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
, hope we can download model again soon…
