Hello together,
i have a ReLU-NN class as follows
  20 class ReLU_NN(tr.nn.Module):                                                     
  21     '''                                                                          
  22     Class for a ReLU-NN with variable size.                                      
  23     Input:                                                                       
  24         -nn_list = [input dim., first hidden layer size,...,                     
  25                     last hidden layer size, output dim.]                         
  26                                                                                  
  27     '''                                                                          
  28     def __init__(self, nn_list):                                                 
  29         super(ReLU_NN, self).__init__()                                          
  30         self.nn_list = nn_list                                                   
  31         self.hidden  = tr.nn.ModuleList()                                        
  32         for i in range(len(nn_list)-1):                                          
  33             self.hidden.append(tr.nn.Linear(nn_list[i], nn_list[i+1]).double())

and now i am trying to add some save methods:
 332     def save_state_dict(self, PATH):                                             
 333         '''                                                                      
 334         A common PyTorch convention is to save models using either a .pt or      
 335         .pth file extension.                                                     
 336         '''                                                                      
 337         tr.save(self.state_dict(), PATH)                                         
 338                                                                                  
 339                                                                                  
 340     @classmethod                                                                 
 341     def load_state_dict(cls, PATH):                                              
 342         '''                                                                      
 343                                                                                  
 344         '''                                                                      
 345         state_dict = tr.load(PATH)                                               
 346         # Attention: here is assumed that all layers have bias                   
 347         nn_list = [None]*(len(state_dict)//2+1)                                  
 348         nn_list[0] = state_dict['hidden.0.weight'].shape[1]                      
 349         for i in range(len(state_dict)//2):                                      
 350             nn_list[i+1] = state_dict[f'hidden.{i}.weight'].shape[0]             
 351                                                                                  
 352         model = cls(nn_list)                                                     
 353         print(model.nn_list)                                                     
 354         model.load_state_dict(tr.load(PATH))                                     
 355                                                                                  
 356         return model

I call the methods as follows:
 76 nn_list = [P, neurons1, 1]                                                       
 77 model   = ReLU_NN(nn_list)                                                       
 78 model.initialize_uniform(u=(-0.1,0.1), c=0.1)                                    
 79                                                                                  
 80 PATH = '/home/lewin/01_studium/6_Masterarbeit/01_Programme/03_test_algorithms/testmodel.pt'
 81 print(isinstance(PATH,str))                                                      
 82 model.save_state_dict(PATH)                                                      
 83 print(model.nn_list)                                                             
 84 model2 = ReLU_NN.load_state_dict(PATH)                                           
 85 print(model2.state_dict())

This gives the output:
True
[4, 32, 1]
[4, 32, 1]
Traceback (most recent call last):
  File "/home/lewin/.local/lib/python3.8/site-packages/torch/serialization.py", line 311, in _check_seekable
    f.seek(f.tell())
AttributeError: 'collections.OrderedDict' object has no attribute 'seek'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "04_greedy_nn.py", line 84, in <module>
    model2 = ReLU_NN.load_state_dict(PATH)
  File "/home/lewin/01_studium/6_Masterarbeit/01_Programme/aux_files/aux_PyTorch.py", line 354, in load_state_dict
    model.load_state_dict(tr.load(PATH))
  File "/home/lewin/01_studium/6_Masterarbeit/01_Programme/aux_files/aux_PyTorch.py", line 345, in load_state_dict
    state_dict = tr.load(PATH) 
  File "/home/lewin/.local/lib/python3.8/site-packages/torch/serialization.py", line 584, in load
    with _open_file_like(f, 'rb') as opened_file:
  File "/home/lewin/.local/lib/python3.8/site-packages/torch/serialization.py", line 239, in _open_file_like
    return _open_buffer_reader(name_or_buffer)
  File "/home/lewin/.local/lib/python3.8/site-packages/torch/serialization.py", line 224, in __init__
    _check_seekable(buffer)
  File "/home/lewin/.local/lib/python3.8/site-packages/torch/serialization.py", line 314, in _check_seekable
    raise_err_msg(["seek", "tell"], e)
  File "/home/lewin/.local/lib/python3.8/site-packages/torch/serialization.py", line 307, in raise_err_msg
    raise type(e)(msg)
AttributeError: 'collections.OrderedDict' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead.

The first True comes from line 81 and the nn_lists do coincide. I think the solution to this problem looks something like Trying to load a torch model via Dropbox 3, but i don’t get it.