737544f1c5
* add config * add cityscapes config * add default value to docstring * fix lint * add deit-s and deit-b * add readme * add eps at norm_cfg * add drop_path_rate experiment * add deit case at init_weight * add upernet result * update result and add upernet 160k config * update upernet result and fix settings * Update iters number * update result and delete some configs * fix import error * fix drop_path_rate * update result and restore config * update benchmark result * remove cityscapes exp * remove neck * neck exp * add more configs * fix init error * fix ffn setting * update result * update results * update result * update results and fill table * delete or rename configs * fix link delimiter * rename configs and fix link * rename neck to mln
37 lines
931 B
Python
37 lines
931 B
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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model = dict(
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decode_head=dict(num_classes=150), 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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optimizer = dict(
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_delete_=True,
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type='AdamW',
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lr=0.00006,
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betas=(0.9, 0.999),
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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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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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