fruits_config.py

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=30,
        in_channels=512,
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
        topk=(1, 5)),
    init_cfg=dict(
        type='Pretrained',
        checkpoint=
        'https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth'
    ))
dataset_type = 'CustomDataset'
data_preprocessor = dict(
    num_classes=30,
    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(
    pin_memory=True,
    persistent_workers=True,
    collate_fn=dict(type='default_collate'),
    batch_size=32,
    num_workers=5,
    dataset=dict(
        type='CustomDataset',
        data_root='F:\\openmmlab\\mmpretrain-main\\data\\fruits_data\\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(
    pin_memory=True,
    persistent_workers=True,
    collate_fn=dict(type='default_collate'),
    batch_size=32,
    num_workers=5,
    dataset=dict(
        type='CustomDataset',
        data_root='F:\\openmmlab\\mmpretrain-main\\data\\fruits_data\\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_dataloader = dict(
    pin_memory=True,
    collate_fn=dict(type='default_collate'),
    persistent_workers=True,
    batch_size=32,
    num_workers=5,
    dataset=dict(
        type='CustomDataset',
        data_root='F:\\openmmlab\\mmpretrain-main\\data\\fruits_data\\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=3)
test_evaluator = dict(type='Accuracy', topk=3)
optim_wrapper = dict(
    optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
param_scheduler = dict(
    type='MultiStepLR', by_epoch=True, milestones=[30, 40, 50], gamma=0.1)
train_cfg = dict(by_epoch=True, max_epochs=50, val_interval=1)
val_cfg = dict()
test_cfg = dict()
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=True,
        show=False,
        wait_time=2,
        out_dir='results',
        interval=1))
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 = './fruits/epoch_50.pth'
resume = False
randomness = dict(seed=None, deterministic=False)
launcher = 'none'
work_dir = './fruits'