import argparse import torch import os import json import pandas as pd from tqdm import tqdm import shortuuid from minigemini.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from minigemini.conversation import conv_templates, SeparatorStyle from minigemini.model.builder import load_pretrained_model from minigemini.utils import disable_torch_init from minigemini.mm_utils import tokenizer_image_token, process_images, load_image_from_base64, get_model_name_from_path from PIL import Image import math all_options = ['A', 'B', 'C', 'D'] def split_list(lst, n): """Split a list into n (roughly) equal-sized chunks""" chunk_size = math.ceil(len(lst) / n) # integer division return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] def get_chunk(lst, n, k): chunks = split_list(lst, n) return chunks[k] def is_none(value): if value is None: return True if type(value) is float and math.isnan(value): return True if type(value) is str and value.lower() == 'nan': return True if type(value) is str and value.lower() == 'none': return True return False def get_options(row, options): parsed_options = [] for option in options: option_value = row[option] if is_none(option_value): break parsed_options.append(option_value) return parsed_options def eval_model(args): # Model disable_torch_init() model_path = os.path.expanduser(args.model_path) model_name = get_model_name_from_path(model_path) tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name) questions = pd.read_table(os.path.expanduser(args.question_file)) questions = get_chunk(questions, args.num_chunks, args.chunk_idx) answers_file = os.path.expanduser(args.answers_file) os.makedirs(os.path.dirname(answers_file), exist_ok=True) ans_file = open(answers_file, "w") if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode: args.conv_mode = args.conv_mode + '_mmtag' print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.') for index, row in tqdm(questions.iterrows(), total=len(questions)): options = get_options(row, all_options) cur_option_char = all_options[:len(options)] if args.all_rounds: num_rounds = len(options) else: num_rounds = 1 for round_idx in range(num_rounds): idx = row['index'] question = row['question'] hint = row['hint'] image = load_image_from_base64(row['image']) if not is_none(hint): question = hint + '\n' + question for option_char, option in zip(all_options[:len(options)], options): question = question + '\n' + option_char + '. ' + option qs = cur_prompt = question if hasattr(model, "update_prompt"): model.update_prompt([[cur_prompt]]) if model.config.mm_use_im_start_end: qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs else: qs = DEFAULT_IMAGE_TOKEN + '\n' + qs if args.single_pred_prompt: if args.lang == 'cn': qs = qs + '\n' + "请直接回答选项字母。" else: qs = qs + '\n' + "Answer with the option's letter from the given choices directly." conv = conv_templates[args.conv_mode].copy() conv.append_message(conv.roles[0], qs) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() if hasattr(model.config, 'image_size_aux'): if not hasattr(image_processor, 'image_size_raw'): image_processor.image_size_raw = image_processor.crop_size.copy() image_processor.crop_size['height'] = model.config.image_size_aux image_processor.crop_size['width'] = model.config.image_size_aux image_processor.size['shortest_edge'] = model.config.image_size_aux image_tensor = process_images([image], image_processor, model.config)[0] image_grid = getattr(model.config, 'image_grid', 1) if hasattr(model.config, 'image_size_aux'): raw_shape = [image_processor.image_size_raw['height'] * image_grid, image_processor.image_size_raw['width'] * image_grid] image_tensor_aux = image_tensor image_tensor = torch.nn.functional.interpolate(image_tensor[None], size=raw_shape, mode='bilinear', align_corners=False)[0] else: image_tensor_aux = [] if image_grid >= 2: raw_image = image_tensor.reshape(3, image_grid, image_processor.image_size_raw['height'], image_grid, image_processor.image_size_raw['width']) raw_image = raw_image.permute(1, 3, 0, 2, 4) raw_image = raw_image.reshape(-1, 3, image_processor.image_size_raw['height'], image_processor.image_size_raw['width']) if getattr(model.config, 'image_global', False): global_image = image_tensor if len(global_image.shape) == 3: global_image = global_image[None] global_image = torch.nn.functional.interpolate(global_image, size=[image_processor.image_size_raw['height'], image_processor.image_size_raw['width']], mode='bilinear', align_corners=False) # [image_crops, image_global] raw_image = torch.cat([raw_image, global_image], dim=0) image_tensor = raw_image.contiguous() images = image_tensor[None].to(dtype=model.dtype, device='cuda', non_blocking=True) images_aux = image_tensor_aux[None].to(dtype=model.dtype, device='cuda', non_blocking=True) if len(image_tensor_aux)>0 else None with torch.inference_mode(): output_ids = model.generate( input_ids, images=images, images_aux=images_aux, do_sample=True if args.temperature > 0 else False, temperature=args.temperature, top_p=args.top_p, num_beams=args.num_beams, # no_repeat_ngram_size=3, max_new_tokens=1024, bos_token_id=tokenizer.bos_token_id, # Begin of sequence token eos_token_id=tokenizer.eos_token_id, # End of sequence token pad_token_id=tokenizer.pad_token_id, # Pad token use_cache=True) outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() ans_id = shortuuid.uuid() ans_file.write(json.dumps({"question_id": idx, "round_id": round_idx, "prompt": cur_prompt, "text": outputs, "options": options, "option_char": cur_option_char, "answer_id": ans_id, "model_id": model_name, "metadata": {}}) + "\n") ans_file.flush() # rotate options options = options[1:] + options[:1] cur_option_char = cur_option_char[1:] + cur_option_char[:1] ans_file.close() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--model-path", type=str, default="facebook/opt-350m") parser.add_argument("--model-base", type=str, default=None) parser.add_argument("--image-folder", type=str, default="") parser.add_argument("--question-file", type=str, default="tables/question.jsonl") parser.add_argument("--answers-file", type=str, default="answer.jsonl") parser.add_argument("--conv-mode", type=str, default="llava_v1") parser.add_argument("--num-chunks", type=int, default=1) parser.add_argument("--chunk-idx", type=int, default=0) parser.add_argument("--temperature", type=float, default=0.2) parser.add_argument("--top_p", type=float, default=None) parser.add_argument("--num_beams", type=int, default=1) parser.add_argument("--all-rounds", action="store_true") parser.add_argument("--single-pred-prompt", action="store_true") parser.add_argument("--lang", type=str, default="en") args = parser.parse_args() eval_model(args)