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import math
from typing import List
from .utils.judge_util import build_judge
from .image_base import ImageBaseDataset
from .mmlongbench import concat_images, MMLongBench_auxeval, anls_compute
from ..smp import *
FAIL_MSG = 'Failed to obtain answer via API.'
def DUDE_acc(result_file):
data = load(result_file)
overall_score = 0.0
score_list = list()
for i in range(len(data)):
item = data.iloc[i]
if isinstance(item['answer'], float) and math.isnan(item['answer']):
item['answer'] = 'Not answerable'
item['answer'] = item['answer'].lower()
item['pred'] = item['pred'].lower()
score = anls_compute(item['answer'], item['pred'])
score_list.append(score)
overall_score += score
data['score'] = score_list
dump(data, result_file)
res = dict()
res['category'], res['num'], res['avg_score'] = ['anls'], [len(data)], [overall_score / len(data)]
res = pd.DataFrame(res)
return res
class DUDE(ImageBaseDataset):
TYPE = 'VQA'
DATASET_URL = {
'DUDE': 'https://opencompass.openxlab.space/utils/VLMEval/DUDE.tsv',
'DUDE_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/DUDE_MINI.tsv',
}
DATASET_MD5 = {
'DUDE': '130d860d08206e1e407cd77150c10d88',
'DUDE_MINI': 'e0c0d998114f0cca7516d12039d2b538',
}
SUPPORTED_MODELS = {
'GPT4': (1, 1),
'GPT4V': (1, 1),
'GPT4V_HIGH': (1, 1),
'GPT4o': (1, 1),
'GPT4o_HIGH': (1, 1),
'GPT4o_MINI': (1, 1),
'XComposer2d5': (1, -1),
'XComposer2_4KHD': (1, -1),
'MiniCPM-Llama3-V-2_5': (1, 5),
'InternVL-Chat-V1-5': (5, 2),
}
def __init__(self, dataset, **kwargs):
self.model_list = list(self.SUPPORTED_MODELS.keys())
model_name = kwargs['model']
if not listinstr(self.model_list, model_name):
raise AssertionError("{} doesn't support the evaluation on DUDE.".format(model_name))
super(DUDE, self).__init__(dataset)
self.is_api = True if listinstr(['GPT4'], model_name) else False
self.max_pages = 120
concat_num, column_num = self.SUPPORTED_MODELS.get(model_name)
self.concat_num = concat_num
self.column_num = column_num
def prepare_tsv(self, url, file_md5=None):
data_root = LMUDataRoot()
os.makedirs(data_root, exist_ok=True)
file_name = url.split('/')[-1]
data_path = osp.join(data_root, file_name)
if osp.exists(data_path) and (file_md5 is None or md5(data_path) == file_md5):
pass
else:
warnings.warn('The dataset tsv is not downloaded')
download_file(url, data_path)
return load(data_path)
def dump_image(self, origin_line):
os.makedirs(self.img_root, exist_ok=True)
try:
import fitz
except:
warnings.warn('Please use `pip install pymupdf` to parse PDF files.')
line = origin_line.copy()
if not isinstance(line['image_path'], List):
line['image_path'] = [line['image_path']]
line['image_path'] = line['image_path'][:self.max_pages]
skip_pdf_parse = True
for im_name in line['image_path']:
path = osp.join(self.img_root, im_name)
if not read_ok(path):
skip_pdf_parse = False
break
# Just for being compatible with the zooped loop: zip(line['image'], line['image_path'])
if skip_pdf_parse:
line['image'] = line['image_path']
else:
pdf_data = base64.b64decode(line['image'])
pdf_file = io.BytesIO(pdf_data)
encoded_images = []
with fitz.open(stream=pdf_file, filetype='pdf') as doc:
doc = doc[:self.max_pages]
for page in doc:
image = page.get_pixmap(dpi=144)
image_file = io.BytesIO(image.tobytes(output='png'))
image = Image.open(image_file)
encoded_image = encode_image_to_base64(image)
encoded_images.append(encoded_image)
line['image'] = encoded_images
print('process {}'.format(line['doc_id']))
if 'image' in line:
if isinstance(line['image'], list):
tgt_path = []
assert 'image_path' in line
for img, im_name in zip(line['image'], line['image_path']):
path = osp.join(self.img_root, im_name)
if not read_ok(path):
decode_base64_to_image_file(img, path)
tgt_path.append(path)
else:
tgt_path = osp.join(self.img_root, f"{line['index']}.jpg")
if not read_ok(tgt_path):
decode_base64_to_image_file(line['image'], tgt_path)
tgt_path = [tgt_path]
else:
assert 'image_path' in line
tgt_path = toliststr(line['image_path'])
if self.concat_num > 0 and not self.is_api:
concatenated_images = concat_images(tgt_path, max_concat=self.concat_num, column_num=self.column_num)
old_tgt_path = tgt_path
assert isinstance(old_tgt_path, list)
if self.column_num != -1:
tgt_path = [
'_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat{}_{}.jpg'.format(self.concat_num, i)
for i in range(len(concatenated_images))
]
else:
tgt_path = ['_'.join(old_tgt_path[0].split('_')[:-1]) + '_concat_all.jpg']
for path, concatenated_image in zip(tgt_path, concatenated_images):
if not read_ok(path):
decode_base64_to_image_file(encode_image_to_base64(concatenated_image), path)
num_images, image_size = len(old_tgt_path), concatenated_image.size
print('concat {} images to a new one with size {}. save at {}'.format(num_images, image_size, path))
return tgt_path
@classmethod
def evaluate(self, eval_file, **judge_kwargs):
logger = get_logger('Evaluation')
model = judge_kwargs['model']
suffix = eval_file.split('.')[-1]
storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
if osp.exists(storage):
logger.warning(f'GPT scoring file {storage} already exists, will reuse it in DUDE_eval. ')
else:
data = load(eval_file)
model = build_judge(max_tokens=128, **judge_kwargs)
lt = len(data)
lines = [data.iloc[i] for i in range(lt)]
tups = [(model, line) for line in lines]
indices = [line['index'] for line in lines]
ans = {}
if osp.exists(tmp_file):
ans = load(tmp_file)
tups = [x for x, i in zip(tups, indices) if i not in ans]
indices = [i for i in indices if i not in ans]
if len(indices):
new_results = list()
for model, line in tqdm(tups):
res = MMLongBench_auxeval(model, line)
new_results.append(res)
log_map, res_map, pred_map = {}, {}, {}
all_inds = [line['index'] for line in lines]
for k, v in zip(all_inds, new_results):
log_map[k] = v['log']
res_map[k] = v['res']
pred_map[k] = v['pred']
data['res'] = [res_map[idx] for idx in data['index']]
data['log'] = [log_map[idx] for idx in data['index']]
data['pred'] = [pred_map[idx] for idx in data['index']]
dump(data, storage)
score = DUDE_acc(storage)
score_pth = storage.replace('.xlsx', '_score.csv')
dump(score, score_pth)
logger.info(f'DUDE successfully finished evaluating {eval_file}, results saved in {score_pth}')
logger.info('Score: ')
logger.info(score)
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