channel_transpose

ChannelTranspose

Bases: NumpyOp

Transpose the data (for example to make it channel-width-height instead of width-height-channel).

Parameters:

Name	Type	Description	Default
inputs	Union[str, Iterable[str]]	Key(s) of images to be modified.	required
outputs	Union[str, Iterable[str]]	Key(s) into which to write the modified images.	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
axes	Iterable[int]	The permutation order.	(2, 0, 1)

Source code in fastestimator/fastestimator/op/numpyop/univariate/channel_transpose.py

@traceable()
class ChannelTranspose(NumpyOp):
    """Transpose the data (for example to make it channel-width-height instead of width-height-channel).

    Args:
        inputs: Key(s) of images to be modified.
        outputs: Key(s) into which to write the modified images.
        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".
        axes: The permutation order.
    """
    def __init__(self,
                 inputs: Union[str, Iterable[str]],
                 outputs: Union[str, Iterable[str]],
                 mode: Union[None, str, Iterable[str]] = None,
                 ds_id: Union[None, str, Iterable[str]] = None,
                 axes: Iterable[int] = (2, 0, 1)):
        super().__init__(inputs=inputs, outputs=outputs, mode=mode, ds_id=ds_id)
        self.axes = axes
        self.in_list, self.out_list = True, True

    def forward(self, data: List[np.ndarray], state: Dict[str, Any]) -> List[np.ndarray]:
        return [np.transpose(elem, self.axes) for elem in data]