diff --git a/resnet/CMakeLists.txt b/resnet/CMakeLists.txt index 887c449..1eb5725 100644 --- a/resnet/CMakeLists.txt +++ b/resnet/CMakeLists.txt @@ -20,5 +20,9 @@ add_executable(resnet50 ${PROJECT_SOURCE_DIR}/resnet50.cpp) target_link_libraries(resnet50 nvinfer) target_link_libraries(resnet50 cudart) +add_executable(resnext50 ${PROJECT_SOURCE_DIR}/resnext50_32x4d.cpp) +target_link_libraries(resnext50 nvinfer) +target_link_libraries(resnext50 cudart) + add_definitions(-O2 -pthread) diff --git a/resnet/resnext50_32x4d.cpp b/resnet/resnext50_32x4d.cpp new file mode 100644 index 0000000..a4a0896 --- /dev/null +++ b/resnet/resnext50_32x4d.cpp @@ -0,0 +1,357 @@ +#include "NvInfer.h" +#include "cuda_runtime_api.h" +#include "common.h" +#include +#include +#include +#include +#include +#include + +// 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 weight files have a simple space delimited format: +// [type] [size] +std::map loadWeights(const std::string file) +{ + std::cout << "Loading weights: " << file << std::endl; + std::map weightMap; + + // 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 && "Invalid 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 *beta = (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] = beta[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* bottleneck(INetworkDefinition *network, std::map& weightMap, ITensor& input, int inch, int outch, int stride, std::string lname) { + Weights emptywts{DataType::kFLOAT, nullptr, 0}; + int groups = 32; + int width = outch * 4 / 64 * 32; + + IConvolutionLayer* conv1 = network->addConvolution(input, width, DimsHW{1, 1}, weightMap[lname + "conv1.weight"], emptywts); + assert(conv1); + + 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->addConvolution(*relu1->getOutput(0), width, DimsHW{3, 3}, weightMap[lname + "conv2.weight"], emptywts); + assert(conv2); + conv2->setStride(DimsHW{stride, stride}); + conv2->setPadding(DimsHW{1, 1}); + conv2->setNbGroups(groups); + + IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + "bn2", 1e-5); + + IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU); + assert(relu2); + + IConvolutionLayer* conv3 = network->addConvolution(*relu2->getOutput(0), outch * 4, DimsHW{1, 1}, weightMap[lname + "conv3.weight"], emptywts); + assert(conv3); + + IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + "bn3", 1e-5); + + IElementWiseLayer* ew1; + if (stride != 1 || inch != outch * 4) { + IConvolutionLayer* conv4 = network->addConvolution(input, outch * 4, DimsHW{1, 1}, weightMap[lname + "downsample.0.weight"], emptywts); + assert(conv4); + conv4->setStride(DimsHW{stride, stride}); + + IScaleLayer* bn4 = addBatchNorm2d(network, weightMap, *conv4->getOutput(0), lname + "downsample.1", 1e-5); + ew1 = network->addElementWise(*bn4->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM); + } else { + ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM); + } + IActivationLayer* relu3 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU); + assert(relu3); + return relu3; +} + +// Creat the engine using only the API and not any parser. +ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType dt) +{ + INetworkDefinition* network = builder->createNetwork(); + + // Create input tensor of shape { 1, 1, 32, 32 } 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("../resnext50.wts"); + Weights emptywts{DataType::kFLOAT, nullptr, 0}; + + // Add convolution layer with 6 outputs and a 5x5 filter. + IConvolutionLayer* conv1 = network->addConvolution(*data, 64, DimsHW{7, 7}, weightMap["conv1.weight"], emptywts); + assert(conv1); + conv1->setStride(DimsHW{2, 2}); + conv1->setPadding(DimsHW{3, 3}); + + IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "bn1", 1e-5); + + // Add activation layer using the ReLU algorithm. + IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU); + assert(relu1); + + // Add max pooling layer with stride of 2x2 and kernel size of 2x2. + IPoolingLayer* pool1 = network->addPooling(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3}); + assert(pool1); + pool1->setStride(DimsHW{2, 2}); + pool1->setPadding(DimsHW{1, 1}); + + IActivationLayer* x = bottleneck(network, weightMap, *pool1->getOutput(0), 64, 64, 1, "layer1.0."); + x = bottleneck(network, weightMap, *x->getOutput(0), 256, 64, 1, "layer1.1."); + x = bottleneck(network, weightMap, *x->getOutput(0), 256, 64, 1, "layer1.2."); + + x = bottleneck(network, weightMap, *x->getOutput(0), 256, 128, 2, "layer2.0."); + x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.1."); + x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.2."); + x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.3."); + + x = bottleneck(network, weightMap, *x->getOutput(0), 512, 256, 2, "layer3.0."); + x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.1."); + x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.2."); + x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.3."); + x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.4."); + x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.5."); + + x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 512, 2, "layer4.0."); + x = bottleneck(network, weightMap, *x->getOutput(0), 2048, 512, 1, "layer4.1."); + x = bottleneck(network, weightMap, *x->getOutput(0), 2048, 512, 1, "layer4.2."); + + IPoolingLayer* pool2 = network->addPooling(*x->getOutput(0), PoolingType::kAVERAGE, DimsHW{7, 7}); + assert(pool2); + pool2->setStride(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); + builder->setMaxWorkspaceSize(1 << 20); + ICudaEngine* engine = builder->buildCudaEngine(*network); + 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); + + // Create model to populate the network, then set the outputs and create an engine + ICudaEngine* engine = createEngine(maxBatchSize, builder, DataType::kFLOAT); + assert(engine != nullptr); + + // Serialize the engine + (*modelStream) = engine->serialize(); + + // Close everything down + engine->destroy(); + builder->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::getNbBindings() number of buffers. + assert(engine.getNbBindings() == 2); + void* buffers[2]; + + // In order to bind the buffers, we need to know 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 << "./resnext -s // serialize model to plan file" << std::endl; + std::cerr << "./resnext -d // deserialize 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("resnext50.engine"); + 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("resnext50.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); + file.read(trtModelStream, size); + file.close(); + } + } else { + return -1; + } + + + // Subtract mean from image + 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); + + // Run inference + 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 histogram 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; +}