avalanche.benchmarks.classic.SplitImageNet

avalanche.benchmarks.classic.SplitImageNet(dataset_root: str | ~pathlib.Path, *, n_experiences=10, per_exp_classes=None, return_task_id=False, seed=0, fixed_class_order=None, shuffle: bool = True, class_ids_from_zero_in_each_exp: bool = False, class_ids_from_zero_from_first_exp: bool = False, train_transform: ~typing.Any | None = Compose(     Resize(size=256, interpolation=bilinear, max_size=None, antialias=True)     CenterCrop(size=(224, 224))     ToTensor()     Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ), eval_transform: ~typing.Any | None = Compose(     Resize(size=256, interpolation=bilinear, max_size=None, antialias=True)     CenterCrop(size=(224, 224))     ToTensor()     Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ), meta_root: ~pathlib.Path | str | None = None)[source]

Creates a CL benchmark using the ImageNet dataset.

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

The returned benchmark will return experiences containing all patterns of a subset of classes, which means that each class is only seen “once”. This is one of the most common scenarios in the Continual Learning literature. Common names used in literature to describe this kind of scenario are “Class Incremental”, “New Classes”, etc. By default, an equal amount of classes will be assigned to each experience.

This generator doesn’t force a choice on the availability of task labels, a choice that is left to the user (see the return_task_id parameter for more info on task labels).

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 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:

  • dataset_root – Base path where Imagenet data is stored.

  • n_experiences – The number of experiences in the current benchmark.

  • per_exp_classes – Is not None, a dictionary whose keys are (0-indexed) experience IDs and their values are the number of classes to include in the respective experiences. The dictionary doesn’t have to contain a key for each experience! All the remaining exps will contain an equal amount of the remaining classes. The remaining number of classes must be divisible without remainder by the remaining number of experiences. For instance, if you want to include 50 classes in the first experience while equally distributing remaining classes across remaining experiences, just pass the “{0: 50}” dictionary as the per_experience_classes parameter. Defaults to None.

  • return_task_id – if True, a progressive task id is returned for every experience. If False, all experiences will have a task ID of 0.

  • seed – A valid int used to initialize the random number generator. Can be None.

  • fixed_class_order – A list of class IDs used to define the class order. If None, value of seed will be used to define the class order. If non-None, seed parameter will be ignored. Defaults to None.

  • shuffle – If true, the class order in the incremental experiences is randomly shuffled. Default to True.

  • class_ids_from_zero_in_each_exp – If True, original class IDs will be mapped to range [0, n_classes_in_exp) for each experience. Defaults to False. Mutually exclusive with the class_ids_from_zero_from_first_exp parameter.

  • class_ids_from_zero_from_first_exp – If True, original class IDs will be remapped so that they will appear as having an ascending order. For instance, if the resulting class order after shuffling (or defined by fixed_class_order) is [23, 34, 11, 7, 6, …] and class_ids_from_zero_from_first_exp is True, then all the patterns belonging to class 23 will appear as belonging to class “0”, class “34” will be mapped to “1”, class “11” to “2” and so on. This is very useful when drawing confusion matrices and when dealing with algorithms with dynamic head expansion. Defaults to False. Mutually exclusive with the class_ids_from_zero_in_each_exp parameter.

  • 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). If no transformation is passed, the default train transformation will be used.

  • 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). If no transformation is passed, the default test transformation will be used.

  • meta_root – Directory where the ILSVRC2012_devkit_t12.tar.gz file can be found. The first time you use this dataset, the meta file will be extracted from the archive and a meta.bin file will be created in the meta_root directory. Defaults to None, which means that the meta file is expected to be in the path provied in the root argument. This is an additional argument not found in the original ImageNet class from the torchvision package. For more info, see the meta_root argument in the AvalancheImageNet class.

Returns:

A properly initialized NCScenario instance.