添加链接描述
install onnx_tensort
这是一个将onnx 转换成tensort的小工具
和tensorrt 的内置工具类似tensorrt转onnx为engine的用法
你需要下载onnx 源码放在3dparty 下
你还需要下载tensorrt8
这个原来项目代码在这里
我在网盘里面 放了整个项目和数据
链接: https://pan.baidu.com/s/1pHs5Qdeqmz4ppGDHPSTnOQ?pwd=7djr
提取码: 7djr
cd onnx-tensorrt
mkdir build && cd build
cmake .. -DTENSORRT_ROOT=/home/oem/Downloads/onnx-tensorrt-master/TensorRT-8.2.1.8 && make -j8
复制onnx文件到build文件夹 并且执行
onnx2trt my_model.onnx -t my_model.onnx.txt
TensorRT Backend For ONNX
Parses ONNX models for execution with TensorRT.
See also the TensorRT documentation.
For the list of recent changes, see the changelog.
For a list of commonly seen issues and questions, see the FAQ.
For business inquiries, please contact researchinquiries@nvidia.com
For press and other inquiries, please contact Hector Marinez at hmarinez@nvidia.com
Supported TensorRT Versions
Development on the Master branch is for the latest version of TensorRT 8.2.3.0 with full-dimensions and dynamic shape support.
For previous versions of TensorRT, refer to their respective branches.
Full Dimensions + Dynamic Shapes
Building INetwork objects in full dimensions mode with dynamic shape support requires calling the following API:
C++
const auto explicitBatch = 1U << static_cast<uint32_t>(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
builder->createNetworkV2(explicitBatch)
Python
import tensorrt
explicit_batch = 1 << (int)(tensorrt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
builder.create_network(explicit_batch)
For examples of usage of these APIs see:
- sampleONNXMNIST
- sampleDynamicReshape
Supported Operators
Current supported ONNX operators are found in the operator support matrix.
Installation
Dependencies
- Protobuf >= 3.0.x
- TensorRT 8.2.3.0
- TensorRT 8.2.3.0 open source libaries (master branch)
Building
For building within docker, we recommend using and setting up the docker containers as instructed in the main (TensorRT repository)[https://github.com/NVIDIA/TensorRT#setting-up-the-build-environment] to build the onnx-tensorrt library.
Once you have cloned the repository, you can build the parser libraries and executables by running:
cd onnx-tensorrt
mkdir build && cd build
cmake .. -DTENSORRT_ROOT=/home/oem/Downloads/onnx-tensorrt-master/TensorRT-8.2.1.8 && make -j8
// Ensure that you update your LD_LIBRARY_PATH to pick up the location of the newly built library:
export LD_LIBRARY_PATH=$PWD:$LD_LIBRARY_PATH
Note that this project has a dependency on CUDA. By default the build will look in /usr/local/cuda for the CUDA toolkit installation. If your CUDA path is different, overwrite the default path by providing -DCUDA_TOOLKIT_ROOT_DIR=<path_to_cuda_install> in the CMake command.
For building only the libraries, append -DBUILD_LIBRARY_ONLY=1 to the CMake build command.
Experimental Ops
All experimental operators will be considered unsupported by the ONNX-TRT’s supportsModel() function.
NonMaxSuppression is available as an experimental operator in TensorRT 8. It has the limitation that the output shape is always padded to length [max_output_boxes_per_class, 3], therefore some post processing is required to extract the valid indices.
Executable Usage
ONNX models can be converted to serialized TensorRT engines using the onnx2trt executable:
onnx2trt my_model.onnx -o my_engine.trt
ONNX models can also be converted to human-readable text:
onnx2trt my_model.onnx -t my_model.onnx.txt
ONNX models can also be optimized by ONNX’s optimization libraries (added by dsandler).
To optimize an ONNX model and output a new one use -m to specify the output model name and -O to specify a semicolon-separated list of optimization passes to apply:
onnx2trt my_model.onnx -O "pass_1;pass_2;pass_3" -m my_model_optimized.onnx
See more all available optimization passes by running:
onnx2trt -p
See more usage information by running:
onnx2trt -h
Python Modules
Python bindings for the ONNX-TensorRT parser are packaged in the shipped .whl files. Install them with
python3 -m pip install <tensorrt_install_dir>/python/tensorrt-8.x.x.x-cp<python_ver>-none-linux_x86_64.whl
TensorRT 8.2.1.8 supports ONNX release 1.8.0. Install it with:
python3 -m pip install onnx==1.8.0
The ONNX-TensorRT backend can be installed by running:
python3 setup.py install
ONNX-TensorRT Python Backend Usage
The TensorRT backend for ONNX can be used in Python as follows:
import onnx
import onnx_tensorrt.backend as backend
import numpy as np
model = onnx.load("/path/to/model.onnx")
engine = backend.prepare(model, device='CUDA:1')
input_data = np.random.random(size=(32, 3, 224, 224)).astype(np.float32)
output_data = engine.run(input_data)[0]
print(output_data)
print(output_data.shape)
C++ Library Usage
The model parser library, libnvonnxparser.so, has its C++ API declared in this header:
NvOnnxParser.h
Tests
After installation (or inside the Docker container), ONNX backend tests can be run as follows:
Real model tests only:
python onnx_backend_test.py OnnxBackendRealModelTest
All tests:
python onnx_backend_test.py
You can use -v flag to make output more verbose.
Pre-trained Models
Pre-trained models in ONNX format can be found at the ONNX Model Zoo
