reference/generated/tensordict.TensorDictBase.repeat

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TensorDictBase.repeat

TensorDictBase.repeat(*repeats: int, inplace: bool = False) → Self

Repeats this tensor along the specified dimensions.

Unlike expand(), this function copies the tensor’s data.

Warning

repeat() behaves differently from repeat(), but is more similar to numpy.tile(). For the operator similar to numpy.repeat(), see repeat_interleave().

Parameters:

repeats (Size, int..., tuple of int or list of int) – The number of times to repeat this tensor along each dimension.

Keyword Arguments:

inplace (bool, optional) – If True, this tensordict’s identity and key set are preserved; each leaf storage is replaced by its repeated counterpart one leaf at a time, keeping peak memory close to 1x. LazyStackedTensorDict is not supported in this mode (the stack dim’s repeat factor would rebuild the stack’s constituents list) — call to_tensordict() first. Defaults to False.

Examples

>>> import torch
>>>
>>> from tensordict import TensorDict
>>>
>>> td = TensorDict(
...     {
...         "a": torch.randn(3, 4, 5),
...         "b": TensorDict({
...             "c": torch.randn(3, 4, 10, 1),
...             "a string": "a string!",
...         }, batch_size=[3, 4, 10])
...     }, batch_size=[3, 4],
... )
>>> print(td.repeat(1, 2))
TensorDict(
    fields={
        a: Tensor(shape=torch.Size([3, 8, 5]), device=cpu, dtype=torch.float32, is_shared=False),
        b: TensorDict(
            fields={
                a string: NonTensorData(data=a string!, batch_size=torch.Size([3, 8, 10]), device=None),
                c: Tensor(shape=torch.Size([3, 8, 10, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([3, 8, 10]),
            device=None,
            is_shared=False)},
    batch_size=torch.Size([3, 8]),
    device=None,
    is_shared=False)

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