38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
_base_ = ['./mask2former_swin-t_8xb2-90k_cityscapes-512x1024.py']
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pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa
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depths = [2, 2, 18, 2]
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model = dict(
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backbone=dict(
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depths=depths, init_cfg=dict(type='Pretrained',
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checkpoint=pretrained)))
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# set all layers in backbone to lr_mult=0.1
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# set all norm layers, position_embeding,
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# query_embeding, level_embeding to decay_multi=0.0
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backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
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backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
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embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
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custom_keys = {
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'backbone': dict(lr_mult=0.1, decay_mult=1.0),
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'backbone.patch_embed.norm': backbone_norm_multi,
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'backbone.norm': backbone_norm_multi,
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'absolute_pos_embed': backbone_embed_multi,
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'relative_position_bias_table': backbone_embed_multi,
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'query_embed': embed_multi,
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'query_feat': embed_multi,
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'level_embed': embed_multi
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}
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custom_keys.update({
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f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi
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for stage_id, num_blocks in enumerate(depths)
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for block_id in range(num_blocks)
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})
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custom_keys.update({
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f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi
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for stage_id in range(len(depths) - 1)
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})
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# optimizer
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optim_wrapper = dict(
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paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
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