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ood.py
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ood.py
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import os
import sys
import argparse
import datetime
import time
import csv
import os.path as osp
import numpy as np
import warnings
import importlib
warnings.filterwarnings('ignore')
import torch
import torch.nn as nn
from torch.optim import lr_scheduler
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.utils as vutils
import datasets.datasets as datasets
from models.models import ConvNet
from models.resnet import ResNet34
from models.resnetABN import resnet34ABN
from models import gan
from utils import Logger, save_networks, save_GAN, load_networks, mkdir_if_missing
from core import train, train_cs, test
parser = argparse.ArgumentParser("ARPLoss")
# dataset
parser.add_argument('--dataroot', type=str, default='./data')
parser.add_argument('--outf', type=str, default='./log')
parser.add_argument('--dataset', type=str, default='mnist')
parser.add_argument('--out-dataset', type=str, default='kmnist')
parser.add_argument('--workers', default=4, type=int,
help="number of data loading workers (default: 4)")
# optimization
parser.add_argument('--batch-size', type=int, default=128)
parser.add_argument('--lr', type=float, default=0.0001, help="learning rate for model")
parser.add_argument('--gan_lr', type=float, default=0.0002, help="learning rate for gan")
parser.add_argument('--max-epoch', type=int, default=100)
parser.add_argument('--stepsize', type=int, default=30)
parser.add_argument('--temp', type=float, default=1.0, help="temp")
parser.add_argument('--loss', type=str, default='ARPLoss')
# model
parser.add_argument('--weight-pl', type=float, default=0.1, help="weight for RPL loss")
parser.add_argument('--beta', type=float, default=0.1, help="weight for entropy loss")
parser.add_argument('--model', type=str, default='cnn')
# misc
parser.add_argument('--nz', type=int, default=100)
parser.add_argument('--ns', type=int, default=1)
parser.add_argument('--eval-freq', type=int, default=1)
parser.add_argument('--print-freq', type=int, default=100)
parser.add_argument('--gpu', type=str, default='0')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--use-cpu', action='store_true')
parser.add_argument('--eval', action='store_true', help="Eval", default=False)
parser.add_argument('--cs', action='store_true', help="Confusing Samples", default=False)
args = parser.parse_args()
options = vars(args)
sys.stdout = Logger(osp.join(options['outf'], 'logs.txt'))
def main():
torch.manual_seed(options['seed'])
os.environ['CUDA_VISIBLE_DEVICES'] = options['gpu']
use_gpu = torch.cuda.is_available()
if options['use_cpu']: use_gpu = False
feat_dim = 2 if 'cnn' in options['model'] else 512
options.update(
{
'feat_dim': feat_dim,
'use_gpu': use_gpu
}
)
if use_gpu:
print("Currently using GPU: {}".format(options['gpu']))
cudnn.benchmark = True
torch.cuda.manual_seed_all(options['seed'])
else:
print("Currently using CPU")
dataset = datasets.create(options['dataset'], **options)
out_dataset = datasets.create(options['out_dataset'], **options)
trainloader, testloader = dataset.trainloader, dataset.testloader
outloader = out_dataset.testloader
options.update(
{
'num_classes': dataset.num_classes
}
)
print("Creating model: {}".format(options['model']))
if 'cnn' in options['model']:
net = ConvNet(num_classes=dataset.num_classes)
else:
if options['cs']:
net = resnet34ABN(num_classes=dataset.num_classes, num_bns=2)
else:
net = ResNet34(dataset.num_classes)
if options['cs']:
print("Creating GAN")
nz = options['nz']
netG = gan.Generator32(1, nz, 64, 3) # ngpu, nz, ngf, nc
netD = gan.Discriminator32(1, 3, 64) # ngpu, nc, ndf
fixed_noise = torch.FloatTensor(64, nz, 1, 1).normal_(0, 1)
criterionD = nn.BCELoss()
Loss = importlib.import_module('loss.'+options['loss'])
criterion = getattr(Loss, options['loss'])(**options)
if use_gpu:
net = nn.DataParallel(net, device_ids=[i for i in range(len(options['gpu'].split(',')))]).cuda()
criterion = criterion.cuda()
if options['cs']:
netG = nn.DataParallel(netG, device_ids=[i for i in range(len(options['gpu'].split(',')))]).cuda()
netD = nn.DataParallel(netD, device_ids=[i for i in range(len(options['gpu'].split(',')))]).cuda()
fixed_noise.cuda()
model_path = os.path.join(options['outf'], 'models', options['dataset'])
file_name = '{}_{}_{}_{}_{}'.format(options['model'], options['dataset'], options['loss'], str(options['weight_pl']), str(options['cs']))
if options['eval']:
net, criterion = load_networks(net, model_path, file_name, criterion=criterion)
results = test(net, criterion, testloader, outloader, epoch=0, **options)
print("Acc (%): {:.3f}\t AUROC (%): {:.3f}\t OSCR (%): {:.3f}\t".format(results['ACC'], results['AUROC'], results['OSCR']))
return
params_list = [{'params': net.parameters()},
{'params': criterion.parameters()}]
optimizer = torch.optim.Adam(params_list, lr=options['lr'])
if options['cs']:
optimizerD = torch.optim.Adam(netD.parameters(), lr=options['gan_lr'], betas=(0.5, 0.999))
optimizerG = torch.optim.Adam(netG.parameters(), lr=options['gan_lr'], betas=(0.5, 0.999))
if options['stepsize'] > 0:
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[30, 60, 90, 120])
start_time = time.time()
score_now = 0.0
for epoch in range(options['max_epoch']):
print("==> Epoch {}/{}".format(epoch+1, options['max_epoch']))
if options['cs']:
train_cs(net, netD, netG, criterion, criterionD,
optimizer, optimizerD, optimizerG,
trainloader, epoch=epoch, **options)
train(net, criterion, optimizer, trainloader, epoch=epoch, **options)
if options['eval_freq'] > 0 and (epoch+1) % options['eval_freq'] == 0 or (epoch+1) == options['max_epoch']:
print("==> Test")
results = test(net, criterion, testloader, outloader, epoch=epoch, **options)
print("Acc (%): {:.3f}\t AUROC (%): {:.3f}\t OSCR (%): {:.3f}\t".format(results['ACC'], results['AUROC'], results['OSCR']))
save_networks(net, model_path, file_name, criterion=criterion)
if options['cs']:
save_GAN(netG, netD, model_path, file_name)
fake = netG(fixed_noise)
GAN_path = os.path.join(model_path, 'samples')
mkdir_if_missing(GAN_path)
vutils.save_image(fake.data, '%s/gan_samples_epoch_%03d.png'%(GAN_path, epoch), normalize=True)
if options['stepsize'] > 0: scheduler.step()
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
print("Finished. Total elapsed time (h:m:s): {}".format(elapsed))
if __name__ == '__main__':
main()