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fda

FDA

Bases: ImageOnlyAlbumentation

Fourier Domain Adaptation. A simple style transfer can be implemented using this op.

Parameters:

Name Type Description Default
reference_images Union[Any, Iterable[Any]]

Sequence of objects that will be converted to images by read_fn. Can either be path of images or numpy arrays (depends upon read_fn).

 required
beta_limit Union[float, Tuple[float, float]]

Beta coefficient for FDA.

 0.1
read_fn Callable

User-defined function to read image, tensor or numpy array. Function should get an element of reference_images

 lambda x: x
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
Source code in fastestimator/fastestimator/op/numpyop/univariate/fda.py 
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@traceable()
class FDA(ImageOnlyAlbumentation):
    """Fourier Domain Adaptation. A simple style transfer can be implemented using this op.

    Args:
        reference_images: Sequence of objects that will be converted to images by read_fn. Can either be path
            of images or numpy arrays (depends upon read_fn).
        beta_limit: Beta coefficient for FDA.
        read_fn: User-defined function to read image, tensor or numpy array. Function should get an element
            of reference_images
        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".
    """
    def __init__(self,
                 inputs: Union[str, Iterable[str]],
                 outputs: Union[str, Iterable[str]],
                 reference_images: Union[Any, Iterable[Any]],
                 mode: Union[None, str, Iterable[str]] = None,
                 ds_id: Union[None, str, Iterable[str]] = None,
                 beta_limit: Union[float, Tuple[float, float]] = 0.1,
                 read_fn: Callable = lambda x: x, # for reading tensor to numpy array
                 ):
        super().__init__(FDAAlb(reference_images=reference_images,
                                beta_limit=beta_limit,
                                read_fn=read_fn,
                                always_apply=True),
                         inputs=inputs,
                         outputs=outputs,
                         mode=mode,
                         ds_id=ds_id)