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vit-base-p16_ft-8xb128-coslr-100e_in1k.py
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vit-base-p16_ft-8xb128-coslr-100e_in1k.py
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# mmcls:: means we use the default settings from MMClassification
_base_ = [
'mmcls::_base_/datasets/imagenet_bs64_swin_224.py',
'mmcls::_base_/schedules/imagenet_bs1024_adamw_swin.py',
'mmcls::_base_/default_runtime.py'
]
data_preprocessor = dict(
num_classes=1000,
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
to_rgb=True,
)
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='BEiT',
arch='base',
img_size=224,
patch_size=16,
drop_path_rate=0.1,
avg_token=True,
output_cls_token=False,
use_abs_pos_emb=False,
use_rel_pos_bias=True,
use_shared_rel_pos_bias=False),
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=0.02)]),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]))
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='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='PackClsInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='ResizeEdge',
scale=256,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackClsInputs')
]
train_dataloader = dict(batch_size=128, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(batch_size=128, dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
# optimizer wrapper
optim_wrapper = dict(
optimizer=dict(
type='AdamW',
lr=4e-3,
weight_decay=0.05,
eps=1e-8,
betas=(0.9, 0.999),
model_type='vit',
layer_decay_rate=0.65),
constructor='mmselfsup.LearningRateDecayOptimWrapperConstructor',
paramwise_cfg=dict(
_delete_=True,
custom_keys={
# the following configurations are designed for BEiT
'.ln': dict(decay_mult=0.0),
'.bias': dict(decay_mult=0.0),
'q_bias': dict(decay_mult=0.0),
'v_bias': dict(decay_mult=0.0),
'.cls_token': dict(decay_mult=0.0),
'.pos_embed': dict(decay_mult=0.0),
'.gamma': 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',
by_epoch=True,
begin=20,
end=100,
eta_min=1e-6,
convert_to_iter_based=True)
]
# runtime settings
default_hooks = dict(
# save checkpoint per epoch.
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=2))
train_cfg = dict(by_epoch=True, max_epochs=100)
randomness = dict(seed=0)