from_any#

tensordict.from_any(obj, *, auto_batch_size: bool = False, batch_dims: int | None = None, device: device | None = None, batch_size: Size | None = None)#

Converts a dict, tuple, dataclass, array or file to a tensordict, recursively.

The conversion depends on the type of obj: see TensorDictBase.from_any for the list. A tensor or a tensor collection is returned as it is.

Parameters:

obj – the object 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, except for a dict, where batch_size wins, and for an h5py.File, where auto_batch_size wins. 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, except for a dict, where batch_size wins and no batch size is computed, and for an h5py.File, where auto_batch_size wins and batch_size is ignored. Defaults to None.

Returns:

A tensordict (a PersistentTensorDict for an HDF5 file), a tensor or a NonTensorStack for a list, or obj itself for the other types.

Examples

>>> import torch
>>> from tensordict import from_any
>>> td = from_any({"obs": torch.zeros(3, 4), "pair": (torch.zeros(3), 1)})
>>> sorted(td.keys(include_nested=True, leaves_only=True), key=str)
[('pair', '0'), ('pair', '1'), 'obs']

from_any#

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