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OOD_Baseline_and_ODIN.py
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"""
Created on Sun Oct 21 2018
@author: Kimin Lee
"""
from __future__ import print_function
import argparse
import torch
import data_loader
import numpy as np
import calculate_log as callog
import models
import os
import lib_generation
from torchvision import transforms
from torch.autograd import Variable
parser = argparse.ArgumentParser(description='PyTorch code: Mahalanobis detector')
parser.add_argument('--batch_size', type=int, default=200, metavar='N', help='batch size for data loader')
parser.add_argument('--dataset', required=True, help='cifar10 | cifar100 | svhn')
parser.add_argument('--dataroot', default='./data', help='path to dataset')
parser.add_argument('--outf', default='./output/', help='folder to output results')
parser.add_argument('--num_classes', type=int, default=10, help='the # of classes')
parser.add_argument('--net_type', required=True, help='resnet | densenet')
parser.add_argument('--gpu', type=int, default=0, help='gpu index')
args = parser.parse_args()
print(args)
def main():
# set the path to pre-trained model and output
pre_trained_net = './pre_trained/' + args.net_type + '_' + args.dataset + '.pth'
args.outf = args.outf + args.net_type + '_' + args.dataset + '/'
if os.path.isdir(args.outf) == False:
os.mkdir(args.outf)
torch.cuda.manual_seed(0)
torch.cuda.set_device(args.gpu)
# check the in-distribution dataset
if args.dataset == 'cifar100':
args.num_classes = 100
if args.dataset == 'svhn':
out_dist_list = ['cifar10', 'imagenet_resize', 'lsun_resize']
else:
out_dist_list = ['svhn', 'imagenet_resize', 'lsun_resize']
# load networks
if args.net_type == 'densenet':
if args.dataset == 'svhn':
model = models.DenseNet3(100, int(args.num_classes))
model.load_state_dict(torch.load(pre_trained_net, map_location = "cuda:" + str(args.gpu)))
else:
model = torch.load(pre_trained_net, map_location = "cuda:" + str(args.gpu))
in_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((125.3/255, 123.0/255, 113.9/255), (63.0/255, 62.1/255.0, 66.7/255.0)),])
elif args.net_type == 'resnet':
model = models.ResNet34(num_c=args.num_classes)
model.load_state_dict(torch.load(pre_trained_net, map_location = "cuda:" + str(args.gpu)))
in_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),])
model.cuda()
print('load model: ' + args.net_type)
# load dataset
print('load target data: ', args.dataset)
train_loader, test_loader = data_loader.getTargetDataSet(args.dataset, args.batch_size, in_transform, args.dataroot)
# measure the performance
M_list = [0, 0.0005, 0.001, 0.0014, 0.002, 0.0024, 0.005, 0.01, 0.05, 0.1, 0.2]
T_list = [1, 10, 100, 1000]
base_line_list = []
ODIN_best_tnr = [0, 0, 0]
ODIN_best_results = [0 , 0, 0]
ODIN_best_temperature = [-1, -1, -1]
ODIN_best_magnitude = [-1, -1, -1]
for T in T_list:
for m in M_list:
magnitude = m
temperature = T
lib_generation.get_posterior(model, args.net_type, test_loader, magnitude, temperature, args.outf, True)
out_count = 0
print('Temperature: ' + str(temperature) + ' / noise: ' + str(magnitude))
for out_dist in out_dist_list:
out_test_loader = data_loader.getNonTargetDataSet(out_dist, args.batch_size, in_transform, args.dataroot)
print('Out-distribution: ' + out_dist)
lib_generation.get_posterior(model, args.net_type, out_test_loader, magnitude, temperature, args.outf, False)
if temperature == 1 and magnitude == 0:
test_results = callog.metric(args.outf, ['PoT'])
base_line_list.append(test_results)
else:
val_results = callog.metric(args.outf, ['PoV'])
if ODIN_best_tnr[out_count] < val_results['PoV']['TNR']:
ODIN_best_tnr[out_count] = val_results['PoV']['TNR']
ODIN_best_results[out_count] = callog.metric(args.outf, ['PoT'])
ODIN_best_temperature[out_count] = temperature
ODIN_best_magnitude[out_count] = magnitude
out_count += 1
# print the results
mtypes = ['TNR', 'AUROC', 'DTACC', 'AUIN', 'AUOUT']
print('Baseline method: in_distribution: ' + args.dataset + '==========')
count_out = 0
for results in base_line_list:
print('out_distribution: '+ out_dist_list[count_out])
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100.*results['PoT']['TNR']), end='')
print(' {val:6.2f}'.format(val=100.*results['PoT']['AUROC']), end='')
print(' {val:6.2f}'.format(val=100.*results['PoT']['DTACC']), end='')
print(' {val:6.2f}'.format(val=100.*results['PoT']['AUIN']), end='')
print(' {val:6.2f}\n'.format(val=100.*results['PoT']['AUOUT']), end='')
print('')
count_out += 1
print('ODIN method: in_distribution: ' + args.dataset + '==========')
count_out = 0
for results in ODIN_best_results:
print('out_distribution: '+ out_dist_list[count_out])
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100.*results['PoT']['TNR']), end='')
print(' {val:6.2f}'.format(val=100.*results['PoT']['AUROC']), end='')
print(' {val:6.2f}'.format(val=100.*results['PoT']['DTACC']), end='')
print(' {val:6.2f}'.format(val=100.*results['PoT']['AUIN']), end='')
print(' {val:6.2f}\n'.format(val=100.*results['PoT']['AUOUT']), end='')
print('temperature: ' + str(ODIN_best_temperature[count_out]))
print('magnitude: '+ str(ODIN_best_magnitude[count_out]))
print('')
count_out += 1
if __name__ == '__main__':
main()