optim_wrapper = dict(
    optimizer=dict(
        type='SGD',
        lr=0.0001,
        momentum=0.9,
        weight_decay=0.0001,
        _scope_='mmpretrain'),
    clip_grad=None)
param_scheduler = [
    dict(type='CosineAnnealingLR', eta_min=1e-05, by_epoch=False, begin=0)
]
train_cfg = dict(by_epoch=True, max_epochs=10, val_interval=1)
val_cfg = dict()
test_cfg = dict()
auto_scale_lr = dict(base_batch_size=512)
model = dict(
    type='ImageClassifier',
    backbone=dict(
        frozen_stages=2,
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(3, ),
        style='pytorch',
        init_cfg=dict(
            type='Pretrained',
            checkpoint=
            'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth',
            prefix='backbone')),
    neck=dict(type='GlobalAveragePooling'),
    head=dict(
        type='LinearClsHead',
        num_classes=2,
        in_channels=2048,
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
        topk=1))
dataset_type = 'CustomDataset'
data_preprocessor = dict(
    num_classes=2,
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    to_rgb=True)
bgr_mean = [103.53, 116.28, 123.675]
bgr_std = [57.375, 57.12, 58.395]
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='RandomResizedCrop',
        scale=224,
        backend='pillow',
        interpolation='bicubic'),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(type='JPEG', compress_val=65, prob=0.1),
    dict(type='GaussianBlur', radius=1.5, prob=0.1),
    dict(type='PackInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='ResizeEdge',
        scale=256,
        edge='short',
        backend='pillow',
        interpolation='bicubic'),
    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=256,
    num_workers=10,
    dataset=dict(
        type='ConcatDataset',
        datasets=[
            dict(
                type='CustomDataset',
                data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset',
                ann_file=
                '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/IF80w.csv',
                pipeline=[
                    dict(type='LoadImageFromFile'),
                    dict(
                        type='RandomResizedCrop',
                        scale=224,
                        backend='pillow',
                        interpolation='bicubic'),
                    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
                    dict(type='JPEG', compress_val=65, prob=0.1),
                    dict(type='GaussianBlur', radius=1.5, prob=0.1),
                    dict(type='PackInputs')
                ]),
            dict(
                type='CustomDataset',
                data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset',
                ann_file=
                '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/if-dpmsolver++-50-20w.tsv',
                pipeline=[
                    dict(type='LoadImageFromFile'),
                    dict(
                        type='RandomResizedCrop',
                        scale=224,
                        backend='pillow',
                        interpolation='bicubic'),
                    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
                    dict(type='JPEG', compress_val=65, prob=0.1),
                    dict(type='GaussianBlur', radius=1.5, prob=0.1),
                    dict(type='PackInputs')
                ]),
            dict(
                type='CustomDataset',
                data_root='',
                ann_file=
                '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/cc1m.csv',
                pipeline=[
                    dict(type='LoadImageFromFile'),
                    dict(
                        type='RandomResizedCrop',
                        scale=224,
                        backend='pillow',
                        interpolation='bicubic'),
                    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
                    dict(type='JPEG', compress_val=65, prob=0.1),
                    dict(type='GaussianBlur', radius=1.5, prob=0.1),
                    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=256,
    num_workers=10,
    dataset=dict(
        type='ConcatDataset',
        datasets=[
            dict(
                type='CustomDataset',
                data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset',
                ann_file=
                '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/if-dpmsolver++-25-1w.tsv',
                pipeline=[
                    dict(type='LoadImageFromFile'),
                    dict(
                        type='RandomResizedCrop',
                        scale=224,
                        backend='pillow',
                        interpolation='bicubic'),
                    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
                    dict(type='JPEG', compress_val=65, prob=0.1),
                    dict(type='GaussianBlur', radius=1.5, prob=0.1),
                    dict(type='PackInputs')
                ]),
            dict(
                type='CustomDataset',
                data_root='',
                ann_file=
                '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/cc1w.csv',
                pipeline=[
                    dict(type='LoadImageFromFile'),
                    dict(
                        type='RandomResizedCrop',
                        scale=224,
                        backend='pillow',
                        interpolation='bicubic'),
                    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
                    dict(type='JPEG', compress_val=65, prob=0.1),
                    dict(type='GaussianBlur', radius=1.5, prob=0.1),
                    dict(type='PackInputs')
                ])
        ]),
    sampler=dict(type='DefaultSampler', shuffle=False))
val_evaluator = dict(type='Accuracy', topk=1)
test_dataloader = dict(
    pin_memory=True,
    persistent_workers=True,
    collate_fn=dict(type='default_collate'),
    batch_size=256,
    num_workers=10,
    dataset=dict(
        type='ConcatDataset',
        datasets=[
            dict(
                type='CustomDataset',
                data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset',
                ann_file=
                '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/if-dpmsolver++-25-1w.tsv',
                pipeline=[
                    dict(type='LoadImageFromFile'),
                    dict(
                        type='RandomResizedCrop',
                        scale=224,
                        backend='pillow',
                        interpolation='bicubic'),
                    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
                    dict(type='JPEG', compress_val=65, prob=0.1),
                    dict(type='GaussianBlur', radius=1.5, prob=0.1),
                    dict(type='PackInputs')
                ]),
            dict(
                type='CustomDataset',
                data_root='',
                ann_file=
                '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/cc1w.csv',
                pipeline=[
                    dict(type='LoadImageFromFile'),
                    dict(
                        type='RandomResizedCrop',
                        scale=224,
                        backend='pillow',
                        interpolation='bicubic'),
                    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
                    dict(type='JPEG', compress_val=65, prob=0.1),
                    dict(type='GaussianBlur', radius=1.5, prob=0.1),
                    dict(type='PackInputs')
                ])
        ]),
    sampler=dict(type='DefaultSampler', shuffle=False))
test_evaluator = dict(type='Accuracy', topk=1)
custom_hooks = [dict(type='EMAHook', momentum=0.0001, priority='ABOVE_NORMAL')]
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))
env_cfg = dict(
    cudnn_benchmark=True,
    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'),
        dict(type='TensorboardVisBackend')
    ])
log_level = 'INFO'
load_from = None
resume = False
randomness = dict(seed=None, deterministic=False)
launcher = 'slurm'
work_dir = 'workdir/resnet50_2xb256_IF_1m_lr1e-4_aug_1e-1'