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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')) | |