################################################################################
# Copyright (c) 2021 ContinualAI. #
# Copyrights licensed under the MIT License. #
# See the accompanying LICENSE file for terms. #
# #
# Date: 20-05-2020 #
# Author: Vincenzo Lomonaco #
# E-mail: contact@continualai.org #
# Website: continualai.org #
################################################################################
from pathlib import Path
from typing import Union, Optional, Any
from avalanche.benchmarks.classic.classic_benchmarks_utils import (
check_vision_benchmark,
)
from avalanche.benchmarks.datasets import ImageNet
from avalanche.benchmarks import nc_benchmark
from torchvision import transforms
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
_default_train_transform = transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]
)
_default_eval_transform = transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]
)
[docs]def SplitImageNet(
dataset_root: Union[str, Path],
*,
n_experiences=10,
per_exp_classes=None,
return_task_id=False,
seed=0,
fixed_class_order=None,
shuffle: bool = True,
train_transform: Optional[Any] = _default_train_transform,
eval_transform: Optional[Any] = _default_eval_transform
):
"""
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 :class:`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".
:param dataset_root: Base path where Imagenet data is stored.
:param n_experiences: The number of experiences in the current benchmark.
:param 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.
:param 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.
:param seed: A valid int used to initialize the random number generator.
Can be None.
:param 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.
:param shuffle: If true, the class order in the incremental experiences is
randomly shuffled. Default to True.
:param 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.
:param 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.
:returns: A properly initialized :class:`NCScenario` instance.
"""
train_set, test_set = _get_imagenet_dataset(dataset_root)
if return_task_id:
return nc_benchmark(
train_dataset=train_set,
test_dataset=test_set,
n_experiences=n_experiences,
task_labels=True,
per_exp_classes=per_exp_classes,
seed=seed,
fixed_class_order=fixed_class_order,
shuffle=shuffle,
class_ids_from_zero_in_each_exp=True,
train_transform=train_transform,
eval_transform=eval_transform,
)
else:
return nc_benchmark(
train_dataset=train_set,
test_dataset=test_set,
n_experiences=n_experiences,
task_labels=False,
per_exp_classes=per_exp_classes,
seed=seed,
fixed_class_order=fixed_class_order,
shuffle=shuffle,
train_transform=train_transform,
eval_transform=eval_transform,
)
def _get_imagenet_dataset(root):
train_set = ImageNet(root, split="train")
test_set = ImageNet(root, split="val")
return train_set, test_set
__all__ = ["SplitImageNet"]
if __name__ == "__main__":
import sys
benchmark_instance = SplitImageNet(
"/ssd2/datasets/imagenet/",
train_transform=transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
]
),
)
check_vision_benchmark(benchmark_instance, show_without_transforms=False)
sys.exit(0)