The range here is large enough for the // network, but is chosen for example purposes only. float maxMean = samplesCommon::getMaxValue(static_cast<const float*>(meanWeights.values), samplesCommon::volume(inputDims)); auto mean = network->addConstant(nvinfer1::Dims3(1, inputDims.d[1],...
CMakeList.txt cmake_minimum_required(VERSION 3.5) project(example) set(CMAKE_CXX_STANDARD 14) # CUDA find_package(CUDA REQUIRED) message(STATUS "Find CUDA include at ${CUDA_INCLUDE_DIRS}") message(STATUS "Find CUDA libraries: ${CUDA_LIBRARIES}") # TensorRT set(TENSORRT_ROOT /workspace/doc...
注意,这里triton models指的是triton server中的model概念,一个服务就是一个model。 # Create the model repository that will be used by the Triton servercd /tensorrtllm_backend # 假设tensorrtllm_backend源码下载到这个位置mkdir triton_model_repo# Copy the example models to the model repositorycp -r al...
head=create_head(nf,3,None,ps=0.5,bn_final=None)model=nn.Sequential(body,head)state=torch.load('new-mobilenetv2-128_S.pth',map_location=device)model.load_state_dict(state['model'],strict=True)example=torch.rand(1,3,128,128).cuda()model.to(device)# 导出onnx模型 torch_out=torch.onnx...
参数初始化主要由initializeSampleParams函数来完成,这个函数的详细注释如下,具体就是根据输入数据和网络文件所在的文件夹去读取LeNet的Caffe原始模型文件和均值文件,另外设置一些如输出Tensor名字,batch大小,运行时精度模式等关键参数,最后返回一个params对象。注意这里使用的LeNet模型是Caffe的原始模型,因为TensorRT是直接支持...
TensorRT C++ 模型推理我用了上述的 Github 仓库。该仓库也包含了 TensorRT Python 模型推理的源码。对于 YOLO C++ 部署只需要下载文件夹tensorRT_Pro/example-simple_yolo/即可。 该开源项目有以下优点 依赖少:仅依赖官方的 TensorRT 和 OpenCV 文件少:只有simple_yolo.hpp和simple_yolo.cu两个文件 ...
Here we take TensorRT C++ runtime APIs as an example to run the exported RVM TRT model. rvm_infer.cc complete code. 1. Load the model: create runtime , deserialize the data of the TRT model file static Logger logger{Logger::Severity::kINFO}; auto runtime = std::unique_ptr<nvinfer...
if c % 2 == 0: s += c return s class ExampleModel(torch.nn.Module): def __init__(self): super().__init__() def forward(self, items): return sum_even(items) def build_engine(model_file): builder = trt.Builder(TRT_LOGGER) ...
# 若传入 scriptModule,需要外加配置 example_outputs,用来获取输出的shape和dtype,无需运行模型 #之前模型使用记录法得到,这里无需运行模型,但要给出输入及输出参数形状;一般无特殊情况,跟踪法使用更多。 dynamic_axes = {'input': {0: 'batch'}, 'output': {0: 'batch'}} # 配置动态分辨率 ...
user parameterization and is provided as a fallback option if the other calibrators yield poor results. Calibration happens after Layer fusion by default. You can customize this calibrator to implement percentile max, for example, 99.99% percentile max is observed to have best accuracy for NVIDIA ...