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_base_ = [
'../_base_/models/san_vit-b16.py',
'../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
crop_size = (640, 640)
metainfo = dict(
classes=('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'),
palette=[[128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
[128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
[192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
[192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
[128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]])
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(metainfo=metainfo, 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='voc'),
decode_head=dict(num_classes=20))
# 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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