def setUp(self):
TorchUtils.set_rnd_seed(1)
self.general_args = {
'experiment_type': 'mnist_adversarial',
'output_root': './src/tests/logs',
'param_file_path': None
}
self.params_overload = {"adv_attack_test": dict()}
self.test_name = self.id().split('.')[-1]
logger_utilities.init_logger(
logger_name=self.test_name,
output_root=self.general_args['output_root'])
self.logger = logger_utilities.get_logger()
import jsonargparse
import os
import torch
import time
import logger_utilities
from experimnet_utilities import Experiment
from utilities import TorchUtils
from adversarial.attacks import get_attack
TorchUtils.set_rnd_seed(1)
# Uncomment for performance. Comment for debug and reproducibility
# torch.backends.cudnn.enable = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
import json
def eval_adversarial_dataset(model,
dataloader,
attack,
save_adv_sample: bool = False):
"""
Evaluate model performance on dataloader with attack
:param model:
:param dataloader:
:param attack:
:param save_adv_sample:
:return:
"""
try:
torch.cuda.reset_max_memory_allocated()