TensorDictBase.from_tuple#

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

Converts a tuple to a tensordict with the keys "0", "1", …

Each element is converted with from_any(), so a nested tuple or dict becomes a nested tensordict and a number becomes a tensor.

Parameters:

obj (tuple) – the 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

>>> import torch
>>> from tensordict import TensorDict
>>> td = TensorDict.from_tuple((torch.zeros(3, 4), 1), auto_batch_size=True)
>>> td["0"].shape, td["1"], td.batch_size
(torch.Size([3, 4]), tensor(1), torch.Size([]))
>>> td = TensorDict.from_tuple((torch.zeros(3, 4), torch.ones(3)), auto_batch_size=True)
>>> td.batch_size
torch.Size([3])

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

TensorDictBase.from_tuple#

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