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# %%
import os
import json
import numpy as np
from tqdm import tqdm
from copy import deepcopy
from argparse import ArgumentParser
cmd_args = True
parser = ArgumentParser()
parser.add_argument('--test_dir', default='./test_webpages', help='the directory of test webpages.')
parser.add_argument('--save_dir', default='./save_results', help='the directory for saving result info jsonl file.')
parser.add_argument('--model_name', default="Qwen2.5-VL-32B-Instruct", help='using the vlms for your inference')
if not cmd_args:
args = parser.parse_args([]) # You can directly set above parameters in the default.
else:
args = parser.parse_args()
MODEL_NAME = args.model_name
save_path = os.path.join(args.save_dir, f'{MODEL_NAME}.jsonl')
data_info = args.test_dir
with open(data_info) as f:
data_info_list = f.readlines()
data_info_list = [json.loads(item) for item in data_info_list]
data_info_dict = {
item['name']: item for item in data_info_list
}
result_sample = {
'groupLayoutScore': [],
'overallScore': [],
'relativeLayoutScore': [],
'relativeStyleScore': []
}
ranges_count = [0, 50, 100, 150, 200, 400]
# ranges_count = [0, 200, 400]
ranges = {
key: {
'data': deepcopy(result_sample),
'industries': {}
} for key in ranges_count
}
industries = {}
with open(save_path) as f:
ori_data = f.readlines()
result = deepcopy(result_sample)
ori_data = [json.loads(item) for item in ori_data]
for item in tqdm(ori_data):
if item['name'] not in data_info_dict:
continue
data_info = data_info_dict[item['name']]
element_count = data_info.get('element_count', 0)
for key in result:
if str(item[key]) == 'nan' or item[key] < 0:
continue
result[key].append(item[key])
append_key = None
for range_key in ranges:
if element_count > range_key:
append_key = range_key
ranges[append_key]['data'][key].append(item[key])
industry = data_info['industry']
if industry not in ranges[append_key]['industries']:
ranges[append_key]['industries'][industry] = deepcopy(result_sample)
ranges[append_key]['industries'][industry][key].append(item[key])
if industry not in industries:
industries[industry] = deepcopy(result_sample)
industries[industry][key].append(item[key])
for key in result:
print(key, np.mean(result[key]))
for key in ranges:
range_item = ranges[key]
data = range_item['data']
for metric_key in data:
print(f'{key} {metric_key} {np.mean(data[metric_key]):.2f}')
print('---')
for industry_key in industries:
idu_item = industries[industry_key]
for metric_key in idu_item:
print(f'{industry_key} {metric_key} {np.mean(idu_item[metric_key]):.2f}')
print('---')
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