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visualize.py
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import argparse
import os
import pathlib
import numpy as np
import pandas as pd
from utils import get_targeted_classes
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--tsvpath', type=str, default='results.tsv', help='path of tsv file (dont add .tsv)')
parser.add_argument('--dirpath', type=str, default='./', help='root directory of saved results')
return parser.parse_args()
def parse_pretrain_dirname(dirname, preargnamelist):
arglist = dirname.split('_')
assert(len(arglist) == len(preargnamelist))
pretrain_args = {}
for idx, name in enumerate(preargnamelist):
pretrain_args[name] = arglist[idx]
return pretrain_args
def parse_unlearn_dirname(dirname, unargnamelist):
arglist = dirname.split('_')
assert(len(arglist) >= 2)
un_args = dict((arg, '') for arg in unargnamelist)
if arglist[0] == 'Naive':
un_args['unlearn_method'], un_args['exp_name'] = arglist[0], arglist[1]
un_args['deletion_size'] = 0
return un_args
un_args['deletion_size'], un_args['unlearn_method'], un_args['exp_name'] = arglist[0], arglist[1], arglist[2]
if arglist[1] in ['EU', 'CF', 'Mixed', 'Scrub', 'BadT', 'SSD', 'AscentLearn', 'ScrubNew', 'ALnew']:
assert(len(arglist) >= 5)
un_args['train_iters'], un_args['k'] = arglist[3], arglist[4]
if arglist[1] in ['Mixed']:
assert(len(arglist) >= 6)
un_args['factor'] = arglist[5]
if arglist[1] in ['AscentLearn', 'ALnew']:
assert(len(arglist) >= 6)
un_args['ascLRscale'] = arglist[5]
if arglist[1] == 'InfRe':
assert(len(arglist) >= 8)
un_args['msteps'], un_args['rsteps'], un_args['ascLRscale'] = arglist[5], arglist[6], arglist[7]
if arglist[1] == 'Scrub':
assert(len(arglist) >= 8)
un_args['kd_T'], un_args['alpha'], un_args['msteps'] = arglist[5:8]
if arglist[1] == 'ScrubNew':
assert(len(arglist) >= 8)
un_args['kd_T'], un_args['alpha'], un_args['ascLRscale'] = arglist[5:8]
if arglist[1] == 'SSD':
assert(len(arglist) >= 7)
un_args['SSDdampening'], un_args['selectwt'] = arglist[5], arglist[6]
return un_args
def compute_accuracy(preds, y):
return np.equal(np.argmax(preds, axis=1), y).mean()
def parse_unpath(un_path, pre_args, un_args, args, headers):
ret = dict((key, '') for key in headers)
ret.update(pre_args)
ret.update(un_args)
tr_preds = np.load(un_path + f'/preds_train.npy')
tr_y = np.load(un_path + f'/targetstrain.npy')
te_preds = np.load(un_path + f'/preds_test.npy')
te_y = np.load(un_path + f'/targetstest.npy')
un_time = np.load(un_path + f'/unlearn_time.npy')
ret['unlearn_time'] = un_time
forget_idx = np.load(args.dirpath+'/'+pre_args['dataset']+'_'+pre_args['dataset_method']+'_'+pre_args['forget_set_size']+'_manip.npy')
if un_args['deletion_size'] != 0:
delete_idx = np.load(args.dirpath+'/'+pre_args['dataset']+'_'+pre_args['dataset_method']+'_'+pre_args['forget_set_size']+'_'+un_args['deletion_size']+'_deletion.npy')
ret['train_clean_acc'] = compute_accuracy(tr_preds, tr_y)
delete_acc, delete_err = 0.0, 101.0
if pre_args['dataset_method'] == 'poisoning':
tr_adv_preds = np.load(un_path + f'/preds_adv_train.npy')
tr_adv_y = np.load(un_path + f'/targetsadv_train.npy')
tr_wrong = np.zeros(tr_adv_y.shape)
te_adv_preds = np.load(un_path + f'/preds_adv_test.npy')
te_adv_y = np.load(un_path + f'/targetsadv_test.npy')
forget_acc = compute_accuracy(tr_adv_preds[forget_idx], tr_adv_y[forget_idx])
if un_args['deletion_size'] != 0:
delete_err = compute_accuracy(tr_adv_preds[delete_idx], tr_wrong['delete_idx'])
delete_acc = compute_accuracy(tr_adv_preds[delete_idx], tr_adv_y[delete_idx])
test_acc = compute_accuracy(te_adv_preds, te_adv_y)
forget_clean_acc = compute_accuracy(tr_preds[forget_idx], tr_y[forget_idx])
test_clean_acc = compute_accuracy(te_preds, te_y)
print(forget_acc, test_acc, forget_clean_acc, test_clean_acc)
ret['delete_acc'], ret['delete_err'], ret['manip_acc'], ret['test_acc'], ret['manip_clean_acc'], ret['test_clean_acc'] =\
delete_acc, delete_err, forget_acc, test_acc, forget_clean_acc, test_clean_acc
if pre_args['dataset_method'] == 'labelrandom':
forget_acc = compute_accuracy(tr_preds[forget_idx], tr_y[forget_idx])
test_acc = compute_accuracy(te_preds, te_y)
print(forget_acc, test_acc)
ret['forget_acc'], ret['test_acc'] = forget_acc, test_acc
if pre_args['dataset_method'] == 'labeltargeted':
classes = get_targeted_classes(pre_args['dataset'])
te_class_idxes = np.concatenate((np.nonzero(te_y == classes[0]), np.nonzero(te_y == classes[1])), axis=1).squeeze()
retain_idxes =np.setdiff1d(np.arange(len(te_y)), te_class_idxes)
forget_acc = compute_accuracy(tr_preds[forget_idx], tr_y[forget_idx])
tr_wrong = tr_y
tr_wrong[tr_y == classes[0]] = classes[1]
tr_wrong[tr_y == classes[1]] = classes[0]
if un_args['deletion_size'] != 0:
delete_acc = compute_accuracy(tr_preds[delete_idx], tr_y[delete_idx])
delete_err = compute_accuracy(tr_preds[delete_idx], tr_wrong[delete_idx])
test_acc = compute_accuracy(te_preds[te_class_idxes], te_y[te_class_idxes])
test_retain_acc = compute_accuracy(te_preds[retain_idxes], te_y[retain_idxes])
print(forget_acc, test_acc, test_retain_acc)
ret['delete_acc'], ret['delete_err'], ret['manip_acc'], ret['test_acc'], ret['test_retain_acc'] = delete_acc, delete_err, forget_acc, test_acc, test_retain_acc
return ret
if __name__ == '__main__':
# datasets = ['CIFAR10', 'CIFAR100', 'PCAM', 'Pneumonia', 'DermaNet']
# model = ['resnet9', 'resnetwide28x10']
# dataset_method = ['labelrandom', 'labeltargeted', 'poisoning']
# unlearn_method = ['Naive', 'EU', 'CF', 'Mixed', 'Scrub']
preargnamelist = ['dataset', 'model', 'dataset_method', 'forget_set_size', 'patch_size', 'pretrain_iters', 'pretrain_lr']
unargnamelist = ['unlearn_method', 'exp_name', 'train_iters', 'k', 'factor', 'kd_T', 'gamma', 'alpha', 'msteps']
metricslist = ['delete_acc', 'delete_err', 'manip_acc', 'test_acc', 'manip_clean_acc', 'test_clean_acc', 'test_retain_acc']
headers = preargnamelist + unargnamelist + metricslist
args = parse_args()
rows = []
for dirname in next(os.walk(args.dirpath))[1]:
print(dirname)
pre_args = parse_pretrain_dirname(dirname, preargnamelist)
pretrain_path = os.path.join(args.dirpath, dirname)
for undirname in next(os.walk(pretrain_path))[1]:
print(dirname, undirname)
un_args = parse_unlearn_dirname(undirname, unargnamelist)
un_path = os.path.join(pretrain_path, undirname)
row = parse_unpath(un_path, pre_args, un_args, args, headers)
rows.append(row)
df = pd.DataFrame.from_records(rows)
df.to_csv(args.tsvpath, sep='\t')