Pytorch Hub is a pre-trained model repository designed to facilitate research reproducibility.
Pytorch Hub supports publishing pre-trained models(model definitions and pre-trained weights) to a github repository by adding a simple
hubconf.py can have multiple entrypoints. Each entrypoint is defined as a python function (example: a pre-trained model you want to publish).
def entrypoint_name(*args, **kwargs): # args & kwargs are optional, for models which take positional/keyword arguments. ...
Here is a code snippet specifies an entrypoint for
resnet18 model if we expand the implementation in
pytorch/vision/hubconf.py. In most case importing the right function in
hubconf.py is sufficient. Here we just want to use the expanded version as an example to show how it works. You can see the full script in pytorch/vision repo
dependencies = ['torch'] from torchvision.models.resnet 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 model
dependenciesvariable is a list of package names required to load the model. Note this might be slightly different from dependencies required for training a model.
kwargsare passed along to the real callable function.
torch.hub.load_state_dict_from_url(). If less than 2GB, it's recommended to attach it to a project release and use the url from the release. In the example above
pretrained, alternatively you can put the following logic in the entrypoint definition.
if pretrained: # For checkpoint saved in local github repo, e.g. <RELATIVE_PATH_TO_CHECKPOINT>=weights/save.pth dirname = os.path.dirname(__file__) checkpoint = os.path.join(dirname, <RELATIVE_PATH_TO_CHECKPOINT>) state_dict = torch.load(checkpoint) model.load_state_dict(state_dict) # For checkpoint saved elsewhere checkpoint = 'https://download.pytorch.org/models/resnet18-5c106cde.pth' model.load_state_dict(torch.hub.load_state_dict_from_url(checkpoint, progress=False))
Pytorch Hub provides convenient APIs to explore all available models in hub through
torch.hub.list(), show docstring and examples through
torch.hub.help() and load the pre-trained models using
torch.hub.load() are used to instantiate a model. After you have loaded a model, how can you find out what you can do with the model? A suggested workflow is
dir(model)to see all available methods of the model.
help(model.foo)to check what arguments
model.footakes to run
To help users explore without referring to documentation back and forth, we strongly recommend repo owners make function help messages clear and succinct. It's also helpful to include a minimal working example.
The locations are used in the order of
$TORCH_HOME/hub, if environment variable
$XDG_CACHE_HOME/torch/hub, if environment variable
By default, we don't clean up files after loading it. Hub uses the cache by default if it already exists in the directory returned by
Users can force a reload by calling
hub.load(..., force_reload=True). This will delete the existing github folder and downloaded weights, reinitialize a fresh download. This is useful when updates are published to the same branch, users can keep up with the latest release.
Torch hub works by importing the package as if it was installed. There're some side effects introduced by importing in Python. For example, you can see new items in Python caches
sys.path_importer_cache which is normal Python behavior.
A known limitation that worth mentioning here is user CANNOT load two different branches of the same repo in the same python process. It's just like installing two packages with the same name in Python, which is not good. Cache might join the party and give you surprises if you actually try that. Of course it's totally fine to load them in separate processes.