def __getitem__(self, index: int):
if self.shuffle:
self.randomize(self.data)
data1 = self.data[index]
data2 = self.data[index+self.__len__()]
if self.transform is not None:
data1 = apply_transform(self.transform, data1)
data2 = apply_transform(self.transform, data2)
return (data1, data2)
def __call__(self, data):
if len(self.transforms) == 0:
return data
index = self.R.multinomial(1, self.weights).argmax()
_transform = self.transforms[index]
data = apply_transform(_transform, data, self.map_items,
self.unpack_items, self.log_stats)
# if the data is a mapping (dictionary), append the OneOf transform to the end
if isinstance(data, Mapping):
for key in data.keys():
if self.trace_key(key) in data:
self.push_transform(data, key, extra_info={"index": index})
return data
def inverse(self, data):
invertible_transforms = [
t for t in self.flatten().transforms
if isinstance(t, InvertibleTransform)
]
if len(invertible_transforms) == 0:
warnings.warn(
"inverse has been called but no invertible transforms have been supplied"
)
# loop backwards over transforms
for t in reversed(invertible_transforms):
data = apply_transform(t.inverse, data)
return data
def inverse(self, data):
invertible_transforms = [
t for t in self.flatten().transforms
if isinstance(t, InvertibleTransform)
]
if not invertible_transforms:
warnings.warn(
"inverse has been called but no invertible transforms have been supplied"
)
# loop backwards over transforms
for t in reversed(invertible_transforms):
data = apply_transform(t.inverse, data, self.map_items,
self.unpack_items, self.log_stats)
return data
def __call__(self, input_):
for _transform in self.transforms:
input_ = apply_transform(_transform, input_, self.map_items,
self.unpack_items, self.log_stats)
return input_
def __call__(self, input_):
for _transform in self.transforms:
input_ = apply_transform(_transform, input_)
return input_