models#
Module attributes and functions#
- models.get_all_models_legacy()[source]#
- Returns the list of all the available models in the models folder that follow the model naming convention (see Models). 
- models.get_model(args, backbone, loss, transform, dataset)[source]#
- Return the class of the selected continual model among those that are available. If an error was detected while loading the available datasets, it raises the appropriate error message. - Parameters:
- args (Namespace) – the arguments which contains the –model attribute 
- backbone (nn.Module) – the backbone of the model 
- loss – the loss function 
- transform – the transform function 
- dataset – the instance of the dataset 
 
- Return type:
 - Exceptions:
- AssertError: if the model is not available Exception: if an error is detected in the model 
 - Returns:
- the continual model instance 
- Return type:
 
- models.get_model_class(args)[source]#
- Return the class of the selected continual model among those that are available. If an error was detected while loading the available datasets, it raises the appropriate error message. - Parameters:
- args (Namespace) – the arguments which contains the –model attribute 
- Return type:
 - Exceptions:
- AssertError: if the model is not available Exception: if an error is detected in the model 
 - Returns:
- the continual model class 
- Return type:
 
- models.get_model_names()[source]#
- Return the available continual model names and classes. - Returns:
- A dictionary containing the names of the available continual models and their classes. 
- Return type:
 
- models.register_model(name)[source]#
- Decorator to register a ContinualModel. The decorator should be used on a class that inherits from ContinualModel. The registered model can be accessed using the get_model function and can include additional keyword arguments to be set during parsing. - Differently from the register_dataset and register_backbone functions, this decorator does not infer the arguments from the signature of the class. Instead, to define model-specific arguments, you should define the get_parser function in the model class, which should return a parser with the additional arguments.