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_base_ = [
'../_base_/models/setr_mla.py',
'../_base_/datasets/FoodSeg103_768x768.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
backbone=dict(
img_size=768,
model_name='vit_base_patch16_224',
embed_dim=768,
depth=12,
num_heads=12,
pos_embed_interp=True,
drop_rate=0.,
mla_channels=256,
mla_index=(5,7,9,11)
),
decode_head=dict(img_size=768,mla_channels=256,mlahead_channels=128,num_classes=104),
auxiliary_head=[
dict(
type='VIT_MLA_AUXIHead',
in_channels=256,
channels=512,
in_index=0,
img_size=768,
num_classes=104,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='VIT_MLA_AUXIHead',
in_channels=256,
channels=512,
in_index=1,
img_size=768,
num_classes=104,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='VIT_MLA_AUXIHead',
in_channels=256,
channels=512,
in_index=2,
img_size=768,
num_classes=104,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='VIT_MLA_AUXIHead',
in_channels=256,
channels=512,
in_index=3,
img_size=768,
num_classes=104,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
])
optimizer = dict(lr=0.002, weight_decay=0.0,
paramwise_cfg = dict(custom_keys={'head': dict(lr_mult=10.)})
)
crop_size = (768, 768)
test_cfg = dict(mode='slide', crop_size=crop_size, stride=(512, 512))
find_unused_parameters = True
data = dict(samples_per_gpu=1)
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