avalanche.benchmarks.classic.OpenLORIS

avalanche.benchmarks.classic.OpenLORIS(*, factor: Literal['clutter', 'illumination', 'occlusion', 'pixel', 'mixture-iros'] = 'clutter', train_transform: Any | None = None, eval_transform: Any | None = None, dataset_root: str | Path | None = None)[source]

Creates a CL benchmark for OpenLORIS.

If the dataset is not present in the computer, this method will NOT be able automatically download and store it.

This generator can be used to obtain scenarios based on different “factors”. Valid factors include ‘clutter’, ‘illumination’, ‘occlusion’, ‘pixel’, or ‘mixture-iros’.

The benchmark instance returned by this method will have two fields, train_stream and test_stream, which can be iterated to obtain training and test Experience. Each Experience contains the dataset and the associated task label.

The task label 0 will be assigned to each experience.

The benchmark API is quite simple and is uniform across all benchmark generators. It is recommended to check the tutorial of the “benchmark” API, which contains usage examples ranging from “basic” to “advanced”.

Parameters:
  • factor – OpenLORIS main factors, indicating different environmental variations. It can be chosen between ‘clutter’, ‘illumination’, ‘occlusion’, ‘pixel’, or ‘mixture-iros’. The first three factors are included in the ICRA 2020 paper and the last factor (mixture-iros) is the benchmark setting for IROS 2019 Lifelong robotic vision competition.

  • train_transform – The transformation to apply to the training data, e.g. a random crop, a normalization or a concatenation of different transformations (see torchvision.transform documentation for a comprehensive list of possible transformations). Defaults to None.

  • eval_transform – The transformation to apply to the test data, e.g. a random crop, a normalization or a concatenation of different transformations (see torchvision.transform documentation for a comprehensive list of possible transformations). Defaults to None.

  • dataset_root – The root path of the dataset. Defaults to None, which means that the default location for ‘openloris’ will be used.

Returns:

a properly initialized GenericCLScenario instance.