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add test_mmpretrain test_mmrotate and test_mmseg  
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# Copyright (c) OpenMMLab. All rights reserved.
model = dict(
    type='ImageClassifier',
    backbone=dict(
        type='ResNet',
        depth=18,
        num_stages=4,
        out_indices=(3, ),
        style='pytorch'),
    neck=dict(type='GlobalAveragePooling'),
    head=dict(
        type='LinearClsHead',
        num_classes=1000,
        in_channels=512,
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
        topk=(1, 5)))
dataset_type = 'ImageNet'
data_preprocessor = dict(
    num_classes=1000,
    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),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(type='PackInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='ResizeEdge', scale=256, edge='short'),
    dict(type='CenterCrop', crop_size=224),
    dict(type='PackInputs')
]
train_dataloader = dict(
    batch_size=2,
    num_workers=1,
    dataset=dict(
        type='ImageNet',
        data_root='tests/test_codebase/test_mmpretrain/data/imgs',
        ann_file='ann.txt',
        data_prefix='train',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='RandomResizedCrop', scale=224),
            dict(type='RandomFlip', prob=0.5, direction='horizontal'),
            dict(type='PackInputs')
        ]),
    sampler=dict(type='DefaultSampler', shuffle=True))
val_dataloader = dict(
    batch_size=2,
    num_workers=1,
    dataset=dict(
        type='ImageNet',
        data_root='tests/test_codebase/test_mmpretrain/data/imgs',
        ann_file='ann.txt',
        data_prefix='val',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='ResizeEdge', scale=256, edge='short'),
            dict(type='CenterCrop', crop_size=224),
            dict(type='PackInputs')
        ]),
    sampler=dict(type='DefaultSampler', shuffle=False))
val_evaluator = dict(type='Accuracy', topk=(1, 5))
test_dataloader = dict(
    batch_size=2,
    num_workers=1,
    dataset=dict(
        type='ImageNet',
        data_root='tests/test_codebase/test_mmpretrain/data/imgs',
        ann_file='ann.txt',
        data_prefix='val',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='ResizeEdge', scale=256, edge='short'),
            dict(type='CenterCrop', crop_size=224),
            dict(type='PackInputs')
        ]),
    sampler=dict(type='DefaultSampler', shuffle=False))
test_evaluator = dict(type='Accuracy', topk=(1, 5))
optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001))
param_scheduler = dict(
    type='MultiStepLR', by_epoch=True, milestones=[30, 60, 90], gamma=0.1)
train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1)
val_cfg = dict()
test_cfg = dict()
auto_scale_lr = dict(base_batch_size=256)
default_scope = 'mmpretrain'
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=100),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=1),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='VisualizationHook', enable=False))
env_cfg = dict(
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='UniversalVisualizer', vis_backends=[dict(type='LocalVisBackend')])
log_level = 'INFO'
load_from = None
resume = False
randomness = dict(seed=None, deterministic=False)