OpenLORIS 指标计算

continual_learning_metric_calc.py

import csv

'''
suit for lifelong-robotic-vision/OpenLORIS-Object 's csv format result
every line of csv contains accuracy of every task on test set after training a single task
'''

raw_result = []
with open('openloris.csv', newline='') as csvfile:
    spamreader = csv.reader(csvfile, delimiter=' ', quotechar='|')
    for row in spamreader:
        if row == []:
            continue
        k = [float(i) for i in row[0].split(',')]
        raw_result.append(k)

cls_cnt = 69

# Accuracy
sum_of = 0
cnt_of = 0
for idx in range(len(raw_result)):
    cls = idx%cls_cnt
    for i in range(len(raw_result[idx])):
        if (i%cls_cnt) <= cls:
            sum_of += raw_result[idx][i]
            cnt_of += 1
print(f'Accuracy: {sum_of/cnt_of*100:.3f} %')

# Backward Transfer(BWT)
sum_of = 0
cnt_of = 0
for idx in range(len(raw_result)):
    cls = idx%cls_cnt
    for i in range(len(raw_result[idx])):
        if (i%cls_cnt) < cls:
            sum_of += raw_result[idx][i]
            cnt_of += 1
print(f'BWT: {sum_of/cnt_of*100:.3f} %')

# Forward Transfer(FWT)
sum_of = 0
cnt_of = 0
for idx in range(len(raw_result)):
    cls = idx%cls_cnt
    for i in range(len(raw_result[idx])):
        if (i%cls_cnt) > cls:
            sum_of += raw_result[idx][i]
            cnt_of += 1
print(f'FWT: {sum_of/cnt_of*100:.3f} %')

# Over-all accuracy
sum_of = 0
cnt_of = 0
for t in raw_result:
    for i in t:
        sum_of += i
        cnt_of += 1
print(f'Over-all accuracy: {sum_of/cnt_of*100:.3f} %')