mirror of
https://github.com/mii443/usls.git
synced 2025-12-03 02:58:22 +00:00
Bump the version to v0.0.11
- ONNXRuntime -> 1.19.x - CUDA -> 12.x - TensorRT -> 10.x
This commit is contained in:
Jamjamjon
GitHub
10
Cargo.toml
10
Cargo.toml
@@ -1,6 +1,6 @@
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[package]
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name = "usls"
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version = "0.0.10"
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version = "0.0.11"
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edition = "2021"
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description = "A Rust library integrated with ONNXRuntime, providing a collection of ML models."
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repository = "https://github.com/jamjamjon/usls"
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@@ -11,11 +11,13 @@ exclude = ["assets/*", "examples/*", "scripts/*", "runs/*"]
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[dependencies]
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clap = { version = "4.2.4", features = ["derive"] }
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ndarray = { version = "0.15.6", features = ["rayon"] }
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ort = { version = "2.0.0-rc.2", git = "https://github.com/pykeio/ort.git", default-features = false, features = [
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ndarray = { version = "0.16.1", features = ["rayon"] }
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ort = { version = "2.0.0-rc.5", default-features = false, features = [
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"load-dynamic",
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"copy-dylibs",
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"half",
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"cann",
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"rknpu",
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"ndarray",
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"cuda",
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"tensorrt",
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@@ -24,7 +26,7 @@ ort = { version = "2.0.0-rc.2", git = "https://github.com/pykeio/ort.git", defau
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"rocm",
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"openvino",
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"operator-libraries"
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], rev = "467d127c5877b099e1d0f605d38b74d221b6121c"}
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]}
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anyhow = { version = "1.0.75" }
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regex = { version = "1.5.4" }
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rand = { version = "0.8.5" }
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161
README.md
161
README.md
@@ -1,92 +1,109 @@
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# usls
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<p align="center">
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<h2 align="center">usls</h2>
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</p>
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[](https://crates.io/crates/usls) [](https://github.com/microsoft/onnxruntime/releases) [](https://developer.nvidia.com/cuda-toolkit-archive) [](https://developer.nvidia.com/tensorrt)
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[](https://docs.rs/usls) 
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<p align="center">
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| <a href="https://docs.rs/usls"><strong>Documentation</strong></a> |
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<br>
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<br>
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<a href='https://github.com/microsoft/onnxruntime/releases'>
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<img src='https://img.shields.io/badge/ONNXRuntime-v1.19.x-239DFF?style=for-the-badge&logo=onnx' alt='ONNXRuntime Release Page'>
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</a>
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<a href='https://developer.nvidia.com/cuda-toolkit-archive'>
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<img src='https://img.shields.io/badge/CUDA-12.x-76B900?style=for-the-badge&logo=nvidia' alt='CUDA Toolkit Page'>
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</a>
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<a href='https://developer.nvidia.com/tensorrt'>
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<img src='https://img.shields.io/badge/TensorRT-10.x.x.x-76B900?style=for-the-badge&logo=nvidia' alt='TensorRT Page'>
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</a>
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</p>
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<p align="center">
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<a href='https://crates.io/crates/usls'>
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<img src='https://img.shields.io/crates/v/usls.svg?style=for-the-badge&logo=rust' alt='Crates Page'>
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</a>
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<!-- Documentation Badge -->
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<!-- <a href="https://docs.rs/usls">
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<img src='https://img.shields.io/badge/Documents-usls-000000?style=for-the-badge&logo=docs.rs' alt='Documentation'>
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</a> -->
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<!-- Downloads Badge -->
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<a href="">
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<img alt="Crates.io Total Downloads" src="https://img.shields.io/crates/d/usls?style=for-the-badge&color=3ECC5F">
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</a>
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</p>
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**`usls`** is a Rust library integrated with **ONNXRuntime** that provides a collection of state-of-the-art models for **Computer Vision** and **Vision-Language** tasks, including:
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A Rust library integrated with **ONNXRuntime**, providing a collection of **Computer Vison** and **Vision-Language** models including [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv6](https://github.com/meituan/YOLOv6), [YOLOv7](https://github.com/WongKinYiu/yolov7), [YOLOv8](https://github.com/ultralytics/ultralytics), [YOLOv9](https://github.com/WongKinYiu/yolov9), [YOLOv10](https://github.com/THU-MIG/yolov10), [RTDETR](https://arxiv.org/abs/2304.08069), [SAM](https://github.com/facebookresearch/segment-anything), [MobileSAM](https://github.com/ChaoningZhang/MobileSAM), [EdgeSAM](https://github.com/chongzhou96/EdgeSAM), [SAM-HQ](https://github.com/SysCV/sam-hq), [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM), [CLIP](https://github.com/openai/CLIP), [BLIP](https://arxiv.org/abs/2201.12086), [DINOv2](https://github.com/facebookresearch/dinov2), [YOLO-World](https://github.com/AILab-CVC/YOLO-World), [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), [Depth-Anything](https://github.com/LiheYoung/Depth-Anything), [GroundingDINO](https://github.com/IDEA-Research/GroundingDINO) and others.
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| Segment Anything |
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| :------------------------------------------------------: |
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| <img src='examples/sam/demo.png' width="800px"> |
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| YOLO + SAM |
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| :------------------------------------------------------: |
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| <img src='examples/yolo-sam/demo.png' width="800px"> |
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| Monocular Depth Estimation |
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| :--------------------------------------------------------------: |
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| <img src='examples/depth-anything/demo.png' width="800px"> |
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| Panoptic Driving Perception | Text-Detection-Recognition |
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| :----------------------------------------------------: | :------------------------------------------------: |
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| <img src='examples/yolop/demo.png' width="385px"> | <img src='examples/db/demo.png' width="385x"> |
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- **YOLO Models**: [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv6](https://github.com/meituan/YOLOv6), [YOLOv7](https://github.com/WongKinYiu/yolov7), [YOLOv8](https://github.com/ultralytics/ultralytics), [YOLOv9](https://github.com/WongKinYiu/yolov9), [YOLOv10](https://github.com/THU-MIG/yolov10)
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- **SAM Models**: [SAM](https://github.com/facebookresearch/segment-anything), [SAM2](https://github.com/facebookresearch/segment-anything-2), [MobileSAM](https://github.com/ChaoningZhang/MobileSAM), [EdgeSAM](https://github.com/chongzhou96/EdgeSAM), [SAM-HQ](https://github.com/SysCV/sam-hq), [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM)
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- **Vision Models**: [RTDETR](https://arxiv.org/abs/2304.08069), [RTMO](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmo), [DB](https://arxiv.org/abs/1911.08947), [SVTR](https://arxiv.org/abs/2205.00159), [Depth-Anything-v1-v2](https://github.com/LiheYoung/Depth-Anything), [DINOv2](https://github.com/facebookresearch/dinov2), [MODNet](https://github.com/ZHKKKe/MODNet)
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- **Vision-Language Models**: [CLIP](https://github.com/openai/CLIP), [BLIP](https://arxiv.org/abs/2201.12086), [GroundingDINO](https://github.com/IDEA-Research/GroundingDINO), [YOLO-World](https://github.com/AILab-CVC/YOLO-World)
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<details>
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<summary>Click to expand Supported Models</summary>
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## Supported Models
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| Model | Task / Type | Example | CUDA<br />f32 | CUDA<br />f16 | TensorRT<br />f32 | TensorRT<br />f16 |
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| :---------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | :--------------------------: | :-----------: | :-----------: | :------------------------: | :-----------------------: |
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| [YOLOv5](https://github.com/ultralytics/yolov5) | Classification<br />Object Detection<br />Instance Segmentation | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [YOLOv6](https://github.com/meituan/YOLOv6) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [YOLOv7](https://github.com/WongKinYiu/yolov7) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [YOLOv8](https://github.com/ultralytics/ultralytics) | Object Detection<br />Instance Segmentation<br />Classification<br />Oriented Object Detection<br />Keypoint Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [YOLOv9](https://github.com/WongKinYiu/yolov9) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [YOLOv10](https://github.com/THU-MIG/yolov10) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [RTDETR](https://arxiv.org/abs/2304.08069) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) | Instance Segmentation | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [SAM](https://github.com/facebookresearch/segment-anything) | Segment Anything | [demo](examples/sam) | ✅ | ✅ | | |
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| [MobileSAM](https://github.com/ChaoningZhang/MobileSAM) | Segment Anything | [demo](examples/sam) | ✅ | ✅ | | |
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| [EdgeSAM](https://github.com/chongzhou96/EdgeSAM) | Segment Anything | [demo](examples/sam) | ✅ | ✅ | | |
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| [SAM-HQ](https://github.com/SysCV/sam-hq) | Segment Anything | [demo](examples/sam) | ✅ | ✅ | | |
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| [YOLO-World](https://github.com/AILab-CVC/YOLO-World) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [DINOv2](https://github.com/facebookresearch/dinov2) | Vision-Self-Supervised | [demo](examples/dinov2) | ✅ | ✅ | ✅ | ✅ |
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| [CLIP](https://github.com/openai/CLIP) | Vision-Language | [demo](examples/clip) | ✅ | ✅ | ✅ visual<br />❌ textual | ✅ visual<br />❌ textual |
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| [BLIP](https://github.com/salesforce/BLIP) | Vision-Language | [demo](examples/blip) | ✅ | ✅ | ✅ visual<br />❌ textual | ✅ visual<br />❌ textual |
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| [DB](https://arxiv.org/abs/1911.08947) | Text Detection | [demo](examples/db) | ✅ | ✅ | ✅ | ✅ |
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| [SVTR](https://arxiv.org/abs/2205.00159) | Text Recognition | [demo](examples/svtr) | ✅ | ✅ | ✅ | ✅ |
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| [RTMO](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmo) | Keypoint Detection | [demo](examples/rtmo) | ✅ | ✅ | ❌ | ❌ |
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| [YOLOPv2](https://arxiv.org/abs/2208.11434) | Panoptic Driving Perception | [demo](examples/yolop) | ✅ | ✅ | ✅ | ✅ |
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| [Depth-Anything<br />(v1, v2)](https://github.com/LiheYoung/Depth-Anything) | Monocular Depth Estimation | [demo](examples/depth-anything) | ✅ | ✅ | ❌ | ❌ |
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| [MODNet](https://github.com/ZHKKKe/MODNet) | Image Matting | [demo](examples/modnet) | ✅ | ✅ | ✅ | ✅ |
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| [GroundingDINO](https://github.com/IDEA-Research/GroundingDINO) | Open-Set Detection With Language | [demo](examples/grounding-dino) | ✅ | ✅ | | |
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## Installation
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Refer to [ort docs](https://ort.pyke.io/setup/linking)
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<details close>
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<summary>For Linux or MacOS users</summary>
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- Download from [ONNXRuntime Releases](https://github.com/microsoft/onnxruntime/releases)
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- Then linking
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```Shell
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export ORT_DYLIB_PATH=/Users/qweasd/Desktop/onnxruntime-osx-arm64-1.17.1/lib/libonnxruntime.1.17.1.dylib
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```
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| Model | Task / Type | Example | CUDA f32 | CUDA f16 | TensorRT f32 | TensorRT f16 |
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|---------------------------------------------------------------------|----------------------------------------------------------------------------------------------|----------------------------|----------|----------|--------------|--------------|
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| [YOLOv5](https://github.com/ultralytics/yolov5) | Classification<br>Object Detection<br>Instance Segmentation | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [YOLOv6](https://github.com/meituan/YOLOv6) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [YOLOv7](https://github.com/WongKinYiu/yolov7) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
|
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| [YOLOv8](https://github.com/ultralytics/ultralytics) | Object Detection<br>Instance Segmentation<br>Classification<br>Oriented Object Detection<br>Keypoint Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [YOLOv9](https://github.com/WongKinYiu/yolov9) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [YOLOv10](https://github.com/THU-MIG/yolov10) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
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| [RTDETR](https://arxiv.org/abs/2304.08069) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
|
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| [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) | Instance Segmentation | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
|
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| [SAM](https://github.com/facebookresearch/segment-anything) | Segment Anything | [demo](examples/sam) | ✅ | ✅ | | |
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| [SAM2](https://github.com/facebookresearch/segment-anything-2) | Segment Anything | [demo](examples/sam) | ✅ | ✅ | | |
|
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| [MobileSAM](https://github.com/ChaoningZhang/MobileSAM) | Segment Anything | [demo](examples/sam) | ✅ | ✅ | | |
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| [EdgeSAM](https://github.com/chongzhou96/EdgeSAM) | Segment Anything | [demo](examples/sam) | ✅ | ✅ | | |
|
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| [SAM-HQ](https://github.com/SysCV/sam-hq) | Segment Anything | [demo](examples/sam) | ✅ | ✅ | | |
|
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| [YOLO-World](https://github.com/AILab-CVC/YOLO-World) | Object Detection | [demo](examples/yolo) | ✅ | ✅ | ✅ | ✅ |
|
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| [DINOv2](https://github.com/facebookresearch/dinov2) | Vision-Self-Supervised | [demo](examples/dinov2) | ✅ | ✅ | ✅ | ✅ |
|
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| [CLIP](https://github.com/openai/CLIP) | Vision-Language | [demo](examples/clip) | ✅ | ✅ | ✅ Visual<br>❌ Textual | ✅ Visual<br>❌ Textual |
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| [BLIP](https://github.com/salesforce/BLIP) | Vision-Language | [demo](examples/blip) | ✅ | ✅ | ✅ Visual<br>❌ Textual | ✅ Visual<br>❌ Textual |
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| [DB](https://arxiv.org/abs/1911.08947) | Text Detection | [demo](examples/db) | ✅ | ✅ | ✅ | ✅ |
|
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| [SVTR](https://arxiv.org/abs/2205.00159) | Text Recognition | [demo](examples/svtr) | ✅ | ✅ | ✅ | ✅ |
|
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| [RTMO](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmo) | Keypoint Detection | [demo](examples/rtmo) | ✅ | ✅ | ❌ | ❌ |
|
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| [YOLOPv2](https://arxiv.org/abs/2208.11434) | Panoptic Driving Perception | [demo](examples/yolop) | ✅ | ✅ | ✅ | ✅ |
|
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| [Depth-Anything](https://github.com/LiheYoung/Depth-Anything) | Monocular Depth Estimation | [demo](examples/depth-anything) | ✅ | ✅ | ❌ | ❌ |
|
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| [MODNet](https://github.com/ZHKKKe/MODNet) | Image Matting | [demo](examples/modnet) | ✅ | ✅ | ✅ | ✅ |
|
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| [GroundingDINO](https://github.com/IDEA-Research/GroundingDINO) | Open-Set Detection With Language | [demo](examples/grounding-dino) | ✅ | ✅ | | |
|
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|
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</details>
|
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|
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## Quick Start
|
||||
|
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## ⛳️ Linking
|
||||
|
||||
- #### For detailed setup instructions, refer to the [ORT documentation](https://ort.pyke.io/setup/linking).
|
||||
|
||||
- #### For Linux or macOS users</summary>
|
||||
1. Download the ONNXRuntime package from the [Releases page](https://github.com/microsoft/onnxruntime/releases).
|
||||
2. Set up the library path by exporting the `ORT_DYLIB_PATH` environment variable:
|
||||
```shell
|
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export ORT_DYLIB_PATH=/path/to/onnxruntime/lib/libonnxruntime.so.1.19.0
|
||||
```
|
||||
|
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|
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## 🎈 Quick Start
|
||||
|
||||
```Shell
|
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cargo run -r --example yolo # blip, clip, yolop, svtr, db, ...
|
||||
```
|
||||
|
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## Integrate into your own project
|
||||
|
||||
|
||||
## 🥂 Integrate Into Your Own Project
|
||||
|
||||
Add `usls` as a dependency to your project's `Cargo.toml`
|
||||
```Shell
|
||||
# Add `usls` as a dependency to your project's `Cargo.toml`
|
||||
cargo add usls
|
||||
|
||||
# Or you can use specific commit
|
||||
usls = { git = "https://github.com/jamjamjon/usls", rev = "???sha???"}
|
||||
|
||||
```
|
||||
|
||||
Or use a specific commit:
|
||||
```Toml
|
||||
[dependencies]
|
||||
usls = { git = "https://github.com/jamjamjon/usls", rev = "commit-sha" }
|
||||
```
|
||||
|
||||
## 📌 License
|
||||
This project is licensed under [LICENSE](LICENSE).
|
||||
|
||||
@@ -290,7 +290,6 @@ impl OrtEngine {
|
||||
}
|
||||
let xs = Xs::from(xs);
|
||||
for _ in 0..self.num_dry_run {
|
||||
// self.run(xs.as_ref())?;
|
||||
self.run(xs.clone())?;
|
||||
}
|
||||
self.ts.clear();
|
||||
|
||||
@@ -81,7 +81,8 @@ impl DB {
|
||||
let (ratio, _, _) = Ops::scale_wh(image_width, image_height, w as f32, h as f32);
|
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let v = luma
|
||||
.into_owned()
|
||||
.into_raw_vec()
|
||||
.into_raw_vec_and_offset()
|
||||
.0
|
||||
.iter()
|
||||
.map(|x| {
|
||||
if x <= &self.binary_thresh {
|
||||
|
||||
@@ -49,7 +49,7 @@ impl DepthAnything {
|
||||
let mut ys: Vec<Y> = Vec::new();
|
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for (idx, luma) in xs[0].axis_iter(Axis(0)).enumerate() {
|
||||
let (w1, h1) = (xs0[idx].width(), xs0[idx].height());
|
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let v = luma.into_owned().into_raw_vec();
|
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let v = luma.into_owned().into_raw_vec_and_offset().0;
|
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let max_ = v.iter().max_by(|x, y| x.total_cmp(y)).unwrap();
|
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let min_ = v.iter().min_by(|x, y| x.total_cmp(y)).unwrap();
|
||||
let v = v
|
||||
|
||||
@@ -52,7 +52,7 @@ impl MODNet {
|
||||
let (w1, h1) = (xs0[idx].width(), xs0[idx].height());
|
||||
let luma = luma.mapv(|x| (x * 255.0) as u8);
|
||||
let luma = Ops::resize_luma8_vec(
|
||||
&luma.into_raw_vec(),
|
||||
&luma.into_raw_vec_and_offset().0,
|
||||
self.width() as _,
|
||||
self.height() as _,
|
||||
w1 as _,
|
||||
|
||||
@@ -247,7 +247,8 @@ impl SAM {
|
||||
for (mask, iou) in masks.axis_iter(Axis(0)).zip(confs.axis_iter(Axis(0))) {
|
||||
let (i, conf) = match iou
|
||||
.to_owned()
|
||||
.into_raw_vec()
|
||||
.into_raw_vec_and_offset()
|
||||
.0
|
||||
.into_iter()
|
||||
.enumerate()
|
||||
.max_by(|a, b| a.1.total_cmp(&b.1))
|
||||
@@ -264,7 +265,7 @@ impl SAM {
|
||||
let (h, w) = mask.dim();
|
||||
let luma = if self.use_low_res_mask {
|
||||
Ops::resize_lumaf32_vec(
|
||||
&mask.to_owned().into_raw_vec(),
|
||||
&mask.into_owned().into_raw_vec_and_offset().0,
|
||||
w as _,
|
||||
h as _,
|
||||
image_width as _,
|
||||
@@ -274,7 +275,8 @@ impl SAM {
|
||||
)?
|
||||
} else {
|
||||
mask.mapv(|x| if x > 0. { 255u8 } else { 0u8 })
|
||||
.into_raw_vec()
|
||||
.into_raw_vec_and_offset()
|
||||
.0
|
||||
};
|
||||
|
||||
let luma: image::ImageBuffer<image::Luma<_>, Vec<_>> =
|
||||
|
||||
@@ -215,7 +215,7 @@ impl Vision for YOLO {
|
||||
} else {
|
||||
slice_clss.into_owned()
|
||||
};
|
||||
let mut probs = Prob::default().with_probs(&x.into_raw_vec());
|
||||
let mut probs = Prob::default().with_probs(&x.into_raw_vec_and_offset().0);
|
||||
if let Some(names) = &self.names {
|
||||
probs =
|
||||
probs.with_names(&names.iter().map(|x| x.as_str()).collect::<Vec<_>>());
|
||||
@@ -417,12 +417,12 @@ impl Vision for YOLO {
|
||||
|
||||
// coefs * proto => mask
|
||||
let coefs = Array::from_shape_vec((1, nm), coefs).ok()?; // (n, nm)
|
||||
let proto = proto.into_shape((nm, mh * mw)).ok()?; // (nm, mh * mw)
|
||||
let proto = proto.to_shape((nm, mh * mw)).ok()?; // (nm, mh * mw)
|
||||
let mask = coefs.dot(&proto); // (mh, mw, n)
|
||||
|
||||
// Mask rescale
|
||||
let mask = Ops::resize_lumaf32_vec(
|
||||
&mask.into_raw_vec(),
|
||||
&mask.into_raw_vec_and_offset().0,
|
||||
mw as _,
|
||||
mh as _,
|
||||
image_width as _,
|
||||
|
||||
@@ -191,7 +191,15 @@ impl YOLOPv2 {
|
||||
h1: f32,
|
||||
) -> Result<Vec<imageproc::contours::Contour<i32>>> {
|
||||
let mask = mask.mapv(|x| if x < thresh { 0u8 } else { 255u8 });
|
||||
let mask = Ops::resize_luma8_vec(&mask.into_raw_vec(), w0, h0, w1, h1, false, "Bilinear")?;
|
||||
let mask = Ops::resize_luma8_vec(
|
||||
&mask.into_raw_vec_and_offset().0,
|
||||
w0,
|
||||
h0,
|
||||
w1,
|
||||
h1,
|
||||
false,
|
||||
"Bilinear",
|
||||
)?;
|
||||
let mask: image::ImageBuffer<image::Luma<_>, Vec<_>> =
|
||||
image::ImageBuffer::from_raw(w1 as _, h1 as _, mask)
|
||||
.ok_or(anyhow::anyhow!("Failed to build image"))?;
|
||||
|
||||
Reference in New Issue
Block a user