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optim_wrapper = dict(
optimizer=dict(
type='AdamW',
lr=0.001,
weight_decay=0.05,
eps=1e-08,
betas=(0.9, 0.999),
_scope_='mmpretrain'),
paramwise_cfg=dict(
norm_decay_mult=0.0,
bias_decay_mult=0.0,
flat_decay_mult=0.0,
custom_keys=dict({
'.absolute_pos_embed': dict(decay_mult=0.0),
'.relative_position_bias_table': dict(decay_mult=0.0)
})),
type='AmpOptimWrapper',
dtype='bfloat16',
clip_grad=dict(max_norm=5.0))
param_scheduler = [
dict(type='CosineAnnealingLR', eta_min=1e-05, by_epoch=True, begin=0)
]
train_cfg = dict(by_epoch=True, max_epochs=20, val_interval=1)
val_cfg = dict()
test_cfg = dict()
auto_scale_lr = dict(base_batch_size=1024)
model = dict(
type='ImageClassifier',
backbone=dict(
type='SwinTransformer', arch='base', img_size=224, drop_path_rate=0.5),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=2,
in_channels=1024,
init_cfg=None,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
cal_acc=False),
init_cfg=[
dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.0),
dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
],
train_cfg=None)
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='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=128,
num_workers=10,
dataset=dict(
type='CustomDataset',
data_root='',
ann_file=
'/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/stablediffusionV1-5R2-dpmsolver-25-5m.csv',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=224,
backend='pillow',
interpolation='bicubic'),
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=256,
num_workers=10,
dataset=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='ResizeEdge',
scale=256,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs')
]),
sampler=dict(type='DefaultSampler', shuffle=False))
val_evaluator = [
dict(type='Accuracy', topk=1),
dict(type='SingleLabelMetric', average=None)
]
test_dataloader = dict(
pin_memory=True,
persistent_workers=True,
collate_fn=dict(type='default_collate'),
batch_size=256,
num_workers=10,
dataset=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='ResizeEdge',
scale=256,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs')
]),
sampler=dict(type='DefaultSampler', shuffle=False))
test_evaluator = [
dict(type='Accuracy', topk=1),
dict(type='SingleLabelMetric', average=None)
]
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/swin_base_8xb128_1e-3lr_5m'
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