mixmim_mixmim-base_16xb128-coslr-300e_in1k.py

_base_ = '../_base_/default_runtime.py'

# dataset settings
dataset_type = 'ImageNet'
data_root = 'data/imagenet/'
data_preprocessor = dict(
    type='SelfSupDataPreprocessor',
    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',
        scale=224,
        crop_ratio_range=(0.2, 1.0),
        backend='pillow',
        interpolation='bicubic'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackInputs')
]

train_dataloader = dict(
    batch_size=128,
    num_workers=8,
    persistent_workers=True,
    pin_memory=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    collate_fn=dict(type='default_collate'),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='meta/train.txt',
        data_prefix=dict(img_path='train/'),
        pipeline=train_pipeline))

# model settings
model = dict(
    type='MixMIM',
    backbone=dict(
        type='MixMIMPretrainTransformer',
        arch='B',
        drop_rate=0.0,
        drop_path_rate=0.0,  # drop_path_rate=0.0 during pretraining
        mask_ratio=0.5),
    neck=dict(
        type='MixMIMPretrainDecoder',
        num_patches=49,
        encoder_stride=32,
        embed_dim=1024,
        decoder_embed_dim=512,
        decoder_depth=8,
        decoder_num_heads=16),
    head=dict(
        type='MixMIMPretrainHead',
        norm_pix=True,
        loss=dict(type='PixelReconstructionLoss', criterion='L2')))

# optimizer wrapper
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(
        type='AdamW',
        lr=1.5e-4 * (2048 / 256),
        betas=(0.9, 0.95),
        weight_decay=0.05),
    paramwise_cfg=dict(custom_keys={
        'ln': dict(decay_mult=0.0),
        'bias': dict(decay_mult=0.0)
    }))

param_scheduler = [
    dict(
        type='LinearLR',
        start_factor=1e-4,
        by_epoch=True,
        begin=0,
        end=40,
        convert_to_iter_based=True),
    dict(
        type='CosineAnnealingLR',
        T_max=260,
        by_epoch=True,
        begin=40,
        end=300,
        convert_to_iter_based=True)
]

train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300)
default_hooks = dict(
    checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=1))

randomness = dict(seed=0, diff_rank_seed=True)

# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=2048)