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_base_ = [
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py',
'../_base_/datasets/ade20k.py'
]
# model settings
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' # noqa
ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
crop_size = (512, 512)
data_preprocessor = dict(
type='SegDataPreProcessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255,
size=(512, 512),
test_cfg=dict(size_divisor=32))
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
pretrained=None,
backbone=dict(
type='MSCAN',
init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
embed_dims=[32, 64, 160, 256],
mlp_ratios=[8, 8, 4, 4],
drop_rate=0.0,
drop_path_rate=0.1,
depths=[3, 3, 5, 2],
attention_kernel_sizes=[5, [1, 7], [1, 11], [1, 21]],
attention_kernel_paddings=[2, [0, 3], [0, 5], [0, 10]],
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='BN', requires_grad=True)),
decode_head=dict(
type='LightHamHead',
in_channels=[64, 160, 256],
in_index=[1, 2, 3],
channels=256,
ham_channels=256,
dropout_ratio=0.1,
num_classes=150,
norm_cfg=ham_norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
ham_kwargs=dict(
MD_S=1,
MD_R=16,
train_steps=6,
eval_steps=7,
inv_t=100,
rand_init=True)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'))
# dataset settings
train_dataloader = dict(batch_size=16)
# optimizer
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_block': dict(decay_mult=0.),
'norm': dict(decay_mult=0.),
'head': dict(lr_mult=10.)
}))
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
dict(
type='PolyLR',
power=1.0,
begin=1500,
end=160000,
eta_min=0.0,
by_epoch=False,
)
]
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