o iA^@sdZddlmZmZddlZddlmZmZm Z m Z ddl m Z m Z ddlmZmZmZmZmZmZmZmZmZmZmZddlmZmZmZmZmZm Z dd l!m"Z"erYddl#Z#er`ddl$Z$e %e&Z'e"d d Gd d d eZ(d gZ)dS)zImage processor class for Beit.)OptionalUnionN)INIT_SERVICE_KWARGSBaseImageProcessor BatchFeature get_size_dict)resizeto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_torch_availableis_torch_tensoris_vision_availablelogging)requires)vision)backendsc%seZdZdZdgZeedddejddddddddf de d e e e e fd ed e d e e e e fd ee efde de de eeeefde eeeefde ddffdd Zejddfdejd e e e fd ede ee efde ee efdejf ddZdedejfddZ            d/dede e de e d e e e e fd e ed e e d e e e e fde e d e ede e de eeeefde eeeefde ee effddZ            d/dede e d e e e e fd e ed e e d e e e e fde e d e ede e de eeeefde eeeefde ee efde ee efdejfd d!Z       d0d"ede e d e e e e fd e ed e e d e e e e fde e de ee effd#d$Zd1fd%d& Zedddddddddddddejdfd'ed(e ede e d e e e e fd e ed e e d e e e e fde e d e ede e de eeeefde eeeefde e d)e ee efdede ee efdejjf"d*d+Z d1d,e ee!fd-d.Z"Z#S)2BeitImageProcessoraK Constructs a BEiT image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`): Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_center_crop (`bool`, *optional*, defaults to `True`): Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image is padded with 0's and then center cropped. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`): Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`. Can be overridden by the `crop_size` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): The mean to use if normalizing the image. This is a float or list of floats of length of the number of channels of the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): The standard deviation to use if normalizing the image. This is a float or list of floats of length of the number of channels of the image. Can be overridden by the `image_std` parameter in the `preprocess` method. do_reduce_labels (`bool`, *optional*, defaults to `False`): 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. Can be overridden by the `do_reduce_labels` parameter in the `preprocess` method. pixel_values)extraTNgp?F do_resizesizeresampledo_center_crop crop_sizerescale_factor do_rescale do_normalize image_mean image_stddo_reduce_labelsreturnc  stjdi| |dur|nddd}t|}|dur|nddd}t|dd}||_||_||_||_||_||_||_ ||_ | durG| nt |_ | durP| nt |_| |_dS)N)heightwidthr&) param_name)super__init__rr"r#r$r%r&r(r'r)r r*r r+r,) selfr"r#r$r%r&r'r(r)r*r+r,kwargs __class__r3b/home/ubuntu/.local/lib/python3.10/site-packages/transformers/models/beit/image_processing_beit.pyr5gs   zBeitImageProcessor.__init__image data_formatinput_data_formatcKsVt|ddd}d|vsd|vrtd|t|f|d|df|||d|S)a Resize an image to (size["height"], size["width"]). Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PIL.Image.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. Tr#default_to_squarer2r/r0z@The `size` argument must contain `height` and `width` keys. Got )r#r$r<r=)r ValueErrorkeysr )r6r;r#r$r<r=r7r3r3r:r szBeitImageProcessor.resizelabelcCs,t|}d||dk<|d}d||dk<|S)Nr)r)r6rBr3r3r: reduce_labels   zBeitImageProcessor.reduce_labelcCsf|r||}|r|j|||| d}|r|j||| d}|r&|j|| | d}| r1|j|| | | d}|S)N)r;r#r$r=)r;r#r=)r;scaler=)r;meanstdr=)rFr center_croprescale normalize)r6r;r,r"r#r$r%r&r(r'r)r*r+r=r3r3r: _preprocesss zBeitImageProcessor._preprocesscCslt|}|rt|rtd| durt|} |j|d|||||||| | | | d }| dur4t|| | d}|S)zPreprocesses a single image.zIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.NF) r,r"r#r$r%r&r(r'r)r*r+r=)input_channel_dim)rrlogger warning_oncerrMr )r6r;r"r#r$r%r&r(r'r)r*r+r<r=r3r3r:_preprocess_images2 z$BeitImageProcessor._preprocess_imagesegmentation_mapc Cst|}|jdkr|d}d} tj}n d} |durt|dd}|j|||||||ddtjd }| r8tj|d d }|tj }|S) z'Preprocesses a single segmentation map.)N.TFNrD) num_channels) r;r,r"r$r#r%r&r)r(r=r)axis) rndimr FIRSTrrMnpsqueezeastypeint64) r6rRr"r#r$r%r&r,r=added_dimensionr3r3r:_preprocess_segmentation_maps0    z/BeitImageProcessor._preprocess_segmentation_mapc stj|fd|i|S)Nsegmentation_maps)r4__call__)r6imagesr^r7r8r3r:r_+szBeitImageProcessor.__call__r`r^return_tensorsc sdurn j dur n j t ddd dur n j dur(n jdur1n jtddddurAn j durJ n j durSn jdur\n j duren j durnn j t |}|durt |dd}|durt |stdt |std t  d  f d d |D}d |i}|durч fdd |D}||d<t||dS)aI Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. segmentation_maps (`ImageInput`, *optional*) Segmentation maps to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after resizing. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image. crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the image after center crop. If one edge the image is smaller than `crop_size`, it will be padded with zeros and then cropped do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between [0 - 1]. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`): Image mean. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. 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. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: Use the channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. NTr#r>r&rS)expected_ndimszwInvalid segmentation_maps type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.) r(r'r)r*r+r%r&r"r#r$cs0g|]} j| d qS)) r;r"r%r(r)r$r#r'r&r*r+r<r=)rQ).0img) r&r<r%r)r(r"r*r+r=r$r'r6r#r3r: s$z1BeitImageProcessor.preprocess..r c s$g|]}j|dqS))rRr,r"r$r#r%r&)r])rcrR)r&r%r,r"r$r6r#r3r:res labels)data tensor_type)r"r#rr$r%r&r(r'r)r*r+r,rrr@rr)r6r`r^r"r#r$r%r&r(r'r)r*r+r,rar<r=rgr3)r&r<r%r)r,r(r"r*r+r=r$r'r6r#r: preprocess0s\K "   zBeitImageProcessor.preprocess target_sizescs|j}|durHt|t|krtdt|r|}gtt|D]"}tjjj ||j dd||ddd}|dj dd} |q#S|j ddfd d tj dDS) a6 Converts the output of [`BeitForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch. Args: outputs ([`BeitForSemanticSegmentation`]): Raw outputs of the model. target_sizes (`list[Tuple]` of length `batch_size`, *optional*): List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, predictions will not be resized. Returns: semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each `torch.Tensor` correspond to a semantic class id. NzTMake sure that you pass in as many target sizes as the batch dimension of the logitsr)dimbilinearF)r#mode align_cornersrDcsg|]}|qSr3r3)rcisemantic_segmentationr3r:reszIBeitImageProcessor.post_process_semantic_segmentation..)logitslenr@rnumpyrangetorchnn functional interpolate unsqueezeargmaxappendshape)r6outputsrjrridxresized_logits semantic_mapr3rpr:"post_process_semantic_segmentations&  z5BeitImageProcessor.post_process_semantic_segmentation) NNNNNNNNNNNN)NNNNNNN)N)$__name__ __module__ __qualname____doc__model_input_namesrrrBICUBICboolrdictstrintrfloatlistr5rXndarrayr r rrFrMrQr]r_rWrPILImageritupler __classcell__r3r3r8r:r9s*     $  $       $      0 )       r)*rtypingrrrtrXimage_processing_utilsrrrrimage_transformsr r image_utilsr r r rrrrrrrrutilsrrrrrrutils.import_utilsrrrv get_loggerrrOr__all__r3r3r3r:s&4   A