def short_task_incremental_setting(config: Config):
setting = TaskIncrementalSLSetting(
dataset="mnist", nb_tasks=5, monitor_training_performance=True,
)
setting.config = config
setting.prepare_data()
setting.setup()
# Testing this out: Shortening the train datasets:
setting.train_datasets = [
random_subset(task_dataset, 100) for task_dataset in setting.train_datasets
]
setting.val_datasets = [
random_subset(task_dataset, 100) for task_dataset in setting.val_datasets
]
setting.test_datasets = [
random_subset(task_dataset, 100) for task_dataset in setting.test_datasets
]
assert len(setting.train_datasets) == 5
assert len(setting.val_datasets) == 5
assert len(setting.test_datasets) == 5
assert all(len(dataset) == 100 for dataset in setting.train_datasets)
assert all(len(dataset) == 100 for dataset in setting.val_datasets)
assert all(len(dataset) == 100 for dataset in setting.test_datasets)
# Assert that calling setup doesn't overwrite the datasets.
setting.setup()
assert len(setting.train_datasets) == 5
assert len(setting.val_datasets) == 5
assert len(setting.test_datasets) == 5
assert all(len(dataset) == 100 for dataset in setting.train_datasets)
assert all(len(dataset) == 100 for dataset in setting.val_datasets)
assert all(len(dataset) == 100 for dataset in setting.test_datasets)
return setting
def short_sl_track_setting(config: Config):
setting = SettingProxy(
ClassIncrementalSetting,
"sl_track",
# dataset="synbols",
# nb_tasks=12,
# class_order=class_order,
# monitor_training_performance=True,
)
setting.config = config
# TODO: This could be a bit more convenient.
setting.data_dir = config.data_dir
assert setting.config == config
assert setting.data_dir == config.data_dir
assert setting.nb_tasks == 12
# For now we'll just shorten the tests by shortening the datasets.
samples_per_task = 100
setting.batch_size = 10
setting.setup()
# Testing this out: Shortening the train datasets:
setting.train_datasets = [
random_subset(task_dataset, samples_per_task)
for task_dataset in setting.train_datasets
]
setting.val_datasets = [
random_subset(task_dataset, samples_per_task)
for task_dataset in setting.val_datasets
]
setting.test_datasets = [
random_subset(task_dataset, samples_per_task)
for task_dataset in setting.test_datasets
]
assert len(setting.train_datasets) == setting.nb_tasks
assert len(setting.val_datasets) == setting.nb_tasks
assert len(setting.test_datasets) == setting.nb_tasks
assert all(len(dataset) == samples_per_task for dataset in setting.train_datasets)
assert all(len(dataset) == samples_per_task for dataset in setting.val_datasets)
assert all(len(dataset) == samples_per_task for dataset in setting.test_datasets)
# Assert that calling setup doesn't overwrite the datasets.
setting.setup()
assert len(setting.train_datasets) == setting.nb_tasks
assert len(setting.val_datasets) == setting.nb_tasks
assert len(setting.test_datasets) == setting.nb_tasks
assert all(len(dataset) == samples_per_task for dataset in setting.train_datasets)
assert all(len(dataset) == samples_per_task for dataset in setting.val_datasets)
assert all(len(dataset) == samples_per_task for dataset in setting.test_datasets)
return setting