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_base_ = './maskformer_r50-d32_8xb2-160k_ade20k-512x512.py'
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa
backbone_norm_cfg = dict(type='LN', requires_grad=True)
depths = [2, 2, 6, 2]
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
pretrain_img_size=224,
embed_dims=96,
patch_size=4,
window_size=7,
mlp_ratio=4,
depths=depths,
num_heads=[3, 6, 12, 24],
strides=(4, 2, 2, 2),
out_indices=(0, 1, 2, 3),
qkv_bias=True,
qk_scale=None,
patch_norm=True,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.3,
use_abs_pos_embed=False,
act_cfg=dict(type='GELU'),
norm_cfg=backbone_norm_cfg,
init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)),
decode_head=dict(
type='MaskFormerHead',
in_channels=[96, 192, 384,
768], # input channels of pixel_decoder modules
))
# optimizer
optimizer = dict(
type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01)
# set all layers in backbone to lr_mult=1.0
# set all norm layers, position_embeding,
# query_embeding to decay_multi=0.0
backbone_norm_multi = dict(lr_mult=1.0, decay_mult=0.0)
backbone_embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
embed_multi = dict(decay_mult=0.0)
custom_keys = {
'backbone': dict(lr_mult=1.0),
'backbone.patch_embed.norm': backbone_norm_multi,
'backbone.norm': backbone_norm_multi,
'relative_position_bias_table': backbone_embed_multi,
'query_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(
_delete_=True,
type='OptimWrapper',
optimizer=optimizer,
clip_grad=dict(max_norm=0.01, norm_type=2),
paramwise_cfg=dict(custom_keys=custom_keys))
# learning policy
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
dict(
type='PolyLR',
eta_min=0.0,
power=1.0,
begin=1500,
end=160000,
by_epoch=False,
)
]
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