56 lines
1.4 KiB
Python
56 lines
1.4 KiB
Python
_base_ = './_base_.py'
|
|
|
|
model = dict(
|
|
backbone=dict(
|
|
stem_channels=512,
|
|
drop_path_rate=0.4,
|
|
stage_blocks=[2, 2, 48, 4],
|
|
groups=[16, 32, 64, 128],
|
|
dw_kernel_size=5,
|
|
level2_post_norm=True,
|
|
level2_post_norm_block_ids=[5, 11, 17, 23, 29, 35, 41, 47],
|
|
center_feature_scale=True,
|
|
use_clip_projector=True,
|
|
),
|
|
neck=None,
|
|
head=dict(in_channels=768))
|
|
|
|
train_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='RandomResizedCrop',
|
|
scale=512,
|
|
backend='pillow',
|
|
interpolation='bicubic'),
|
|
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
|
|
dict(type='PackInputs'),
|
|
]
|
|
|
|
test_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='ResizeEdge',
|
|
scale=512,
|
|
edge='short',
|
|
backend='pillow',
|
|
interpolation='bicubic'),
|
|
dict(type='CenterCrop', crop_size=512),
|
|
dict(type='PackInputs'),
|
|
]
|
|
|
|
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
|
|
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
|
|
test_dataloader = val_dataloader
|
|
|
|
optim_wrapper = dict(optimizer=dict(lr=5e-6))
|
|
param_scheduler = [
|
|
dict(
|
|
type='LinearLR',
|
|
by_epoch=True,
|
|
begin=0,
|
|
end=2,
|
|
convert_to_iter_based=True),
|
|
dict(type='CosineAnnealingLR', T_max=18, by_epoch=True, begin=2, end=20)
|
|
]
|
|
train_cfg = dict(by_epoch=True, max_epochs=20, val_interval=1)
|