HubHop
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checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_small_20220308-e638c41c.pth' # noqa
# model settings
backbone_norm_cfg = dict(type='LN')
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,
backbone=dict(
type='PCPVT',
init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
in_channels=3,
embed_dims=[64, 128, 320, 512],
num_heads=[1, 2, 5, 8],
patch_sizes=[4, 2, 2, 2],
strides=[4, 2, 2, 2],
mlp_ratios=[8, 8, 4, 4],
out_indices=(0, 1, 2, 3),
qkv_bias=True,
norm_cfg=backbone_norm_cfg,
depths=[3, 4, 6, 3],
sr_ratios=[8, 4, 2, 1],
norm_after_stage=False,
drop_rate=0.0,
attn_drop_rate=0.,
drop_path_rate=0.2),
decode_head=dict(
type='UPerHead',
in_channels=[64, 128, 320, 512],
in_index=[0, 1, 2, 3],
pool_scales=(1, 2, 3, 6),
channels=512,
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=320,
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'))