diff --git a/PyTorch/contrib/cv/detection/DETR/README.md b/PyTorch/contrib/cv/detection/DETR/README.md index 62dca0eecea3c890e4eba049076301b70c6c98d4..10766ac8520a76122805589cee3933204de12864 100644 --- a/PyTorch/contrib/cv/detection/DETR/README.md +++ b/PyTorch/contrib/cv/detection/DETR/README.md @@ -147,7 +147,9 @@ DETR提出了一种将对象检测视为直接集合预测问题,能够一次 无。 +# 公网地址说明 +代码涉及公网地址参考 public_address_statement.md diff --git a/PyTorch/contrib/cv/detection/DETR/public_address_statement.md b/PyTorch/contrib/cv/detection/DETR/public_address_statement.md new file mode 100644 index 0000000000000000000000000000000000000000..e6f1c73a7b6bb791894256cddb2f4dbfec97dac1 --- /dev/null +++ b/PyTorch/contrib/cv/detection/DETR/public_address_statement.md @@ -0,0 +1,11 @@ +| 类型 | 开源代码地址 | 文件名 | 公网IP地址/公网URL地址/域名/邮箱地址 | 用途说明 | +|----------| ------------ |---------------------------| ------------------------------------ |--------| +| 开发引入 | / | DETR/1.5_requirements.txt | https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI&egg=pycocotools | 下载依赖 | +| 开发引入 | / | DETR/1.8_requirements.txt | https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI&egg=pycocotools | 下载依赖 | +| 开源代码引入 | https://github.com/facebookresearch/detr | DETR/hubconf.py | https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth | 下载权重文件 | +| 开源代码引入 | https://github.com/facebookresearch/detr | DETR/hubconf.py | https://dl.fbaipublicfiles.com/detr/detr-r50-dc5-f0fb7ef5.pth | 下载权重文件 | +| 开源代码引入 | https://github.com/facebookresearch/detr | DETR/hubconf.py | https://dl.fbaipublicfiles.com/detr/detr-r101-2c7b67e5.pth | 下载权重文件 | +| 开源代码引入 | https://github.com/facebookresearch/detr | DETR/hubconf.py | https://dl.fbaipublicfiles.com/detr/detr-r101-dc5-a2e86def.pth | 下载权重文件 | +| 开源代码引入 | https://github.com/facebookresearch/detr | DETR/hubconf.py | https://dl.fbaipublicfiles.com/detr/detr-r50-panoptic-00ce5173.pth | 下载权重文件 | +| 开源代码引入 | https://github.com/facebookresearch/detr | DETR/hubconf.py | https://dl.fbaipublicfiles.com/detr/detr-r50-dc5-panoptic-da08f1b1.pth | 下载权重文件 | +| 开源代码引入 | https://github.com/facebookresearch/detr | DETR/hubconf.py | https://dl.fbaipublicfiles.com/detr/detr-r101-panoptic-40021d53.pth | 下载权重文件 | \ No newline at end of file diff --git a/PyTorch/contrib/cv/detection/FAN/README.md b/PyTorch/contrib/cv/detection/FAN/README.md index f209ba6b061321781ac7a6ba0854eb3bafd1cb76..358bffc84a2ffae0085c0cb042d296612fee0ffa 100644 --- a/PyTorch/contrib/cv/detection/FAN/README.md +++ b/PyTorch/contrib/cv/detection/FAN/README.md @@ -151,4 +151,8 @@ FAN是一个目标对齐检测网络,通过对目标标志的检测,既能 ## FAQ -无。 \ No newline at end of file +无。 + +# 公网地址说明 + +代码涉及公网地址参考 public_address_statement.md \ No newline at end of file diff --git a/PyTorch/contrib/cv/detection/FAN/public_address_statement.md b/PyTorch/contrib/cv/detection/FAN/public_address_statement.md new file mode 100644 index 0000000000000000000000000000000000000000..23516cee31bc6066d8946a7f80a5921711aa62f8 --- /dev/null +++ b/PyTorch/contrib/cv/detection/FAN/public_address_statement.md @@ -0,0 +1,19 @@ +| 类型 | 开源代码地址 | 文件名 | 公网IP地址/公网URL地址/域名/邮箱地址 | 用途说明 | +|--------|--------------------------------------------|----------------------------------------------------| ---------------------------------- |--------| +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/conda/meta.yaml | https://github.com/1adrianb/face-alignment | 下载依赖 | +| 开发引入 | / | FAN/Dockerfile | https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh | 下载依赖 | +| 开发引入 | / | FAN/Dockerfile | https://github.com/1adrianb/face-alignment | 下载依赖 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/__init__.py | adrian@adrianbulat.com | 邮箱 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/api.py | https://www.adrianbulat.com/downloads/python-fan/2DFAN4-cd938726ad.zip | 下载权重文件 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/api.py | https://www.adrianbulat.com/downloads/python-fan/3DFAN4-4a694010b9.zip | 下载权重文件 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/api.py | https://www.adrianbulat.com/downloads/python-fan/depth-6c4283c0e0.zip | 下载权重文件 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/api.py | https://www.adrianbulat.com/downloads/python-fan/2DFAN4_1.6-c827573f02.zip | 下载权重文件 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/api.py | https://www.adrianbulat.com/downloads/python-fan/3DFAN4_1.6-ec5cf40a1d.zip | 下载权重文件 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/api.py | https://www.adrianbulat.com/downloads/python-fan/depth_1.6-2aa3f18772.zip | 下载权重文件 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/api.py | https://www.adrianbulat.com/downloads/python-fan/2DFAN4_1.5-a60332318a.zip | 下载权重文件 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/api.py | https://www.adrianbulat.com/downloads/python-fan/3DFAN4_1.5-176570af4d.zip | 下载权重文件 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/api.py | https://www.adrianbulat.com/downloads/python-fan/depth_1.5-bc10f98e39.zip | 下载权重文件 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/detection/dlib/dlib_detector.py | https://www.adrianbulat.com/downloads/dlib/mmod_human_face_detector.dat | 下载权重文件 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/face_alignment/detection/sfd/sfd_detector.py | https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth | 下载权重文件 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/setup.py | adrian@adrianbulat.com | 邮箱 | +| 开源代码引入 | https://github.com/1adrianb/face-alignment | FAN/setup.py | https://github.com/1adrianb/face-alignment | 下载依赖 | \ No newline at end of file diff --git a/PyTorch/contrib/cv/detection/FCOS/README.md b/PyTorch/contrib/cv/detection/FCOS/README.md index 1a373f2a2b35d40ecb91682188fc7b37b77b1910..51061847d6542fc8cbc0569bfdad5f3cef6fda0d 100644 --- a/PyTorch/contrib/cv/detection/FCOS/README.md +++ b/PyTorch/contrib/cv/detection/FCOS/README.md @@ -217,3 +217,6 @@ FCOS是一个全卷积的one-stage目标检测模型,相比其他目标检测 无。 +# 公网地址说明 + +代码涉及公网地址参考 public_address_statement.md \ No newline at end of file diff --git a/PyTorch/contrib/cv/detection/FCOS/public_address_statement.md b/PyTorch/contrib/cv/detection/FCOS/public_address_statement.md new file mode 100644 index 0000000000000000000000000000000000000000..9b459a0df564d82f22389cfff9110d37c435ac92 --- /dev/null +++ b/PyTorch/contrib/cv/detection/FCOS/public_address_statement.md @@ -0,0 +1,19 @@ +| 类型 | 开源代码地址 | 文件名 | 公网IP地址/公网URL地址/域名/邮箱地址 | 用途说明 | +|--------|--------------------------------------------------------------------|----------------------------------------------------------------------------| --------------------------------- |---------| +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/.pre-commit-config.yaml | https://gitlab.com/pycqa/flake8.git | 下载依赖 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/.pre-commit-config.yaml | https://github.com/asottile/seed-isort-config | 下载依赖 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/.pre-commit-config.yaml | https://github.com/timothycrosley/isort | 下载依赖 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/.pre-commit-config.yaml | https://github.com/pre-commit/mirrors-yapf | 下载依赖 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/.pre-commit-config.yaml | https://github.com/pre-commit/pre-commit-hooks | 下载依赖 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/.pre-commit-config.yaml | https://github.com/myint/docformatter | 下载依赖 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py | https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth | 下载预训练权重 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py | https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth | 下载预训练权重 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car.py | https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth | 下载预训练权重 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/docker/Dockerfile | https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html | 下载依赖 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/docker/Dockerfile | https://github.com/open-mmlab/mmdetection.git | 下载依赖 | +| 开发引入 | / | FCOS/Dockerfile | https://github.com/open-mmlab/mmcv.git | 下载依赖 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/docs/stat.py | https://github.com/open-mmlab/mmdetection/blob/master/ | 下载依赖 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/mmdet/datasets/lvis.py | http://images.cocodataset.org/ | 下载图片 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/setup.py | openmmlab@gmail.com | 邮箱 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/setup.py | https://github.com/open-mmlab/mmdetection | 下载依赖 | +| 开源代码引入 | https://github.com/open-mmlab/mmdetection/tree/v2.9.0/configs/fcos | FCOS/tests/async_benchmark.py | https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth | 下载预训练权重 | diff --git "a/docs/AICC\344\272\221\344\270\212\350\256\255\347\273\203\346\214\207\345\274\225_v1.0.md" "b/docs/AICC\344\272\221\344\270\212\350\256\255\347\273\203\346\214\207\345\274\225_v1.0.md" new file mode 100644 index 0000000000000000000000000000000000000000..8a755bf3994d21e684345effa2f0d20f7507dea7 --- /dev/null +++ "b/docs/AICC\344\272\221\344\270\212\350\256\255\347\273\203\346\214\207\345\274\225_v1.0.md" @@ -0,0 +1,103 @@ +# AICC云上训练指引 + +- [AICC简介](#0) + +- [准备工作](#1) + + - [准备镜像](#1_1) + - [准备适配代码](#1_2) + +- [云上训练](#2) + + + + + +## AICC简介 + +AICC(AI开发平台产业联盟)是由中国电子信息产业发展研究院、中国电子标准研究院、中国电子学会、中国人工智能学会等单位共同发起成立的,旨在推动人工智能产业发展的联盟。AICC的成员包括了众多的企业、高校、研究机构等,其中包括了华为云、阿里云、腾讯云等云计算厂商。 + +而ModelArts是华为云推出的一款人工智能开发平台,提供了丰富的AI开发工具和服务,包括了模型训练、模型部署、数据管理等功能。ModelArts也是AICC的成员之一,同时也是AICC推荐的人工智能开发平台之一。 + + + +## 准备工作 + +在拥有了AICC账号后,要进行云上训练需要2点: +1、运行环境,即modelarts镜像,镜像内包含了基础的第三方库以及[MoXing库](https://support.huaweicloud.com/moxing-devg-modelarts/modelarts_11_0001.html "MoXing"),因为ModelArts服务器无法直接访问obs桶的数据,而MoXing能实现ModelArts服务器与obs桶之间的数据传输。 +2、修改代码以适配ModelArts,主要涉及3中情况: + 1)数据集和预训练权重,从OBS桶上传到ModelArts服务器 + 2)训练后的数据,从ModelArts服务器传回OBS桶 + 3)训练时需要的额外第三方库,如何安装 + +### 准备镜像 +**步骤1** 登录[AscendHub](https://ascendhub.huawei.com/#/detail/ascend-pytorch-arm-modelarts "AscendHub"),下载“ascend-pytorch-arm-modelarts”镜像至本地。如[图1 下载镜像](#1)所示: + **图1** 下载镜像 + +​ 下载镜像 + +​ **说明:** 点击“立即下载”后,弹出“镜像下载”窗口中有 前提条件 和 操作步骤,请详细阅读! + +**步骤2** 登录[武汉AICC]((https://auth.ovaijisuan.com/authui/login#/login) "武汉aicc"),进入“容器镜像服务”,创建组织。如[图2 创建镜像组织](#2)所示: + **图2** 创建镜像组织 + +​ 创建镜像组织 + +**步骤3** 进入“容器镜像服务-我的镜像”,任选一种方式上传镜像。如[图3 上传镜像](#3)所示: + **图3** 上传镜像 + +​ 上传镜像 + +​ **说明:** 1、上传镜像需要指定组织名称; +​ 2、上传成功后,在界面上可看到刚刚上传的镜像。 + +**---结束** + + + +### 准备适配代码 + +1、导入moxing包。如[图4 导包moxing](#4)所示 + + **图4** 导包moxing + +​ 导包moxing + +2、将OBS桶内数据集上传至ModelArts服务器 +下图中args.data_url为ModelArts控制台上选择的obs桶目录;CACHE_DATA_URL为ModelArts服务器上的目录;如[图4 数据上传至modelarts服务器](#4)所示: + **图5** 数据上传至modelarts服务器 + +​ 数据上传至modelarts服务器 + +​ **说明:** 1、预训练权重上传方式一样,只需修改变量名和对应的目录。 + +3、将ModelArts服务器下载至OBS桶 +下图中config.train_url为ModelArts控制台上选择的obs桶目录;如[图5 数据取回至obs桶](#5)所示: + **图6** 数据取回至obs桶 + +​ 数据取回至obs桶 + +4、安装第三方库 +需要安装的第三方库,可以写入根目录的requirements.txt;镜像会默认安装根目录下的requirements.txt + +​ **说明:** + +​ 1)某些模型适配了modelarts,其根目录下有个modelarts文件夹;一般在云上训练时,会把该文件夹下的train_start.py文件复制到根目录下,作为云上训练的启动文件(如果不复制到根目录,会报导包错误) + +​ 2)默认支持单机单卡训练,若要支持单机多卡和多机多卡,须在代码中添加相应代码;并在训练时选择对应训练资源即可。 + +**---结束** + + + +## 云上训练 + +参照[ModelArts云上训练教程](https://www.hiascend.com/document/detail/zh/ModelZoo/Documentation/cmtt/cmtt_0001.html "ModelArts云上训练教程") + + + +注意:训练作业中,选择“准备镜像-步骤3”中上传的"ascend-pytorch-arm-modelarts"镜像;如[图6 训练作业](#5)所示: + +​ **图6** 数据取回至obs桶 + +训练作业设置 \ No newline at end of file diff --git "a/images/ModelArts\344\272\221\344\270\212\350\256\255\347\273\203\346\214\207\345\274\225/ckpt_air1.png" "b/images/ModelArts\344\272\221\344\270\212\350\256\255\347\273\203\346\214\207\345\274\225/ckpt_air1.png" new file mode 100644 index 0000000000000000000000000000000000000000..7d39ede1ceaadda0d878dce5b1d2bdce3d8fb288 Binary files /dev/null and "b/images/ModelArts\344\272\221\344\270\212\350\256\255\347\273\203\346\214\207\345\274\225/ckpt_air1.png" differ diff --git "a/images/ModelArts\344\272\221\344\270\212\350\256\255\347\273\203\346\214\207\345\274\225/ckpt_air2.png" "b/images/ModelArts\344\272\221\344\270\212\350\256\255\347\273\203\346\214\207\345\274\225/ckpt_air2.png" new file mode 100644 index 0000000000000000000000000000000000000000..5d23c0f1e4fd516d3505dc8a86815d5414434b4d Binary files /dev/null and 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