diff --git a/scripts/MiniMonkey.py b/scripts/MiniMonkey.py new file mode 100644 index 0000000..d9cc098 --- /dev/null +++ b/scripts/MiniMonkey.py @@ -0,0 +1,313 @@ +import json +from argparse import ArgumentParser +import torch +import os +import json +from tqdm import tqdm +from PIL import Image +import math +import multiprocessing +from multiprocessing import Pool, Queue, Manager +from PIL import Image +from transformers import AutoModel, CLIPImageProcessor +from transformers import AutoTokenizer +import torchvision.transforms as T +from torchvision.transforms.functional import InterpolationMode + +#https://github.com/Yuliang-Liu/Monkey/tree/main/project/mini_monkey + +IMAGENET_MEAN = (0.485, 0.456, 0.406) +IMAGENET_STD = (0.229, 0.224, 0.225) + + +def build_transform(input_size): + MEAN, STD = IMAGENET_MEAN, IMAGENET_STD + transform = T.Compose([ + T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), + T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), + T.ToTensor(), + T.Normalize(mean=MEAN, std=STD) + ]) + return transform + + +def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): + best_ratio_diff = float('inf') + best_ratio = (1, 1) + area = width * height + for ratio in target_ratios: + target_aspect_ratio = ratio[0] / ratio[1] + ratio_diff = abs(aspect_ratio - target_aspect_ratio) + if ratio_diff < best_ratio_diff: + best_ratio_diff = ratio_diff + best_ratio = ratio + elif ratio_diff == best_ratio_diff: + if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: + best_ratio = ratio + return best_ratio + + +def dynamic_preprocess(image, min_num=5, max_num=6, image_size=448, use_thumbnail=False): + orig_width, orig_height = image.size + aspect_ratio = orig_width / orig_height + + # calculate the existing image aspect ratio + target_ratios = set( + (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 + i * j <= max_num and i * j >= min_num) + target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) + + # find the closest aspect ratio to the target + target_aspect_ratio = find_closest_aspect_ratio( + aspect_ratio, target_ratios, orig_width, orig_height, image_size) + + # calculate the target width and height + target_width = image_size * target_aspect_ratio[0] + target_height = image_size * target_aspect_ratio[1] + blocks = target_aspect_ratio[0] * target_aspect_ratio[1] + + # resize the image + resized_img = image.resize((target_width, target_height)) + processed_images = [] + for i in range(blocks): + box = ( + (i % (target_width // image_size)) * image_size, + (i // (target_width // image_size)) * image_size, + ((i % (target_width // image_size)) + 1) * image_size, + ((i // (target_width // image_size)) + 1) * image_size + ) + # split the image + split_img = resized_img.crop(box) + processed_images.append(split_img) + assert len(processed_images) == blocks + if use_thumbnail and len(processed_images) != 1: + thumbnail_img = image.resize((image_size, image_size)) + processed_images.append(thumbnail_img) + return processed_images, target_aspect_ratio + +def dynamic_preprocess2(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False, prior_aspect_ratio=None): + orig_width, orig_height = image.size + aspect_ratio = orig_width / orig_height + + # calculate the existing image aspect ratio + target_ratios = set( + (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 + i * j <= max_num and i * j >= min_num) + target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) + + new_target_ratios = [] + if prior_aspect_ratio is not None: + for i in target_ratios: + if prior_aspect_ratio[0]%i[0] !=0 or prior_aspect_ratio[1]%i[1] !=0: + new_target_ratios.append(i) + else: + continue + # find the closest aspect ratio to the target + target_aspect_ratio = find_closest_aspect_ratio( + aspect_ratio, new_target_ratios, orig_width, orig_height, image_size) + + # calculate the target width and height + target_width = image_size * target_aspect_ratio[0] + target_height = image_size * target_aspect_ratio[1] + blocks = target_aspect_ratio[0] * target_aspect_ratio[1] + + # resize the image + resized_img = image.resize((target_width, target_height)) + processed_images = [] + for i in range(blocks): + box = ( + (i % (target_width // image_size)) * image_size, + (i // (target_width // image_size)) * image_size, + ((i % (target_width // image_size)) + 1) * image_size, + ((i // (target_width // image_size)) + 1) * image_size + ) + # split the image + split_img = resized_img.crop(box) + processed_images.append(split_img) + assert len(processed_images) == blocks + if use_thumbnail and len(processed_images) != 1: + thumbnail_img = image.resize((image_size, image_size)) + processed_images.append(thumbnail_img) + return processed_images + +def load_image(image_file, input_size=448, min_num=1, max_num=6): + image = Image.open(image_file).convert('RGB') + transform = build_transform(input_size=input_size) + images, target_aspect_ratio = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, min_num=min_num, max_num=max_num) + pixel_values = [transform(image) for image in images] + pixel_values = torch.stack(pixel_values) + return pixel_values, target_aspect_ratio + +def load_image2(image_file, input_size=448, target_aspect_ratio=(1,1), min_num=1, max_num=6): + image = Image.open(image_file).convert('RGB') + transform = build_transform(input_size=input_size) + 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) + pixel_values = [transform(image) for image in images] + pixel_values = torch.stack(pixel_values) + return pixel_values + +def split_list(lst, n): + length = len(lst) + avg = length // n # 每份的大小 + result = [] # 存储分割后的子列表 + for i in range(n - 1): + result.append(lst[i*avg:(i+1)*avg]) + result.append(lst[(n-1)*avg:]) + return result + +def save_json(json_list,save_path): + with open(save_path, 'w') as file: + json.dump(json_list, file,indent=4) + +def _get_args(): + parser = ArgumentParser() + parser.add_argument("--image_folder", type=str, default="./OCRBench_Images") + parser.add_argument("--output_folder", type=str, default="./resutls") + parser.add_argument("--OCRBench_file", type=str, default="./OCRBench/OCRBench.json") + parser.add_argument("--model_path", type=str, default='mx262/MiniMokney')#TODO Set the address of your model's weights + parser.add_argument("--save_name", type=str, default="MiniMokney") #TODO Set the name of the JSON file you save in the output_folder. + parser.add_argument("--num_workers", type=int, default=1) + args = parser.parse_args() + return args + +OCRBench_score = {"Regular Text Recognition":0,"Irregular Text Recognition":0,"Artistic Text Recognition":0,"Handwriting Recognition":0, +"Digit String Recognition":0,"Non-Semantic Text Recognition":0,"Scene Text-centric VQA":0,"Doc-oriented VQA":0,"Doc-oriented VQA":0, +"Key Information Extraction":0,"Handwritten Mathematical Expression Recognition":0} +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, +"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} +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, +"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} + +def eval_worker(args, data, eval_id, output_queue): + print(f"Process {eval_id} start.") + checkpoint = args.model_path + model = AutoModel.from_pretrained( + checkpoint, + torch_dtype=torch.bfloat16, + low_cpu_mem_usage=True, + trust_remote_code=True).eval().to(f'cuda:{eval_id}') + + tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True) + + for i in tqdm(range(len(data))): + dataset_name = data[i]["dataset_name"] + + image_path = os.path.join(args.image_folder, data[i]['image_path']) + qs = data[i]['question'] + + pixel_values, target_aspect_ratio = load_image(image_path, min_num=9, max_num=24) + pixel_values = pixel_values.to(f'cuda:{eval_id}').to(torch.bfloat16) + pixel_values2 = load_image2(image_path, target_aspect_ratio=target_aspect_ratio, min_num=5, max_num=8) + pixel_values2 = pixel_values2.to(f'cuda:{eval_id}').to(torch.bfloat16) + pixel_values = torch.cat((pixel_values[:-1], pixel_values2[:-1], pixel_values[-1:]), 0) + + generation_config = dict( + num_beams=1, + max_new_tokens=512, + do_sample=False, + ) + question = '\n'+qs+ '\nAnswer the question using a single word or phrase.' + response = model.chat(tokenizer, pixel_values, target_aspect_ratio, question, generation_config) + data[i]['predict'] = response + output_queue.put({eval_id: data}) + print(f"Process {eval_id} has completed.") + +if __name__=="__main__": + multiprocessing.set_start_method('spawn') + args = _get_args() + + if os.path.exists(os.path.join(args.output_folder,f"{args.save_name}.json")): + data_path = os.path.join(args.output_folder,f"{args.save_name}.json") + print(f"output_path:{data_path} exist! Only generate the results that were not generated in {data_path}.") + else: + data_path = args.OCRBench_file + + with open(data_path, "r") as f: + data = json.load(f) + + data_list = split_list(data, args.num_workers) + + output_queue = Manager().Queue() + + pool = Pool(processes=args.num_workers) + for i in range(len(data_list)): + pool.apply_async(eval_worker, args=(args, data_list[i], i, output_queue)) + pool.close() + pool.join() + + results = {} + while not output_queue.empty(): + result = output_queue.get() + results.update(result) + + data = [] + for i in range(len(data_list)): + data.extend(results[i]) + + for i in range(len(data)): + data_type = data[i]["type"] + dataset_name = data[i]["dataset_name"] + answers = data[i]["answers"] + if data[i].get('predict',0)==0: + continue + predict = data[i]['predict'] + data[i]['result'] = 0 + if dataset_name == "HME100k": + if type(answers)==list: + for j in range(len(answers)): + answer = answers[j].strip().replace("\n"," ").replace(" ","") + predict = predict.strip().replace("\n"," ").replace(" ","") + if answer in predict: + data[i]['result'] = 1 + else: + answers = answers.strip().replace("\n"," ").replace(" ","") + predict = predict.strip().replace("\n"," ").replace(" ","") + if answers in predict: + data[i]['result'] = 1 + else: + if type(answers)==list: + for j in range(len(answers)): + answer = answers[j].lower().strip().replace("\n"," ") + predict = predict.lower().strip().replace("\n"," ") + if answer in predict: + data[i]['result'] = 1 + else: + answers = answers.lower().strip().replace("\n"," ") + predict = predict.lower().strip().replace("\n"," ") + if answers in predict: + data[i]['result'] = 1 + save_json(data, os.path.join(args.output_folder,f"{args.save_name}.json")) + if len(data)==1000: + for i in range(len(data)): + if data[i].get("result",100)==100: + continue + OCRBench_score[data[i]['type']] += data[i]['result'] + 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'] + 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'] + print("###########################OCRBench##############################") + print(f"Text Recognition(Total 300):{recognition_score}") + print("------------------Details of Recognition Score-------------------") + print(f"Regular Text Recognition(Total 50): {OCRBench_score['Regular Text Recognition']}") + print(f"Irregular Text Recognition(Total 50): {OCRBench_score['Irregular Text Recognition']}") + print(f"Artistic Text Recognition(Total 50): {OCRBench_score['Artistic Text Recognition']}") + print(f"Handwriting Recognition(Total 50): {OCRBench_score['Handwriting Recognition']}") + print(f"Digit String Recognition(Total 50): {OCRBench_score['Digit String Recognition']}") + print(f"Non-Semantic Text Recognition(Total 50): {OCRBench_score['Non-Semantic Text Recognition']}") + print("----------------------------------------------------------------") + print(f"Scene Text-centric VQA(Total 200): {OCRBench_score['Scene Text-centric VQA']}") + print("----------------------------------------------------------------") + print(f"Doc-oriented VQA(Total 200): {OCRBench_score['Doc-oriented VQA']}") + print("----------------------------------------------------------------") + print(f"Key Information Extraction(Total 200): {OCRBench_score['Key Information Extraction']}") + print("----------------------------------------------------------------") + print(f"Handwritten Mathematical Expression Recognition(Total 100): {OCRBench_score['Handwritten Mathematical Expression Recognition']}") + print("----------------------Final Score-------------------------------") + print(f"Final Score(Total 1000): {Final_score}") + else: + for i in range(len(data)): + num_all[data[i]['dataset_name']] += 1 + if data[i].get("result",100)==100: + continue + AllDataset_score[data[i]['dataset_name']] += data[i]['result'] + for key in AllDataset_score.keys(): + print(f"{key}: {AllDataset_score[key]/float(num_all[key])}")