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TensorRT

  • “WARNING: Half2 support requested on hardware without native FP16 support, performance will be negatively affected.”

    Fp16 mode requires a device with full-rate fp16 support.

  • “error: parameter check failed at: engine.cpp::setBindingDimensions::1046, condition: profileMinDims.d[i] <= dimensions.d[i]”

    When building an ICudaEngine from an INetworkDefinition that has dynamically resizable inputs, users need to specify at least one optimization profile. Which can be set in deploy config:

    backend_config = dict(
      common_config=dict(max_workspace_size=1 << 30),
      model_inputs=[
          dict(
              input_shapes=dict(
                  input=dict(
                      min_shape=[1, 3, 320, 320],
                      opt_shape=[1, 3, 800, 1344],
                      max_shape=[1, 3, 1344, 1344])))
      ])
    

    The input tensor shape should be limited between min_shape and max_shape.

  • “error: [TensorRT] INTERNAL ERROR: Assertion failed: cublasStatus == CUBLAS_STATUS_SUCCESS”

    TRT 7.2.1 switches to use cuBLASLt (previously it was cuBLAS). cuBLASLt is the defaulted choice for SM version >= 7.0. You may need CUDA-10.2 Patch 1 (Released Aug 26, 2020) to resolve some cuBLASLt issues. Another option is to use the new TacticSource API and disable cuBLASLt tactics if you dont want to upgrade.

Libtorch

  • Error: libtorch/share/cmake/Caffe2/Caffe2Config.cmake:96 (message):Your installed Caffe2 version uses cuDNN but I cannot find the cuDNN libraries.  Please set the proper cuDNN prefixes and / or install cuDNN.

    May export CUDNN_ROOT=/root/path/to/cudnn to resolve the build error.

Windows

  • Error: similar like this OSError: [WinError 1455] The paging file is too small for this operation to complete. Error loading "C:\Users\cx\miniconda3\lib\site-packages\torch\lib\cudnn_cnn_infer64_8.dll" or one of its dependencies

    Solution: according to this post, the issue may be caused by NVidia and will fix in CUDA release 11.7. For now one could use the fixNvPe.py script to modify the nvidia dlls in the pytorch lib dir.

    python fixNvPe.py --input=C:\Users\user\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\lib\*.dll

    You can find your pytorch installation path with:

    import torch
    print(torch.__file__)
    
  • 编译时enable_language(CUDA) 报错

    -- Selecting Windows SDK version 10.0.19041.0 to target Windows 10.0.19044.
    -- Found CUDA: C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.1 (found version "11.1")
    CMake Error at C:/Software/cmake/cmake-3.23.1-windows-x86_64/share/cmake-3.23/Modules/CMakeDetermineCompilerId.cmake:491 (message):
      No CUDA toolset found.
    Call Stack (most recent call first):
      C:/Software/cmake/cmake-3.23.1-windows-x86_64/share/cmake-3.23/Modules/CMakeDetermineCompilerId.cmake:6 (CMAKE_DETERMINE_COMPILER_ID_BUILD)
      C:/Software/cmake/cmake-3.23.1-windows-x86_64/share/cmake-3.23/Modules/CMakeDetermineCompilerId.cmake:59 (__determine_compiler_id_test)
      C:/Software/cmake/cmake-3.23.1-windows-x86_64/share/cmake-3.23/Modules/CMakeDetermineCUDACompiler.cmake:339 (CMAKE_DETERMINE_COMPILER_ID)
      C:/workspace/mmdeploy-0.6.0-windows-amd64-cuda11.1-tensorrt8.2.3.0/sdk/lib/cmake/MMDeploy/MMDeployConfig.cmake:27 (enable_language)
      CMakeLists.txt:5 (find_package)
    

    原因: CUDA Toolkit 11.1安装在Visual Studio之前,造成VS的插件没有安装。或者VS的版本过新,使得CUDA Toolkit的安装的时候跳过了VS插件的安装

    解决方法: 可以通过手工拷贝插件的方式来解决这个问题。比如将C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.1\extras\visual_studio_integration\MSBuildExtensions中的四个文件拷贝到C:\Software\Microsoft Visual Studio\2022\Community\Msbuild\Microsoft\VC\v170\BuildCustomizations 目录下。具体路径根据实际情况进行更改。

ONNX Runtime

  • Windows系统下,转模型可视化时以及SDK推理时遇到

    onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Failed to load library, error code: 193
    

    原因: 在较新的windows系统中,系统路径下下有两个onnxruntime.dll,且会优先加载,造成冲突。

    C:\Windows\SysWOW64\onnxruntime.dll
    C:\Windows\System32\onnxruntime.dll
    

    解决方法: 以下两个方案任选其一

    1. 将下载的onnxruntime中的lib目录下的dll拷贝到mmdeploy_onnxruntime_ops.dll的同级目录(推荐使用Everything 进行查找)

    2. 将系统路径下的这两个dll改名,使其加载不到,可能涉及到修改文件权限的操作

Pip

  • pip installed package but could not import them.

    Make sure your are using conda pip.

    $ which pip
    # /path/to/.local/bin/pip
    /path/to/miniconda3/lib/python3.9/site-packages/pip
    
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