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266 lines
7.1 KiB
Markdown
266 lines
7.1 KiB
Markdown
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# InsightFace in OneFlow
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[English](README.md) **|** [简体中文](README_CH.md)
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It introduces how to train InsightFace in OneFlow, and do verification over the validation datasets via the well-toned networks.
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## Contents
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\- [InsightFace in OneFlow](#insightface-in-oneflow)
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\- [Contents](#contents)
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\- [Background](#background)
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\- [InsightFace opensource project](#insightface-opensource-project)
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\- [Implementation in OneFlow](#implementation-in-oneflow)
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\- [Preparations](#preparations)
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\- [Install OneFlow](#install-oneflow)
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\- [Data preparations](#data-preparations)
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\- [1. Download datasets](#1-download-datasets)
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\- [2. Transformation from MS1M recordio to OFRecord](#2-transformation-from-ms1m-recordio-to-ofrecord)
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\- [Training and verification](#training-and-verification)
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\- [Training](#training)
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\- [OneFLow2ONNX](#OneFLow2ONNX)
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## Background
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### InsightFace opensource project
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[InsightFace](https://github.com/deepinsight/insightface) is an open-source 2D&3D deep face analysis toolbox, mainly based on MXNet.
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In InsightFace, it supports:
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- 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.
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* Backbones of ResNet, MobilefaceNet, InceptionResNet_v2, and other deep-learning networks to apply in facial recognition.
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* Implementation of different loss functions, including SphereFace Loss、Softmax Loss、SphereFace Loss, etc.
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### Implementation in OneFlow
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Based upon the currently existing work of Insightface, OneFlow ported basic models from it, and now OneFlow supports:
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- Training datasets of MS1M、Glint360k, and validation datasets of Lfw、Cfp_fp and Agedb_30, scripts for training and validating.
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- Backbones of ResNet100 and MobileFaceNet to recognize faces.
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- Loss function, e.g. Softmax Loss and Margin Softmax Loss（including Arcface、Cosface and Combined Loss）.
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- Model parallelism and [Partial FC](https://github.com/deepinsight/insightface/tree/760d6de043d7f654c5963391271f215dab461547/recognition/partial_fc#partial-fc) optimization.
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- Model transformation via MXNet.
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To be coming further:
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- Additional datasets transformation.
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- Plentiful backbones.
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- Full-scale loss functions implementation.
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- Incremental tutorial on the distributed configuration.
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This project is open for every developer to PR, new implementation and animated discussion will be most welcome.
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## Preparations
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First of all, before execution, please make sure that:
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1. Install OneFlow
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2. Prepare training and validation datasets in form of OFRecord.
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### Install OneFlow
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According to steps in [Install OneFlow](https://github.com/Oneflow-Inc/oneflow#install-oneflow) install the newest release master whl packages.
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```
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python3 -m pip install oneflow -f https://oneflow-staging.oss-cn-beijing.aliyuncs.com/branch/master/cu102/6aa719d70119b65837b25cc5f186eb19ef2b7891/index.html --user
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```
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### Data preparations
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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.
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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:
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[MS1M-ArcFace(face_emore)](http://oneflow-public.oss-cn-beijing.aliyuncs.com/face_dataset/train_ofrecord.tar.gz)
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[MS1MV3](https://oneflow-public.oss-cn-beijing.aliyuncs.com/facedata/MS1V3/oneflow/ms1m-retinaface-t1.zip)
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It illustrates how to convert downloaded datasets into OFRecords, and take MS1M-ArcFace as an example in the following.
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#### 1. Download datasets
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The structure of the downloaded MS1M-ArcFace is shown as follown：
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```
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faces_emore/
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train.idx
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train.rec
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property
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lfw.bin
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cfp_fp.bin
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agedb_30.bin
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```
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The first three files are MXNet recordio format files of MS1M training dataset, the last three `.bin` files are different validation datasets.
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#### 2. Transformation from MS1M recordio to OFRecord
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Only need to execute 2.1 or 2.2
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2.1 Use Python scripts directly
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Run
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```
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python tools/mx_recordio_2_ofrecord_shuffled_npart.py --data_dir datasets/faces_emore --output_filepath faces_emore/ofrecord/train --num_part 16
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```
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And you will get the number of `part_num` parts of OFRecord, it's 16 parts in this example, it showed like this
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```
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tree ofrecord/test/
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ofrecord/test/
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|-- _SUCCESS
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|-- part-00000
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|-- part-00001
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|-- part-00002
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|-- part-00003
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|-- part-00004
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|-- part-00005
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|-- part-00006
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|-- part-00007
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|-- part-00008
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|-- part-00009
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|-- part-00010
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|-- part-00011
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|-- part-00012
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|-- part-00013
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|-- part-00014
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`-- part-00015
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0 directories, 17 files
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```
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2.2 Use Python scripts + Spark Shuffle + Spark partition
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Run
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```
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python tools/dataset_convert/mx_recordio_2_ofrecord.py --data_dir datasets/faces_emore --output_filepath faces_emore/ofrecord/train
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```
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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.
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1. Get jar package available
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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)
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2. Run in Spark
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Assign that you have already installed and configured Spark.
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Run
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```
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//Start Spark
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./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/
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// shuffle and partition in 16 parts
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import org.oneflow.Spark.functions._
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Spark.read.chunk("data_path").shuffle().repartition(16).write.chunk("new_data_path")
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sc.formatFilenameAsOneflowStyle("new_data_path")
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```
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Hence you will get 16 parts of OFRecords, it shown like this
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```
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tree ofrecord/test/
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ofrecord/test/
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|-- _SUCCESS
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|-- part-00000
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|-- part-00001
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|-- part-00002
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|-- part-00003
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|-- part-00004
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|-- part-00005
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|-- part-00006
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|-- part-00007
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|-- part-00008
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|-- part-00009
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|-- part-00010
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|-- part-00011
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|-- part-00012
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|-- part-00013
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|-- part-00014
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`-- part-00015
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0 directories, 17 files
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```
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## Training and verification
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### Training
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To reduce the usage cost of user, OneFlow draws close the scripts to Torch style, you can directly modify parameters via configs/*.py
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#### eager
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```
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./train_ddp.sh
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```
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#### Graph
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```
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train_graph_distributed.sh
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```
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### Varification
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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.
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```
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./val.sh
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```
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## OneFLow2ONNX
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```
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pip install oneflow-onnx==0.5.1
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./convert.sh
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``` |