art.preprocessing.expectation_over_transformation
¶
Module providing expectation over transformations.
EOT Image Center Crop - PyTorch¶
- class art.preprocessing.expectation_over_transformation.EoTImageCenterCropPyTorch(nb_samples: int, clip_values: Tuple[float, float], size: int = 5, label_type: str = 'classification', apply_fit: bool = False, apply_predict: bool = True)¶
This module implements Expectation over Transformation preprocessing for image center crop in PyTorch.
- __init__(nb_samples: int, clip_values: Tuple[float, float], size: int = 5, label_type: str = 'classification', apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTImageCenterCropPyTorch.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of the form (min, max) representing the minimum and maximum values allowed for features.size (
int
) – Maximal size of the crop on all sides of the image in pixels.label_type (
str
) – String defining the type of labels. Currently supported: classification, object_detectionapply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Image Rotation - TensorFlow V2¶
- class art.preprocessing.expectation_over_transformation.EoTImageRotationTensorFlow(nb_samples: int, clip_values: Tuple[float, float], angles: Union[float, Tuple[float, float]] = 45.0, label_type: str = 'classification', apply_fit: bool = False, apply_predict: bool = True)¶
This module implements Expectation over Transformation preprocessing for image rotation in TensorFlow.
- __init__(nb_samples: int, clip_values: Tuple[float, float], angles: Union[float, Tuple[float, float]] = 45.0, label_type: str = 'classification', apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTImageRotationTensorFlow.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of the form (min, max) representing the minimum and maximum values allowed for features.angles – A positive scalar angle in degrees defining the uniform sampling range from negative to positive angles_range.
label_type (
str
) – String defining the type of labels. Currently supported: classificationapply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Image Rotation - PyTorch¶
- class art.preprocessing.expectation_over_transformation.EoTImageRotationPyTorch(nb_samples: int, clip_values: Tuple[float, float], angles: Union[float, Tuple[float, float], List[float]] = 45.0, label_type: str = 'classification', apply_fit: bool = False, apply_predict: bool = True)¶
This module implements Expectation over Transformation preprocessing for image rotation in PyTorch.
- __init__(nb_samples: int, clip_values: Tuple[float, float], angles: Union[float, Tuple[float, float], List[float]] = 45.0, label_type: str = 'classification', apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTImageRotationPyTorch.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of the form (min, max) representing the minimum and maximum values allowed for features.angles – In degrees and counter-clockwise. If a positive scalar it defines the uniform sampling range from negative to positive value. If a tuple of two scalar angles it defines the uniform sampling range from minimum to maximum angles. If a list of scalar values it defines the discrete angles that will be sampled. For label_type=”object_detection” only a list of multiples of 90 degrees is supported.
label_type (
str
) – String defining the type of labels. Currently supported: classification, object_detectionapply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Brightness - PyTorch¶
- class art.preprocessing.expectation_over_transformation.EoTBrightnessPyTorch(nb_samples: int, clip_values: Tuple[float, float], delta: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True)¶
This module implements EoT of changes in brightness by addition of uniformly sampled delta.
- __init__(nb_samples: int, clip_values: Tuple[float, float], delta: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTBrightnessPyTorch.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of float representing minimum and maximum values of input (min, max).delta – Range to sample the delta (addition) to the pixel values to adjust the brightness. A single float is translated to range [-delta, delta] or a tuple of floats is used to create sampling range [delta[0], delta[1]]. The applied delta is sampled uniformly from this range for each image.
apply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Brightness - TensorFlow V2¶
- class art.preprocessing.expectation_over_transformation.EoTBrightnessTensorFlow(nb_samples: int, clip_values: Tuple[float, float], delta: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True)¶
This module implements EoT of changes in brightness by addition of uniformly sampled delta.
- __init__(nb_samples: int, clip_values: Tuple[float, float], delta: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTBrightnessTensorFlow.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of float representing minimum and maximum values of input (min, max).delta – Range to sample the delta (addition) to the pixel values to adjust the brightness. A single float is translated to range [-delta, delta] or a tuple of floats is used to create sampling range [delta[0], delta[1]]. The applied delta is sampled uniformly from this range for each image.
apply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Contrast - PyTorch¶
- class art.preprocessing.expectation_over_transformation.EoTContrastPyTorch(nb_samples: int, clip_values: Tuple[float, float], contrast_factor: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True)¶
This module implements EoT of changes in contrast with uniformly sampled factor.
- __init__(nb_samples: int, clip_values: Tuple[float, float], contrast_factor: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTContrastPyTorch.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of float representing minimum and maximum values of input (min, max).contrast_factor – Range to sample the contrast factor adjust the contrast. A single float is translated to range [-delta, delta] or a tuple of floats is used to create sampling range [delta[0], delta[1]]. The applied delta is sampled uniformly from this range for each image.
apply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Contrast - TensorFlow V2¶
- class art.preprocessing.expectation_over_transformation.EoTContrastTensorFlow(nb_samples: int, clip_values: Tuple[float, float], contrast_factor: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True)¶
This module implements EoT of changes in contrast with uniformly sampled factor.
- __init__(nb_samples: int, clip_values: Tuple[float, float], contrast_factor: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTContrastTensorFlow.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of float representing minimum and maximum values of input (min, max).contrast_factor – Range to sample the contrast factor adjust the contrast. A single float is translated to range [-delta, delta] or a tuple of floats is used to create sampling range [delta[0], delta[1]]. The applied delta is sampled uniformly from this range for each image.
apply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Gaussian Noise - PyTorch¶
- class art.preprocessing.expectation_over_transformation.EoTGaussianNoisePyTorch(nb_samples: int, clip_values: Tuple[float, float], std: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True)¶
This module implements EoT of adding Gaussian noise with uniformly sampled standard deviation.
- __init__(nb_samples: int, clip_values: Tuple[float, float], std: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTBrightnessPyTorch.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of float representing minimum and maximum values of input (min, max).std – Range to sample the standard deviation for the Gaussian distribution. A single float is translated to range [0, std]. The applied delta is sampled uniformly from this range for each image.
apply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Gaussian Noise - TensorFlow V2¶
- class art.preprocessing.expectation_over_transformation.EoTGaussianNoiseTensorFlow(nb_samples: int, clip_values: Tuple[float, float], std: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True)¶
This module implements EoT of adding Gaussian noise with uniformly sampled standard deviation.
- __init__(nb_samples: int, clip_values: Tuple[float, float], std: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTGaussianNoiseTensorFlow.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of float representing minimum and maximum values of input (min, max).std – Range to sample the standard deviation for the Gaussian distribution. A single float is translated to range [0, std]. The applied delta is sampled uniformly from this range for each image.
apply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Shot Noise - PyTorch¶
- class art.preprocessing.expectation_over_transformation.EoTShotNoisePyTorch(nb_samples: int, clip_values: Tuple[float, float], lam: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True)¶
This module implements EoT of adding shot noise (Poisson) with uniformly sampled rate parameter.
- __init__(nb_samples: int, clip_values: Tuple[float, float], lam: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTShotNoisePyTorch.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of float representing minimum and maximum values of input (min, max).lam – Range to sample the rate of the Poisson distribution. A single float is translated to range [0.0, lam] or a tuple of floats is used to create sampling range [lam[0], lam[1]]. The applied delta is sampled uniformly from this range for each image.
apply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Shot Noise - TensorFlow V2¶
- class art.preprocessing.expectation_over_transformation.EoTShotNoiseTensorFlow(nb_samples: int, clip_values: Tuple[float, float], lam: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True)¶
This module implements EoT of adding shot noise (Poisson) with uniformly sampled rate parameter.
- __init__(nb_samples: int, clip_values: Tuple[float, float], lam: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTShotNoiseTensorFlow.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of float representing minimum and maximum values of input (min, max).lam – Range to sample the rate of the Poisson distribution. A single float is translated to range [0.0, lam] or a tuple of floats is used to create sampling range [lam[0], lam[1]]. The applied delta is sampled uniformly from this range for each image.
apply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Zoom Blur - PyTorch¶
- class art.preprocessing.expectation_over_transformation.EoTZoomBlurPyTorch(nb_samples: int, clip_values: Tuple[float, float], zoom: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True)¶
This module implements EoT of zoom blur with uniformly sampled zoom factor.
- __init__(nb_samples: int, clip_values: Tuple[float, float], zoom: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTZoomBlurPyTorch.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of float representing minimum and maximum values of input (min, max).zoom – Range to sample the zoom factor. A single float is translated to range [1.0, zoom] or a tuple of floats is used to create sampling range [zoom[0], zoom[1]]. The applied zoom is sampled uniformly from this range for each image.
apply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.
EOT Zoom Blur - TensorFlow V2¶
- class art.preprocessing.expectation_over_transformation.EoTZoomBlurTensorFlow(nb_samples: int, clip_values: Tuple[float, float], zoom: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True)¶
This module implements EoT of zoom blur with uniformly sampled zoom factor.
- __init__(nb_samples: int, clip_values: Tuple[float, float], zoom: Union[float, Tuple[float, float]], apply_fit: bool = False, apply_predict: bool = True) None ¶
Create an instance of EoTZoomBlurTensorFlow.
- Parameters:
nb_samples (
int
) – Number of random samples per input sample.clip_values (
Tuple
) – Tuple of float representing minimum and maximum values of input (min, max).zoom – Range to sample the zoom factor. A single float is translated to range [1.0, zoom] or a tuple of floats is used to create sampling range [zoom[0], zoom[1]]. The applied zoom is sampled uniformly from this range for each image.
apply_fit (
bool
) – True if applied during fitting/training.apply_predict (
bool
) – True if applied during predicting.