arcface_pytorch

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README2.md

-# ArcFace
-## 论文
-- https://arxiv.org/pdf/1801.07698.pdf
-## 模型结构
-这篇文章提出一种新的用于人脸识别的损失函数：additive angular margin loss，直接在角度空间（angular space）中最大化分类界限，基于该损失函数训练得到人脸识别算法ArcFace。
-<div align=center>
-    <img src="./docs/arcface.png"/>
-</div>
-
-## 算法原理
-通过训练深度卷积神经网络嵌入 (DCNN Embedding) 来进行人脸识别。  
-ArcFace训练流程：  
-设类别数（人脸ID数量）为 $n$,DCNN的最后一个FC 层的权重为$W\subset {\mathbb{R}}^{d \times n}$，输入$W$的特征$x_i$的维度为$d$。  
-1、分别归一化输入特征$x_i \subset {\mathbb{R}}^{b}$和FC层权重$W_j \in {\mathbb{R}}^{1 \times b}$（张量除以欧几里得范数标量），令所得归一化特征$\frac{x_i}{\|x_i\|}$与第$j \in {1,2,...,y_i,...,n}$个类别的FC层权重$\frac{{W_j}^T}{\|W_j\|} \in {\mathbb{R}}^{1\times d}$点乘得到FC层的第$j$个输出$cos \theta_j \in {\mathbb{R}}^{1\times1}$（数量积公式：${W_j}^{T}\cdot x_i=\|W_j\|\|x_i\|cos\theta_j$）,表示**将特征$x_i$预测为第$j$类的预测值**。  
-2、设特征$x_i$的真实类别为第$y_j$个类别，单独取出Target权重$\frac{{W_{y_j}}^T}{\|W_{y_i}\|}$计算$\theta_{y_i}=arccos(cos\theta_{y_i})=arccos(\frac{{W_{y_j}}^T}{\|W_{y_i}\|}\cdot\frac{x_i}{\|x_i\|})$可得归一化特征$\frac{x_i}{\|x_i\|}$与归一化**target权重**$\frac{{W_{y_j}}^T}{\|W_{y_i}\|}$之间的夹角—— **Target角度$\theta_{y_i}$**  
-3、通过把一个自定义的**加性角度边距 (additive angular margin)** $m$加到$\theta_{y_i}$，得到$\theta_{y_i}+m$，用于**调整Target角度**。   
-4、计算经调整的Target角度的余弦，得到仅关于特征$x_i$的真实类别$y_i$的**新Target Logit $cos(\theta_{y_i}+m)$**。  
-5、通过自定义的特征范数$s$重缩放所有Logit（除Target Logit变为$cos(\theta_{y_i}+m)$）外其余原Logit仍为$cos\theta_j$，矩阵运算时需用相当于 0/1 mask的one-hot labels区分）得到新 Logit $s∗cos \theta_j, j\in{1,2,..,y_i,..,n}$。
-6、对上述过程得到的**新Logit**按通常方式计算Softmax Loss。  
-
-<div align=center>
-    <img src="./docs/train.jpg"/>
-</div>
-
-
-## 环境配置
-### Docker（方法一）
-从[光源](https://www.sourcefind.cn/#/service-list)中拉取docker镜像：
-```
-docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk23.10-py310
-```
-创建容器并挂载目录进行开发：
-```
-docker run -it --name {name} --shm-size=1024G  --device=/dev/kfd --device=/dev/dri/ --privileged --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal:ro -v {}:{} {docker_image} /bin/bash
-# 修改1 {name} 需要改为自定义名称，建议命名{框架_dtk版本_使用者姓名},如果有特殊用途可在命名框架前添加命名
-# 修改2 {docker_image} 需要需要创建容器的对应镜像名称，如: pytorch:1.10.0-centos7.6-dtk-23.04-py37-latest【镜像名称:tag名称】
-# 修改3 -v 挂载路径到容器指定路径
-pip install -r requirements.txt
-```
-### Dockerfile（方法二）
-```
-cd docker
-docker build --no-cache -t arcface_pytorch:1.0 .
-docker run -it --name {name} --shm-size=1024G  --device=/dev/kfd --device=/dev/dri/ --privileged --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal:ro -v {}:{} {docker_image} /bin/bash 
-pip install -r requirements.txt
-```
-### Anaconda（方法三）
-线上节点推荐使用conda进行环境配置。
-创建python=3.10的conda环境并激活
-```
-conda create -n arcface python=3.10
-conda activate arcface
-```
-
-关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
-```
-DTK驱动：dtk23.10
-python：python3.10
-pytorch:1.13.1
-torchvision:0.14.1
-```
-安装其他依赖包
-```
-pip install -r requirements.txt
-```
-## 数据集
-`MS1MV2`
-
-- 训练集[face_train.zip](https://pan.baidu.com/s/1S6LJZGdqcZRle1vlcMzHOQ)，
-下载后解压到当前目录  
-数据目录结构如下：
-```
- ── faces_emore
-    |   agedb_30.bin
-    |   calfw.bin
-    |   cfp_ff.bin
-    |   cfp_fp.bin
-    |   cplfw.bin
-    |   lfw.bin
-    |   property
-    |   train.idx
-    |   train.rec
-    |   vgg2_fp.bin
-```
-- 测试集[face_val.zip]()
-
-
-- 项目中已提供用于试验训练的迷你数据集[datasets](https://pan.baidu.com/s/1oKRgOW7jCLxzPZoQofl1mQ?pwd=0okl)，下载后解压即可。
-## 训练
-Backbone使用ResNet100，在MS1MV3数据机上的预训练权重文件[model.pt](https://pan.baidu.com/s/1W-TisIZtZmRQz32hq5T6Uw?pwd=1234)  
-
-### 单机单卡
-```
-python train_v2.py configs/ms1mv2_r100
-```
-
-### 单机多卡
-```
-torchrun --nproc_per_node=4 train_v2.py configs/ms1mv2_r100
-```
-
-## 精度
-使用权重文件[model.pt](https://pan.baidu.com/s/1W-TisIZtZmRQz32hq5T6Uw?pwd=1234)，测试模型精度：
-```
-python eval_ijbc.py --model-prefix model.pt --image-path IJB_release/IJBC/ --network r100
-```
-
-### 精度
-测试数据：[test](http://images.cocodataset.org/zips/test2017.zip)
-
-测试指标：
-| 模型 | 数据类型 | map0.5:0.95 | map0.5 |
-| :------: | :------: | :------: | :------: |
-| yolo9-c-converted | 全精度 | 0.530 | 0.703 |
-| yolo9-e-converted | 全精度 | 0.556 | 0.728 |
-| yolo9-c | 全精度 | 0.530 | 0.703 |
-| yolo9-e | 全精度 | 0.556 | 0.728 |
-| gelan-c | 全精度 | 0.526 | 0.695 |
-| gelan-e | 全精度 | 0.550 | 0.719 |
-
-
-## 应用场景
-### 算法类别
-人脸识别
-
-### 热点应用行业
-安防,交通,教育
-
-
-## 源码仓库及问题反馈
-[https://developer.hpccube.com/codes/modelzoo/yolov9_pytorch](https://developer.hpccube.com/codes/modelzoo/yolov9_pytorch)
-## 参考资料
-[https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch)
-
-

configs/ms1mv2_r100.py

@@ -19,7 +19,7 @@ config.lr = 0.1
 config.verbose = 2000
 config.dali = False
 
-config.rec = "/train_tmp/faces_emore"
+config.rec = "faces_emore"
 config.num_classes = 85742
 config.num_image = 5822653
 config.num_epoch = 20

model.properties

+# 模型唯一标识
+modelCode=676
+# 模型名称
+modelName=arcface_pytorch
+# 模型描述
+modelDescription=ArcFace设计了新的用于人脸识别的损失函数,
+# 应用场景
+appScenario=推理,训练,人脸识别,安防,交通,教育
+# 框架类型
+frameType=pytorch

requirement.txt

@@ -2,5 +2,8 @@ tensorboard
 easydict
 mxnet
 onnx
-sklearn
-opencv-python
\ No newline at end of file
+scipy==1.7.1 
+scikit-learn
+opencv-python
+menpo
+prettytable
\ No newline at end of file