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compute_forgetting.py
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compute_forgetting.py
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import os
import copy
import sys
import argparse
import shutil
import time
import math
import random
import numpy as np
import torch
parser = argparse.ArgumentParser('argument for training')
parser.add_argument('--result_folder', type=str, default='/datasets/work/d61-eif/source/', help='path to custom dataset')
parser.add_argument('--n_task', type=int, default=10, help='dataset name')
parser.add_argument('--n_class_per_task', type=int, default=10, help='n_class_per_task')
opt = parser.parse_args()
results = []
for task in range(opt.n_task):
with open(os.path.join(opt.result_folder, "acc_buffer_{}.txt".format(task)), "r") as result_file:
lines = result_file.readlines()
results_task = [0]*opt.n_class_per_task*opt.n_task
i = 0
for l in lines:
as_list = l.split(" ")
if len(as_list) == 1:
# print(as_list)
results_task[i] = float(as_list[0].replace('\n', ''))
i = i + 1
results.append(results_task)
print(results)
def forgetting(results):
n_tasks = len(results)
li = list()
for i in range(n_tasks - 1):
results[i] += [0.0] * (n_tasks - len(results[i]))
np_res = np.array(results)
maxx = np.max(np_res, axis=0)
for i in range(n_tasks - 1):
li.append(maxx[i] - results[-1][i])
return np.mean(li)
print(forgetting(results))