o V h@sddlmZmZmZmZmZmZmZddlZddlm Z ddl m Z ddl mZGdddeZGdd d eZGd d d eZGd d d eZdS))AnyCallableDictListOptionalSequenceUnionN)nn) transforms) TransformcsNeZdZdZdeeddffdd Zdedefdd Zde fd d Z Z S) ComposeaComposes several transforms together. This transform does not support torchscript. Please, see the note below. Args: transforms (list of ``Transform`` objects): list of transforms to compose. Example: >>> transforms.Compose([ >>> transforms.CenterCrop(10), >>> transforms.PILToTensor(), >>> transforms.ConvertImageDtype(torch.float), >>> ]) .. note:: In order to script the transformations, please use ``torch.nn.Sequential`` as below. >>> transforms = torch.nn.Sequential( >>> transforms.CenterCrop(10), >>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), >>> ) >>> scripted_transforms = torch.jit.script(transforms) Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require `lambda` functions or ``PIL.Image``. r returnNcs2tt|tstd|std||_dS)N5Argument transforms should be a sequence of callableszPass at least one transform)super__init__ isinstancer TypeError ValueErrorr selfr  __class__X/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/transforms/v2/_container.pyr(s   zCompose.__init__inputscGs2t|dk}|jD] }||}|r|n|f}q |SN)lenr rrneeds_unpacking transformoutputsrrrforward0s  zCompose.forwardcC*g}|jD] }|d|qd|SNz  r appendjoinr format_stringtrrr extra_repr7  zCompose.extra_repr) __name__ __module__ __qualname____doc__rrrrr"strr, __classcell__rrrrr s r cszeZdZdZejZddeee e j fde ddffdd Z deeeffd d Zd edefd d ZdefddZZS) RandomApplyaApply randomly a list of transformations with a given probability. .. note:: In order to script the transformation, please use ``torch.nn.ModuleList`` as input instead of list/tuple of transforms as shown below: >>> transforms = transforms.RandomApply(torch.nn.ModuleList([ >>> transforms.ColorJitter(), >>> ]), p=0.3) >>> scripted_transforms = torch.jit.script(transforms) Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require `lambda` functions or ``PIL.Image``. Args: transforms (sequence or torch.nn.Module): list of transformations p (float): probability of applying the list of transforms ?r pr NcsVtt|ttjfstd||_d|kr!dks&tdtd||_ dS)NzJArgument transforms should be a sequence of callables or a `nn.ModuleList`gg?z@`p` should be a floating point value in the interval [0.0, 1.0].) rrrrr ModuleListrr rr6rr r6rrrrTs  zRandomApply.__init__cCs|j|jdS)Nr r6r9)rrrr _extract_params_for_v1_transform_sz,RandomApply._extract_params_for_v1_transformrcGsRt|dk}td|jkr|r|S|dS|jD] }||}|r#|n|f}q|S)Nrr)rtorchrandr6r rrrrr"bs  zRandomApply.forwardcCr#r$r&r)rrrr,mr-zRandomApply.extra_repr)r5)r.r/r0r1 _transformsr4_v1_transform_clsrrrr r7floatrrr2rr:r"r,r3rrrrr4>s*  r4csPeZdZdZ d deedeeeddffdd Z de de fd d Z Z S) RandomChoiceaApply single transformation randomly picked from a list. This transform does not support torchscript. Args: transforms (sequence or torch.nn.Module): list of transformations p (list of floats or None, optional): probability of each transform being picked. If ``p`` doesn't sum to 1, it is automatically normalized. If ``None`` (default), all transforms have the same probability. Nr r6r cst|ts td|durdgt|}nt|t|kr+tdt|dt|t||_t|fdd|D|_ dS)Nrrz4Length of p doesn't match the number of transforms: z != csg|]}|qSrr).0probtotalrr sz)RandomChoice.__init__..) rrrrrrrr sumr6r8rrCrrs  zRandomChoice.__init__rcGs*ttt|jd}|j|}||Sr)intr; multinomialtensorr6r )rridxr rrrr"s zRandomChoice.forward)N) r.r/r0r1rrrrr?rrr"r3rrrrr@ts r@cs@eZdZdZdeeddffdd Zdedefdd ZZ S) RandomOrderzApply a list of transformations in a random order. This transform does not support torchscript. Args: transforms (sequence or torch.nn.Module): list of transformations r r Ncs&t|ts tdt||_dS)Nr)rrrrrr rrrrrs   zRandomOrder.__init__rcGsFt|dk}tt|jD]}|j|}||}|r|n|f}q|Sr)rr;randpermr )rrrrJr r!rrrr"s  zRandomOrder.forward) r.r/r0r1rrrrr"r3rrrrrKsrK)typingrrrrrrrr;r torchvisionr r=torchvision.transforms.v2r r r4r@rKrrrrs$   46%