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resize3d

Resize3D

Bases: TensorOp

Resize a 3D tensor (supports multi-io).

Parameters:

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

Key(s) of the input tensor.

 required
outputs Union[str, Iterable[str]]

Key(s) of the output tensor.

 required
output_shape Sequence[int]

The desired output shape for the input tensor exculding batch and channels (H, W, D).

 required
resize_mode str

The resize mode of the operation ('area' or 'nearest'). 'area' : Uses pixel area relation for resampling. This is best suited for reducing the size of an image (shrinking). When used for zooming into the image, it uses the nearest method. 'nearest' : Uses the nearest neighbor concept for interpolation.

 'nearest'
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/resize3d.py 
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@traceable()
class Resize3D(TensorOp):
    """Resize a 3D tensor (supports multi-io).

        Args:
            inputs: Key(s) of the input tensor.
            outputs: Key(s) of the output tensor.
            output_shape: The desired output shape for the input tensor exculding batch and channels (H, W, D).
            resize_mode: The resize mode of the operation ('area' or 'nearest').
                'area' : Uses pixel area relation for resampling. This is best suited for reducing the size of an image
                        (shrinking). When used for zooming into the image, it uses the nearest method.
                'nearest' : Uses the nearest neighbor concept for interpolation.
            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]],
                 output_shape: Sequence[int],
                 resize_mode: str = 'nearest',
                 mode: Union[None, str, Iterable[str]] = None,
                 ds_id: Union[None, str, Iterable[str]] = None):

        super().__init__(inputs=to_list(inputs), outputs=to_list(outputs), mode=mode, ds_id=ds_id)
        assert resize_mode in ['nearest', 'area'], "Only following resize modes are supported: 'nearest', 'area' "
        if len(output_shape) != 3:
            raise ValueError("The output shape is expected to be (H, W, D) excluding batch size and channels.")
        self.output_shape = output_shape
        self.resize_mode = resize_mode

    def forward(self, data: List[Tensor], state: Dict[str, Any]) -> Union[Tensor, List[Tensor]]:
        return [resize_3d(elem, self.output_shape, self.resize_mode) for elem in data]