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 main branch is for the latest version of TensorRT 8.6 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 8.6
• TensorRT 8.6 open source libaries (main branch)

Building

For building within docker, we recommend using and setting up the docker containers as instructed in the main TensorRT repository 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=<path_to_trt> && make -j
// 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.

InstanceNormalizaiton Performance

In TensorRT 8.6 there are two implementations of InstanceNormalization that may perform differently depending on various parameters. By default the parser will insert an InstanceNormalization plugin layer as it performs best for general use cases. Users that want to benchmark using the native TensorRT implementation of InstanceNorm can set the parser flag kNATIVE_INSTANCENORM prior to parsing the model. For building version compatible or hardware compatible engines, this flag must be set.

C++ Example:

auto flag = 1U << static_cast<uint32_t>(nvonnxparser::OnnxParserFlag::kNATIVE_INSTANCENORM);
parser->setFlags(flag);

Python Example:

flag = 1 << (int)(trt.OnnxParserFlag.NATIVE_INSTANCENORM)
parser.flags = flag

Executable Usage

There are currently two officially supported tools for users to quickly check if an ONNX model can parse and build into a TensorRT engine from an ONNX file.

For C++ users, there is the trtexec binary that is typically found in the <tensorrt_root_dir>/bin directory. The basic command of running an ONNX model is:

trtexec --onnx=model.onnx

Refer to the link or run trtexec -h for more information on CLI options.

For Python users, there is the polygraphy tool. The basic command for running an onnx model is:

polygraphy run model.onnx --trt

Refer to the link or run polygraphy run -h for more information on CLI options.

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 8.6 supports ONNX release 1.13.1. Install it with:

python3 -m pip install onnx==1.13.1

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