# PLSC **Repository Path**: yuyangup/PLSC ## Basic Information - **Project Name**: PLSC - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-12-13 - **Last Updated**: 2021-12-13 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # PLSC ## 1. Introduction [PLSC](https://github.com/PaddlePaddle/PLSC) is an open source Paddle Large Scale Classification Tools, which supports 60 million classes on single node 8 NVIDIA V100 (32G). ## 2. Environment Preparation ### 2.1 Install Paddle from Source Code ```shell 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 Install Paddle from PyPI ```shell # python required 3.x or later # paddlepaddle required 2.2.0rc0 or later pip install paddlepaddle-gpu==2.2.0rc0 ``` ### 2.3 Download PLSC ```shell 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](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_). * MS1M_v2: MS1M-ArcFace * MS1M_v3: MS1M-RetinaFace ### 3.2 Extract MXNet Dataset to Images ```shell 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. ``` 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. ```shell 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. ```shell 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 ```bash sh scripts/train_static.sh ``` #### Dynamic Mode ```bash sh scripts/train_dynamic.sh ``` During training, you can view loss changes in real time through `VisualDL`, For more information, please refer to [VisualDL](https://github.com/PaddlePaddle/VisualDL/). ## 5. Model Evaluation The model evaluation process can be started as follows. #### Static Mode ```bash sh scripts/validation_static.sh ``` #### Dynamic Mode ```bash sh scripts/validation_dynamic.sh ``` ## 6. Export Model PaddlePaddle supports inference using prediction engines. Firstly, you should export inference model. #### Static Mode ```bash sh scripts/export_static.sh ``` #### Dynamic Mode ```bash 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. ```bash sh scripts/inference.sh ``` ## 8. Model Performance ### 8.1 Accuracy on Verification Datasets **Configuration：** * GPU: 8 NVIDIA Tesla V100 32G * Precison: FP16 * BatchSize: 128/1024 | Mode | Datasets | backbone | Ratio | agedb30 | cfp_fp | lfw | log | last checkpoint | | ------- | :------: | :------- | ----- | :------ | :----- | :--- | :--- | :--- | | Static | MS1MV3 | r50 | 0.1 | 0.98317 | 0.98943| 0.99850 | [log](experiments/logs/static/ms1mv3_r50_static_128_fp16_0.1/training.log) | [checkpoint](https://paddle-model-ecology.bj.bcebos.com/model/insight-face/distributed/ms1mv3_r50_static_128_fp16_0.1_epoch_24.tgz) | | Static | MS1MV3 | r50 | 1.0 | 0.98283 | 0.98843| 0.99850 | [log](experiments/logs/static/ms1mv3_r50_static_128_fp16_1.0/training.log) | [checkpoint](https://paddle-model-ecology.bj.bcebos.com/model/insight-face/distributed/ms1mv3_r50_static_128_fp16_1.0_epoch_24.tgz) | | Dynamic | MS1MV3 | r50 | 0.1 | 0.98367 | 0.98971| 0.99850 | [log](experiments/logs/dynamic/ms1mv3_r50_dynamic_128_fp16_0.1/training.log) | [checkpoint](https://plsc.bj.bcebos.com/pretrained_model/ms1mv3_r50_dynamic_128_fp16_0.1_eopch_24.tgz) | | Dynamic | MS1MV3 | r50 | 1.0 | 0.98333 | 0.99043| 0.99850 | [log](experiments/logs/dynamic/ms1mv3_r50_dynamic_128_fp16_1.0/training.log) | [checkpoint](https://plsc.bj.bcebos.com/pretrained_model/ms1mv3_r50_dynamic_128_fp16_1.0_eopch_24.tgz) | ### 8.2 Maximum Number of Identities **Configuration：** * GPU: 8 NVIDIA Tesla V100 32G (32510MiB) * BatchSize: 64/512 * SampleRatio: 0.1 | Mode | Precision | Res50 | Res100 | | ------------------------- | --------- | -------- | -------- | | Framework1 (static) | AMP | 42000000 (31792MiB)| 39000000 (31938MiB)| | Framework2 (dynamic) | AMP | 30000000 (31702MiB)| 29000000 (32286MiB)| | Paddle (static) | FP16 | 60000000 (32018MiB)| 60000000 (32018MiB)| | Paddle (dynamic) | FP16 | 67000000 (31970MiB)| 67000000 (31970MiB)| **Note:** config environment variable by ``export FLAGS_allocator_strategy=naive_best_fit`` ### 8.3 Throughtput **Configuration：** * BatchSize: 128/1024 * SampleRatio: 0.1 * Datasets: MS1MV3 ![insightface_throughtput](experiments/images/throughtput.png) ## 9. Demo Combined with face detection model, we can complete the face recognition process. Firstly, use the fllowing commands to download the models. ```bash # 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. ```bash # 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. ```bash # 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.