def new_backend_tensor(
x,
dtype=None,
device=None,
requires_grad: bool = None,
copy: bool = False,
pin_memory: bool = False,
):
kwargs = update_with_backend_values(requires_grad=requires_grad,
device=device,
copy=copy,
dtype=dtype)
if Backend.is_numpy():
return new_numpy_array(x,
dtype=kwargs.get("dtype"),
copy=kwargs.get("copy"))
elif Backend.is_torch():
return new_torch_tensor(
x,
dtype=kwargs.get("dtype"),
copy=kwargs.get("copy"),
device=kwargs.get("device"),
requires_grad=kwargs.get("requires_grad"),
pin_memory=pin_memory,
)
def get_function(name):
if name in functions.fractalai.AVAILABLE_FUNCTIONS:
return getattr(functions.fractalai, name)
elif name in functions.batching.AVAILABLE_FUNCTIONS:
return getattr(functions.batching, name)
elif name in functions.images.AVAILABLE_FUNCTIONS:
return getattr(functions.images, name)
elif name in functions.notebook.AVAILABLE_FUNCTIONS:
return getattr(functions.notebook, name)
elif Backend.is_numpy():
backend = functions.numpy
else:
backend = functions.pytorch
return getattr(backend, name)
def __new_getattr(name):
if name in DATA_TYPE_NAMES:
return getattr(_data_types, name)()
elif name in AVAILABLE_FUNCTIONS:
return getattr(API, name)
try:
return __old_getattr(name)
except AttributeError as e:
if Backend.is_numpy():
val = getattr(numpy, name)
return __backend_wrap(val) if callable(val) else val
elif Backend.is_torch():
val = getattr(torch, name)
return __backend_wrap(val) if callable(val) else val
raise e
def to_backend(x: "Tensor",
requires_grad: bool = None,
device: str = None,
copy: bool = None) -> Tensor:
kwargs = update_with_backend_values(requires_grad=requires_grad,
device=device,
copy=copy)
if Backend.is_numpy():
return to_numpy(x, kwargs.get("copy"))
return to_torch(
x,
requires_grad=kwargs.get("requires_grad"),
device=kwargs.get("device"),
copy=kwargs.get("copy"),
)
def to_node_id(x):
if Backend.is_numpy():
return str(x) if Backend.use_true_hash() else int(x)
elif Backend.is_torch():
return int(x)
