# 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) \- [Training and verification](#training-and-verification) \- [Training](#training) \- [OneFLow2ONNX](#OneFLow2ONNX) ## 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 oneflow -f https://oneflow-staging.oss-cn-beijing.aliyuncs.com/branch/master/cu102/6aa719d70119b65837b25cc5f186eb19ef2b7891/index.html --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 --num_part 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 ``` ## 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 #### eager ``` ./train_ddp.sh ``` #### Graph ``` train_graph_distributed.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 ``` ## OneFLow2ONNX ``` pip install oneflow-onnx==0.5.1 ./convert.sh ```