evaluate_cifar10.py

# -*- coding: utf-8 -*-

### basic modules
import numpy as np
import time, pickle, os, sys, json, PIL, tempfile, warnings, importlib, math, copy, shutil, setproctitle

### torch modules
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR, MultiStepLR
import torch.nn.functional as F
from torch import autograd

from torch.utils.data import Dataset, DataLoader, TensorDataset
from torch.optim.lr_scheduler import StepLR, MultiStepLR
import utils, data_load, BCP

if __name__ == "__main__":
    args = utils.argparser()
    print(args)
    setproctitle.setproctitle(args.prefix)
    test_log = open(args.prefix + "_test.log", "w")
    
    _, test_loader = data_load.data_loaders(args.data, args.test_batch_size, args.normalization, args.augmentation, args.drop_last, args.shuffle)
    torch.manual_seed(args.seed)
    torch.cuda.manual_seed(args.seed)

    args.print = True
    t = 100
    
    try:
        aa = torch.load(args.test_pth,map_location='cuda:'+str(args.cuda_ids))['state_dict'][0]
    except:
        aa = torch.load(args.test_pth,map_location='cuda:'+str(args.cuda_ids))['state_dict']
    model_eval = utils.select_model(args.data, args.model)
    model_eval.load_state_dict(aa)
    print('std testing ...')
    std_err = utils.evaluate(test_loader, model_eval, t, test_log, args.verbose)
    print('verification testing ...')
    if args.method=='BCP':
        last_err = BCP.evaluate_BCP(test_loader, model_eval, args.epsilon, t, test_log, args.verbose, args, None)
    print('pgd testing ...')
    pgd_err = utils.evaluate_pgd(test_loader, model_eval, args)
    print('Best model evaluation:', std_err.item(), pgd_err.item(), last_err.item())