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AE_DS_certify.py
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from architectures import get_architecture, IMAGENET_CLASSIFIERS, AUTOENCODER_ARCHITECTURES, DENOISERS_ARCHITECTURES
from core import Smooth
from datasets import get_dataset, DATASETS, get_num_classes
from time import time
import argparse
import datetime
import os
import torch
from torch.utils.data import DataLoader
from robustness import datasets as dataset_r
from robustness.tools.imagenet_helpers import common_superclass_wnid, ImageNetHierarchy
parser = argparse.ArgumentParser(description='Certify many examples')
parser.add_argument("--dataset", choices=DATASETS, help="which dataset")
parser.add_argument("--sigma", type=float, help="noise hyperparameter")
parser.add_argument("--outfile", type=str, help="output file")
parser.add_argument("--batch", type=int, default=1000, help="batch size")
parser.add_argument("--skip", type=int, default=1, help="how many examples to skip")
parser.add_argument("--max", type=int, default=-1, help="stop after this many examples")
parser.add_argument("--split", choices=["train", "test"], default="test", help="train or test set")
parser.add_argument("--N0", type=int, default=100)
parser.add_argument("--N", type=int, default=10000, help="number of samples to use")
parser.add_argument("--alpha", type=float, default=0.001, help="failure probability")
parser.add_argument('--philly_imagenet_path', type=str, default='',
help='Path to imagenet on philly')
parser.add_argument('--azure_datastore_path', type=str, default='',
help='Path to imagenet on azure')
parser.add_argument('--l2radius', type=float, help='l2 radius')
# Model Arch & Checkpoint
parser.add_argument('--model_type', default='DS', type=str,
help="Denoiser + (AutoEncoder) + classifier/reconstructor",
choices=['DS', 'AE_DS'])
parser.add_argument("--base_classifier", type=str, help="path to saved pytorch model of base classifier")
parser.add_argument('--pretrained-denoiser', type=str, default='',
help='Path to a denoiser to attached before classifier during certificaiton.')
parser.add_argument('--pretrained-encoder', default='', type=str,
help='path to a pretrained encoder')
parser.add_argument('--pretrained-decoder', default='', type=str,
help='path to a pretrained decoder')
parser.add_argument('--encoder_arch', type=str, default='cifar_encoder', choices=AUTOENCODER_ARCHITECTURES)
parser.add_argument('--decoder_arch', type=str, default='cifar_decoder', choices=AUTOENCODER_ARCHITECTURES)
parser.add_argument('--arch', type=str, choices=DENOISERS_ARCHITECTURES)
parser.add_argument('--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
args = parser.parse_args()
if __name__ == "__main__":
# --------------------- Dataset Loading ----------------------
if args.dataset == 'cifar10' or args.dataset == 'stl10' or args.dataset == 'mnist':
pin_memory = (args.dataset == "imagenet")
test_dataset = get_dataset(args.dataset, 'test')
test_loader = DataLoader(test_dataset, shuffle=False, batch_size=1,
num_workers=args.workers, pin_memory=pin_memory)
elif args.dataset == 'restricted_imagenet':
in_path = '/localscratch2/damondemon/datasets/imagenet'
in_info_path = '/localscratch2/damondemon/datasets/imagenet_info'
in_hier = ImageNetHierarchy(in_path, in_info_path)
superclass_wnid = ['n02084071', 'n02120997', 'n01639765', 'n01662784', 'n02401031', 'n02131653', 'n02484322',
'n01976957', 'n02159955', 'n01482330']
class_ranges, label_map = in_hier.get_subclasses(superclass_wnid, balanced=True)
custom_dataset = dataset_r.CustomImageNet(in_path, class_ranges)
_, test_loader = custom_dataset.make_loaders(workers=4, batch_size=1)
# --------------------- Model Loading -------------------------
# a) Classifier / Reconstructor
checkpoint = torch.load(args.base_classifier)
base_classifier = get_architecture(checkpoint['arch'], args.dataset)
base_classifier.load_state_dict(checkpoint['state_dict'])
# b) Denoiser
if args.pretrained_denoiser:
checkpoint = torch.load(args.pretrained_denoiser)
assert checkpoint['arch'] == args.arch
denoiser = get_architecture(checkpoint['arch'], args.dataset)
denoiser.load_state_dict(checkpoint['state_dict'])
else:
denoiser = get_architecture(args.arch, args.dataset)
# c) AutoEncoder
if args.model_type == 'AE_DS':
checkpoint = torch.load(args.pretrained_encoder)
assert checkpoint['arch'] == args.encoder_arch
encoder = get_architecture(checkpoint['arch'], args.dataset)
encoder.load_state_dict(checkpoint['state_dict'])
checkpoint = torch.load(args.pretrained_decoder)
assert checkpoint['arch'] == args.decoder_arch
decoder = get_architecture(checkpoint['arch'], args.dataset)
decoder.load_state_dict(checkpoint['state_dict'])
base_classifier = torch.nn.Sequential(denoiser, encoder, decoder, base_classifier)
else:
base_classifier = torch.nn.Sequential(denoiser, base_classifier)
base_classifier = base_classifier.eval().cuda()
# create the smooothed classifier g
smoothed_classifier = Smooth(base_classifier, get_num_classes(args.dataset), args.sigma)
# prepare output file
if not os.path.exists(args.outfile.split('sigma')[0]):
os.makedirs(args.outfile.split('sigma')[0])
f = open(args.outfile, 'w')
print("idx\tlabel\tpredict\tradius\tSta_correct\ttime\tcount\tSta_count", file=f, flush=True)
print("idx\tlabel\tpredict\tradius\tSta_correct\ttime\tcount\tSta_count", flush=True)
f.close()
# iterate through the dataset
count = 0
sta_count = 0
for i, (x, label) in enumerate(test_loader):
# only certify every args.skip examples, and stop after args.max examples
if i % args.skip != 0:
continue
if i == args.max:
break
before_time = time()
# certify the prediction of g around x
x = x.cuda()
prediction, radius = smoothed_classifier.certify(x, args.N0, args.N, args.alpha, args.batch)
after_time = time()
# correct = int(prediction == label)
correct = int(prediction == label and radius > args.l2radius)
sta_correct = int(prediction == label)
count += correct
sta_count += sta_correct
time_elapsed = str(datetime.timedelta(seconds=(after_time - before_time)))
f = open(args.outfile, 'a')
print("{}\t{}\t{}\t{:.3}\t{}\t{}\t{}\t{}".format(
i, label, prediction, radius, sta_correct, time_elapsed, count, sta_count), file=f, flush=True)
print("{}\t{}\t{}\t{:.3}\t{}\t{}\t{}\t{}".format(
i, label, prediction, radius, sta_correct, time_elapsed, count, sta_count), flush=True)
f.close()