def omniglot(folder, shots, ways, shuffle=True, test_shots=None,
seed=None, **kwargs):
"""Helper function to create a meta-dataset for the Omniglot dataset.
Parameters
----------
folder : string
Root directory where the dataset folder `omniglot` exists.
shots : int
Number of (training) examples per class in each task. This corresponds
to `k` in `k-shot` classification.
ways : int
Number of classes per task. This corresponds to `N` in `N-way`
classification.
shuffle : bool (default: `True`)
Shuffle the examples when creating the tasks.
test_shots : int, optional
Number of test examples per class in each task. If `None`, then the
number of test examples is equal to the number of training examples per
class.
seed : int, optional
Random seed to be used in the meta-dataset.
kwargs
Additional arguments passed to the `Omniglot` class.
See also
--------
`datasets.Omniglot` : Meta-dataset for the Omniglot dataset.
"""
if 'num_classes_per_task' in kwargs:
warnings.warn('Both arguments `ways` and `num_classes_per_task` were '
'set in the helper function for the number of classes per task. '
'Ignoring the argument `ways`.', stacklevel=2)
ways = kwargs['num_classes_per_task']
if 'transform' not in kwargs:
kwargs['transform'] = Compose([Resize(28), ToTensor()])
if 'target_transform' not in kwargs:
kwargs['target_transform'] = Categorical(ways)
if 'class_augmentations' not in kwargs:
kwargs['class_augmentations'] = [Rotation([90, 180, 270])]
if test_shots is None:
test_shots = shots
dataset = Omniglot(folder, num_classes_per_task=ways, **kwargs)
dataset = ClassSplitter(dataset, shuffle=shuffle,
num_train_per_class=shots, num_test_per_class=test_shots)
dataset.seed(seed)
return dataset
def create_og_data_loader(
root,
meta_split,
k_way,
n_shot,
input_size,
n_query,
batch_size,
num_workers,
download=False,
use_vinyals_split=False,
seed=None,
):
"""Create a torchmeta BatchMetaDataLoader for Omniglot
Args:
root: Path to Omniglot data root folder (containing an 'omniglot'` subfolder with the
preprocess json-Files or downloaded zip-files).
meta_split: see torchmeta.datasets.Omniglot
k_way: Number of classes per task
n_shot: Number of samples per class
input_size: Images are resized to this size.
n_query: Number of test images per class
batch_size: Meta batch size
num_workers: Number of workers for data preprocessing
download: Download (and dataset specific preprocessing that needs to be done on the
downloaded files).
use_vinyals_split: see torchmeta.datasets.Omniglot
seed: Seed to be used in the meta-dataset
Returns:
A torchmeta :class:`BatchMetaDataLoader` object.
"""
dataset = Omniglot(
root,
num_classes_per_task=k_way,
transform=Compose([Resize(input_size), ToTensor()]),
target_transform=Categorical(num_classes=k_way),
class_augmentations=[Rotation([90, 180, 270])],
meta_split=meta_split,
download=download,
use_vinyals_split=use_vinyals_split,
)
dataset = ClassSplitter(dataset,
shuffle=True,
num_train_per_class=n_shot,
num_test_per_class=n_query)
dataset.seed = seed
dataloader = BatchMetaDataLoader(dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=True)
return dataloader