TensorDictBase.from_any#

classmethod TensorDictBase.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.

from_any picks the conversion from the type of obj, and applies it to the values inside obj too. It accepts more types than the TensorDict constructor, at the cost of some overhead and of opinionated choices about how each type maps to entries:

a dict or collections.UserDict: from_dict();
a named tuple: from_namedtuple();
a tuple: from_tuple(), with the keys "0", "1", … This includes the results of torch.return_types (max, sort, topk, …), which also emit a FutureWarning. Starting with TensorDict 0.17, the default will change to their field names ("values", "indices", …), as from_namedtuple() gives;
a dataclass instance: from_dataclass() (the result is a tensordict, not a tensorclass). A dataclass type whose fields all have defaults gives a tensordict of the defaults too, with a FutureWarning. Starting with TensorDict 0.17, the default will change to returning the type unchanged, as for any other class;
a NumPy structured array: from_struct_array();
a pandas DataFrame: from_pandas();
an open h5py.File: a PersistentTensorDict (see from_h5()).

A tensor or a tensor collection is returned as it is. A list becomes a tensor if torch.tensor() takes it, and a NonTensorStack otherwise. Any other object is returned unchanged.

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 dataclasses
>>> import torch
>>> from tensordict import TensorDict
>>> @dataclasses.dataclass
... class Data:
...     obs: torch.Tensor
...     extra: tuple
>>> data = Data(obs=torch.zeros(3, 4), extra=(torch.ones(3), {"x": torch.ones(3)}))
>>> td = TensorDict.from_any(data, auto_batch_size=True)
>>> td.batch_size
torch.Size([3])
>>> sorted(td.keys(include_nested=True, leaves_only=True), key=str)
[('extra', '0'), ('extra', '1', 'x'), 'obs']
>>> TensorDict.from_any([1, 2, 3])
tensor([1, 2, 3])

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

TensorDictBase.from_any#

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