def _detail_torch_tensor(worker: AbstractWorker,
tensor_tuple: tuple) -> torch.Tensor:
"""
This function converts a serialized torch tensor into a torch tensor
using pickle.
Args:
tensor_tuple (bin): serialized obj of torch tensor. It's a tuple where
the first value is the ID, the second vlaue is the binary for the
PyTorch object, the third value is the chain of tensor abstractions,
and the fourth object is the chain of gradients (.grad.grad, etc.)
Returns:
torch.Tensor: a torch tensor that was serialized
"""
tensor_id, tensor_bin, chain, grad_chain, tags, description = tensor_tuple
tensor = _deserialize_tensor(tensor_bin)
# note we need to do this explicitly because torch.load does not
# include .grad informatino
if grad_chain is not None:
tensor.grad = _detail_torch_tensor(worker, grad_chain)
initialize_tensor(
hook_self=syft.torch.hook,
cls=tensor,
torch_tensor=True,
owner=worker,
id=tensor_id,
init_args=[],
kwargs={},
)
if tags is not None:
tags = list(tags)
for i in range(len(tags)):
tag = tags[i]
if isinstance(tag, bytes):
tag = tag.decode("utf-8")
tags[i] = tag
tensor.tags = tags
if description is not None:
if isinstance(description, bytes):
description = description.decode("utf-8")
tensor.description = description
if chain is not None:
chain = syft.serde._detail(worker, chain)
tensor.child = chain
tensor.is_wrapper = True
return tensor
def new___init__(cls, *args, owner=None, id=None, register=True, **kwargs):
initialize_tensor(
hook_self=hook_self,
cls=cls,
id=id,
torch_tensor=torch_tensor,
init_args=args,
init_kwargs=kwargs,
)
