[MMSIG-87] Migrate SoftWingLoss config to 1.x (open-mmlab#2287)

configs/face_2d_keypoint/topdown_regression/README.md

@@ -15,3 +15,4 @@ Result on WFLW test set
 | Model                                                           | Input Size | NME  |                              ckpt                              |                              log                              |
 | :-------------------------------------------------------------- | :--------: | :--: | :------------------------------------------------------------: | :-----------------------------------------------------------: |
 | [ResNet-50](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8xb64-210e_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) |
+| [ResNet-50+SoftWingLoss](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_softwingloss_8xb64-210e_wflw-256x256.py) |  256x256   | 4.67 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss-4d34f22a_20211212.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss_20211212.log.json) |

configs/face_2d_keypoint/topdown_regression/wflw/resnet_softwingloss_wflw.md

@@ -0,0 +1,75 @@
+<!-- [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>
+
+<!-- [ALGORITHM] -->
+
+<details>
+<summary align="right"><a href="https://ieeexplore.ieee.org/document/9442331/">SoftWingloss (TIP'2021)</a></summary>
+
+```bibtex
+@article{lin2021structure,
+  title={Structure-Coherent Deep Feature Learning for Robust Face Alignment},
+  author={Lin, Chunze and Zhu, Beier and Wang, Quan and Liao, Renjie and Qian, Chen and Lu, Jiwen and Zhou, Jie},
+  journal={IEEE Transactions on Image Processing},
+  year={2021},
+  publisher={IEEE}
+}
+```
+
+</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+SoftWingLoss](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_softwingloss_8xb64-210e_wflw-256x256.py) |  256x256   | 4.44 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss-4d34f22a_20211212.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss_20211212.log.json) |

configs/face_2d_keypoint/topdown_regression/wflw/resnet_softwingloss_wflw.yml

@@ -0,0 +1,16 @@
+Models:
+- Config: configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_softwingloss_8xb64-210e_wflw-256x256.py
+  In Collection: ResNet
+  Metadata:
+    Architecture:
+    - DeepPose
+    - ResNet
+    - SoftWingloss
+    Training Data: WFLW
+  Name: td-reg_res50_softwingloss_8xb64-210e_wflw-256x256
+  Results:
+  - Dataset: WFLW
+    Metrics:
+      NME: 4.44
+    Task: Face 2D Keypoint
+  Weights: https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss-4d34f22a_20211212.pth

configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_softwingloss_8xb64-210e_wflw-256x256.py

@@ -0,0 +1,122 @@
+_base_ = ['../../../_base_/default_runtime.py']
+
+# runtime
+train_cfg = dict(max_epochs=210, val_interval=10)
+
+# 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='SoftWingLoss', 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='less'))
+
+# evaluators
+val_evaluator = dict(
+    type='NME',
+    norm_mode='keypoint_distance',
+)
+test_evaluator = val_evaluator