insightface/recognition/arcface_paddle/README.md

PLSC

1. Introduction

PLSC is an open source Paddle Large Scale Classification Tools, which supports 60 million classes on 8 NVIDIA V100 (32G).

2. Environment preparation

2.1 Install Paddle from source code

git clone https://github.com/PaddlePaddle/Paddle.git

cd /path/to/Paddle/

mkdir build && cd build

cmake .. -DWITH_TESTING=ON -DWITH_GPU=ON -DWITH_GOLANG=OFF -DWITH_STYLE_CHECK=ON -DCMAKE_INSTALL_PREFIX=$PWD/output -DWITH_DISTRIBUTE=ON -DCMAKE_BUILD_TYPE=Release -DPY_VERSION=3.7 -DCUDA_ARCH_NAME=All -DPADDLE_VERSION=2.2.0

make -j20 && make install -j20

pip install output/opt/paddle/share/wheels/paddlepaddle_gpu-2.2.0-cp37-cp37m-linux_x86_64.whl

2.2 Download PLSC

git clone https://github.com/PaddlePaddle/PLSC.git

cd /path/to/PLSC/

3. Data preparation

3.1 Download dataset

Download the dataset from insightface datasets.

3.2 Extract MXNet Dataset to images

python tools/mx_recordio_2_images.py --root_dir ms1m-retinaface-t1/ --output_dir MS1M_v3/

After finishing unzipping the dataset, the folder structure is as follows.

arcface_paddle/MS1M_v3
|_ images
|  |_ 00000001.jpg
|  |_ ...
|  |_ 05179510.jpg
|_ label.txt
|_ agedb_30.bin
|_ cfp_ff.bin
|_ cfp_fp.bin
|_ lfw.bin

Label file format is as follows.

# delimiter: "\t"
# the following the content of label.txt
images/00000001.jpg 0
...

If you want to use customed dataset, you can arrange your data according to the above format.

3.3 Transform between original image files and bin files

If you want to convert original image files to bin files used directly for training process, you can use the following command to finish the conversion.

python tools/convert_image_bin.py --image_path="your/input/image/path" --bin_path="your/output/bin/path" --mode="image2bin"

If you want to convert bin files to original image files, you can use the following command to finish the conversion.

python tools/convert_image_bin.py --image_path="your/input/bin/path" --bin_path="your/output/image/path" --mode="bin2image"

4. How to Training

4.1 Single node, 8 GPUs:

Static Mode

sh scripts/train_static.sh

Dynamic Mode

sh scripts/train_dynamic.sh

During training, you can view loss changes in real time through VisualDL, For more information, please refer to VisualDL.

5. Model evaluation

The model evaluation process can be started as follows.

Static Mode

sh scripts/validation_static.sh

Dynamic Mode

sh scripts/validation_dynamic.sh

6. Export model

PaddlePaddle supports inference using prediction engines. Firstly, you should export inference model.

Static Mode

sh scripts/export_static.sh

Dynamic Mode

sh scripts/export_dynamic.sh

We also support export to onnx model, you only need to set --export_type onnx.

7. Model inference

The model inference process supports paddle save inference model and onnx model.

sh scripts/inference.sh

8. Model performance

8.1 Accuracy on Verification Datasets

Configuration：

• GPU: 8 NVIDIA Tesla V100 32G
• Precison: Pure FP16
• BatchSize: 128/1024

Mode	Datasets	backbone	Ratio	agedb30	cfp_fp	lfw	log	checkpoint
Static	MS1MV3	r50	0.1	0.98317	0.98943	0.99850	log	checkpoint
Static	MS1MV3	r50	1.0	0.98283	0.98843	0.99850	log	checkpoint
Dynamic	MS1MV3	r50	0.1	0.98333	0.98900	0.99833	log	checkpoint
Dynamic	MS1MV3	r50	1.0	0.98317	0.98900	0.99833	log	checkpoint

8.2 Maximum Number of Identities

Configuration：

• GPU: 8 NVIDIA Tesla V100 32G
• BatchSize: 64/512
• SampleRatio: 0.1

Mode	Precision	Res50	Res100
Framework1 (static)	AMP	42000000	39000000
Framework2 (dynamic)	AMP	30000000	29000000
Paddle (static)	Pure FP16	60000000	60000000
Paddle (dynamic)	Pure FP16	59000000	59000000

Note: config environment variable export FLAGS_allocator_strategy=naive_best_fit

8.3 Throughtput

Configuration：

• BatchSize: 128/1024
• SampleRatio: 0.1
• Datasets: MS1MV3

9. Demo

Combined with face detection model, we can complete the face recognition process.

Firstly, use the fllowing commands to download the models.

# Create models directory
mkdir -p models

# Download blazeface face detection model and extract it
wget https://paddle-model-ecology.bj.bcebos.com/model/insight-face/blazeface_fpn_ssh_1000e_v1.0_infer.tar -P models/
tar -xzf models/blazeface_fpn_ssh_1000e_v1.0_infer.tar -C models/
rm -rf models/blazeface_fpn_ssh_1000e_v1.0_infer.tar

# Download static ResNet50 PartialFC 0.1 model and extract it
wget https://paddle-model-ecology.bj.bcebos.com/model/insight-face/distributed/ms1mv3_r50_static_128_fp16_0.1_epoch_24.tgz -P models/
tar -xf models/ms1mv3_r50_static_128_fp16_0.1_epoch_24.tgz -C models/
rm -rf models/ms1mv3_r50_static_128_fp16_0.1_epoch_24.tgz

# Export static save inference model
python tools/export.py --is_static True --export_type paddle --backbone FresResNet50 --embedding_size 512 --checkpoint_dir models/ms1mv3_r50_static_128_fp16_0.1_epoch_24 --output_dir models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer
rm -rf models/ms1mv3_r50_static_128_fp16_0.1_epoch_24

Then, use the following commands to download the gallery, demo image and font file for visualization. And we generate gallery features.

# Download gallery, query and font file
mkdir -p images/
git clone https://github.com/littletomatodonkey/insight-face-paddle /tmp/insight-face-paddle
cp -r /tmp/insight-face-paddle/demo/friends/gallery/ images/
cp -r /tmp/insight-face-paddle/demo/friends/query/ images/
mkdir -p assets
cp /tmp/insight-face-paddle/SourceHanSansCN-Medium.otf assets/
rm -rf /tmp/insight-face-paddle

# Build index file
python tools/test_recognition.py \
    --rec \
    --rec_model_file_path models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer/FresResNet50.pdmodel \
    --rec_params_file_path models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer/FresResNet50.pdiparams \
    --build_index=images/gallery/index.bin \
    --img_dir=images/gallery \
    --label=images/gallery/label.txt

Use the following command to run the whole face recognition demo.

# detection + recogniotion process
python tools/test_recognition.py \
    --det \
    --det_model_file_path models/blazeface_fpn_ssh_1000e_v1.0_infer/inference.pdmodel \
    --det_params_file_path models/blazeface_fpn_ssh_1000e_v1.0_infer/inference.pdiparams \
    --rec \
    --rec_model_file_path models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer/FresResNet50.pdmodel \
    --rec_params_file_path models/ms1mv3_r50_static_128_fp16_0.1_epoch_24_infer/FresResNet50.pdiparams \
    --index=images/gallery/index.bin \
    --input=images/query/friends2.jpg \
    --cdd_num 10 \
    --rec_thresh 0.4 \
    --output="./output"

The final result is save in folder output/, which is shown as follows.