PhddlZddlZddlZddlmZmZddlmZgdZ ejdGddeZ Gd d eZ Gd d Z Gd dZGddZGddZy)N) RandomCropRandomResizedCrop)_functional_video)RandomCropVideoRandomResizedCropVideoCenterCropVideoNormalizeVideo ToTensorVideoRandomHorizontalFlipVideozThe 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.c$eZdZdZdZdefdZy)rc~t|tjrt|t|f|_y||_yN) isinstancenumbersNumberintsize)selfrs sC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\torchvision/transforms/_transforms_video.py__init__zRandomCropVideo.__init__s+ dGNN +TCI.DIDIcv|j||j\}}}}tj|||||S)z Args: clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W) Returns: torch.tensor: randomly cropped/resized video clip. size is (C, T, OH, OW) ) get_paramsrFcroprclipijhws r__call__zRandomCropVideo.__call__#s7__T4995 1avvdAq!Q''rreturncN|jjd|jdS)N(size=)) __class____name__rrs r__repr__zRandomCropVideo.__repr__.s$..))*& 1==rNr) __module__ __qualname__rr#strr+rrrrs (>#>rrc,eZdZ ddZdZdefdZy)rct|tr$t|dk7rtd|||_n ||f|_||_||_||_y)Nz2size should be tuple (height, width), instead got )rtuplelen ValueErrorrinterpolation_modescaleratio)rrr8r9r7s rrzRandomResizedCropVideo.__init__3sV dE "4yA~ #UVZU[!\]]DIt DI"4  rc |j||j|j\}}}}tj||||||j |j S)z Args: clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W) Returns: torch.tensor: randomly cropped/resized video clip. size is (C, T, H, W) )rr8r9r resized_croprr7rs rr#zRandomResizedCropVideo.__call__EsK__T4::tzzB 1a~~dAq!Q 4;R;RSSrr$c |jjd|jd|jd|jd|j d S)Nr&z, interpolation_mode=z, scale=z, ratio=r')r(r)rr7r8r9r*s rr+zRandomResizedCropVideo.__repr__Psp..))*& ;PQUQhQhPiiqrvr|r|q}~FGKGQGQFRRST TrN))g{Gz?g?)g?gUUUUUU?bilinearr,r0rrrr2s&$% $ TT#Trrc$eZdZdZdZdefdZy)r c~t|tjrt|t|f|_y||_yr)rrrr crop_size)rr@s rrzCenterCropVideo.__init__Us+ i 0!)nc)n=DN&DNrcBtj||jS)z Args: clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W) Returns: torch.tensor: central cropping of video clip. Size is (C, T, crop_size, crop_size) )r center_cropr@rrs rr#zCenterCropVideo.__call__[s}}T4>>22rr$cN|jjd|jdS)Nz (crop_size=r')r(r)r@r*s rr+zCenterCropVideo.__repr__es%..))*+dnn5EQGGrNr,r0rrr r Ts' 3H#Hrr c*eZdZdZddZdZdefdZy)r z Normalize the video clip by mean subtraction and division by standard deviation Args: mean (3-tuple): pixel RGB mean std (3-tuple): pixel RGB standard deviation inplace (boolean): whether do in-place normalization c.||_||_||_yr)meanstdinplace)rrGrHrIs rrzNormalizeVideo.__init__rs  rcntj||j|j|jS)zj Args: clip (torch.tensor): video clip to be normalized. Size is (C, T, H, W) )r normalizerGrHrIrCs rr#zNormalizeVideo.__call__ws% {{4DHHdllCCrr$c|jjd|jd|jd|jdS)Nz(mean=z, std=z , inplace=r')r(r)rGrHrIr*s rr+zNormalizeVideo.__repr__~s=..))*& 6$((:VZVbVbUccdeerN)Fr)r-r.__doc__rr#r/r+r0rrr r is  Df#frr c(eZdZdZdZdZdefdZy)r z{ Convert tensor data type from uint8 to float, divide value by 255.0 and permute the dimensions of clip tensor cyrr0r*s rrzToTensorVideo.__init__s rc,tj|S)z Args: clip (torch.tensor, dtype=torch.uint8): Size is (T, H, W, C) Return: clip (torch.tensor, dtype=torch.float): Size is (C, T, H, W) )r to_tensorrCs rr#zToTensorVideo.__call__s{{4  rr$c.|jjSr)r(r)r*s rr+zToTensorVideo.__repr__s~~&&&rNrMr0rrr r s  !'#'rr c*eZdZdZddZdZdefdZy)r z Flip the video clip along the horizontal direction with a given probability Args: p (float): probability of the clip being flipped. Default value is 0.5 c||_yr)p)rrVs rrz"RandomHorizontalFlipVideo.__init__s rcrtj|jkrtj|}|S)z Args: clip (torch.tensor): Size is (C, T, H, W) Return: clip (torch.tensor): Size is (C, T, H, W) )randomrVrhfliprCs rr#z"RandomHorizontalFlipVideo.__call__s( ==?TVV #774=D rr$cN|jjd|jdS)Nz(p=r')r(r)rVr*s rr+z"RandomHorizontalFlipVideo.__repr__s$..))*#dffXQ77rN)g?rMr0rrr r s  8#8rr )rrXwarningstorchvision.transformsrrrr__all__warnrrr r r r r0rrr`sz @$  > >j>,T.TDHH*ff2'',88r