forked from fudan-zvg/SETR
/
SETR_MLA_DeiT_768x768_40k_cityscapes_bs_8.py
62 lines (59 loc) · 2.09 KB
/
SETR_MLA_DeiT_768x768_40k_cityscapes_bs_8.py
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_base_ = [
'../_base_/models/setr_mla.py',
'../_base_/datasets/cityscapes_768x768.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
model = dict(
backbone=dict(img_size=768, pos_embed_interp=True, drop_rate=0., mla_channels=256,
model_name='deit_base_distilled_path16_384', mla_index=(2, 5, 8, 11), embed_dim=768, depth=12, num_heads=12),
decode_head=dict(img_size=768, mla_channels=256,
mlahead_channels=128, num_classes=19),
auxiliary_head=[
dict(
type='VIT_MLA_AUXIHead',
in_channels=256,
channels=512,
in_index=0,
img_size=768,
num_classes=19,
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=19,
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=19,
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=19,
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)