from typing import Dict, List
import torch
import csv
import argparse
from perceptual_advex.utilities import add_dataset_model_arguments, \
get_dataset_model
from perceptual_advex.attacks import *
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Adversarial training evaluation')
add_dataset_model_arguments(parser, include_checkpoint=True)
parser.add_argument('attacks', metavar='attack', type=str, nargs='+',
help='attack names')
parser.add_argument('--batch_size', type=int, default=100,
help='number of examples/minibatch')
parser.add_argument('--parallel', type=int, default=1,
help='number of GPUs to train on')
parser.add_argument('--num_batches', type=int, required=False,
help='number of batches (default entire dataset)')
parser.add_argument('--per_example', action='store_true', default=False,
help='output per-example accuracy')
parser.add_argument('--output', type=str, help='output CSV')
args = parser.parse_args()
dataset, model = get_dataset_model(args)
from perceptual_advex import evaluation
from perceptual_advex.utilities import add_dataset_model_arguments, \
get_dataset_model, calculate_accuracy, get_vae_model
from perceptual_advex.attacks import *
from perceptual_advex.ci_attacks2 import *
from perceptual_advex.cd_attacks2 import *
from perceptual_advex.models import FeatureModel
from perceptual_advex.hidden_attacks import *
from perceptual_advex.distances import L2Distance, LinfDistance
VAL_ITERS = 100
if __name__ == '__main__':
parser = argparse.ArgumentParser()
add_dataset_model_arguments(parser)
parser.add_argument('--num_epochs', type=int, required=False,
help='number of epochs trained')
parser.add_argument('--batch_size', type=int, default=100,
help='number of examples/minibatch')
parser.add_argument('--val_batches', type=int, default=10,
help='number of batches to validate on')
parser.add_argument('--log_dir', type=str, default='data/logs')
parser.add_argument('--parallel', type=int, default=1,
help='number of GPUs to train on')
parser.add_argument('--only_attack_correct', action='store_true',
default=False, help='only attack examples that '
'are classified correctly')
