Step6-1: Build Custom Ops# TensorRT Custom Ops cd ${MMDEPLOY_DIR} mkdir -p build && cd build cmake -DCMAKE_CXX_COMPILER=g++-7 \ -DMMDEPLOY_TARGET_BACKENDS=trt \ -DTENSORRT_DIR=${TENSORRT_DIR} \ -DCUDNN_DIR=${CUDNN_DIR} .. make -j$(nproc) Step6-2: install Model Con...
$env:TENSORRT_DIR = "F:\env\TensorRT" # Windows: 上边命令代表新建一个系统变量,变量名为:TENSORRT_DIR 变量值为:F:\env\TensorRT # Linux: vim ~/.bashrc #在最后一行加入 export TENSORRT_DIR=/home/gy77/TensorRT source ~/.bashrc $env:Path = "F:\env\TensorRT\lib" # Windows: 上边命令代表...
Step4: Install TensorRT install TensorRT throughtar file After installation, you’d better add TensorRT environment variables to bashrc by cd /the/path/of/tensorrt/tar/gz/file tar -zxvf TensorRT-8.2.3.0.Linux.x86_64-gnu.cuda-11.4.cudnn8.2.tar.gz # 将下面的导入到 ~/.bashrc export TENSORRT_...
重新进入conda环境 conda deactivate conda activate mmdeploy3.8 python -c "import tensorrt; print(tensorrt.__version__)" # 将会打印出 TensorRT 版本 # 为之后编译MMDeploy设置环境变量(加入~/.bashrc中) export TENSORRT_DIR=/usr/include/aarch64-linux-gnu #将 cuda 路径和 lib 路径写入到环境变量 `$P...
环境: cuda 11.1 torch1.8 tensorrt8.2.0.6 执行 tools/onnx2tensorrt.py 报错“ImportError: cannot import name 'create_trt_engine' from 'mmdeploy.backend.tensorrt'” tensorrt已经按文档安装并且在build/lib/libmmdeploy_tensorrt_ops.so文件
Consolidate compiler generated dependencies of target mmdeploy_tensorrt_ops_obj [ 95%] Built target mmdeploy_tensorrt_ops_obj [100%] Built target mmdeploy_tensorrt_ops But I meet the error: 2022-02-04 16:12:04,247 - mmdeploy - INFO - torch2onnx success. ...
JetPack SDK 自带 TensorRT。 但是为了能够在 Conda 环境中成功导入,我们需要将 TensorRT 拷贝进先前创建的 Conda 环境中。 cp -r /usr/lib/python${PYTHON_VERSION}/dist-packages/tensorrt* ~/archiconda3/envs/mmdeploy/lib/python${PYTHON_VERSION}/site-packages/ conda deactivate conda activate mmdeploy pyt...
print('Skip building ext ops due to the absence of torch.') pwd=os.path.dirname(__file__) version_file='mmdeploy/version.py' defreadme(): withopen(os.path.join(pwd,'README.md'),encoding='utf-8')asf: content=f.read() returncontent ...
"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 cu...
部署流水线 PyTorch - ONNX - ONNX Runtime/TensorRT 的示例及常见部署问题的解决方法。 MMDeploy C/C++ 推理 SDK。 1.MMDeploy 模型部署流程 Model Converter 能将PyTorch 模型转换为 ONNX、TorchScript 等和设备无关的 IR 模型;也能将 ONNX 模型转换为推理后端模型。