Questions on Adapting SAM+ Model to New Architecture and Handling Channel Mismatch

[Climb-one]

Dear Author

I have successfully reproduced the results from your paper“SAM-Assisted Remote Sensing Imagery Semantic
Segmentation with Object and Boundary Constraints” and I am truly impressed with the outcome. Thank you for your team's contribution to advancing research in this field!

I am now trying to apply the "SAM+ model" you proposed to adapt to a new model, and I would like to ask what specific modifications I need to make for this adaptation. Could you kindly provide a detailed explanation?

Additionally, I encountered an issue with channel mismatch. Should I modify the model to accommodate SAM? If so, I would appreciate any guidance on how to handle this issue.

Looking forward to your advice and thank you in advance for your time.

Best regards！

Here is the error message I am encountering:
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Traceback (most recent call last):
File "/remote-home/cs_iot_zqw/SSRS/SSRS-main/SAM_RS/DCSwin_train_Urban_DCSwin.py", line 228, in
train(net, optimizer, 50, scheduler)
File "/remote-home/cs_iot_zqw/SSRS/SSRS-main/SAM_RS/DCSwin_train_Urban_DCSwin.py", line 180, in train
output = net(data)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 184, in forward
return self.module(*inputs[0], **module_kwargs[0])
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/remote-home/cs_iot_zqw/SSRS/SSRS-main/SAM_RS/model/DCSwin.py", line 913, in forward
x = self.decoder(x1, x2, x3, x4)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/remote-home/cs_iot_zqw/SSRS/SSRS-main/SAM_RS/model/DCSwin.py", line 882, in forward
out1, out2 = self.dcfam(x1, x2, x3, x4)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/remote-home/cs_iot_zqw/SSRS/SSRS-main/SAM_RS/model/DCSwin.py", line 851, in forward
out4 = self.conv4(x4) + self.down34(self.pa(self.down232(x2)))
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/container.py", line 219, in forward
input = module(input)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 458, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/home/zqw/anaconda3/envs/SAM_RS/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 454, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [768, 768, 1, 1], expected input[10, 1024, 8, 8] to have 768 channels, but got 1024 channels instead