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import argparse | |
import torch | |
import os | |
import json | |
from tqdm import tqdm | |
import shortuuid | |
from cumo.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN | |
from cumo.conversation import conv_templates, SeparatorStyle | |
from cumo.model.builder import load_pretrained_model | |
from cumo.utils import disable_torch_init | |
from cumo.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path | |
from datasets import load_dataset, concatenate_datasets | |
from cumo.eval.mmmu_utils.data_utils import load_yaml, save_json, CAT_SHORT2LONG | |
from cumo.eval.mmmu_utils.eval_utils import parse_multi_choice_response, parse_open_response | |
from PIL import Image | |
import math | |
import re | |
def process_single_sample(data): | |
question = data['question'] | |
o_imgs_paths = [] | |
for option in data['options']: | |
current_o_imgs_paths = re.findall("<img='(.*?)'>", option) | |
for img_path in current_o_imgs_paths: | |
o_imgs_paths.append(img_path) | |
if len(o_imgs_paths) > 1: # multiple images in options, used for random selection | |
return {'id': data['id'], 'question': question, 'options': data['options'], 'answer': data['answer'], | |
'image': None, 'question_type': data['question_type']} | |
else: | |
return {'id': data['id'], 'question': question, 'options': data['options'], 'answer': data['answer'], | |
'image': data['image_1'], 'question_type': data['question_type']} | |
def construct_prompt(sample): | |
question = sample['question'] | |
options = eval(sample['options']) | |
example = "" | |
if sample['question_type'] == 'multiple-choice': | |
start_chr = 'A' | |
prediction_range = [] | |
index2ans = {} | |
for option in options: | |
prediction_range.append(start_chr) | |
example += f"({start_chr}) {option}\n" | |
index2ans[start_chr] = option | |
start_chr = chr(ord(start_chr) + 1) | |
empty_prompt = question + '\n' + example + '\n' + "Answer with the option's letter from the given choices directly" | |
res_dict = {} | |
res_dict['index2ans'] = index2ans | |
res_dict['correct_choice'] = sample['answer'] | |
res_dict['all_choices'] = prediction_range | |
res_dict['empty_prompt'] = empty_prompt | |
res_dict['final_input_prompt'] = empty_prompt | |
res_dict['gt_content'] = options[ord(sample['answer'].upper()) - ord('A')] | |
else: | |
empty_prompt = question + '\n' + "Answer the question using a single word or phrase." | |
res_dict = {} | |
res_dict['empty_prompt'] = empty_prompt | |
res_dict['final_input_prompt'] = empty_prompt | |
res_dict['gt_content'] = sample['answer'] | |
res_dict.update(sample) | |
return res_dict | |
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 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) | |
model.config.training = False | |
# run for each subject | |
sub_dataset_list = [] | |
for subject in CAT_SHORT2LONG.values(): | |
print("loading ", subject) | |
sub_dataset = load_dataset(args.data_path, subject, split=args.split) | |
sub_dataset_list.append(sub_dataset) | |
# merge all dataset | |
dataset = concatenate_datasets(sub_dataset_list) | |
answers_file = os.path.expanduser(args.answers_file) | |
os.makedirs(os.path.dirname(answers_file), exist_ok=True) | |
out_samples = dict() | |
for sample in tqdm(dataset, total=len(dataset)): | |
sample = process_single_sample(sample) | |
sample = construct_prompt(sample) | |
qs = sample['final_input_prompt'].replace('<image 1>', '').strip() | |
if sample['image'] is not None: | |
image_tensor = process_images([sample['image'].convert('RGB')], image_processor, model.config)[0] | |
images = image_tensor.unsqueeze(0).half().cuda() | |
image_sizes = [sample['image'].size] | |
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 | |
image_sizes = 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, | |
image_sizes=image_sizes, | |
do_sample=True if args.temperature > 0 else False, | |
#temperature=args.temperature, | |
max_new_tokens=1024, | |
pad_token_id=tokenizer.eos_token_id, | |
use_cache=True, | |
) | |
response = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() | |
if sample['question_type'] == 'multiple-choice': | |
pred_ans = parse_multi_choice_response(response, sample['all_choices'], sample['index2ans']) | |
else: # open question | |
pred_ans = response | |
out_samples[sample['id']] = pred_ans | |
save_json(answers_file, out_samples) | |
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('--data_path', type=str, default="MMMU/MMMU") # hf dataset path. | |
parser.add_argument('--split', type=str, default='validation') | |
parser.add_argument("--answer-prompter", action="store_true") | |
parser.add_argument("--single-pred-prompt", action="store_true") | |
args = parser.parse_args() | |
eval_model(args) | |