Update gen_table.py

gen_table.py

@@ -10,13 +10,6 @@ import pandas as pd
 from meta_data import DEFAULT_BENCH, META_FIELDS, RESULTS
 
 
-def listinstr(lst, s):
-    assert isinstance(lst, list)
-    for item in lst:
-        if item in s:
-            return True
-    return False
-
 def load_results_local():
     with open(RESULTS, 'r') as infile:
         data = json.load(infile)
@@ -26,11 +19,6 @@ def nth_large(val, vals):
     return sum([1 for v in vals if v > val]) + 1
 
 
-def format_timestamp(timestamp):
-    date = timestamp[:2] + '.' + timestamp[2:4] + '.' + timestamp[4:6]
-    time = timestamp[6:8] + ':' + timestamp[8:10] + ':' + timestamp[10:12]
-    return date + ' ' + time
-
 
 def model_size_flag(sz, FIELDS):
     if pd.isna(sz) and 'Unknown' in FIELDS:
@@ -76,70 +64,6 @@ def BUILD_L1_DF(results, fields):
     return df, check_box
 
 
-def BUILD_L2_DF(results, dataset):
-    res = defaultdict(list)
-    sub = [v for v in results.values() if dataset in v]
-    assert len(sub)
-    fields = list(sub[0][dataset].keys())
-
-    non_overall_fields = [x for x in fields if 'Overall' not in x]
-    overall_fields = [x for x in fields if 'Overall' in x]
-    if dataset == 'MME':
-        non_overall_fields = [x for x in non_overall_fields if not listinstr(['Perception', 'Cognition'], x)]
-        overall_fields = overall_fields + ['Perception', 'Cognition']
-    if dataset == 'OCRBench':
-        non_overall_fields = [x for x in non_overall_fields if not listinstr(['Final Score'], x)]
-        overall_fields = ['Final Score']
-    print(overall_fields)
-    print(non_overall_fields)
-
-    for m in results:
-        item = results[m]
-        if dataset not in item:
-            continue
-        meta = item['META']
-        for k in META_FIELDS:
-            if k == 'Param (B)':
-                param = meta['Parameters']
-                res[k].append(float(param.replace('B', '')) if param != '' else None)
-            elif k == 'Method':
-                name, url = meta['Method']
-                res[k].append(f'<a href="{url}">{name}</a>')
-            else:
-                res[k].append(meta[k])
-        fields = [x for x in fields]
-
-        for d in non_overall_fields:
-            res[d].append(item[dataset][d])
-        for d in overall_fields:
-            res[d].append(item[dataset][d])
-
-    df = pd.DataFrame(res)
-    print(df)
-    all_fields = overall_fields + non_overall_fields
-    # Use the first 5 non-overall fields as required fields
-    # required_fields = overall_fields if len(overall_fields) else non_overall_fields[:5]
-    required_fields = all_fields
-
-    if dataset == 'OCRBench':
-        df = df.sort_values('Final Score')
-    elif dataset == 'COCO_VAL':
-        df = df.sort_values('CIDEr')
-    else:
-        df = df.sort_values('Overall')
-    df = df.iloc[::-1]
-
-    check_box = {}
-    check_box['essential'] = ['Method', 'Param (B)']
-    check_box['required'] = required_fields
-    check_box['all'] = all_fields
-    type_map = defaultdict(lambda: 'number')
-    type_map['Method'] = 'html'
-    type_map['Language Model'] = type_map['Vision Model'] = type_map['OpenSource'] = type_map['Verified'] = 'str'
-    check_box['type_map'] = type_map
-    return df, check_box
-
-
 def generate_table(results, fields):
 
     def get_mmbench_v11(item):