130 lines
3.2 KiB
Python
130 lines
3.2 KiB
Python
_base_ = [
|
|
'../../_base_/models/mixmim/mixmim_base.py',
|
|
'../../_base_/datasets/imagenet_bs64_swin_224.py',
|
|
'../../_base_/default_runtime.py'
|
|
]
|
|
|
|
# dataset settings
|
|
dataset_type = 'ImageNet'
|
|
data_root = 'data/imagenet/'
|
|
|
|
data_preprocessor = dict(
|
|
mean=[123.675, 116.28, 103.53],
|
|
std=[58.395, 57.12, 57.375],
|
|
to_rgb=True,
|
|
)
|
|
|
|
bgr_mean = data_preprocessor['mean'][::-1]
|
|
bgr_std = data_preprocessor['std'][::-1]
|
|
|
|
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=[round(x) for x in bgr_mean], interpolation='bicubic')),
|
|
dict(
|
|
type='RandomErasing',
|
|
erase_prob=0.25,
|
|
mode='rand',
|
|
min_area_ratio=0.02,
|
|
max_area_ratio=1 / 3,
|
|
fill_color=bgr_mean,
|
|
fill_std=bgr_std),
|
|
dict(type='PackInputs'),
|
|
]
|
|
|
|
train_dataloader = dict(
|
|
batch_size=128,
|
|
num_workers=16,
|
|
dataset=dict(
|
|
type=dataset_type,
|
|
data_root=data_root,
|
|
ann_file='meta/train.txt',
|
|
data_prefix='train',
|
|
pipeline=train_pipeline),
|
|
sampler=dict(type='DefaultSampler', shuffle=True),
|
|
persistent_workers=True,
|
|
)
|
|
|
|
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'),
|
|
]
|
|
|
|
val_dataloader = dict(
|
|
batch_size=64,
|
|
num_workers=8,
|
|
pin_memory=True,
|
|
collate_fn=dict(type='default_collate'),
|
|
dataset=dict(
|
|
type=dataset_type,
|
|
data_root=data_root,
|
|
ann_file='meta/val.txt',
|
|
data_prefix='val',
|
|
pipeline=test_pipeline),
|
|
sampler=dict(type='DefaultSampler', shuffle=False),
|
|
persistent_workers=True,
|
|
)
|
|
test_dataloader = val_dataloader
|
|
|
|
# optimizer
|
|
optim_wrapper = dict(
|
|
type='OptimWrapper',
|
|
optimizer=dict(
|
|
type='AdamW',
|
|
lr=5e-4 * (8 * 128 / 256),
|
|
betas=(0.9, 0.999),
|
|
weight_decay=0.05),
|
|
constructor='LearningRateDecayOptimWrapperConstructor',
|
|
paramwise_cfg=dict(
|
|
layer_decay_rate=0.7,
|
|
custom_keys={
|
|
'.ln': dict(decay_mult=0.0), # do not decay on ln and bias
|
|
'.bias': dict(decay_mult=0.0)
|
|
}))
|
|
|
|
param_scheduler = [
|
|
dict(
|
|
type='LinearLR',
|
|
start_factor=1e-6,
|
|
by_epoch=True,
|
|
begin=0,
|
|
end=5,
|
|
convert_to_iter_based=True),
|
|
dict(
|
|
type='CosineAnnealingLR',
|
|
T_max=95,
|
|
eta_min=1e-6,
|
|
by_epoch=True,
|
|
begin=5,
|
|
end=100,
|
|
convert_to_iter_based=True)
|
|
]
|
|
|
|
train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=10)
|
|
val_cfg = dict()
|
|
test_cfg = dict()
|
|
|
|
default_hooks = dict(
|
|
# save checkpoint per epoch.
|
|
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=1))
|