def copy_torch(x: torch.Tensor, requires_grad, device=None) -> torch.Tensor:
grad = requires_grad if requires_grad is not None else Backend.requires_grad(
)
device = torch.device(
device) if device is not None else Backend.get_device()
new_tensor = x.clone()
if device is not None and new_tensor.device == device:
new_tensor = new_tensor.to(device)
if grad is not None and not grad:
new_tensor = new_tensor.detach()
return new_tensor
def to_torch(x,
requires_grad: bool = None,
device: str = None,
copy: bool = False):
use_grad = requires_grad if requires_grad is not None else Backend.requires_grad(
)
device = device if device is not None else Backend.get_device()
if isinstance(x, torch.Tensor):
return (copy_torch(x, device=device, requires_grad=use_grad)
if copy else _assign_device_and_grad(
x, device=device, requires_grad=use_grad))
return new_torch_tensor(x, device=device, copy=copy)
def permutation(x):
idx = torch.randperm(x.shape[0])
sample = x[idx].to(Backend.get_device()).detach()
return to_backend(sample)
