53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
_base_ = [
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'../_base_/models/upernet_swin.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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crop_size = (512, 512)
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data_preprocessor = dict(size=crop_size)
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checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa
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model = dict(
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data_preprocessor=data_preprocessor,
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backbone=dict(
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init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
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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=150),
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auxiliary_head=dict(in_channels=384, num_classes=150))
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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=160000,
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by_epoch=False,
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)
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]
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# By default, models are trained on 8 GPUs with 2 images per GPU
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train_dataloader = dict(batch_size=2)
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val_dataloader = dict(batch_size=1)
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test_dataloader = val_dataloader
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