how to use SimMIMSwinTransformer model as backbone to do the downsteam task

[YuchenGUOGYC]

I use the selfup model to train a swin-large model, then I try to use the it as a backbone in mmdetection, then I write config code like this:
model = dict(
backbone=dict(
delete=True, # Delete the backbone field in base
type='mmpretrain.SimMIMSwinTransformer', # Using SimMIMSwinTransformer from mmpretrain
arch='large',
img_size=256,
stage_cfgs=dict(block_cfgs=dict(window_size=12)),
pad_small_map=True,
init_cfg=dict(
type='Pretrained',
checkpoint=pretrained,
prefix='backbone.'),
#frozen_stages=4 #
), # The pre-trained weights of backbone network in mmpretrain have prefix='backbone.'. The prefix in the keys will be removed so that these weights can be normally loaded.
# Modify in_channels
neck=dict(
type='PAFPN',
in_channels=[128, 256, 512, 1024],
out_channels=256,
num_outs=5),)
then it report issue like below:

Traceback (most recent call last):
File "/home/ec2-user/oetz/github_x/github/roof-counting-mmd/build_ins/train.py", line 128, in
main()
File "/home/ec2-user/oetz/github_x/github/roof-counting-mmd/build_ins/train.py", line 124, in main
runner.train()
File "/opt/conda/envs/pytorch/lib/python3.9/site-packages/mmengine/runner/runner.py", line 1777, in train
model = self.train_loop.run() # type: ignore
File "/opt/conda/envs/pytorch/lib/python3.9/site-packages/mmengine/runner/loops.py", line 96, in run
self.run_epoch()
File "/opt/conda/envs/pytorch/lib/python3.9/site-packages/mmengine/runner/loops.py", line 113, in run_epoch
self.run_iter(idx, data_batch)
File "/opt/conda/envs/pytorch/lib/python3.9/site-packages/mmengine/runner/loops.py", line 129, in run_iter
outputs = self.runner.model.train_step(
File "/opt/conda/envs/pytorch/lib/python3.9/site-packages/mmengine/model/base_model/base_model.py", line 114, in train_step
losses = self._run_forward(data, mode='loss') # type: ignore
File "/opt/conda/envs/pytorch/lib/python3.9/site-packages/mmengine/model/base_model/base_model.py", line 361, in _run_forward
results = self(**data, mode=mode)
File "/opt/conda/envs/pytorch/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ec2-user/oetz/github_x/github/roof-counting-mmd/build_ins/mmdet/models/detectors/base.py", line 92, in forward
return self.loss(inputs, data_samples)
File "/home/ec2-user/oetz/github_x/github/roof-counting-mmd/build_ins/mmdet/models/detectors/two_stage.py", line 160, in loss
x = self.extract_feat(batch_inputs)
File "/home/ec2-user/oetz/github_x/github/roof-counting-mmd/build_ins/mmdet/models/detectors/two_stage.py", line 110, in extract_feat
x = self.backbone(batch_inputs)
File "/opt/conda/envs/pytorch/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'mask'

how I can solve it ?