TensorRT backend for ONNX

Parses ONNX models for execution with TensorRT.

Supported TensorRT Versions

Development on the Master branch is for the latest version of TensorRT 7.0 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:

Supported Operators

Current supported ONNX operators are found in the operator support matrix.

Installation

Dependencies

Building

For building on master, we recommend following the instructions on the master branch of TensorRT as there are new dependencies that were introduced to support these new features.

To build on older branches refer to their respective READMEs.

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

Python modules

Python bindings for the ONNX-TensorRT parser are packaged in the shipped .whl files. Install them with

pip install <tensorrt_install_dir>/python/tensorrt-7.x.x.x-cp27-none-linux_x86_64.whl

TensorRT 7.0 supports ONNX release 1.6.0. Install it with:

pip install onnx==1.6.0

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)

C++ library usage

The model parser library, libnvonnxparser.so, has its C++ API declared in this header:

NvOnnxParser.h

Docker image

Tar-Based TensorRT

Build the onnx_tensorrt Docker image using tar-based TensorRT by running:

git clone --recurse-submodules https://github.com/onnx/onnx-tensorrt.git
cd onnx-tensorrt
cp /path/to/TensorRT-7.x.x.tar.gz .
docker build -f docker/onnx-tensorrt-tar.Dockerfile --tag=onnx-tensorrt:7.x.x .

Deb-Based TensorRT

Build the onnx_tensorrt Docker image using deb-based TensorRT by running:

git clone --recurse-submodules https://github.com/onnx/onnx-tensorrt.git
cd onnx-tensorrt
cp /path/to/nv-tensorrt-repo-ubuntu1x04-cudax.x-trt7.x.x.x-ga-yyyymmdd_1-1_amd64.deb .
docker build -f docker/onnx-tensorrt-deb.Dockerfile --tag=onnx-tensorrt:7.x.x.x .

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

vanmaxim/onnx-tensorrt