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# model settings
backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
norm_cfg = dict(type='SyncBN', requires_grad=True)
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)
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
pretrained='pretrain/jx_vit_large_p16_384-b3be5167.pth',
backbone=dict(
type='VisionTransformer',
img_size=(768, 768),
patch_size=16,
in_channels=3,
embed_dims=1024,
num_layers=24,
num_heads=16,
out_indices=(9, 14, 19, 23),
drop_rate=0.1,
norm_cfg=backbone_norm_cfg,
with_cls_token=True,
interpolate_mode='bilinear',
),
decode_head=dict(
type='SETRUPHead',
in_channels=1024,
channels=256,
in_index=3,
num_classes=19,
dropout_ratio=0,
norm_cfg=norm_cfg,
num_convs=4,
up_scale=2,
kernel_size=3,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
auxiliary_head=[
dict(
type='SETRUPHead',
in_channels=1024,
channels=256,
in_index=0,
num_classes=19,
dropout_ratio=0,
norm_cfg=norm_cfg,
num_convs=1,
up_scale=4,
kernel_size=3,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='SETRUPHead',
in_channels=1024,
channels=256,
in_index=1,
num_classes=19,
dropout_ratio=0,
norm_cfg=norm_cfg,
num_convs=1,
up_scale=4,
kernel_size=3,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='SETRUPHead',
in_channels=1024,
channels=256,
in_index=2,
num_classes=19,
dropout_ratio=0,
norm_cfg=norm_cfg,
num_convs=1,
up_scale=4,
kernel_size=3,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
],
train_cfg=dict(),
test_cfg=dict(mode='whole'))
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