deit-small_pt-4xb256_in1k.py

# In small and tiny arch, remove drop path and EMA hook comparing with the
# original config
_base_ = [
    '../_base_/datasets/imagenet_bs64_swin_224.py',
    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
    '../_base_/default_runtime.py'
]

# model settings
model = dict(
    type='ImageClassifier',
    backbone=dict(
        type='VisionTransformer',
        arch='deit-small',
        img_size=224,
        patch_size=16),
    neck=None,
    head=dict(
        type='VisionTransformerClsHead',
        num_classes=1000,
        in_channels=384,
        loss=dict(
            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
    ),
    init_cfg=[
        dict(type='TruncNormal', layer='Linear', std=.02),
        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
    ],
    train_cfg=dict(augments=[
        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
    ]))

# data settings
data = dict(samples_per_gpu=256, workers_per_gpu=5)

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(paramwise_cfg=paramwise_cfg)