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reshape

Reshape

Bases: TensorOp

Reshape a input tensor to conform to a given shape.

Parameters:

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

Key of the input tensor that is to be reshaped.

 required
outputs Union[str, List[str]]

Key of the output tensor that has been reshaped.

 required
shape Union[int, Tuple[int, ...]]

Target shape.

 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/reshape.py 
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@traceable()
class Reshape(TensorOp):
    """Reshape a input tensor to conform to a given shape.

    Args:
        inputs: Key of the input tensor that is to be reshaped.
        outputs: Key of the output tensor that has been reshaped.
        shape: Target shape.
        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, List[str]],
                 outputs: Union[str, List[str]],
                 shape: Union[int, Tuple[int, ...]],
                 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.shape = list(shape)
        self.in_list, self.out_list = True, True

    def forward(self, data: List[Tensor], state: Dict[str, Any]) -> List[Tensor]:
        return [reshape(elem, self.shape) for elem in data]