[MMSIG-92] Integrate WFLW deeppose model to dev-1.x branch

configs/face_2d_keypoint/topdown_regression/README.md

@@ -0,0 +1,17 @@
+# Top-down regression-based pose estimation
+
+Top-down methods divide the task into two stages: object detection, followed by single-object pose estimation given object bounding boxes. At the 2nd stage, regression based methods directly regress the keypoint coordinates given the features extracted from the bounding box area, following the paradigm introduced in [Deeppose: Human pose estimation via deep neural networks](http://openaccess.thecvf.com/content_cvpr_2014/html/Toshev_DeepPose_Human_Pose_2014_CVPR_paper.html).
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/15977946/146515040-a82a8a29-d6bc-42f1-a2ab-7dfa610ce363.png">
+</div>
+
+## Results and Models
+
+### WFLW Dataset
+
+Result on WFLW test set
+
+| Model                                                           | Input Size | NME  |                              ckpt                              |                              log                              |
+| :-------------------------------------------------------------- | :--------: | :--: | :------------------------------------------------------------: | :-----------------------------------------------------------: |
+| [ResNet-50](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8x64e-210e_wflw-256x256.py) |  256x256   | 4.88 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256-92d0ba7f_20210303.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_20210303.log.json) |

configs/face_2d_keypoint/topdown_regression/wflw/resnet_wflw.md

@@ -0,0 +1,58 @@
+<!-- [ALGORITHM] -->
+
+<details>
+<summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2014/html/Toshev_DeepPose_Human_Pose_2014_CVPR_paper.html">DeepPose (CVPR'2014)</a></summary>
+
+```bibtex
+@inproceedings{toshev2014deeppose,
+  title={Deeppose: Human pose estimation via deep neural networks},
+  author={Toshev, Alexander and Szegedy, Christian},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={1653--1660},
+  year={2014}
+}
+```
+
+</details>
+
+<!-- [BACKBONE] -->
+
+<details>
+<summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html">ResNet (CVPR'2016)</a></summary>
+
+```bibtex
+@inproceedings{he2016deep,
+  title={Deep residual learning for image recognition},
+  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={770--778},
+  year={2016}
+}
+```
+
+</details>
+
+<!-- [DATASET] -->
+
+<details>
+<summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2018/html/Wu_Look_at_Boundary_CVPR_2018_paper.html">WFLW (CVPR'2018)</a></summary>
+
+```bibtex
+@inproceedings{wu2018look,
+  title={Look at boundary: A boundary-aware face alignment algorithm},
+  author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={2129--2138},
+  year={2018}
+}
+```
+
+</details>
+
+Results on WFLW dataset
+
+The model is trained on WFLW train set.
+
+| Model                                                           | Input Size | NME  |                              ckpt                              |                              log                              |
+| :-------------------------------------------------------------- | :--------: | :--: | :------------------------------------------------------------: | :-----------------------------------------------------------: |
+| [ResNet-50](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8x64e-210e_wflw-256x256.py) |  256x256   | 4.88 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256-92d0ba7f_20210303.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_20210303.log.json) |

configs/face_2d_keypoint/topdown_regression/wflw/resnet_wflw.yml

@@ -0,0 +1,15 @@
+Models:
+- Config: configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8x64e-210e_wflw-256x256.py
+  In Collection: ResNet
+  Metadata:
+    Architecture:
+    - DeepPose
+    - ResNet
+    Training Data: WFLW
+  Name: td-reg_res50_8x64e-210e_wflw-256x256
+  Results:
+  - Dataset: WFLW
+    Metrics:
+      NME: 4.88
+    Task: Face 2D Keypoint
+  Weights: https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256-92d0ba7f_20210303.pth

configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8x64e-210e_wflw-256x256.py

@@ -0,0 +1,122 @@
+_base_ = ['../../../_base_/default_runtime.py']
+
+# runtime
+train_cfg = dict(max_epochs=210, val_interval=1)
+
+# optimizer
+optim_wrapper = dict(optimizer=dict(
+    type='Adam',
+    lr=5e-4,
+))
+
+# learning policy
+param_scheduler = [
+    dict(
+        type='LinearLR', begin=0, end=500, start_factor=0.001,
+        by_epoch=False),  # warm-up
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=210,
+        milestones=[170, 200],
+        gamma=0.1,
+        by_epoch=True)
+]
+
+# automatically scaling LR based on the actual training batch size
+auto_scale_lr = dict(base_batch_size=512)
+
+# codec settings
+codec = dict(type='RegressionLabel', input_size=(256, 256))
+
+# model settings
+model = dict(
+    type='TopdownPoseEstimator',
+    data_preprocessor=dict(
+        type='PoseDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True),
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='RegressionHead',
+        in_channels=2048,
+        num_joints=98,
+        loss=dict(type='SmoothL1Loss', use_target_weight=True),
+        decoder=codec),
+    train_cfg=dict(),
+    test_cfg=dict(
+        flip_test=True,
+        shift_coords=True,
+    ))
+
+# base dataset settings
+dataset_type = 'WFLWDataset'
+data_mode = 'topdown'
+data_root = 'data/wflw/'
+
+# pipelines
+train_pipeline = [
+    dict(type='LoadImage'),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(
+        type='RandomBBoxTransform',
+        scale_factor=[0.75, 1.25],
+        rotate_factor=60),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+val_pipeline = [
+    dict(type='LoadImage'),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='PackPoseInputs')
+]
+
+# dataloaders
+train_dataloader = dict(
+    batch_size=64,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='annotations/face_landmarks_wflw_train.json',
+        data_prefix=dict(img='images/'),
+        pipeline=train_pipeline,
+    ))
+val_dataloader = dict(
+    batch_size=32,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='annotations/face_landmarks_wflw_test.json',
+        data_prefix=dict(img='images/'),
+        test_mode=True,
+        pipeline=val_pipeline,
+    ))
+test_dataloader = val_dataloader
+
+# hooks
+default_hooks = dict(checkpoint=dict(save_best='NME', rule='greater'))
+
+# evaluators
+val_evaluator = dict(
+    type='NME',
+    norm_mode='keypoint_distance',
+)
+test_evaluator = val_evaluator