TensorRT backend for ONNX
Parses ONNX models for execution with TensorRT.
See also the TensorRT documentation.
ONNX 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)
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
See more usage information by running:
onnx2trt -h
C++ library usage
The model parser library, libnvonnxparser.so, has a C++ API declared in this header:
NvOnnxParser.h
TensorRT engines built using this parser must use the plugin factory provided in libnvonnxparser_runtime.so, which has a C++ API declared in this header:
NvOnnxParserRuntime.h
Installation
Dependencies
Download the code
Clone the code from GitHub.
git clone --recursive https://github.com/onnx/onnx-tensorrt.git
Executable and libraries
Suppose your TensorRT library is located at /opt/tensorrt
. Build the onnx2trt
executable and the libnvonnxparser*
libraries using CMake:
mkdir build
cd build
cmake .. -DTENSORRT_ROOT=/opt/tensorrt
make -j8
sudo make install
Python modules
Build the Python wrappers and modules by running:
python setup.py build
sudo python setup.py install
Docker image
Build the onnx_tensorrt Docker image by running:
cp /path/to/TensorRT-3.0.*.tar.gz .
docker build -t onnx_tensorrt .
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 Caffe2 models in ONNX format can be found at https://github.com/onnx/models