Create MiniMonkey.py
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scripts/MiniMonkey.py
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313
scripts/MiniMonkey.py
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import json
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from argparse import ArgumentParser
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import torch
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import os
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import json
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from tqdm import tqdm
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from PIL import Image
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import math
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import multiprocessing
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from multiprocessing import Pool, Queue, Manager
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from PIL import Image
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from transformers import AutoModel, CLIPImageProcessor
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from transformers import AutoTokenizer
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import torchvision.transforms as T
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from torchvision.transforms.functional import InterpolationMode
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#https://github.com/Yuliang-Liu/Monkey/tree/main/project/mini_monkey
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IMAGENET_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_STD = (0.229, 0.224, 0.225)
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def build_transform(input_size):
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MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
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transform = T.Compose([
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T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
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T.ToTensor(),
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T.Normalize(mean=MEAN, std=STD)
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])
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return transform
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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 dynamic_preprocess(image, min_num=5, max_num=6, image_size=448, 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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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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# 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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def dynamic_preprocess2(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False, prior_aspect_ratio=None):
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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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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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new_target_ratios = []
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if prior_aspect_ratio is not None:
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for i in target_ratios:
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if prior_aspect_ratio[0]%i[0] !=0 or prior_aspect_ratio[1]%i[1] !=0:
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new_target_ratios.append(i)
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else:
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continue
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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, new_target_ratios, orig_width, orig_height, image_size)
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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
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def load_image(image_file, input_size=448, min_num=1, max_num=6):
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image = Image.open(image_file).convert('RGB')
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transform = build_transform(input_size=input_size)
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images, target_aspect_ratio = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, min_num=min_num, max_num=max_num)
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pixel_values = [transform(image) for image in images]
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pixel_values = torch.stack(pixel_values)
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return pixel_values, target_aspect_ratio
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def load_image2(image_file, input_size=448, target_aspect_ratio=(1,1), min_num=1, max_num=6):
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image = Image.open(image_file).convert('RGB')
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transform = build_transform(input_size=input_size)
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images = dynamic_preprocess2(image, image_size=input_size, prior_aspect_ratio=target_aspect_ratio, use_thumbnail=True, min_num=min_num, max_num=max_num)
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pixel_values = [transform(image) for image in images]
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pixel_values = torch.stack(pixel_values)
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return pixel_values
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def split_list(lst, n):
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length = len(lst)
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avg = length // n # 每份的大小
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result = [] # 存储分割后的子列表
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for i in range(n - 1):
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result.append(lst[i*avg:(i+1)*avg])
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result.append(lst[(n-1)*avg:])
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return result
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def save_json(json_list,save_path):
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with open(save_path, 'w') as file:
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json.dump(json_list, file,indent=4)
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def _get_args():
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parser = ArgumentParser()
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parser.add_argument("--image_folder", type=str, default="./OCRBench_Images")
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parser.add_argument("--output_folder", type=str, default="./resutls")
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parser.add_argument("--OCRBench_file", type=str, default="./OCRBench/OCRBench.json")
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parser.add_argument("--model_path", type=str, default='mx262/MiniMokney')#TODO Set the address of your model's weights
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parser.add_argument("--save_name", type=str, default="MiniMokney") #TODO Set the name of the JSON file you save in the output_folder.
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parser.add_argument("--num_workers", type=int, default=1)
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args = parser.parse_args()
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return args
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OCRBench_score = {"Regular Text Recognition":0,"Irregular Text Recognition":0,"Artistic Text Recognition":0,"Handwriting Recognition":0,
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"Digit String Recognition":0,"Non-Semantic Text Recognition":0,"Scene Text-centric VQA":0,"Doc-oriented VQA":0,"Doc-oriented VQA":0,
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"Key Information Extraction":0,"Handwritten Mathematical Expression Recognition":0}
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AllDataset_score = {"IIIT5K":0,"svt":0,"IC13_857":0,"IC15_1811":0,"svtp":0,"ct80":0,"cocotext":0,"ctw":0,"totaltext":0,"HOST":0,"WOST":0,"WordArt":0,"IAM":0,"ReCTS":0,"ORAND":0,"NonSemanticText":0,"SemanticText":0,
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"STVQA":0,"textVQA":0,"ocrVQA":0,"ESTVQA":0,"ESTVQA_cn":0,"docVQA":0,"infographicVQA":0,"ChartQA":0,"ChartQA_Human":0,"FUNSD":0,"SROIE":0,"POIE":0,"HME100k":0}
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num_all = {"IIIT5K":0,"svt":0,"IC13_857":0,"IC15_1811":0,"svtp":0,"ct80":0,"cocotext":0,"ctw":0,"totaltext":0,"HOST":0,"WOST":0,"WordArt":0,"IAM":0,"ReCTS":0,"ORAND":0,"NonSemanticText":0,"SemanticText":0,
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"STVQA":0,"textVQA":0,"ocrVQA":0,"ESTVQA":0,"ESTVQA_cn":0,"docVQA":0,"infographicVQA":0,"ChartQA":0,"ChartQA_Human":0,"FUNSD":0,"SROIE":0,"POIE":0,"HME100k":0}
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def eval_worker(args, data, eval_id, output_queue):
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print(f"Process {eval_id} start.")
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checkpoint = args.model_path
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model = AutoModel.from_pretrained(
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checkpoint,
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True).eval().to(f'cuda:{eval_id}')
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tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
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for i in tqdm(range(len(data))):
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dataset_name = data[i]["dataset_name"]
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image_path = os.path.join(args.image_folder, data[i]['image_path'])
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qs = data[i]['question']
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pixel_values, target_aspect_ratio = load_image(image_path, min_num=9, max_num=24)
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pixel_values = pixel_values.to(f'cuda:{eval_id}').to(torch.bfloat16)
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pixel_values2 = load_image2(image_path, target_aspect_ratio=target_aspect_ratio, min_num=5, max_num=8)
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pixel_values2 = pixel_values2.to(f'cuda:{eval_id}').to(torch.bfloat16)
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pixel_values = torch.cat((pixel_values[:-1], pixel_values2[:-1], pixel_values[-1:]), 0)
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generation_config = dict(
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num_beams=1,
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max_new_tokens=512,
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do_sample=False,
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)
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question = '<image>\n'+qs+ '\nAnswer the question using a single word or phrase.'
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response = model.chat(tokenizer, pixel_values, target_aspect_ratio, question, generation_config)
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data[i]['predict'] = response
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output_queue.put({eval_id: data})
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print(f"Process {eval_id} has completed.")
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if __name__=="__main__":
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multiprocessing.set_start_method('spawn')
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args = _get_args()
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if os.path.exists(os.path.join(args.output_folder,f"{args.save_name}.json")):
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data_path = os.path.join(args.output_folder,f"{args.save_name}.json")
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print(f"output_path:{data_path} exist! Only generate the results that were not generated in {data_path}.")
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else:
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data_path = args.OCRBench_file
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with open(data_path, "r") as f:
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data = json.load(f)
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data_list = split_list(data, args.num_workers)
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output_queue = Manager().Queue()
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pool = Pool(processes=args.num_workers)
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for i in range(len(data_list)):
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pool.apply_async(eval_worker, args=(args, data_list[i], i, output_queue))
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pool.close()
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pool.join()
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results = {}
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while not output_queue.empty():
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result = output_queue.get()
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results.update(result)
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data = []
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for i in range(len(data_list)):
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data.extend(results[i])
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for i in range(len(data)):
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data_type = data[i]["type"]
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dataset_name = data[i]["dataset_name"]
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answers = data[i]["answers"]
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if data[i].get('predict',0)==0:
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continue
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predict = data[i]['predict']
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data[i]['result'] = 0
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if dataset_name == "HME100k":
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if type(answers)==list:
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for j in range(len(answers)):
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answer = answers[j].strip().replace("\n"," ").replace(" ","")
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predict = predict.strip().replace("\n"," ").replace(" ","")
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if answer in predict:
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data[i]['result'] = 1
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else:
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answers = answers.strip().replace("\n"," ").replace(" ","")
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predict = predict.strip().replace("\n"," ").replace(" ","")
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if answers in predict:
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data[i]['result'] = 1
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else:
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if type(answers)==list:
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for j in range(len(answers)):
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answer = answers[j].lower().strip().replace("\n"," ")
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predict = predict.lower().strip().replace("\n"," ")
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if answer in predict:
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data[i]['result'] = 1
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else:
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answers = answers.lower().strip().replace("\n"," ")
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predict = predict.lower().strip().replace("\n"," ")
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if answers in predict:
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data[i]['result'] = 1
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save_json(data, os.path.join(args.output_folder,f"{args.save_name}.json"))
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if len(data)==1000:
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for i in range(len(data)):
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if data[i].get("result",100)==100:
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continue
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OCRBench_score[data[i]['type']] += data[i]['result']
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recognition_score = OCRBench_score['Regular Text Recognition']+OCRBench_score['Irregular Text Recognition']+OCRBench_score['Artistic Text Recognition']+OCRBench_score['Handwriting Recognition']+OCRBench_score['Digit String Recognition']+OCRBench_score['Non-Semantic Text Recognition']
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Final_score = recognition_score+OCRBench_score['Scene Text-centric VQA']+OCRBench_score['Doc-oriented VQA']+OCRBench_score['Key Information Extraction']+OCRBench_score['Handwritten Mathematical Expression Recognition']
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print("###########################OCRBench##############################")
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print(f"Text Recognition(Total 300):{recognition_score}")
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print("------------------Details of Recognition Score-------------------")
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print(f"Regular Text Recognition(Total 50): {OCRBench_score['Regular Text Recognition']}")
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print(f"Irregular Text Recognition(Total 50): {OCRBench_score['Irregular Text Recognition']}")
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print(f"Artistic Text Recognition(Total 50): {OCRBench_score['Artistic Text Recognition']}")
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print(f"Handwriting Recognition(Total 50): {OCRBench_score['Handwriting Recognition']}")
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print(f"Digit String Recognition(Total 50): {OCRBench_score['Digit String Recognition']}")
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print(f"Non-Semantic Text Recognition(Total 50): {OCRBench_score['Non-Semantic Text Recognition']}")
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print("----------------------------------------------------------------")
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print(f"Scene Text-centric VQA(Total 200): {OCRBench_score['Scene Text-centric VQA']}")
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print("----------------------------------------------------------------")
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print(f"Doc-oriented VQA(Total 200): {OCRBench_score['Doc-oriented VQA']}")
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print("----------------------------------------------------------------")
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print(f"Key Information Extraction(Total 200): {OCRBench_score['Key Information Extraction']}")
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print("----------------------------------------------------------------")
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print(f"Handwritten Mathematical Expression Recognition(Total 100): {OCRBench_score['Handwritten Mathematical Expression Recognition']}")
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print("----------------------Final Score-------------------------------")
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print(f"Final Score(Total 1000): {Final_score}")
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else:
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for i in range(len(data)):
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num_all[data[i]['dataset_name']] += 1
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if data[i].get("result",100)==100:
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continue
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AllDataset_score[data[i]['dataset_name']] += data[i]['result']
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for key in AllDataset_score.keys():
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print(f"{key}: {AllDataset_score[key]/float(num_all[key])}")
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