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

configs/face_2d_keypoint/topdown_regression/README.md

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+# Top-down regression-based pose estimation

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

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+_base_ = [
+    '../../../_base_/default_runtime.py', '../../../_base_/datasets/wflw.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=[0.25], rotate_factor=80),
+    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