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image_processor_mle.py

@@ -0,0 +1,443 @@
+# copied from ViTImageProcessor (https://github.com/huggingface/transformers/blob/v4.37.2/src/transformers/models/vit/image_processing_vit.py)
+
+"""Image processor class for WD v14 Tagger."""
+
+from typing import Optional, List, Dict, Union, Tuple
+
+import numpy as np
+import cv2
+from PIL import Image
+
+from transformers.image_processing_utils import (
+    BaseImageProcessor,
+    BatchFeature,
+    get_size_dict,
+)
+from transformers.image_transforms import (
+    rescale,
+    to_channel_dimension_format,
+    _rescale_for_pil_conversion,
+    to_pil_image,
+)
+from transformers.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 transformers.utils import TensorType, logging
+
+logger = logging.get_logger(__name__)
+
+
+def resize_by_factor(
+    image: np.ndarray,
+    resize_factor: int,
+    resample: PILImageResampling = None,
+    data_format: Optional[Union[str, ChannelDimension]] = None,
+    input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    return_numpy: bool = True,
+):
+    """
+    Resizes `image` to `(height, width)` specified by `size` using the PIL library.
+
+    Args:
+        image (`np.ndarray`):
+            The image to resize.
+        resize_factor (`int`):
+            Value for padding the image to a multiple of the factor.
+        resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+            The filter to user for resampling.
+        data_format (`ChannelDimension`, *optional*):
+            The channel dimension format of the output image. If unset, will use the inferred format from the input.
+        return_numpy (`bool`, *optional*, defaults to `True`):
+            Whether or not to return the resized image as a numpy array. If False a `PIL.Image.Image` object is
+            returned.
+        input_data_format (`ChannelDimension`, *optional*):
+            The channel dimension format of the input image. If unset, will use the inferred format from the input.
+
+    Returns:
+        `np.ndarray`: The resized image.
+    """
+
+    resample = resample if resample is not None else PILImageResampling.BILINEAR
+
+    # For all transformations, we want to keep the same data format as the input image unless otherwise specified.
+    # The resized image from PIL will always have channels last, so find the input format first.
+    if input_data_format is None:
+        input_data_format = infer_channel_dimension_format(image)
+    data_format = input_data_format if data_format is None else data_format
+
+    # To maintain backwards compatibility with the resizing done in previous image feature extractors, we use
+    # the pillow library to resize the image and then convert back to numpy
+    do_rescale = False
+    if not isinstance(image, Image.Image):
+        do_rescale = _rescale_for_pil_conversion(image)
+        image = to_pil_image(
+            image, do_rescale=do_rescale, input_data_format=input_data_format
+        )
+
+    assert isinstance(image, Image.Image)
+
+    width, height = (
+        int(np.ceil(image.size[0] // resize_factor) * resize_factor),
+        int(np.ceil(image.size[1] // resize_factor) * resize_factor),
+    )
+    # solid image
+    new_image = Image.new(image.mode, (width, height), "white")
+
+    # paste original image on top left
+    new_image.paste(image)
+
+    if return_numpy:
+        new_image = np.array(new_image)
+        # If the input image channel dimension was of size 1, then it is dropped when converting to a PIL image
+        # so we need to add it back if necessary.
+        new_image = (
+            np.expand_dims(new_image, axis=-1) if new_image.ndim == 2 else new_image
+        )
+        # The image is always in channels last format after converting from a PIL image
+        new_image = to_channel_dimension_format(
+            new_image, data_format, input_channel_dim=ChannelDimension.LAST
+        )
+        # If an image was rescaled to be in the range [0, 255] before converting to a PIL image, then we need to
+        # rescale it back to the original range.
+        new_image = rescale(new_image, 1 / 255) if do_rescale else new_image
+
+    return new_image
+
+
+def greyscale(
+    image: np.ndarray,
+    data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
+    input_data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
+    return_numpy: bool = True,
+):
+    """
+    Convert `image` to `greyscale` using the PIL library.
+
+    Args:
+        image (`np.ndarray`):
+            The image to greyscale.
+    Returns:
+        `np.ndarray`: The greyscaled image.
+    """
+
+    if not isinstance(image, Image.Image):
+        do_rescale = _rescale_for_pil_conversion(image)
+        image = to_pil_image(
+            image, do_rescale=do_rescale, input_data_format=input_data_format
+        )
+
+    assert isinstance(image, Image.Image)
+
+    # do greyscale
+    image = image.convert("L")
+
+    if return_numpy:
+        image = np.array(image)
+
+        # If the input image channel dimension was of size 1, then it is dropped when converting to a PIL image
+        # so we need to add it back if necessary.
+        image = np.expand_dims(image, axis=-1) if image.ndim == 2 else image
+
+        # The image is always in channels last format after converting from a PIL image
+        image = to_channel_dimension_format(
+            image, data_format, input_channel_dim=ChannelDimension.LAST
+        )
+        # If an image was rescaled to be in the range [0, 255] before converting to a PIL image, then we need to
+        # rescale it back to the original range.
+        image = rescale(image, 1 / 255) if do_rescale else image
+
+    return image
+
+
+class MLEImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a MLE image processor.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
+            size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
+        resize_factor (`int`, *optional*, defaults to `16`):
+            Value for padding the image to a multiple of the factor.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
+            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
+            `preprocess` method.
+        do_rescale (`bool`, *optional*, defaults to `False`):
+            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+            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_normalize (`bool`, *optional*, defaults to `False`):
+            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.
+    """
+
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        resize_factor: int = 16,
+        do_greyscale: bool = True,
+        resample: PILImageResampling = PILImageResampling.BILINEAR,
+        do_rescale: bool = True,
+        rescale_factor: Union[int, float] = 1.0,
+        do_normalize: bool = False,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.do_resize = do_resize
+        self.resize_factor = resize_factor
+        self.do_greyscale = do_greyscale
+        self.do_rescale = do_rescale
+        self.do_normalize = do_normalize
+        self.resample = resample
+        self.rescale_factor = rescale_factor
+        self.image_mean = (
+            image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN[0]
+        )
+        self.image_std = (
+            image_std if image_std is not None else IMAGENET_STANDARD_STD[0]
+        )
+
+    def resize(
+        self,
+        image: np.ndarray,
+        resize_factor: int,
+        resample: PILImageResampling = PILImageResampling.BILINEAR,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Resize an image to `(size["height"], size["width"])`.
+
+        Args:
+            image (`np.ndarray`):
+                Image to resize.
+            resize_factor (`int`):
+                Value for padding the image to a multiple of the factor.
+            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
+            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.
+        """
+
+        return resize_by_factor(
+            image,
+            resize_factor=resize_factor,
+            resample=resample,
+            data_format=data_format,
+            input_data_format=input_data_format,
+            **kwargs,
+        )
+
+    def greyscale(
+        self,
+        image: np.ndarray,
+        data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
+        input_data_format: Optional[
+            Union[str, ChannelDimension]
+        ] = ChannelDimension.FIRST,
+        **kwargs,
+    ):
+        """
+        Convert an image to greyscale.
+
+        Args:
+            image (`np.ndarray`):
+                Image to greyscale
+
+        Returns:
+            `np.ndarray`: The greyscaled image.
+        """
+
+        return greyscale(
+            image,
+            data_format=data_format,
+            input_data_format=input_data_format,
+            **kwargs,
+        )
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        do_resize: Optional[bool] = None,
+        resize_factor: Optional[int] = None,
+        do_greyscale: Optional[bool] = None,
+        resample: PILImageResampling = None,
+        do_rescale: Optional[bool] = None,
+        rescale_factor: Optional[float] = None,
+        do_normalize: Optional[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: Union[str, ChannelDimension] = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ):
+        """
+        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.
+            resize_factor (`int`, *optional*, defaults to `self.resize_factor`):
+                Value for padding the image to a multiple of the factor.
+            resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
+                `PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has
+                an effect if `do_resize` is set to `True`.
+            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.
+            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.
+        """
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        do_greyscale = do_greyscale if do_greyscale is not None else self.do_greyscale
+        resample = resample if resample is not None else self.resample
+        rescale_factor = (
+            rescale_factor if rescale_factor is not None else self.rescale_factor
+        )
+        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
+
+        resize_factor = (
+            resize_factor if resize_factor is not None else self.resize_factor
+        )
+
+        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 resize_factor is None:
+            raise ValueError("Resize factor must be specified if do_resize is True.")
+
+        if do_rescale and rescale_factor is None:
+            raise ValueError("Rescale factor must be specified if do_rescale is True.")
+
+        # All transformations expect numpy arrays.
+        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:
+            # We assume that all images have the same channel dimension format.
+            input_data_format = infer_channel_dimension_format(images[0])
+
+        if do_resize:
+            images = [
+                self.resize(
+                    image=image,
+                    resize_factor=resize_factor,
+                    resample=resample,
+                    input_data_format=input_data_format,
+                )
+                for image in images
+            ]
+
+        if do_greyscale:
+            images = [
+                self.greyscale(
+                    image=image,
+                    data_format=data_format,
+                    input_data_format=input_data_format,
+                )
+                for image in images
+            ]
+            # the channel would be set to 1, so input data format could't be estimated
+            input_data_format = ChannelDimension.FIRST
+
+        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)

modeling_mle.py

@@ -391,23 +391,26 @@ class MLEForAnimeLineExtraction(MLEPretrainedModel):
 
         self.model = MLEModel(config)
 
-    def postprocess(self, output_tensor: torch.Tensor, input_shape: torch.Size):
-        pixel_values = output_tensor[0, 0, :, :]
+    def postprocess(self, output_tensor: torch.Tensor, input_shape: tuple[int, int]):
+        pixel_values = output_tensor[:, 0, :, :]
         pixel_values = torch.clip(pixel_values, 0, 255)
 
-        pixel_values = pixel_values[0 : input_shape[2], 0 : input_shape[3]]
+        pixel_values = pixel_values[:, 0 : input_shape[0], 0 : input_shape[1]]
         return pixel_values
 
     def forward(
         self, pixel_values: torch.Tensor, return_dict: bool = True
     ) -> tuple[torch.Tensor, ...] | MLEForAnimeLineExtractionOutput:
+        # height, width
+        input_image_size = (pixel_values.shape[2], pixel_values.shape[3])
+
         model_output = self.model(pixel_values)
 
         if not return_dict:
-            return (model_output, self.postprocess(model_output, pixel_values.shape))
+            return (model_output, self.postprocess(model_output, input_image_size))
 
         else:
             return MLEForAnimeLineExtractionOutput(
                 last_hidden_state=model_output,
-                pixel_values=self.postprocess(model_output, pixel_values.shape),
+                pixel_values=self.postprocess(model_output, input_image_size),
             )