mmpretrain/configs/repmlp/repmlp-base_8xb64_in1k-256px.py

_base_ = [
    '../_base_/models/repmlp-base_224.py',
    '../_base_/datasets/imagenet_bs64_pil_resize.py',
    '../_base_/schedules/imagenet_bs4096_AdamW.py',
    '../_base_/default_runtime.py'
]

# model settings
model = dict(backbone=dict(img_size=256))

# dataset settings
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='RandomResizedCrop', scale=256),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(type='PackClsInputs'),
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='ResizeEdge', scale=292, edge='short', backend='pillow'),
    dict(type='CenterCrop', crop_size=256),
    dict(type='PackClsInputs'),
]

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

# runtime settings
default_hooks = dict(optimizer=dict(grad_clip=dict(max_norm=1.0)))