82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
_base_ = [
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'../_base_/models/setr_mla.py', '../_base_/datasets/ade20k.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
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]
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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model = dict(
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pretrained='pretrain/vit_large_patch16_384.pth',
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backbone=dict(img_size=(512, 512), drop_rate=0.),
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decode_head=dict(num_classes=150),
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auxiliary_head=[
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dict(
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type='FCNHead',
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in_channels=256,
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channels=256,
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in_index=0,
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dropout_ratio=0,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='ReLU'),
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num_convs=0,
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kernel_size=1,
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concat_input=False,
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num_classes=150,
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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='FCNHead',
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in_channels=256,
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channels=256,
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in_index=1,
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dropout_ratio=0,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='ReLU'),
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num_convs=0,
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kernel_size=1,
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concat_input=False,
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num_classes=150,
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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='FCNHead',
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in_channels=256,
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channels=256,
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in_index=2,
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dropout_ratio=0,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='ReLU'),
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num_convs=0,
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kernel_size=1,
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concat_input=False,
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num_classes=150,
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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='FCNHead',
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in_channels=256,
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channels=256,
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in_index=3,
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dropout_ratio=0,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='ReLU'),
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num_convs=0,
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kernel_size=1,
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concat_input=False,
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num_classes=150,
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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=(512, 512), stride=(341, 341)),
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)
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optimizer = dict(
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lr=0.001,
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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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# num_gpus: 8 -> batch_size: 8
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data = dict(samples_per_gpu=1)
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