codingutf8 2022 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license image processor class for deit from typing import dict list optional union import numpy as np from imageprocessingutils import baseimageprocessor batchfeature getsizedict from imagetransforms import resize tochanneldimensionformat from imageutils import imagenetstandardmean imagenetstandardstd channeldimension imageinput pilimageresampling inferchanneldimensionformat isscaledimage makelistofimages tonumpyarray validimages from utils import tensortype isvisionavailable logging if isvisionavailable import pil logger logging getloggername class deitimageprocessorbaseimageprocessor r constructs a deit image processor args doresize bool optional defaults to true whether to resize the image s height width dimensions to the specified size can be overridden by doresize in preprocess size dictstr int optional defaults to height 256 width 256 size of the image after resize can be overridden by size in preprocess resample pilimageresampling filter optional defaults to resampling bicubic resampling filter to use if resizing the image can be overridden by resample in preprocess docentercrop bool optional defaults to true whether to center crop the image if the input size is smaller than cropsize along any edge the image is padded with 0 s and then center cropped can be overridden by docentercrop in preprocess cropsize dictstr int optional defaults to height 224 width 224 desired output size when applying centercropping can be overridden by cropsize in preprocess rescalefactor int or float optional defaults to 1255 scale factor to use if rescaling the image can be overridden by the rescalefactor parameter in the preprocess method dorescale bool optional defaults to true whether to rescale the image by the specified scale rescalefactor can be overridden by the dorescale parameter in the preprocess method donormalize bool optional defaults to true whether to normalize the image can be overridden by the donormalize parameter in the preprocess method imagemean float or listfloat optional defaults to imagenetstandardmean mean to use if normalizing the image this is a float or list of floats the length of the number of channels in the image can be overridden by the imagemean parameter in the preprocess method imagestd float or listfloat optional defaults to imagenetstandardstd standard deviation to use if normalizing the image this is a float or list of floats the length of the number of channels in the image can be overridden by the imagestd parameter in the preprocess method copied from transformers models vit imageprocessingvit vitimageprocessor resize with pilimageresampling bilinearpilimageresampling bicubic resize an image to sizeheight sizewidth args image np ndarray image to resize size dictstr int dictionary in the format height int width int specifying the size of the output image resample pilimageresampling optional defaults to pilimageresampling bicubic pilimageresampling filter to use when resizing the image e g pilimageresampling bicubic dataformat channeldimension or str optional the channel dimension format for the output image if unset the channel dimension format of the input image is used can be one of channelsfirst or channeldimension first image in numchannels height width format channelslast or channeldimension last image in height width numchannels format none or channeldimension none image in height width format inputdataformat 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 channelsfirst or channeldimension first image in numchannels height width format channelslast or channeldimension last image in height width numchannels format none or channeldimension none image in height width format returns np ndarray the resized image 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 dorescalefalse doresize bool optional defaults to self doresize whether to resize the image size dictstr int optional defaults to self size size of the image after resize resample pilimageresampling optional defaults to self resample pilimageresampling filter to use if resizing the image only has an effect if doresize is set to true docentercrop bool optional defaults to self docentercrop whether to center crop the image cropsize dictstr int optional defaults to self cropsize size of the image after center crop if one edge the image is smaller than cropsize it will be padded with zeros and then cropped dorescale bool optional defaults to self dorescale whether to rescale the image values between 0 1 rescalefactor float optional defaults to self rescalefactor rescale factor to rescale the image by if dorescale is set to true donormalize bool optional defaults to self donormalize whether to normalize the image imagemean float or listfloat optional defaults to self imagemean image mean imagestd float or listfloat optional defaults to self imagestd image standard deviation returntensors str or tensortype optional the type of tensors to return can be one of none 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 dataformat channeldimension or str optional defaults to channeldimension first the channel dimension format for the output image can be one of channeldimension first image in numchannels height width format channeldimension last image in height width numchannels format inputdataformat 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 channelsfirst or channeldimension first image in numchannels height width format channelslast or channeldimension last image in height width numchannels format none or channeldimension none image in height width format all transformations expect numpy arrays we assume that all images have the same channel dimension format coding utf 8 2022 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license image processor class for deit constructs a deit 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 do_resize in preprocess size dict str int optional defaults to height 256 width 256 size of the image after resize can be overridden by size in preprocess resample pilimageresampling filter optional defaults to resampling bicubic resampling filter to use if resizing the image can be overridden by resample in preprocess 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 do_center_crop in preprocess crop_size dict str int optional defaults to height 224 width 224 desired output size when applying center cropping can be overridden by crop_size in preprocess 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 mean to use if normalizing the image this is a float or list of floats the length of the number of channels in 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 standard deviation to use if normalizing the image this is a float or list of floats the length of the number of channels in the image can be overridden by the image_std parameter in the preprocess method copied from transformers models vit image_processing_vit vitimageprocessor resize with pilimageresampling bilinear pilimageresampling bicubic resize an image to size height size width args image np ndarray image to resize size dict str int dictionary in the format height int width int specifying the size of the output image resample pilimageresampling optional defaults to pilimageresampling bicubic pilimageresampling filter to use when resizing the image e g pilimageresampling bicubic data_format channeldimension or str optional the channel dimension format for the output image if unset the channel dimension format of the input image is used 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 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 returns np ndarray the resized image 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 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 resize resample pilimageresampling optional defaults to self resample pilimageresampling filter to use if resizing the image 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 return_tensors str or tensortype optional the type of tensors to return can be one of none 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 channeldimension first image in num_channels height width format channeldimension last image in height width num_channels format 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 all transformations expect numpy arrays we assume that all images have the same channel dimension format
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    infer_channel_dimension_format,
    is_scaled_image,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


logger = logging.get_logger(__name__)


class DeiTImageProcessor(BaseImageProcessor):
    r

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        resample: PILImageResampling = PIL.Image.BICUBIC,
        do_center_crop: bool = True,
        crop_size: Dict[str, int] = None,
        rescale_factor: Union[int, float] = 1 / 255,
        do_rescale: bool = True,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        size = size if size is not None else {"height": 256, "width": 256}
        size = get_size_dict(size)
        crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
        crop_size = get_size_dict(crop_size, param_name="crop_size")

        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_center_crop = do_center_crop
        self.crop_size = crop_size
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD

    
    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BICUBIC,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        
        size = get_size_dict(size)
        if "height" not in size or "width" not in size:
            raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
        output_size = (size["height"], size["width"])
        return resize(
            image,
            size=output_size,
            resample=resample,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )

    def preprocess(
        self,
        images: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        resample=None,
        do_center_crop: bool = None,
        crop_size: Dict[str, int] = None,
        do_rescale: bool = None,
        rescale_factor: float = None,
        do_normalize: bool = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> PIL.Image.Image:
        
        do_resize = do_resize if do_resize is not None else self.do_resize
        resample = resample if resample is not None else self.resample
        do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
        image_mean = image_mean if image_mean is not None else self.image_mean
        image_std = image_std if image_std is not None else self.image_std

        size = size if size is not None else self.size
        size = get_size_dict(size)
        crop_size = crop_size if crop_size is not None else self.crop_size
        crop_size = get_size_dict(crop_size, param_name="crop_size")

        images = make_list_of_images(images)

        if not valid_images(images):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."
            )

        if do_resize and size is None or resample is None:
            raise ValueError("Size and resample must be specified if do_resize is True.")

        if do_center_crop and crop_size is None:
            raise ValueError("Crop size must be specified if do_center_crop is True.")

        if do_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True.")

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("Image mean and std must be specified if do_normalize is True.")

        
        images = [to_numpy_array(image) for image in images]

        if is_scaled_image(images[0]) and do_rescale:
            logger.warning_once(
                "It 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."
            )

        if input_data_format is None:
            
            input_data_format = infer_channel_dimension_format(images[0])

        if do_resize:
            images = [
                self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
                for image in images
            ]

        if do_center_crop:
            images = [
                self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) for image in images
            ]

        if do_rescale:
            images = [
                self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
                for image in images
            ]

        if do_normalize:
            images = [
                self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
                for image in images
            ]

        images = [
            to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
        ]

        data = {"pixel_values": images}
        return BatchFeature(data=data, tensor_type=return_tensors)