How to load a torchvision model from disk? - pytorch

Question

I'm trying to follow the solution from the top answer here to load an object detection model from the .pth file.
os.environ['TORCH_HOME'] = '../input/torchvision-fasterrcnn-resnet-50/' #setting the environment variable
model = detection.fasterrcnn_resnet50_fpn(pretrained=False).to(DEVICE)
I get the following error
NotADirectoryError: [Errno 20] Not a directory: '../input/torchvision-fasterrcnn-resnet-50/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth/hub'
google did not reveal an answer to the error and I don't exactly know what it means except for the obvious (that folder 'hub' is missing).
Do I have to unpack or create a folder?
I have tried loading the weights but I get the same error message.
this is how I load the model
model = detection.fasterrcnn_resnet50_fpn(pretrained=True)
checkpoint = torch.load('../input/torchvision-fasterrcnn-resnet-50/model.pth.tar')
model.load_state_dict(checkpoint['state_dict'])
thank you for your help!
Full Error Trace:
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
URLError Traceback (most recent call last)
/tmp/ipykernel_42/1218627017.py in <module>
1 # to load
----> 2 model = detection.fasterrcnn_resnet50_fpn(pretrained=True)
3 checkpoint = torch.load('../input/torchvision-fasterrcnn-resnet-50/model.pth.tar')
4 model.load_state_dict(checkpoint['state_dict'])
/opt/conda/lib/python3.7/site-packages/torchvision/models/detection/faster_rcnn.py in fasterrcnn_resnet50_fpn(pretrained, progress, num_classes, pretrained_backbone, trainable_backbone_layers, **kwargs)
360 if pretrained:
361 state_dict = load_state_dict_from_url(model_urls['fasterrcnn_resnet50_fpn_coco'],
--> 362 progress=progress)
363 model.load_state_dict(state_dict)
364 return model
/opt/conda/lib/python3.7/site-packages/torch/hub.py in load_state_dict_from_url(url, model_dir, map_location, progress, check_hash, file_name)
553 r = HASH_REGEX.search(filename) # r is Optional[Match[str]]
554 hash_prefix = r.group(1) if r else None
--> 555 download_url_to_file(url, cached_file, hash_prefix, progress=progress)
556
557 if _is_legacy_zip_format(cached_file):
/opt/conda/lib/python3.7/site-packages/torch/hub.py in download_url_to_file(url, dst, hash_prefix, progress)
423 # certificates in older Python
424 req = Request(url, headers={"User-Agent": "torch.hub"})
--> 425 u = urlopen(req)
426 meta = u.info()
427 if hasattr(meta, 'getheaders'):
/opt/conda/lib/python3.7/urllib/request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
220 else:
221 opener = _opener
--> 222 return opener.open(url, data, timeout)
223
224 def install_opener(opener):
/opt/conda/lib/python3.7/urllib/request.py in open(self, fullurl, data, timeout)
523 req = meth(req)
524
--> 525 response = self._open(req, data)
526
527 # post-process response
/opt/conda/lib/python3.7/urllib/request.py in _open(self, req, data)
541 protocol = req.type
542 result = self._call_chain(self.handle_open, protocol, protocol +
--> 543 '_open', req)
544 if result:
545 return result
/opt/conda/lib/python3.7/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args)
501 for handler in handlers:
502 func = getattr(handler, meth_name)
--> 503 result = func(*args)
504 if result is not None:
505 return result
/opt/conda/lib/python3.7/urllib/request.py in https_open(self, req)
1391 def https_open(self, req):
1392 return self.do_open(http.client.HTTPSConnection, req,
-> 1393 context=self._context, check_hostname=self._check_hostname)
1394
1395 https_request = AbstractHTTPHandler.do_request_
/opt/conda/lib/python3.7/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
1350 encode_chunked=req.has_header('Transfer-encoding'))
1351 except OSError as err: # timeout error
-> 1352 raise URLError(err)
1353 r = h.getresponse()
1354 except:
URLError: <urlopen error [Errno -3] Temporary failure in name resolution>

Answer 1

If you are loading a pretrained network, you don't need to load the model from torchvision pretrained (as in pretrained by torchvision on ImageNet using pretrained=True). You have two options:
Either set pretrained=False and load you weights using:
checkpoint = torch.load('../input/torchvision-fasterrcnn-resnet-50/model.pth.tar')
model.load_state_dict(checkpoint['state_dict'])
Or if you decide to change TORCH_HOME (which is not ideal) you need to keep the same directory structure Torchvision has which would be:
inputs/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
In practice, you wouldn't change TORCH_HOME just to load one model.

Answer 2

I found the solution digging deep into github, to the problem, which is a little hidden.
detection.()
has a default argument besides pretrained, it's called pretrained_backbone which by default is set to true, which if True sets the models to download from a dictionary path of urls.
this will work:
detection.fasterrcnn_resnet50_fpn(pretrained=False, pretrained_backbone = False, num_classes = 91).
then load the model as usual.
num_classes is expected, in the docs it's a default = 91 but in github i saw it as None, which is why I added it here for saftey.