mmpretrain/configs/beit/benchmarks/beit-base-p16_8xb64_in1k.py

# Copyright (c) OpenMMLab. All rights reserved.
# This is a BETA new format config file, and the usage may change recently.
from mmengine.config import read_base

with read_base():
    from ..._base_.datasets.imagenet_bs64_swin_224 import *
    from ..._base_.schedules.imagenet_bs1024_adamw_swin import *
    from ..._base_.default_runtime import *

from mmengine.model import ConstantInit, TruncNormalInit

from mmpretrain.models import (BEiTViT, ImageClassifier, LabelSmoothLoss,
                               LinearClsHead)
from mmpretrain.models.utils.batch_augments import CutMix, Mixup

data_preprocessor = dict(
    num_classes=1000,
    # RGB format normalization parameters
    mean=[127.5, 127.5, 127.5],
    std=[127.5, 127.5, 127.5],
    # convert image from BGR to RGB
    to_rgb=True,
)

model = dict(
    type=ImageClassifier,
    backbone=dict(
        type=BEiTViT,
        arch='base',
        img_size=224,
        patch_size=16,
        out_type='avg_featmap',
        use_abs_pos_emb=False,
        use_rel_pos_bias=True,
        use_shared_rel_pos_bias=False,
    ),
    neck=None,
    head=dict(
        type=LinearClsHead,
        num_classes=1000,
        in_channels=768,
        loss=dict(type=LabelSmoothLoss, label_smooth_val=0.1, mode='original'),
    ),
    init_cfg=[
        dict(type=TruncNormalInit, layer='Linear', std=.02),
        dict(type=ConstantInit, layer='LayerNorm', val=1., bias=0.),
    ],
    train_cfg=dict(
        augments=[dict(type=Mixup, alpha=0.8),
                  dict(type=CutMix, alpha=1.0)]))