# onnx-tensorrt

**Repository Path**: Mr_xiaolong/onnx-tensorrt

## Basic Information

- **Project Name**: onnx-tensorrt
- **Description**: 来自 https://github.com/onnx/onnx-tensorrt
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 0
- **Created**: 2021-06-09
- **Last Updated**: 2022-06-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<!--- SPDX-License-Identifier: Apache-2.0 -->

# TensorRT Backend For ONNX

Parses ONNX models for execution with [TensorRT](https://developer.nvidia.com/tensorrt).

See also the [TensorRT documentation](https://docs.nvidia.com/deeplearning/sdk/#inference).

For the list of recent changes, see the [changelog](docs/Changelog.md).

For a list of commonly seen issues and questions, see the [FAQ](docs/faq.md).

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 8.0.1.6](https://developer.nvidia.com/nvidia-tensorrt-download) 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](https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/sampleOnnxMNIST)
* [sampleDynamicReshape](https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/sampleDynamicReshape)

## Supported Operators

Current supported ONNX operators are found in the [operator support matrix](docs/operators.md).

# Installation

### Dependencies

 - [Protobuf >= 3.0.x](https://github.com/google/protobuf/releases)
 - [TensorRT 8.0.1.6](https://developer.nvidia.com/tensorrt)
 - [TensorRT 8.0.1.6 open source libaries (master branch)](https://github.com/NVIDIA/TensorRT/)

### 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=<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

For building only the libraries, append `-DBUILD_LIBRARY_ONLY=1` to the CMake build command. If your model has Gather or GatherElements operations with negative indices, add `-DSUPPORT_NEGATIVE_GATHER` to the build command. Note that enabling negative-indices gather will have a performance impact on gathers with non-negative indices.

### 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](https://gitlab-master.nvidia.com/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 8.0.1.6 supports ONNX release 1.6.0. Install it with:

    python3 -m pip install onnx==1.6.0

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:

```python
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](https://github.com/onnx/models)