real_trojan_detector.py

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import os
import numpy as np
import skimage.io
import random
import torch
import warnings 
warnings.filterwarnings("ignore")
import demo


def real_trojan_detector(model_filepath, result_filepath, scratch_dirpath, examples_dirpath, example_img_format='png'):

    print('model_filepath = {}'.format(model_filepath))
    print('result_filepath = {}'.format(result_filepath))
    print('scratch_dirpath = {}'.format(scratch_dirpath))
    print('examples_dirpath = {}'.format(examples_dirpath))
    trojan_probability=demo.demo_trojan_detector(model_filepath, result_filepath, scratch_dirpath, examples_dirpath, example_img_format)
    with open(result_filepath, 'w') as fh:
        fh.write("{}".format(trojan_probability))


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description='Fake Trojan Detector to Demonstrate Test and Evaluation Infrastructure.')
    parser.add_argument('--model_filepath', type=str, help='File path to the pytorch model file to be evaluated.', default='./model.pt')
    parser.add_argument('--result_filepath', type=str, help='File path to the file where output result should be written. After execution this file should contain a single line with a single floating point trojan probability.', default='./output')
    parser.add_argument('--scratch_dirpath', type=str, help='File path to the folder where scratch disk space exists. This folder will be empty at execution start and will be deleted at completion of execution.', default='./scratch')
    parser.add_argument('--examples_dirpath', type=str, help='File path to the folder of examples which might be useful for determining whether a model is poisoned.', default='./example')


    args = parser.parse_args()
    real_trojan_detector(args.model_filepath, args.result_filepath, args.scratch_dirpath, args.examples_dirpath)