def hash_type():
funcs = {
"numpy":
lambda x: numpy.dtype("<U64")
if Backend.use_true_hash() else numpy.uint64,
"torch":
lambda x: torch.int64,
}
return Backend.execute(None, funcs)
def copy(x, requires_grad: bool = None):
if x is None:
return
if not dtype.is_tensor(x):
x = JudoTensor(x)
funcs = {
"numpy": lambda x: x.copy(),
"torch": lambda x: copy_torch(x, requires_grad),
}
return Backend.execute(x, funcs)
def __getattr__(cls, item):
funcs = {
"numpy": lambda x: getattr(cls._numpy_random_state, x),
"torch": lambda x: getattr(cls._torch_random_state, x),
}
return Backend.execute(item, funcs)
def float64():
funcs = {
"numpy": lambda x: numpy.float64,
"torch": lambda x: torch.float64
}
return Backend.execute(None, funcs)
def int32():
funcs = {"numpy": lambda x: numpy.int32, "torch": lambda x: torch.int32}
return Backend.execute(None, funcs)
def uint8():
funcs = {"numpy": lambda x: numpy.uint8, "torch": lambda x: torch.uint8}
return Backend.execute(None, funcs)
def bool():
funcs = {"numpy": lambda x: numpy.bool_, "torch": lambda x: torch.bool}
return Backend.execute(None, funcs)
def as_tensor(x, *args, **kwargs):
funcs = {
"numpy": lambda x: numpy.ascontiguousarray(x, *args, **kwargs),
"torch": lambda x: torch.as_tensor(x, *args, **kwargs),
}
return Backend.execute(x, funcs)
def astype(x, dtype):
funcs = {
"numpy": lambda x: x.astype(dtype),
"torch": lambda x: x.to(dtype),
}
return Backend.execute(x, funcs)
def true_hash_tensor(cls, x):
funcs = {
"numpy": cls.hash_numpy,
"torch": cls.hash_torch,
}
return Backend.execute(x, funcs)
