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- _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco.py']
- pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
- depths = [2, 2, 6, 2]
- model = dict(
- type='Mask2Former',
- backbone=dict(
- _delete_=True,
- type='SwinTransformer',
- embed_dims=96,
- depths=depths,
- num_heads=[3, 6, 12, 24],
- window_size=7,
- mlp_ratio=4,
- qkv_bias=True,
- qk_scale=None,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.3,
- patch_norm=True,
- out_indices=(0, 1, 2, 3),
- with_cp=False,
- convert_weights=True,
- frozen_stages=-1,
- init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
- panoptic_head=dict(
- type='Mask2FormerHead', in_channels=[96, 192, 384, 768]),
- init_cfg=None)
- # set all layers in backbone to lr_mult=0.1
- # set all norm layers, position_embeding,
- # query_embeding, level_embeding to decay_multi=0.0
- backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
- backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
- embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
- custom_keys = {
- 'backbone': dict(lr_mult=0.1, decay_mult=1.0),
- 'backbone.patch_embed.norm': backbone_norm_multi,
- 'backbone.norm': backbone_norm_multi,
- 'absolute_pos_embed': backbone_embed_multi,
- 'relative_position_bias_table': backbone_embed_multi,
- 'query_embed': embed_multi,
- 'query_feat': embed_multi,
- 'level_embed': embed_multi
- }
- custom_keys.update({
- f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi
- for stage_id, num_blocks in enumerate(depths)
- for block_id in range(num_blocks)
- })
- custom_keys.update({
- f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi
- for stage_id in range(len(depths) - 1)
- })
- # optimizer
- optim_wrapper = dict(
- paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
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