TensorDictBase.from_namedtuple#

classmethod TensorDictBase.from_namedtuple(named_tuple, *, auto_batch_size: bool = False, batch_dims: int | None = None, device: device | None = None, batch_size: Size | None = None)#

Converts a namedtuple to a TensorDict recursively.

Parameters:

named_tuple – The namedtuple instance to be converted.

Keyword Arguments:

auto_batch_size (bool, optional) – if True, the batch size will be computed automatically. Defaults to False.
batch_dims (int, optional) – If auto_batch_size is True, defines how many dimensions the output tensordict should have. Defaults to None (full batch-size at each level).
device (device, optional) – The device on which the TensorDict will be created. Defaults to None.
batch_size (Size, optional) – The batch size of the TensorDict. Defaults to None.

Returns:

A TensorDict representation of the input namedtuple.

Examples

>>> from tensordict import TensorDict
>>> import torch
>>> data = TensorDict({
...     "a_tensor": torch.zeros((3)),
...     "nested": {"a_tensor": torch.zeros((3)), "a_string": "zero!"}}, [3])
>>> nt = data.to_namedtuple()
>>> print(nt)
GenericDict(a_tensor=tensor([0., 0., 0.]), nested=GenericDict(a_tensor=tensor([0., 0., 0.]), a_string='zero!'))
>>> TensorDict.from_namedtuple(nt, auto_batch_size=True)
TensorDict(
    fields={
        a_tensor: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
        nested: TensorDict(
            fields={
                a_string: NonTensorData(data=zero!, batch_size=torch.Size([3]), device=None),
                a_tensor: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([3]),
            device=None,
            is_shared=False)},
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)

Every tensordict class and every tensorclass has this method. For the other methods, see Methods by task.

TensorDictBase.from_namedtuple#

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