diff --git a/README.md b/README.md index 4bee544..74f9eeb 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ All the models are implemented in pytorch or mxnet first, and export a weights f ## News +- `13 Sep 2020`. Add crnn, and got 1000fps on GTX1080. - `7 Sep 2020`. Implement retinaface(mobilenet0.25), and got 333fps on GTX1080. - `28 Aug 2020`. [BaofengZan](https://github.com/BaofengZan) added a tutorial for compiling and running tensorrtx on windows. - `16 Aug 2020`. [upczww](https://github.com/upczww) added a python wrapper for yolov5. @@ -64,6 +65,7 @@ Following models are implemented. |[arcface](./arcface)| LResNet50E-IR, weights from [deepinsight/insightface](https://github.com/deepinsight/insightface) | |[retinafaceAntiCov](./retinafaceAntiCov)| mobilenet0.25, weights from [deepinsight/insightface](https://github.com/deepinsight/insightface), retinaface anti-COVID-19, detect face and mask attribute | |[dbnet](./dbnet)| Scene Text Detection, weights from [BaofengZan/DBNet.pytorch](https://github.com/BaofengZan/DBNet.pytorch) | +|[crnn](./crnn)| pytorch implementation from [meijieru/crnn.pytorch](https://github.com/meijieru/crnn.pytorch) | ## Tricky Operations @@ -87,6 +89,7 @@ Some tricky operations encountered in these models, already solved, but might ha |mish| mish activation is implemented as a plugin, mish is used in yolov4 | |prelu| mxnet's prelu activation with trainable gamma is implemented as a plugin, used in arcface | |HardSwish| hard_swish = x * hard_sigmoid, used in yolov5 v3.0 | +|LSTM| Implemented pytorch nn.LSTM() with tensorrt api | ## Speed Benchmark @@ -107,6 +110,7 @@ Some tricky operations encountered in these models, already solved, but might ha | RetinaFace(resnet50) | Xeon E5-2620/GTX1080 | 1 | FP32 | 480x640 | 90 | | RetinaFace(mobilenet0.25) | Xeon E5-2620/GTX1080 | 1 | FP32 | 480x640 | 333 | | ArcFace(LResNet50E-IR) | Xeon E5-2620/GTX1080 | 1 | FP32 | 112x112 | 333 | +| CRNN | Xeon E5-2620/GTX1080 | 1 | FP32 | 32x100 | 1000 | Help wanted, if you got speed results, please add an issue or PR. diff --git a/crnn/CMakeLists.txt b/crnn/CMakeLists.txt new file mode 100644 index 0000000..d159cf5 --- /dev/null +++ b/crnn/CMakeLists.txt @@ -0,0 +1,33 @@ +cmake_minimum_required(VERSION 2.6) + +project(crnn) + +add_definitions(-std=c++11) + +option(CUDA_USE_STATIC_CUDA_RUNTIME OFF) +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_BUILD_TYPE Debug) + +find_package(CUDA REQUIRED) + +include_directories(${PROJECT_SOURCE_DIR}/include) +if (CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64") + message("embed_platform on") + include_directories(/usr/local/cuda/targets/aarch64-linux/include) + link_directories(/usr/local/cuda/targets/aarch64-linux/lib) +else() + message("embed_platform off") + include_directories(/usr/local/cuda/include) + link_directories(/usr/local/cuda/lib64) +endif() + +find_package(OpenCV) +include_directories(OpenCV_INCLUDE_DIRS) + +add_executable(crnn ${PROJECT_SOURCE_DIR}/crnn.cpp) +target_link_libraries(crnn nvinfer) +target_link_libraries(crnn cudart) +target_link_libraries(crnn ${OpenCV_LIBS}) + +add_definitions(-O2 -pthread) + diff --git a/crnn/README.md b/crnn/README.md new file mode 100644 index 0000000..0c67152 --- /dev/null +++ b/crnn/README.md @@ -0,0 +1,44 @@ +# crnn + +The Pytorch implementation is [meijieru/crnn.pytorch](https://github.com/meijieru/crnn.pytorch). + +## How to Run + +``` +1. generate crnn.wts from pytorch + +git clone https://github.com/wang-xinyu/tensorrtx.git +git clone https://github.com/meijieru/crnn.pytorch.git +// download its weights 'crnn.pth' +// copy tensorrtx/crnn/genwts.py into crnn.pytorch/ +// go to crnn.pytorch/ +python genwts.py +// a file 'crnn.wts' will be generated. + +2. build tensorrtx/crnn and run + +// put crnn.wts into tensorrtx/crnn +// go to tensorrtx/crnn +mkdir build +cd build +cmake .. +make +sudo ./crnn -s // serialize model to plan file i.e. 'crnn.engine' +// copy crnn.pytorch/data/demo.png here +sudo ./crnn -d // deserialize plan file and run inference + +3. check the output as follows: + +raw: a-----v--a-i-l-a-bb-l-e--- +sim: available + +``` + +## More Information + +See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx) + +## Acknowledgment + +Thanks for the donation for this crnn tensorrt implementation from @雍. + diff --git a/crnn/crnn.cpp b/crnn/crnn.cpp new file mode 100644 index 0000000..4da950f --- /dev/null +++ b/crnn/crnn.cpp @@ -0,0 +1,418 @@ +#include +#include +#include +#include +#include "NvInfer.h" +#include "cuda_runtime_api.h" +#include "logging.h" + +#define CHECK(status) \ + do\ + {\ + auto ret = (status);\ + if (ret != 0)\ + {\ + std::cerr << "Cuda failure: " << ret << std::endl;\ + abort();\ + }\ + } while (0) + +#define USE_FP16 // comment out this if want to use FP32 +#define DEVICE 0 // GPU id +#define BATCH_SIZE 1 + +// stuff we know about the network and the input/output blobs +static const int INPUT_H = 32; +static const int INPUT_W = 100; +static const int OUTPUT_SIZE = 26 * 37; +const char* INPUT_BLOB_NAME = "data"; +const char* OUTPUT_BLOB_NAME = "prob"; +static Logger gLogger; + +const int ks[] = {3, 3, 3, 3, 3, 3, 2}; +const int ps[] = {1, 1, 1, 1, 1, 1, 0}; +const int ss[] = {1, 1, 1, 1, 1, 1, 1}; +const int nm[] = {64, 128, 256, 256, 512, 512, 512}; +const std::string alphabet = "-0123456789abcdefghijklmnopqrstuvwxyz"; + +using namespace nvinfer1; + +std::string strDecode(std::vector& preds, bool raw) { + std::string str; + if (raw) { + for (auto v: preds) { + str.push_back(alphabet[v]); + } + } else { + for (size_t i = 0; i < preds.size(); i++) { + if (preds[i] == 0 || (i > 0 && preds[i - 1] == preds[i])) continue; + str.push_back(alphabet[preds[i]]); + } + } + return str; +} + +// 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. please check if the .wts file path is right!!!!!!"); + + // 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; + + 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; +} + +ILayer* convRelu(INetworkDefinition *network, std::map& weightMap, ITensor& input, int i, bool use_bn = false) { + int nOut = nm[i]; + IConvolutionLayer* conv = network->addConvolutionNd(input, nOut, DimsHW{ks[i], ks[i]}, weightMap["cnn.conv" + std::to_string(i) + ".weight"], weightMap["cnn.conv" + std::to_string(i) + ".bias"]); + assert(conv); + conv->setStrideNd(DimsHW{ss[i], ss[i]}); + conv->setPaddingNd(DimsHW{ps[i], ps[i]}); + ILayer *tmp = conv; + if (use_bn) { + tmp = addBatchNorm2d(network, weightMap, *conv->getOutput(0), "cnn.batchnorm" + std::to_string(i), 1e-5); + } + auto relu = network->addActivation(*tmp->getOutput(0), ActivationType::kRELU); + assert(relu); + return relu; +} + +void splitLstmWeights(std::map& weightMap, std::string lname) { + int weight_size = weightMap[lname].count; + for (int i = 0; i < 4; i++) { + Weights wt{DataType::kFLOAT, nullptr, 0}; + wt.count = weight_size / 4; + float *val = reinterpret_cast(malloc(sizeof(float) * wt.count)); + memcpy(val, (float*)weightMap[lname].values + wt.count * i, sizeof(float) * wt.count); + wt.values = val; + weightMap[lname + std::to_string(i)] = wt; + } +} + +ILayer* addLSTM(INetworkDefinition *network, std::map& weightMap, ITensor& input, int nHidden, std::string lname) { + splitLstmWeights(weightMap, lname + ".weight_ih_l0"); + splitLstmWeights(weightMap, lname + ".weight_hh_l0"); + splitLstmWeights(weightMap, lname + ".bias_ih_l0"); + splitLstmWeights(weightMap, lname + ".bias_hh_l0"); + splitLstmWeights(weightMap, lname + ".weight_ih_l0_reverse"); + splitLstmWeights(weightMap, lname + ".weight_hh_l0_reverse"); + splitLstmWeights(weightMap, lname + ".bias_ih_l0_reverse"); + splitLstmWeights(weightMap, lname + ".bias_hh_l0_reverse"); + Dims dims = input.getDimensions(); + std::cout << "lstm input shape: " << dims.nbDims << " [" << dims.d[0] << " " << dims.d[1] << " " << dims.d[2] << "]"<< std::endl; + auto lstm = network->addRNNv2(input, 1, nHidden, dims.d[1], RNNOperation::kLSTM); + lstm->setDirection(RNNDirection::kBIDIRECTION); + lstm->setWeightsForGate(0, RNNGateType::kINPUT, true, weightMap[lname + ".weight_ih_l00"]); + lstm->setWeightsForGate(0, RNNGateType::kFORGET, true, weightMap[lname + ".weight_ih_l01"]); + lstm->setWeightsForGate(0, RNNGateType::kCELL, true, weightMap[lname + ".weight_ih_l02"]); + lstm->setWeightsForGate(0, RNNGateType::kOUTPUT, true, weightMap[lname + ".weight_ih_l03"]); + + lstm->setWeightsForGate(0, RNNGateType::kINPUT, false, weightMap[lname + ".weight_hh_l00"]); + lstm->setWeightsForGate(0, RNNGateType::kFORGET, false, weightMap[lname + ".weight_hh_l01"]); + lstm->setWeightsForGate(0, RNNGateType::kCELL, false, weightMap[lname + ".weight_hh_l02"]); + lstm->setWeightsForGate(0, RNNGateType::kOUTPUT, false, weightMap[lname + ".weight_hh_l03"]); + + lstm->setBiasForGate(0, RNNGateType::kINPUT, true, weightMap[lname + ".bias_ih_l00"]); + lstm->setBiasForGate(0, RNNGateType::kFORGET, true, weightMap[lname + ".bias_ih_l01"]); + lstm->setBiasForGate(0, RNNGateType::kCELL, true, weightMap[lname + ".bias_ih_l02"]); + lstm->setBiasForGate(0, RNNGateType::kOUTPUT, true, weightMap[lname + ".bias_ih_l03"]); + + lstm->setBiasForGate(0, RNNGateType::kINPUT, false, weightMap[lname + ".bias_hh_l00"]); + lstm->setBiasForGate(0, RNNGateType::kFORGET, false, weightMap[lname + ".bias_hh_l01"]); + lstm->setBiasForGate(0, RNNGateType::kCELL, false, weightMap[lname + ".bias_hh_l02"]); + lstm->setBiasForGate(0, RNNGateType::kOUTPUT, false, weightMap[lname + ".bias_hh_l03"]); + + lstm->setWeightsForGate(1, RNNGateType::kINPUT, true, weightMap[lname + ".weight_ih_l0_reverse0"]); + lstm->setWeightsForGate(1, RNNGateType::kFORGET, true, weightMap[lname + ".weight_ih_l0_reverse1"]); + lstm->setWeightsForGate(1, RNNGateType::kCELL, true, weightMap[lname + ".weight_ih_l0_reverse2"]); + lstm->setWeightsForGate(1, RNNGateType::kOUTPUT, true, weightMap[lname + ".weight_ih_l0_reverse3"]); + + lstm->setWeightsForGate(1, RNNGateType::kINPUT, false, weightMap[lname + ".weight_hh_l0_reverse0"]); + lstm->setWeightsForGate(1, RNNGateType::kFORGET, false, weightMap[lname + ".weight_hh_l0_reverse1"]); + lstm->setWeightsForGate(1, RNNGateType::kCELL, false, weightMap[lname + ".weight_hh_l0_reverse2"]); + lstm->setWeightsForGate(1, RNNGateType::kOUTPUT, false, weightMap[lname + ".weight_hh_l0_reverse3"]); + + lstm->setBiasForGate(1, RNNGateType::kINPUT, true, weightMap[lname + ".bias_ih_l0_reverse0"]); + lstm->setBiasForGate(1, RNNGateType::kFORGET, true, weightMap[lname + ".bias_ih_l0_reverse1"]); + lstm->setBiasForGate(1, RNNGateType::kCELL, true, weightMap[lname + ".bias_ih_l0_reverse2"]); + lstm->setBiasForGate(1, RNNGateType::kOUTPUT, true, weightMap[lname + ".bias_ih_l0_reverse3"]); + + lstm->setBiasForGate(1, RNNGateType::kINPUT, false, weightMap[lname + ".bias_hh_l0_reverse0"]); + lstm->setBiasForGate(1, RNNGateType::kFORGET, false, weightMap[lname + ".bias_hh_l0_reverse1"]); + lstm->setBiasForGate(1, RNNGateType::kCELL, false, weightMap[lname + ".bias_hh_l0_reverse2"]); + lstm->setBiasForGate(1, RNNGateType::kOUTPUT, false, weightMap[lname + ".bias_hh_l0_reverse3"]); + return lstm; +} + +// Creat 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 shape {C, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME + ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{1, INPUT_H, INPUT_W}); + assert(data); + + std::map weightMap = loadWeights("../crnn.wts"); + + // cnn + auto x = convRelu(network, weightMap, *data, 0); + auto p = network->addPoolingNd(*x->getOutput(0), PoolingType::kMAX, DimsHW{2, 2}); + p->setStrideNd(DimsHW{2, 2}); + x = convRelu(network, weightMap, *p->getOutput(0), 1); + p = network->addPoolingNd(*x->getOutput(0), PoolingType::kMAX, DimsHW{2, 2}); + p->setStrideNd(DimsHW{2, 2}); + x = convRelu(network, weightMap, *p->getOutput(0), 2, true); + x = convRelu(network, weightMap, *x->getOutput(0), 3); + p = network->addPoolingNd(*x->getOutput(0), PoolingType::kMAX, DimsHW{2, 2}); + p->setStrideNd(DimsHW{2, 1}); + p->setPaddingNd(DimsHW{0, 1}); + x = convRelu(network, weightMap, *p->getOutput(0), 4, true); + x = convRelu(network, weightMap, *x->getOutput(0), 5); + p = network->addPoolingNd(*x->getOutput(0), PoolingType::kMAX, DimsHW{2, 2}); + p->setStrideNd(DimsHW{2, 1}); + p->setPaddingNd(DimsHW{0, 1}); + x = convRelu(network, weightMap, *p->getOutput(0), 6, true); + + auto sfl = network->addShuffle(*x->getOutput(0)); + sfl->setFirstTranspose(Permutation{1, 2, 0}); + + // rnn + auto lstm0 = addLSTM(network, weightMap, *sfl->getOutput(0), 256, "rnn.0.rnn"); + auto sfl0 = network->addShuffle(*lstm0->getOutput(0)); + sfl0->setReshapeDimensions(Dims4{26, 1, 1, 512}); + auto fc0 = network->addFullyConnected(*sfl0->getOutput(0), 256, weightMap["rnn.0.embedding.weight"], weightMap["rnn.0.embedding.bias"]); + + sfl = network->addShuffle(*fc0->getOutput(0)); + sfl->setFirstTranspose(Permutation{2, 3, 0, 1}); + sfl->setReshapeDimensions(Dims3{1, 26, 256}); + + auto lstm1 = addLSTM(network, weightMap, *sfl->getOutput(0), 256, "rnn.1.rnn"); + auto sfl1 = network->addShuffle(*lstm1->getOutput(0)); + sfl1->setReshapeDimensions(Dims4{26, 1, 1, 512}); + auto fc1 = network->addFullyConnected(*sfl1->getOutput(0), 37, weightMap["rnn.1.embedding.weight"], weightMap["rnn.1.embedding.bias"]); + Dims dims = fc1->getOutput(0)->getDimensions(); + std::cout << "fc1 shape " << dims.d[0] << " " << dims.d[1] << " " << dims.d[2] << std::endl; + + fc1->getOutput(0)->setName(OUTPUT_BLOB_NAME); + network->markOutput(*fc1->getOutput(0)); + + // Build engine + builder->setMaxBatchSize(maxBatchSize); + config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB +#ifdef USE_FP16 + config->setFlag(BuilderFlag::kFP16); +#endif + std::cout << "Building engine, please wait for a while..." << std::endl; + ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config); + std::cout << "Build engine successfully!" << 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 the 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(); +} + +void doInference(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* input, float* output, int batchSize) { + // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host + CHECK(cudaMemcpyAsync(buffers[0], input, batchSize * 1 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream)); + context.enqueue(batchSize, buffers, stream, nullptr); + CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream)); + cudaStreamSynchronize(stream); +} + +int main(int argc, char** argv) { + cudaSetDevice(DEVICE); + // create a model using the API directly and serialize it to a stream + char *trtModelStream{nullptr}; + size_t size{0}; + if (argc == 2 && std::string(argv[1]) == "-s") { + IHostMemory* modelStream{nullptr}; + APIToModel(BATCH_SIZE, &modelStream); + assert(modelStream != nullptr); + std::ofstream p("crnn.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 0; + } else if (argc == 2 && std::string(argv[1]) == "-d") { + std::ifstream file("crnn.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 { + std::cerr << "arguments not right!" << std::endl; + std::cerr << "./crnn -s // serialize model to plan file" << std::endl; + std::cerr << "./crnn -d ../samples // deserialize plan file and run inference" << std::endl; + return -1; + } + + // prepare input data --------------------------- + static float data[BATCH_SIZE * 1 * INPUT_H * INPUT_W]; + //for (int i = 0; i < 1 * INPUT_H * INPUT_W; i++) + // data[i] = 1.0; + static float prob[BATCH_SIZE * OUTPUT_SIZE]; + IRuntime* runtime = createInferRuntime(gLogger); + assert(runtime != nullptr); + ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size); + assert(engine != nullptr); + IExecutionContext* context = engine->createExecutionContext(); + assert(context != nullptr); + delete[] trtModelStream; + 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); + assert(inputIndex == 0); + assert(outputIndex == 1); + // Create GPU buffers on device + CHECK(cudaMalloc(&buffers[inputIndex], BATCH_SIZE * 1 * INPUT_H * INPUT_W * sizeof(float))); + CHECK(cudaMalloc(&buffers[outputIndex], BATCH_SIZE * OUTPUT_SIZE * sizeof(float))); + // Create stream + cudaStream_t stream; + CHECK(cudaStreamCreate(&stream)); + + cv::Mat img = cv::imread("demo.png"); + if (img.empty()) { + std::cerr << "demo.png not found !!!" << std::endl; + return -1; + } + cv::cvtColor(img, img, CV_BGR2GRAY); + cv::resize(img, img, cv::Size(INPUT_W, INPUT_H)); + for (int i = 0; i < INPUT_H * INPUT_W; i++) { + data[i] = ((float)img.at(i) / 255.0 - 0.5) * 2.0; + } + + // Run inference + auto start = std::chrono::system_clock::now(); + doInference(*context, stream, buffers, data, prob, BATCH_SIZE); + auto end = std::chrono::system_clock::now(); + std::cout << std::chrono::duration_cast(end - start).count() << "ms" << std::endl; + + std::vector preds; + for (int i = 0; i < 26; i++) { + int maxj = 0; + for (int j = 1; j < 37; j++) { + if (prob[37 * i + j] > prob[37 * i + maxj]) maxj = j; + } + preds.push_back(maxj); + } + std::cout << "raw: " << strDecode(preds, true) << std::endl; + std::cout << "sim: " << strDecode(preds, false) << std::endl; + + // Release stream and buffers + cudaStreamDestroy(stream); + CHECK(cudaFree(buffers[inputIndex])); + CHECK(cudaFree(buffers[outputIndex])); + // 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 < OUTPUT_SIZE; i++) + //{ + // std::cout << prob[i] << ", "; + // if (i % 10 == 0) std::cout << std::endl; + //} + //std::cout << std::endl; + + return 0; +} diff --git a/crnn/genwts.py b/crnn/genwts.py new file mode 100644 index 0000000..693162c --- /dev/null +++ b/crnn/genwts.py @@ -0,0 +1,35 @@ +import torch +from torch.autograd import Variable +import utils +import models.crnn as crnn +import struct + +model_path = './data/crnn.pth' + +model = crnn.CRNN(32, 1, 37, 256) +if torch.cuda.is_available(): + model = model.cuda() +print('loading pretrained model from %s' % model_path) +model.load_state_dict(torch.load(model_path)) + +image = torch.ones(1, 1, 32, 100) +if torch.cuda.is_available(): + image = image.cuda() + +model.eval() +print(model) +print('image shape ', image.shape) +preds = model(image) + +f = open("crnn.wts", 'w') +f.write("{}\n".format(len(model.state_dict().keys()))) +for k,v in model.state_dict().items(): + print('key: ', k) + print('value: ', v.shape) + vr = v.reshape(-1).cpu().numpy() + f.write("{} {}".format(k, len(vr))) + for vv in vr: + f.write(" ") + f.write(struct.pack(">f", float(vv)).hex()) + f.write("\n") + diff --git a/crnn/logging.h b/crnn/logging.h new file mode 100644 index 0000000..602b69f --- /dev/null +++ b/crnn/logging.h @@ -0,0 +1,503 @@ +/* + * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TENSORRT_LOGGING_H +#define TENSORRT_LOGGING_H + +#include "NvInferRuntimeCommon.h" +#include +#include +#include +#include +#include +#include +#include + +using Severity = nvinfer1::ILogger::Severity; + +class LogStreamConsumerBuffer : public std::stringbuf +{ +public: + LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog) + : mOutput(stream) + , mPrefix(prefix) + , mShouldLog(shouldLog) + { + } + + LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other) + : mOutput(other.mOutput) + { + } + + ~LogStreamConsumerBuffer() + { + // std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence + // std::streambuf::pptr() gives a pointer to the current position of the output sequence + // if the pointer to the beginning is not equal to the pointer to the current position, + // call putOutput() to log the output to the stream + if (pbase() != pptr()) + { + putOutput(); + } + } + + // synchronizes the stream buffer and returns 0 on success + // synchronizing the stream buffer consists of inserting the buffer contents into the stream, + // resetting the buffer and flushing the stream + virtual int sync() + { + putOutput(); + return 0; + } + + void putOutput() + { + if (mShouldLog) + { + // prepend timestamp + std::time_t timestamp = std::time(nullptr); + tm* tm_local = std::localtime(×tamp); + std::cout << "["; + std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/"; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/"; + std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-"; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":"; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":"; + std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] "; + // std::stringbuf::str() gets the string contents of the buffer + // insert the buffer contents pre-appended by the appropriate prefix into the stream + mOutput << mPrefix << str(); + // set the buffer to empty + str(""); + // flush the stream + mOutput.flush(); + } + } + + void setShouldLog(bool shouldLog) + { + mShouldLog = shouldLog; + } + +private: + std::ostream& mOutput; + std::string mPrefix; + bool mShouldLog; +}; + +//! +//! \class LogStreamConsumerBase +//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer +//! +class LogStreamConsumerBase +{ +public: + LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog) + : mBuffer(stream, prefix, shouldLog) + { + } + +protected: + LogStreamConsumerBuffer mBuffer; +}; + +//! +//! \class LogStreamConsumer +//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages. +//! Order of base classes is LogStreamConsumerBase and then std::ostream. +//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field +//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream. +//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream. +//! Please do not change the order of the parent classes. +//! +class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream +{ +public: + //! \brief Creates a LogStreamConsumer which logs messages with level severity. + //! Reportable severity determines if the messages are severe enough to be logged. + LogStreamConsumer(Severity reportableSeverity, Severity severity) + : LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity) + , std::ostream(&mBuffer) // links the stream buffer with the stream + , mShouldLog(severity <= reportableSeverity) + , mSeverity(severity) + { + } + + LogStreamConsumer(LogStreamConsumer&& other) + : LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog) + , std::ostream(&mBuffer) // links the stream buffer with the stream + , mShouldLog(other.mShouldLog) + , mSeverity(other.mSeverity) + { + } + + void setReportableSeverity(Severity reportableSeverity) + { + mShouldLog = mSeverity <= reportableSeverity; + mBuffer.setShouldLog(mShouldLog); + } + +private: + static std::ostream& severityOstream(Severity severity) + { + return severity >= Severity::kINFO ? std::cout : std::cerr; + } + + static std::string severityPrefix(Severity severity) + { + switch (severity) + { + case Severity::kINTERNAL_ERROR: return "[F] "; + case Severity::kERROR: return "[E] "; + case Severity::kWARNING: return "[W] "; + case Severity::kINFO: return "[I] "; + case Severity::kVERBOSE: return "[V] "; + default: assert(0); return ""; + } + } + + bool mShouldLog; + Severity mSeverity; +}; + +//! \class Logger +//! +//! \brief Class which manages logging of TensorRT tools and samples +//! +//! \details This class provides a common interface for TensorRT tools and samples to log information to the console, +//! and supports logging two types of messages: +//! +//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal) +//! - Test pass/fail messages +//! +//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is +//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location. +//! +//! In the future, this class could be extended to support dumping test results to a file in some standard format +//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run). +//! +//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger +//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT +//! library and messages coming from the sample. +//! +//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the +//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger +//! object. + +class Logger : public nvinfer1::ILogger +{ +public: + Logger(Severity severity = Severity::kWARNING) + : mReportableSeverity(severity) + { + } + + //! + //! \enum TestResult + //! \brief Represents the state of a given test + //! + enum class TestResult + { + kRUNNING, //!< The test is running + kPASSED, //!< The test passed + kFAILED, //!< The test failed + kWAIVED //!< The test was waived + }; + + //! + //! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger + //! \return The nvinfer1::ILogger associated with this Logger + //! + //! TODO Once all samples are updated to use this method to register the logger with TensorRT, + //! we can eliminate the inheritance of Logger from ILogger + //! + nvinfer1::ILogger& getTRTLogger() + { + return *this; + } + + //! + //! \brief Implementation of the nvinfer1::ILogger::log() virtual method + //! + //! Note samples should not be calling this function directly; it will eventually go away once we eliminate the + //! inheritance from nvinfer1::ILogger + //! + void log(Severity severity, const char* msg) override + { + LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl; + } + + //! + //! \brief Method for controlling the verbosity of logging output + //! + //! \param severity The logger will only emit messages that have severity of this level or higher. + //! + void setReportableSeverity(Severity severity) + { + mReportableSeverity = severity; + } + + //! + //! \brief Opaque handle that holds logging information for a particular test + //! + //! This object is an opaque handle to information used by the Logger to print test results. + //! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used + //! with Logger::reportTest{Start,End}(). + //! + class TestAtom + { + public: + TestAtom(TestAtom&&) = default; + + private: + friend class Logger; + + TestAtom(bool started, const std::string& name, const std::string& cmdline) + : mStarted(started) + , mName(name) + , mCmdline(cmdline) + { + } + + bool mStarted; + std::string mName; + std::string mCmdline; + }; + + //! + //! \brief Define a test for logging + //! + //! \param[in] name The name of the test. This should be a string starting with + //! "TensorRT" and containing dot-separated strings containing + //! the characters [A-Za-z0-9_]. + //! For example, "TensorRT.sample_googlenet" + //! \param[in] cmdline The command line used to reproduce the test + // + //! \return a TestAtom that can be used in Logger::reportTest{Start,End}(). + //! + static TestAtom defineTest(const std::string& name, const std::string& cmdline) + { + return TestAtom(false, name, cmdline); + } + + //! + //! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments + //! as input + //! + //! \param[in] name The name of the test + //! \param[in] argc The number of command-line arguments + //! \param[in] argv The array of command-line arguments (given as C strings) + //! + //! \return a TestAtom that can be used in Logger::reportTest{Start,End}(). + static TestAtom defineTest(const std::string& name, int argc, char const* const* argv) + { + auto cmdline = genCmdlineString(argc, argv); + return defineTest(name, cmdline); + } + + //! + //! \brief Report that a test has started. + //! + //! \pre reportTestStart() has not been called yet for the given testAtom + //! + //! \param[in] testAtom The handle to the test that has started + //! + static void reportTestStart(TestAtom& testAtom) + { + reportTestResult(testAtom, TestResult::kRUNNING); + assert(!testAtom.mStarted); + testAtom.mStarted = true; + } + + //! + //! \brief Report that a test has ended. + //! + //! \pre reportTestStart() has been called for the given testAtom + //! + //! \param[in] testAtom The handle to the test that has ended + //! \param[in] result The result of the test. Should be one of TestResult::kPASSED, + //! TestResult::kFAILED, TestResult::kWAIVED + //! + static void reportTestEnd(const TestAtom& testAtom, TestResult result) + { + assert(result != TestResult::kRUNNING); + assert(testAtom.mStarted); + reportTestResult(testAtom, result); + } + + static int reportPass(const TestAtom& testAtom) + { + reportTestEnd(testAtom, TestResult::kPASSED); + return EXIT_SUCCESS; + } + + static int reportFail(const TestAtom& testAtom) + { + reportTestEnd(testAtom, TestResult::kFAILED); + return EXIT_FAILURE; + } + + static int reportWaive(const TestAtom& testAtom) + { + reportTestEnd(testAtom, TestResult::kWAIVED); + return EXIT_SUCCESS; + } + + static int reportTest(const TestAtom& testAtom, bool pass) + { + return pass ? reportPass(testAtom) : reportFail(testAtom); + } + + Severity getReportableSeverity() const + { + return mReportableSeverity; + } + +private: + //! + //! \brief returns an appropriate string for prefixing a log message with the given severity + //! + static const char* severityPrefix(Severity severity) + { + switch (severity) + { + case Severity::kINTERNAL_ERROR: return "[F] "; + case Severity::kERROR: return "[E] "; + case Severity::kWARNING: return "[W] "; + case Severity::kINFO: return "[I] "; + case Severity::kVERBOSE: return "[V] "; + default: assert(0); return ""; + } + } + + //! + //! \brief returns an appropriate string for prefixing a test result message with the given result + //! + static const char* testResultString(TestResult result) + { + switch (result) + { + case TestResult::kRUNNING: return "RUNNING"; + case TestResult::kPASSED: return "PASSED"; + case TestResult::kFAILED: return "FAILED"; + case TestResult::kWAIVED: return "WAIVED"; + default: assert(0); return ""; + } + } + + //! + //! \brief returns an appropriate output stream (cout or cerr) to use with the given severity + //! + static std::ostream& severityOstream(Severity severity) + { + return severity >= Severity::kINFO ? std::cout : std::cerr; + } + + //! + //! \brief method that implements logging test results + //! + static void reportTestResult(const TestAtom& testAtom, TestResult result) + { + severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # " + << testAtom.mCmdline << std::endl; + } + + //! + //! \brief generate a command line string from the given (argc, argv) values + //! + static std::string genCmdlineString(int argc, char const* const* argv) + { + std::stringstream ss; + for (int i = 0; i < argc; i++) + { + if (i > 0) + ss << " "; + ss << argv[i]; + } + return ss.str(); + } + + Severity mReportableSeverity; +}; + +namespace +{ + +//! +//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE +//! +//! Example usage: +//! +//! LOG_VERBOSE(logger) << "hello world" << std::endl; +//! +inline LogStreamConsumer LOG_VERBOSE(const Logger& logger) +{ + return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE); +} + +//! +//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO +//! +//! Example usage: +//! +//! LOG_INFO(logger) << "hello world" << std::endl; +//! +inline LogStreamConsumer LOG_INFO(const Logger& logger) +{ + return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO); +} + +//! +//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING +//! +//! Example usage: +//! +//! LOG_WARN(logger) << "hello world" << std::endl; +//! +inline LogStreamConsumer LOG_WARN(const Logger& logger) +{ + return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING); +} + +//! +//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR +//! +//! Example usage: +//! +//! LOG_ERROR(logger) << "hello world" << std::endl; +//! +inline LogStreamConsumer LOG_ERROR(const Logger& logger) +{ + return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR); +} + +//! +//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR +// ("fatal" severity) +//! +//! Example usage: +//! +//! LOG_FATAL(logger) << "hello world" << std::endl; +//! +inline LogStreamConsumer LOG_FATAL(const Logger& logger) +{ + return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR); +} + +} // anonymous namespace + +#endif // TENSORRT_LOGGING_H