tensorrt的安装和使用

安装

提前安装好 CUDA 和 CUDNN，登录 NVIDIA 官方网站下载和主机 CUDA 版本适配的 TensorRT 压缩包即可。

以 CUDA 版本是 10.2 为例，选择适配 CUDA 10.2 的 tar 包，然后执行类似如下的命令安装并测试：

#安装c++版本
cd /the/path/of/tensorrt/tar/gz/file 
tar -zxvf TensorRT-8.2.5.1.linux.x86_64-gnu.cuda-10.2.cudnn8.2.tar.gz 
export TENSORRT_DIR=$(pwd)/TensorRT-8.2.5.1 
export LD_LIBRARY_PATH=$TENSORRT_DIR/lib:$LD_LIBRARY_PATH 

#安装python版本
pip install TensorRT-8.2.5.1/python/tensorrt-8.2.5.1-cp37-none-linux_x86_64.whl 
python -c "import tensorrt;print(tensorrt.__version__)" #打印8.2.5.1，则说明安装成功

构建trt模型

手动搭建

使用python接口

import tensorrt as trt 
 
verbose = True 
IN_NAME = 'input' 
OUT_NAME = 'output' 
IN_H = 224 
IN_W = 224 
BATCH_SIZE = 1 
 
EXPLICIT_BATCH = 1 << (int)( 
    trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) 
 
TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) if verbose else trt.Logger() 
with trt.Builder(TRT_LOGGER) as builder, builder.create_builder_config( 
) as config, builder.create_network(EXPLICIT_BATCH) as network: 
    # define network 
    input_tensor = network.add_input( 
        name=IN_NAME, dtype=trt.float32, shape=(BATCH_SIZE, 3, IN_H, IN_W)) 
    pool = network.add_pooling( 
        input=input_tensor, type=trt.PoolingType.MAX, window_size=(2, 2)) 
    pool.stride = (2, 2) 
    pool.get_output(0).name = OUT_NAME 
    network.mark_output(pool.get_output(0)) 
 
    # serialize the model to engine file 
    profile = builder.create_optimization_profile() 
    profile.set_shape_input('input', *[[BATCH_SIZE, 3, IN_H, IN_W]]*3)  
    builder.max_batch_size = 1 
    config.max_workspace_size = 1 << 30 
    engine = builder.build_engine(network, config) 
    with open('model_python_trt.engine', mode='wb') as f: 
        f.write(bytearray(engine.serialize())) 
        print("generating file done!")

使用c++接口

#include  
#include  
 
#include  
#include <../samples/common/logger.h> 
 
using namespace nvinfer1; 
using namespace sample; 
 
const char* IN_NAME = "input"; 
const char* OUT_NAME = "output"; 
static const int IN_H = 224; 
static const int IN_W = 224; 
static const int BATCH_SIZE = 1; 
static const int EXPLICIT_BATCH = 1 << (int)(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH); 
 
int main(int argc, char** argv) 
{ 
        // Create builder 
        Logger m_logger; 
        IBuilder* builder = createInferBuilder(m_logger); 
        IBuilderConfig* config = builder->createBuilderConfig(); 
 
        // Create model to populate the network 
        INetworkDefinition* network = builder->createNetworkV2(EXPLICIT_BATCH); 
        ITensor* input_tensor = network->addInput(IN_NAME, DataType::kFLOAT, Dims4{ BATCH_SIZE, 3, IN_H, IN_W }); 
        IPoolingLayer* pool = network->addPoolingNd(*input_tensor, PoolingType::kMAX, DimsHW{ 2, 2 }); 
        pool->setStrideNd(DimsHW{ 2, 2 }); 
        pool->getOutput(0)->setName(OUT_NAME); 
        network->markOutput(*pool->getOutput(0)); 
 
        // Build engine 
        IOptimizationProfile* profile = builder->createOptimizationProfile(); 
        profile->setDimensions(IN_NAME, OptProfileSelector::kMIN, Dims4(BATCH_SIZE, 3, IN_H, IN_W)); 
        profile->setDimensions(IN_NAME, OptProfileSelector::kOPT, Dims4(BATCH_SIZE, 3, IN_H, IN_W)); 
        profile->setDimensions(IN_NAME, OptProfileSelector::kMAX, Dims4(BATCH_SIZE, 3, IN_H, IN_W)); 
        config->setMaxWorkspaceSize(1 << 20); 
        ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config); 
 
        // Serialize the model to engine file 
        IHostMemory* modelStream{ nullptr }; 
        assert(engine != nullptr); 
        modelStream = engine->serialize(); 
 
        std::ofstream p("model.engine", std::ios::binary); 
        if (!p) { 
                std::cerr << "could not open output file to save model" << std::endl; 
                return -1; 
        } 
        p.write(reinterpret_cast(modelStream->data()), modelStream->size()); 
        std::cout << "generating file done!" << std::endl; 
 
        // Release resources 
        modelStream->destroy(); 
        network->destroy(); 
        engine->destroy(); 
        builder->destroy(); 
        config->destroy(); 
        return 0; 
} 

onnx模型转换

trtexec

失败尝试：下载tensorrt的官方镜像，试图使用镜像中的trtexec，但是在驱动版本为515.105的A800机器上，不论哪个版本的镜像，都会报cuda版本跟驱动版本不匹配的错误

docker pull nvcr.io/nvidia/tensorrt:xx.xx-py3
#第一个xx表示年份，第二个xx表示月份，如22.09，23.01

成功尝试：直接下载tensorrt的tar包，然后在bin目录下执行trtexec命令，好像是不依赖cuda和cudnn的，但是同样要注意，不同版本的tensorrt编译出来的engine文件，在别的版本tensorrt下可能不能运行。

使用python接口

import torch 
import onnx 
import tensorrt as trt 
 
 
onnx_model = 'model.onnx' 
 
class NaiveModel(torch.nn.Module): 
    def __init__(self): 
        super().__init__() 
        self.pool = torch.nn.MaxPool2d(2, 2) 
 
    def forward(self, x): 
        return self.pool(x) 
 
device = torch.device('cuda:0') 
 
# generate ONNX model 
torch.onnx.export(NaiveModel(), torch.randn(1, 3, 224, 224), onnx_model, input_names=['input'], output_names=['output'], opset_version=11) 
onnx_model = onnx.load(onnx_model) 
 
# create builder and network 
logger = trt.Logger(trt.Logger.ERROR) 
builder = trt.Builder(logger) 
EXPLICIT_BATCH = 1 << (int)( 
    trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) 
network = builder.create_network(EXPLICIT_BATCH) 
 
# parse onnx 
parser = trt.OnnxParser(network, logger) 
 
if not parser.parse(onnx_model.SerializeToString()): 
    error_msgs = '' 
    for error in range(parser.num_errors): 
        error_msgs += f'{parser.get_error(error)}\n' 
    raise RuntimeError(f'Failed to parse onnx, {error_msgs}') 
 
config = builder.create_builder_config() 
config.max_workspace_size = 1<<20 
profile = builder.create_optimization_profile() 
 
profile.set_shape('input', [1,3 ,224 ,224], [1,3,224, 224], [1,3 ,224 ,224]) 
config.add_optimization_profile(profile) 
# create engine 
with torch.cuda.device(device): 
    engine = builder.build_engine(network, config) 
 
with open('model.engine', mode='wb') as f: 
    f.write(bytearray(engine.serialize())) 
    print("generating file done!") 

使用c++接口

#include  
#include  
 
#include  
#include  
#include <../samples/common/logger.h> 
 
using namespace nvinfer1; 
using namespace nvonnxparser; 
using namespace sample; 
 
int main(int argc, char** argv) 
{ 
        // Create builder 
        Logger m_logger; 
        IBuilder* builder = createInferBuilder(m_logger); 
        const auto explicitBatch = 1U << static_cast(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH); 
        IBuilderConfig* config = builder->createBuilderConfig(); 
 
        // Create model to populate the network 
        INetworkDefinition* network = builder->createNetworkV2(explicitBatch); 
 
        // Parse ONNX file 
        IParser* parser = nvonnxparser::createParser(*network, m_logger); 
        bool parser_status = parser->parseFromFile("model.onnx", static_cast(ILogger::Severity::kWARNING)); 
 
        // Get the name of network input 
        Dims dim = network->getInput(0)->getDimensions(); 
        if (dim.d[0] == -1)  // -1 means it is a dynamic model 
        { 
                const char* name = network->getInput(0)->getName(); 
                IOptimizationProfile* profile = builder->createOptimizationProfile(); 
                profile->setDimensions(name, OptProfileSelector::kMIN, Dims4(1, dim.d[1], dim.d[2], dim.d[3])); 
                profile->setDimensions(name, OptProfileSelector::kOPT, Dims4(1, dim.d[1], dim.d[2], dim.d[3])); 
                profile->setDimensions(name, OptProfileSelector::kMAX, Dims4(1, dim.d[1], dim.d[2], dim.d[3])); 
                config->addOptimizationProfile(profile); 
        } 
 
 
        // Build engine 
        config->setMaxWorkspaceSize(1 << 20); 
        ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config); 
 
        // Serialize the model to engine file 
        IHostMemory* modelStream{ nullptr }; 
        assert(engine != nullptr); 
        modelStream = engine->serialize(); 
 
        std::ofstream p("model.engine", std::ios::binary); 
        if (!p) { 
                std::cerr << "could not open output file to save model" << std::endl; 
                return -1; 
        } 
        p.write(reinterpret_cast(modelStream->data()), modelStream->size()); 
        std::cout << "generate file success!" << std::endl; 
 
        // Release resources 
        modelStream->destroy(); 
        network->destroy(); 
        engine->destroy(); 
        builder->destroy(); 
        config->destroy(); 
        return 0; 
} 

模型推理

使用python接口

#输入一个 1x3x224x224 的张量，输出一个 1x3x112x112 的张量
from typing import Union, Optional, Sequence,Dict,Any 
 
import torch 
import tensorrt as trt 
 
class TRTWrapper(torch.nn.Module): 
    def __init__(self,engine: Union[str, trt.ICudaEngine], 
                 output_names: Optional[Sequence[str]] = None) -> None: 
        super().__init__() 
        self.engine = engine 
        if isinstance(self.engine, str): 
            with trt.Logger() as logger, trt.Runtime(logger) as runtime: 
                with open(self.engine, mode='rb') as f: 
                    engine_bytes = f.read() 
                self.engine = runtime.deserialize_cuda_engine(engine_bytes) 
        self.context = self.engine.create_execution_context() 
        names = [_ for _ in self.engine] 
        input_names = list(filter(self.engine.binding_is_input, names)) 
        self._input_names = input_names 
        self._output_names = output_names 
 
        if self._output_names is None: 
            output_names = list(set(names) - set(input_names)) 
            self._output_names = output_names 
 
    def forward(self, inputs: Dict[str, torch.Tensor]): 
        assert self._input_names is not None 
        assert self._output_names is not None 
        bindings = [None] * (len(self._input_names) + len(self._output_names)) 
        profile_id = 0 
        for input_name, input_tensor in inputs.items(): 
            # check if input shape is valid 
            profile = self.engine.get_profile_shape(profile_id, input_name) 
            assert input_tensor.dim() == len( 
                profile[0]), 'Input dim is different from engine profile.' 
            for s_min, s_input, s_max in zip(profile[0], input_tensor.shape, 
                                             profile[2]): 
                assert s_min <= s_input <= s_max, \ 
                    'Input shape should be between ' \ 
                    + f'{profile[0]} and {profile[2]}' \ 
                    + f' but get {tuple(input_tensor.shape)}.' 
            idx = self.engine.get_binding_index(input_name) 
 
            # All input tensors must be gpu variables 
            assert 'cuda' in input_tensor.device.type 
            input_tensor = input_tensor.contiguous() 
            if input_tensor.dtype == torch.long: 
                input_tensor = input_tensor.int() 
            self.context.set_binding_shape(idx, tuple(input_tensor.shape)) 
            bindings[idx] = input_tensor.contiguous().data_ptr() 
 
        # create output tensors 
        outputs = {} 
        for output_name in self._output_names: 
            idx = self.engine.get_binding_index(output_name) 
            dtype = torch.float32 
            shape = tuple(self.context.get_binding_shape(idx)) 
 
            device = torch.device('cuda') 
            output = torch.empty(size=shape, dtype=dtype, device=device) 
            outputs[output_name] = output 
            bindings[idx] = output.data_ptr() 
        self.context.execute_async_v2(bindings, 
                                      torch.cuda.current_stream().cuda_stream) 
        return outputs 
 
model = TRTWrapper('model.engine', ['output']) 
output = model(dict(input = torch.randn(1, 3, 224, 224).cuda())) 
print(output) 

c++接口

#include  
#include  
 
#include  
#include <../samples/common/logger.h> 
 
#define CHECK(status) \ 
    do\ 
    {\ 
        auto ret = (status);\ 
        if (ret != 0)\ 
        {\ 
            std::cerr << "Cuda failure: " << ret << std::endl;\ 
            abort();\ 
        }\ 
    } while (0) 
 
using namespace nvinfer1; 
using namespace sample; 
 
const char* IN_NAME = "input"; 
const char* OUT_NAME = "output"; 
static const int IN_H = 224; 
static const int IN_W = 224; 
static const int BATCH_SIZE = 1; 
static const int EXPLICIT_BATCH = 1 << (int)(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH); 
 
 
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(IN_NAME); 
        const int outputIndex = engine.getBindingIndex(OUT_NAME); 
 
        // Create GPU buffers on device 
        CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * IN_H * IN_W * sizeof(float))); 
        CHECK(cudaMalloc(&buffers[outputIndex], batchSize * 3 * IN_H * IN_W /4 * 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 * IN_H * IN_W * sizeof(float), cudaMemcpyHostToDevice, stream)); 
        context.enqueue(batchSize, buffers, stream, nullptr); 
        CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * 3 * IN_H * IN_W / 4 * 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) 
{ 
        // create a model using the API directly and serialize it to a stream 
        char *trtModelStream{ nullptr }; 
        size_t size{ 0 }; 
 
        std::ifstream file("model.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(); 
        } 
 
        Logger m_logger; 
        IRuntime* runtime = createInferRuntime(m_logger); 
        assert(runtime != nullptr); 
        ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr); 
        assert(engine != nullptr); 
        IExecutionContext* context = engine->createExecutionContext(); 
        assert(context != nullptr); 
 
        // generate input data 
        float data[BATCH_SIZE * 3 * IN_H * IN_W]; 
        for (int i = 0; i < BATCH_SIZE * 3 * IN_H * IN_W; i++) 
                data[i] = 1; 
 
        // Run inference 
        float prob[BATCH_SIZE * 3 * IN_H * IN_W /4]; 
        doInference(*context, data, prob, BATCH_SIZE); 
 
        // Destroy the engine 
        context->destroy(); 
        engine->destroy(); 
        runtime->destroy(); 
        return 0; 
}