Error in loading state_dict of the pre-trained the model

[ramy-bat]

Dear all,
By loading the state_dict, the attached error is faced:

It seems that the pre-trained model uses another normalization than the original one.

Thanks in advance for your quick help

[LavenderLA]

Thanks for your interest in our work. We've update the pre-trained model link in README.md.
BTW, if you'd like to reproduce the results in our paper, please add --refine to use refine blocks. Otherwise you'll encounter error like "Unexpected key(s) in state_dict: 'refine_block...'".

[ramy-bat]

Thanks for your quick response.
I have added the refine (--refine) but the problem seems that the keys of the model are renamed differently as the following:

it works with --refine after replacing the names self.gn1, self.gn2 and self.gn3 with self.bn1, self.bn2 and self.bn3 in model\flot\gconv.py respectively.
I am not quite sure which update you mean, but I have downloaded your pre-trained model again and it shows me new error seems to be generated from different torch version: :

However, the old model works correctly now after the aforementioned modifications.

[weiyithu]

Thanks for your interest. We have tested the new model and it works fine. Which torch version do you use ? I wonder it may be caused by different torch version. Also, you should check that whether you can reproduce our result with your aforementioned modifications under this torch version.

[gengshi]

@ramy-bat the OrdictDict on typing problem is because of the python version. Changing the python version to python3.8 works for me. hope to work for you

[gengshi]

@ramy-bat if you dont want to run on python3.8, try install python3.8 on your local PC (mac for me) and install pytorch-cpu, then convert the state_dict to collections.OrderedDict, since this API is available for both <=python3.6 and >=python3.7, then save and load the ckpt in python3.6 or lower, an example:

run in python3.8:

  import torch
  from collections import OrderedDict
  ckpt = torch.load('best_checkpoint.params', 'cpu')
  ckpt['state_dict'] =  OrderedDict(ckpt['state_dict'])
  torch.save(ckpt, 'best_checkpoint.params.py36')

then on your python3.6 or lower version, just load the converted best_checkpoint.params.py36 for the PV-RAFT refine

The above solution works for me