# Custom operators for ONNX Runtime in MMCV

## Introduction of ONNX Runtime

**ONNX Runtime** is a cross-platform inferencing and training accelerator compatible with many popular ML/DNN frameworks. Check its [github](https://github.com/microsoft/onnxruntime) for more information.

## Introduction of ONNX

**ONNX** stands for **Open Neural Network Exchange**, which acts as *Intermediate Representation(IR)* for ML/DNN models from many frameworks. Check its [github](https://github.com/onnx/onnx) for more information.

## Why include custom operators for ONNX Runtime in MMCV

- To verify the correctness of exported ONNX models in ONNX Runtime.
- To ease the deployment of ONNX models with custom operators from `mmcv.ops` in ONNX Runtime.

## List of operators for ONNX Runtime supported in MMCV

|                        Operator                        |  CPU  |  GPU  | MMCV Releases |
| :----------------------------------------------------: | :---: | :---: | :-----------: |
|      [SoftNMS](onnxruntime_custom_ops.md#softnms)      |   Y   |   N   |     1.2.3     |
|     [RoIAlign](onnxruntime_custom_ops.md#roialign)     |   Y   |   N   |     1.2.5     |
|          [NMS](onnxruntime_custom_ops.md#nms)          |   Y   |   N   |     1.2.7     |
| [grid_sampler](onnxruntime_custom_ops.md#grid_sampler) |   Y   |   N   |    master     |

## How to build custom operators for ONNX Runtime

*Please be noted that only **onnxruntime>=1.5.1** of CPU version on Linux platform is tested by now.*

### Prerequisite

- Clone repository

```bash
git clone https://github.com/open-mmlab/mmcv.git
```

- Download `onnxruntime-linux-x64-1.5.1.tgz` from ONNX Runtime [releases](https://github.com/microsoft/onnxruntime/releases/tag/v1.5.1), extract it, expose `ONNXRUNTIME_DIR` and finally add the lib path to `LD_LIBRARY_PATH` as below:

```bash

wget https://github.com/microsoft/onnxruntime/releases/download/v1.5.1/onnxruntime-linux-x64-1.5.1.tgz

tar -zxvf onnxruntime-linux-x64-1.5.1.tgz
cd onnxruntime-linux-x64-1.5.1
export ONNXRUNTIME_DIR=$(pwd)
export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH
```

### Build on Linux

```bash
cd mmcv # to MMCV root directory
MMCV_WITH_OPS=1 MMCV_WITH_ORT=1 pip install -e .
```

## How to do inference using exported ONNX models with custom operators in ONNX Runtime in python

Install ONNX Runtime with `pip`

```bash
pip install onnxruntime==1.5.1
```

Inference Demo

```python
import os

import numpy as np
import onnxruntime as ort

from mmcv.ops import get_onnxruntime_op_path

ort_custom_op_path = get_onnxruntime_op_path()
assert os.path.exists(ort_custom_op_path)
session_options = ort.SessionOptions()
session_options.register_custom_ops_library(ort_custom_op_path)
# exported ONNX model with custom operators
onnx_file = 'sample.onnx'
input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)
sess = ort.InferenceSession(onnx_file, session_options)
onnx_results = sess.run(None, {'input' : input_data})
```

## How to add a new custom operator for ONNX Runtime in MMCV

### Reminder

- The custom operator is not included in [supported operator list](https://github.com/microsoft/onnxruntime/blob/master/docs/OperatorKernels.md) in ONNX Runtime.
- The custom operator should be able to be exported to ONNX.

### Main procedures

Take custom operator `soft_nms` for example.

1. Add header `soft_nms.h` to ONNX Runtime include directory `mmcv/ops/csrc/onnxruntime/`
2. Add source `soft_nms.cpp` to ONNX Runtime source directory `mmcv/ops/csrc/onnxruntime/cpu/`
3. Register `soft_nms` operator in [onnxruntime_register.cpp](../mmcv/ops/csrc/onnxruntime/cpu/onnxruntime_register.cpp)

    ```c++
    #include "soft_nms.h"

    SoftNmsOp c_SoftNmsOp;

    if (auto status = ortApi->CustomOpDomain_Add(domain, &c_SoftNmsOp)) {
    return status;
    }
    ```

4. Add unit test into `tests/test_ops/test_onnx.py`
   Check [here](../tests/test_ops/test_onnx.py) for examples.

**Finally, welcome to send us PR of adding custom operators for ONNX Runtime in MMCV.** :nerd_face:

## Known Issues

- None

## References

- [How to export Pytorch model with custom op to ONNX and run it in ONNX Runtime](https://github.com/onnx/tutorials/blob/master/PyTorchCustomOperator/README.md)
- [How to add a custom operator/kernel in ONNX Runtime](https://github.com/microsoft/onnxruntime/blob/master/docs/AddingCustomOp.md)
