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from typing import List, Optional, Union
from torch_geometric.data import Data, HeteroData
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform
@functional_transform('to_device')
class ToDevice(BaseTransform):
r"""Performs tensor device conversion, either for all attributes of the
:obj:`~torch_geometric.data.Data` object or only the ones given by
:obj:`attrs` (functional name: :obj:`to_device`).
Args:
device (torch.device): The destination device.
attrs (List[str], optional): If given, will only perform tensor device
conversion for the given attributes. (default: :obj:`None`)
non_blocking (bool, optional): If set to :obj:`True` and tensor
values are in pinned memory, the copy will be asynchronous with
respect to the host. (default: :obj:`False`)
"""
def __init__(
self,
device: Union[int, str],
attrs: Optional[List[str]] = None,
non_blocking: bool = False,
) -> None:
self.device = device
self.attrs = attrs or []
self.non_blocking = non_blocking
def forward(
self,
data: Union[Data, HeteroData],
) -> Union[Data, HeteroData]:
return data.to(self.device, *self.attrs,
non_blocking=self.non_blocking)
def __repr__(self) -> str:
return f'{self.__class__.__name__}({self.device})'
|
