o oiX@s|ddlmZmZddlmZddlmZddlm Z ddl m Z gdZ GdddeZ Gd d d eZGd d d eZGd ddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdd d eZGd!d"d"eZGd#d$d$eZGd%d&d&eZGd'd(d(eZGd)d*d*eZGd+d,d,eZdS)-)OptionalTupleN) OperationBase)Tensor) STEFunction) AutoContrast BrightnessContrastEqualizeGrayHorizontalFlipHueInvert PosterizeRotateSaturate SharpnessShearXShearYSolarize SolarizeAdd TranslateX TranslateY VerticalFlipc s6eZdZdZ d dedededd ffd d ZZS) raApply auto_contrast operation. Args: initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. ?皙?Finitial_probability temperaturesymmetric_megnitudereturnNcs"tjtjd|dd||ddSNF same_on_batchpinitial_magnituderr)super__init__KRandomAutoContrast)selfrrr __class__[/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/auto/operations/ops.pyr';s   zAutoContrast.__init__)rrF)__name__ __module__ __qualname____doc__floatboolr' __classcell__r-r-r+r.r0s rc ReZdZdZ     ddeededeeefd ed ed d f fd d ZZ S)raApply brightness operation. Args: initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. initial_magnitude: the initial magnitude. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. r皙?g?rFr%rmagnitude_rangerrrNc*tjtj|d|dd|fg||ddS)NFr!brightness_factorr$)r&r'r(RandomBrightnessr*r%rr9rrr+r-r.r'S  zBrightness.__init__rrr7rF r/r0r1r2rr3rr4r'r5r-r-r+r.rF( rc r6)r aApply contrast operation. Args: initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. initial_magnitude: the initial magnitude. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. rr7rFr%rr9rrrNcr:)NFr!contrast_factorr$)r&r'r(RandomContrastr=r+r-r.r'pr>zContrast.__init__r?r@r-r-r+r.r crAr c ReZdZdZ     ddeeded eeefd ed ed d f fdd ZZ S)r a Apply hue operation. Args: initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. initial_magnitude: the initial magnitude. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. rgrrFr%rr9rrrNcr:)NFr! hue_factorr$)r&r'r( RandomHuer=r+r-r.r'r>z Hue.__init__)rErrFrFr@r-r-r+r.r rAr c r6)raApply saturation operation. Args: initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. initial_magnitude: the initial magnitude. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. rr7rFr%rr9rrrNcr:)NFr!saturation_factorr$)r&r'r(RandomSaturationr=r+r-r.r'r>zSaturate.__init__r?r@r-r-r+r.rrArc0eZdZdZd dededdffdd ZZS) r aApply equalize operation. Args: initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. temperature: temperature for RelaxedBernoulli distribution used during training. Note: Equalize cannot update probabilities yet. Note: STE gradient estimator applied for back propagation. rrrrrNcs$tjtjd|dd|dtddS)NFr!r%rrgradient_estimator)r&r'r(RandomEqualizerr*rrr+r-r.r's  zEqualize.__init__rrr/r0r1r2r3r'r5r-r-r+r.r s$r crK) r a&Apply grayscale operation. Args: initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. temperature: temperature for RelaxedBernoulli distribution used during training. rrrrrNc"tjtjd|dd|dddSr )r&r'r(RandomGrayscalerOr+r-r.r'   z Gray.__init__rPrQr-r-r+r.r $ r crK) ra#Apply invert operation. Args: initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. temperature: temperature for RelaxedBernoulli distribution used during training. rrrrrNcrRr )r&r'r( RandomInvertrOr+r-r.r'rTzInvert.__init__rPrQr-r-r+r.rrUrc sheZdZdZededefddZ    dd eed ed e eefdede ddf fdd Z Z S)ra[Apply posterize operation. Args: initial_magnitude: the initial magnitude. initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. Note: STE gradient estimator applied for back propagation. magnitudercCs|S)N)longrWr-r-r._process_magnitude szPosterize._process_magnitude@r?g @rFr%rr9rrNcs0tjtj|d|dd|fg||tjtddS)NFr! bits_factor)r%rr magnitude_fnrM)r&r'r(RandomPosterizerrZrr=r+r-r.r's zPosterize.__init__)r[rr\rF r/r0r1r2 staticmethodrrZrr3rr4r'r5r-r-r+r.rs, rc r6)raYApply solarize operation. Args: initial_magnitude: the initial magnitude. initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. magnitude_range: the sampling range for random sampling and clamping the optimized symmetric_megnitude: if to randomly assign the magnitude as negative or not.magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. Note: STE gradient estimator applied for back propagation. rrEr]rFr%rr9rrrNcs.tjtj|dd|dd|fg||tddS)NrEF) additionsr"r# thresholdsrLr&r'r(RandomSolarizerr=r+r-r.r'1 zSolarize.__init__)rrrcrFr@r-r-r+r.r!( rc rD)raApply solarize-addition operation with a fixed thresholds of 0.5. Args: initial_magnitude: the initial magnitude. initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. Note: STE gradient estimator applied for back propagation. rErg333333ӿ333333?rFr%rr9rrrNcs.tjtjd|d|dd|fg||tddS)NrF)rerdr"r#rdrLrfr=r+r-r.r'RrhzSolarizeAdd.__init__)rErrjrFr@r-r-r+r.rBrirc r6)raApply sharpness operation. Args: initial_magnitude: the initial magnitude. initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. rrgffffff?rFr%rr9rrrNcr:)NFr! sharpnessr$)r&r'r(RandomSharpnessr=r+r-r.r'pr>zSharpness.__init__)rrrlrFr@r-r-r+r.rcrArcrK) r a,Apply horizontal flip operation. Args: initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. temperature: temperature for RelaxedBernoulli distribution used during training. rrrrrNcrRr )r&r'r(RandomHorizontalFliprOr+r-r.r'rTzHorizontalFlip.__init__rPrQr-r-r+r.r rUr crK) rzApply vertical flip operation. Args: initial_magnitude: the initial magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. rrrrrNcrRr )r&r'r(RandomVerticalFliprOr+r-r.r'rTzVerticalFlip.__init__rPrQr-r-r+r.rs$rc rD)raApply rotate operation. Args: initial_magnitude: the initial magnitude. initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. .@rrEg>@rTr%rr9rrrNcsX|r|ddkrtd|jjd|ddtjtj|d|dd|fg||ddS) NrLower bound of = is a symmetric operation. The lower bound must above 0. Got .Fr!degreesr$) ValueErrorr,r/r&r'r(RandomRotationr=r+r-r.r's  zRotate.__init__)rqrrrrTr@r-r-r+r.rrArc heZdZdZededefddZ     dd eed ed e eefd ede ddf fdd Z Z S)raApply shear operation along x-axis. Args: initial_magnitude: the initial magnitude. initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. rWrcC|dSNr-rYr-r-r.rZzShearX._process_magnituderrrErkTr%rr9rrNcs^|r|ddkrtd|jjd|ddtjtj|d|ddd|fg||tjd dS) NrrsrtruFTr"r# align_cornersshear_xr%rrr_) rwr,r/r&r'r( RandomShearrrZr=r+r-r.r's  zShearX.__init__rrr~rTrar-r-r+r.r,  rc ry)raApply shear operation along y-axis. Args: initial_magnitude: the initial magnitude. initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. rWrcCrzr{r-rYr-r-r.rZr}zShearY._process_magnituderrr~Tr%rr9rrNcsn|r|ddkrtd|jjd|ddtjtjdd|d|dfd|dd d |fg||tjd dS) NrrsrtrurEFTrshear_yr) rwr,r/r&r'r(rrrZr=r+r-r.r's$  zShearY.__init__rrar-r-r+r.rrrc rD)ra Apply translate operation along x-axis. Args: initial_magnitude: the initial magnitude. initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. r8rrErrTr%rr9rrrNcsZ|r|ddkrtd|jjd|ddtjtj|d|ddd|fg||d dS) NrrsrtruFTr translate_xr$rwr,r/r&r'r(RandomTranslater=r+r-r.r')s  zTranslateX.__init__r8rrrTr@r-r-r+r.rrArc rD)ra Apply translate operation along y-axis. Args: initial_magnitude: the initial magnitude. initial_probability: the initial probability. If None, the augmentation will be randomly applied according to he augmentation sampling range. magnitude_range: the sampling range for random sampling and clamping the optimized magnitude. temperature: temperature for RelaxedBernoulli distribution used during training. symmetric_megnitude: if to randomly assign the magnitude as negative or not. r8rrrTr%rr9rrrNcs\|r|ddkrtd|jjd|ddtjtjd|d|ddd|fg||d dS) NrrsrtruFTr translate_yr$rr=r+r-r.r'Ks  zTranslateY.__init__rr@r-r-r+r.r>rAr) typingrrkornia.augmentation augmentationr((kornia.augmentation.auto.operations.baser kornia.corerkornia.grad_estimatorr__all__rrr r rr r rrrrrr rrrrrrr-r-r-r.s2    &!!"(-"