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# Distributed Arcface Training in Pytorch
The "arcface_torch" repository is the official implementation of the ArcFace algorithm. It supports distributed and sparse training with multiple distributed training examples, including several memory-saving techniques such as mixed precision training and gradient checkpointing. It also supports training for ViT models and datasets including WebFace42M and Glint360K, two of the largest open-source datasets. Additionally, the repository comes with a built-in tool for converting to ONNX format, making it easy to submit to MFR evaluation systems.
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/killing-two-birds-with-one-stone-efficient/face-verification-on-ijb-c)](https://paperswithcode.com/sota/face-verification-on-ijb-c?p=killing-two-birds-with-one-stone-efficient)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/killing-two-birds-with-one-stone-efficient/face-verification-on-ijb-b)](https://paperswithcode.com/sota/face-verification-on-ijb-b?p=killing-two-birds-with-one-stone-efficient)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/killing-two-birds-with-one-stone-efficient/face-verification-on-agedb-30)](https://paperswithcode.com/sota/face-verification-on-agedb-30?p=killing-two-birds-with-one-stone-efficient)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/killing-two-birds-with-one-stone-efficient/face-verification-on-cfp-fp)](https://paperswithcode.com/sota/face-verification-on-cfp-fp?p=killing-two-birds-with-one-stone-efficient)
## Requirements
To avail the latest features of PyTorch, we have upgraded to version 1.12.0.
- Install [PyTorch](https://pytorch.org/get-started/previous-versions/) (torch>=1.12.0).
- (Optional) Install [DALI](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/), our doc for [install_dali.md](docs/install_dali.md).
- `pip install -r requirement.txt`.
## How to Training
To train a model, execute the `train_v2.py` script with the path to the configuration files. The sample commands provided below demonstrate the process of conducting distributed training.
### 1. To run on one GPU:
```shell
python train_v2.py configs/ms1mv3_r50_onegpu
```
Note:
It is not recommended to use a single GPU for training, as this may result in longer training times and suboptimal performance. For best results, we suggest using multiple GPUs or a GPU cluster.
### 2. To run on a machine with 8 GPUs:
```shell
torchrun --nproc_per_node=8 train_v2.py configs/ms1mv3_r50
```
### 3. To run on 2 machines with 8 GPUs each:
Node 0:
```shell
torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=12581 train_v2.py configs/wf42m_pfc02_16gpus_r100
```
Node 1:
```shell
torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=12581 train_v2.py configs/wf42m_pfc02_16gpus_r100
```
### 4. Run ViT-B on a machine with 24k batchsize:
```shell
torchrun --nproc_per_node=8 train_v2.py configs/wf42m_pfc03_40epoch_8gpu_vit_b
```
## Download Datasets or Prepare Datasets
- [MS1MV2](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_#ms1m-arcface-85k-ids58m-images-57) (87k IDs, 5.8M images)
- [MS1MV3](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_#ms1m-retinaface) (93k IDs, 5.2M images)
- [Glint360K](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc#4-download) (360k IDs, 17.1M images)
- [WebFace42M](docs/prepare_webface42m.md) (2M IDs, 42.5M images)
- [Your Dataset, Click Here!](docs/prepare_custom_dataset.md)
Note:
If you want to use DALI for data reading, please use the script 'scripts/shuffle_rec.py' to shuffle the InsightFace style rec before using it.
Example:
`python scripts/shuffle_rec.py ms1m-retinaface-t1`
You will get the "shuffled_ms1m-retinaface-t1" folder, where the samples in the "train.rec" file are shuffled.
## Model Zoo
- The models are available for non-commercial research purposes only.
- All models can be found in here.
- [Baidu Yun Pan](https://pan.baidu.com/s/1CL-l4zWqsI1oDuEEYVhj-g): e8pw
- [OneDrive](https://1drv.ms/u/s!AswpsDO2toNKq0lWY69vN58GR6mw?e=p9Ov5d)
### Performance on IJB-C and [**ICCV2021-MFR**](https://github.com/deepinsight/insightface/blob/master/challenges/mfr/README.md)
ICCV2021-MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face
recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities.
As the result, we can evaluate the FAIR performance for different algorithms.
For **ICCV2021-MFR-ALL** set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). The
globalised multi-racial testset contains 242,143 identities and 1,624,305 images.
#### 1. Training on Single-Host GPU
| Datasets | Backbone | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | log |
|:---------------|:--------------------|:------------|:------------|:------------|:------------------------------------------------------------------------------------------------------------------------------------|
| MS1MV2 | mobilefacenet-0.45G | 62.07 | 93.61 | 90.28 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv2_mbf/training.log) |
| MS1MV2 | r50 | 75.13 | 95.97 | 94.07 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv2_r50/training.log) |
| MS1MV2 | r100 | 78.12 | 96.37 | 94.27 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv2_r100/training.log) |
| MS1MV3 | mobilefacenet-0.45G | 63.78 | 94.23 | 91.33 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_mbf/training.log) |
| MS1MV3 | r50 | 79.14 | 96.37 | 94.47 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_r50/training.log) |
| MS1MV3 | r100 | 81.97 | 96.85 | 95.02 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_r100/training.log) |
| Glint360K | mobilefacenet-0.45G | 70.18 | 95.04 | 92.62 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_mbf/training.log) |
| Glint360K | r50 | 86.34 | 97.16 | 95.81 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_r50/training.log) |
| Glint360k | r100 | 89.52 | 97.55 | 96.38 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_r100/training.log) |
| WF4M | r100 | 89.87 | 97.19 | 95.48 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf4m_r100/training.log) |
| WF12M-PFC-0.2 | r100 | 94.75 | 97.60 | 95.90 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf12m_pfc02_r100/training.log) |
| WF12M-PFC-0.3 | r100 | 94.71 | 97.64 | 96.01 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf12m_pfc03_r100/training.log) |
| WF12M | r100 | 94.69 | 97.59 | 95.97 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf12m_r100/training.log) |
| WF42M-PFC-0.2 | r100 | 96.27 | 97.70 | 96.31 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf42m_pfc02_r100/training.log) |
| WF42M-PFC-0.2 | ViT-T-1.5G | 92.04 | 97.27 | 95.68 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf42m_pfc02_40epoch_8gpu_vit_t/training.log) |
| WF42M-PFC-0.3 | ViT-B-11G | 97.16 | 97.91 | 97.05 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_b_8gpu/training.log) |
#### 2. Training on Multi-Host GPU
| Datasets | Backbone(bs*gpus) | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | Throughout | log |
|:-----------------|:------------------|:------------|:------------|:------------|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------------|
| WF42M-PFC-0.2 | r50(512*8) | 93.83 | 97.53 | 96.16 | ~5900 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r50_bs4k_pfc02/training.log) |
| WF42M-PFC-0.2 | r50(512*16) | 93.96 | 97.46 | 96.12 | ~11000 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r50_lr01_pfc02_bs8k_16gpus/training.log) |
| WF42M-PFC-0.2 | r50(128*32) | 94.04 | 97.48 | 95.94 | ~17000 | click me |
| WF42M-PFC-0.2 | r100(128*16) | 96.28 | 97.80 | 96.57 | ~5200 | click me |
| WF42M-PFC-0.2 | r100(256*16) | 96.69 | 97.85 | 96.63 | ~5200 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r100_bs4k_pfc02/training.log) |
| WF42M-PFC-0.0018 | r100(512*32) | 93.08 | 97.51 | 95.88 | ~10000 | click me |
| WF42M-PFC-0.2 | r100(128*32) | 96.57 | 97.83 | 96.50 | ~9800 | click me |
`r100(128*32)` means backbone is r100, batchsize per gpu is 128, the number of gpus is 32.
#### 3. ViT For Face Recognition
| Datasets | Backbone(bs) | FLOPs | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | Throughout | log |
|:--------------|:--------------|:------|:------------|:------------|:------------|:-----------|:-----------------------------------------------------------------------------------------------------------------------------|
| WF42M-PFC-0.3 | r18(128*32) | 2.6 | 79.13 | 95.77 | 93.36 | - | click me |
| WF42M-PFC-0.3 | r50(128*32) | 6.3 | 94.03 | 97.48 | 95.94 | - | click me |
| WF42M-PFC-0.3 | r100(128*32) | 12.1 | 96.69 | 97.82 | 96.45 | - | click me |
| WF42M-PFC-0.3 | r200(128*32) | 23.5 | 97.70 | 97.97 | 96.93 | - | click me |
| WF42M-PFC-0.3 | VIT-T(384*64) | 1.5 | 92.24 | 97.31 | 95.97 | ~35000 | click me |
| WF42M-PFC-0.3 | VIT-S(384*64) | 5.7 | 95.87 | 97.73 | 96.57 | ~25000 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_s_64gpu/training.log) |
| WF42M-PFC-0.3 | VIT-B(384*64) | 11.4 | 97.42 | 97.90 | 97.04 | ~13800 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_b_64gpu/training.log) |
| WF42M-PFC-0.3 | VIT-L(384*64) | 25.3 | 97.85 | 98.00 | 97.23 | ~9406 | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_l_64gpu/training.log) |
`WF42M` means WebFace42M, `PFC-0.3` means negivate class centers sample rate is 0.3.
#### 4. Noisy Datasets
| Datasets | Backbone | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | log |
|:-------------------------|:---------|:------------|:------------|:------------|:---------|
| WF12M-Flip(40%) | r50 | 43.87 | 88.35 | 80.78 | click me |
| WF12M-Flip(40%)-PFC-0.1* | r50 | 80.20 | 96.11 | 93.79 | click me |
| WF12M-Conflict | r50 | 79.93 | 95.30 | 91.56 | click me |
| WF12M-Conflict-PFC-0.3* | r50 | 91.68 | 97.28 | 95.75 | click me |
`WF12M` means WebFace12M, `+PFC-0.1*` denotes additional abnormal inter-class filtering.
## Speed Benchmark
<div><img src="https://github.com/anxiangsir/insightface_arcface_log/blob/master/pfc_exp.png" width = "90%" /></div>
**Arcface-Torch** is an efficient tool for training large-scale face recognition training sets. When the number of classes in the training sets exceeds one million, the partial FC sampling strategy maintains the same accuracy while providing several times faster training performance and lower GPU memory utilization. The partial FC is a sparse variant of the model parallel architecture for large-scale face recognition, utilizing a sparse softmax that dynamically samples a subset of class centers for each training batch. During each iteration, only a sparse portion of the parameters are updated, leading to a significant reduction in GPU memory requirements and computational demands. With the partial FC approach, it is possible to train sets with up to 29 million identities, the largest to date. Furthermore, the partial FC method supports multi-machine distributed training and mixed precision training.
More details see
[speed_benchmark.md](docs/speed_benchmark.md) in docs.
> 1. Training Speed of Various Parallel Techniques (Samples per Second) on a Tesla V100 32GB x 8 System (Higher is Optimal)
`-` means training failed because of gpu memory limitations.
| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
|:--------------------------------|:--------------|:---------------|:---------------|
| 125000 | 4681 | 4824 | 5004 |
| 1400000 | **1672** | 3043 | 4738 |
| 5500000 | **-** | **1389** | 3975 |
| 8000000 | **-** | **-** | 3565 |
| 16000000 | **-** | **-** | 2679 |
| 29000000 | **-** | **-** | **1855** |
> 2. GPU Memory Utilization of Various Parallel Techniques (MB per GPU) on a Tesla V100 32GB x 8 System (Lower is Optimal)
| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
|:--------------------------------|:--------------|:---------------|:---------------|
| 125000 | 7358 | 5306 | 4868 |
| 1400000 | 32252 | 11178 | 6056 |
| 5500000 | **-** | 32188 | 9854 |
| 8000000 | **-** | **-** | 12310 |
| 16000000 | **-** | **-** | 19950 |
| 29000000 | **-** | **-** | 32324 |
## Citations
```
@inproceedings{deng2019arcface,
title={Arcface: Additive angular margin loss for deep face recognition},
author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={4690--4699},
year={2019}
}
@inproceedings{An_2022_CVPR,
author={An, Xiang and Deng, Jiankang and Guo, Jia and Feng, Ziyong and Zhu, XuHan and Yang, Jing and Liu, Tongliang},
title={Killing Two Birds With One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month={June},
year={2022},
pages={4042-4051}
}
@inproceedings{zhu2021webface260m,
title={Webface260m: A benchmark unveiling the power of million-scale deep face recognition},
author={Zhu, Zheng and Huang, Guan and Deng, Jiankang and Ye, Yun and Huang, Junjie and Chen, Xinze and Zhu, Jiagang and Yang, Tian and Lu, Jiwen and Du, Dalong and Zhou, Jie},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10492--10502},
year={2021}
}
```
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# 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)
backbones/__init__.py 0 → 100644
View file @ 9b87ce51
from .iresnet import iresnet18, iresnet34, iresnet50, iresnet100, iresnet200
from .mobilefacenet import get_mbf
def get_model(name, **kwargs):
# resnet
if name == "r18":
return iresnet18(False, **kwargs)
elif name == "r34":
return iresnet34(False, **kwargs)
elif name == "r50":
return iresnet50(False, **kwargs)
elif name == "r100":
return iresnet100(False, **kwargs)
elif name == "r200":
return iresnet200(False, **kwargs)
elif name == "r2060":
from .iresnet2060 import iresnet2060
return iresnet2060(False, **kwargs)
elif name == "mbf":
fp16 = kwargs.get("fp16", False)
num_features = kwargs.get("num_features", 512)
return get_mbf(fp16=fp16, num_features=num_features)
elif name == "mbf_large":
from .mobilefacenet import get_mbf_large
fp16 = kwargs.get("fp16", False)
num_features = kwargs.get("num_features", 512)
return get_mbf_large(fp16=fp16, num_features=num_features)
elif name == "vit_t":
num_features = kwargs.get("num_features", 512)
from .vit import VisionTransformer
return VisionTransformer(
img_size=112, patch_size=9, num_classes=num_features, embed_dim=256, depth=12,
num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0.1)
elif name == "vit_t_dp005_mask0": # For WebFace42M
num_features = kwargs.get("num_features", 512)
from .vit import VisionTransformer
return VisionTransformer(
img_size=112, patch_size=9, num_classes=num_features, embed_dim=256, depth=12,
num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.0)
elif name == "vit_s":
num_features = kwargs.get("num_features", 512)
from .vit import VisionTransformer
return VisionTransformer(
img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=12,
num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0.1)
elif name == "vit_s_dp005_mask_0": # For WebFace42M
num_features = kwargs.get("num_features", 512)
from .vit import VisionTransformer
return VisionTransformer(
img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=12,
num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.0)
elif name == "vit_b":
# this is a feature
num_features = kwargs.get("num_features", 512)
from .vit import VisionTransformer
return VisionTransformer(
img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=24,
num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0.1, using_checkpoint=True)
elif name == "vit_b_dp005_mask_005": # For WebFace42M
# this is a feature
num_features = kwargs.get("num_features", 512)
from .vit import VisionTransformer
return VisionTransformer(
img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=24,
num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.05, using_checkpoint=True)
elif name == "vit_l_dp005_mask_005": # For WebFace42M
# this is a feature
num_features = kwargs.get("num_features", 512)
from .vit import VisionTransformer
return VisionTransformer(
img_size=112, patch_size=9, num_classes=num_features, embed_dim=768, depth=24,
num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.05, using_checkpoint=True)
elif name == "vit_h": # For WebFace42M
num_features = kwargs.get("num_features", 512)
from .vit import VisionTransformer
return VisionTransformer(
img_size=112, patch_size=9, num_classes=num_features, embed_dim=1024, depth=48,
num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0, using_checkpoint=True)
else:
raise ValueError()
backbones/iresnet.py 0 → 100644
View file @ 9b87ce51
import torch
from torch import nn
from torch.utils.checkpoint import checkpoint
__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']
using_ckpt = False
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes,
out_planes,
kernel_size=1,
stride=stride,
bias=False)
class IBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
super(IBasicBlock, self).__init__()
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
self.conv1 = conv3x3(inplanes, planes)
self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
self.prelu = nn.PReLU(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
self.downsample = downsample
self.stride = stride
def forward_impl(self, x):
identity = x
out = self.bn1(x)
out = self.conv1(out)
out = self.bn2(out)
out = self.prelu(out)
out = self.conv2(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
def forward(self, x):
if self.training and using_ckpt:
return checkpoint(self.forward_impl, x)
else:
return self.forward_impl(x)
class IResNet(nn.Module):
fc_scale = 7 * 7
def __init__(self,
block, layers, dropout=0, num_features=512, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
super(IResNet, self).__init__()
self.extra_gflops = 0.0
self.fp16 = fp16
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
self.prelu = nn.PReLU(self.inplanes)
self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=2,
dilate=replace_stride_with_dilation[2])
self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
self.dropout = nn.Dropout(p=dropout, inplace=True)
self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
self.features = nn.BatchNorm1d(num_features, eps=1e-05)
nn.init.constant_(self.features.weight, 1.0)
self.features.weight.requires_grad = False
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, 0, 0.1)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
if zero_init_residual:
for m in self.modules():
if isinstance(m, IBasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
)
layers = []
layers.append(
block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(self.inplanes,
planes,
groups=self.groups,
base_width=self.base_width,
dilation=self.dilation))
return nn.Sequential(*layers)
def forward(self, x):
with torch.cuda.amp.autocast(self.fp16):
x = self.conv1(x)
x = self.bn1(x)
x = self.prelu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.bn2(x)
x = torch.flatten(x, 1)
x = self.dropout(x)
x = self.fc(x.float() if self.fp16 else x)
x = self.features(x)
return x
def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
model = IResNet(block, layers, **kwargs)
if pretrained:
raise ValueError()
return model
def iresnet18(pretrained=False, progress=True, **kwargs):
return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,
progress, **kwargs)
def iresnet34(pretrained=False, progress=True, **kwargs):
return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,
progress, **kwargs)
def iresnet50(pretrained=False, progress=True, **kwargs):
return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
progress, **kwargs)
def iresnet100(pretrained=False, progress=True, **kwargs):
return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,
progress, **kwargs)
def iresnet200(pretrained=False, progress=True, **kwargs):
return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,
progress, **kwargs)
backbones/iresnet2060.py 0 → 100644
View file @ 9b87ce51
import torch
from torch import nn
assert torch.__version__ >= "1.8.1"
from torch.utils.checkpoint import checkpoint_sequential
__all__ = ['iresnet2060']
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes,
out_planes,
kernel_size=1,
stride=stride,
bias=False)
class IBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
super(IBasicBlock, self).__init__()
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05, )
self.conv1 = conv3x3(inplanes, planes)
self.bn2 = nn.BatchNorm2d(planes, eps=1e-05, )
self.prelu = nn.PReLU(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn3 = nn.BatchNorm2d(planes, eps=1e-05, )
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.bn1(x)
out = self.conv1(out)
out = self.bn2(out)
out = self.prelu(out)
out = self.conv2(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
class IResNet(nn.Module):
fc_scale = 7 * 7
def __init__(self,
block, layers, dropout=0, num_features=512, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
super(IResNet, self).__init__()
self.fp16 = fp16
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
self.prelu = nn.PReLU(self.inplanes)
self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=2,
dilate=replace_stride_with_dilation[2])
self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05, )
self.dropout = nn.Dropout(p=dropout, inplace=True)
self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
self.features = nn.BatchNorm1d(num_features, eps=1e-05)
nn.init.constant_(self.features.weight, 1.0)
self.features.weight.requires_grad = False
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, 0, 0.1)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
if zero_init_residual:
for m in self.modules():
if isinstance(m, IBasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
)
layers = []
layers.append(
block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(self.inplanes,
planes,
groups=self.groups,
base_width=self.base_width,
dilation=self.dilation))
return nn.Sequential(*layers)
def checkpoint(self, func, num_seg, x):
if self.training:
return checkpoint_sequential(func, num_seg, x)
else:
return func(x)
def forward(self, x):
with torch.cuda.amp.autocast(self.fp16):
x = self.conv1(x)
x = self.bn1(x)
x = self.prelu(x)
x = self.layer1(x)
x = self.checkpoint(self.layer2, 20, x)
x = self.checkpoint(self.layer3, 100, x)
x = self.layer4(x)
x = self.bn2(x)
x = torch.flatten(x, 1)
x = self.dropout(x)
x = self.fc(x.float() if self.fp16 else x)
x = self.features(x)
return x
def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
model = IResNet(block, layers, **kwargs)
if pretrained:
raise ValueError()
return model
def iresnet2060(pretrained=False, progress=True, **kwargs):
return _iresnet('iresnet2060', IBasicBlock, [3, 128, 1024 - 128, 3], pretrained, progress, **kwargs)
backbones/mobilefacenet.py 0 → 100644
View file @ 9b87ce51
'''
Adapted from https://github.com/cavalleria/cavaface.pytorch/blob/master/backbone/mobilefacenet.py
Original author cavalleria
'''
import torch.nn as nn
from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module
import torch
class Flatten(Module):
def forward(self, x):
return x.view(x.size(0), -1)
class ConvBlock(Module):
def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
super(ConvBlock, self).__init__()
self.layers = nn.Sequential(
Conv2d(in_c, out_c, kernel, groups=groups, stride=stride, padding=padding, bias=False),
BatchNorm2d(num_features=out_c),
PReLU(num_parameters=out_c)
)
def forward(self, x):
return self.layers(x)
class LinearBlock(Module):
def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
super(LinearBlock, self).__init__()
self.layers = nn.Sequential(
Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False),
BatchNorm2d(num_features=out_c)
)
def forward(self, x):
return self.layers(x)
class DepthWise(Module):
def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):
super(DepthWise, self).__init__()
self.residual = residual
self.layers = nn.Sequential(
ConvBlock(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)),
ConvBlock(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride),
LinearBlock(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
)
def forward(self, x):
short_cut = None
if self.residual:
short_cut = x
x = self.layers(x)
if self.residual:
output = short_cut + x
else:
output = x
return output
class Residual(Module):
def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)):
super(Residual, self).__init__()
modules = []
for _ in range(num_block):
modules.append(DepthWise(c, c, True, kernel, stride, padding, groups))
self.layers = Sequential(*modules)
def forward(self, x):
return self.layers(x)
class GDC(Module):
def __init__(self, embedding_size):
super(GDC, self).__init__()
self.layers = nn.Sequential(
LinearBlock(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)),
Flatten(),
Linear(512, embedding_size, bias=False),
BatchNorm1d(embedding_size))
def forward(self, x):
return self.layers(x)
class MobileFaceNet(Module):
def __init__(self, fp16=False, num_features=512, blocks=(1, 4, 6, 2), scale=2):
super(MobileFaceNet, self).__init__()
self.scale = scale
self.fp16 = fp16
self.layers = nn.ModuleList()
self.layers.append(
ConvBlock(3, 64 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1))
)
if blocks[0] == 1:
self.layers.append(
ConvBlock(64 * self.scale, 64 * self.scale, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64)
)
else:
self.layers.append(
Residual(64 * self.scale, num_block=blocks[0], groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
)
self.layers.extend(
[
DepthWise(64 * self.scale, 64 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128),
Residual(64 * self.scale, num_block=blocks[1], groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
DepthWise(64 * self.scale, 128 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256),
Residual(128 * self.scale, num_block=blocks[2], groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
DepthWise(128 * self.scale, 128 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512),
Residual(128 * self.scale, num_block=blocks[3], groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
])
self.conv_sep = ConvBlock(128 * self.scale, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0))
self.features = GDC(num_features)
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
with torch.cuda.amp.autocast(self.fp16):
for func in self.layers:
x = func(x)
x = self.conv_sep(x.float() if self.fp16 else x)
x = self.features(x)
return x
def get_mbf(fp16, num_features, blocks=(1, 4, 6, 2), scale=2):
return MobileFaceNet(fp16, num_features, blocks, scale=scale)
def get_mbf_large(fp16, num_features, blocks=(2, 8, 12, 4), scale=4):
return MobileFaceNet(fp16, num_features, blocks, scale=scale)
backbones/vit.py 0 → 100644
View file @ 9b87ce51
import torch
import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from typing import Optional, Callable
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU6, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class VITBatchNorm(nn.Module):
def __init__(self, num_features):
super().__init__()
self.num_features = num_features
self.bn = nn.BatchNorm1d(num_features=num_features)
def forward(self, x):
return self.bn(x)
class Attention(nn.Module):
def __init__(self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_scale: Optional[None] = None,
attn_drop: float = 0.,
proj_drop: float = 0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
with torch.cuda.amp.autocast(True):
batch_size, num_token, embed_dim = x.shape
#qkv is [3,batch_size,num_heads,num_token, embed_dim//num_heads]
qkv = self.qkv(x).reshape(
batch_size, num_token, 3, self.num_heads, embed_dim // self.num_heads).permute(2, 0, 3, 1, 4)
with torch.cuda.amp.autocast(False):
q, k, v = qkv[0].float(), qkv[1].float(), qkv[2].float()
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(batch_size, num_token, embed_dim)
with torch.cuda.amp.autocast(True):
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(self,
dim: int,
num_heads: int,
num_patches: int,
mlp_ratio: float = 4.,
qkv_bias: bool = False,
qk_scale: Optional[None] = None,
drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
act_layer: Callable = nn.ReLU6,
norm_layer: str = "ln",
patch_n: int = 144):
super().__init__()
if norm_layer == "bn":
self.norm1 = VITBatchNorm(num_features=num_patches)
self.norm2 = VITBatchNorm(num_features=num_patches)
elif norm_layer == "ln":
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.Identity()
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
act_layer=act_layer, drop=drop)
self.extra_gflops = (num_heads * patch_n * (dim//num_heads)*patch_n * 2) / (1000**3)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
with torch.cuda.amp.autocast(True):
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class PatchEmbed(nn.Module):
def __init__(self, img_size=108, patch_size=9, in_channels=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * \
(img_size[0] // patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv2d(in_channels, embed_dim,
kernel_size=patch_size, stride=patch_size)
def forward(self, x):
batch_size, channels, height, width = x.shape
assert height == self.img_size[0] and width == self.img_size[1], \
f"Input image size ({height}*{width}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class VisionTransformer(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self,
img_size: int = 112,
patch_size: int = 16,
in_channels: int = 3,
num_classes: int = 1000,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.,
qkv_bias: bool = False,
qk_scale: Optional[None] = None,
drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
hybrid_backbone: Optional[None] = None,
norm_layer: str = "ln",
mask_ratio = 0.1,
using_checkpoint = False,
):
super().__init__()
self.num_classes = num_classes
# num_features for consistency with other models
self.num_features = self.embed_dim = embed_dim
if hybrid_backbone is not None:
raise ValueError
else:
self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim)
self.mask_ratio = mask_ratio
self.using_checkpoint = using_checkpoint
num_patches = self.patch_embed.num_patches
self.num_patches = num_patches
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
patch_n = (img_size//patch_size)**2
self.blocks = nn.ModuleList(
[
Block(dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
num_patches=num_patches, patch_n=patch_n)
for i in range(depth)]
)
self.extra_gflops = 0.0
for _block in self.blocks:
self.extra_gflops += _block.extra_gflops
if norm_layer == "ln":
self.norm = nn.LayerNorm(embed_dim)
elif norm_layer == "bn":
self.norm = VITBatchNorm(self.num_patches)
# features head
self.feature = nn.Sequential(
nn.Linear(in_features=embed_dim * num_patches, out_features=embed_dim, bias=False),
nn.BatchNorm1d(num_features=embed_dim, eps=2e-5),
nn.Linear(in_features=embed_dim, out_features=num_classes, bias=False),
nn.BatchNorm1d(num_features=num_classes, eps=2e-5)
)
self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
torch.nn.init.normal_(self.mask_token, std=.02)
trunc_normal_(self.pos_embed, std=.02)
# trunc_normal_(self.cls_token, std=.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token'}
def get_classifier(self):
return self.head
def random_masking(self, x, mask_ratio=0.1):
"""
Perform per-sample random masking by per-sample shuffling.
Per-sample shuffling is done by argsort random noise.
x: [N, L, D], sequence
"""
N, L, D = x.size() # batch, length, dim
len_keep = int(L * (1 - mask_ratio))
noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
# sort noise for each sample
# ascend: small is keep, large is remove
ids_shuffle = torch.argsort(noise, dim=1)
ids_restore = torch.argsort(ids_shuffle, dim=1)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
x_masked = torch.gather(
x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
# generate the binary mask: 0 is keep, 1 is remove
mask = torch.ones([N, L], device=x.device)
mask[:, :len_keep] = 0
# unshuffle to get the binary mask
mask = torch.gather(mask, dim=1, index=ids_restore)
return x_masked, mask, ids_restore
def forward_features(self, x):
B = x.shape[0]
x = self.patch_embed(x)
x = x + self.pos_embed
x = self.pos_drop(x)
if self.training and self.mask_ratio > 0:
x, _, ids_restore = self.random_masking(x)
for func in self.blocks:
if self.using_checkpoint and self.training:
from torch.utils.checkpoint import checkpoint
x = checkpoint(func, x)
else:
x = func(x)
x = self.norm(x.float())
if self.training and self.mask_ratio > 0:
mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] - x.shape[1], 1)
x_ = torch.cat([x[:, :, :], mask_tokens], dim=1) # no cls token
x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle
x = x_
return torch.reshape(x, (B, self.num_patches * self.embed_dim))
def forward(self, x):
x = self.forward_features(x)
x = self.feature(x)
return x
configs/3millions.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# configs for test speed
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 0.1
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 5e-4
config.batch_size = 512 # total_batch_size = batch_size * num_gpus
config.lr = 0.1 # batch size is 512
config.rec = "synthetic"
config.num_classes = 30 * 10000
config.num_image = 100000
config.num_epoch = 30
config.warmup_epoch = -1
config.val_targets = []
configs/base.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
# Margin Base Softmax
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r50"
config.resume = False
config.save_all_states = False
config.output = "ms1mv3_arcface_r50"
config.embedding_size = 512
# Partial FC
config.sample_rate = 1
config.interclass_filtering_threshold = 0
config.fp16 = False
config.batch_size = 128
# For SGD
config.optimizer = "sgd"
config.lr = 0.1
config.momentum = 0.9
config.weight_decay = 5e-4
# For AdamW
# config.optimizer = "adamw"
# config.lr = 0.001
# config.weight_decay = 0.1
config.verbose = 2000
config.frequent = 10
# For Large Sacle Dataset, such as WebFace42M
config.dali = False
config.dali_aug = False
# Gradient ACC
config.gradient_acc = 1
# setup seed
config.seed = 2048
# dataload numworkers
config.num_workers = 2
# WandB Logger
config.wandb_key = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
config.suffix_run_name = None
config.using_wandb = False
config.wandb_entity = "entity"
config.wandb_project = "project"
config.wandb_log_all = True
config.save_artifacts = False
config.wandb_resume = False # resume wandb run: Only if the you wand t resume the last run that it was interrupted
\ No newline at end of file
configs/glint360k_mbf.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 1e-4
config.batch_size = 128
config.lr = 0.1
config.verbose = 2000
config.dali = False
config.rec = "/train_tmp/glint360k"
config.num_classes = 360232
config.num_image = 17091657
config.num_epoch = 20
config.warmup_epoch = 0
config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]
configs/glint360k_r100.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 1e-4
config.batch_size = 128
config.lr = 0.1
config.verbose = 2000
config.dali = False
config.rec = "/train_tmp/glint360k"
config.num_classes = 360232
config.num_image = 17091657
config.num_epoch = 20
config.warmup_epoch = 0
config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]
configs/glint360k_r50.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.0, 0.4)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 1e-4
config.batch_size = 128
config.lr = 0.1
config.verbose = 2000
config.dali = False
config.rec = "/train_tmp/glint360k"
config.num_classes = 360232
config.num_image = 17091657
config.num_epoch = 20
config.warmup_epoch = 0
config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]
configs/ms1mv2_mbf.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 1e-4
config.batch_size = 128
config.lr = 0.1
config.verbose = 2000
config.dali = False
config.rec = "/train_tmp/faces_emore"
config.num_classes = 85742
config.num_image = 5822653
config.num_epoch = 40
config.warmup_epoch = 0
config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]
configs/ms1mv2_r100.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 5e-4
config.batch_size = 128
config.lr = 0.1
config.verbose = 2000
config.dali = False
config.rec = "/train_tmp/faces_emore"
config.num_classes = 85742
config.num_image = 5822653
config.num_epoch = 20
config.warmup_epoch = 0
config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]
configs/ms1mv2_r50.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 5e-4
config.batch_size = 128
config.lr = 0.1
config.verbose = 2000
config.dali = False
config.rec = "/train_tmp/faces_emore"
config.num_classes = 85742
config.num_image = 5822653
config.num_epoch = 20
config.warmup_epoch = 0
config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]
configs/ms1mv3_mbf.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "mbf"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 1e-4
config.batch_size = 128
config.lr = 0.1
config.verbose = 2000
config.dali = False
config.rec = "/train_tmp/ms1m-retinaface-t1"
config.num_classes = 93431
config.num_image = 5179510
config.num_epoch = 40
config.warmup_epoch = 0
config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]
configs/ms1mv3_r100.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r100"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 5e-4
config.batch_size = 128
config.lr = 0.1
config.verbose = 2000
config.dali = False
config.rec = "/train_tmp/ms1m-retinaface-t1"
config.num_classes = 93431
config.num_image = 5179510
config.num_epoch = 20
config.warmup_epoch = 0
config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]
configs/ms1mv3_r50.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 5e-4
config.batch_size = 128
config.lr = 0.1
config.verbose = 2000
config.dali = False
config.rec = "/train_tmp/ms1m-retinaface-t1"
config.num_classes = 93431
config.num_image = 5179510
config.num_epoch = 20
config.warmup_epoch = 0
config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]
configs/ms1mv3_r50_onegpu.py 0 → 100644
View file @ 9b87ce51
from easydict import EasyDict as edict
# make training faster
# our RAM is 256G
# mount -t tmpfs -o size=140G tmpfs /train_tmp
config = edict()
config.margin_list = (1.0, 0.5, 0.0)
config.network = "r50"
config.resume = False
config.output = None
config.embedding_size = 512
config.sample_rate = 1.0
config.fp16 = True
config.momentum = 0.9
config.weight_decay = 5e-4
config.batch_size = 128
config.lr = 0.02
config.verbose = 2000
config.dali = False
config.rec = "/train_tmp/ms1m-retinaface-t1"
config.num_classes = 93431
config.num_image = 5179510
config.num_epoch = 20
config.warmup_epoch = 0
config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]
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