rand_increasing_policies = [
    dict(type='AutoContrast'),
    dict(type='Equalize'),
    dict(type='Invert'),
    dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
    dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)),
    dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)),
    dict(
        type='SolarizeAdd',
        magnitude_key='magnitude',
        magnitude_range=(0, 110)),
    dict(
        type='ColorTransform',
        magnitude_key='magnitude',
        magnitude_range=(0, 0.9)),
    dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
    dict(
        type='Brightness', magnitude_key='magnitude',
        magnitude_range=(0, 0.9)),
    dict(
        type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
    dict(
        type='Shear',
        magnitude_key='magnitude',
        magnitude_range=(0, 0.3),
        direction='horizontal'),
    dict(
        type='Shear',
        magnitude_key='magnitude',
        magnitude_range=(0, 0.3),
        direction='vertical'),
    dict(
        type='Translate',
        magnitude_key='magnitude',
        magnitude_range=(0, 0.45),
        direction='horizontal'),
    dict(
        type='Translate',
        magnitude_key='magnitude',
        magnitude_range=(0, 0.45),
        direction='vertical')
]
dataset_type = 'ImageNet'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='RandomResizedCrop',
        size=224,
        backend='pillow',
        interpolation='bicubic'),
    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
    dict(
        type='RandAugment',
        policies=rand_increasing_policies,
        num_policies=2,
        total_level=10,
        magnitude_level=9,
        magnitude_std=0.5,
        hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),
    dict(
        type='RandomErasing',
        erase_prob=0.25,
        mode='rand',
        min_area_ratio=0.02,
        max_area_ratio=0.3333333333333333,
        fill_color=[103.53, 116.28, 123.675],
        fill_std=[57.375, 57.12, 58.395]),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='ToTensor', keys=['gt_label']),
    dict(type='Collect', keys=['img', 'gt_label'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='Resize',
        size=(256, -1),
        backend='pillow',
        interpolation='bicubic'),
    dict(type='CenterCrop', crop_size=224),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='Collect', keys=['img'])
]
data = dict(
    samples_per_gpu=32,
    workers_per_gpu=4,
    train=dict(
        type='ImageNet',
        data_prefix='/data/vdb/ziyuan.tw/yimian/gzn/datasets/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='RandomResizedCrop',
                size=224,
                backend='pillow',
                interpolation='bicubic'),
            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
            dict(
                type='RandAugment',
                policies=[
                    dict(type='AutoContrast'),
                    dict(type='Equalize'),
                    dict(type='Invert'),
                    dict(
                        type='Rotate',
                        magnitude_key='angle',
                        magnitude_range=(0, 30)),
                    dict(
                        type='Posterize',
                        magnitude_key='bits',
                        magnitude_range=(4, 0)),
                    dict(
                        type='Solarize',
                        magnitude_key='thr',
                        magnitude_range=(256, 0)),
                    dict(
                        type='SolarizeAdd',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 110)),
                    dict(
                        type='ColorTransform',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.9)),
                    dict(
                        type='Contrast',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.9)),
                    dict(
                        type='Brightness',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.9)),
                    dict(
                        type='Sharpness',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.9)),
                    dict(
                        type='Shear',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.3),
                        direction='horizontal'),
                    dict(
                        type='Shear',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.3),
                        direction='vertical'),
                    dict(
                        type='Translate',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.45),
                        direction='horizontal'),
                    dict(
                        type='Translate',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.45),
                        direction='vertical')
                ],
                num_policies=2,
                total_level=10,
                magnitude_level=9,
                magnitude_std=0.5,
                hparams=dict(pad_val=[104, 116, 124],
                             interpolation='bicubic')),
            dict(
                type='RandomErasing',
                erase_prob=0.25,
                mode='rand',
                min_area_ratio=0.02,
                max_area_ratio=0.3333333333333333,
                fill_color=[103.53, 116.28, 123.675],
                fill_std=[57.375, 57.12, 58.395]),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='ToTensor', keys=['gt_label']),
            dict(type='Collect', keys=['img', 'gt_label'])
        ],
        ann_file='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/train_mmcls.txt',
        classes='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/classname.txt'),
    val=dict(
        type='ImageNet',
        data_prefix='/data/vdb/ziyuan.tw/yimian/gzn/datasets/',
        ann_file='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/val_mmcls.txt',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='Resize',
                size=(256, -1),
                backend='pillow',
                interpolation='bicubic'),
            dict(type='CenterCrop', crop_size=224),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ],
        classes='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/classname.txt'),
    test=dict(
        type='ImageNet',
        data_prefix='/data/vdb/ziyuan.tw/yimian/gzn/datasets/',
        ann_file='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/val_mmcls.txt',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='Resize',
                size=(256, -1),
                backend='pillow',
                interpolation='bicubic'),
            dict(type='CenterCrop', crop_size=224),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ],
        classes='/data/vdb/ziyuan.tw/yimian/gzn/datasets/virgo_data/dailytags/classname.txt'))
evaluation = dict(interval=2, metric='accuracy', save_best='auto')
paramwise_cfg = dict(
    norm_decay_mult=0.0,
    bias_decay_mult=0.0,
    custom_keys={
        '.cls_token': dict(decay_mult=0.0),
        '.pos_embed': dict(decay_mult=0.0)
    })

optimizer = dict(
    type='AdamW',
    lr=2e-5, #5e-4 * 32 * 1 / 512, 1.25e-4  
    weight_decay=0.1,
    eps=1e-8,
    betas=(0.9, 0.999),
    paramwise_cfg=paramwise_cfg)
optimizer_config = dict(grad_clip=dict(max_norm=5.0))

# learning policy
lr_config = dict(
    policy='CosineAnnealing',
    by_epoch=False,
    min_lr_ratio=1e-2, 
    warmup='linear',
    warmup_ratio=1e-3,
    warmup_iters=20,
    warmup_by_epoch=True)
runner = dict(type='EpochBasedRunner', max_epochs=300)
checkpoint_config = dict(interval=1, max_keep_ckpts=20, create_symlink=True)
log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
model = dict(
    type='ImageClassifier',
    backbone=dict(
        type='NextViT',
        arch='small',
        path_dropout=0.2,
    ),
    neck=dict(type='GlobalAveragePooling'),
    head=dict(
        type='LinearClsHead',
        num_classes=1296,
        in_channels=1024,
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
    ),
)
custom_hooks = [dict(type='EMAHook', momentum=4e-05, priority='ABOVE_NORMAL')]
work_dir = './work_dir/'
gpu_ids = range(0, 32)