reference/generated/tensordict.TensorDictBase.to_dict

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TensorDictBase.to_dict

TensorDictBase.to_dict(*, retain_none: bool = True, convert_tensors: bool | Literal['numpy'] = False, tolist_first: bool = False) → dict[str, Any]

Returns a dictionary with key-value pairs matching those of the tensordict.

Parameters:

• retain_none (bool) – if True, the None values from tensorclass instances will be written in the dictionary. Otherwise, they will be discarded. Default: True.

• convert_tensors (bool, "numpy") – if True, tensors will be converted to lists when creating the dictionary. If “numpy”, tensors will be converted to numpy arrays. Otherwise, they will remain as tensors. Default: False.

• tolist_first (bool) – if True, the tensordict will be converted to a list first when it has batch dimensions. Default: False.

Returns:

A dictionary representation of the tensordict.

See also

tolist()

Examples

>>> import torch
>>> from tensordict import TensorDict
>>>
>>> td = TensorDict(
...     a=torch.arange(6).view(2, 3),
...     b=TensorDict(c=torch.arange(4).reshape(2, 2), batch_size=(2, 2)),
...     batch_size=(2,)
... )
>>> print(td.to_dict())
{'a': tensor([[0, 1, 2],
        [3, 4, 5]]), 'b': {'c': tensor([[0, 1],
        [2, 3]])}}
>>> print(td.to_dict(convert_tensors=True))
{'a': [[0, 1, 2], [3, 4, 5]], 'b': {'c': [[0, 1], [2, 3]]}}

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