import os.path as osp from util import load_file, save_to import argparse map_name = {'CW': 'Why', 'CH': 'How', 'TN': 'Bef&Aft', 'TC': 'When', 'DC': 'Cnt', 'DL': 'Loc', 'DO': 'Other', 'C': 'Acc_C', 'T': 'Acc_T', 'D': 'Acc_D'} def accuracy_metric(sample_list, result): group = {'CW':[], 'CH':[], 'TN':[], 'TC':[], 'DC':[], 'DL':[], 'DO':[]} for id, row in sample_list.iterrows(): qns_id = str(row['video_id']) + '_' + str(row['qid']) qtype = str(row['type']) #(combine temporal qns of previous and next as 'TN') if qtype == 'TP': qtype = 'TN' group[qtype].append(qns_id) preds = result group_acc = {'CW': 0, 'CH': 0, 'TN': 0, 'TC': 0, 'DC': 0, 'DL': 0, 'DO': 0} group_cnt = {'CW': 0, 'CH': 0, 'TN': 0, 'TC': 0, 'DC': 0, 'DL': 0, 'DO': 0} overall_acc = {'C':0, 'T':0, 'D':0} overall_cnt = {'C':0, 'T':0, 'D':0} all_acc = 0 all_cnt = 0 for qtype, qns_ids in group.items(): # print(qtype, len(qns_ids)) cnt = 0 acc = 0 for qid in qns_ids: cnt += 1 answer = preds[qid]['answer'] pred = preds[qid]['prediction'] if answer == pred: acc += 1 group_cnt[qtype] = cnt group_acc[qtype] += acc overall_acc[qtype[0]] += acc overall_cnt[qtype[0]] += cnt all_acc += acc all_cnt += cnt for qtype, value in overall_acc.items(): group_acc[qtype] = value group_cnt[qtype] = overall_cnt[qtype] for qtype in group_acc: if group_cnt[qtype] == 0: continue print(map_name[qtype], end='\t') print('') for qtype, acc in group_acc.items(): if group_cnt[qtype] == 0: continue print('{:.2f}'.format(acc*100.0/group_cnt[qtype]), end ='\t') print('') print('Acc: {:.2f}'.format(all_acc*100.0/all_cnt)) def accuracy_metric_sub(sample_list, result, sub_ids): sub_ids = [int(id) for id in sub_ids] subset = sample_list.iloc[sub_ids] accuracy_metric(subset, result) def eval_ground(sample_list, gsubset, result): gids = [] vids = [] for idx, row in sample_list.iterrows(): vid, qid = str(row['video_id']), str(row['qid']) if vid in gsubset and qid in gsubset[vid]['location'].keys(): vids.append(vid) gids.append(idx) print("Video: {} Questions: {}".format(len(set(vids)), len(gids))) subset = sample_list.iloc[gids] accuracy_metric(subset, result) def eval_ground_nov(sample_list, gsubset, result, nov): gids = [] vids = [] ks = [] for idx, row in sample_list.iterrows(): vid, qid = str(row['video_id']), str(row['qid']) k = f'{vid}_{qid}' if nov[k]['answer'] == nov[k]['prediction']: continue if vid in gsubset and qid in gsubset[vid]['location'].keys(): vids.append(vid) gids.append(idx) ks.append(k) print("Video: {} Questions: {}".format(len(set(vids)), len(gids))) subset = sample_list.iloc[gids] accuracy_metric(subset, result) # save_to('./aba/vqa_sub.json', ks) def eval_ground_nov_nosig(sample_list, gsubset, result, nov, sigF): gids = [] vids = [] ks = [] # print(len(sample_list), len(sigF)) for idx, row in sample_list.iterrows(): vid, qid = str(row['video_id']), str(row['qid']) k = f'{vid}_{qid}' if nov[k]['answer'] == nov[k]['prediction'] or sigF[k]['answer'] == sigF[k]['prediction']: continue if vid in gsubset and qid in gsubset[vid]['location'].keys(): vids.append(vid) gids.append(idx) ks.append(k) print("Video: {} Questions: {}".format(len(set(vids)), len(gids))) subset = sample_list.iloc[gids] accuracy_metric(subset, result) # save_to('./aba/vidqa_sub.json', ks) def eval_ground_vqa(sample_list, gsubset, result, nov, noq, novq): gids = [] vids = [] for idx, row in sample_list.iterrows(): vid, qid = str(row['video_id']), str(row['qid']) k = f'{vid}_{qid}' if nov[k]['answer'] == nov[k]['prediction'] or noq[k]['answer'] == noq[k]['prediction'] or novq[k]['answer'] == novq[k]['prediction']: continue if vid in gsubset and qid in gsubset[vid]['location'].keys(): vids.append(vid) gids.append(idx) print("Video: {} Questions: {}".format(len(set(vids)), len(gids))) subset = sample_list.iloc[gids] accuracy_metric(subset, result) def eval_ground_gqa(sample_list, gsubset, result, nov, neg, pos): gids = [] vids = [] ks = [] for idx, row in sample_list.iterrows(): vid, qid = str(row['video_id']), str(row['qid']) k = f'{vid}_{qid}' if nov[k]['answer'] == nov[k]['prediction'] or neg[k]['answer'] == neg[k]['prediction'] or pos[k]['answer'] != pos[k]['prediction']: continue if vid in gsubset and qid in gsubset[vid]['location'].keys(): vids.append(vid) gids.append(idx) ks.append(k) print("Video: {} Questions: {}".format(len(set(vids)), len(gids))) subset = sample_list.iloc[gids] accuracy_metric(subset, result) # save_to('./aba/gdqa_sub.json', ks) def main(result_file, mode='val'): dataset_dir = '../data/datasets/nextgqa/' data_set = mode sample_list_file = osp.join(dataset_dir, data_set+'.csv') print('Evaluating {}'.format(result_file)) sample_list = load_file(sample_list_file) result = load_file(result_file) print(len(result)) # accuracy_metric(sample_list, result) # if mode == 'val': # print('==============ATP-hard Subset================') # hard_subset = osp.join(dataset_dir, 'atp-hard-ct4.txt') # sub_ids = load_file(hard_subset) # accuracy_metric_sub(sample_list, result, sub_ids) print('===============Ground Subset=================') gsub_file = osp.join(dataset_dir, f'gsub_{mode}.json') gsubset = load_file(gsub_file) eval_ground(sample_list, gsubset, result) # res_dir = '../data/save_models/nextqa/' res_dir = '../data/gmodels/nextgqa/' # print('==============Ground VQA Subset==============') # nov_file = f'{res_dir}/BlindQA/{mode}-res.json' # nov = load_file(nov_file) # eval_ground_nov(sample_list, gsubset, result, nov) # print('==============Ground GQA Subset==============') # res_dir = '../data/gmodels/NG+/' # neg_file = f'{res_dir}/TempCLIP-NEG/{mode}-res.json' # neg = load_file(neg_file) # pos_file = f'{res_dir}/TempCLIP-POS/{mode}-res.json' # pos = load_file(pos_file) # eval_ground_gqa(sample_list, gsubset, result, nov, neg, pos) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--mode", type=str, default='val', choices=['val','test']) parser.add_argument("--folder", type=str) args = parser.parse_args() res_dir = '../data/gmodels/'+args.folder #res_dir = '../data/models/nextqa/' mode = args.mode model_prefix = 'res' result_file = '{}/{}-{}.json'.format(res_dir, mode, model_prefix) main(result_file, mode)