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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Modified from HunyuanVL image processor for BrainOCR.
"""Image processor class for BrainOCR."""
# isort: skip_file
import math
import numpy as np
import torchvision.transforms as transforms
from transformers import AutoImageProcessor
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_transforms import (
convert_to_rgb,
)
from transformers.image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_flat_list_of_images,
make_list_of_images,
valid_images,
validate_preprocess_arguments,
)
from transformers.utils import TensorType, logging
from transformers.video_utils import VideoInput, make_batched_videos
logger = logging.get_logger(__name__)
def smart_resize(
height: int,
width: int,
factor: int = 16,
min_pixels: int = 512 * 512,
max_pixels: int = 2048 * 2048,
):
"""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 max(height, width) / min(height, width) > 200:
raise ValueError(
"absolute aspect ratio must be smaller than 200, got "
f"{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 = max(factor, math.floor(height / beta / factor) * factor)
w_bar = max(factor, 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 BrainOCRImageProcessor(BaseImageProcessor):
model_input_names = [
"pixel_values",
"image_grid_thw",
"pixel_values_videos",
"video_grid_thw",
]
def __init__(
self,
do_resize: bool = True,
size: dict[str, int] | None = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: int | float = 1 / 255,
do_normalize: bool = True,
image_mean: float | list[float] | None = None,
image_std: float | list[float] | None = None,
do_convert_rgb: bool = True,
min_pixels: int | None = None,
max_pixels: int | None = None,
patch_size: int = 16,
temporal_patch_size: int = 2,
merge_size: int = 2,
**kwargs,
) -> None:
super().__init__(**kwargs)
if size is not None and (
"shortest_edge" not in size or "longest_edge" not in size
):
raise ValueError(
"size must contain 'shortest_edge' and 'longest_edge' keys."
)
else:
size = {"shortest_edge": 512 * 512, "longest_edge": 2048 * 2048}
if min_pixels is not None:
size["shortest_edge"] = min_pixels
if max_pixels is not None:
size["longest_edge"] = max_pixels
self.min_pixels = size["shortest_edge"]
self.max_pixels = size["longest_edge"]
self.size = size
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.patch_size = patch_size
self.temporal_patch_size = temporal_patch_size
self.merge_size = merge_size
self.do_convert_rgb = do_convert_rgb
def _preprocess(
self,
images: ImageInput | VideoInput,
do_resize: bool | None = None,
size: dict[str, int] | None = None,
resample: PILImageResampling = None,
do_rescale: bool | None = None,
rescale_factor: float | None = None,
do_normalize: bool | None = None,
image_mean: float | list[float] | None = None,
image_std: float | list[float] | None = None,
patch_size: int = 16,
temporal_patch_size: int = 2,
merge_size: int = 2,
do_convert_rgb: bool | None = None,
data_format: ChannelDimension | None = ChannelDimension.FIRST,
input_data_format: str | ChannelDimension | None = None,
):
images = make_list_of_images(images)
if do_convert_rgb:
images = [convert_to_rgb(image) for image in images]
width, height = images[0].width, images[0].height
resized_width, resized_height = width, height
processed_images = []
for image in images:
if do_resize:
resized_height, resized_width = smart_resize(
height=height,
width=width,
factor=patch_size * merge_size,
min_pixels=self.min_pixels,
max_pixels=self.max_pixels,
)
image = image.resize((resized_width, resized_height))
if do_normalize:
image = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(self.image_mean, self.image_std),
]
)(image)
processed_images.append(image)
patches = np.array(processed_images)
channel = patches.shape[1]
grid_t = patches.shape[0] // temporal_patch_size
grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
patches = patches.reshape(
1,
channel,
grid_h // merge_size,
merge_size,
patch_size,
grid_w // merge_size,
merge_size,
patch_size,
)
patches = patches.transpose(0, 2, 3, 5, 6, 1, 4, 7)
flatten_patches = patches.reshape(
1 * grid_h * grid_w, channel * patch_size * patch_size
)
return flatten_patches, (grid_t, grid_h, grid_w)
def preprocess(
self,
images: ImageInput,
videos: VideoInput = None,
do_resize: bool | None = None,
size: dict[str, int] | None = None,
min_pixels: int | None = None,
max_pixels: int | None = None,
resample: PILImageResampling = None,
do_rescale: bool | None = None,
rescale_factor: float | None = None,
do_normalize: bool | None = None,
image_mean: float | list[float] | None = None,
image_std: float | list[float] | None = None,
patch_size: int | None = None,
temporal_patch_size: int | None = None,
merge_size: int | None = None,
do_convert_rgb: bool | None = None,
return_tensors: str | TensorType | None = None,
data_format: ChannelDimension | None = ChannelDimension.FIRST,
input_data_format: str | ChannelDimension | None = None,
):
min_pixels = min_pixels if min_pixels is not None else self.min_pixels
max_pixels = max_pixels if max_pixels is not None else self.max_pixels
if size is not None:
if "shortest_edge" not in size or "longest_edge" not in size:
raise ValueError(
"size must contain 'shortest_edge' and 'longest_edge' keys."
)
min_pixels = size["shortest_edge"]
elif min_pixels is not None and max_pixels is not None:
size = {"shortest_edge": min_pixels, "longest_edge": max_pixels}
else:
size = {**self.size}
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_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
patch_size = patch_size if patch_size is not None else self.patch_size
temporal_patch_size = (
temporal_patch_size
if temporal_patch_size is not None
else self.temporal_patch_size
)
merge_size = merge_size if merge_size is not None else self.merge_size
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_flat_list_of_images(images)
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,
)
data = {}
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,
size=size,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
patch_size=patch_size,
temporal_patch_size=temporal_patch_size,
merge_size=merge_size,
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.update(
{"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}
)
if videos is not None:
videos = make_batched_videos(videos)
pixel_values_videos, vision_grid_thws_videos = [], []
for images in videos:
patches, video_grid_thw = self._preprocess(
images,
do_resize=do_resize,
size=size,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
patch_size=patch_size,
temporal_patch_size=temporal_patch_size,
merge_size=merge_size,
data_format=data_format,
do_convert_rgb=do_convert_rgb,
input_data_format=input_data_format,
)
pixel_values_videos.extend(patches)
vision_grid_thws_videos.append(video_grid_thw)
data.update(
{
"pixel_values_videos": np.array(pixel_values_videos),
"video_grid_thw": np.array(vision_grid_thws_videos),
}
)
return BatchFeature(data=data, tensor_type=return_tensors)
def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
min_pixels = (
images_kwargs["min_pixels"]
if "min_pixels" in images_kwargs
else self.size["shortest_edge"]
)
max_pixels = (
images_kwargs["max_pixels"]
if "max_pixels" in images_kwargs
else self.size["longest_edge"]
)
patch_size = images_kwargs.get("patch_size", self.patch_size)
merge_size = images_kwargs.get("merge_size", self.merge_size)
factor = patch_size * merge_size
resized_height, resized_width = smart_resize(
height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
)
grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
return grid_h * (grid_w + 1) + 2
AutoImageProcessor.register("BrainOCRImageProcessor", BrainOCRImageProcessor)
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