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_base_ = './ocrnet_hr18_512x1024_80k_cityscapes.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w48',
backbone=dict(
extra=dict(
stage2=dict(num_channels=(48, 96)),
stage3=dict(num_channels=(48, 96, 192)),
stage4=dict(num_channels=(48, 96, 192, 384)))),
decode_head=[
dict(
type='FCNHead',
in_channels=[48, 96, 192, 384],
channels=sum([48, 96, 192, 384]),
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
kernel_size=1,
num_convs=1,
norm_cfg=norm_cfg,
concat_input=False,
dropout_ratio=-1,
num_classes=19,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
dict(
type='OCRHead',
in_channels=[48, 96, 192, 384],
channels=512,
ocr_channels=256,
input_transform='resize_concat',
in_index=(0, 1, 2, 3),
norm_cfg=norm_cfg,
dropout_ratio=-1,
num_classes=19,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
])
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