mmpretrain/configs/repvgg/repvgg-B3_8xb32_in1k.py

_base_ = [
    '../_base_/models/repvgg-B3_lbs-mixup_in1k.py',
    '../_base_/datasets/imagenet_bs32_pil_resize.py',
    '../_base_/schedules/imagenet_bs256_coslr.py',
    '../_base_/default_runtime.py'
]

# schedule settings
optim_wrapper = dict(
    paramwise_cfg=dict(
        bias_decay_mult=0.0,
        custom_keys={
            'branch_3x3.norm': dict(decay_mult=0.0),
            'branch_1x1.norm': dict(decay_mult=0.0),
            'branch_norm.bias': dict(decay_mult=0.0),
        }))

data_preprocessor = dict(
    # RGB format normalization parameters
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    # convert image from BGR to RGB
    to_rgb=True,
)

bgr_mean = data_preprocessor['mean'][::-1]
bgr_std = data_preprocessor['std'][::-1]

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='RandomResizedCrop', scale=224, backend='pillow'),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(
        type='RandAugment',
        policies='timm_increasing',
        num_policies=2,
        total_level=10,
        magnitude_level=7,
        magnitude_std=0.5,
        hparams=dict(pad_val=[round(x) for x in bgr_mean])),
    dict(type='PackInputs'),
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
    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 = dict(dataset=dict(pipeline=test_pipeline))

# schedule settings
param_scheduler = dict(
    type='CosineAnnealingLR',
    T_max=200,
    by_epoch=True,
    begin=0,
    end=200,
    convert_to_iter_based=True)

train_cfg = dict(by_epoch=True, max_epochs=200)

default_hooks = dict(
    checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))