_base_ = [
    '../_base_/models/resnet50.py',
    '../_base_/datasets/imagenet_bs256_rsb_a12.py',
    '../_base_/schedules/imagenet_bs2048_rsb.py',
    '../_base_/default_runtime.py'
]

# model settings
model = dict(
    backbone=dict(
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        drop_path_rate=0.05,
    ),
    head=dict(
        loss=dict(
            type='LabelSmoothLoss',
            label_smooth_val=0.1,
            mode='original',
            use_sigmoid=True,
        )),
    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)