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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)
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