59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
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=None,
|
|
backbone=dict(
|
|
type='BEiT',
|
|
img_size=(640, 640),
|
|
patch_size=16,
|
|
in_channels=3,
|
|
embed_dims=768,
|
|
num_layers=12,
|
|
num_heads=12,
|
|
mlp_ratio=4,
|
|
out_indices=(3, 5, 7, 11),
|
|
qv_bias=True,
|
|
attn_drop_rate=0.0,
|
|
drop_path_rate=0.1,
|
|
norm_cfg=dict(type='LN', eps=1e-6),
|
|
act_cfg=dict(type='GELU'),
|
|
norm_eval=False,
|
|
init_values=0.1),
|
|
neck=dict(type='Feature2Pyramid', embed_dim=768, rescales=[4, 2, 1, 0.5]),
|
|
decode_head=dict(
|
|
type='UPerHead',
|
|
in_channels=[768, 768, 768, 768],
|
|
in_index=[0, 1, 2, 3],
|
|
pool_scales=(1, 2, 3, 6),
|
|
channels=768,
|
|
dropout_ratio=0.1,
|
|
num_classes=150,
|
|
norm_cfg=norm_cfg,
|
|
align_corners=False,
|
|
loss_decode=dict(
|
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
|
auxiliary_head=dict(
|
|
type='FCNHead',
|
|
in_channels=768,
|
|
in_index=2,
|
|
channels=256,
|
|
num_convs=1,
|
|
concat_input=False,
|
|
dropout_ratio=0.1,
|
|
num_classes=150,
|
|
norm_cfg=norm_cfg,
|
|
align_corners=False,
|
|
loss_decode=dict(
|
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
|
|
# model training and testing settings
|
|
train_cfg=dict(),
|
|
test_cfg=dict(mode='whole'))
|