For --model mae_vit_base_patch8_128, KeyError

[robmarkcole]

Running eurosat_finetune, from the error:

    model = models_vit_tensor.__dict__[args.model](drop_path_rate=args.drop_path,
KeyError: 'mae_vit_base_patch8_128'

Adding print(list(models_vit_tensor.__dict__.keys()) I see:

['__name__', '__doc__', '__package__', '__loader__', '__spec__', '__file__', '__cached__', '__builtins__', 'partial', 'torch', 'nn', 

'Attention', 'Block', 'PatchEmbed', 'Linear_Block', 'Linear_Attention', 'VisionTransformer', 'vit_huge_patch14', 'vit_base_patch16', 

'vit_base_patch8', 'vit_base_patch8_128', 'vit_base_patch8_channel10', 'vit_base_patch16_128', 'vit_large_patch16', 

'vit_large_patch8_128', 'vit_huge_patch8_128', 'vit_base_patch8_120']

Possibly missing from the script: import models_mae_spectral and at line 273: model = models_mae_spectral.__dict__[args.model]()
However I then get AttributeError: 'MaskedAutoencoderViT' object has no attribute 'head'

[moonboy12138]

Thank you for your kind reminder. Please use the right command --model vit_base_patch8_128 during finetuning. We will correct this as soon as possible.

[robmarkcole]

I then get

[11:43:49.174991] Load pre-trained checkpoint from: /teamspace/studios/this_studio/ieee_tpami_spectralgpt/weights/SpectralGPT+.pth
Traceback (most recent call last):
  File "/teamspace/studios/this_studio/ieee_tpami_spectralgpt/main_finetune.py", line 455, in <module>
    main(args)
  File "/teamspace/studios/this_studio/ieee_tpami_spectralgpt/main_finetune.py", line 293, in main
    if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:
KeyError: 'pos_embed_spatial'

[moonboy12138]

You can simply modify lines 291-292 by deleting 'pos_embed_spatial' in main_finetune.py to run the code. If the same error persists, try applying the same method again.

[robmarkcole]

OK after removing 'pos_embed_spatial', 'pos_embed_temporal' I can proceed

[Ahuiforever]

OK after removing 'pos_embed_spatial', 'pos_embed_temporal' I can proceed

I suggest to modify the source code in the following way in case of other potential problems.

from

if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:

to

if k in checkpoint_model and k in state_dict and checkpoint_model[k].shape != state_dict[k].shape: