get_resize_output_image_size..constrain_to_multiple_of>sQ #. !H ,  1; 3>*X5A ; #.)H4A)rheightwidth)rN)shapeabsr) rrrrr+ input_height input_width output_height output_width scale_height scale_width new_height new_widths r*get_resize_output_image_sizer=8s !, 1 1"# 6L"-M!/L,K q; #a,&6"7 7&L'K),*EPXYJ()BXVI : 77r-cj\rSrSr%Sr\\\S'\\\S'\\\S'\\\S'\\\S'Sr g ) DPTFastImageProcessorKwargs_a ensure_multiple_of (`int`, *optional*, defaults to 1): If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. Can be overidden by `ensure_multiple_of` in `preprocess`. do_pad (`bool`, *optional*, defaults to `False`): Whether to apply center padding. This was introduced in the DINOv2 paper, which uses the model in combination with DPT. size_divisor (`int`, *optional*): If `do_pad` is `True`, pads the image dimensions to be divisible by this value. This was introduced in the DINOv2 paper, which uses the model in combination with DPT. keep_aspect_ratio (`bool`, *optional*, defaults to `False`): If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Can be overidden by `keep_aspect_ratio` in `preprocess`. do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`): Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255. ensure_multiple_of size_divisordo_padrdo_reduce_labelsN) __name__ __module__ __qualname____firstlineno____doc__rint__annotations__bool__static_attributes__rEr-r*r?r?_s;&! %3- TN~%tn$r-r?c'\rSrSr\R r\r\ r SSS.r Sr Sr SrSrSrSrSrSrSrSrSr\rS(S S S \S S S\S\\S\SS 4SjjrS)S S S\SS 4SjjrS\S S\S\S \S \S S\S\S\S\S\S\\ \\\4S\\ \\\4S\S\\S\S\\S \\S!\\ \!\"4S\#4&S"jr$S*S#S$S%\\ \"\\%\\4S4S\\&\!\"44S&jjr'S'r(g)+DPTImageProcessorFastzir0TFgp?r/Nimagersize interpolationzF.InterpolationMode antialiasrArrcUR(aUR(d[SUR535e[ UURUR4UUS9n[ 5R XX4S9$)a Resize an image to `(size["height"], size["width"])`. Args: image (`torch.Tensor`): Image to resize. size (`SizeDict`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`): `InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`. antialias (`bool`, *optional*, defaults to `True`): Whether to use antialiasing when resizing the image ensure_multiple_of (`int`, *optional*): If `do_resize` is `True`, the image is resized to a size that is a multiple of this value keep_aspect_ratio (`bool`, *optional*, defaults to `False`): If `True`, and `do_resize` is `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Returns: `torch.Tensor`: The resized image. zDThe size dictionary must contain the keys 'height' and 'width'. Got )rrr)rTrU)r1r2 ValueErrorkeysr=r resize)selfrRrSrTrUrArrs r*rYDPTImageProcessorFast.resizesn:{{$**cdhdmdmdocpqr r2 djj1/'  &'..uQ^.ttr-rBcURSSup4SnU"X25upgU"XB5upXX4n [R"X5$)aL Center pad a batch of images to be a multiple of `size_divisor`. Args: image (`torch.Tensor`): Image to pad. Can be a batch of images of dimensions (N, C, H, W) or a single image of dimensions (C, H, W). size_divisor (`int`): The width and height of the image will be padded to a multiple of this number. r.NcX[R"X- 5U-nX - nUS-nX4- nXE4$)N)r#r%)rSrBnew_sizepad_size pad_size_leftpad_size_rights r*_get_pad1DPTImageProcessorFast.pad_image.._get_pads9yy!45 DHH$MM%5N 0 0r-)r3Fpad) rZrRrBr1r2rcpad_top pad_bottompad_left pad_rightpaddings r* pad_imageDPTImageProcessorFast.pad_imagesP BC(  1'v<&u;i<uuU$$r-imagesrD do_resizedo_center_crop crop_size do_rescalerescale_factor do_normalize image_mean image_stdrCdisable_groupingreturn_tensorsc ,U(aURU5n[UUS9unn0nUR5H%unnU(aURUUUUU S9nUUU'M' [ UU5n[UUS9unn0nUR5HQunnU(aUR UU5nU(aUR UU5nURUXXU 5nUUU'MS [ UU5nU(a[R"USS9OUn[SU0S9$)N)rw)rRrSrTrArr)dim pixel_values)data) reduce_labelritemsrYr center_croprlrescale_and_normalizetorchstackr)rZrnrDrorSrTrprqrrrsrtrurvrrArCrBrwrxkwargsgrouped_imagesgrouped_images_indexresized_images_groupedr3stacked_imagesresized_imagesprocessed_images_groupedprocessed_imagess r* _preprocess!DPTImageProcessorFast._preprocesssI, &&v.F0EV^n/o,,!#%3%9%9%; !E>!%("/'9&7 "-"-; "5 )&<((>@TU0E^fv/w,,#% %3%9%9%; !E>!%!1!1.)!L!% !M!77 LV_N/= $U +&<**BDXYCQ5;;'7Q?Wg.2B!CDDr-outputsr target_sizesc[US5 URnUb#[U5[U5:wa [S5e/nUcS/[U5-OUn[ X25HqupVUbV[ R RRURS5RS5USSS9R5nURS U05 Ms U$) aj Converts the raw output of [`DepthEstimatorOutput`] into final depth predictions and depth PIL images. Only supports PyTorch. Args: outputs ([`DepthEstimatorOutput`]): Raw outputs of the model. target_sizes (`TensorType` or `List[Tuple[int, int]]`, *optional*): Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size (height, width) of each image in the batch. If left to None, predictions will not be resized. Returns: `List[Dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth predictions. rNz]Make sure that you pass in as many target sizes as the batch dimension of the predicted depthrr/bicubicF)rSmode align_cornerspredicted_depth) rrlenrWziprnnr interpolate unsqueezesqueezeappend)rZrrrresultsdepth target_sizes r*post_process_depth_estimation3DPTImageProcessorFast.post_process_depth_estimation s( $(!11  $3+?3|CT+To 8D8LvO 44R^ "%o"D E&++77OOA&003+Iej8') NN-u5 6 #Er-rE)NTr/F)r/r!))rFrGrHrIrBICUBICresampler rurrvrSrorrrtrCrsrArrDrqrpr? valid_kwargsrrMrrKrYrllistfloatrstrrrrtupledictrrNrEr-r*rPrPzse!))H'J%IC (DIJL FNIN.L 04,-"'&u&u&u- &u  &u %SM &u &u &uV%%%  %89E^$9E9E 9E  9E  56 9E9E9E9E9E9EU5$u+#5679EE%e"4569E 9E%SM9E !9E"sm#9E$#4.%9E&!sJ!78'9E* +9E|RV'''uZeCHo1F%LMN' d3 ?# $ ''r-rP))r#collections.abcrtypingrrr"transformers.image_processing_basertransformers.image_transformsrr 3transformers.models.beit.image_processing_beit_fastr image_processing_utils_fastr image_utilsr rrrutilsrrrrrrmodeling_outputsrrtorchvision.transforms.v2rretorchvision.transformsrKrMr=r?rP__all__rEr-r*rs" $11;OV 8  96$8$8sHSM)*$8$8 $8  $8N%"A%6y2yyx # #r-