https://github.com/oneflow-inc/oneflow_face
https://github.com/oneflow-inc/oneflow_face
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/oneflow-inc/oneflow_face
- Owner: Oneflow-Inc
- Created: 2020-04-28T05:27:02.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-08-10T03:21:03.000Z (almost 4 years ago)
- Last Synced: 2025-04-17T07:17:47.898Z (about 1 year ago)
- Language: Python
- Size: 451 KB
- Stars: 12
- Watchers: 49
- Forks: 5
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# InsightFace in OneFlow
[English](README.md) **|** [简体中文](README_CH.md)
It introduces how to train InsightFace in OneFlow, and do verification over the validation datasets via the well-toned networks.
## Contents
\- [InsightFace in OneFlow](#insightface-in-oneflow)
\- [Contents](#contents)
\- [Background](#background)
\- [InsightFace opensource project](#insightface-opensource-project)
\- [Implementation in OneFlow](#implementation-in-oneflow)
\- [Preparations](#preparations)
\- [Install OneFlow](#install-oneflow)
\- [Data preparations](#data-preparations)
\- [1. Download datasets](#1-download-datasets)
\- [2. Transformation from MS1M recordio to OFRecord](#2-transformation-from-ms1m-recordio-to-ofrecord)
\- [Pretrained model](#Pretrained-model)
\- [Training and verification](#training-and-verification)
\- [Training](#training)
\- [Varification](#varification)
\- [Benchmark](#benchmark)
## Background
### InsightFace opensource project
[InsightFace](https://github.com/deepinsight/insightface) is an open-source 2D&3D deep face analysis toolbox, mainly based on MXNet.
In InsightFace, it supports:
- Datasets typically used for face recognition, such as CASIA-Webface、MS1M、VGG2(Provided with the form of a binary file which could run in MXNet, [here](https://github.com/deepinsight/insightface/wiki/Dataset-Zoo) is more details about the datasets and how to download.
* Backbones of ResNet, MobilefaceNet, InceptionResNet_v2, and other deep-learning networks to apply in facial recognition.
* Implementation of different loss functions, including SphereFace Loss、Softmax Loss、SphereFace Loss, etc.
### Implementation in OneFlow
Based upon the currently existing work of Insightface, OneFlow ported basic models from it, and now OneFlow supports:
- Training datasets of MS1M、Glint360k, and validation datasets of Lfw、Cfp_fp and Agedb_30, scripts for training and validating.
- Backbones of ResNet100 and MobileFaceNet to recognize faces.
- Loss function, e.g. Softmax Loss and Margin Softmax Loss（including Arcface、Cosface and Combined Loss）.
- Model parallelism and [Partial FC](https://github.com/deepinsight/insightface/tree/760d6de043d7f654c5963391271f215dab461547/recognition/partial_fc#partial-fc) optimization.
- Model transformation via MXNet.
To be coming further:
- Additional datasets transformation.
- Plentiful backbones.
- Full-scale loss functions implementation.
- Incremental tutorial on the distributed configuration.
This project is open for every developer to PR, new implementation and animated discussion will be most welcome.
## Preparations
First of all, before execution, please make sure that:
1. Install OneFlow
2. Prepare training and validation datasets in form of OFRecord.
### Install OneFlow
According to steps in [Install OneFlow](https://github.com/Oneflow-Inc/oneflow#install-oneflow) install the newest release master whl packages.
```
python3 -m pip install --find-links https://release.oneflow.info oneflow_cu102 --user
```
### Data preparations
According to [Load and Prepare OFRecord Datasets](https://docs.oneflow.org/en/extended_topics/how_to_make_ofdataset.html), datasets should be converted into the form of OFREcord, to test InsightFace.
It has provided a set of datasets related to face recognition tasks, which have been pre-processed via face alignment or other processions already in [InsightFace](https://github.com/deepinsight/insightface). The corresponding datasets could be downloaded from [here](https://github.com/deepinsight/insightface/wiki/Dataset-Zoo) and should be converted into OFRecord, which performs better in OneFlow. Considering the cumbersome steps, it is suggested to download converted OFrecord datasets:
[MS1M-ArcFace(face_emore)](http://oneflow-public.oss-cn-beijing.aliyuncs.com/face_dataset/train_ofrecord.tar.gz)
[MS1MV3](https://oneflow-public.oss-cn-beijing.aliyuncs.com/facedata/MS1V3/oneflow/ms1m-retinaface-t1.zip)
It illustrates how to convert downloaded datasets into OFRecords, and take MS1M-ArcFace as an example in the following.
#### 1. Download datasets
The structure of the downloaded MS1M-ArcFace is shown as follown：
```
faces_emore/
train.idx
train.rec
property
lfw.bin
cfp_fp.bin
agedb_30.bin
```
The first three files are MXNet recordio format files of MS1M training dataset, the last three `.bin` files are different validation datasets.
#### 2. Transformation from MS1M recordio to OFRecord
Only need to execute 2.1 or 2.2
2.1 Use Python scripts directly
Run
```
python tools/mx_recordio_2_ofrecord_shuffled_npart.py --data_dir datasets/faces_emore --output_filepath faces_emore/ofrecord/train --part_num 16
```
And you will get the number of `part_num` parts of OFRecord, it's 16 parts in this example, it showed like this
```
tree ofrecord/test/
ofrecord/test/
|-- _SUCCESS
|-- part-00000
|-- part-00001
|-- part-00002
|-- part-00003
|-- part-00004
|-- part-00005
|-- part-00006
|-- part-00007
|-- part-00008
|-- part-00009
|-- part-00010
|-- part-00011
|-- part-00012
|-- part-00013
|-- part-00014
`-- part-00015
0 directories, 17 files
```
2.2 Use Python scripts + Spark Shuffle + Spark partition
Run
```
python tools/dataset_convert/mx_recordio_2_ofrecord.py --data_dir datasets/faces_emore --output_filepath faces_emore/ofrecord/train
```
And you will get one part of OFRecord(`part-0`) with all data in this way. Then you should use Spark to shuffle and partition.
1. Get jar package available
You can download Spark-oneflow-connector-assembly-0.1.0.jar via [Github](https://github.com/Oneflow-Inc/spark-oneflow-connector) or [OSS](https://oneflow-public.oss-cn-beijing.aliyuncs.com/spark-oneflow-connector/spark-oneflow-connector-assembly-0.1.1.jar)
2. Run in Spark
Assign that you have already installed and configured Spark.
Run
```
//Start Spark
./Spark-2.4.3-bin-hadoop2.7/bin/Spark-shell --jars ~/Spark-oneflow-connector-assembly-0.1.0.jar --driver-memory=64G --conf Spark.local.dir=/tmp/
// shuffle and partition in 16 parts
import org.oneflow.Spark.functions._
Spark.read.chunk("data_path").shuffle().repartition(16).write.chunk("new_data_path")
sc.formatFilenameAsOneflowStyle("new_data_path")
```
Hence you will get 16 parts of OFRecords, it shown like this
```
tree ofrecord/test/
ofrecord/test/
|-- _SUCCESS
|-- part-00000
|-- part-00001
|-- part-00002
|-- part-00003
|-- part-00004
|-- part-00005
|-- part-00006
|-- part-00007
|-- part-00008
|-- part-00009
|-- part-00010
|-- part-00011
|-- part-00012
|-- part-00013
|-- part-00014
`-- part-00015
0 directories, 17 files
```
## Pretrained model
The accuracy comparison of OneFlow and MXNet pretrained models on the verification set of the 1:1 verification accuracy on insightface recognition test (IFRT) are as follows:
| **Framework** | **African** | **Caucasian** | **Indian** | **Asian** | **All** |
| ------------- | ----------- | ------------- | ---------- | --------- | ------- |
| OneFlow | 90.4076 | 94.583 | 93.702 | 68.754 | 89.684 |
| MXNet | 90.45 | 94.60 | 93.96 | 63.91 | 88.23 |
The download link of the OneFlow pretrain model:[of_005_model.tar.gz](http://oneflow-public.oss-cn-beijing.aliyuncs.com/face_dataset/pretrained_model/of_glint360k_partial_fc/of_005_model.tar.gz)
We also provide the MXNet model which converted from OneFlow:[of_to_mxnet_model_005.tar.gz](http://oneflow-public.oss-cn-beijing.aliyuncs.com/face_dataset/pretrained_model/of_2_mxnet_glint360k_partial_fc/of_to_mxnet_model_005.tar.gz)
## OneFLow2ONNX
```
pip install oneflow-onnx==0.3.4
./convert.sh
```
## Training and verification
### Training
To reduce the usage cost of user, OneFlow draws close the scripts to Torch style, you can directly modify parameters via configs/*.py
```
./run.sh
```
### Varification
Moreover, OneFlow offers a validation script to do verification separately, val.py, which facilitates you to check the precision of the pre-training model saved.
```
./val.sh
```
## Benchmark
### Training Speed Benchmark
#### Face_emore Dataset & FP32
| Backbone | GPU | model_parallel | partial_fc | BatchSize / it | Throughput img / sec |
| -------- | ------------------------ | -------------- | ---------- | -------------- | -------------------- |
| R100 | 8 * Tesla V100-SXM2-16GB | False | False | 64 | 1836.8 |
| R100 | 8 * Tesla V100-SXM2-16GB | True | False | 64 | 1854.15 |
| R100 | 8 * Tesla V100-SXM2-16GB | True | True | 64 | 1872.81 |
| R100 | 8 * Tesla V100-SXM2-16GB | False | False | 96(Max) | 1931.76 |
| R100 | 8 * Tesla V100-SXM2-16GB | True | False | 115(Max) | 1921.87 |
| R100 | 8 * Tesla V100-SXM2-16GB | True | True | 120(Max) | 1962.76 |
| Y1 | 8 * Tesla V100-SXM2-16GB | False | False | 256 | 14298.02 |
| Y1 | 8 * Tesla V100-SXM2-16GB | True | False | 256 | 14049.75 |
| Y1 | 8 * Tesla V100-SXM2-16GB | False | False | 350(Max) | 14756.03 |
| Y1 | 8 * Tesla V100-SXM2-16GB | True | True | 400(Max) | 14436.38 |
#### Glint360k Dataset & FP32
| Backbone | GPU | partial_fc sample_ratio | BatchSize / it | Throughput img / sec |
| -------- | ------------------------ | ----------------------- | -------------- | -------------------- |
| R100 | 8 * Tesla V100-SXM2-16GB | 0.1 | 64 | 1858.57 |
| R100 | 8 * Tesla V100-SXM2-16GB | 0.1 | 115 | 1933.88 |
### Evaluation on Lfw, Cfp_fp, Agedb_30
- Data Parallelism
| Backbone | Dataset | Lfw | Cfp_fp | Agedb_30 |
| ------------- | ------- | ------ | ------ | -------- |
| R100 | MS1M | 99.717 | 98.643 | 98.150 |
| MobileFaceNet | MS1M | 99.5 | 92.657 | 95.6 |
- Model Parallelism
| Backbone | Dataset | Lfw | Cfp_fp | Agedb_30 |
| ------------- | ------- | ------ | ------ | -------- |
| R100 | MS1M | 99.733 | 98.329 | 98.033 |
| MobileFaceNet | MS1M | 99.483 | 93.457 | 95.7 |
- Partial FC
| Backbone | Dataset | Lfw | Cfp_fp | Agedb_30 |
| -------- | ------- | ------ | ------ | -------- |
| R100 | MS1M | 99.817 | 98.443 | 98.217 |
### Evaluation on IFRT
r denotes the sampling rate of negative class centers.
| Backbone | Dataset | African | Caucasian | Indian | Asian | ALL |
| -------- | -------------------- | ------- | --------- | ------ | ------ | ------ |
| R100 | **Glint360k**(r=0.1) | 90.4076 | 94.583 | 93.702 | 68.754 | 89.684 |
### Max num_classses
| node_num | gpu_num_per_node | batch_size_per_device | fp16 | Model Parallel | Partial FC | num_classes |
| -------- | ---------------- | --------------------- | ---- | -------------- | ---------- | ----------- |
| 1 | 1 | 64 | True | True | True | 2000000 |
| 1 | 8 | 64 | True | True | True | 13500000 |
More test details could refer to [OneFlow DLPerf](https://github.com/Oneflow-Inc/DLPerf#insightface).