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_base_ = [
'../_base_/models/san_vit-b16.py',
'../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
crop_size = (640, 640)
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='ResizeShortestEdge', scale=crop_size, max_size=2560),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs')
]
# By default, models are trained on 8 GPUs with 2 images per GPU
train_dataloader = dict(batch_size=2)
val_dataloader = dict(batch_size=1, dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
data_preprocessor = dict(
mean=[122.7709, 116.7460, 104.0937],
std=[68.5005, 66.6322, 70.3232],
size_divisor=640,
test_cfg=dict(size_divisor=32))
model = dict(
data_preprocessor=data_preprocessor,
pretrained='pretrain/vit_base_patch16_224.pth',
text_encoder=dict(dataset_name='pascal_context'),
decode_head=dict(num_classes=59))
# AdamW optimizer, no weight decay for position embedding & layer norm
# in backbone
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=dict(
type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01),
paramwise_cfg=dict(
custom_keys={
'pos_embed': dict(decay_mult=0.),
'cls_token': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)
}))
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
dict(
type='PolyLR',
eta_min=0.0,
power=1.0,
begin=1500,
end=160000,
by_epoch=False,
)
]
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