/
vit-base-p16_8xb256-coslr-100e_in1k.py
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vit-base-p16_8xb256-coslr-100e_in1k.py
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_base_ = [
'../../_base_/datasets/imagenet_bs64_swin_224.py',
'../../_base_/schedules/imagenet_bs1024_adamw_swin.py',
'../../_base_/default_runtime.py'
]
# dataset
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=224,
backend='pillow',
interpolation='bicubic'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies='timm_increasing',
num_policies=2,
total_level=10,
magnitude_level=9,
magnitude_std=0.5,
hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),
dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
dict(
type='RandomErasing',
erase_prob=0.25,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=0.3333333333333333,
fill_color=[103.53, 116.28, 123.675],
fill_std=[57.375, 57.12, 58.395]),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs'),
]
train_dataloader = dict(batch_size=256, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(batch_size=256, dataset=dict(pipeline=test_pipeline))
# If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='VisionTransformer',
arch='base',
img_size=224,
patch_size=16,
drop_path_rate=0.1,
out_type='avg_featmap',
final_norm=False,
init_cfg=dict(type='Pretrained', checkpoint='', prefix='backbone.')),
neck=None,
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
init_cfg=[
dict(type='TruncNormal', layer='Linear', std=2e-5, bias=2e-5)
]),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]))
# optimizer wrapper
optim_wrapper = dict(
optimizer=dict(
type='AdamW', lr=8e-3, weight_decay=0.05, betas=(0.9, 0.999)),
constructor='LearningRateDecayOptimWrapperConstructor',
paramwise_cfg=dict(
layer_decay_rate=0.65,
custom_keys={
'.ln': dict(decay_mult=0.0),
'.bias': dict(decay_mult=0.0),
'.cls_token': dict(decay_mult=0.0),
'.pos_embed': dict(decay_mult=0.0)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=20,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=80,
by_epoch=True,
begin=20,
end=100,
eta_min=1e-6,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=100)
default_hooks = dict(
# save checkpoint per epoch.
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
randomness = dict(seed=0)