503 lines
20 KiB
Python
503 lines
20 KiB
Python
import math
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from typing import List, Tuple
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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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from config import IMAGE_SIZE, BASE_SIZE, CROP_MODE, MIN_CROPS, MAX_CROPS, PROMPT, TOKENIZER
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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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# print(f'width: {width}, height: {height}, best_ratio: {best_ratio}')
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return best_ratio
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def count_tiles(orig_width, orig_height, min_num=MIN_CROPS, max_num=MAX_CROPS, image_size=640, use_thumbnail=False):
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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 = set(
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(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
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i * j <= max_num and i * j >= min_num)
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# print(target_ratios)
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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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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return target_aspect_ratio
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def dynamic_preprocess(image, min_num=MIN_CROPS, max_num=MAX_CROPS, image_size=640, use_thumbnail=False):
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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 = set(
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(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
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i * j <= max_num and i * j >= min_num)
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# print(target_ratios)
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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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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# print(target_aspect_ratio)
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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__(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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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 = TOKENIZER,
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candidate_resolutions: Tuple[Tuple[int, int]] = [[1024, 1024]],
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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.candidate_resolutions = candidate_resolutions # placeholder no use
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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 = patch_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 = downsample_ratio
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self.downsample_ratio = 4
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self.image_transform = ImageTransform(mean=image_mean, std=image_std, normalize=normalize)
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self.tokenizer = tokenizer
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# self.tokenizer = add_special_token(tokenizer)
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self.tokenizer.padding_side = 'left' # must set this，padding side with make a difference in batch inference
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# add the pad_token as special token to use 'tokenizer.pad_token' 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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# image_token_id = self.tokenizer.vocab.get(image_token)
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# if image_token_id is None:
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# special_tokens = [image_token]
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# special_tokens_dict = {"additional_special_tokens": special_tokens}
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# self.tokenizer.add_special_tokens(special_tokens_dict)
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self.image_token_id = self.tokenizer.vocab.get(image_token)
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# add five special tokens for grounding-related tasks
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# <|ref|>, <|/ref|>, <|det|>, <|/det|>, <|grounding|>
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# special_tokens = ['<|ref|>', '<|/ref|>', '<|det|>', '<|/det|>', '<|grounding|>']
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# special_tokens_dict = {"additional_special_tokens": special_tokens}
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# special_tokens = ['<image>','<|ref|>', '<|/ref|>', '<|det|>', '<|/det|>', '<|grounding|>', '<td>', '</td>', '<tr>', '</tr>']
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# special_tokens_dict = {"additional_special_tokens": special_tokens}
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# self.tokenizer.add_special_tokens(special_tokens_dict)
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# # add special tokens for SFT data
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# special_tokens = ["<|User|>", "<|Assistant|>"]
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# special_tokens_dict = {"additional_special_tokens": special_tokens}
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# self.tokenizer.add_special_tokens(special_tokens_dict)
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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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# def select_best_resolution(self, image_size):
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# # used for cropping
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# original_width, original_height = image_size
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# best_fit = None
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# max_effective_resolution = 0
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# min_wasted_resolution = float("inf")
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# for width, height in self.candidate_resolutions:
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# scale = min(width / original_width, height / original_height)
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# downscaled_width, downscaled_height = int(
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# original_width * scale), int(original_height * scale)
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# effective_resolution = min(downscaled_width * downscaled_height,
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# original_width * original_height)
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# wasted_resolution = (width * height) - effective_resolution
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# if effective_resolution > max_effective_resolution or (
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# effective_resolution == max_effective_resolution
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# and wasted_resolution < min_wasted_resolution):
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# max_effective_resolution = effective_resolution
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# min_wasted_resolution = wasted_resolution
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# best_fit = (width, height)
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# return best_fit
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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,
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inference_mode: bool = True,
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**kwargs,
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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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conversations (List[Dict]): conversations with a list of messages;
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images (List[ImageType]): the list of images;
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inference_mode (bool): if True, then remove the last eos token;
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system_prompt (str): the system prompt;
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**kwargs:
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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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sft_format = prompt
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input_ids, pixel_values, images_crop, images_seq_mask, images_spatial_crop, num_image_tokens, _ = images[0]
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return {
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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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# 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,
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inference_mode: bool = True,
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**kwargs,
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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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inference_mode (bool): if True, then remove the last eos token;
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**kwargs:
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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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- images (torch.FloatTensor): [n_images, 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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prepare = self.process_one(
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prompt=prompt,
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images=images,
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inference_mode=inference_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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# print(conversation)
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conversation = PROMPT
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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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image_shapes = []
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num_image_tokens = []
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tokenized_str = []
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# print('image: ', len(images))
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for text_sep, image in zip(text_splits, images):
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"""encode text_sep"""
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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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"""select best resolution for anyres"""
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# if cropping:
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# best_width, best_height = self.select_best_resolution(image.size)
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# else:
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# best_width, best_height = self.image_size, self.image_size
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image_shapes.append(image.size)
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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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else:
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if cropping:
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# print('image-size: ', image.size)
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# best_width, best_height = select_best_resolution(image.size, self.candidate_resolutions)
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# print('image ', image.size)
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# print('open_size:', image.size)
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images_crop_raw, crop_ratio = dynamic_preprocess(image, image_size=IMAGE_SIZE)
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# print('crop_ratio: ', crop_ratio)
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else:
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# best_width, best_height = self.image_size, self.image_size
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crop_ratio = [1, 1]
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# print(image.size, (best_width, best_height)) # check the select_best_resolutions func
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# print(crop_ratio)
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"""process the global view"""
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# if cropping
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if self.image_size <= 640 and not cropping:
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# print('directly resize')
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image = image.resize((self.image_size, self.image_size))
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global_view = ImageOps.pad(image, (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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images_list.append(self.image_transform(global_view))
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"""record height / width crop num"""
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# width_crop_num, height_crop_num = best_width // self.image_size, best_height // self.image_size
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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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"""process the local views"""
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# local_view = ImageOps.pad(image, (best_width, best_height),
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# color=tuple(int(x * 255) for x in self.image_transform.mean))
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# for i in range(0, best_height, self.image_size):
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# for j in range(0, best_width, self.image_size):
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# images_crop_list.append(
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# self.image_transform(local_view.crop((j, i, j + self.image_size, i + self.image_size))))
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for i in range(len(images_crop_raw)):
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images_crop_list.append(self.image_transform(images_crop_raw[i]))
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# """process the global view"""
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# global_view = ImageOps.pad(image, (self.image_size, self.image_size),
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# color=tuple(int(x * 255) for x in self.image_transform.mean))
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# images_list.append(self.image_transform(global_view))
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# """process the local views"""
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# local_view = ImageOps.pad(image, (best_width, best_height),
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# color=tuple(int(x * 255) for x in self.image_transform.mean))
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# for i in range(0, best_height, self.image_size):
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# for j in range(0, best_width, self.image_size):
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# images_list.append(
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# self.image_transform(local_view.crop((j, i, j + self.image_size, i + self.image_size))))
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# """add image tokens"""
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"""add image tokens"""
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num_queries = math.ceil((self.image_size // self.patch_size) / self.downsample_ratio)
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num_queries_base = math.ceil((self.base_size // self.patch_size) / self.downsample_ratio)
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tokenized_image = ([self.image_token_id] * num_queries_base + [self.image_token_id]) * 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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tokenized_image += ([self.image_token_id] * (num_queries * num_width_tiles) + [self.image_token_id]) * (
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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(
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images_seq_mask), f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}"
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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 len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str), \
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(f"tokenized_str's length {len(tokenized_str)}, input_ids' length {len(masked_tokenized_str)}, "
|
||
f"imags_seq_mask's length {len(images_seq_mask)}, are not equal")
|
||
|
||
input_ids = torch.LongTensor(tokenized_str)
|
||
target_ids = torch.LongTensor(masked_tokenized_str)
|
||
images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
|
||
|
||
# set input_ids < 0 | input_ids == self.image_token_id as ignore_id
|
||
target_ids[(input_ids < 0) |
|
||
(input_ids == self.image_token_id)] = self.ignore_id
|
||
input_ids[input_ids < 0] = self.pad_id
|
||
|
||
inference_mode = True
|
||
|
||
if inference_mode:
|
||
# Remove the ending eos token
|
||
assert input_ids[-1] == self.eos_id
|
||
input_ids = input_ids[:-1]
|
||
target_ids = target_ids[:-1]
|
||
images_seq_mask = images_seq_mask[:-1]
|
||
|
||
if len(images_list) == 0:
|
||
pixel_values = torch.zeros((1, 3, self.base_size, self.base_size))
|
||
images_spatial_crop = torch.zeros((1, 1), dtype=torch.long)
|
||
images_crop = torch.zeros((1, 3, self.image_size, self.image_size)).unsqueeze(0)
|
||
else:
|
||
pixel_values = torch.stack(images_list, dim=0)
|
||
images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
|
||
if images_crop_list:
|
||
images_crop = torch.stack(images_crop_list, dim=0).unsqueeze(0)
|
||
else:
|
||
images_crop = torch.zeros((1, 3, self.image_size, self.image_size)).unsqueeze(0)
|
||
|
||
input_ids = input_ids.unsqueeze(0)
|
||
|
||
|
||
return [[input_ids, pixel_values, images_crop, images_seq_mask, images_spatial_crop, num_image_tokens, image_shapes]]
|
||
|
||
|
||
AutoProcessor.register("DeepseekVLV2Processor", DeepseekOCRProcessor)
|