image_processing_keye.py

# coding=utf-8
#
# 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 Keye."""

import math
from typing import Dict, List, Optional, Union

import numpy as np
import torch
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from torchvision.transforms import functional as TF
from transformers.image_transforms import (
    convert_to_rgb,
    resize,
    to_channel_dimension_format,
)
from transformers.image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    infer_channel_dimension_format,
    is_scaled_image,
    is_valid_image,
    make_list_of_images,
    to_numpy_array,
    valid_images,
    validate_preprocess_arguments,
)
from transformers.utils import TensorType, is_vision_available, logging


logger = logging.get_logger(__name__)


if is_vision_available():
    from PIL import Image

ImageInput = Union[
    "PIL.Image.Image",
    np.ndarray,
    "torch.Tensor",
    List["PIL.Image.Image"],
    List[np.ndarray],
    List["torch.Tensor"],
]  # noqa


VideoInput = Union[
    List["PIL.Image.Image"],
    "np.ndarray",
    "torch.Tensor",
    List["np.ndarray"],
    List["torch.Tensor"],
    List[List["PIL.Image.Image"]],
    List[List["np.ndarrray"]],
    List[List["torch.Tensor"]],
]  # noqa


def make_batched_images(images) -> List[List[ImageInput]]:
    """
    Accepts images in list or nested list format, and makes a list of images for preprocessing.

    Args:
        images (`Union[List[List[ImageInput]], List[ImageInput], ImageInput]`):
            The input image.

    Returns:
        list: A list of images.
    """
    if (
        isinstance(images, (list, tuple))
        and isinstance(images[0], (list, tuple))
        and is_valid_image(images[0][0])
    ):
        return [img for img_list in images for img in img_list]

    elif isinstance(images, (list, tuple)) and is_valid_image(images[0]):
        return images

    elif is_valid_image(images):
        return [images]

    raise ValueError(f"Could not make batched images from {images}")


def adjust_size(size, patch_size):
    num_patches = size // patch_size
    if num_patches % 2 != 0:  # 如果是奇数，减1
        num_patches -= 1
    return num_patches * patch_size


def make_batched_videos(videos) -> List[VideoInput]:
    if (
        isinstance(videos, (list, tuple))
        and isinstance(videos[0], (list, tuple))
        and is_valid_image(videos[0][0])
    ):
        return videos

    elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
        if isinstance(videos[0], Image.Image):
            return [videos]
        elif len(videos[0].shape) == 4:
            return [list(video) for video in videos]

    elif is_valid_image(videos) and len(videos.shape) == 4:
        return [list(videos)]

    raise ValueError(f"Could not make batched video from {videos}")


def smart_resize(
    height: int,
    width: int,
    factor: int = 28,
    min_pixels: int = 28 * 28 * 130,
    max_pixels: int = 28 * 28 * 1280,
):
    """Rescales the image so that the following conditions are met:

    1. Both dimensions (height and width) are divisible by 'factor'.

    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].

    3. The aspect ratio of the image is maintained as closely as possible.

    """
    # if height < factor or width < factor:
    #    raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}")
    # if int(height < factor//4) + int(width < factor//4):
    #     raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor//4}")

    if height < factor:
        print(f"smart_resize: height={height} < factor={factor}, reset height=factor")
        width = round((width * factor) / height)
        height = factor

    if width < factor:
        print(f"smart_resize: width={width} < factor={factor}, reset width=factor")
        height = round((height * factor) / width)
        width = factor

    if max(height, width) / min(height, width) > 200:
        raise ValueError(
            f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}"
        )
    h_bar = round(height / factor) * factor
    w_bar = round(width / factor) * factor
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = math.floor(height / beta / factor) * factor
        w_bar = math.floor(width / beta / factor) * factor
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = math.ceil(height * beta / factor) * factor
        w_bar = math.ceil(width * beta / factor) * factor
    return h_bar, w_bar


class SiglipImageProcessor(BaseImageProcessor):
    r"""
    Constructs a Siglip image processor that dynamically resizes images based on the original images.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's (height, width) dimensions.
        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
            Resampling filter to use when resizing the image.
        do_rescale (`bool`, *optional*, defaults to `True`):
            Whether to rescale the image by the specified scale `rescale_factor`.
        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
            Scale factor to use if rescaling the image.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether to normalize the image.
        image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
            Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
        image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
            Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
        do_convert_rgb (`bool`, *optional*, defaults to `True`):
            Whether to convert the image to RGB.
        min_pixels (`int`, *optional*, defaults to `28 * 28 * 130`):
            The min pixels of the image to resize the image.
        max_pixels (`int`, *optional*, defaults to `28 * 28 * 1670`):
            The max pixels of the image to resize the image.
        patch_size (`int`, *optional*, defaults to 14):
            The spacial patch size of the vision encoder.
        temporal_patch_size (`int`, *optional*, defaults to 2):
            The temporal patch size of the vision encoder.
        merge_size (`int`, *optional*, defaults to 2):
            The merge size of the vision encoder to llm encoder.
    """

    model_input_names = [
        "pixel_values",
        "image_grid_thw",
        "pixel_values_videos",
        "video_grid_thw",
    ]

    def __init__(
        self,
        do_resize: bool = True,
        resample: PILImageResampling = PILImageResampling.BICUBIC,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_convert_rgb: bool = True,
        min_pixels: int = 28 * 28 * 130,
        max_pixels: int = 28 * 28 * 1280,
        patch_size: int = 14,
        temporal_patch_size: int = 1,
        merge_size: int = 2,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        self.do_resize = do_resize
        self.resample = resample
        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 OPENAI_CLIP_MEAN
        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
        self.min_pixels = min_pixels
        self.max_pixels = max_pixels
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.merge_size = merge_size
        self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels}  # not used
        self.do_convert_rgb = do_convert_rgb

    def mvit_rescale(self, image: Image.Image, merge_size: int = 2) -> Image.Image:
        try:
            w, h = image.size
        except:
            raise ValueError(str((type(image), image)))
        patch_size = self.patch_size

        if (w // patch_size) * (h // patch_size) > self.in_token_limit:
            scale = math.sqrt(
                self.in_token_limit / ((w // patch_size) * (h // patch_size))
            )
            new_w, new_h = int(w * scale), int(h * scale)

            image = image.resize((new_w, new_h), Image.Resampling.BICUBIC)
        if self.pad_input:
            new_w, new_h = image.size
            pad_size_h = merge_size * patch_size
            pad_size_w = merge_size * patch_size

            pad_h = (pad_size_h - new_h % pad_size_h) % pad_size_h
            pad_w = (pad_size_w - new_w % pad_size_w) % pad_size_w

            image = TF.pad(image, (0, 0, pad_w, pad_h))
        else:
            new_w, new_h = image.size
            new_w = new_w - new_w % patch_size
            new_h = new_h - new_h % patch_size

            new_w = adjust_size(new_w, patch_size)
            new_h = adjust_size(new_h, patch_size)

            image = TF.center_crop(image, (new_h, new_w))

        w, h = image.size
        if w // patch_size >= 512 or h // patch_size >= 512:
            new_h = min(patch_size * 510, h)
            new_w = min(patch_size * 510, w)
            image = TF.center_crop(image, (new_h, new_w))
            # raise ValueError("Exceed pos emb")
        return image

    def _preprocess(
        self,
        images: Union[ImageInput, VideoInput],
        do_resize: bool = None,
        resample: PILImageResampling = 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,
        do_convert_rgb: bool = None,
        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ):
        """
        Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.

        Args:
            images (`ImageInput`):
                Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`.
            vision_info (`List[Dict]`, *optional*):
                Optional list of dictionaries containing additional information about vision inputs.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image.
            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
                Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image.
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Scale factor to use if rescaling the image.
            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`):
                Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
                Whether to convert the image to RGB.
            data_format (`ChannelDimension`, *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. 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.   - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
        """
        images = make_list_of_images(images)

        if do_convert_rgb:
            images = [convert_to_rgb(image) for image in images]

        # 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])

        height, width = get_image_size(images[0], channel_dim=input_data_format)
        resized_height, resized_width = height, width
        processed_images = []

        for image in images:
            if do_resize:
                resized_height, resized_width = smart_resize(
                    height,
                    width,
                    factor=self.patch_size * self.merge_size,
                    min_pixels=self.min_pixels,
                    max_pixels=self.max_pixels,
                )
                image = resize(
                    image,
                    size=(resized_height, resized_width),
                    resample=resample,
                    input_data_format=input_data_format,
                )

            if do_rescale:
                image = self.rescale(
                    image, scale=rescale_factor, input_data_format=input_data_format
                )

            if do_normalize:
                image = self.normalize(
                    image=image,
                    mean=image_mean,
                    std=image_std,
                    input_data_format=input_data_format,
                )
            image = to_channel_dimension_format(
                image, data_format, input_channel_dim=input_data_format
            )
            processed_images.append(image)

        patches = np.array(processed_images)
        if data_format == ChannelDimension.LAST:
            patches = patches.transpose(0, 3, 1, 2)
        if patches.shape[0] == 1:
            patches = np.tile(patches, (self.temporal_patch_size, 1, 1, 1))
        init_patches = patches
        channel = patches.shape[1]
        grid_t = patches.shape[0] // self.temporal_patch_size
        grid_h, grid_w = (
            resized_height // self.patch_size,
            resized_width // self.patch_size,
        )
        patches = patches.reshape(
            grid_t,
            self.temporal_patch_size,
            channel,
            grid_h,
            self.patch_size,
            grid_w,
            self.patch_size,
        )
        patches = patches.transpose(0, 3, 5, 2, 1, 4, 6)
        assert self.temporal_patch_size == 1
        flatten_patches = patches.reshape(
            grid_t * grid_h * grid_w, channel, self.patch_size, self.patch_size
        )
        return flatten_patches, (grid_t, grid_h, grid_w)

    def preprocess(
        self,
        images: ImageInput,
        videos: VideoInput = None,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        resample: PILImageResampling = 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,
        do_convert_rgb: bool = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ):
        """
        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`.
            videos (`VideoInput`):
                Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If
                passing in videos 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. Shortest edge of the image is resized to size["shortest_edge"], with
                the longest edge resized to keep the input aspect ratio.
            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_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image.
            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 to use for normalization. Only has an effect if `do_normalize` is set to `True`.
            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
                `True`.
            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
                Whether to convert the image to RGB.
            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
        size = size if size is not None else self.size
        resample = resample if resample is not None else self.resample
        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
        do_convert_rgb = (
            do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
        )

        if images is not None:
            images = make_batched_images(images)
        if videos is not None:
            videos = make_batched_videos(videos)

        if images is not None and 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."
            )

        validate_preprocess_arguments(
            rescale_factor=rescale_factor,
            do_normalize=do_normalize,
            image_mean=image_mean,
            image_std=image_std,
            do_resize=do_resize,
            size=size,
            resample=resample,
        )

        if images is not None:
            pixel_values, vision_grid_thws = [], []
            for image in images:
                patches, image_grid_thw = self._preprocess(
                    image,
                    do_resize=do_resize,
                    resample=resample,
                    do_rescale=do_rescale,
                    rescale_factor=rescale_factor,
                    do_normalize=do_normalize,
                    image_mean=image_mean,
                    image_std=image_std,
                    data_format=data_format,
                    do_convert_rgb=do_convert_rgb,
                    input_data_format=input_data_format,
                )
                pixel_values.extend(patches)
                vision_grid_thws.append(image_grid_thw)
            pixel_values = np.array(pixel_values)
            vision_grid_thws = np.array(vision_grid_thws)
            data = {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}

        if videos is not None:
            pixel_values, vision_grid_thws = [], []
            for images in videos:
                patches, video_grid_thw = self._preprocess(
                    images,
                    do_resize=do_resize,
                    resample=resample,
                    do_rescale=do_rescale,
                    rescale_factor=rescale_factor,
                    do_normalize=do_normalize,
                    image_mean=image_mean,
                    image_std=image_std,
                    data_format=data_format,
                    do_convert_rgb=do_convert_rgb,
                    input_data_format=input_data_format,
                )
                pixel_values.extend(patches)
                vision_grid_thws.append(video_grid_thw)
            pixel_values = np.array(pixel_values)
            vision_grid_thws = np.array(vision_grid_thws)
            data = {
                "pixel_values_videos": pixel_values,
                "video_grid_thw": vision_grid_thws,
            }

        return BatchFeature(data=data, tensor_type=return_tensors)