# usls

A Rust library integrated with **ONNXRuntime**, providing a collection of **Computer Vison** and **Vision-Language** models including [YOLOv8](https://github.com/ultralytics/ultralytics) `(Classification, Segmentation, Detection and Pose Detection)`, [YOLOv9](https://github.com/WongKinYiu/yolov9), [RTDETR](https://arxiv.org/abs/2304.08069), [CLIP](https://github.com/openai/CLIP), [DINOv2](https://github.com/facebookresearch/dinov2), [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM), [YOLO-World](https://github.com/AILab-CVC/YOLO-World), [BLIP](https://arxiv.org/abs/2201.12086), and others. Many execution providers are supported, sunch as `CUDA`, `TensorRT` and `CoreML`.

## Supported Models

|         Model         |         Example         |     CUDA(f32)     |     CUDA(f16)     |       TensorRT(f32)       |       TensorRT(f16)       |
| :-------------------: | :----------------------: | :----------------: | :----------------: | :------------------------: | :-----------------------: |
|   YOLOv8-detection   |   [demo](examples/yolov8)   |         ✅         |         ✅         |             ✅             |            ✅            |
|      YOLOv8-pose      |   [demo](examples/yolov8)   |         ✅         |         ✅         |             ✅             |            ✅            |
| YOLOv8-classification |   [demo](examples/yolov8)   |         ✅         |         ✅         |             ✅             |            ✅            |
|  YOLOv8-segmentation  |   [demo](examples/yolov8)   |         ✅         |         ✅         |             ✅             |            ✅            |
|      YOLOv8-OBB      |    ***TODO***    | ***TODO*** | ***TODO*** |     ***TODO***     |    ***TODO***    |
|        YOLOv9        |   [demo](examples/yolov9)   |         ✅         |         ✅         |             ✅             |            ✅            |
|        RT-DETR        |   [demo](examples/rtdetr)   |         ✅         |         ✅         |             ✅             |            ✅            |
|        FastSAM        |  [demo](examples/fastsam)  |         ✅         |         ✅         |             ✅             |            ✅            |
|      YOLO-World      | [demo](examples/yolo-world) |         ✅         |         ✅         |             ✅             |            ✅            |
|        DINOv2        |   [demo](examples/dinov2)   |         ✅         |         ✅         |             ✅             |            ✅            |
|         CLIP         |    [demo](examples/clip)    |         ✅         |         ✅         | ✅ visual<br />❌ textual | ✅ visual<br />❌ textual |
|         BLIP         |    [demo](examples/blip)    |         ✅         |         ✅         | ✅ visual<br />❌ textual | ✅ visual<br />❌ textual |
|     OCR(DB, SVTR)     |    ***TODO***    | ***TODO*** | ***TODO*** |     ***TODO***     |    ***TODO***    |

## Solution Models

Additionally, this repo also provides some solution models such as pedestrian `fall detection`, `head detection`, `trash detection`, and more.

|          Model          |             Example             |  
| :---------------------: | :------------------------------: |
| face-landmark detection |    [demo](examples/yolov8-face)    |
|     head detection     |    [demo](examples/yolov8-head)    |
|     fall detection     |  [demo](examples/yolov8-falldown)  |
|    trash detection    | [demo](examples/yolov8-plastic-bag) | 

## Demo

```
cargo run -r --example yolov8   # fastsam, yolov9, blip, clip, dinov2, yolo-world...
```

## Integrate into your own project

#### 1. Install [ort](https://github.com/pykeio/ort)

check **[ort guide](https://ort.pyke.io/setup/linking)**

<details close>
<summary>For Linux or MacOS users</summary>

- Firstly, download from latest release from [ONNXRuntime Releases](https://github.com/microsoft/onnxruntime/releases)
- Then linking
  ```shell
  export ORT_DYLIB_PATH=/Users/qweasd/Desktop/onnxruntime-osx-arm64-1.17.1/lib/libonnxruntime.1.17.1.dylib
  ```

</details>

#### 2. Add `usls` as a dependency to your project's `Cargo.toml`

```shell
cargo add --git https://github.com/jamjamjon/usls

# or
cargo add usls 
```


#### 3. Set `Options` and build model
```Rust
let options = Options::default()
    .with_model("../models/yolov8m-seg-dyn-f16.onnx")
    .with_trt(0) // using cuda(0) by default
// when model with dynamic shapes
    .with_i00((1, 2, 4).into()) // dynamic batch
    .with_i02((416, 640, 800).into())   // dynamic height
    .with_i03((416, 640, 800).into())   // dynamic width
    .with_confs(&[0.4, 0.15]) // person: 0.4, others: 0.15
    .with_dry_run(3)
    .with_saveout("YOLOv8");    // save results
let mut model = YOLO::new(&options)?;
```

#### 4. Prepare inputs, and then you're ready to go

- Build `DataLoader` to load images

```Rust
let dl = DataLoader::default()
    .with_batch(model.batch.opt as usize)
    .load("./assets/")?;

for (xs, _paths) in dl {
    let _y = model.run(&xs)?;
}
```

- Or simply read one image

```Rust
let x = DataLoader::try_read("./assets/bus.jpg")?;
let _y = model.run(&[x])?;
```


## Script: converte ONNX model from `float32` to `float16`

```python
import onnx
from pathlib import Path
from onnxconverter_common import float16

model_f32 = "onnx_model.onnx"
model_f16 = float16.convert_float_to_float16(onnx.load(model_f32))
saveout = Path(model_f32).with_name(Path(model_f32).stem + "-f16.onnx")
onnx.save(model_f16, saveout)
```