57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
_base_ = [
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'../_base_/models/upernet_swin.py', '../_base_/datasets/levir_256x256.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py'
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]
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crop_size = (256, 256)
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norm_cfg = dict(type='BN', requires_grad=True)
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data_preprocessor = dict(
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size=crop_size,
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type='SegDataPreProcessor',
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mean=[123.675, 116.28, 103.53, 123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375, 58.395, 57.12, 57.375])
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model = dict(
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data_preprocessor=data_preprocessor,
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backbone=dict(
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in_channels=6,
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embed_dims=96,
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depths=[2, 2, 6, 2],
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num_heads=[3, 6, 12, 24],
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window_size=7,
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use_abs_pos_embed=False,
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drop_path_rate=0.3,
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patch_norm=True),
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decode_head=dict(in_channels=[96, 192, 384, 768], num_classes=2),
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auxiliary_head=dict(in_channels=384, num_classes=2))
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# AdamW optimizer, no weight decay for position embedding & layer norm
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# in backbone
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optim_wrapper = dict(
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_delete_=True,
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type='OptimWrapper',
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optimizer=dict(
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type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01),
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paramwise_cfg=dict(
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custom_keys={
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'absolute_pos_embed': dict(decay_mult=0.),
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'relative_position_bias_table': dict(decay_mult=0.),
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'norm': dict(decay_mult=0.)
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}))
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param_scheduler = [
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dict(
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type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
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dict(
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type='PolyLR',
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eta_min=0.0,
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power=1.0,
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begin=1500,
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end=20000,
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by_epoch=False,
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)
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]
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train_dataloader = dict(batch_size=4)
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val_dataloader = dict(batch_size=1)
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test_dataloader = val_dataloader
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