_base_ = [
    'configs/_base_/models/tiny_efficientnet_b2.py',
    'configs/_base_/datasets/tiny_imagenet_bs32.py',
    'configs/_base_/schedules/imagenet_bs256.py',
    'configs/_base_/default_runtime.py',
]

# dataset settings
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='EfficientNetRandomCrop', scale=260),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(type='PackInputs'),
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='EfficientNetCenterCrop', crop_size=260),
    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))
root@K100_AI02:/renzhc/workdir/mmpretrain# cat configs/_base_/models/tiny_efficientnet_b2.py 
# model settings
model = dict(
    type='ImageClassifier',
    backbone=dict(type='EfficientNet', arch='b2'),
    neck=dict(type='GlobalAveragePooling'),
    head=dict(
        type='LinearClsHead',
        num_classes=200,
        in_channels=1408,
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
        topk=(1, 5),
    ))