annotator/dwpose/dwpose_config/dwpose-l_384x288.py

# runtime
max_epochs = 270
stage2_num_epochs = 30
base_lr = 4e-3

train_cfg = dict(max_epochs=max_epochs, val_interval=10)
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=(288, 384),
    sigma=(6., 6.93),
    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=1.,
        widen_factor=1.,
        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/'
            'rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth'  # noqa
        )),
    head=dict(
        type='RTMCCHead',
        in_channels=1024,
        out_channels=133,
        input_size=codec['input_size'],
        in_featuremap_size=(9, 12),
        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 = 'CocoWholeBodyDataset'
data_mode = 'topdown'
data_root = '/data/'

backend_args = dict(backend='local')
# backend_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         f'{data_root}': 's3://openmmlab/datasets/detection/coco/',
#         f'{data_root}': 's3://openmmlab/datasets/detection/coco/'
#     }))

# 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=[0.6, 1.4], rotate_factor=80),
    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=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')
]

datasets = []
dataset_coco=dict(
    type=dataset_type,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='coco/annotations/coco_wholebody_train_v1.0.json',
    data_prefix=dict(img='coco/train2017/'),
    pipeline=[],
)
datasets.append(dataset_coco)

scene = ['Magic_show', 'Entertainment', 'ConductMusic', 'Online_class', 
         'TalkShow', 'Speech', 'Fitness', 'Interview', 'Olympic', 'TVShow', 
         'Singing', 'SignLanguage', 'Movie', 'LiveVlog', 'VideoConference']

for i in range(len(scene)):
    datasets.append(
        dict(
            type=dataset_type,
            data_root=data_root,
            data_mode=data_mode,
            ann_file='UBody/annotations/'+scene[i]+'/keypoint_annotation.json',
            data_prefix=dict(img='UBody/images/'+scene[i]+'/'),
            pipeline=[],
        )
    )

# data loaders
train_dataloader = dict(
    batch_size=32,
    num_workers=10,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type='CombinedDataset',
        metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
        datasets=datasets,
        pipeline=train_pipeline,
        test_mode=False,
    ))
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='coco/annotations/coco_wholebody_val_v1.0.json',
        bbox_file=f'{data_root}coco/person_detection_results/'
        'COCO_val2017_detections_AP_H_56_person.json',
        data_prefix=dict(img='coco/val2017/'),
        test_mode=True,
        pipeline=val_pipeline,
    ))
test_dataloader = val_dataloader

# hooks
default_hooks = dict(
    checkpoint=dict(
        save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=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='CocoWholeBodyMetric',
    ann_file=data_root + 'coco/annotations/coco_wholebody_val_v1.0.json')
test_evaluator = val_evaluator