reference/generated/tensordict.TensorDictBase.numpy

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TensorDictBase.numpy

TensorDictBase.numpy() → ndarray | dict[str, Any]

Converts a tensordict to a (possibly nested) dictionary of numpy arrays.

Non-tensor data is exposed as such.

Examples

>>> from tensordict import TensorDict
>>> import torch
>>> data = TensorDict({"a": {"b": torch.zeros(()), "c": "a string!"}})
>>> print(data)
TensorDict(
    fields={
        a: TensorDict(
            fields={
                b: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                c: NonTensorData(data=a string!, batch_size=torch.Size([]), device=None)},
            batch_size=torch.Size([]),
            device=None,
            is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False)
>>> print(data.numpy())
{'a': {'b': array(0., dtype=float32), 'c': 'a string!'}}

seealso: to_struct_array() to convert to a struct array.

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