pip install icgen
for a development installation see CONTRIBUTING.md
import icgen
dataset_generator = icgen.ICDatasetGenerator(
data_path="datasets",
min_resolution=16,
max_resolution=512,
max_log_res_deviation=1, # Sample only 1 log resolution from the native one
min_classes=2,
max_classes=100,
min_examples_per_class=20,
max_examples_per_class=100_000,
)
dev_data, test_data, dataset_info = dataset_generator.get_dataset(
dataset="cifar10", augment=True, download=True
)
The augment
parameter controls whether the original dataset is modified.
Options only affect sampling with augment=True
and the min max ranges do not filter datasets.
The data is left at the original resolution, so it can be resized under user control. This is necessary to for example avoid resizing twice which can hurt performance.
You can also sample from a list of datasets
dataset_generator.get_dataset(datasets=["cifar100", "emnist/balanced"], download=True)
We provide some lists of available datasets
import icgen
icgen.DATASETS_TRAIN
icgen.DATASETS_VAL
icgen.DATASETS_TEST
icgen.DATASETS
or on the commandline you can get the names with
python -m icgen.dataset_names
To download datasets ahead of time you can run
python -m icgen.download --data_path DATA_PATH --datasets D1 D2 D3
or directly download a complete group
python -m icgen.download --data_path DATA_PATH --dataset_group GROUP # all, train, dev, test
Alternatively, you can also use the download=True
flag of the dataset_generator.get_dataset
function.
In distributed applications it may be necessary to sample datasets on one machine and then use them on another one. Conversely, for reproducibility it may be necessary to store the exact dataset which was used. For these cases icgen uses a dataset identifier which uniquely identifies datasets.