_base_ = [
    '../../../../_base_/default_runtime.py',
    '../../../../_base_/datasets/mpii.py'
]
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')

optimizer = dict(
    type='Adam',
    lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[170, 200])
total_epochs = 210
log_config = dict(
    interval=50, hooks=[
        dict(type='TextLoggerHook'),
    ])

target_type = 'GaussianHeatmap'
channel_cfg = dict(
    num_output_channels=16,
    dataset_joints=16,
    dataset_channel=list(range(16)),
    inference_channel=list(range(16)))

# model settings
model = dict(
    type='TopDown',
    pretrained='https://download.openmmlab.com/mmpose/'
    'pretrain_models/hrnet_w32-36af842e.pth',
    backbone=dict(
        type='HRNet',
        in_channels=3,
        extra=dict(
            stage1=dict(
                num_modules=1,
                num_branches=1,
                block='BOTTLENECK',
                num_blocks=(4, ),
                num_channels=(64, )),
            stage2=dict(
                num_modules=1,
                num_branches=2,
                block='BASIC',
                num_blocks=(4, 4),
                num_channels=(32, 64)),
            stage3=dict(
                num_modules=4,
                num_branches=3,
                block='BASIC',
                num_blocks=(4, 4, 4),
                num_channels=(32, 64, 128)),
            stage4=dict(
                num_modules=3,
                num_branches=4,
                block='BASIC',
                num_blocks=(4, 4, 4, 4),
                num_channels=(32, 64, 128, 256))),
    ),
    keypoint_head=dict(
        type='TopdownHeatmapSimpleHead',
        in_channels=32,
        out_channels=channel_cfg['num_output_channels'],
        num_deconv_layers=0,
        extra=dict(final_conv_kernel=1, ),
        loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
    train_cfg=dict(),
    test_cfg=dict(
        flip_test=True,
        post_process='default',
        shift_heatmap=False,
        target_type=target_type,
        modulate_kernel=11,
        use_udp=True))

data_cfg = dict(
    image_size=[256, 256],
    heatmap_size=[64, 64],
    num_output_channels=channel_cfg['num_output_channels'],
    num_joints=channel_cfg['dataset_joints'],
    dataset_channel=channel_cfg['dataset_channel'],
    inference_channel=channel_cfg['inference_channel'],
    use_gt_bbox=True,
    bbox_file=None,
)

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='TopDownGetBboxCenterScale', padding=1.25),
    dict(type='TopDownRandomFlip', flip_prob=0.5),
    dict(
        type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
    dict(type='TopDownAffine', use_udp=True),
    dict(type='ToTensor'),
    dict(
        type='NormalizeTensor',
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]),
    dict(
        type='TopDownGenerateTarget',
        sigma=2,
        encoding='UDP',
        target_type=target_type),
    dict(
        type='Collect',
        keys=['img', 'target', 'target_weight'],
        meta_keys=[
            'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
            'rotation', 'flip_pairs'
        ]),
]

val_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='TopDownGetBboxCenterScale', padding=1.25),
    dict(type='TopDownAffine', use_udp=True),
    dict(type='ToTensor'),
    dict(
        type='NormalizeTensor',
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]),
    dict(
        type='Collect',
        keys=['img'],
        meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']),
]

test_pipeline = val_pipeline

data_root = 'data/mpii'
data = dict(
    samples_per_gpu=64,
    workers_per_gpu=2,
    val_dataloader=dict(samples_per_gpu=32),
    test_dataloader=dict(samples_per_gpu=32),
    train=dict(
        type='TopDownMpiiDataset',
        ann_file=f'{data_root}/annotations/mpii_train.json',
        img_prefix=f'{data_root}/images/',
        data_cfg=data_cfg,
        pipeline=train_pipeline,
        dataset_info={{_base_.dataset_info}}),
    val=dict(
        type='TopDownMpiiDataset',
        ann_file=f'{data_root}/annotations/mpii_val.json',
        img_prefix=f'{data_root}/images/',
        data_cfg=data_cfg,
        pipeline=val_pipeline,
        dataset_info={{_base_.dataset_info}}),
    test=dict(
        type='TopDownMpiiDataset',
        ann_file=f'{data_root}/annotations/mpii_val.json',
        img_prefix=f'{data_root}/images/',
        data_cfg=data_cfg,
        pipeline=test_pipeline,
        dataset_info={{_base_.dataset_info}}),
)
