o wi{0 @s0ddlZddlmZddlmZmZmZddlmZddl m Z m Z ddl m Z ddlmZdd lmZmZmZmZdd lmZmZmZmZmZmZerRdd lmZerYddlZercdd lm Z!n erldd l"m Z!d ddee#ee#fde$de#def ddZ%GdddeZ&eGddde Z'dgZ(dS)N)Iterable) TYPE_CHECKINGOptionalUnion) BatchFeature)group_images_by_shapereorder_images)BeitImageProcessorFast)DefaultFastImageProcessorKwargs)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDPILImageResamplingSizeDict) TensorTypeauto_docstringis_torch_availableis_torchvision_availableis_torchvision_v2_availablerequires_backends)DepthEstimatorOutput) functional input_image torch.Tensor output_sizekeep_aspect_ratiomultiplereturnc Csddd}|jdd\}}|\}}||} ||} |r-td| td| kr+| } n| } || ||d} || ||d} t| | dS) NrcSsPt|||}|dur||krt|||}||kr&t|||}|SN)roundmathfloorceil)valrmin_valmax_valxr'`/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/transformers/models/dpt/modular_dpt.pyconstrain_to_multiple_of>s z>get_resize_output_image_size..constrain_to_multiple_of)rheightwidth)rN)shapeabsr) rrrrr) input_height input_width output_height output_width scale_height scale_width new_height new_widthr'r'r(get_resize_output_image_size8s   r9c@sNeZdZUdZeeed<eeed<eeed<eeed<eeed<dS)DPTFastImageProcessorKwargsa 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____doc__rint__annotations__boolr'r'r'r(r:_s     r:c'@seZdZejZeZeZ dddZ dZ dZ dZ dZdZdZdZdZdZdZdZeZdd Z    d,d d d ed ddedeededd fddZ d-d d dedd fddZded deded ed eddededededed ee eeefd!ee eeefdedeed"edeed#eed$ee e!e"fde#f&d%d&Z$ d.d'd(d)ee e"ee%eefdfdee&e!e"ffd*d+Z'dS)/DPTImageProcessorFastir,TFgp?r+NcCstd)NzNo need to override this method)NotImplementedErrorr'r'r'r( from_dictszDPTImageProcessorFast.from_dictimagersize interpolationzF.InterpolationMode antialiasr;rrcCsJ|jr|jstd|t||j|jf||d}tj||||dS)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)rKrL)r-r. ValueErrorkeysr9r resize)selfrIrJrKrLr;rrr'r'r(rOs  zDPTImageProcessorFast.resizer<c CsN|jdd\}}dd}|||\}}|||\}} ||| |f} t|| S)a 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*NcSs2t|||}||}|d}||}||fS)N)r r")rJr<new_sizepad_size pad_size_leftpad_size_rightr'r'r(_get_pads z1DPTImageProcessorFast.pad_image.._get_pad)r/Fpad) rPrIr<r-r.rVpad_top pad_bottompad_left pad_rightpaddingr'r'r( pad_images   zDPTImageProcessorFast.pad_imageimagesr> do_resizedo_center_crop crop_size do_rescalerescale_factor do_normalize image_mean image_stdr=disable_groupingreturn_tensorsc Ks|r||}t||d\}}i}|D]\}}|r%|j||||| d}|||<qt||}t||d\}}i}|D]"\}}|rI|||}|rQ|||}|||| | | | }|||<q=t||}|rptj |dd}|S|}|S)N)rh)rIrJrKr;rr)dim) reduce_labelritemsrOr center_cropr^rescale_and_normalizetorchstack)rPr_r>r`rJrKrarbrcrdrerfrgrr;r=r<rhrikwargsgrouped_imagesgrouped_images_indexresized_images_groupedr/stacked_imagesresized_imagesprocessed_images_groupedprocessed_imagesr'r'r( _preprocesss>        z!DPTImageProcessorFast._preprocessoutputsr target_sizescCst|d|j}|durt|t|krtdg}|dur%dgt|n|}t||D]"\}}|durGtjjj| d d|ddd }| d |iq,|S) a 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. roNz]Make sure that you pass in as many target sizes as the batch dimension of the predicted depthrr+bicubicF)rJmode align_cornerspredicted_depth) rrlenrMzipronnr interpolate unsqueezesqueezeappend)rPrzr{rresultsdepth target_sizer'r'r(post_process_depth_estimations" z3DPTImageProcessorFast.post_process_depth_estimation)NTr+F)r+r)(r?r@rArBICUBICresampler rfr rgrJr`rcrer=rdr;rr>rbrar: valid_kwargsrHrrErrCrOr^listfloatrstrrrrytupledictrr'r'r'r(rFzs  +       >rF))r collections.abcrtypingrrr"transformers.image_processing_basertransformers.image_transformsrr3transformers.models.beit.image_processing_beit_fastr image_processing_utils_fastr image_utilsr r rrutilsrrrrrrmodeling_outputsrrotorchvision.transforms.v2rrWtorchvision.transformsrCrEr9r:rF__all__r'r'r'r(sB        ' @