def __init__(self, path):
# type: (str) -> None
"""
Class constructor.
:param path: The folder in which to download MNIST.
"""
super(MNIST_TRAIN, self).__init__()
self.path = path
self.normal_class = None
# Get train and test split
self.train_split = datasets.MNIST(self.path, train=True, download=True, transform=None)
self.test_split = datasets.MNIST(self.path, train=False, download=True, transform=None)
# Shuffle training indexes to build a validation set (see val())
train_idx = np.arange(len(self.train_split))
np.random.shuffle(train_idx)
self.shuffled_train_idx = train_idx
# Transform zone
self.train_transform = transforms.Compose([ToFloatTensor2D()])
self.val_transform = transforms.Compose([ToFloatTensor2D()])
self.test_transform = transforms.Compose([ToFloat32(), OCToFloatTensor2D()])
self.transform = None
# Other utilities
self.mode = None
self.length = None
self.val_idxs = None
def __init__(self,
path,
n_class=10,
select=None,
select_novel_classes=None):
# type: (str) -> None
"""
Class constructor.
:param path: The folder in which to download MNIST.
"""
super(FMNIST, self).__init__()
self.path = path
self.n_class = n_class
self.normal_class = None
self.select = select
if select_novel_classes != None:
self.select_novel_classes = [
int(novel_class) for novel_class in select_novel_classes
]
else:
self.select_novel_classes = None
self.name = 'fmnist'
# Get train and test split
self.train_split = datasets.FashionMNIST(self.path,
train=True,
download=True,
transform=None)
self.test_split = datasets.FashionMNIST(self.path,
train=False,
download=True,
transform=None)
# Shuffle training indexes to build a validation set (see val())
train_idx = np.arange(len(self.train_split))
np.random.shuffle(train_idx)
self.shuffled_train_idx = train_idx
# Transform zone
self.val_transform = transforms.Compose([ToFloatTensor2D()])
self.train_transform = transforms.Compose([ToFloatTensor2D()])
self.test_transform = transforms.Compose(
[ToFloat32(), OCToFloatTensor2D()])
self.transform = None
# Other utilities
self.mode = None
self.length = None
# val idx in normal class (all possible classes)
self.val_idxs = None
# train idx in normal class
self.train_idxs = None
# test idx with 50%->90% normal class(50% -> 10% novelty)
self.test_idxs = None