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EDTER_BIMLA_320x320_40k_nyud_rgb_bs_4.py
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EDTER_BIMLA_320x320_40k_nyud_rgb_bs_4.py
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_base_ = [
'../_base_/models/edter_bimla.py',
'../_base_/datasets/nyud_rgb.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
model = dict(
backbone=dict(img_size=320,pos_embed_interp=True,drop_rate=0.,mla_channels=256,mla_index=(5,11,17,23)),
decode_head=dict(img_size=320,mla_channels=256,mlahead_channels=64,num_classes=1),
auxiliary_head=[
dict(
type='VIT_BIMLA_AUXIHead',
in_channels=256,
channels=512,
in_index=0,
img_size=320,
num_classes=1,
align_corners=False,
loss_decode=dict(
type='HEDLoss', use_sigmoid=True, loss_weight=0.4)),
dict(
type='VIT_BIMLA_AUXIHead',
in_channels=256,
channels=512,
in_index=1,
img_size=320,
num_classes=1,
align_corners=False,
loss_decode=dict(
type='HEDLoss', use_sigmoid=True, loss_weight=0.4)),
dict(
type='VIT_BIMLA_AUXIHead',
in_channels=256,
channels=512,
in_index=2,
img_size=320,
num_classes=1,
align_corners=False,
loss_decode=dict(
type='HEDLoss', use_sigmoid=True, loss_weight=0.4)),
dict(
type='VIT_BIMLA_AUXIHead',
in_channels=256,
channels=512,
in_index=3,
img_size=320,
num_classes=1,
align_corners=False,
loss_decode=dict(
type='HEDLoss', use_sigmoid=True , loss_weight=0.4)),
dict(
type='VIT_BIMLA_AUXIHead',
in_channels=256,
channels=512,
in_index=4,
img_size=320,
num_classes=1,
align_corners=False,
loss_decode=dict(
type='HEDLoss', use_sigmoid=True, loss_weight=0.4)),
dict(
type='VIT_BIMLA_AUXIHead',
in_channels=256,
channels=512,
in_index=5,
img_size=320,
num_classes=1,
align_corners=False,
loss_decode=dict(
type='HEDLoss', use_sigmoid=True, loss_weight=0.4)),
dict(
type='VIT_BIMLA_AUXIHead',
in_channels=256,
channels=512,
in_index=6,
img_size=320,
num_classes=1,
align_corners=False,
loss_decode=dict(
type='HEDLoss', use_sigmoid=True, loss_weight=0.4)),
dict(
type='VIT_BIMLA_AUXIHead',
in_channels=256,
channels=512,
in_index=7,
img_size=320,
num_classes=1,
align_corners=False,
loss_decode=dict(
type='HEDLoss', use_sigmoid=True , loss_weight=0.4)),
])
optimizer = dict(lr=1e-6, weight_decay=0.0002,
paramwise_cfg = dict(custom_keys={'head': dict(lr_mult=10.)})
)
lr_config = dict(policy='poly', power=0.9, min_lr=1e-8, by_epoch=False)
test_cfg = dict(mode='slide', crop_size=(320, 320), stride=(280, 280))
find_unused_parameters = True
data = dict(samples_per_gpu=1)