reference/generated/tensordict.TensorDictBase.values

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TensorDictBase.values

TensorDictBase.values(include_nested: bool = False, leaves_only: bool = False, is_leaf=None, *, sort: bool = False) → Iterator[Tensor | TensorCollection]

Returns a generator of the values of the tensordict.

By default, only the top-level values are returned, and a nested tensordict is returned as one value. Non-tensor data comes as a NonTensorData, not as the object that td[key] returns. See Keys, nested keys, and locking for nested keys and leaves.

Parameters:

• include_nested (bool, optional) – if True, the entries of the nested tensordicts are also returned, under nested keys (tuples of strings). Defaults to False.

• leaves_only (bool, optional) – if True, only the leaves are returned, and nested tensordicts are skipped. Defaults to False.

• is_leaf (Callable, optional) – a function that takes a class and returns True if values of that class count as leaves. It filters the result only when leaves_only=True. It does not stop the recursion: with include_nested=True, a nested tensordict is still searched even if is_leaf returns True for it. Defaults to None, which uses default_is_leaf(): tensordicts, tensorclasses and non-tensor data are not leaves. Pass is_leaf_nontensor() to count non-tensor data as leaves.

Keyword Arguments:

sort (bool, optional) – if True, the keys are sorted. Nested keys are sorted by their joined name: ("a", "key") sorts as "a.key". Sorting takes extra time on large tensordicts. Defaults to False.

Examples

>>> import torch
>>> from tensordict import TensorDict
>>> td = TensorDict(
...     a=torch.zeros(2),
...     nested=TensorDict(b=torch.ones(2, 3), batch_size=[2]),
...     batch_size=[2],
... )
>>> [type(value).__name__ for value in td.values()]
['Tensor', 'TensorDict']
>>> [value.shape for value in td.values(include_nested=True, leaves_only=True)]
[torch.Size([2]), torch.Size([2, 3])]

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

Tutorials that use values

Benchmarking TensorDict serialization speed

Benchmarking TensorDict serialization speed