average

Average

Bases: TensorOp

Compute the average across tensors.

Parameters:

Name	Type	Description	Default
inputs	Union[str, Iterable[str]]	Keys of tensors to be averaged.	required
outputs	str	The key under which to save the output.	required
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/average.py

@traceable()
class Average(TensorOp):
    """Compute the average across tensors.

    Args:
        inputs: Keys of tensors to be averaged.
        outputs: The key under which to save the output.
        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: str,
                 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.in_list, self.out_list = True, False

    def forward(self, data: List[Tensor], state: Dict[str, Any]) -> Tensor:
        result = zeros_like(data[0])
        for tensor in data:
            result += tensor
        return result / len(data)