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permute

Permute

Bases: TensorOp

Permute a input tensor.

Parameters:

Name Type Description Default
inputs Union[str, Iterable[str]]

Key of the input tensor that is to be normalized.

 required
outputs Union[str, Iterable[str]]

Key of the output tensor that has been normalized.

 required
permutation Sequence[int]

Sequence[int]

 (0, 3, 1, 2)
mode Union[None, str, Iterable[str]]

What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument like "!infer" or "!train".

 None
ds_id Union[None, str, Iterable[str]]

What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all ds_ids except for a particular one, you can pass an argument like "!ds1".

 None
Source code in fastestimator/fastestimator/op/tensorop/permute.py 
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@traceable()
class Permute(TensorOp):
    """Permute a input tensor.

    Args:
        inputs: Key of the input tensor that is to be normalized.
        outputs: Key of the output tensor that has been normalized.
        permutation: Sequence[int]
        mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
            regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
            like "!infer" or "!train".
        ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
            ds_ids except for a particular one, you can pass an argument like "!ds1".
    """
    def __init__(self,
                 inputs: Union[str, Iterable[str]],
                 outputs: Union[str, Iterable[str]],
                 permutation: Sequence[int] = (0, 3, 1, 2),
                 mode: Union[None, str, Iterable[str]] = None,
                 ds_id: Union[None, str, Iterable[str]] = None) -> None:
        super().__init__(inputs=inputs, outputs=outputs, mode=mode, ds_id=ds_id)
        self.permutation = permutation

    def forward(self, data: List[Tensor], state: Dict[str, Any]) -> Union[Tensor, List[Tensor]]:
        return permute(data, self.permutation)