3j,SSKrSSKJrJrJrJrJrJrJr SSK r SSK J r J r J r JrJrJrJrJr SSKJr SSKJr \\r\\ R2\\\ R244r"SS\ 5rg) N)AnyDictListOptionalTupleUnioncast)AugmentationSequential ColorJitterImageSequential RandomAffine RandomErasingRandomGaussianNoiseRandomHorizontalFlipRandomRotation90)_AugmentationBase) ParamItemc`^\rSrSrSrSSSSSSS S S S S S. S\\\4S\S\S\S\S\S\ S\\ R\ \\44S\\ R\ \\44S\S\ \S\ \S\SS 4U4SjjjrS\\ R\ \\44S\\ R\ \\44S \S\ \\\4S!44S"jrS#\SS 4S$jrS%\S\S\ \ \ \\\ R,444S&jrS%\S\S'\ RS\4S(jrS)\S*\S'\ RS\4S+jrS/S%\S,\ \\S\ \S#\ \S\4 U4S-jjjrS.rU=r$)0!AdaptiveDiscriminatorAugmentation(aImplementation of Adaptive Discriminator Augmentation for GANs training as introduced in :cite:`Karras2020ada`. adjust a global probability p over all augmentations list to select a subset of images to augment based on an exponential moving average of the Discriminator's accuracy labeling real samples. Args: *args: a list of kornia augmentation modules, set to a default list if not specified. initial_p: initial global probability `p` for applying the augmentations adjustment_speed: float step size for updating the global probability `p` max_p: maximum allowed value for `p` target_real_acc: target Discriminator accuracy to guide `p` adjustments ema_lambda: EMA smoothing factor. The real accuracy EMA is what's used to determine the `p` update update_every: `p` update frequency (in steps) erasing_scale: scale range used for `RandomErasing` if default augmentations are used erasing_ratio: aspect ratio range used for `RandomErasing` if default augmentations are used erasing_fill_value: fill value used in `RandomErasing` same_on_batch: apply the same transformation across the batch data_keys: input types to apply augmentations on **kwargs: Additional keyword arguments passed to `AugmentationSequential` Examples: >>> from kornia.augmentation.presets.ada import AdaptiveDiscriminatorAugmentation >>> original = torch.randn(2, 3, 16, 16) >>> ada = AdaptiveDiscriminatorAugmentation() >>> augmented = ada(original) This example demonstrates using default augmentations with AdaptiveDiscriminatorAugmentation in a GAN training loop. >>> import kornia.augmentation as K >>> from kornia.augmentation.presets.ada import AdaptiveDiscriminatorAugmentation >>> originals = torch.randn(2, 3, 5, 6) >>> aug_list = [ ... K.RandomRotation90(times=(0, 3), p=1), ... K.RandomAffine(degrees=10, translate=(.1, .1), scale=(.9, 1.1), p=1), ... K.ColorJitter(brightness=.2, contrast=.2, saturation=.2, hue=.1, p=1), ... ] >>> ada = AdaptiveDiscriminatorAugmentation(*aug_list) >>> augmented = ada(original) This example demonstrates using custom augmentations with AdaptiveDiscriminatorAugmentation. gh㈵>g{Gz?g?g333333?gGz?)g{Gz?gQ?)g333333?gffffff @gNF) initial_padjustment_speedmax_ptarget_real_acc ema_lambda update_every erasing_scale erasing_ratioerasing_fill_value data_keys same_on_batchargsrrrrrrrr r!r"r#kwargsreturnc X>U (dURXxU 5n [TU]"U U bU OS/U S.U D6 US::a[SUS35eSUs=::aS::dO [SUS35eSUs=::aS::dO [S US35eUS:a[S US 35eSUs=::aS::dO [S US35eSUs=::aS::dO [S US35eX:a[R "SUSUS3SS9 UnXlX lX0lX@l XPl X`l SUl SUl g)Ninput)r"r#rzInvalid `adjustment_speed` (u) — must be greater than 0zInvalid `target_real_acc` (u) — must be in [0, 1]zInvalid `ema_lambda` (zInvalid `update_every` (u) — must be at least 1zInvalid `max_p` (zInvalid `initial_p` (z `initial_p` (z) is greater than `max_p` (z#), resetting `initial_p` to `max_p`r ) stacklevelg?)default_ada_transfromssuper__init__ ValueErrorwarningswarnprrrrr real_acc_ema _num_calls)selfrrrrrrrr r!r"r#r$r% __class__s Y/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/augmentation/presets/ada.pyr-*AdaptiveDiscriminatorAugmentation.__init__fsz ..}M_`D  $ (   q ;>UbURU5 URS:XaU$Uc6URb&URVs/sHoURPM snOS/nUR XS9upg[ R "[ R"U4UR[ RUS95R5nUR5(dU$URXUS9n [[[T U]=U UUS95n UR!XU5$s snf)aGApply augmentations to a subset of input tensors with global probability `p`. This method applies the augmentation pipeline to a subset of input samples, randomly selected via a Bernoulli distribution with probability `p` if `real_acc` is provided, the internal probability `p` is updated via the `update` method. Non-augmented samples retain their original values, and the output matches the input structure. `real_acc` is the Discriminator's accuracy on real images; for example, `(real_logits > 0).float().mean().item()` if using logits andn assuming real labels are positive. rr()r")dtyperU)r"rZ)rkr")rNr1r"namerXrc bernoullifullfloat32boolanyr\r _inputs_typer,forwardri) r4rPrkr"rJkrWrUrZselected_inputsaugmented_inputsr5s r6ru)AdaptiveDiscriminatorAugmentation.forwards$   KK ! 66Q;M  >>-"&0A0 "66v6S ??5::zmTVV5==ag#hinnp||~~M(,(;(;Fbj(;(k  GO#   !!&HEE11sD)r3rrrr1r2rr)NNN)__name__ __module__ __qualname____firstlineno____doc__rrr floatintrcrdrr_data_keys_typerrrr-r+rNrtstrrUrXr\rirrru__static_attributes__ __classcell__)r5s@r6rr(sw;@ "&!% BNBL$'/3(->&78>> >  >  >>>U\\5+>>?>U\\5+>>?>">O,> ~>> >>@ U\\5#667 U\\5#667   u&78#= >  4EuEE, "" "/> " sHU3 #4566 7 "$\$o$Y^YeYe$jv$  ,,   4-1/3$( 2F2Fi)2FO, 2F 5/ 2F  2F2Fr8r)r/typingrrrrrrr rcr r r rrrrrbasercontainer.paramsrrrrdrtrr8r6rsh$@@@    %(s)U\\4U\\(9#::; JF(>JFr8