| | import argparse |
| | import torch |
| | import os |
| | import json |
| | import pandas as pd |
| | from tqdm import tqdm |
| | import shortuuid |
| |
|
| | from llava.constants import MM_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN |
| | from llava.conversation import conv_templates, SeparatorStyle |
| | from llava.model.builder import load_pretrained_model |
| | from llava.utils import disable_torch_init |
| | from llava.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) |
| | 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): |
| | |
| | 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 model.config.mm_use_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, MM_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() |
| |
|
| | image_tensor = process_images([image], image_processor, model.config)[0] |
| |
|
| | with torch.inference_mode(): |
| | output_ids = model.generate( |
| | input_ids, |
| | images=image_tensor.unsqueeze(0).half().cuda(), |
| | image_sizes=[image.size], |
| | do_sample=True if args.temperature > 0 else False, |
| | temperature=args.temperature, |
| | top_p=args.top_p, |
| | num_beams=args.num_beams, |
| | |
| | max_new_tokens=1024, |
| | 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() |
| |
|
| | |
| | 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) |
| |
|
