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439 lines
15 KiB
Python
439 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# adapted from https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSeek-OCR-master/DeepSeek-OCR-vllm/process/image_process.py
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import math
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import torch
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import torchvision.transforms as T
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from PIL import Image, ImageOps
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from transformers import AutoProcessor, BatchFeature, LlamaTokenizerFast
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from transformers.processing_utils import ProcessorMixin
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# TODO(Isotr0py): change modes for variants
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# see: https://github.com/deepseek-ai/DeepSeek-OCR/blob/8cf003d38821fa1b19c73da3bd1b0dc262ea8136/DeepSeek-OCR-master/DeepSeek-OCR-vllm/config.py#L1-L6
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# Tiny: base_size = 512, image_size = 512, crop_mode = False
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# Small: base_size = 640, image_size = 640, crop_mode = False
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# Base: base_size = 1024, image_size = 1024, crop_mode = False
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# Large: base_size = 1280, image_size = 1280, crop_mode = False
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# Gundam: base_size = 1024, image_size = 640, crop_mode = True
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BASE_SIZE = 1024
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IMAGE_SIZE = 640
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CROP_MODE = True
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# TODO(Isotr0py): Expose as mm_kwargs
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MIN_CROPS = 2
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MAX_CROPS = 6 # max:9; If your GPU memory is small, it is recommended to set it to 6.
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def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
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best_ratio_diff = float("inf")
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best_ratio = (1, 1)
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area = width * height
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for ratio in target_ratios:
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target_aspect_ratio = ratio[0] / ratio[1]
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ratio_diff = abs(aspect_ratio - target_aspect_ratio)
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if ratio_diff < best_ratio_diff:
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best_ratio_diff = ratio_diff
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best_ratio = ratio
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elif ratio_diff == best_ratio_diff:
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
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best_ratio = ratio
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return best_ratio
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def calculate_aspect_ratios(
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min_num: int = MIN_CROPS, max_num: int = MAX_CROPS
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) -> list[tuple[int, int]]:
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target_ratios: set[tuple[int, int]] = set(
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(i, j)
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for n in range(min_num, max_num + 1)
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for i in range(1, n + 1)
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for j in range(1, n + 1)
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if i * j <= max_num and i * j >= min_num
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)
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sorted_target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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return sorted_target_ratios
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def count_tiles(
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orig_width,
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orig_height,
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min_num=MIN_CROPS,
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max_num=MAX_CROPS,
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image_size=640,
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use_thumbnail=False,
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):
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aspect_ratio = orig_width / orig_height
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# calculate the existing image aspect ratio
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target_ratios = calculate_aspect_ratios(min_num, max_num)
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# find the closest aspect ratio to the target
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target_aspect_ratio = find_closest_aspect_ratio(
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aspect_ratio, target_ratios, orig_width, orig_height, image_size
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)
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return target_aspect_ratio
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def dynamic_preprocess(
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image, min_num=MIN_CROPS, max_num=MAX_CROPS, image_size=640, use_thumbnail=False
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):
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orig_width, orig_height = image.size
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aspect_ratio = orig_width / orig_height
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# calculate the existing image aspect ratio
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target_ratios = calculate_aspect_ratios(min_num, max_num)
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# find the closest aspect ratio to the target
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target_aspect_ratio = find_closest_aspect_ratio(
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aspect_ratio, target_ratios, orig_width, orig_height, image_size
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)
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# calculate the target width and height
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target_width = image_size * target_aspect_ratio[0]
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target_height = image_size * target_aspect_ratio[1]
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
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# resize the image
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resized_img = image.resize((target_width, target_height))
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processed_images = []
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for i in range(blocks):
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box = (
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(i % (target_width // image_size)) * image_size,
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(i // (target_width // image_size)) * image_size,
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((i % (target_width // image_size)) + 1) * image_size,
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((i // (target_width // image_size)) + 1) * image_size,
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)
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# split the image
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split_img = resized_img.crop(box)
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processed_images.append(split_img)
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assert len(processed_images) == blocks
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if use_thumbnail and len(processed_images) != 1:
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thumbnail_img = image.resize((image_size, image_size))
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processed_images.append(thumbnail_img)
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return processed_images, target_aspect_ratio
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class ImageTransform:
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def __init__(
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self,
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mean: tuple[float, float, float] = (0.5, 0.5, 0.5),
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std: tuple[float, float, float] = (0.5, 0.5, 0.5),
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normalize: bool = True,
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):
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self.mean = mean
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self.std = std
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self.normalize = normalize
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transform_pipelines = [T.ToTensor()]
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if normalize:
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transform_pipelines.append(T.Normalize(mean, std))
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self.transform = T.Compose(transform_pipelines)
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def __call__(self, pil_img: Image.Image):
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x = self.transform(pil_img)
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return x
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class DeepseekOCRProcessor(ProcessorMixin):
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tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
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attributes = ["tokenizer"]
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def __init__(
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self,
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tokenizer: LlamaTokenizerFast,
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patch_size: int = 16,
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downsample_ratio: int = 4,
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image_mean: tuple[float, float, float] = (0.5, 0.5, 0.5),
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image_std: tuple[float, float, float] = (0.5, 0.5, 0.5),
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normalize: bool = True,
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image_token: str = "<image>",
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pad_token: str = "<｜▁pad▁｜>",
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add_special_token: bool = False,
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sft_format: str = "deepseek",
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mask_prompt: bool = True,
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ignore_id: int = -100,
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**kwargs,
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):
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self.image_size = IMAGE_SIZE
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self.base_size = BASE_SIZE
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self.patch_size = 16
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self.image_mean = image_mean
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self.image_std = image_std
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self.normalize = normalize
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self.downsample_ratio = 4
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self.image_transform = ImageTransform(
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mean=image_mean, std=image_std, normalize=normalize
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)
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self.tokenizer = tokenizer
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self.tokenizer.padding_side = "left" # must set this，padding side with make a difference in batch inference # noqa: E501
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# add the pad_token as special token to use 'tokenizer.pad_token'
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# and 'tokenizer.pad_token_id'
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if self.tokenizer.pad_token is None:
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self.tokenizer.add_special_tokens({"pad_token": pad_token})
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# add image token
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self.image_token_id = self.tokenizer.vocab.get(image_token)
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self.image_token = image_token
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self.pad_token = pad_token
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self.add_special_token = add_special_token
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self.sft_format = sft_format
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self.mask_prompt = mask_prompt
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self.ignore_id = ignore_id
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super().__init__(
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tokenizer,
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**kwargs,
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)
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@property
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def bos_id(self):
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return self.tokenizer.bos_token_id
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@property
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def eos_id(self):
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return self.tokenizer.eos_token_id
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@property
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def pad_id(self):
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return self.tokenizer.pad_token_id
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def encode(self, text: str, bos: bool = True, eos: bool = False):
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t = self.tokenizer.encode(text, add_special_tokens=False)
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if bos:
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t = [self.bos_id] + t
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if eos:
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t = t + [self.eos_id]
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return t
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def decode(self, t: list[int], **kwargs) -> str:
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return self.tokenizer.decode(t, **kwargs)
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def process_one(
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self,
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prompt: str,
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images: list[Image.Image],
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crop_mode: bool = CROP_MODE,
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):
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"""
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Args:
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prompt (str): the formatted prompt;
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images (List[ImageType]): the list of images;
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crop_mode (bool): if True, then crop the image;
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Returns:
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outputs (BaseProcessorOutput): the output of the processor,
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- input_ids (torch.LongTensor): [N + image tokens]
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- target_ids (torch.LongTensor): [N + image tokens]
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- pixel_values (torch.FloatTensor): [n_patches, 3, H, W]
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- image_id (int): the id of the image token
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- num_image_tokens (List[int]): the number of image tokens
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"""
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assert prompt is not None and images is not None, (
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"prompt and images must be used at the same time."
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)
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sft_format = prompt
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(
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input_ids,
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pixel_values,
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images_crop,
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images_seq_mask,
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images_spatial_crop,
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num_image_tokens,
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_,
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) = self.tokenize_with_images(
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conversation=sft_format,
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images=images,
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bos=True,
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eos=True,
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cropping=crop_mode,
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)
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prepare = BatchFeature(
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data=dict(
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input_ids=input_ids,
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pixel_values=pixel_values,
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images_crop=images_crop,
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images_seq_mask=images_seq_mask,
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images_spatial_crop=images_spatial_crop,
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num_image_tokens=num_image_tokens,
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),
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tensor_type="pt",
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)
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return prepare
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def __call__(
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self,
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*,
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prompt: str,
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images: list[Image.Image],
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crop_mode: bool = CROP_MODE,
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**kwargs,
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):
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prepare = self.process_one(
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prompt=prompt,
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images=images,
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crop_mode=crop_mode,
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)
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return prepare
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def tokenize_with_images(
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self,
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conversation: str,
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images: list[Image.Image],
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bos: bool = True,
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eos: bool = True,
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cropping: bool = True,
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):
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"""Tokenize text with <image> tags."""
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assert conversation.count(self.image_token) == len(images)
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text_splits = conversation.split(self.image_token)
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images_list, images_crop_list, images_seq_mask, images_spatial_crop = (
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[],
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[],
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[],
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[],
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)
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image_shapes = []
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num_image_tokens = []
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tokenized_str = []
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for text_sep, image in zip(text_splits, images):
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tokenized_sep = self.encode(text_sep, bos=False, eos=False)
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tokenized_str += tokenized_sep
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images_seq_mask += [False] * len(tokenized_sep)
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image_shapes.append(image.size)
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images_crop_raw = []
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if image.size[0] <= 640 and image.size[1] <= 640:
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crop_ratio = [1, 1]
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elif cropping:
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images_crop_raw, crop_ratio = dynamic_preprocess(
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image, image_size=IMAGE_SIZE
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)
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else:
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crop_ratio = [1, 1]
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if self.image_size <= 640 and not cropping:
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image = image.resize((self.image_size, self.image_size))
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global_view = ImageOps.pad(
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image,
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(self.base_size, self.base_size),
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color=tuple(int(x * 255) for x in self.image_transform.mean),
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)
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images_list.append(self.image_transform(global_view))
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num_width_tiles, num_height_tiles = crop_ratio
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images_spatial_crop.append([num_width_tiles, num_height_tiles])
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if num_width_tiles > 1 or num_height_tiles > 1:
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for cropped_image in images_crop_raw:
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images_crop_list.append(self.image_transform(cropped_image))
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num_queries = math.ceil(
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(self.image_size // self.patch_size) / self.downsample_ratio
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)
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num_queries_base = math.ceil(
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(self.base_size // self.patch_size) / self.downsample_ratio
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)
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tokenized_image = (
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[self.image_token_id] * num_queries_base + [self.image_token_id]
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) * num_queries_base
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tokenized_image += [self.image_token_id]
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if num_width_tiles > 1 or num_height_tiles > 1:
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local_row = [self.image_token_id] * (num_queries * num_width_tiles + 1)
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tokenized_image += local_row * (num_queries * num_height_tiles)
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tokenized_str += tokenized_image
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images_seq_mask += [True] * len(tokenized_image)
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num_image_tokens.append(len(tokenized_image))
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"""process the last text split"""
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tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
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tokenized_str += tokenized_sep
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images_seq_mask += [False] * len(tokenized_sep)
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"""add the bos and eos tokens"""
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if bos:
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tokenized_str = [self.bos_id] + tokenized_str
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images_seq_mask = [False] + images_seq_mask
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if eos:
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tokenized_str = tokenized_str + [self.eos_id]
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images_seq_mask = images_seq_mask + [False]
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assert len(tokenized_str) == len(images_seq_mask), (
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f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} "
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f"is not equal to images_seq_mask's length {len(images_seq_mask)}."
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)
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masked_tokenized_str = []
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for token_index in tokenized_str:
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if token_index != self.image_token_id:
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masked_tokenized_str.append(token_index)
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else:
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masked_tokenized_str.append(self.ignore_id)
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assert (
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len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str)
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), (
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f"tokenized_str's length {len(tokenized_str)}, "
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f"input_ids' length {len(masked_tokenized_str)}, "
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f"images_seq_mask's length {len(images_seq_mask)}, are not equal."
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)
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input_ids = torch.LongTensor(tokenized_str)
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target_ids = torch.LongTensor(masked_tokenized_str)
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images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
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# set input_ids < 0 | input_ids == self.image_token_id as ignore_id
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target_ids[(input_ids < 0) | (input_ids == self.image_token_id)] = (
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self.ignore_id
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)
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input_ids[input_ids < 0] = self.pad_id
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# Remove the ending eos token
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assert input_ids[-1] == self.eos_id
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input_ids = input_ids[:-1]
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target_ids = target_ids[:-1]
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images_seq_mask = images_seq_mask[:-1]
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if len(images_list) == 0:
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pixel_values = torch.zeros((0, 3, self.base_size, self.base_size))
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images_spatial_crop = torch.zeros((0, 2), dtype=torch.long)
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images_crop = torch.zeros((0, 3, self.image_size, self.image_size))
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else:
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pixel_values = torch.stack(images_list, dim=0)
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images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
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if images_crop_list:
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images_crop = torch.stack(images_crop_list, dim=0)
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else:
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images_crop = torch.zeros((0, 3, self.image_size, self.image_size))
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input_ids = input_ids.unsqueeze(0)
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return (
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input_ids,
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pixel_values,
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images_crop,
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images_seq_mask,
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images_spatial_crop,
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num_image_tokens,
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image_shapes,
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)
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AutoProcessor.register("DeepseekOCRProcessor", DeepseekOCRProcessor)
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