From 7c1a145c346241df048c0525de11580b3c45c756 Mon Sep 17 00:00:00 2001 From: liufqing <82146488+liufqing@users.noreply.github.com> Date: Thu, 30 Sep 2021 15:18:04 +0800 Subject: [PATCH] add resnet34 (#741) * hello * add resnet34 --- resnet/CMakeLists.txt | 4 + resnet/README.md | 10 +- resnet/resnet34.cpp | 355 ++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 366 insertions(+), 3 deletions(-) create mode 100644 resnet/resnet34.cpp diff --git a/resnet/CMakeLists.txt b/resnet/CMakeLists.txt index a762214..f735609 100644 --- a/resnet/CMakeLists.txt +++ b/resnet/CMakeLists.txt @@ -21,6 +21,10 @@ add_executable(resnet18 ${PROJECT_SOURCE_DIR}/resnet18.cpp) target_link_libraries(resnet18 nvinfer) target_link_libraries(resnet18 cudart) +add_executable(resnet34 ${PROJECT_SOURCE_DIR}/resnet34.cpp) +target_link_libraries(resnet34 nvinfer) +target_link_libraries(resnet34 cudart) + add_executable(resnet50 ${PROJECT_SOURCE_DIR}/resnet50.cpp) target_link_libraries(resnet50 nvinfer) target_link_libraries(resnet50 cudart) diff --git a/resnet/README.md b/resnet/README.md index 7320680..df19d8b 100644 --- a/resnet/README.md +++ b/resnet/README.md @@ -13,11 +13,11 @@ Following tricks are used in this resnet, nothing special, residual connection a ## TensorRT C++ API ``` -// 1a. generate resnet18.wts or resnet50.wts from [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet) +// 1a. generate resnet18.wts,resnet34.wts or resnet50.wts from [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet) // 1b. generate wide_resnet50.wts from [BlueMirrors/torchtrtz](https://github.com/BlueMirrors/torchtrtz) -// 2. put resnet18.wts or resnet50.wts into tensorrtx/resnet +// 2. put resnet18.wts,resnet34 or resnet50.wts into tensorrtx/resnet // 3. build and run @@ -34,6 +34,10 @@ make sudo ./resnet18 -s // serialize model to plan file i.e. 'resnet18.engine' sudo ./resnet18 -d // deserialize plan file and run inference +or +sudo ./resnet34 -s // serialize model to plan file i.e. 'resnet34.engine' +sudo ./resnet34 -d // deserialize plan file and run inference + or sudo ./resnet50 -s // serialize model to plan file i.e. 'resnet50.engine' @@ -51,7 +55,7 @@ sudo ./wide_resnet50 -d // deserialize plan file and run inference // 4. see if the output is same as -- [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet) - for resnet18, resnet50, resnext50 +- [pytorchx/resnet](https://github.com/wang-xinyu/pytorchx/tree/master/resnet) - for resnet18, resnet34, resnet50, resnext50 - [BlueMirrors/torchtrtz](https://github.com/BlueMirrors/torchtrtz) - for wide_resnet50 ``` diff --git a/resnet/resnet34.cpp b/resnet/resnet34.cpp new file mode 100644 index 0000000..e75b0f7 --- /dev/null +++ b/resnet/resnet34.cpp @@ -0,0 +1,355 @@ +#include "NvInfer.h" +#include "cuda_runtime_api.h" +#include "logging.h" +#include +#include +#include +#include +#include +#include +#include + +#define CHECK(status) \ + do\ + {\ + auto ret = (status);\ + if(ret != 0)\ + {\ + std::cerr << "Cuda failure: " << ret << std::endl;\ + abort();\ + }\ + } while(0) + +// stuff we know about the network and the input/output blobs +static const int INPUT_H = 224; +static const int INPUT_W = 224; +static const int OUTPUT_SIZE = 1000; + +const char* INPUT_BLOB_NAME = "data"; +const char* OUTPUT_BLOB_NAME = "prob"; + +using namespace nvinfer1; + +static Logger gLogger; + +// Load weights from files shared with TensorRT samples. +// TensorRT weigths files have a simple space delimited format: +// [tpyt] [size] +std::map loadWeights(const std::string file) +{ + std::cout << "Loading weights: " << file << std::endl; + std::mapweightMap; + + // Open weights file + std::ifstream input(file); + assert(input.is_open() && "Unable to load weight file."); + + // Read number of weight blobs + int32_t count; + input >> count; + assert(count > 0 && "Invalis weight map file"); + + while (count--) + { + Weights wt{ DataType::kFLOAT, nullptr,0 }; + uint32_t size; + + // Read name and type of blob + std::string name; + input >> name >> std::dec >> size; + wt.type = DataType::kFLOAT; + + // Load blob + uint32_t* val = reinterpret_cast(malloc(sizeof(val)* size)); + for (uint32_t x = 0, y = size; x < y; ++x) + { + input >> std::hex >> val[x]; + } + wt.values = val; + + wt.count = size; + weightMap[name] = wt; + } + + return weightMap; + +} + +IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map& weightMap, ITensor& input, std::string lname, float eps) { + float *gamma = (float*)weightMap[lname + ".weight"].values; + float *bata = (float*)weightMap[lname + ".bias"].values; + float *mean = (float*)weightMap[lname + ".running_mean"].values; + float *var = (float*)weightMap[lname + ".running_var"].values; + int len = weightMap[lname + ".running_var"].count; + std::cout << "len " << len << std::endl; + + float *scval = reinterpret_cast(malloc(sizeof(float) * len)); + for(int i=0; i < len; i++){ + scval[i] = gamma[i] / sqrt(var[i] + eps); + } + Weights scale{ DataType::kFLOAT,scval,len }; + + float *shval = reinterpret_cast(malloc(sizeof(float) * len)); + for (int i = 0; i < len; i++) { + shval[i] = bata[i] - mean[i] * gamma[i] / sqrt(var[i] + eps); + } + Weights shift{ DataType::kFLOAT, shval, len }; + + float *pval = reinterpret_cast(malloc(sizeof(float) * len)); + for (int i = 0; i < len; i++) { + pval[i] = 1.0; + } + Weights power{ DataType::kFLOAT, pval, len }; + + weightMap[lname + ".scale"] = scale; + weightMap[lname + ".shift"] = shift; + weightMap[lname + ".power"] = power; + IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power); + assert(scale_1); + return scale_1; +} + +IActivationLayer* basicBlock(INetworkDefinition* network, std::map& weightMap, ITensor& input, int inch, int outch, int stride, std::string lname) { + Weights emptywts{ DataType::kFLOAT, nullptr, 0 }; + + IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ 3,3 }, weightMap[lname + "conv1.weight"], emptywts); + assert(conv1); + conv1->setStrideNd(DimsHW{ stride,stride }); + conv1->setPaddingNd(DimsHW{ 1,1 }); + + IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "bn1", 1e-5); + + IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU); + assert(relu1); + + IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{ 3,3 }, weightMap[lname + "conv2.weight"], emptywts); + assert(conv2); + conv2->setPaddingNd(DimsHW{ 1,1 }); + + IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + "bn2", 1e-5); + + IElementWiseLayer* ew1; + if (inch != outch) { + IConvolutionLayer* conv3 = network->addConvolutionNd(input, outch, DimsHW{ 1,1 }, weightMap[lname + "downsample.0.weight"], emptywts); + assert(conv3); + conv3->setStrideNd(DimsHW{ stride, stride }); + IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + "downsample.1", 1e-5); + ew1 = network->addElementWise(*bn3->getOutput(0), *bn2->getOutput(0), ElementWiseOperation::kSUM); + + }else { + ew1 = network->addElementWise(input, *bn2->getOutput(0), + ElementWiseOperation::kSUM); + + } + IActivationLayer* relu2 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU); + assert(relu2); + return relu2; +} + +// Create the engine using only the API and not any parser. +ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) { + INetworkDefinition* network = builder->createNetworkV2(0U); + + // Create input tensor of shpae { 3, INPUT_H INPPUT_W} with name INPUT_BLOB_NAME + ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3,INPUT_H,INPUT_W }); + assert(data); + + std::map weightMap = loadWeights("../resnet34.wts"); + Weights emptywts{ DataType::kFLOAT,nullptr,0 }; + + IConvolutionLayer* conv1 = network->addConvolutionNd(*data, 64, DimsHW{ 7,7 }, weightMap["conv1.weight"], emptywts); + assert(conv1); + conv1->setStrideNd(DimsHW{ 2,2 }); + conv1->setPaddingNd(DimsHW{ 3,3 }); + + IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "bn1", 1e-5); + IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU); + assert(relu1); + + IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{ 3,3 }); + assert(pool1); + pool1->setStrideNd(DimsHW{ 2,2 }); + pool1->setPaddingNd(DimsHW{ 1,1 }); + + IActivationLayer* relu2 = basicBlock(network, weightMap, *pool1->getOutput(0), 64, 64, 1, "layer1.0."); + IActivationLayer* relu3 = basicBlock(network, weightMap, *relu2->getOutput(0), 64, 64, 1, "layer1.1."); + IActivationLayer* relu4 = basicBlock(network, weightMap, *relu3->getOutput(0), 64, 64, 1, "layer1.2."); + IActivationLayer* relu5 = basicBlock(network, weightMap, *relu4->getOutput(0), 64, 128, 2, "layer2.0."); + IActivationLayer* relu6 = basicBlock(network, weightMap, *relu5->getOutput(0), 128, 128, 1, "layer2.1."); + IActivationLayer* relu7 = basicBlock(network, weightMap, *relu6->getOutput(0), 128, 128, 1, "layer2.2."); + IActivationLayer* relu8 = basicBlock(network, weightMap, *relu7->getOutput(0), 128, 128, 1, "layer2.3."); + IActivationLayer* relu9 = basicBlock(network, weightMap, *relu8->getOutput(0), 128, 256, 2, "layer3.0."); + IActivationLayer* relu10 = basicBlock(network, weightMap, *relu9->getOutput(0), 256, 256, 1, "layer3.1."); + IActivationLayer* relu11 = basicBlock(network, weightMap, *relu10->getOutput(0), 256, 256, 1, "layer3.2."); + IActivationLayer* relu12 = basicBlock(network, weightMap, *relu11->getOutput(0), 256, 256, 1, "layer3.3."); + IActivationLayer* relu13 = basicBlock(network, weightMap, *relu12->getOutput(0), 256, 256, 1, "layer3.4."); + IActivationLayer* relu14 = basicBlock(network, weightMap, *relu13->getOutput(0), 256, 256, 1, "layer3.5."); + IActivationLayer* relu15 = basicBlock(network, weightMap, *relu14->getOutput(0), 256, 512, 2, "layer4.0."); + IActivationLayer* relu16 = basicBlock(network, weightMap, *relu15->getOutput(0), 512, 512, 1, "layer4.1."); + IActivationLayer* relu17 = basicBlock(network, weightMap, *relu16->getOutput(0), 512, 512, 1, "layer4.2."); + IPoolingLayer* pool2 = network->addPoolingNd(*relu17->getOutput(0), PoolingType::kAVERAGE, DimsHW{ 7,7 }); + assert(pool2); + pool2->setStrideNd(DimsHW{ 1,1 }); + IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool2->getOutput(0), 1000, weightMap["fc.weight"], weightMap["fc.bias"]); + assert(fc1); + + fc1->getOutput(0)->setName(OUTPUT_BLOB_NAME); + std::cout << "set name out" << std::endl; + network->markOutput(*fc1->getOutput(0)); + + // Build engine + builder->setMaxBatchSize(maxBatchSize); + config->setMaxWorkspaceSize(1 << 20); + ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config); + std::cout << "build out" << std::endl; + + // Don't need the network any more + network->destroy(); + + // Release host memory + for (auto& mem : weightMap) { + free((void*)(mem.second.values)); + } + + return engine; +} + +void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream) +{ + // Create builder + IBuilder* builder = createInferBuilder(gLogger); + IBuilderConfig* config = builder->createBuilderConfig(); + + // Create model to populate the network, then set outputs and create an engine + ICudaEngine* engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT); + assert(engine != nullptr); + + // Serialize the engine + (*modelStream) = engine->serialize(); + + // Close everything down + engine->destroy(); + builder->destroy(); + config->destroy(); +} + +void doInference(IExecutionContext& context, float* input, float* output, int batchSize) +{ + const ICudaEngine& engine = context.getEngine(); + + // Pointers to input and output device buffers to pass to engine. + // Engine requires exactly IEngine::getNbBingdings() number of buffers. + assert(engine.getNbBindings() == 2); + void* buffers[2]; + + // In order to bind the buffers, we need to konow the names of the input and output tensors. + // Note that indices are guaranteed to be less than IEngine::getNbBindings() + const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME); + const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME); + + // Create GPU buffers on device + CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H* INPUT_W * sizeof(float))); + CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float))); + + // Create stream + cudaStream_t stream; + CHECK(cudaStreamCreate(&stream)); + + // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host + CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream)); + context.enqueue(batchSize, buffers, stream, nullptr); + CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream)); + cudaStreamSynchronize(stream); + + // Release stream and buffers + cudaStreamDestroy(stream); + CHECK(cudaFree(buffers[inputIndex])); + CHECK(cudaFree(buffers[outputIndex])); + +} + +int main(int argc, char** argv) +{ + if (argc != 2) { + std::cerr << "arguments not right!" << std::endl; + std::cerr << "./resnet34 -s // serialize model to plan file" << std::endl; + std::cerr << "./resnet34 -d // desrialize plan file and run inference" << std::endl; + return -1; + } + + // Create a model using the API directly and serialize it to a stream + char *trtModelStream{ nullptr }; + size_t size(0); + + if (std::string(argv[1]) == "-s") { + IHostMemory* modelStream{ nullptr }; + APIToModel(1, &modelStream); + assert(modelStream != nullptr); + + std::ofstream p("resnet34.engine", std::ios::binary); + if (!p) { + std::cerr << "could not open plan output file" << std::endl; + return -1; + } + p.write(reinterpret_cast(modelStream->data()), modelStream->size()); + modelStream->destroy(); + return 1; + }else if (std::string(argv[1]) == "-d") { + std::ifstream file("resnet34.engine", std::ios::binary); + if (file.good()) { + file.seekg(0, file.end); + size = file.tellg(); + file.seekg(0, file.beg); + trtModelStream = new char[size]; + assert(trtModelStream, size); + file.read(trtModelStream, size); + file.close(); + } + }else { + return -1; + + } + + // Subtract mean from image + static float data[3 * INPUT_H * INPUT_W]; + for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++) { + data[i] = 1.0; + } + + IRuntime* runtime = createInferRuntime(gLogger); + assert(runtime != nullptr); + ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr); + assert(engine != nullptr); + IExecutionContext* context = engine->createExecutionContext(); + assert(context != nullptr); + delete[] trtModelStream; + + // Run inference + static float prob[OUTPUT_SIZE]; + for (int i = 0; i < 100; i++) { + auto start = std::chrono::system_clock::now(); + doInference(*context, data, prob, 1); + auto end = std::chrono::system_clock::now(); + std::cout << std::chrono::duration_cast(end - start).count() << "ms" << std::endl; + } + + // Destroy the engine + context->destroy(); + engine->destroy(); + runtime->destroy(); + + // Print historgram of the output distribution + std::cout << "\nOutput:\n\n"; + for (unsigned int i = 0; i < 10; i++) + { + std::cout << prob[i] << ","; + } + std::cout << std::endl; + for (unsigned int i = 0; i < 10; i++) + { + std::cout << prob[OUTPUT_SIZE - 10 + i] << ","; + } + std::cout << std::endl; + + return 0; +} \ No newline at end of file