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from_float

FromFloat

Bases: ImageOnlyAlbumentation

Takes an input float image in range [0, 1.0] and then multiplies by max_value to get an int image.

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
max_value Optional[float]

The maximum value to serve as the multiplier. If None it will be inferred by dtype.

 None
dtype Union[str, dtype]

The data type to cast the output as.

 'uint16'
Image types

float32

Source code in fastestimator/fastestimator/op/numpyop/univariate/from_float.py 
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@traceable()
class FromFloat(ImageOnlyAlbumentation):
    """Takes an input float image in range [0, 1.0] and then multiplies by `max_value` to get an int image.

    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".
        max_value: The maximum value to serve as the multiplier. If None it will be inferred by dtype.
        dtype: The data type to cast the output as.

    Image types:
        float32
    """
    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,
                 max_value: Optional[float] = None,
                 dtype: Union[str, np.dtype] = "uint16"):
        super().__init__(FromFloatAlb(max_value=max_value, dtype=dtype, always_apply=True),
                         inputs=inputs,
                         outputs=outputs,
                         mode=mode,
                         ds_id=ds_id)