_base_ = ['../../../_base_/default_runtime.py']

# runtime
train_cfg = dict(max_epochs=210, val_interval=10)

# optimizer
custom_imports = dict(
    imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'],
    allow_failed_imports=False)

optim_wrapper = dict(
    optimizer=dict(
        type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1),
    paramwise_cfg=dict(
        num_layers=12,
        layer_decay_rate=0.8,
        custom_keys={
            'bias': dict(decay_multi=0.0),
            'pos_embed': dict(decay_mult=0.0),
            'relative_position_bias_table': dict(decay_mult=0.0),
            'norm': dict(decay_mult=0.0),
        },
    ),
    constructor='LayerDecayOptimWrapperConstructor',
    clip_grad=dict(max_norm=1., norm_type=2),
)

# 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)

# hooks
default_hooks = dict(
    checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1))

# codec settings
codec = dict(
    type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)

# 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='mmpretrain.VisionTransformer',
        arch={
            'embed_dims': 384,
            'num_layers': 12,
            'num_heads': 12,
            'feedforward_channels': 384 * 4
        },
        img_size=(256, 192),
        patch_size=16,
        qkv_bias=True,
        drop_path_rate=0.1,
        with_cls_token=False,
        out_type='featmap',
        patch_cfg=dict(padding=2),
        init_cfg=dict(
            type='Pretrained',
            checkpoint='https://download.openmmlab.com/mmpose/'
            'v1/pretrained_models/mae_pretrain_vit_small_20230913.pth'),
    ),
    neck=dict(type='FeatureMapProcessor', scale_factor=4.0, apply_relu=True),
    head=dict(
        type='HeatmapHead',
        in_channels=384,
        out_channels=17,
        deconv_out_channels=[],
        deconv_kernel_sizes=[],
        final_layer=dict(kernel_size=3, padding=1),
        loss=dict(type='KeypointMSELoss', use_target_weight=True),
        decoder=codec,
    ),
    test_cfg=dict(
        flip_test=True,
        flip_mode='heatmap',
        shift_heatmap=False,
    ))

# base dataset settings
data_root = 'data/coco/'
dataset_type = 'CocoDataset'
data_mode = 'topdown'

# pipelines
train_pipeline = [
    dict(type='LoadImage'),
    dict(type='GetBBoxCenterScale'),
    dict(type='RandomFlip', direction='horizontal'),
    dict(type='RandomHalfBody'),
    dict(type='RandomBBoxTransform'),
    dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True),
    dict(type='GenerateTarget', encoder=codec),
    dict(type='PackPoseInputs')
]
val_pipeline = [
    dict(type='LoadImage'),
    dict(type='GetBBoxCenterScale'),
    dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True),
    dict(type='PackPoseInputs')
]

# data loaders
train_dataloader = dict(
    batch_size=64,
    num_workers=4,
    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/person_keypoints_train2017.json',
        data_prefix=dict(img='train2017/'),
        pipeline=train_pipeline,
    ))
val_dataloader = dict(
    batch_size=32,
    num_workers=4,
    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/person_keypoints_val2017.json',
        bbox_file='data/coco/person_detection_results/'
        'COCO_val2017_detections_AP_H_56_person.json',
        data_prefix=dict(img='val2017/'),
        test_mode=True,
        pipeline=val_pipeline,
    ))
test_dataloader = val_dataloader

# evaluators
val_evaluator = dict(
    type='CocoMetric',
    ann_file=data_root + 'annotations/person_keypoints_val2017.json')
test_evaluator = val_evaluator
