65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
_base_ = [
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'../_base_/models/setr_pup.py', '../_base_/datasets/cityscapes_768x768.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
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]
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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crop_size = (768, 768)
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model = dict(
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pretrained=None,
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backbone=dict(
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drop_rate=0.,
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init_cfg=dict(
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type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')),
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auxiliary_head=[
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dict(
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type='SETRUPHead',
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in_channels=1024,
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channels=256,
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in_index=0,
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num_classes=19,
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dropout_ratio=0,
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norm_cfg=norm_cfg,
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num_convs=2,
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up_scale=4,
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kernel_size=3,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
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dict(
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type='SETRUPHead',
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in_channels=1024,
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channels=256,
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in_index=1,
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num_classes=19,
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dropout_ratio=0,
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norm_cfg=norm_cfg,
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num_convs=2,
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up_scale=4,
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kernel_size=3,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
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dict(
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type='SETRUPHead',
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in_channels=1024,
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channels=256,
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in_index=2,
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num_classes=19,
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dropout_ratio=0,
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norm_cfg=norm_cfg,
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num_convs=2,
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up_scale=4,
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kernel_size=3,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4))
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],
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test_cfg=dict(mode='slide', crop_size=crop_size, stride=(512, 512)))
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optimizer = dict(
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weight_decay=0.0,
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paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)}))
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data = dict(samples_per_gpu=1)
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