a d+@sddlmZddlmZmZmZmZmZddlZddl m Z ddlm Z ddl m Z ddlmZddlmZmZmZee ee e ffZGdd d e jZGd d d eZdS) )Enum)AnyDictOptionalTupleUnionN)Tensor) Bernoulli)RandomGeneratorBase)_adapted_sampling_transform_output_shapeoverride_parameterscspeZdZdZd*eeeeddfdd Zed d d Ze e d d dZ e e d ddZ e e e dddZ ejeee fdddZe eee feeefe dddZejejddddZejeeee dddZeee feeefe eee feeeffd d!d"Zeee fd d#d$Ze eee feeefe dd%d&Zd+e eeee fe d'd(d)ZZS),_BasicAugmentationBasea_BasicAugmentationBase base class for customized augmentation implementations. Plain augmentation base class without the functionality of transformation matrix calculations. By default, the random computations will be happened on CPU with ``torch.get_default_dtype()``. To change this behaviour, please use ``set_rng_device_and_dtype``. For automatically generating the corresponding ``__repr__`` with full customized parameters, you may need to implement ``_param_generator`` by inheriting ``RandomGeneratorBase`` for generating random parameters and put all static parameters inside ``self.flags``. You may take the advantage of ``PlainUniformGenerator`` to generate simple uniform parameters with less boilerplate code. Args: p: probability for applying an augmentation. This param controls the augmentation probabilities element-wise. p_batch: probability for applying an augmentation to a batch. This param controls the augmentation probabilities batch-wise. same_on_batch: apply the same transformation across the batch. keepdim: whether to keep the output shape the same as input ``True`` or broadcast it to the batch form ``False``. ??FN)pp_batch same_on_batchkeepdimreturncst||_||_||_||_i|_|dks8|dkrDt|j|_|dksT|dkr`t|j|_ d|_ i|_ | t dt dS)Ngrcpu)super__init__rrrr_paramsr _p_gen _p_batch_gen_param_generatorflagsset_rng_device_and_dtypetorchdeviceZget_default_dtype)selfrrrr __class__a/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/augmentation/base.pyr$s   z_BasicAugmentationBase.__init__rcCsd|jd|jd|j}t|jtr) r!r5rBrO in_tensorZ input_shaper?rr<r$r$r%forwards   z_BasicAugmentationBase.forward)rrFF)N)r/ __module__ __qualname____doc__floatrHrr+r3rr8r;rr>rrQrr@rrDr rErrMrPrVrWrrZ __classcell__r$r$r"r%rs, $    $rcseZdZdZdeeeeeeddfdd Zee d d d Z e d fd d Z e e dddZ e ee e fee efe dddZde ee e fee efee e dddZde ee e feee efe dddZZS)_AugmentationBasea_AugmentationBase base class for customized augmentation implementations. Advanced augmentation base class with the functionality of transformation matrix calculations. Args: p: probability for applying an augmentation. This param controls the augmentation probabilities element-wise for a batch. p_batch: probability for applying an augmentation to a batch. This param controls the augmentation probabilities batch-wise. same_on_batch: apply the same transformation across the batch. keepdim: whether to keep the output shape the same as input ``True`` or broadcast it to the batch form ``False``. NFrr)return_transformrrrrrcs>tj||||d||_||_||_||dur:tddS)N)rrrz`return_transform` is deprecated. Please access the transformation matrix with `.transform_matrix`. For chained matrices, please use `AugmentationSequential`.)rrrrra ValueError)r!rarrrrr"r$r%rsz_AugmentationBase.__init__r&cCs|jSr6)_transform_matrixr!r$r$r%transform_matrixsz"_AugmentationBase.transform_matrixcs|jjdtdS)Nr(r))r#r/rr3rdr"r$r%r3sz_AugmentationBase.__repr__r4cCstdSr6r9r7r$r$r%identity_matrixsz!_AugmentationBase.identity_matrixrAcCstdSr6r9rCr$r$r%compute_transformationsz(_AugmentationBase.compute_transformation)r5rBr transformrcCstdSr6r9)r!r5rBrrhr$r$r%rDsz!_AugmentationBase.apply_transform)rYrBrrcCs|dur|j}|d}|s.|}||}nt|rZ|j|||d}|j||||d}nH|}||}|j||||d||<|j||||||d||<||_|S)NrL)rBr)rBrrh)ranyrfallrgrDclonerc)r!rYrBrrUr<Z trans_matrixr$r$r%rWs"   z_AugmentationBase.apply_func)NFrrF)N)N)r/r[r\r]rrHr^rpropertyrrer+r3rfrrrgrDrWr_r$r$r"r%r`s:$ r`)enumrtypingrrrrrrZtorch.nnnnrZtorch.distributionsr Z$kornia.augmentation.random_generatorr Zkornia.augmentation.utilsr r r ZTensorWithTransformMatModulerr`r$r$r$r%s     &