[Feature] Support LaPa Dataset (open-mmlab#2281)

configs/face_2d_keypoint/rtmpose/README.md

@@ -22,3 +22,11 @@ Results on WFLW  dataset
 |   Model   | Input Size | NME  |           Details and Download            |
 | :-------: | :--------: | :--: | :---------------------------------------: |
 | RTMPose-m |  256x256   | 4.01 | [rtmpose_wflw.md](./wflw/rtmpose_wflw.md) |
+
+### LaPa Dataset
+
+Results on LaPa dataset
+
+|   Model   | Input Size | NME  |           Details and Download            |
+| :-------: | :--------: | :--: | :---------------------------------------: |
+| RTMPose-m |  256x256   | 1.29 | [rtmpose_lapa.md](./wflw/rtmpose_lapa.md) |

configs/face_2d_keypoint/rtmpose/lapa/rtmpose-m_8xb64-120e_lapa-256x256.py

@@ -0,0 +1,247 @@
+_base_ = ['../../../_base_/default_runtime.py']
+
+# runtime
+max_epochs = 120
+stage2_num_epochs = 10
+base_lr = 4e-3
+
+train_cfg = dict(max_epochs=max_epochs, val_interval=1)
+randomness = dict(seed=21)
+
+# optimizer
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
+    paramwise_cfg=dict(
+        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=1.0e-5,
+        by_epoch=False,
+        begin=0,
+        end=1000),
+    dict(
+        # use cosine lr from 150 to 300 epoch
+        type='CosineAnnealingLR',
+        eta_min=base_lr * 0.05,
+        begin=max_epochs // 2,
+        end=max_epochs,
+        T_max=max_epochs // 2,
+        by_epoch=True,
+        convert_to_iter_based=True),
+]
+
+# automatically scaling LR based on the actual training batch size
+auto_scale_lr = dict(base_batch_size=512)
+
+# codec settings
+codec = dict(
+    type='SimCCLabel',
+    input_size=(256, 256),
+    sigma=(5.66, 5.66),
+    simcc_split_ratio=2.0,
+    normalize=False,
+    use_dark=False)
+
+# 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(
+        _scope_='mmdet',
+        type='CSPNeXt',
+        arch='P5',
+        expand_ratio=0.5,
+        deepen_factor=0.67,
+        widen_factor=0.75,
+        out_indices=(4, ),
+        channel_attention=True,
+        norm_cfg=dict(type='SyncBN'),
+        act_cfg=dict(type='SiLU'),
+        init_cfg=dict(
+            type='Pretrained',
+            prefix='backbone.',
+            checkpoint='https://download.openmmlab.com/mmpose/v1/projects/'
+            'rtmposev1/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth'  # noqa
+        )),
+    head=dict(
+        type='RTMCCHead',
+        in_channels=768,
+        out_channels=106,
+        input_size=codec['input_size'],
+        in_featuremap_size=(8, 8),
+        simcc_split_ratio=codec['simcc_split_ratio'],
+        final_layer_kernel_size=7,
+        gau_cfg=dict(
+            hidden_dims=256,
+            s=128,
+            expansion_factor=2,
+            dropout_rate=0.,
+            drop_path=0.,
+            act_fn='SiLU',
+            use_rel_bias=False,
+            pos_enc=False),
+        loss=dict(
+            type='KLDiscretLoss',
+            use_target_weight=True,
+            beta=10.,
+            label_softmax=True),
+        decoder=codec),
+    test_cfg=dict(flip_test=True, ))
+
+# base dataset settings
+dataset_type = 'LapaDataset'
+data_mode = 'topdown'
+data_root = 'data/LaPa/'
+
+backend_args = dict(backend='local')
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         f'{data_root}': 's3://openmmlab/datasets/pose/LaPa/',
+#         f'{data_root}': 's3://openmmlab/datasets/pose/LaPa/'
+#     }))
+
+# pipelines
+train_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform', scale_factor=[0.5, 1.5], rotate_factor=80),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(type='PhotometricDistortion'),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.2),
+            dict(type='MedianBlur', p=0.2),
+            dict(
+                type='CoarseDropout',
+                max_holes=1,
+                max_height=0.4,
+                max_width=0.4,
+                min_holes=1,
+                min_height=0.2,
+                min_width=0.2,
+                p=1.0),
+        ]),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+val_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='PackPoseInputs')
+]
+
+train_pipeline_stage2 = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    # dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform',
+        shift_factor=0.,
+        scale_factor=[0.75, 1.25],
+        rotate_factor=60),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.1),
+            dict(type='MedianBlur', p=0.1),
+            dict(
+                type='CoarseDropout',
+                max_holes=1,
+                max_height=0.4,
+                max_width=0.4,
+                min_holes=1,
+                min_height=0.2,
+                min_width=0.2,
+                p=0.5),
+        ]),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+
+# data loaders
+train_dataloader = dict(
+    batch_size=32,
+    num_workers=10,
+    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/lapa_train.json',
+        data_prefix=dict(img='train/images/'),
+        pipeline=train_pipeline,
+    ))
+val_dataloader = dict(
+    batch_size=32,
+    num_workers=10,
+    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/lapa_val.json',
+        data_prefix=dict(img='val/images/'),
+        test_mode=True,
+        pipeline=val_pipeline,
+    ))
+test_dataloader = dict(
+    batch_size=32,
+    num_workers=10,
+    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/lapa_test.json',
+        data_prefix=dict(img='test/images/'),
+        test_mode=True,
+        pipeline=val_pipeline,
+    ))
+
+# hooks
+default_hooks = dict(
+    checkpoint=dict(
+        save_best='NME', rule='less', max_keep_ckpts=1, interval=1))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0002,
+        update_buffers=True,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - stage2_num_epochs,
+        switch_pipeline=train_pipeline_stage2)
+]
+
+# evaluators
+val_evaluator = dict(
+    type='NME',
+    norm_mode='keypoint_distance',
+)
+test_evaluator = val_evaluator

configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.md

@@ -0,0 +1,40 @@
+<!-- [ALGORITHM] -->
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/2212.07784">RTMDet (ArXiv 2022)</a></summary>
+
+```bibtex
+@misc{lyu2022rtmdet,
+      title={RTMDet: An Empirical Study of Designing Real-Time Object Detectors},
+      author={Chengqi Lyu and Wenwei Zhang and Haian Huang and Yue Zhou and Yudong Wang and Yanyi Liu and Shilong Zhang and Kai Chen},
+      year={2022},
+      eprint={2212.07784},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+</details>
+
+<!-- [DATASET] -->
+
+<details>
+<summary align="right"><a href="https://aaai.org/ojs/index.php/AAAI/article/view/6832/6686">LaPa (AAAI'2020)</a></summary>
+
+```bibtex
+@inproceedings{liu2020new,
+  title={A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing.},
+  author={Liu, Yinglu and Shi, Hailin and Shen, Hao and Si, Yue and Wang, Xiaobo and Mei, Tao},
+  booktitle={AAAI},
+  pages={11637--11644},
+  year={2020}
+}
+```
+
+</details>
+
+Results on COCO-WholeBody-Face val set
+
+| Arch                                                           | Input Size | NME  |                              ckpt                              |                              log                               |
+| :------------------------------------------------------------- | :--------: | :--: | :------------------------------------------------------------: | :------------------------------------------------------------: |
+| [pose_rtmpose_m](/configs/face_2d_keypoint/rtmpose/lapa/rtmpose-m_8xb64-120e_lapa-256x256.py) |  256x256   | 1.29 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-lapa_pt-aic-coco_120e-256x256-762b1ae2_20230422.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-lapa_pt-aic-coco_120e-256x256-762b1ae2_20230422.json) |

configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.yml

@@ -0,0 +1,15 @@
+Models:
+- Config: configs/face_2d_keypoint/rtmpose/lapa/rtmpose-m_8xb64-120e_lapa-256x256.py
+  In Collection: RTMPose
+  Alias: face
+  Metadata:
+    Architecture:
+    - RTMPose
+    Training Data: LaPa
+  Name: rtmpose-m_8xb64-120e_lapa-256x256
+  Results:
+  - Dataset: WFLW
+    Metrics:
+      NME: 1.29
+    Task: Face 2D Keypoint
+  Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-lapa_pt-aic-coco_120e-256x256-762b1ae2_20230422.pth

demo/topdown_demo_with_mmdet.py

@@ -214,7 +214,8 @@ def main():
 
         if output_file:
             img_vis = visualizer.get_image()
-            mmcv.imwrite(mmcv.rgb2bgr(img_vis), output_file)
+            if args.show:
+                mmcv.imwrite(mmcv.rgb2bgr(img_vis), output_file)
 
     elif input_type in ['webcam', 'video']:
         from mmpose.visualization import FastVisualizer

docs/en/dataset_zoo/2d_face_keypoint.md

@@ -10,6 +10,7 @@ MMPose supported datasets:
 - [AFLW](#aflw-dataset) \[ [Homepage](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/) \]
 - [COFW](#cofw-dataset) \[ [Homepage](http://www.vision.caltech.edu/xpburgos/ICCV13/) \]
 - [COCO-WholeBody-Face](#coco-wholebody-face) \[ [Homepage](https://github.com/jin-s13/COCO-WholeBody/) \]
+- [LaPa](#lapa-dataset) \[ [Homepage](https://github.com/JDAI-CV/lapa-dataset) \]
 
 ## 300W Dataset
 
@@ -325,3 +326,58 @@ mmpose
 Please also install the latest version of [Extended COCO API](https://github.com/jin-s13/xtcocoapi) to support COCO-WholeBody evaluation:
 
 `pip install xtcocotools`
+
+## LaPa
+
+<!-- [DATASET] -->
+
+<details>
+<summary align="right"><a href="https://aaai.org/ojs/index.php/AAAI/article/view/6832/6686">LaPa (AAAI'2020)</a></summary>
+
+```bibtex
+@inproceedings{liu2020new,
+  title={A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing.},
+  author={Liu, Yinglu and Shi, Hailin and Shen, Hao and Si, Yue and Wang, Xiaobo and Mei, Tao},
+  booktitle={AAAI},
+  pages={11637--11644},
+  year={2020}
+}
+```
+
+</details>
+
+<div align="center">
+  <img src="https://github.com/lucia123/lapa-dataset/raw/master/sample.png" height="200px">
+</div>
+
+For [LaPa](https://github.com/JDAI-CV/lapa-dataset) dataset, images can be downloaded from [their github page](https://github.com/JDAI-CV/lapa-dataset).
+
+Download and extract them under $MMPOSE/data, and use our `tools/dataset_converters/lapa2coco.py` to make them look like this:
+
+```text
+mmpose
+├── mmpose
+├── docs
+├── tests
+├── tools
+├── configs
+`── data
+    │── LaPa
+        │-- annotations
+        │   │-- lapa_train.json
+        │   |-- lapa_val.json
+        │   |-- lapa_test.json
+        │-- train
+        │   │-- images
+        │   │-- labels
+        │   │-- landmarks
+        │-- val
+        │   │-- images
+        │   │-- labels
+        │   │-- landmarks
+        `-- test
+        │   │-- images
+        │   │-- labels
+        │   │-- landmarks
+
+```