_base_ = [
    'mmcls::_base_/datasets/imagenet_bs256_rsb_a12.py',
    'mmcls::_base_/schedules/imagenet_bs2048_rsb.py',
    'mmcls::_base_/default_runtime.py'
]

model = dict(
    type='ImageClassifier',
    backbone=dict(
        type='mmdet.CSPNeXt',
        arch='P5',
        out_indices=(4, ),
        expand_ratio=0.5,
        deepen_factor=0.33,
        widen_factor=0.5,
        channel_attention=True,
        norm_cfg=dict(type='BN'),
        act_cfg=dict(type='mmdet.SiLU')),
    neck=dict(type='GlobalAveragePooling'),
    head=dict(
        type='LinearClsHead',
        num_classes=1000,
        in_channels=512,
        loss=dict(
            type='LabelSmoothLoss',
            label_smooth_val=0.1,
            mode='original',
            loss_weight=1.0),
        topk=(1, 5)),
    train_cfg=dict(augments=[
        dict(type='Mixup', alpha=0.2),
        dict(type='CutMix', alpha=1.0)
    ]))

# dataset settings
train_dataloader = dict(sampler=dict(type='RepeatAugSampler', shuffle=True))

# schedule settings
optim_wrapper = dict(
    optimizer=dict(weight_decay=0.01),
    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.),
)

param_scheduler = [
    # warm up learning rate scheduler
    dict(
        type='LinearLR',
        start_factor=0.0001,
        by_epoch=True,
        begin=0,
        end=5,
        # update by iter
        convert_to_iter_based=True),
    # main learning rate scheduler
    dict(
        type='CosineAnnealingLR',
        T_max=595,
        eta_min=1.0e-6,
        by_epoch=True,
        begin=5,
        end=600)
]

train_cfg = dict(by_epoch=True, max_epochs=600)