Trust_repo to either False, True or "check". If None: this will raise a warning, inviting the user to set If it is not present in that list, theīehaviour will fall back onto the trust_repo=False option. If "check", the repo will be checked against the list of If True, the repo will be added to the trusted list and loaded If False, a prompt will ask the user whether the repo should
#Github find file name code
Only run code from repos that they trust. This parameter was introduced in v1.12 and helps ensuring that users Requests to the GitHub API you can specify a non-default GitHub token by setting the Specified by the github argument properly belongs to the repo owner. Skip_validation ( bool, optional) – if False, torchhub will check that the branch or commit If ref is not specified, the default branch is assumed to be main ifįorce_reload ( bool, optional) – whether to discard the existing cache and force a fresh download. Github ( str) – a string with format “repo_owner/repo_name” with an optional List all callable entrypoints available in the repo specified by github. list ( github, force_reload = False, skip_validation = False, trust_repo = None ) ¶ Through (), show docstring and examples through Pytorch Hub provides convenient APIs to explore all available models in hub The published models should be at least in a branch/tag. load_state_dict_from_url ( checkpoint, progress = False )) Important Notice ¶ load_state_dict ( state_dict ) # For checkpoint saved elsewhere checkpoint = '' model. If pretrained : # For checkpoint saved in local GitHub repo, e.g. In the example above 18 handles pretrained, alternatively you can put the following logic in the entrypoint definition. If less than 2GB, it’s recommended to attach it to a project release Pretrained weights can either be stored locally in the GitHub repo, or loadable by tokenizers.Ĭallables prefixed with underscore are considered as helper functions which won’t show up in (). It’s highly recommended to add a few examples here.Įntrypoint function can either return a model(nn.module), or auxiliary tools to make the user workflow smoother, e.g. It explains what does the model do and whatĪre the allowed positional/keyword arguments. Note this mightīe slightly different from dependencies required for training a model.Īrgs and kwargs are passed along to the real callable function.ĭocstring of the function works as a help message. Extending torch.func with autograd.Functionĭependencies = from import resnet18 as _resnet18 # resnet18 is the name of entrypoint def resnet18 ( pretrained = False, ** kwargs ): """ # This docstring shows up in hub.help() Resnet18 model pretrained (bool): kwargs, load pretrained weights into the model """ # Call the model, load pretrained weights model = _resnet18 ( pretrained = pretrained, ** kwargs ) return modelĭependencies variable is a list of package names required to load the model.CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.
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