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danfenghong / Information_Fusion_CasFormer

Li, Chenyu, Bing Zhang, Danfeng Hong, Jun Zhou, Gemine Vivone, Shutao Li, and Jocelyn Chanussot. "CasFormer: Cascaded transformers for fusion-aware computational hyperspectral imaging." Information Fusion, 2024, 102408.
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how to process the CAVE dataset #1

Open AAAeatwhattoday opened 3 months ago

AAAeatwhattoday commented 3 months ago

Hi, thanks for your excellent work. I'm using CAVE to achieve the fusion between HSI and MSI and found your team work. In your dataset file and paper, i found there are two different setting in CAVE dataset: CAVE_28_Channles and CAVE_31_Channels . Could you please tell me how to downsample for the cave dataset ?(code or the method you used) Looking forward to your reply!

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supercrj520 commented 2 weeks ago

I get the following error when trying to achieve yours effective simply “ModuleNotFoundError: No module named 'architecture'”Could you please tell me what should I do? Thanks

AAAeatwhattoday commented 2 weeks ago

I get the following error when trying to achieve yours effective simply “ModuleNotFoundError: No module named 'architecture'”Could you please tell me what should I do? Thanks

Could you please paste your all error message？

supercrj520 commented 2 weeks ago

.conda\envs\CAS\python.exe .conda\envs\Information_Fusion_CasFormer-main\Information_Fusion_CasFormer-main\test_code\test.py .conda\envs\Information_Fusion_CasFormer-main\Information_Fusion_CasFormer-main\test_code\test.py:59: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. model = torch.load(pretrained_model_path) Traceback (most recent call last): File ".conda\envs\Information_Fusion_CasFormer-main\Information_Fusion_CasFormer-main\test_code\test.py", line 67, in main() File ".conda\envs\Information_Fusion_CasFormer-main\Information_Fusion_CasFormer-main\test_code\test.py", line 59, in main model = torch.load(pretrained_model_path) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File ".conda\envs\CAS\Lib\site-packages\torch\serialization.py", line 1097, in load return _load( ^^^^^^ File ".conda\envs\CAS\Lib\site-packages\torch\serialization.py", line 1525, in _load result = unpickler.load() ^^^^^^^^^^^^^^^^ File ".conda\envs\CAS\Lib\site-packages\torch\serialization.py", line 1515, in find_class return super().find_class(mod_name, name) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ModuleNotFoundError: No module named 'architecture'

Process finished with exit code 1

supercrj520 commented 2 weeks ago

I get the following error when trying to achieve yours effective simply “ModuleNotFoundError: No module named 'architecture'”Could you please tell me what should I do? Thanks

Could you please paste your all error message？

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