datasets.QM9
- class datasets.QM9(*args: Any, **kwargs: Any)[source]
QM9 benchmark database for organic molecules.
The QM9 database contains small organic molecules with up to nine non-hydrogen atoms from including C, O, N, F. This class adds convenient functions to download QM9 from figshare and load the data into pytorch.
References
- Parameters:
datapath – path to dataset
batch_size – (train) batch size
num_train – number of training examples
num_val – number of validation examples
num_test – number of test examples
split_file – path to npz file with data partitions
format – dataset format
load_properties – subset of properties to load
remove_uncharacterized – do not include uncharacterized molecules.
val_batch_size – validation batch size. If None, use test_batch_size, then batch_size.
test_batch_size – test batch size. If None, use val_batch_size, then batch_size.
transforms – Transform applied to each system separately before batching.
train_transforms – Overrides transform_fn for training.
val_transforms – Overrides transform_fn for validation.
test_transforms – Overrides transform_fn for testing.
num_workers – Number of data loader workers.
num_val_workers – Number of validation data loader workers (overrides num_workers).
num_test_workers – Number of test data loader workers (overrides num_workers).
property_units – Dictionary from property to corresponding unit as a string (eV, kcal/mol, …).
distance_unit – Unit of the atom positions and cell as a string (Ang, Bohr, …).
data_workdir – Copy data here as part of setup, e.g. cluster scratch for faster performance.