Updata dtk to 24.04

Contributors.md

+# Contributors
+None

README.md

@@ -23,9 +23,9 @@ DETR将目标检测看作一种set prediction问题, 并提出了一个十分简
 ### Docker（方法一）
 
 ```
-docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04.1-py38-latest
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
 
-docker run -it -v /path/your_code_data/:/path/ your_code_data/ --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
+docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal:ro --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
 
 cd /your_code_path/detr_pytorch
 pip install -r requirements.txt
@@ -35,31 +35,29 @@ pip install -r requirements.txt
 
 ```
 cd ./docker
-cp ../requirements.txt requirements.txt
 
 docker build --no-cache -t detr:latest .
-docker run -it -v /path/your_code_data/:/path/your_code_data/ --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
+docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal:ro --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
 
 cd /your_code_path/detr_pytorch
 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: dtk23.04.1
-python: python3.8
-torch: 1.13.1
-torchvision: 0.14.1
+DTK软件栈: dtk24.04
+python: python3.10
+torch: 2.1.0+git00661e0.abi0.dtk2404
+torchvision: 0.16.0+gitc9e7141.abi0.dtk2404.torch2.1
 ```
 
 Tips: 以上dtk软件栈、python、torch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requirements.txt安装
 
-```
+```bash
 pip install -r requirements.txt
 ```
 
@@ -77,60 +75,62 @@ COCO2017
 
 数据集的目录结构如下:
 
-```
+```bash
 ├── COCO2017
-│   ├── images
 │   ├── train2017
 │   ├── val2017
-│       └── test2017
-│   ├── annotations
+│   ├── test2017
+│   └── annotations
 │       ├── instances_train2017.json
 │       └── instances_val2017.json
 ```
 
-训练/验证集数据准备:
+训练/验证集数据准备:训练/验证集都是采用的**COCO**的数据格式, 如果使用自己的标注数据, 请先将标注数据转换成**COCO**的格式, 并按照上面的目录结构进行存放。
 
-训练/验证集都是采用的COCO的数据格式, 如果使用自己的标注数据, 请先将标注数据转换成COCO的格式, 并按照上面的目录结构进行存放。
+本项目提供了`coco128`数据集可以进行功能验证使用，目录结构如下：
+```bash
+├── coco128
+│   ├── train2017
+│   └── annotations
+│       └── train.json
+```
 
 ## 训练
-
 ### 单机多卡
 
-```
+```bash
+# --nproc_per_node 数据与显卡数量一致
+# coco_path是训练数据集地址，数据是coco format
 bash train.sh
 ```
 
 ## 推理
-
-验证前需提前准备好预训练模型, 可从最下面的**参考资料**项目中提供的模型下载, 并将coco_path设置为当前环境中推理数据的对应地址, 数据应为COCO数据格式。
+验证前需提前准备好预训练模型, 可从[参考资料](#参考资料)项目中提供的模型下载, 并将coco_path设置为当前环境中推理数据的对应地址, 数据应为COCO数据格式。
 
 如果想要查看预测效果（预测结果输出到图片上）, 请执行(其余参数如backbone等需与训练模型参数需一致, 详情请根据训练参数配置):
 
-```
+```bash
 python test.py --pre_trained_model <checkpoint path> --coco_path <coco path>
 ```
 
 #### 单卡推理
-
-```
+```bash
 bash val.sh
 ```
 
 ## result
 
 <div align=center>
-    <img src="./doc/000000001503.jpg"/>
+    <img src="./doc/results.jpg"/>
 </div>
 
 ### 精度
-
-在COCO2017的val数据集上进行R50 backbone的单卡测试, 结果如下表所示:
+在COCO2017的val数据集上进行单卡测试, 结果如下表所示:
 
 | device | backbone | schedule | epoch | box AP |
 | :------: | :------: | :------: | :------: | :------: |
-| Z100L  | R50 | 500 | 5 | 39.8 |
-| V100S | R50 | 500 | 5 | 39.9 |
-
+| Z100L  | R50 | 500 | 300 | 41.4 |
+| V100 | R50 | 500 | 300 | 42 |
 
 ## 应用场景
 ### 算法类别

coco128/README.txt

+# Introduction
+
+This directory contains software developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information please visit https://www.ultralytics.com.
+
+# Description
+
+The https://github.com/ultralytics/COCO2YOLO repo contains code to convert JSON datasets into YOLO (darknet) format. The code works on Linux, MacOS and Windows.
+
+# Requirements
+
+Python 3.7 or later with the following `pip3 install -U -r requirements.txt` packages:
+
+- `numpy`
+- `tqdm`
+
+# Citation
+
+[![DOI](https://zenodo.org/badge/186122711.svg)](https://zenodo.org/badge/latestdoi/186122711)
+
+# Contact
+
+Issues should be raised directly in the repository. For additional questions or comments please email Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com.
\ No newline at end of file

coco128/labels/train2017/000000000009.txt

+45 0.479492 0.688771 0.955609 0.5955
+45 0.736516 0.247188 0.498875 0.476417
+50 0.637063 0.732938 0.494125 0.510583
+45 0.339438 0.418896 0.678875 0.7815
+49 0.646836 0.132552 0.118047 0.0969375
+49 0.773148 0.129802 0.0907344 0.0972292
+49 0.668297 0.226906 0.131281 0.146896
+49 0.642859 0.0792187 0.148063 0.148062

coco128/labels/train2017/000000000025.txt

+23 0.770336 0.489695 0.335891 0.697559
+23 0.185977 0.901608 0.206297 0.129554

coco128/labels/train2017/000000000030.txt

+58 0.519219 0.451121 0.39825 0.75729
+75 0.501188 0.592138 0.26 0.456192

coco128/labels/train2017/000000000034.txt

+22 0.346211 0.493259 0.689422 0.892118

coco128/labels/train2017/000000000036.txt

+25 0.475759 0.414523 0.951518 0.672422
+0 0.671279 0.617945 0.645759 0.726859

coco128/labels/train2017/000000000042.txt

+16 0.606687 0.341381 0.544156 0.51

coco128/labels/train2017/000000000049.txt

+17 0.597835 0.63755 0.342283 0.36886
+17 0.324291 0.64808 0.219711 0.3164
+0 0.620039 0.5939 0.172415 0.14608
+0 0.385525 0.58557 0.14937 0.12586
+0 0.328898 0.70199 0.0313386 0.06714
+58 0.622546 0.89961 0.185932 0.09446
+0 0.760577 0.69423 0.0285564 0.05486
+0 0.510709 0.69215 0.0187927 0.04682
+0 0.929554 0.67602 0.0388451 0.01844

coco128/labels/train2017/000000000061.txt

+0 0.445688 0.480615 0.075125 0.117295
+0 0.640086 0.471742 0.0508281 0.0814344
+20 0.643211 0.558852 0.129828 0.097623
+20 0.459703 0.592121 0.22175 0.159242
+0 0.435383 0.45832 0.0534531 0.111025

coco128/labels/train2017/000000000064.txt

+2 0.292792 0.729031 0.367417 0.246281
+7 0.239438 0.599242 0.259542 0.0929219
+11 0.279896 0.412773 0.077125 0.117453
+74 0.394146 0.184914 0.321458 0.237984

coco128/labels/train2017/000000000071.txt

+2 0.752648 0.525833 0.0433594 0.033216
+2 0.835727 0.538498 0.0380469 0.028169
+2 0.700875 0.521455 0.0364375 0.0202347
+2 0.937398 0.559777 0.0424219 0.0335915
+6 0.452477 0.576408 0.755359 0.280892
+2 0.794984 0.538779 0.0368125 0.0261502
+2 0.953469 0.514906 0.026125 0.012723
+7 0.539727 0.464061 0.0373281 0.0238967
+7 0.58518 0.471397 0.0379844 0.0165962
+2 0.982555 0.572371 0.0348906 0.0300469
+2 0.767367 0.531279 0.0373906 0.0296948
+2 0.617961 0.476373 0.0250156 0.0100704
+2 0.588344 0.471491 0.0307812 0.0183333
+2 0.560102 0.471303 0.0302344 0.0119953
+2 0.796047 0.521796 0.0299687 0.0145775
+2 0.734094 0.523427 0.029375 0.02723

coco128/labels/train2017/000000000072.txt

+23 0.658478 0.592133 0.677002 0.779766
+23 0.391581 0.556305 0.546862 0.887391

coco128/labels/train2017/000000000073.txt

+3 0.497327 0.511852 0.948637 0.952609
+3 0.240637 0.217617 0.475398 0.424859

coco128/labels/train2017/000000000074.txt

+16 0.32825 0.769577 0.463156 0.242207
+1 0.128828 0.375258 0.249063 0.733333
+0 0.476187 0.289613 0.0287813 0.138099
+0 0.52143 0.258251 0.0211719 0.0608685
+0 0.569492 0.285235 0.0245469 0.122254
+0 0.746734 0.295869 0.0494687 0.0983568
+0 0.444961 0.298779 0.0239531 0.110047
+0 0.450773 0.271209 0.0182656 0.0569249

coco128/labels/train2017/000000000077.txt

+0 0.53553 0.308733 0.2069 0.317147
+0 0.73609 0.272987 0.18926 0.259413
+36 0.13364 0.88656 0.10108 0.06464
+36 0.50059 0.413213 0.05466 0.07608
+0 0.14666 0.667293 0.194 0.441093
+0 0.59734 0.325733 0.0494 0.111253
+36 0.73492 0.39656 0.04812 0.0401067
+0 0.55563 0.265173 0.04586 0.220053

coco128/labels/train2017/000000000078.txt

+74 0.762851 0.196119 0.349886 0.385474