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https://github.com/open-mmlab/mmsegmentation.git
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24f1563571
* [Feature] Add BEiT backbone * fix * fix * fix * fix * add readme * fix * fix * fix * fix * fix * add link * fix memory * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix test_beit.py * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix
31 lines
821 B
Python
31 lines
821 B
Python
_base_ = [
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'../_base_/models/upernet_beit.py', '../_base_/datasets/ade20k_640x640.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
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]
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model = dict(
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pretrained='pretrain/beit_base_patch16_224_pt22k_ft22k.pth',
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test_cfg=dict(mode='slide', crop_size=(640, 640), stride=(426, 426)))
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optimizer = dict(
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_delete_=True,
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type='AdamW',
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lr=3e-5,
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betas=(0.9, 0.999),
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weight_decay=0.05,
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constructor='LayerDecayOptimizerConstructor',
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paramwise_cfg=dict(num_layers=12, layer_decay_rate=0.9))
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lr_config = dict(
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_delete_=True,
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policy='poly',
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warmup='linear',
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warmup_iters=1500,
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warmup_ratio=1e-6,
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power=1.0,
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min_lr=0.0,
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by_epoch=False)
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# By default, models are trained on 8 GPUs with 2 images per GPU
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data = dict(samples_per_gpu=2)
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