swin-large-w14_8xb256-coslr-100e_in1k.py

_base_ = [
    '../../_base_/models/swin_transformer/base_224.py',
    '../../_base_/datasets/imagenet_bs256_swin_192.py',
    '../../_base_/default_runtime.py'
]

# dataset settings
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='RandomResizedCrop',
        scale=224,
        backend='pillow',
        interpolation='bicubic'),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(
        type='RandAugment',
        policies='timm_increasing',
        num_policies=2,
        total_level=10,
        magnitude_level=9,
        magnitude_std=0.5,
        hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),
    dict(
        type='RandomErasing',
        erase_prob=0.25,
        mode='rand',
        min_area_ratio=0.02,
        max_area_ratio=0.3333333333333333,
        fill_color=[103.53, 116.28, 123.675],
        fill_std=[57.375, 57.12, 58.395]),
    dict(type='PackInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='ResizeEdge',
        scale=256,
        edge='short',
        backend='pillow',
        interpolation='bicubic'),
    dict(type='CenterCrop', crop_size=224),
    dict(type='PackInputs')
]

train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader

# model settings
model = dict(
    backbone=dict(
        arch='large',
        img_size=224,
        drop_path_rate=0.2,
        stage_cfgs=dict(block_cfgs=dict(window_size=14)),
        pad_small_map=True,
        init_cfg=dict(type='Pretrained', checkpoint='', prefix='backbone.')),
    head=dict(in_channels=1536))

# optimizer settings
optim_wrapper = dict(
    type='AmpOptimWrapper',
    optimizer=dict(type='AdamW', lr=5e-3, weight_decay=0.05),
    clip_grad=dict(max_norm=5.0),
    constructor='LearningRateDecayOptimWrapperConstructor',
    paramwise_cfg=dict(
        layer_decay_rate=0.7,
        custom_keys={
            '.norm': dict(decay_mult=0.0),
            '.bias': dict(decay_mult=0.0),
            '.absolute_pos_embed': dict(decay_mult=0.0),
            '.relative_position_bias_table': dict(decay_mult=0.0)
        }))

# learning rate scheduler
param_scheduler = [
    dict(
        type='LinearLR',
        start_factor=2.5e-7 / 1.25e-3,
        by_epoch=True,
        begin=0,
        end=20,
        convert_to_iter_based=True),
    dict(
        type='CosineAnnealingLR',
        T_max=100,
        eta_min=1e-6,
        by_epoch=True,
        begin=20,
        end=100,
        convert_to_iter_based=True)
]

# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=100)
val_cfg = dict()
test_cfg = dict()

default_hooks = dict(
    # save checkpoint per epoch.
    checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3),
    logger=dict(type='LoggerHook', interval=100))

randomness = dict(seed=0)