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**/__pycache__
*.pth
backup/
proxy.sh
datasets/annotations/
datasets/train2017/
datasets/val2017/
logs/
*.onnx
*.log
\ No newline at end of file
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# RT-DETR
## 论文
`DETRs Beat YOLOs on Real-time Object Detection`
- https://arxiv.org/abs/2304.08069
## 模型结构
RT-DETR是一种先进的实时物体检测器，它建立在视觉转换器（Vision Transformer）架构之上：
模型利用主干网络的最后三个阶段的输出特征{S3，S4，S5}作为编码器的输入；
混合编码器通过度内特征交互（AIFI）和跨尺度特征融合模块（CCFM）将多尺度特征转换成图像特征序列；
随后采用loU感知查询选择从编码器输出的特征序列中选择固定数量的特征，作为解码器的初始目标查询；
最后解码器通过辅助预测头迭代优化目标查询，生成边界框和置信度得分。
<div align=center>
<img src="./doc/RT-DETR.PNG"/>
</div>
## 算法原理
1、主干网络：对于 backbone 部分，采用了经典的 ResNet 和可缩放的 HGNetv2 两种，两种 backbone 各训练了两个版本 ，以 HGNetv2 为 backbone 的 RT-DETR 包括 L 和 X 版本，以ResNet 为 backbone 的RT-DETR 则包括 RT-DETR-R50 和 RT-DETR-R101 。
RT-DETR-R50/101 做主干方便和现有的 DETR 变体进行对比，而 RT-DETR-HGNet-L/X 则用来和现有的实时检测器进行对比，值得注意的是，HGNetv2是由百度自家研发的主干结构。
与YOLO相似的地方在于，RT-DETR最终会输出三种不同尺寸的特征图，它们相对于输入图像的分辨率下采样倍数分别是 8 倍、16 倍和 32 倍。
2、混合编码器：RT-DETR 采用了一层 Transformer 的 Encoder ，其包括度内特征交互（AIFI）和跨尺度特征融合模块（CCFM）两部分。
它首先将二维的 S5 特征拉成向量，然后交给 AIFI 模块处理，其数学过程就是多头自注意力与 FFN，随后，再将输出 Reshape 回二维，记作 F5，以便去完成后续的所谓的“跨尺度特征融合”。
CCFM模块是由 2 个 1×1 卷积和 N 个 RepBlock 构成的，通过调整 CCFM 中 RepBlock 的数量和 Encoder 的编码维度分别控制 Hybrid Encoder 的深度和宽度，同时对 backbone 进行相应的调整即可实现检测器的缩放。
3、loU：过在训练期间约束检测器对高 IoU 的特征产生高分类分数，对低 IoU 的特征产生低分类分数。从而使得模型根据分类分数选择的 Top-K 特征对应的预测框同时具有高分类分数和高 IoU 分数。
4、解码器：支持不同层数的灵活推理，无需重训练。
<div align=center>
<img src="./doc/RT-DETR.PNG"/>
</div>
## 代码改动说明
项目要求torch==2.0.1、torchvision==0.15.2，dcu的torch==2.1.0、torchvision==0.16.0版本过高。
问题主要集中在torchvison.datapoints、torchvison.transformers等库的调用中，其中torchvision的datapoints依赖库在高版本中完全被移除了。
因此进行了代码适配修改，根据api替换了一遍，由于修改过多不再一一展示，可在仓库内搜索"TODO"查看。
ps：仓库中是改动后的代码，不需再次修改
## 环境配置
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.2-py3.10
docker run -it --name=RT-DETR --network=host --privileged=true --device=/dev/kfd --device=/dev/dri --shm-size=16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /path/your_code_data:/home/RT-DETR -v /opt/hyhal/:/opt/hyhal/:ro <imageID> bash # <imageID>为以上拉取的docker的镜像ID替换
cd RT-DETR
# 安装依赖
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install -r requirements.txt
```
### Dockerfile（方法二）
```
docker build --no-cache -t rtdetr:latest .
docker run -it --name=RT-DETR --network=host --privileged=true --device=/dev/kfd --device=/dev/dri --shm-size=16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /path/your_code_data:/home/RT-DETR -v /opt/hyhal/:/opt/hyhal/:ro rtdetr /bin/bash
cd RT-DETR
# 安装依赖
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install -r requirements.txt
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装: https://developer.hpccube.com/tool/
```
DTK软件栈：dtk24.04.2
python：python3.10
pytorch：2.1.0
torchvision：0.16.0
onnxruntime：1.15.0
```
`Tips：以上dtk软件栈、python、pytorch等DCU相关工具版本需要严格一一对应`
2、其他非特殊库直接按照下面步骤进行安装
```
cd RT-DETR
# 安装依赖
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install -r requirements.txt
```
## 数据集
### 训练数据集
`Coco2017`
仅需要annotations_trainval2017.zip、val2017.zip、train2017.zip作为数据集。可通过[scnet](http://113.200.138.88:18080/aidatasets/coco2017) 或[官网链接](https://cocodataset.org/#download)进行下载，下载后的压缩包需要解压缩。通过官网下载和解压数据集的代码如下：
ps：本仓库内准备了小数据集供训练测试，位于。。。。。
```
cd RT-DETR/datasets
wget -i url.txt
# 解压
apt-get update
apt-get install unzip
unzip annotations_trainval2017.zip;unzip val2017.zip;unzip train2017.zip
```
数据集目录结构如下：
```
RT-DETR/datasets:
── train2017
│   └── ...
── val2017
│   └── ...
── annotations
│   └── ...
```
### 推理数据集
推理测试所用数据已保存在RT-DETR/datasets/000000033109.jpg
## 训练
运行代码时会默认下载辅助模型，存储目录为/root/.cache/torch/hub/checkpoints/
### 单机多卡
```
cd RT-DETR
HIP_VISIBLE_DEVICES=0,1,2,3;torchrun --nproc_per_node=4 rtdetr_pytorch/tools/train.py -c rtdetr_pytorch/configs/rtdetr/rtdetr_r18vd_6x_coco.yml
# -c 配置文件路径
# -r 恢复训练的权重加载路径
```
注：如果挂载目录不是"/home/RT-DETR",需要修改RT-DETR/rtdetr_pytorch/configs/dataset/coco_detection.yml和RT-DETR/rtdetr_pytorch/configs/rtdetr/rtdetr_r18vd_6x_coco.yml中的对应路径。
## 推理
权重可通过[scnet](http://113.200.138.88:18080/aimodels/findsource-dependency/rt-detrv1/-/tree/main/pytorch) 或 [官网链接](https://github.com/lyuwenyu/storage/releases/tag/v0.1)进行下载，通过官网下载的代码如下：
```
cd RT-DETR
wget -i model/url.txt -P model/
```
onnx_infer.py内可以修改onnx模型目录和数据集目录，推理代码如下：
```
cd RT-DETR
# 1、导出onnx文件，以r18vd_6x为例
HIP_VISIBLE_DEVICES=0 python rtdetr_pytorch/tools/export_onnx.py \
-c rtdetr_pytorch/configs/rtdetr/rtdetr_r101vd_6x_coco.yml \
-r model/rtdetr_r101vd_6x_coco_from_paddle.pth \
-f model/onnx/rtdetr_r101vd_6x_coco.onnx \
--check
# -c 模型配置文件的存储目录
# -r 源模型的目录
# -f onnx模型的导出目录
# --check 检查onnx模型是否导出完成
# 2、运行推理
python onnx_infer.py
```
注：如果挂载目录不是"/home/RT-DETR",需要修改RT-DETR/rtdetr_pytorch/configs/dataset/coco_detection.yml和RT-DETR/rtdetr_pytorch/configs/rtdetr/rtdetr_r18vd_6x_coco.yml中的对应路径。
## 评估
评估代码如下：
```
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 rtdetr_pytorch/tools/train.py \
-c rtdetr_pytorch/configs/rtdetr/rtdetr_r101vd_6x_coco.yml \
-r model/rtdetr_r101vd_6x_coco_from_paddle.pth \
--test-only
# -c 配置文件路径
# -r 权重加载路径
# --test-only 只进行评估
```
## result
默认推理结果为：
<div align=center>
<img src="./doc/inference_result.png"/>
</div>
### 精度
| | 测试参数 | 软件栈 | final loss |
| ---------------------------- | ------------------------------- | ---------- | ---------- |
| A800 * 4 （80G，1410 Mhz） | config=rtdetr_r18vd_6x_coco.yml | cuda11.8 | 9.390466 |
| k100ai * 4 （64G，1400 Mhz） | config=rtdetr_r18vd_6x_coco.yml | dtk24.04.2 | 9.400760 |
## 应用场景
### 算法类别
`目标检测`
### 热点应用行业
`制造,交通,电商,广媒,医疗`
## 预训练权重
- http://113.200.138.88:18080/aimodels/findsource-dependency/rt-detrv1/-/tree/main/pytorch
- https://github.com/lyuwenyu/storage/releases/tag/v0.1
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/rt-detr_pytorch
## 参考资料
- https://github.com/huangb23/VTimeLLM
\ No newline at end of file
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简体中文 | [English](README.md)
# RT-DETR
This is the official implementation of the paper "[DETRs Beat YOLOs on Real-time Object Detection](https://arxiv.org/abs/2304.08069)".
<div align="center">
<img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/0ede1dc1-a854-43b6-9986-cf9090f11a61" width=500 >
</div>
## 最新动态
- 发布RT-DETR-R50, RT-DETR-R101模型
- 发布RT-DETR-R50-m模型（scale模型的范例）
- 发布RT-DETR-R34, RT-DETR-R18模型
- 发布RT-DETR-L, RT-DETR-X模型
## 代码仓库
- [RT-DETR-paddle](./rtdetr_paddle)
- [RT-DETR--pytorch](./rtdetr_pytorch)
## 简介
<!-- We propose a **R**eal-**T**ime **DE**tection **TR**ansformer (RT-DETR), the first real-time end-to-end object detector to our best knowledge. Specifically, we design an efficient hybrid encoder to efficiently process multi-scale features by decoupling the intra-scale interaction and cross-scale fusion, and propose IoU-aware query selection to improve the initialization of object queries. In addition, our proposed detector supports flexibly adjustment of the inference speed by using different decoder layers without the need for retraining, which facilitates the practical application of real-time object detectors. Our RT-DETR-L achieves 53.0% AP on COCO val2017 and 114 FPS on T4 GPU, while RT-DETR-X achieves 54.8% AP and 74 FPS, outperforming all YOLO detectors of the same scale in both speed and accuracy. Furthermore, our RT-DETR-R50 achieves 53.1% AP and 108 FPS, outperforming DINO-Deformable-DETR-R50 by 2.2% AP in accuracy and by about 21 times in FPS. -->
RT-DETR是第一个实时端到端目标检测器。具体而言，我们设计了一个高效的混合编码器，通过解耦尺度内交互和跨尺度融合来高效处理多尺度特征，并提出了IoU感知的查询选择机制，以优化解码器查询的初始化。此外，RT-DETR支持通过使用不同的解码器层来灵活调整推理速度，而不需要重新训练，这有助于实时目标检测器的实际应用。RT-DETR-R50在COCO val2017上实现了53.1%的AP，在T4 GPU上实现了108FPS，RT-DETR-R101实现了54.3%的AP和74FPS，在速度和精度方面都优于相同规模的所有YOLO检测器。使用Objects365预训练之后, RT-DETR-R50 和 RT-DETR-R101 分别实现了 55.3% 和 56.2% AP的精度.
若要了解更多细节，请参考我们的论文[paper](https://arxiv.org/abs/2304.08069).
<div align="center">
<img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/c211a164-ddce-4084-8b71-fb73f29f363b" width=500 >
</div>
## 引用RT-DETR
如果需要在你的研究中使用RT-DETR，请通过以下方式引用我们的论文：
```
@misc{lv2023detrs,
title={DETRs Beat YOLOs on Real-time Object Detection},
author={Yian Zhao and Wenyu Lv and Shangliang Xu and Jinman Wei and Guanzhong Wang and Qingqing Dang and Yi Liu and Jie Chen},
year={2023},
eprint={2304.08069},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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English | [简体中文](README_cn.md)
<h2 align="center">RT-DETR: DETRs Beat YOLOs on Real-time Object Detection</h2>
<p align="center">
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<img alt="emal" src="https://img.shields.io/badge/contact_me-email-yellow">
</a>
</p>
---
<div align="center">
<img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/0ede1dc1-a854-43b6-9986-cf9090f11a61" width=500 >
</div>
This is the official implementation of the paper "[DETRs Beat YOLOs on Real-time Object Detection](https://arxiv.org/abs/2304.08069)".
## 🚀 Updates
- \[2024.02.27\] Our work has been accepted to CVPR 2024!
- \[2024.01.23\] Fix difference on data augmentation with paper in rtdetr_pytorch [#84](https://github.com/lyuwenyu/RT-DETR/commit/5dc64138e439247b4e707dd6cebfe19d8d77f5b1).
- \[2023.11.07\] Add pytorch ✅ *rtdetr_r34vd* for requests [#107](https://github.com/lyuwenyu/RT-DETR/issues/107), [#114](https://github.com/lyuwenyu/RT-DETR/issues/114).
- \[2023.11.05\] Upgrade the logic of `remap_mscoco_category` to facilitate training of custom datasets, see detils in [*Train custom data*](./rtdetr_pytorch/) part. [#81](https://github.com/lyuwenyu/RT-DETR/commit/95fc522fd7cf26c64ffd2ad0c622c392d29a9ebf).
- \[2023.10.23\] Add [*discussion for deployments*](https://github.com/lyuwenyu/RT-DETR/issues/95), supported onnxruntime, TensorRT, openVINO.
- \[2023.10.12\] Add tuning code for pytorch version, now you can tuning rtdetr based on pretrained weights.
- \[2023.09.19\] Upload ✅ [*pytorch weights*](https://github.com/lyuwenyu/RT-DETR/issues/42) convert from paddle version.
- \[2023.08.24] Release RT-DETR-R18 pretrained models on objects365. *49.2 mAP* and *217 FPS*.
- \[2023.08.22\] Upload ✅ [*rtdetr_pytorch*](./rtdetr_pytorch/) source code. Please enjoy it!
- \[2023.08.15\] Release RT-DETR-R101 pretrained models on objects365. *56.2 mAP* and *74 FPS*.
- \[2023.07.30\] Release RT-DETR-R50 pretrained models on objects365. *55.3 mAP* and *108 FPS*.
- \[2023.07.28\] Fix some bugs, and add some comments. [1](https://github.com/lyuwenyu/RT-DETR/pull/14), [2](https://github.com/lyuwenyu/RT-DETR/commit/3b5cbcf8ae3b907e6b8bb65498a6be7c6736eabc).
- \[2023.07.13\] Upload ✅ [*training logs on coco*](https://github.com/lyuwenyu/RT-DETR/issues/8).
- \[2023.05.17\] Release RT-DETR-R18, RT-DETR-R34, RT-DETR-R50-m（example for scaled).
- \[2023.04.17\] Release RT-DETR-R50, RT-DETR-R101, RT-DETR-L, RT-DETR-X.
## 📍 Implementations
- 🔥 RT-DETR paddle: [code](./rtdetr_paddle), [weights](./rtdetr_paddle)
- 🔥 RT-DETR pytorch: [code](./rtdetr_pytorch), [weights](./rtdetr_pytorch)
| Model | Epoch | Input shape | Dataset | $AP^{val}$ | $AP^{val}_{50}$| Params(M) | FLOPs(G) | T4 TensorRT FP16(FPS)
|:---:|:---:|:---:| :---:|:---:|:---:|:---:|:---:|:---:|
| RT-DETR-R18 | 6x | 640 | COCO | 46.5 | 63.8 | 20 | 60 | 217 |
| RT-DETR-R34 | 6x | 640 | COCO | 48.9 | 66.8 | 31 | 92 | 161 |
| RT-DETR-R50-m | 6x | 640 | COCO | 51.3 | 69.6 | 36 | 100 | 145 |
| RT-DETR-R50 | 6x | 640 | COCO | 53.1 | 71.3 | 42 | 136 | 108 |
| RT-DETR-R101 | 6x | 640 | COCO | 54.3 | 72.7 | 76 | 259 | 74 |
| RT-DETR-HGNetv2-L | 6x | 640 | COCO | 53.0 | 71.6 | 32 | 110 | 114 |
| RT-DETR-HGNetv2-X | 6x | 640 | COCO | 54.8 | 73.1 | 67 | 234 | 74 |
| RT-DETR-R18 | 5x | 640 | COCO + Objects365 | **49.2** | **66.6** | 20 | 60 | **217** |
| RT-DETR-R50 | 2x | 640 | COCO + Objects365 | **55.3** | **73.4** | 42 | 136 | **108** |
| RT-DETR-R101 | 2x | 640 | COCO + Objects365 | **56.2** | **74.6** | 76 | 259 | **74** |
**Notes:**
- `COCO + Objects365` in the table means finetuned model on COCO using pretrained weights trained on Objects365.
## 💡 Introduction
We propose a **R**eal-**T**ime **DE**tection **TR**ansformer (RT-DETR, aka RTDETR), the first real-time end-to-end object detector to our best knowledge. Our RT-DETR-R50 / R101 achieves 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4 GPU, outperforming previously advanced YOLOs in both speed and accuracy. Furthermore, RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy and about 21 times in FPS. After pre-training with Objects365, RT-DETR-R50 / R101 achieves 55.3% / 56.2% AP.
<div align="center">
<img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/c211a164-ddce-4084-8b71-fb73f29f363b" width=500 >
</div>
## 🦄 Performance
### 🏕️ Complex Scenarios
<div align="center">
<img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/52743892-68c8-4e53-b782-9f89221739e4" width=500 >
</div>
### 🌋 Difficult Conditions
<div align="center">
<img src="https://github.com/lyuwenyu/RT-DETR/assets/77494834/213cf795-6da6-4261-8549-11947292d3cb" width=500 >
</div>
## Citation
If you use `RT-DETR` in your work, please use the following BibTeX entries:
```
@misc{lv2023detrs,
title={DETRs Beat YOLOs on Real-time Object Detection},
author={Yian Zhao and Wenyu Lv and Shangliang Xu and Jinman Wei and Guanzhong Wang and Qingqing Dang and Yi Liu and Jie Chen},
year={2023},
eprint={2304.08069},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
benchmark/README.md 0 → 100644
View file @ 46260e34
# 论文测速使用的部分代码和工具
## 测试YOLO系列的速度 [in progress]
以[yolov8](https://github.com/ultralytics/ultralytics)为例
<details open>
<summary>1. 转onnx </summary>
执行`yolov8_onnx.py`中的`export_onnx`函数，新增代码主要涉及输出格式的转换
</details>
<details>
<summary>2. 插入nms </summary>
使用`utils.py`中的`yolo_insert_nms`函数，导出onnx模型后使用[Netron](https://netron.app/)查看结构. <img width="924" alt="image" src="https://github.com/lyuwenyu/RT-DETR/assets/17582080/cb466483-d3a3-4f23-a68d-7ab8825059c8">
</details>
<details>
<summary>3. 转tensorrt </summary>
可以使用`trtexec.md`中的的脚本转换，或者使用`utils.py`中的Python代码转换
```bash
# trtexec -h
trtexec --onnx=./yolov8l_w_nms.onnx --saveEngine=yolov8l_w_nms.engine --buildOnly --fp16
```
</details>
<details>
<summary>4. trtexec测速 </summary>
可以使用`trtexec.md`中的的脚本转换，去掉`--buildOnly`参数
</details>
<details>
<summary>5. profile分析（可选） </summary>
在4的基础之上加以下命令
```bash
nsys profile --force-overwrite=true -t 'nvtx,cuda,osrt,cudnn' -c cudaProfilerApi -o yolov8l_w_nms
```
可以使用nsys可视化分析
<img width="1090" alt="image" src="https://github.com/lyuwenyu/RT-DETR/assets/17582080/507d8bde-9e7c-4ae5-b571-976c540ef2c6">
</details>
<details>
<summary>6. Python测速或者部署 </summary>
在Coco val数据集上测模型的平均速度使用`trtinfer.py`中的代码推理
</details>
benchmark/dataset.py 0 → 100644
View file @ 46260e34
'''by lyuwenyu
'''
import os
import glob
from PIL import Image
import torch
import torch.utils.data as data
import torchvision
import torchvision.transforms as T
import torchvision.transforms.functional as F
class ToTensor(T.ToTensor):
def __init__(self) -> None:
super().__init__()
def __call__(self, pic):
if isinstance(pic, torch.Tensor):
return pic
return super().__call__(pic)
class PadToSize(T.Pad):
def __init__(self, size, fill=0, padding_mode='constant'):
super().__init__(0, fill, padding_mode)
self.size = size
self.fill = fill
def __call__(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be padded.
Returns:
PIL Image or Tensor: Padded image.
"""
w, h = F.get_image_size(img)
padding = (0, 0, self.size[0] - w, self.size[1] - h)
return F.pad(img, padding, self.fill, self.padding_mode)
class Dataset(data.Dataset):
def __init__(self, img_dir: str='', preprocess: T.Compose=None, device='cuda:0') -> None:
super().__init__()
self.device = device
self.size = 640
self.im_path_list = list(glob.glob(os.path.join(img_dir, '*.jpg')))
if preprocess is None:
self.preprocess = T.Compose([
T.Resize(size=639, max_size=640),
PadToSize(size=(640, 640), fill=114),
ToTensor(),
T.ConvertImageDtype(torch.float),
])
else:
self.preprocess = preprocess
def __len__(self, ):
return len(self.im_path_list)
def __getitem__(self, index):
# im = Image.open(self.img_path_list[index]).convert('RGB')
im = torchvision.io.read_file(self.im_path_list[index])
im = torchvision.io.decode_jpeg(im, mode=torchvision.io.ImageReadMode.RGB, device=self.device)
_, h, w = im.shape # c,h,w
im = self.preprocess(im)
blob = {
'image': im,
'im_shape': torch.tensor([self.size, self.size]).to(im.device),
'scale_factor': torch.tensor([self.size / h, self.size / w]).to(im.device),
'orig_size': torch.tensor([w, h]).to(im.device),
}
return blob
@staticmethod
def post_process():
pass
@staticmethod
def collate_fn():
pass
def draw_nms_result(blob, outputs, draw_score_threshold=0.25, name=''):
'''show result
Keys:
'num_dets', 'det_boxes', 'det_scores', 'det_classes'
'''
for i in range(blob['image'].shape[0]):
det_scores = outputs['det_scores'][i]
det_boxes = outputs['det_boxes'][i][det_scores > draw_score_threshold]
im = (blob['image'][i] * 255).to(torch.uint8)
im = torchvision.utils.draw_bounding_boxes(im, boxes=det_boxes, width=2)
Image.fromarray(im.permute(1, 2, 0).cpu().numpy()).save(f'test_{name}_{i}.jpg')
benchmark/trtexec.md 0 → 100644
View file @ 46260e34
```bash
# build tensorrt engine
trtexec --onnx=./yolov8l_w_nms.onnx --saveEngine=yolov8l_w_nms.engine --buildOnly --fp16
# using dynamic shapes
# --explicitBatch --minShapes=image:1x3x640x640 --optShapes=image:8x3x640x640 --maxShapes=image:16x3x640x640 --shapes=image:8x3x640x640
# timeline
nsys profile --force-overwrite=true -t 'nvtx,cuda,osrt,cudnn' -c cudaProfilerApi -o yolov8l_w_nms trtexec --loadEngine=./yolov8l_w_nms.engine --fp16 --avgRuns=10 --loadInputs='image:input_tensor.bin'
# https://forums.developer.nvidia.com/t/about-loadinputs-in-trtexec/218880
```
benchmark/trtinfer.py 0 → 100644
View file @ 46260e34
'''by lyuwenyu
'''
import time
import contextlib
from collections import namedtuple, OrderedDict
import torch
import numpy as np
import tensorrt as trt
from utils import TimeProfiler
class TRTInference(object):
def __init__(self, engine_path, device='cuda:0', backend='torch', max_batch_size=32, verbose=False):
self.engine_path = engine_path
self.device = device
self.backend = backend
self.max_batch_size = max_batch_size
self.logger = trt.Logger(trt.Logger.VERBOSE) if verbose else trt.Logger(trt.Logger.INFO)
self.engine = self.load_engine(engine_path)
self.context = self.engine.create_execution_context()
self.bindings = self.get_bindings(self.engine, self.context, self.max_batch_size, self.device)
self.bindings_addr = OrderedDict((n, v.ptr) for n, v in self.bindings.items())
self.input_names = self.get_input_names()
self.output_names = self.get_output_names()
if self.backend == 'cuda':
self.stream = cuda.Stream()
self.time_profile = TimeProfiler()
def init(self, ):
self.dynamic = False
def load_engine(self, path):
'''load engine
'''
trt.init_libnvinfer_plugins(self.logger, '')
with open(path, 'rb') as f, trt.Runtime(self.logger) as runtime:
return runtime.deserialize_cuda_engine(f.read())
def get_input_names(self, ):
names = []
for _, name in enumerate(self.engine):
if self.engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:
names.append(name)
return names
def get_output_names(self, ):
names = []
for _, name in enumerate(self.engine):
if self.engine.get_tensor_mode(name) == trt.TensorIOMode.OUTPUT:
names.append(name)
return names
def get_bindings(self, engine, context, max_batch_size=32, device=None):
'''build binddings
'''
Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
bindings = OrderedDict()
# max_batch_size = 1
for i, name in enumerate(engine):
shape = engine.get_tensor_shape(name)
dtype = trt.nptype(engine.get_tensor_dtype(name))
if shape[0] == -1:
dynamic = True
shape[0] = max_batch_size
if engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT: # dynamic
context.set_input_shape(name, shape)
if self.backend == 'cuda':
if engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:
data = np.random.randn(*shape).astype(dtype)
ptr = cuda.mem_alloc(data.nbytes)
bindings[name] = Binding(name, dtype, shape, data, ptr)
else:
data = cuda.pagelocked_empty(trt.volume(shape), dtype)
ptr = cuda.mem_alloc(data.nbytes)
bindings[name] = Binding(name, dtype, shape, data, ptr)
else:
data = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
bindings[name] = Binding(name, dtype, shape, data, data.data_ptr())
return bindings
def run_torch(self, blob):
'''torch input
'''
for n in self.input_names:
if self.bindings[n].shape != blob[n].shape:
self.context.set_input_shape(n, blob[n].shape)
self.bindings[n] = self.bindings[n]._replace(shape=blob[n].shape)
self.bindings_addr.update({n: blob[n].data_ptr() for n in self.input_names})
self.context.execute_v2(list(self.bindings_addr.values()))
outputs = {n: self.bindings[n].data for n in self.output_names}
return outputs
def async_run_cuda(self, blob):
'''numpy input
'''
for n in self.input_names:
cuda.memcpy_htod_async(self.bindings_addr[n], blob[n], self.stream)
bindings_addr = [int(v) for _, v in self.bindings_addr.items()]
self.context.execute_async_v2(bindings=bindings_addr, stream_handle=self.stream.handle)
outputs = {}
for n in self.output_names:
cuda.memcpy_dtoh_async(self.bindings[n].data, self.bindings[n].ptr, self.stream)
outputs[n] = self.bindings[n].data
self.stream.synchronize()
return outputs
def __call__(self, blob):
if self.backend == 'torch':
return self.run_torch(blob)
elif self.backend == 'cuda':
return self.async_run_cuda(blob)
def synchronize(self, ):
if self.backend == 'torch' and torch.cuda.is_available():
torch.cuda.synchronize()
elif self.backend == 'cuda':
self.stream.synchronize()
def warmup(self, blob, n):
for _ in range(n):
_ = self(blob)
def speed(self, blob, n):
self.time_profile.reset()
for _ in range(n):
with self.time_profile:
_ = self(blob)
return self.time_profile.total / n
benchmark/utils.py 0 → 100644
View file @ 46260e34
'''by lyuwenyu
'''
import time
import contextlib
import numpy as np
from PIL import Image
from collections import OrderedDict
import onnx
import torch
import onnx_graphsurgeon
def to_binary_data(path, size=(640, 640), output_name='input_tensor.bin'):
'''--loadInputs='image:input_tensor.bin'
'''
im = Image.open(path).resize(size)
data = np.asarray(im, dtype=np.float32).transpose(2, 0, 1)[None] / 255.
data.tofile(output_name)
def yolo_insert_nms(path, score_threshold=0.01, iou_threshold=0.7, max_output_boxes=300, simplify=False):
'''
http://www.xavierdupre.fr/app/onnxcustom/helpsphinx/api/onnxops/onnx__EfficientNMS_TRT.html
https://huggingface.co/spaces/muttalib1326/Punjabi_Character_Detection/blob/3dd1e17054c64e5f6b2254278f96cfa2bf418cd4/utils/add_nms.py
'''
onnx_model = onnx.load(path)
if simplify:
from onnxsim import simplify
onnx_model, _ = simplify(onnx_model, overwrite_input_shapes={'image': [1, 3, 640, 640]})
graph = onnx_graphsurgeon.import_onnx(onnx_model)
graph.toposort()
graph.fold_constants()
graph.cleanup()
topk = max_output_boxes
attrs = OrderedDict(plugin_version='1',
background_class=-1,
max_output_boxes=topk,
score_threshold=score_threshold,
iou_threshold=iou_threshold,
score_activation=False,
box_coding=0, )
outputs = [onnx_graphsurgeon.Variable('num_dets', np.int32, [-1, 1]),
onnx_graphsurgeon.Variable('det_boxes', np.float32, [-1, topk, 4]),
onnx_graphsurgeon.Variable('det_scores', np.float32, [-1, topk]),
onnx_graphsurgeon.Variable('det_classes', np.int32, [-1, topk])]
graph.layer(op='EfficientNMS_TRT',
name="batched_nms",
inputs=[graph.outputs[0],
graph.outputs[1]],
outputs=outputs,
attrs=attrs, )
graph.outputs = outputs
graph.cleanup().toposort()
onnx.save(onnx_graphsurgeon.export_onnx(graph), f'yolo_w_nms.onnx')
class TimeProfiler(contextlib.ContextDecorator):
def __init__(self, ):
self.total = 0
def __enter__(self, ):
self.start = self.time()
return self
def __exit__(self, type, value, traceback):
self.total += self.time() - self.start
def reset(self, ):
self.total = 0
def time(self, ):
if torch.cuda.is_available():
torch.cuda.synchronize()
return time.time()
benchmark/yolov8_onnx.py 0 → 100644
View file @ 46260e34
'''by lyuwenyu
'''
import torch
import torchvision
import numpy as np
import onnxruntime as ort
from utils import yolo_insert_nms
class YOLOv8(torch.nn.Module):
def __init__(self, name) -> None:
super().__init__()
from ultralytics import YOLO
# Load a model
# build a new model from scratch
# model = YOLO(f'{name}.yaml')
# load a pretrained model (recommended for training)
model = YOLO(f'{name}.pt')
self.model = model.model
def forward(self, x):
'''https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/tasks.py#L216
'''
pred: torch.Tensor = self.model(x)[0] # n 84 8400,
pred = pred.permute(0, 2, 1)
boxes, scores = pred.split([4, 80], dim=-1)
boxes = torchvision.ops.box_convert(boxes, in_fmt='cxcywh', out_fmt='xyxy')
return boxes, scores
def export_onnx(name='yolov8n'):
'''export onnx
'''
m = YOLOv8(name)
x = torch.rand(1, 3, 640, 640)
dynamic_axes = {
'image': {0: '-1'}
}
torch.onnx.export(m, x, f'{name}.onnx',
input_names=['image'],
output_names=['boxes', 'scores'],
opset_version=13,
dynamic_axes=dynamic_axes)
data = np.random.rand(1, 3, 640, 640).astype(np.float32)
sess = ort.InferenceSession(f'{name}.onnx')
_ = sess.run(output_names=None, input_feed={'image': data})
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--name', type=str, default='yolov8l')
parser.add_argument('--score_threshold', type=float, default=0.001)
parser.add_argument('--iou_threshold', type=float, default=0.7)
parser.add_argument('--max_output_boxes', type=int, default=300)
args = parser.parse_args()
export_onnx(name=args.name)
yolo_insert_nms(path=f'{args.name}.onnx',
score_threshold=args.score_threshold,
iou_threshold=args.iou_threshold,
max_output_boxes=args.max_output_boxes, )
datasets/url.txt 0 → 100644
View file @ 46260e34
http://images.cocodataset.org/zips/val2017.zip
http://images.cocodataset.org/zips/train2017.zip
http://images.cocodataset.org/annotations/annotations_trainval2017.zip
\ No newline at end of file
model.properties 0 → 100644
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# 模型唯一标识
modelCode = 1123
# 模型名称
modelName=rt-detr_pytorch
# 模型描述
modelDescription=RT-DETR是百度开发的一种端到端的实时物体检测器，可在保持高精度的同时提供实时性能
# 应用场景
appScenario=推理,训练,目标检测,制造,交通,电商,广媒,医疗
# 框架类型
frameType=pytorch
\ No newline at end of file
model/url.txt 0 → 100644
View file @ 46260e34
https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r18vd_dec3_6x_coco_from_paddle.pth
https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r34vd_dec4_6x_coco_from_paddle.pth
https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r50vd_m_6x_coco_from_paddle.pth
https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r50vd_6x_coco_from_paddle.pth
https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r101vd_6x_coco_from_paddle.pth
# COCO + Objects365表示使用在 Objects365 上训练的预训练权重在 COCO 上微调的模型。
https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r18vd_5x_coco_objects365_from_paddle.pth
https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r50vd_2x_coco_objects365_from_paddle.pth
https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r101vd_2x_coco_objects365_from_paddle.pth
\ No newline at end of file
onnx_infer.py 0 → 100644
View file @ 46260e34
import onnxruntime as ort
from PIL import Image, ImageDraw
from torchvision.transforms import ToTensor
import torch
import time
# print(onnx.helper.printable_graph(mm.graph))
im = Image.open('datasets/000000033109.jpg').convert('RGB') # TODO:修改推理图片路径
im = im.resize((640, 640))
im_data = ToTensor()(im)[None]
print(im_data.shape)
size = torch.tensor([[640, 640]])
sess = ort.InferenceSession("model/onnx/rtdetr_r101vd_6x_coco.onnx") # TODO:修改onnx模型路径
start=time.time()
output = sess.run(
# output_names=['labels', 'boxes', 'scores'],
output_names=None,
input_feed={'images': im_data.data.numpy(), "orig_target_sizes": size.data.numpy()}
)
end=time.time()
print(end-start,1.0/(end-start))
# print(type(output))
# print([out.shape for out in output])
labels, boxes, scores = output
print("labels shape = ",labels)
print("boxes shape = ",boxes)
print("scores shape = ",scores)
draw = ImageDraw.Draw(im)
thrh = 0.6
for i in range(im_data.shape[0]):
scr = scores[i]
lab = labels[i][scr > thrh]
box = boxes[i][scr > thrh]
print(i, sum(scr > thrh))
for b in box:
draw.rectangle(list(b), outline='red',)
draw.text((b[0], b[1]), text=str(lab[i]), fill='blue', )
im.save('result/test.jpg') # TODO:修改推理结果存储路径
\ No newline at end of file
requirements.txt 0 → 100644
View file @ 46260e34
# torch==2.0.1
# torchvision==0.15.2
onnx==1.14.0
onnxruntime==1.15.1
pycocotools
PyYAML
scipy
rtdetr_pytorch/README.md 0 → 100644
View file @ 46260e34
## TODO
<details>
<summary> see details </summary>
- [x] Training
- [x] Evaluation
- [x] Export onnx
- [x] Upload source code
- [x] Upload weight convert from paddle, see [*links*](https://github.com/lyuwenyu/RT-DETR/issues/42)
- [x] Align training details with the [*paddle version*](../rtdetr_paddle/)
- [x] Tuning rtdetr based on [*pretrained weights*](https://github.com/lyuwenyu/RT-DETR/issues/42)
</details>
## Model Zoo
| Model | Dataset | Input Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | #Params(M) | FPS | checkpoint |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
rtdetr_r18vd | COCO | 640 | 46.4 | 63.7 | 20 | 217 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r18vd_dec3_6x_coco_from_paddle.pth)
rtdetr_r34vd | COCO | 640 | 48.9 | 66.8 | 31 | 161 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r34vd_dec4_6x_coco_from_paddle.pth)
rtdetr_r50vd_m | COCO | 640 | 51.3 | 69.5 | 36 | 145 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r50vd_m_6x_coco_from_paddle.pth)
rtdetr_r50vd | COCO | 640 | 53.1 | 71.2| 42 | 108 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r50vd_6x_coco_from_paddle.pth)
rtdetr_r101vd | COCO | 640 | 54.3 | 72.8 | 76 | 74 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r101vd_6x_coco_from_paddle.pth)
rtdetr_18vd | COCO+Objects365 | 640 | 49.0 | 66.5 | 20 | 217 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r18vd_5x_coco_objects365_from_paddle.pth)
rtdetr_r50vd | COCO+Objects365 | 640 | 55.2 | 73.4 | 42 | 108 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r50vd_2x_coco_objects365_from_paddle.pth)
rtdetr_r101vd | COCO+Objects365 | 640 | 56.2 | 74.5 | 76 | 74 | [url<sup>*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r101vd_2x_coco_objects365_from_paddle.pth)
Notes
- `COCO + Objects365` in the table means finetuned model on `COCO` using pretrained weights trained on `Objects365`.
- `url`<sup>`*`</sup> is the url of pretrained weights convert from paddle model for save energy. *It may have slight differences between this table and paper*
<!-- - `FPS` is evaluated on a single T4 GPU with $batch\\_size = 1$ and $tensorrt\\_fp16$ mode -->
## Quick start
<details>
<summary>Install</summary>
```bash
pip install -r requirements.txt
```
</details>
<details>
<summary>Data</summary>
- Download and extract COCO 2017 train and val images.
```
path/to/coco/
annotations/ # annotation json files
train2017/ # train images
val2017/ # val images
```
- Modify config [`img_folder`, `ann_file`](configs/dataset/coco_detection.yml)
</details>
<details>
<summary>Training & Evaluation</summary>
- Training on a Single GPU:
```shell
# training on single-gpu
export CUDA_VISIBLE_DEVICES=0
python tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml
```
- Training on Multiple GPUs:
```shell
# train on multi-gpu
export CUDA_VISIBLE_DEVICES=0,1,2,3
torchrun --nproc_per_node=4 tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml
```
- Evaluation on Multiple GPUs:
```shell
# val on multi-gpu
export CUDA_VISIBLE_DEVICES=0,1,2,3
torchrun --nproc_per_node=4 tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml -r path/to/checkpoint --test-only
```
</details>
<details>
<summary>Export</summary>
```shell
python tools/export_onnx.py -c configs/rtdetr/rtdetr_r18vd_6x_coco.yml -r path/to/checkpoint --check
```
</details>
<details open>
<summary>Train custom data</summary>
1. set `remap_mscoco_category: False`. This variable only works for ms-coco dataset. If you want to use `remap_mscoco_category` logic on your dataset, please modify variable [`mscoco_category2name`](https://github.com/lyuwenyu/RT-DETR/blob/main/rtdetr_pytorch/src/data/coco/coco_dataset.py#L154) based on your dataset.
2. add `-t path/to/checkpoint` (optinal) to tuning rtdetr based on pretrained checkpoint. see [training script details](./tools/README.md).
</details>
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