53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
norm_cfg = dict(type='SyncBN', requires_grad=True)
|
|
custom_imports = dict(imports='mmpretrain.models', allow_failed_imports=False)
|
|
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_32xb128-noema_in1k_20220301-2a0ee547.pth' # noqa
|
|
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='mmpretrain.ConvNeXt',
|
|
arch='base',
|
|
out_indices=[0, 1, 2, 3],
|
|
drop_path_rate=0.4,
|
|
layer_scale_init_value=1.0,
|
|
gap_before_final_norm=False,
|
|
init_cfg=dict(
|
|
type='Pretrained', checkpoint=checkpoint_file,
|
|
prefix='backbone.')),
|
|
decode_head=dict(
|
|
type='UPerHead',
|
|
in_channels=[128, 256, 512, 1024],
|
|
in_index=[0, 1, 2, 3],
|
|
pool_scales=(1, 2, 3, 6),
|
|
channels=512,
|
|
dropout_ratio=0.1,
|
|
num_classes=19,
|
|
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=384,
|
|
in_index=2,
|
|
channels=256,
|
|
num_convs=1,
|
|
concat_input=False,
|
|
dropout_ratio=0.1,
|
|
num_classes=19,
|
|
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'))
|