| import json |
| import string |
| import regex |
|
|
| |
| def normalize_answer(s): |
| def remove_articles(text): |
| return regex.sub(r'\b(a|an|the)\b', ' ', text) |
|
|
| def white_space_fix(text): |
| return ' '.join(text.split()) |
|
|
| def remove_punc(text): |
| exclude = set(string.punctuation) |
| return ''.join(ch for ch in text if ch not in exclude) |
|
|
| def lower(text): |
| return text.lower() |
|
|
| return white_space_fix(remove_articles(remove_punc(lower(s)))) |
|
|
|
|
| def cal_acc_multi(ground_truth, preds, return_id = False): |
| all_num = len(ground_truth) |
| acc_num = 0 |
| ids = [] |
| temp = [] |
| for i, answer_id in enumerate(ground_truth): |
| pred = preds[i] |
| cnt = 0 |
| for aid in answer_id: |
| if pred == aid: |
| cnt += 1 |
| if cnt ==1: |
| acc_num += 1/3 |
| |
| elif cnt == 2: |
| acc_num += 2/3 |
|
|
| elif cnt > 2: |
| acc_num += 1 |
|
|
|
|
| if return_id: |
| return acc_num / all_num, ids |
| else: |
| return acc_num, all_num |
|
|
|
|
| def ensemble(a): |
| return max(a[::-1], key = a.count) |
|
|
| |
| f=open("/root/okvqa/data/okvqa_val.json", "r") |
| answer_dict=json.load(f) |
| f.close() |
| for k in answer_dict.keys(): |
| for a_ind, a in enumerate(answer_dict[k]['multi_answers']): |
| answer_dict[k]['multi_answers'][a_ind] = normalize_answer(answer_dict[k]['multi_answers'][a_ind]) |
|
|
|
|
| |
| f1=open("/mnt/bn/qingyi-hl/finetunedModelonOKVQA/1e-41e-5FTwiki25-From-1e-41e-5PretrainWiki25Epo0/FTwiki25FromPretrainWiki25Epo0-1e41e5/predictions.json", "r") |
| predict0_dict=json.load(f1) |
| for p in predict0_dict.keys(): |
| predict0_dict[p]=normalize_answer(predict0_dict[p]) |
| f1.close() |
| f2=open("/mnt/bn/qingyi-hl/finetunedModelonOKVQA/1e-41e-5FTwiki25-From-1e-41e-5PretrainWiki25Epo1/predictions.json", "r") |
| predict1_dict=json.load(f2) |
| for p in predict1_dict.keys(): |
| predict1_dict[p]=normalize_answer(predict1_dict[p]) |
| f2.close() |
| f3=open("/mnt/bn/qingyi-hl/finetunedModelonOKVQA/1e-41e-5FTwiki25-From-1e-41e-5PretrainWiki25Epo2/predictions.json", "r") |
| predict2_dict=json.load(f3) |
| for p in predict2_dict.keys(): |
| predict2_dict[p]=normalize_answer(predict2_dict[p]) |
| f3.close() |
|
|
|
|
|
|
| answer_list=[] |
| predict0_list=[] |
| predict1_list=[] |
| predict2_list=[] |
| emsemble_predict=[] |
| for k in answer_dict.keys(): |
| answer_list.append( answer_dict[k]['multi_answers']) |
| predict0_list.append( predict0_dict[k]) |
| predict1_list.append( predict1_dict[k]) |
| predict2_list.append( predict2_dict[k]) |
| |
| emsemble_predict.append(ensemble([predict0_dict[k], predict1_dict[k], predict2_dict[k]) |
| |
|
|
|
|
| acc_n0,all_n0=cal_acc_multi(answer_list,predict0_list) |
| acc_n1,all_n1=cal_acc_multi(answer_list,predict1_list) |
| acc_n2,all_n2=cal_acc_multi(answer_list,predict2_list) |
|
|
| acc_ens,all_ens=cal_acc_multi(answer_list,emsemble_predict) |
|
|
| print("0-accuracy",acc_n0/all_n0) |
| print("1-accuracy",acc_n1/all_n1) |
| print("2-accuracy",acc_n2/all_n2) |
|
|
|
|
| print("ensemble-accuracy",acc_ens/all_ens) |
