reference/generated/tensordict.from_namedtuple

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from_namedtuple

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

Converts a named tuple to a tensordict whose keys are its field names.

Each field value is converted with from_any(). See TensorDictBase.from_namedtuple.

Parameters:

named_tuple (NamedTuple) – the named tuple to convert.

Keyword Arguments:

• auto_batch_size (bool, optional) – if True, sets the batch size to the largest leading shape that all the entries share. Exclusive with batch_size. Defaults to False.

• batch_dims (int, optional) – with auto_batch_size, the maximum number of batch dimensions. Ignored without auto_batch_size, with a FutureWarning when it would change the result. Starting with TensorDict 0.17, the default will change to turning on auto_batch_size when batch_dims is given, as from_dict does. Defaults to None.

• device (device, str or int, optional) – the device of the result. Leaves on another device are moved to it. None leaves each leaf where it is, and the result has no device. Defaults to None.

• batch_size (Size or list of int, optional) – the batch size of the result: the leading dimensions that all its entries share. Exclusive with auto_batch_size. Defaults to None, an empty batch size.

Returns:

A TensorDict.

Examples

>>> import torch
>>> from tensordict import from_namedtuple
>>> td = from_namedtuple(torch.tensor([[2, 0, 1]]).max(dim=1))
>>> td["values"], td["indices"]
(tensor([2]), tensor([0]))