullptr, 0};
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// Add convolution layer with 6 outputs and a 5x5 filter.
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IConvolutionLayer* conv1 = network->addConvolution(*data, 64, DimsHW{3, 3}, weightMap["features.0.weight"], weightMap["features.0.bias"]);
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assert(conv1);
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conv1->setPadding(DimsHW{1, 1});
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IActivationLayer* relu1 = network->addActivation(*conv1->getOutput(0), ActivationType::kRELU);
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assert(relu1);
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IPoolingLayer* pool1 = network->addPooling(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
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assert(pool1);
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pool1->setStride(DimsHW{2, 2});
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conv1 = network->addConvolution(*pool1->getOutput(0), 128, DimsHW{3, 3}, weightMap["features.3.weight"], weightMap["features.3.bias"]);
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conv1->setPadding(DimsHW{1, 1});
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relu1 = network->addActivation(*conv1->getOutput(0), ActivationType::kRELU);
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pool1 = network->addPooling(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
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pool1->setStride(DimsHW{2, 2});
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conv1 = network->addConvolution(*pool1->getOutput(0), 256, DimsHW{3, 3}, weightMap["features.6.weight"], weightMap["features.6.bias"]);
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conv1->setPadding(DimsHW{1, 1});
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relu1 = network->addActivation(*conv1->getOutput(0), ActivationType::kRELU);
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conv1 = network->addConvolution(*relu1->getOutput(0), 256, DimsHW{3, 3}, weightMap["features.8.weight"], weightMap["features.8.bias"]);
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conv1->setPadding(DimsHW{1, 1});
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relu1 = network->addActivation(*conv1->getOutput(0), ActivationType::kRELU);
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pool1 = network->addPooling(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
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pool1->setStride(DimsHW{2, 2});
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conv1 = network->addConvolution(*pool1->getOutput(0), 512, DimsHW{3, 3}, weightMap["features.11.weight"], weightMap["features.11.bias"]);
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conv1->setPadding(DimsHW{1, 1});
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relu1 = network->addActivation(*conv1->getOutput(0), ActivationType::kRELU);
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conv1 = network->addConvolution(*relu1->getOutput(0), 512, DimsHW{3, 3}, weightMap["features.13.weight"], weightMap["features.13.bias"]);
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conv1->setPadding(DimsHW{1, 1});
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relu1 = network->addActivation(*conv1->getOutput(0), ActivationType::kRELU);
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pool1 = network->addPooling(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
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pool1->setStride(DimsHW{2, 2});
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conv1 = network->addConvolution(*pool1->getOutput(0), 512, DimsHW{3, 3}, weightMap["features.16.weight"], weightMap["features.16.bias"]);
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conv1->setPadding(DimsHW{1, 1});
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relu1 = network->addActivation(*conv1->getOutput(0), ActivationType::kRELU);
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conv1 = network->addConvolution(*relu1->getOutput(0), 512, DimsHW{3, 3}, weightMap["features.18.weight"], weightMap["features.18.bias"]);
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conv1->setPadding(DimsHW{1, 1});
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relu1 = network->addActivation(*conv1->getOutput(0), ActivationType::kRELU);
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pool1 = network->addPooling(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{2, 2});
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pool1->setStride(DimsHW{2, 2});
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IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool1->getOutput(0), 4096, weightMap["classifier.0.weight"], weightMap["classifier.0.bias"]);
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assert(fc1);
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relu1 = network->addActivation(*fc1->getOutput(0), ActivationType::kRELU);
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fc1 = network->addFullyConnected(*relu1->getOutput(0), 4096, weightMap["classifier.3.weight"], weightMap["classifier.3.bias"]);
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relu1 = network->addActivation(*fc1->getOutput(0), ActivationType::kRELU);
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fc1 = network->addFullyConnected(*relu1->getOutput(0), 1000, weightMap["classifier.6.weight"], weightMap["classifier.6.bias"]);
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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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builder->setMaxWorkspaceSize(1 << 20);
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ICudaEngine* engine = builder->buildCudaEngine(*network);
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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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// Create model to populate the network, then set the outputs and create an engine
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ICudaEngine* engine = createEngine(maxBatchSize, builder, 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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{
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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 << "./vgg -s // serialize model to plan file" << std::endl;
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std::cerr << "./vgg -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("vgg.engine");
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if (!p)
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{
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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("vgg.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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// Subtract mean from image
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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;
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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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// Run inference
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float prob[OUTPUT_SIZE];
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for (int i = 0; i < 100; 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 << i / 10 << 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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