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decorators.py
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decorators.py
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from itertools import chain
import numpy as np
import torch
from abc import ABC, abstractmethod
from functools import update_wrapper, partial
class Decorator(ABC):
def __init__(self, f):
self.func = f
update_wrapper(self, f, updated=[]) # updated=[] so that 'self' attributes are not overwritten
@abstractmethod
def __call__(self, *args, **kwargs):
pass
def __get__(self, instance, owner):
new_f = partial(self.__call__, instance)
update_wrapper(new_f, self.func)
return new_f
def to_tensor(x):
if isinstance(x, np.ndarray) or np.isscalar(x):
return torch.from_numpy(np.array(x)).float()
else:
return x
def to_numpy(x):
if isinstance(x, torch.Tensor):
return x.cpu().detach().numpy()
else:
return x
# noinspection PyPep8Naming
class input_to_tensors(Decorator):
def __call__(self, *args, **kwargs):
new_args = [to_tensor(arg) for arg in args]
new_kwargs = {key: to_tensor(value) for key, value in kwargs.items()}
return self.func(*new_args, **new_kwargs)
# noinspection PyPep8Naming
class output_to_tensors(Decorator):
def __call__(self, *args, **kwargs):
outputs = self.func(*args, **kwargs)
if isinstance(outputs, np.ndarray):
return to_tensor(outputs)
if isinstance(outputs, tuple):
new_outputs = tuple([to_tensor(item) for item in outputs])
return new_outputs
return outputs
# noinspection PyPep8Naming
class input_to_numpy(Decorator):
def __call__(self, *args, **kwargs):
new_args = [to_numpy(arg) for arg in args]
new_kwargs = {key: to_numpy(value) for key, value in kwargs.items()}
return self.func(*new_args, **new_kwargs)
# noinspection PyPep8Naming
class output_to_numpy(Decorator):
def __call__(self, *args, **kwargs):
outputs = self.func(*args, **kwargs)
if isinstance(outputs, torch.Tensor):
return to_numpy(outputs)
if isinstance(outputs, tuple):
new_outputs = tuple([to_numpy(item) for item in outputs])
return new_outputs
return outputs
# noinspection PyPep8Naming
class none_if_missing_arg(Decorator):
def __call__(self, *args, **kwargs):
for arg in chain(args, kwargs.values()):
if arg is None:
return None
return self.func(*args, **kwargs)