add face task (PaddlePaddle#134)

task/recognition/face/README.md

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+# Face Recognition
+
+The face recognition task is a case of achieving large-scale classification on PLSC, 
+and the goal is to implement and reproduce the SOTA algorithm. It has 
+the ability to train tens of millions of identities with high throughput in a single server.
+
+Function has supported:
+* ArcFace
+* CosFace
+* PartialFC
+* SparseMomentum
+* FP16 training
+* DataParallel(backbone layer) + ModelParallel(FC layer) distributed training
+
+Backbone includes:
+* IResNet
+* FaceViT
+
+## Requirements
+To enjoy some new features, PaddlePaddle 2.4 is required. For more installation tutorials 
+refer to [installation.md](../../../tutorials/get_started/installation.md)
+
+## Data Preparation
+
+### Download Dataset
+
+Download the dataset from insightface datasets.
+
+- [MS1MV2](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_#ms1m-arcface-85k-ids58m-images-57) (87k IDs, 5.8M images)
+- [MS1MV3](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_#ms1m-retinaface) (93k IDs, 5.2M images)
+- [Glint360K](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc#4-download) (360k IDs, 17.1M images)
+- [WebFace42M](https://github.com/deepinsight/insightface/blob/master/recognition/arcface_torch/docs/prepare_webface42m.md) (2M IDs, 42.5M images)
+
+Note:
+* MS1MV2: MS1M-ArcFace
+* MS1MV3: MS1M-RetinaFace
+* WebFace42M: cleared WebFace260M
+
+### [Optional] Extract MXNet Dataset to Images
+```shell
+# for example, here extract MS1MV3 dataset
+python -m plsc.data.dataset.tools.mx_recordio_2_images --root_dir /path/to/ms1m-retinaface-t1/ --output_dir ./dataset/MS1M_v3/
+```
+
+### Extract LFW Style bin Dataset to Images
+```shell
+# for example, here extract agedb_30 bin to images
+python -m plsc.data.dataset.tools.lfw_style_bin_dataset_converter --bin_path ./dataset/MS1M_v3/agedb_30.bin --out_dir ./dataset/MS1M_v3/agedb_30 --flip_test
+```
+
+### Dataset Directory
+We put all the data in the `./dataset/` directory, and we also recommend using soft links, for example:
+```shell
+mkdir -p ./dataset/
+ln -s /path/to/MS1M_v3 ./dataset/MS1M_v3
+```
+
+## How to Train
+
+```bash
+# Note: If running on multiple nodes, 
+# set the following environment variables 
+# and then need to run the script on each node.
+export PADDLE_NNODES=1
+export PADDLE_MASTER="127.0.0.1:12538"
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+
+python -m paddle.distributed.launch \
+    --nnodes=$PADDLE_NNODES \
+    --master=$PADDLE_MASTER \
+    --devices=$CUDA_VISIBLE_DEVICES \
+    plsc-train \
+    -c ./configs/IResNet50_MS1MV3_ArcFace_pfc01_1n8c_dp_mp_fp16o1.yaml
+```
+
+## How to Export
+
+```bash
+# In general, we only need to export the 
+# backbone, so we only need to run the 
+# export command on a single device.
+export PADDLE_NNODES=1
+export PADDLE_MASTER="127.0.0.1:12538"
+export CUDA_VISIBLE_DEVICES=0
+python -m paddle.distributed.launch \
+    --nnodes=$PADDLE_NNODES \
+    --master=$PADDLE_MASTER \
+    --devices=$CUDA_VISIBLE_DEVICES \
+    plsc-export \
+    -c ./configs/IResNet50_MS1MV3_ArcFace_pfc01_1n8c_dp_mp_fp16o1.yaml \
+    -o Global.pretrained_model=./output/IResNet50/latest \
+    -o FP16.level=O0 \ # export FP32 model when training with FP16
+    -o Model.data_format=NCHW # IResNet required if training with NHWC 
+```
+
+## Evaluation IJB-C
+```bash
+python onnx_ijbc.py \
+  --model-root ./output/IResNet50.onnx \
+  --image-path ./ijb/IJBC/ \
+  --target IJBC
+```
+
+## Model Zoo
+
+| Datasets | Backbone | Config                                                       | Devices   | PFC  | agedb30 | IJB-C(1E-4) | IJB-C(1E-5) | checkpoint                                                   | log                                                          |
+| :------: | :------- | ------------------------------------------------------------ | --------- | ---- | ------- | ----------- | :---------- | :----------------------------------------------------------- | ------------------------------------------------------------ |
+|  MS1MV3  | Res50    | [config](./configs/IResNet50_MS1MV3_ArcFace_pfc10_1n8c_dp_mp_fp16o1.yaml) | N1C8*A100 | 1.0  | 0.9825  | 96.52       | 94.60       | [download](https://plsc.bj.bcebos.com/models/face/v2.4/IResNet50_MS1MV3_ArcFace_pfc10_1n8c_dp_mp_fp16o1.pdparams) | [download](https://plsc.bj.bcebos.com/models/face/v2.4/IResNet50_MS1MV3_ArcFace_pfc10_1n8c_dp_mp_fp16o1.log) |
+
+## Citations
+
+```
+@misc{plsc,
+    title={PLSC: An Easy-to-use and High-Performance Large Scale Classification Tool},
+    author={PLSC Contributors},
+    howpublished = {\url{https://github.com/PaddlePaddle/PLSC}},
+    year={2022}
+}
+@inproceedings{deng2019arcface,
+  title={Arcface: Additive angular margin loss for deep face recognition},
+  author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={4690--4699},
+  year={2019}
+}
+@inproceedings{An_2022_CVPR,
+    author={An, Xiang and Deng, Jiankang and Guo, Jia and Feng, Ziyong and Zhu, XuHan and Yang, Jing and Liu, Tongliang},
+    title={Killing Two Birds With One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC},
+    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month={June},
+    year={2022},
+    pages={4042-4051}
+}
+```

task/recognition/face/configs/IResNet50_MS1MV3_ArcFace_pfc10_1n8c_dp_mp_fp16o1.yaml

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+# global configs
+Global:
+  task_type: recognition
+  train_epoch_func: defualt_train_one_epoch
+  eval_func: face_verification_eval
+  checkpoint: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 1
+  max_num_latest_checkpoint: 0
+  eval_during_train: True
+  eval_interval: 2000
+  eval_unit: "step"
+  accum_steps: 1
+  epochs: 20
+  print_batch_step: 100
+  use_visualdl: True
+  seed: 2022
+
+# FP16 setting
+FP16:
+  level: O1
+  GradScaler:
+    init_loss_scaling: 27648.0
+
+DistributedStrategy:
+  data_parallel: True
+
+# model architecture
+Model:
+  name: IResNet50
+  num_features : 512
+  data_format : "NHWC"
+  class_num: 93431
+  pfc_config:
+    sample_ratio: 1.0
+    model_parallel: True
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - MarginLoss:
+        m1: 1.0
+        m2: 0.5
+        m3: 0.0
+        s: 64.0
+        model_parallel: True
+        weight: 1.0
+
+LRScheduler:
+  name: Poly
+  learning_rate: 0.1
+  decay_unit: step
+  warmup_steps: 0
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  weight_decay: 5e-4
+  grad_clip:
+    name: ClipGradByGlobalNorm
+    clip_norm: 5.0
+    always_clip: True
+    no_clip_list: ['dist']
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: FaceIdentificationDataset
+      image_root: ./dataset/MS1M_v3/
+      cls_label_path: ./dataset/MS1M_v3/label.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.5, 0.5, 0.5]
+            std: [0.5, 0.5, 0.5]
+            order: ''
+        - ToCHWImage: 
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 8
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: FaceVerificationDataset
+      image_root: ./dataset/MS1M_v3/agedb_30
+      cls_label_path: ./dataset/MS1M_v3/agedb_30/label.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.5, 0.5, 0.5]
+            std: [0.5, 0.5, 0.5]
+            order: ''
+        - ToCHWImage:
+    sampler:
+      name: BatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 0
+      use_shared_memory: True
+
+Metric:
+  Eval:
+    - LFWAcc:
+        flip_test: True
+
+Export:
+  export_type: onnx
+  input_shape: [None, 3, 112, 112]

task/recognition/face/eval_ijbc.sh

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+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+python onnx_ijbc.py \
+  --model-root ./output/IResNet50.onnx \
+  --image-path ./ijb/IJBC/ \
+  --target IJBC

task/recognition/face/export.sh

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+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+export PADDLE_NNODES=1
+export PADDLE_MASTER="127.0.0.1:12538"
+export CUDA_VISIBLE_DEVICES=0
+python -m paddle.distributed.launch \
+    --nnodes=$PADDLE_NNODES \
+    --master=$PADDLE_MASTER \
+    --devices=$CUDA_VISIBLE_DEVICES \
+    plsc-export \
+    -c ./configs/IResNet50_MS1MV3_ArcFace_pfc10_1n8c_dp_mp_fp16o1.yaml \
+    -o Global.pretrained_model=output/IResNet50/latest \
+    -o FP16.level=O0 \
+    -o Model.data_format=NCHW