Issue with conda settings

[Brahmagupta]

Created conda environment with python=3.7,
I tried all available versions of pytorch=1.3.1 (paired with fixed torchvision=0.4.2 version) with different cudatoolkit versions (9.2, 10.0, 10.1 )
it seems like there's some problem between torchvision0.4.2 and PIL, causing a failure in importing torchvision properly.

Then I tried

conda install pytorch=1.4.1 torchvision cudatoolkit=10.1 -c pytorch

it worked and but the results I got are quite different.

I wonder what pytorch versions and cudatoolkit versions are recommended.

[Brahmagupta]

Problem solved by downgrading PIL to a lower version (<7)
#Downgrading PIL
but there's a new issue with loading the pretrained weights..

I'm getting

Traceback (most recent call last):
  File "~/anaconda3/envs/chm/lib/python3.7/tarfile.py", line 187, in nti
    n = int(s.strip() or "0", 8)
ValueError: invalid literal for int() with base 8: '_tensor_'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "~/anaconda3/envs/chm/lib/python3.7/tarfile.py", line 2289, in next
    tarinfo = self.tarinfo.fromtarfile(self)
  File "~/anaconda3/envs/chm/lib/python3.7/tarfile.py", line 1095, in fromtarfile
    obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)
  File "~/anaconda3/envs/chm/lib/python3.7/tarfile.py", line 1037, in frombuf
    chksum = nti(buf[148:156])
  File "~/anaconda3/envs/chm/lib/python3.7/tarfile.py", line 189, in nti
    raise InvalidHeaderError("invalid header")
tarfile.InvalidHeaderError: invalid header

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "~/anaconda3/envs/chm/lib/python3.7/site-packages/torch/serialization.py", line 595, in _load
    return legacy_load(f)
  File "~/anaconda3/envs/chm/lib/python3.7/site-packages/torch/serialization.py", line 506, in legacy_load
    with closing(tarfile.open(fileobj=f, mode='r:', format=tarfile.PAX_FORMAT)) as tar, \
  File "~/anaconda3/envs/chm/lib/python3.7/tarfile.py", line 1593, in open
    return func(name, filemode, fileobj, **kwargs)
  File "~/anaconda3/envs/chm/lib/python3.7/tarfile.py", line 1623, in taropen
    return cls(name, mode, fileobj, **kwargs)
  File "~/anaconda3/envs/chm/lib/python3.7/tarfile.py", line 1486, in __init__
    self.firstmember = self.next()
  File "~/anaconda3/envs/chm/lib/python3.7/tarfile.py", line 2301, in next
    raise ReadError(str(e))
tarfile.ReadError: invalid header

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "../test.py", line 53, in <module>
    model.load_state_dict(torch.load(args.load))
  File "~/anaconda3/envs/chm/lib/python3.7/site-packages/torch/serialization.py", line 426, in load
    return _load(f, map_location, pickle_module, **pickle_load_args)
  File "~/anaconda3/envs/chm/lib/python3.7/site-packages/torch/serialization.py", line 599, in _load
    raise RuntimeError("{} is a zip archive (did you mean to use torch.jit.load()?)".format(f.name))
RuntimeError: ../../Pretrained/pas_psi.pt is a zip archive (did you mean to use torch.jit.load()?)

[Brahmagupta]

Actually, found the solution here Manually Convert the Pretrained Weights to Make it Compatible with Pytorch of Lower Version(<=1.6)

Load the .pt under higher version pytorch and resave it.

[Brahmagupta]

However I would recommend including

conda install -c anaconda "pillow<7"

in the conda setting instruction in README

[Brahmagupta]

I think the results I'm getting now are still quite different from what are presented in the paper...

I wonder if you could provide a full output of

conda list -n chm

?

[Brahmagupta]

nvm It seems like I downloaded the wrong weights .....