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import os
import json
import glob
import h5py
from PIL import Image, ImageOps
from torchmeta.utils.data import Dataset, ClassDataset, CombinationMetaDataset
from torchvision.datasets.utils import list_dir, download_url
from torchmeta.datasets.utils import get_asset
class Omniglot(CombinationMetaDataset):
"""
The Omniglot dataset [1]. A dataset of 1623 handwritten characters from
50 different alphabets.
Parameters
----------
root : string
Root directory where the dataset folder `omniglot` exists.
num_classes_per_task : int
Number of classes per tasks. This corresponds to "N" in "N-way"
classification.
meta_train : bool (default: `False`)
Use the meta-train split of the dataset. If set to `True`, then the
arguments `meta_val` and `meta_test` must be set to `False`. Exactly one
import os
import json
from torchmeta.datasets.cifar100.base import CIFAR100ClassDataset
from torchmeta.datasets.utils import get_asset
from torchmeta.utils.data import ClassDataset, CombinationMetaDataset
class CIFARFS(CombinationMetaDataset):
"""
The CIFAR-FS dataset, introduced in [1]. This dataset contains
images of 100 different classes from the CIFAR100 dataset [2].
Parameters
----------
root : string
Root directory where the dataset folder `cifar100` exists.
num_classes_per_task : int
Number of classes per tasks. This corresponds to `N` in `N-way`
classification.
meta_train : bool (default: `False`)
Use the meta-train split of the dataset. If set to `True`, then the
arguments `meta_val` and `meta_test` must be set to `False`. Exactly one
import numpy as np
from PIL import Image
import os
import io
import json
import glob
import h5py
from torchmeta.utils.data import Dataset, ClassDataset, CombinationMetaDataset
from torchvision.datasets.utils import download_url
from torchmeta.datasets.utils import get_asset
class CUB(CombinationMetaDataset):
"""
The Caltech-UCSD Birds dataset, introduced in [1]. This dataset is based on
images from 200 species of birds from the Caltech-UCSD Birds dataset [2].
Parameters
----------
root : string
Root directory where the dataset folder `cub` exists.
num_classes_per_task : int
Number of classes per tasks. This corresponds to "N" in "N-way"
classification.
meta_train : bool (default: `False`)
Use the meta-train split of the dataset. If set to `True`, then the
arguments `meta_val` and `meta_test` must be set to `False`. Exactly one
import os
import pickle
from PIL import Image
import h5py
import json
from torchmeta.utils.data import Dataset, ClassDataset, CombinationMetaDataset
from torchvision.datasets.utils import download_file_from_google_drive
class MiniImagenet(CombinationMetaDataset):
"""
The Mini-Imagenet dataset, introduced in [1]. This dataset contains images
of 100 different classes from the ILSVRC-12 dataset (Imagenet challenge).
The meta train/validation/test splits are taken from [2] for reproducibility.
Parameters
----------
root : string
Root directory where the dataset folder `miniimagenet` exists.
num_classes_per_task : int
Number of classes per tasks. This corresponds to "N" in "N-way"
classification.
meta_train : bool (default: `False`)
Use the meta-train split of the dataset. If set to `True`, then the
import numpy as np
from PIL import Image
import h5py
import json
import os
import io
import pickle
from torchmeta.utils.data import Dataset, ClassDataset, CombinationMetaDataset
from torchvision.datasets.utils import download_file_from_google_drive
class TieredImagenet(CombinationMetaDataset):
"""
The Tiered-Imagenet dataset, introduced in [1]. This dataset contains images
of 608 different classes from the ILSVRC-12 dataset (Imagenet challenge).
Parameters
----------
root : string
Root directory where the dataset folder `tieredimagenet` exists.
num_classes_per_task : int
Number of classes per tasks. This corresponds to "N" in "N-way"
classification.
meta_train : bool (default: `False`)
Use the meta-train split of the dataset. If set to `True`, then the
arguments `meta_val` and `meta_test` must be set to `False`. Exactly one
import os
import json
from torchmeta.datasets.cifar100.base import CIFAR100ClassDataset
from torchmeta.datasets.utils import get_asset
from torchmeta.utils.data import ClassDataset, CombinationMetaDataset
class FC100(CombinationMetaDataset):
"""
The Fewshot-CIFAR100 dataset, introduced in [1]. This dataset contains
images of 100 different classes from the CIFAR100 dataset [2].
Parameters
----------
root : string
Root directory where the dataset folder `cifar100` exists.
num_classes_per_task : int
Number of classes per tasks. This corresponds to `N` in `N-way`
classification.
meta_train : bool (default: `False`)
Use the meta-train split of the dataset. If set to `True`, then the
arguments `meta_val` and `meta_test` must be set to `False`. Exactly one