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test_model.py
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test_model.py
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'''codes used to evaluate models (acc and asr)
'''
import numpy as np
import torch
import os
from torchvision import transforms,datasets
import argparse
import random
import torch.optim as optim
from torch.optim.lr_scheduler import MultiStepLR
from torch import nn
from PIL import Image
from utils import supervisor, tools, default_args
import config
parser = argparse.ArgumentParser()
parser.add_argument('-dataset', type=str, required=False,
default=default_args.parser_default['dataset'],
choices=default_args.parser_choices['dataset'])
parser.add_argument('-poison_type', type=str, required=False,
choices=default_args.parser_choices['poison_type'],
default=default_args.parser_default['poison_type'])
parser.add_argument('-poison_rate', type=float, required=False,
choices=default_args.parser_choices['poison_rate'],
default=default_args.parser_default['poison_rate'])
parser.add_argument('-cover_rate', type=float, required=False,
choices=default_args.parser_choices['cover_rate'],
default=default_args.parser_default['cover_rate'])
parser.add_argument('-alpha', type=float, required=False,
default=default_args.parser_default['alpha'])
parser.add_argument('-test_alpha', type=float, required=False, default=None)
parser.add_argument('-trigger', type=str, required=False, default=None)
parser.add_argument('-model_path', required=False, default=None)
parser.add_argument('-cleanser', type=str, required=False, default=None,
choices=['SCAn', 'AC', 'SS', 'Strip', 'CT', 'SPECTRE'])
parser.add_argument('-no_normalize', default=False, action='store_true')
parser.add_argument('-no_aug', default=False, action='store_true')
parser.add_argument('-devices', type=str, default='0')
parser.add_argument('-seed', type=int, required=False, default=default_args.seed)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = "%s" % args.devices
if args.trigger is None:
args.trigger = config.trigger_default[args.poison_type]
batch_size = 128
kwargs = {'num_workers': 4, 'pin_memory': True}
tools.setup_seed(args.seed)
if args.dataset == 'cifar10':
data_transform_aug = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261]),
])
data_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261])
])
elif args.dataset == 'gtsrb':
data_transform_aug = transforms.Compose([
transforms.RandomRotation(15),
transforms.ToTensor(),
transforms.Normalize((0.3337, 0.3064, 0.3171), (0.2672, 0.2564, 0.2629))
])
data_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.3337, 0.3064, 0.3171), (0.2672, 0.2564, 0.2629))
])
else:
raise NotImplementedError('dataset %s not supported' % args.dataset)
if args.dataset == 'cifar10':
num_classes = 10
arch = config.arch[args.dataset]
momentum = 0.9
weight_decay = 1e-4
epochs = 200
milestones = torch.tensor([100, 150])
learning_rate = 0.1
elif args.dataset == 'cifar100':
num_classes = 100
raise NotImplementedError('<To Be Implemented> Dataset = %s' % args.dataset)
elif args.dataset == 'gtsrb':
num_classes = 43
arch = config.arch[args.dataset]
momentum = 0.9
weight_decay = 1e-4
epochs = 100
milestones = torch.tensor([40, 80])
learning_rate = 0.1
else:
print('<Undefined Dataset> Dataset = %s' % args.dataset)
raise NotImplementedError('<To Be Implemented> Dataset = %s' % args.dataset)
poison_set_dir = supervisor.get_poison_set_dir(args)
model_path = supervisor.get_model_dir(args, cleanse=(args.cleanser is not None))
arch = config.arch[args.dataset]
model = arch(num_classes=num_classes)
model.load_state_dict(torch.load(model_path))
model = nn.DataParallel(model)
model = model.cuda()
print("Evaluating model '{}'...".format(model_path))
# Set Up Test Set for Debug & Evaluation
test_set_dir = os.path.join('clean_set', args.dataset, 'test_split')
test_set_img_dir = os.path.join(test_set_dir, 'data')
test_set_label_path = os.path.join(test_set_dir, 'labels')
test_set = tools.IMG_Dataset(data_dir=test_set_img_dir,
label_path=test_set_label_path, transforms=data_transform)
test_set_loader = torch.utils.data.DataLoader(
test_set,
batch_size=batch_size, shuffle=False, worker_init_fn=tools.worker_init, **kwargs)
# Poison Transform for Testing
poison_transform = supervisor.get_poison_transform(poison_type=args.poison_type, dataset_name=args.dataset,
target_class=config.target_class[args.dataset], trigger_transform=data_transform,
is_normalized_input=True,
alpha=args.alpha if args.test_alpha is None else args.test_alpha,
trigger_name=args.trigger, args=args)
if args.poison_type == 'TaCT' or args.poison_type == 'SleeperAgent':
source_classes = [config.source_class]
else:
source_classes = None
tools.test(model=model, test_loader=test_set_loader, poison_test=True, poison_transform=poison_transform, num_classes=num_classes, source_classes=source_classes, all_to_all=('all_to_all' in args.poison_type))