Module data_preprocess.fl_datasets.cifar100
Expand source code
import os
from PIL import Image
from ..h5_tff_dataset import H5TFFDataset
class FLCifar100(H5TFFDataset):
"""
CIFAR100 Dataset.
500 clients that were allocated data_preprocess using LDA.
"""
def __init__(self, h5_path, transform, client_id=None, train=True):
if train:
h5_path = os.path.join(h5_path, 'cifar100_fl/fed_cifar100_train.h5')
else:
h5_path = os.path.join(h5_path, 'cifar100_fl/fed_cifar100_test.h5')
super(FLCifar100, self).__init__(h5_path, client_id, 'cifar100_fl', 'image')
self.transform = transform
def __getitem__(self, index):
"""
Args:
index (int): Index of item that is fetched on behalf on current setup client
Returns:
tuple: (image, target) where target is index of the target class.
"""
client, i = self._get_item_preprocess(index)
img = Image.fromarray(self.dataset[client]['image'][i])
x = self.transform(img)
y = self.dataset[client]['label'][i]
return x, y
Classes
class FLCifar100 (h5_path, transform, client_id=None, train=True)
-
CIFAR100 Dataset. 500 clients that were allocated data_preprocess using LDA.
Ctor.
Args
h5_path
:str
- path to HDF5 file with dataset. Not native for systems like TensorFlow
client_id
:int
- switch dataset to work view of client client_id
data_key(str): if h5_path is not in the filesystem and download is True then it will an attempt to download dataset from TFF_DATASETS[data_key] URL download(bool): allow to download dataset
Expand source code
class FLCifar100(H5TFFDataset): """ CIFAR100 Dataset. 500 clients that were allocated data_preprocess using LDA. """ def __init__(self, h5_path, transform, client_id=None, train=True): if train: h5_path = os.path.join(h5_path, 'cifar100_fl/fed_cifar100_train.h5') else: h5_path = os.path.join(h5_path, 'cifar100_fl/fed_cifar100_test.h5') super(FLCifar100, self).__init__(h5_path, client_id, 'cifar100_fl', 'image') self.transform = transform def __getitem__(self, index): """ Args: index (int): Index of item that is fetched on behalf on current setup client Returns: tuple: (image, target) where target is index of the target class. """ client, i = self._get_item_preprocess(index) img = Image.fromarray(self.dataset[client]['image'][i]) x = self.transform(img) y = self.dataset[client]['label'][i] return x, y
Ancestors
- data_preprocess.h5_tff_dataset.H5TFFDataset
- data_preprocess.fl_dataset.FLDataset
- torch.utils.data.dataset.Dataset
- typing.Generic