import argparse import torch import os import json from tqdm import tqdm from dc.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from dc.conversation import conv_templates, SeparatorStyle from dc.model.builder import load_pretrained_model from dc.utils import disable_torch_init from dc.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path from datasets import load_dataset from PIL import Image import math 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 create_one_query(problem, shot_num, shot_type, use_caption): ### [1] Demo prompt demo_prompt = "" ### [2] Test query # problem info question = problem['question'] unit = problem['unit'] choices = problem['choices'] # caption = problem['caption'] precision = problem['precision'] question_type = problem['question_type'] answer_type = problem['answer_type'] # hint if shot_type == 'solution': if question_type == "multi_choice": assert answer_type == "text" hint_text = f"Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end." else: assert answer_type in ["integer", "float", "list"] if answer_type == "integer": hint_text = f"Hint: Please answer the question requiring an integer answer and provide the final value, e.g., 1, 2, 3, at the end." elif answer_type == "float" and precision == 1: hint_text = f"Hint: Please answer the question requiring a floating-point number with one decimal place and provide the final value, e.g., 1.2, 1.3, 1.4, at the end." elif answer_type == "float" and precision == 2: hint_text = f"Hint: Please answer the question requiring a floating-point number with two decimal places and provide the final value, e.g., 1.23, 1.34, 1.45, at the end." elif answer_type == "list": hint_text = f"Hint: Please answer the question requiring a Python list as an answer and provide the final list, e.g., [1, 2, 3], [1.2, 1.3, 1.4], at the end." else: assert shot_type == 'code' hint_text = "Hint: Please generate a python code to solve the problem" # question question_text = f"Question: {question}" if unit: question_text += f" (Unit: {unit})" # choices if choices: # choices: (A) 1.2 (B) 1.3 (C) 1.4 (D) 1.5 texts = ["Choices:"] for i, choice in enumerate(choices): texts.append(f"({chr(ord('A')+i)}) {choice}") choices_text = "\n".join(texts) else: choices_text = "" # prompt if shot_type == 'solution': prompt = "Solution: " else: assert shot_type == 'code' prompt = "Python code: " elements = [hint_text, question_text, choices_text] test_query = "\n".join([e for e in elements if e != ""]) ### [3] Final query query = demo_prompt + "\n\n" + test_query query = query.strip() return query 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, load_8bit=args.load_8bit) questions = json.load(open(os.path.expanduser(args.question_file), "r")) dataset = load_dataset('AI4Math/MathVista')['testmini'] questions = [dict(pid=d['pid'], info=d) for d in dataset] # questions = [dict(pid=pid, info=qs) for pid, qs in questions.items()] 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) terminators = [ tokenizer.eos_token_id ] if args.conv_mode == 'llama_3': if tokenizer.unk_token is None: tokenizer.unk_token = "<|reserved_special_token_0|>" tokenizer.pad_token = tokenizer.unk_token terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] if os.path.exists(answers_file): file = open(answers_file, "r") pred_contents = [json.loads(line) for line in file] done_pid = [sample['pid'] for sample in pred_contents] else: done_pid = [] ans_file = open(answers_file, "a") for i, line in enumerate(tqdm(questions)): idx = line['pid'] info = line['info'] if idx in done_pid: continue qs = create_one_query( problem = info, shot_num = 0, shot_type = 'solution', use_caption = False, ) query = qs if 'image' in info: image_file = info["image"] image = Image.open(os.path.join(args.image_folder, image_file)) 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 if getattr(model.config, 'mm_use_im_start_end', False): qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs else: qs = DEFAULT_IMAGE_TOKEN + '\n' + qs else: images = None images_aux = None 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() 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, max_new_tokens=1024, bos_token_id=tokenizer.bos_token_id, # Begin of sequence token eos_token_id=terminators, # 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() # print(tokenizer.batch_decode(output_ids)[0].strip()) del info['decoded_image'] info['query'] = query info['response'] = outputs ans_file.write(json.dumps(info) + "\n") ans_file.flush() 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.json") parser.add_argument("--answers-file", type=str, default="answer.jsonl") parser.add_argument("--conv-mode", type=str, default="llava_v0") 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("--answer-prompter", action="store_true") parser.add_argument('--load_8bit', type=bool, default=False) parser.add_argument("--single-pred-prompt", action="store_true") args = parser.parse_args() eval_model(args)