|
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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
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/all1.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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
dict(type='PackInputs') |
|
]), |
|
dict( |
|
type='CustomDataset', |
|
data_root='', |
|
ann_file= |
|
'/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/all2.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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
dict(type='PackInputs') |
|
]), |
|
dict( |
|
type='CustomDataset', |
|
data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', |
|
ann_file= |
|
'/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/all3.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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
dict(type='PackInputs') |
|
]), |
|
dict( |
|
type='CustomDataset', |
|
data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', |
|
ann_file= |
|
'/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/stylegan3fake8w.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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
dict(type='PackInputs') |
|
]), |
|
dict( |
|
type='CustomDataset', |
|
data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', |
|
ann_file= |
|
'/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/stylegan3real8w.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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
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/stablediffusionV2-1-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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
dict(type='PackInputs') |
|
]), |
|
dict( |
|
type='CustomDataset', |
|
data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', |
|
ann_file= |
|
'/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV1-5R2-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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
dict(type='PackInputs') |
|
]), |
|
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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
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/stablediffusionV2-1-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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
dict(type='PackInputs') |
|
]), |
|
dict( |
|
type='CustomDataset', |
|
data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', |
|
ann_file= |
|
'/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/stablediffusionV1-5R2-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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
dict(type='PackInputs') |
|
]), |
|
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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
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=0.5), |
|
dict(type='GaussianBlur', radius=1.5, prob=0.5), |
|
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_all_1m_lr1e-4_aug_5e-1' |
|
|