o oiD@sddlmZmZmZmZmZmZmZmZddl Z ddl m Z ddl mZddlmZddlmZmZmZmZddlmZddlmZdd lmZdd lmZd gZGd d d eZGdd d eeZ dede j!de j!fddZ"dS))AnyDictIteratorListOptionalTupleUnioncastN)_AugmentationBase)override_parameters) ImageModuleModuleTensor as_tensor)ImageModuleMixIneye_like)ImageSequentialBase) ParamItemImageSequentialc@sXeZdZUdZeed<edefddZejd deddfd dZd e ddfd d Z dS)ImageModuleForSequentialMixInF_disable_featuresreturncCs|jSNr)selfrW/home/ubuntu/.local/lib/python3.10/site-packages/kornia/augmentation/container/image.pydisable_features&sz.ImageModuleForSequentialMixIn.disable_featuresTvalueNcCs ||_dSrr)rr rrrr* argscGs |D] }t|tfr d|_qdS)NT) isinstancer r)rr"argrrrdisable_item_features.s  z3ImageModuleForSequentialMixIn.disable_item_featuresT) __name__ __module__ __qualname__rbool__annotations__propertyrsetterr r%rrrrr#s rcseZdZdZdddddddddedeed eed eeee eeffd ee e d e d ededdffddZ d eeee eeffdedee eefeffddZd3dede ee e efeffddZdee e efde efddZd4dee edee e effddZdejde efdd Zd!edefd"d#Z   d5d!edee ed$ed%eee efdeef d&d'Zd3d(edefd)d*Zdd+d,d-ed.ee ed/e d0edef fd1d2ZZS)6ra Sequential for creating kornia image processing pipeline. Args: *args : a list of kornia augmentation and image operation modules. same_on_batch: apply the same transformation across the batch. If None, it will not overwrite the function-wise settings. keepdim: whether to keep the output shape the same as input (True) or broadcast it to the batch form (False). If None, it will not overwrite the function-wise settings. random_apply: randomly select a sublist (order agnostic) of args to apply transformation. The selection probability aligns to the ``random_apply_weights``. If int, a fixed number of transformations will be selected. If (a,), x number of transformations (a <= x <= len(args)) will be selected. If (a, b), x number of transformations (a <= x <= b) will be selected. If True, the whole list of args will be processed as a sequence in a random order. If False, the whole list of args will be processed as a sequence in original order. random_apply_weights: a list of selection weights for each operation. The length shall be as same as the number of operations. By default, operations are sampled uniformly. .. note:: Transformation matrix returned only considers the transformation applied in ``kornia.augmentation`` module. Those transformations in ``kornia.geometry`` will not be taken into account. Examples: >>> _ = torch.manual_seed(77) >>> import kornia >>> input = torch.randn(2, 3, 5, 6) >>> aug_list = ImageSequential( ... kornia.color.BgrToRgb(), ... kornia.augmentation.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0), ... kornia.filters.MedianBlur((3, 3)), ... kornia.augmentation.RandomAffine(360, p=1.0), ... kornia.enhance.Invert(), ... kornia.augmentation.RandomMixUpV2(p=1.0), ... same_on_batch=True, ... random_apply=10, ... ) >>> out = aug_list(input) >>> out.shape torch.Size([2, 3, 5, 6]) Reproduce with provided params. >>> out2 = aug_list(input, params=aug_list._params) >>> torch.equal(out, out2) True Perform ``OneOf`` transformation with ``random_apply=1`` and ``random_apply_weights`` in ``ImageSequential``. >>> import kornia >>> input = torch.randn(2, 3, 5, 6) >>> aug_list = ImageSequential( ... kornia.color.BgrToRgb(), ... kornia.augmentation.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0), ... kornia.filters.MedianBlur((3, 3)), ... kornia.augmentation.RandomAffine(360, p=1.0), ... random_apply=1, ... random_apply_weights=[0.5, 0.3, 0.2, 0.5] ... ) >>> out= aug_list(input) >>> out.shape torch.Size([2, 3, 5, 6]) NFraiseT) same_on_batchkeepdim random_applyrandom_apply_weightsif_unsupported_opsr%disable_sequential_featuresr"r/r0r1r2r3r%r4rc s|r|j||r d|_tj|||d||t||_|dur:t|t|kr:tdt|dt|dt|pDt t|f|_ ||_ dS)NT)r/r0zUThe length of `random_apply_weights` must be as same as the number of operations.Got z and .) r%rsuper__init___read_random_applylenr1 ValueErrorrtorchonesr2r3) rr/r0r1r2r3r%r4r" __class__rrr7ts   zImageSequential.__init__ max_lengthcCsBt|tfr |dur d}njt|tfr|dur||df}nYt|tfr+||df}nLt|tfrRt|dkrRt|dtfrRt|dtfrR|d|ddf}n%t|tfrot|dkrot|dtfro|d|df}ntd|d|durt|tfrt|dkrt|dtfrt|dtfstd|d|S) z'Process the scenarios for random apply.FTrrzNon-readable random_apply. Got r5z"Expect a tuple of (int, int). Got )r#r*inttupler9r:AssertionError)rr1r?rrrr8s6   (  z"ImageSequential._read_random_applywith_mixcCst|jtrttjg|jdR}n tdt|j|j }| | }d||<tj ||||kd}d}|rst|dkrstdt|t|t|krst |d|d<|tt|}d}|||fS) a Get a forward sequence when random apply is in need. Args: with_mix: if to require a mix augmentation for the sequence. Note: Mix augmentations (e.g. RandomMixUp) will be only applied once even in a random forward. )rz'random apply should be a tuple. Gotcha r) replacementFrT)r#r1rBrAr;randintitem TypeErrortyper2cloneget_mix_augmentation_indicesnamed_children multinomialsumr9randr*floatrandpermget_children_by_indices)rrD num_samplesmultinomial_weights mix_indicesindices mix_addedrrrget_random_forward_sequences$  &z+ImageSequential.get_random_forward_sequence named_modulescCsddt|DS)zGet all the mix augmentations since they are label-involved. Special operations needed for label-involved augmentations. cSs$g|]\}\}}t|tjr|qSr)r#KMixAugmentationBaseV2).0idx_childrrr s$z@ImageSequential.get_mix_augmentation_indices..) enumerate)rrZrrrrLsz,ImageSequential.get_mix_augmentation_indicesparamscCsZ|dur(||}|jr|dSt|dkr$tdt|d|S||S)NrrzOMultiple mix augmentation is prohibited without enabling random_apply.Detected z mix augmentations.)rLrMr1rYr9r:get_children_by_params)rrcrVrrrget_forward_sequences   z$ImageSequential.get_forward_sequence batch_shapecCsd|}g}|D]'\}}t|ttjtfr ||}t||}nt|d}t||}| |q|Sr) rer#r r[r\rforward_parametersr_get_new_batch_shapeappend)rrfrZrcnamemodule mod_paramparamrrrrgs      z"ImageSequential.forward_parametersinputcCs td|S)zReturn identity matrix.r)rrnrrridentity_matrixr!zImageSequential.identity_matrix recompute extra_argsc Cs|durtd|duri}||}d}t||dur|ngD]\\}}} t|tjfrt| jtr|j} z| |}Wn t yFYnw|rZt |j |dd} | || j| } n|jdurjt|j|j|jd} nt|d|durw| n| |}||| }|jr| |jkr|}q t|tfrt|tjfr|st|j|j|jd} ntttt| j} |j|| ||d}|dur||n|} |dur| n| |}q |S)aCompute the transformation matrix according to the provided parameters. Args: input: the input tensor. params: params for the sequence. recompute: if to recompute the transformation matrix according to the params. default: False. extra_args: Optional dictionary of extra arguments with specific options for different input types. Nzrequires params to be provided.F)in_place)devicedtypez3._transform_matrix is None while `recompute=False`.)rqrr)NotImplementedErrorrezipr#r[GeometricAugmentationBase2Ddatadictshapetransform_tensorr:r flagsgenerate_transformation_matrix_transform_matrixrrtru RuntimeErrortransform_output_tensorr0squeezerAugmentationSequentialr rrrget_transformation_matrixrp)rrnrcrqrrrZres_matr_rkrm ori_shaper}matmaybe_param_data_matrrrrsH "    z)ImageSequential.get_transformation_matrixstrictcCsT|D]#}t|tfr||sdSt|tfrqt|tjr"q|r'dSqdS)aCheck if all transformations are intensity-based. Args: strict: if strict is False, it will allow non-augmentation Modules to be passed. e.g. `kornia.enhance.AdjustBrightness` will be recognized as non-intensity module if strict is set to True. Note: patch processing would break the continuity of labels (e.g. bbounding boxes, masks). FT)childrenr#ris_intensity_onlyr[IntensityAugmentationBase2D)rrr$rrrr8s   z!ImageSequential.is_intensity_onlytensorinput_names_to_handle output_typeinputsrrkwargscsb|js&|j||dtj}||i|}|dkr!|||_|S||_|Stj|i|}|S)aOverwrite the __call__ function to handle various inputs. Args: inputs: Inputs to operate on. input_names_to_handle: List of input names to convert, if None, handle all inputs. output_type: Desired output type ('tensor', 'numpy', or 'pil'). kwargs: Additional arguments. Returns: Callable: Decorated function with converted input and output types. rr)rconvert_input_outputr6__call___detach_tensor_to_cpu _output_image)rrrrrdecorated_forwardrr=rrrPs zImageSequential.__call__r&r)NFN)r'r(r)__doc__r rr*rrArrrQstrr7r8rrYrLrrer;Sizergrrprrrrr __classcell__rrr=rr4sB    ("%(  : rmrfrcCs|j}|dur |St|tr|D]}t||}q|Sd|vrT|dd}|dur?|dkr2|n|d}|dkr>|Sn|St|}|dd|dd<t|S|S)zGet the new batch shape if the augmentation changes the image size. Note: Augmentations that change the image size must provide the parameter `output_size`. N output_size batch_probrrg?) ryr#listrhgetnumelrHr;r)rmrfryprprobnew_batch_shaperrrrhrs&     rh)#typingrrrrrrrr r;kornia.augmentation augmentationr[kornia.augmentation.baser kornia.augmentation.utilsr kornia.corer r rrkornia.core.moduler kornia.utilsrbaserrcr__all__rrrrhrrrrs (       @