# go-rknnlite


![go-rknnlite-logo.jpg](go-rknnlite-logo.jpg)

go-rknnlite provides Go language bindings for the [RKNN Toolkit2](https://github.com/airockchip/rknn-toolkit2/tree/master)
C API interface.  It aims to provide lite bindings in the spirit of the closed source
Python lite bindings used for running AI Inference models on the Rockchip NPU 
via the RKNN software stack.

These bindings are made to work with Rockchips [RK35xx series](https://www.rock-chips.com/a/en/products/RK35_Series/) of processors,
specifically the RK3562, RK3566, RK3568, RK3576, RK3582, and RK3588.


## Usage

To use in your Go project, get the library.
```
go get github.com/swdee/go-rknnlite
```

Or to try the examples clone the git code and data repositories.  
```
git clone https://github.com/swdee/go-rknnlite.git
cd go-rknnlite/example
git clone --depth=1 https://github.com/swdee/go-rknnlite-data.git data
```

Then refer to the Readme files for each example to run on command line.


## Dependencies

The [rknn-toolkit2](https://github.com/airockchip/rknn-toolkit2) must be installed on
your system with C header files and libraries available in the system path,
eg: `/usr/include/rknn_api.h` and `/usr/lib/librknnrt.so`.  If your using an
official OS image provided by your SBC vendor these files probably already exist.

Refer to the official documentation on how to install this on your system as it
will vary based on OS and SBC vendor.

### Verify rknpu Driver

My usage was on the Radxa Rock Pi 5B running the official Debian 12 OS image which
has the rknpu2 driver already installed.

To my knowledge [Armbian](https://www.armbian.com/) and 
[Joshua's Ubuntu](https://github.com/Joshua-Riek/ubuntu-rockchip/) 
OS images also have the driver installed for the support SBC's.

You can test if your OS has the driver installed with.
```
dmesg | grep -i rknpu
```

The output should list the driver and indicate the NPU is initialized.
```
[    5.726221] [drm] Initialized rknpu 0.9.6 20240322 for fdab0000.npu on minor 1
```

### GoCV

The examples make use of [GoCV](https://gocv.io/) for image processing.  Make sure
you have a working installation of GoCV first, see the [How to Install](INSTALL.md)
instructions that provide details on prebuilt docker images or manual
installation.



## Examples

See the [example](example) directory.

* Image Classification
  * [MobileNet Demo](example/mobilenet)
  * [Pooled Runtime Usage](example/pool)
  * [Batch Input Usage](example/batch)
* Object Detection
  * [YOLOv5 Demo](example/yolov5)  
  * [YOLOv8 Demo](example/yolov8)
  * [YOLOv10 Demo](example/yolov10)
  * [YOLOv11 Demo](example/yolov11) 
  * [YOLOX Demo](example/yolox) 
  * [RetinaFace](example/retinaface) - Detect human faces and identify landmark features (eyes, nose, and mouth).
  * [YOLO-NAS Demo](example/yolo-nas)
* Instance Segmentation
  * [YOLOv5-seg Demo](example/yolov5-seg)
  * [YOLOv8-seg Demo](example/yolov8-seg)
* Pose Estimation
  * [YOLOv8-pose Demo](example/yolov8-pose) - Identify and display skeleton/pose of people.
* Oriented Bounding Boxes
  * [YOLOv8-obb Demo](example/yolov8-obb) - Object detection with Oriented Bounding Boxes
* License Plate Recognition
  * [LPRNet Demo](example/lprnet) 
  * [ALPR Demo](example/alpr) - Automatic License Plate Recognition combining Yolov8 and LPRNet Models.
* Text Identification
  * [PPOCR Detect](example/ppocr#ppocr-detect) - Takes an image and detects areas of text.
  * [PPOCR Recognise](example/ppocr#ppocr-recognise) - Takes an area of text and performs OCR on it.
  * [PPOCR System](example/ppocr#ppocr-system) - Combines both Detect and Recognise.
* Tracking
  * [Re-Identification Demo](example/reid) - Re-Identify (ReID) similar objects for tracking, uses batch processing. 
* Streaming
  * [HTTP Stream with ByteTrack Tracking](example/stream) - Demo that streams a video over HTTP with YOLO object detection and ByteTrack object tracking.  
* Slicing Aided Hyper Inference
  * [SAHI YOLO Demo](example/sahi) - YOLO Object detection using SAHI on a 4k image with Pooled inferencing. 
* Depth Estimation
  * [MiDaS v3.1 Demo](example/midas) 


## Converting Inference Models

To convert your inference model into the required `.rknn` format to run on the NPU, see the
[vendor instructions](https://github.com/airockchip/rknn_model_zoo/tree/main/examples/yolov8#4-convert-to-rknn) 
in the Model Zoo.

Each Model has its own `convert.py` script contained in the vendors project.  You may
need to modify this python script for your own Models depending on how they were trained.   

Run the `convert.py` script on your x86 workstation to perform the conversion.

We also provide a [docker image](toolkit/) with the [rknn-toolkit2](https://github.com/airockchip/rknn-toolkit2/) 
and the [Model Zoo](https://github.com/airockchip/rknn_model_zoo) installed
which can be used for compiling your custom models to RKNN format.


## Pooled Runtimes

Running multiple Runtimes in a Pool allows you to take advantage of all three
NPU cores.  For our usage of an EfficentNet-Lite0 model, a single runtime has
an inference speed of 7.9ms per image, however running a Pool of 9 runtimes brings
the average inference speed down to 1.65ms per image.

See the [Pool example](example/pool).


## Runtime

To initialize a new instance of the rknnlite runtime call.

```
rt, err := rknnlite.NewRuntime("path/to/model.file", rknnlite.NPUCoreAuto)
```

You can pin which NPU cores the model runs on by adjusting the second parameter
above to any of the CoreMask values defined.

For convenience you can also initialize the runtime by passing a string value of
the platform your running on.

```
rt, err := rknnlite.NewRuntimeByPlatform("rk3576", "path/to/model.file")
```


### RK356x Platforms


Rockchip models such as the RK356x series feature a single NPU core and
don't support pinning the model to specific NPU cores, so initialise
the Runtime with the `rknnlite.NPUSkipSetCore` flag as follows.

```
rt, err := rknnlite.NewRuntime(*modelFile, rknnlite.NPUSkipSetCore)
```

If you use `rknnlite.NewRuntimeByPlatform()` instead this will be automatically 
set for you.


## Runtime Inference

Once a Runtime has been created inference is performed by passing the input 
tensors.

```
rt.Inference([]gocv.Mat{})
```

The `Inference()` function takes a slice of gocv.Mat's where the number of
elements in the slice corresponds to the total number of input tensors the
Model has.  Typically most models only have a single input tensor so only a single
gocv.Mat would be passed here.

If you want to pass multiple images in a single `Inference()` call, then you need
to use [Batching](example/batch).


## CPU Affinity

The performance of the NPU is effected by which CPU cores your program runs on, so
to achieve maximum performance we need to set the CPU Affinity.

The RK3588 for example has 4 fast Cortex-A76 cores at 2.4Ghz and 4 efficient
Cortex-A55 cores at 1.8Ghz.  By default your Go program will run across all cores
which effects performance, instead set the CPU Affinity to run on the fast Cortex-A76
cores only.

```
// set CPU affinity
err = rknnlite.SetCPUAffinity(rknnlite.RK3588FastCores)
	
if err != nil {
	log.Printf("Failed to set CPU Affinity: %v\n", err)
}
```

Constants have been set for each platform as 
`rknnlite.<platform>FastCores`, `rknnlite.<platform>SlowCores`,
and `rknnlite.<platform>AllCores`.  You can specify you own custom configuration
by defining the core mask.

You can also specify the CPU Affinity by passing a string value for the platform
your running on.
```
err := rknnlite.SetCPUAffinityByPlatform("rk3576", rknnlite.FastCores)
```


### Core Mask

To create the core mask value we will use the RK3588 as an example which has 
CPU cores 0-3 as the slow A55 cores and cores 4-7 being the fast A76 cores.

You can use the provided convenience function to calculate the mask for cores 4-7.

```
mask := rknnlite.CPUCoreMask([]int{4,5,6,7})
```


## NPU Clock Speed

Depending on the OS being used the NPU clock speed and governor may not be ideal
for achieving best performance from the NPU.

First locate the sys path of your NPU by running;
```
for d in /sys/class/devfreq/*; do \
  grep -qi 'rknpu' "$d/device/of_node/compatible" && echo "$d"; \
done
```

On the Rock 5B this outputs `/sys/class/devfreq/fdab0000.npu` and Rock 4D outputs
`/sys/class/devfreq/27700000.npu`.


Next check that the `performance` governor is available;
```
cat /sys/class/devfreq/27700000.npu/available_governors
```

Set governor to `performance` to set maximum NPU clock frequency.
```
echo performance > /sys/class/devfreq/27700000.npu/governor
```


### Permanent Clock Speed

Setting the governor to `performance` in the above command is not permanent 
and setting will be lost on next reboot.   To make it permanent setup a udev rule.

Create file `/etc/udev/rules.d/80-npu-governor.rules` with contents;
```
# When the RK3576 NPU devfreq device shows up, set its governor to "performance"
SUBSYSTEM=="devfreq", KERNEL=="27700000.npu", ATTR{governor}="performance"
```

Then reload udev and load the rule
```
sudo udevadm control --reload
sudo udevadm trigger --action=add /sys/class/devfreq/27700000.npu
```

Verify governor has changed and frequency is set to the maximum.
```
$ sudo cat /sys/class/devfreq/27700000.npu/governor
performance

$ sudo cat /sys/class/devfreq/27700000.npu/cur_freq 
950000000
```

Note: In all of the above commands adjust the sys path to your NPU by 
replacing `27700000.npu` where appropriate.



## PreProcessing

Convenience functions exist for handling preprocessing of images to run inference on. 

The `preprocess.Resizer` provides functions for handling resizing and scaling of input
images to the target size needed for inference input tensors.  It will maintain
aspect ratio by scaling and applying any needed letterbox padding to the source image.

```
// load source image file
img := gocv.IMRead(filename, gocv.IMReadColor)

if img.Empty() {
		log.Fatal("Error reading image from: ", *imgFile)
}

// convert colorspace from GoCV's BGR to RGB as most models have been trained
// using RGB data 
rgbImg := gocv.NewMat()
gocv.CvtColor(img, &rgbImg, gocv.ColorBGRToRGB)

// create new resizer setting the source image size and input tensor sizes
resizer := preprocess.NewResizer(img.Cols(), img.Rows(),
  int(inputAttrs[0].Dims[1]), int(inputAttrs[0].Dims[2]))

// resize image
resizedImg := gocv.NewMat()
resizer.LetterBoxResize(rgbImg, &resizedImg, render.Black)
```

For Object Detection and Instance Segmentation the Resizer is required so 
image mask sizes can be correctly calculated and scaled back for applying as 
an overlay on the source image.


## Renderer

The `render` package provides convenience functions for drawing the bounding box
around objects or segmentation mask/outline.


## Post Processing

If a Model (ie: specific YOLO version) is not yet supported, a post processor 
could be written to handle the outputs from the RKNN engine in the same manner the
YOLOv5 code has been created.   


## Notice

This code is being used in production for Image Classification.  Over time it will be expanded
on to support more features such as Object Detection using YOLO.   The addition of
new features may cause changes or breakages in the API between commits due to the
early nature of how this library evolves.

Ensure you use Go Modules so your code is not effected, but be aware any updates may
require minor changes to your code to support the latest version.

Versioning of the library will be added at a later date once the feature set stablises.

See the [CHANGES](CHANGES.md) file for a list of breaking changes.



## Reference Material

* [rknn-toolkit2](https://github.com/airockchip/rknn-toolkit2) - RKNN software stack
tools and C API.
* [C API Reference Documentation](https://github.com/airockchip/rknn-toolkit2/blob/master/doc/04_Rockchip_RKNPU_API_Reference_RKNNRT_V2.0.0beta0_EN.pdf)
* [RKNN Model Zoo](https://github.com/airockchip/rknn_model_zoo/tree/main/examples) - RKNN maintained Model Zoo with example code