Not sure about eval.py

[l1x]

I am trying to use the code in this repo and the first part work:

 python train.py --path ../imgs/ --name fast --iter 500 --batch_size 2 --im_size 1024
Setting up Perceptual loss...
Loading model from: /data/FastGAN-pytorch/lpips/weights/v0.1/vgg.pth
...[net-lin [vgg]] initialized
...Done
Namespace(batch_size=2, ckpt='None', cuda=0, im_size=1024, iter=500, name='fast', path='../imgs/', start_iter=0)
  GAN: loss d: 2.85532    loss g: -1.84026
100%| .... | 501/501 [03:16<00:00,  2.55it/s]

This generates the following:

 ls train_results/fast/
args.txt  diffaug.py  eval.py  images  models  models.py  operation.py  __pycache__  train.py

I am having trouble when trying to use eval.py

$ python eval.py --n_sample 10
Traceback (most recent call last):
  File "eval.py", line 66, in <module>
    checkpoint = torch.load(ckpt, map_location=lambda a,b: a)
  File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/serialization.py", line 525, in load
    with _open_file_like(f, 'rb') as opened_file:
  File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/serialization.py", line 212, in _open_file_like
    return _open_file(name_or_buffer, mode)
  File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/serialization.py", line 193, in __init__
    super(_open_file, self).__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: './models/60000.pth'

Is there a particular reason it is looking for 60000.pth?

[odegeasslbc]

Sorry, I did not refine the eval.py, it was not ready to be used by others. I would suggest you look into the code in eval.py and modify it for your own purpose of use. Basically, you just need to specify the path of your trained model and use that path instead.

[l1x]

Sorry, I did not refine the eval.py, it was not ready to be used by others. I would suggest you look into the code in eval.py and modify it for your own purpose of use. Basically, you just need to specify the path of your trained model and use that path instead.

Thank you very much!