reference/generated/tensordict.TensorDictBase.from_namedtuple

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

Each field value is converted with from_any(), so a nested named tuple or dict becomes a nested tensordict and a number becomes a tensor. The torch.return_types results of torch.max, torch.sort and the like are accepted too. to_namedtuple() converts back.

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. It applies to the nested tensordicts too. Exclusive with auto_batch_size. Defaults to None, an empty batch size.

Returns:

A TensorDict.

Raises:

TypeError – if both auto_batch_size=True and batch_size are given.

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()
>>> nt
GenericDict(a_tensor=tensor([0., 0., 0.]), nested=GenericDict(a_tensor=tensor([0., 0., 0.]), a_string='zero!'))
>>> td = TensorDict.from_namedtuple(nt, auto_batch_size=True)
>>> td.batch_size, td["nested", "a_string"]
(torch.Size([3]), 'zero!')
>>> sorted(TensorDict.from_namedtuple(torch.tensor([2, 0, 1]).max(0)).keys())
['indices', 'values']

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