Hi All
I am very new to deep learning and pytorch. i am trying to lead the resnet34 model and it fails with 403 forbidden error. I am using Google Colab as the environment for notebooks.
resnet = torchvision.models.resnet34(pretrained=True)
Downloading: "https://download.pytorch.org/models/resnet34-333f7ec4.pth" to /root/.cache/torch/checkpoints/resnet34-333f7ec4.pth
---------------------------------------------------------------------------
HTTPError                                 Traceback (most recent call last)
<ipython-input-16-9b84e690c25c> in <module>()
      1 get_ipython().system('rm -rf /root/.cache/torch')
----> 2 resnet = torchvision.models.resnet34(pretrained=True)

9 frames
/usr/local/lib/python3.6/dist-packages/torchvision/models/resnet.py in resnet34(pretrained, progress, **kwargs)
    247     """
    248     return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
--> 249                    **kwargs)
    250 
    251 

/usr/local/lib/python3.6/dist-packages/torchvision/models/resnet.py in _resnet(arch, block, layers, pretrained, progress, **kwargs)
    221     if pretrained:
    222         state_dict = load_state_dict_from_url(model_urls[arch],
--> 223                                               progress=progress)
    224         model.load_state_dict(state_dict)
    225     return model

/usr/local/lib/python3.6/dist-packages/torch/hub.py in load_state_dict_from_url(url, model_dir, map_location, progress, check_hash)
    490         sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
    491         hash_prefix = HASH_REGEX.search(filename).group(1) if check_hash else None
--> 492         download_url_to_file(url, cached_file, hash_prefix, progress=progress)
    493 
    494     # Note: extractall() defaults to overwrite file if exists. No need to clean up beforehand.

/usr/local/lib/python3.6/dist-packages/torch/hub.py in download_url_to_file(url, dst, hash_prefix, progress)
    389     # We use a different API for python2 since urllib(2) doesn't recognize the CA
    390     # certificates in older Python
--> 391     u = urlopen(url)
    392     meta = u.info()
    393     if hasattr(meta, 'getheaders'):

/usr/lib/python3.6/urllib/request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
    221     else:
    222         opener = _opener
--> 223     return opener.open(url, data, timeout)
    224 
    225 def install_opener(opener):

/usr/lib/python3.6/urllib/request.py in open(self, fullurl, data, timeout)
    530         for processor in self.process_response.get(protocol, []):
    531             meth = getattr(processor, meth_name)
--> 532             response = meth(req, response)
    533 
    534         return response

/usr/lib/python3.6/urllib/request.py in http_response(self, request, response)
    640         if not (200 <= code < 300):
    641             response = self.parent.error(
--> 642                 'http', request, response, code, msg, hdrs)
    643 
    644         return response

/usr/lib/python3.6/urllib/request.py in error(self, proto, *args)
    568         if http_err:
    569             args = (dict, 'default', 'http_error_default') + orig_args
--> 570             return self._call_chain(*args)
    571 
    572 # XXX probably also want an abstract factory that knows when it makes

/usr/lib/python3.6/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args)
    502         for handler in handlers:
    503             func = getattr(handler, meth_name)
--> 504             result = func(*args)
    505             if result is not None:
    506                 return result

/usr/lib/python3.6/urllib/request.py in http_error_default(self, req, fp, code, msg, hdrs)
    648 class HTTPDefaultErrorHandler(BaseHandler):
    649     def http_error_default(self, req, fp, code, msg, hdrs):
--> 650         raise HTTPError(req.full_url, code, msg, hdrs, fp)
    651 
    652 class HTTPRedirectHandler(BaseHandler):

HTTPError: HTTP Error 403: Forbidden