certify.py

# evaluate a smoothed classifier on a dataset
import argparse
import os
import setGPU
from datasets import get_dataset, DATASETS, get_num_classes
from core import Smooth
from time import time
import torch
import datetime
from architectures import get_architecture, get_diffusion_model, DiffSmooth_Classifier

parser = argparse.ArgumentParser(description='Certify many examples')
parser.add_argument("dataset", choices=DATASETS, help="which dataset")
parser.add_argument("classifier", type=str, help="path to saved pytorch model of base classifier")
parser.add_argument("sigma", type=float, help="noise hyperparameter")
parser.add_argument("outfile", type=str, help="output file")
parser.add_argument("--m", type=int, default=5, help="the number of local smoothing noise")
parser.add_argument("--local_noise_sd", type=float, default=0.25, help="the noise magnitude for the local smoothing")
parser.add_argument("--batch", type=int, default=50, help="batch size")
parser.add_argument("--skip", type=int, default=100, 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")
args = parser.parse_args()

if __name__ == "__main__":
    # load the base classifier
    checkpoint = torch.load(args.classifier)
    classifier = get_architecture(checkpoint["arch"], args.dataset)
    classifier.load_state_dict(checkpoint['state_dict'])
    diffusion = get_diffusion_model(args.sigma, args.dataset)
    base_classifier = DiffSmooth_Classifier(args.dataset, classifier, diffusion, args.m, args.local_noise_sd)
    # create the smooothed classifier g
    smoothed_classifier = Smooth(base_classifier, get_num_classes(args.dataset), args.sigma)

    # prepare output file
    f = open(args.outfile, 'w')
    print("idx\tlabel\tpredict\tradius\tcorrect\ttime", file=f, flush=True)
    
    # Check if the directory exists, and if not, create it
    directory = os.path.dirname(args.outfile)
    if not os.path.exists(directory):
        os.makedirs(directory)
        
    # iterate through the dataset
    dataset = get_dataset(args.dataset, args.split)
    for i in range(len(dataset)):

        # only certify every args.skip examples, and stop after args.max examples
        if i % args.skip != 0:
            continue
        if i == args.max:
            break

        (x, label) = dataset[i]

        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)

        time_elapsed = str(datetime.timedelta(seconds=(after_time - before_time)))
        print("{}\t{}\t{}\t{:.3}\t{}\t{}".format(
            i, label, prediction, radius, correct, time_elapsed), file=f, flush=True)

    f.close()