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# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='VIT_MLA',
model_name='vit_large_patch16_384',
img_size=768,
patch_size=16,
in_chans=3,
embed_dim=1024,
depth=24,
num_heads=16,
num_classes=19,
drop_rate=0.1,
norm_cfg=norm_cfg,
pos_embed_interp=True,
align_corners=False,
mla_channels=256,
mla_index=(5,11,17,23)
),
decode_head=dict(
type='VIT_MLAHead',
in_channels=1024,
channels=512,
img_size=768,
mla_channels=256,
mlahead_channels=128,
num_classes=19,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')
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