multimodal/build/lib/open_flamingo/eval/task/mmbench.py

import base64
import io
import random

import pandas as pd
from PIL import Image
from torch.utils.data import Dataset
from open_flamingo.eval.task.utils import get_object_from_text

def decode_base64_to_image(base64_string):
    image_data = base64.b64decode(base64_string)
    image = Image.open(io.BytesIO(image_data))
    return image

class MMBenchDataset(Dataset):
    def __init__(self,
                 data_file,
                 sys_prompt='There are several options:'):
        self.df = pd.read_csv(data_file, sep='\t')
        self.sys_prompt = sys_prompt

    def __len__(self):
        return len(self.df)

    def __getitem__(self, idx):
        index = self.df.iloc[idx]['index']
        image = self.df.iloc[idx]['image']
        image = decode_base64_to_image(image)
        question = self.df.iloc[idx]['question']
        answer = self.df.iloc[idx]['answer'] if 'answer' in self.df.iloc[0].keys() else None
        catetory = self.df.iloc[idx]['category']
        l2_catetory = self.df.iloc[idx]['l2-category']

        option_candidate = ['A', 'B', 'C', 'D', 'E']
        options = {
            cand: self.load_from_df(idx, cand)
            for cand in option_candidate
            if self.load_from_df(idx, cand) is not None
        }
        options_prompt = f'{self.sys_prompt}\n'
        for key, item in options.items():
            options_prompt += f'{key}. {item}\n'

        hint = self.load_from_df(idx, 'hint')
        data = {
            'img': image,
            'question': question,
            'answer': answer,
            'options': options_prompt,
            'category': catetory,
            'l2-category': l2_catetory,
            'options_dict': options,
            'index': index,
            'context': hint,
        }
        return data
    def load_from_df(self, idx, key):
        if key in self.df.iloc[idx] and not pd.isna(self.df.iloc[idx][key]):
            return self.df.iloc[idx][key]
        else:
            return None


def evaluate_mmbench(
    model,
    tokenizer,
    image_processor,
    batch_size=1,
    image_dir_path=None,
    questions_json_path=None,
    annotations_json_path=None,
    vis_embed_size=None,
    rank=0,
    world_size=1,
    id=0,
):
    dataset_name = "mmbench"
    dataset = MMBenchDataset("/gpfs/u/home/LMCG/LMCGljnn/scratch/datasets/raw/mmbench/mmbench_dev_20230712.tsv")
    for sample in dataset:
        print(sample)


if __name__ == '__main__':
    evaluate_mmbench(None, None, None)