45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
_base_ = [
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'../_base_/models/upernet_vit-b16_ln_mln.py',
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'../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
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'../_base_/schedules/schedule_80k.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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model = dict(
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data_preprocessor=data_preprocessor,
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pretrained='pretrain/vit_base_patch16_224.pth',
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decode_head=dict(num_classes=150),
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auxiliary_head=dict(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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'pos_embed': dict(decay_mult=0.),
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'cls_token': 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=80000,
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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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