def test_get_not_empty_vm_id_many_results_by_many_tags(self):
attackers = get_attackers("vm-555")
self.assertIn("vm-333", attackers)
self.assertIn("vm-444", attackers)
self.assertIn("vm-555", attackers)
self.assertIn("vm-666", attackers)
self.assertEqual(4, len(attackers))
def test_get_not_empty_vm_id_one_result(self):
attackers = get_attackers("vm-222")
self.assertEqual(attackers, ["vm-111"])
def test_get_not_empty_vm_id_no_results(self):
attackers = get_attackers("vm-111")
self.assertEqual(attackers, [])
def test_get_not_empty_non_existing_vm_id(self):
attackers = get_attackers("vm-123")
self.assertEqual(attackers, [])
def test_get_empty_vm_id(self):
attackers = get_attackers("")
self.assertEqual(attackers, [])
if __name__ == '__main__':
# add run_type cli args
parser = argparse.ArgumentParser()
# add base cli args
options.base_training_cfgs(parser)
# if run_type_args.run_type=='train_adv':
options.adv_training_cfg(parser)
options.adv_attk_params(parser)
# parse all args
args = vars(parser.parse_args())
args['device'] = get_device(**args)
args['data_loader'] = {
f'{dataset_type}': get_dataloader(dataset_type=dataset_type, **args)
for dataset_type in ['val', 'train']
}
args['model'] = get_torchvision_models(**args)
args['optimizer'] = get_optimizer(**args)
args['lr_scheduler'] = get_lr_scheduler(**args)
args['attackers'] = get_attackers(loss_fn=nn.CrossEntropyLoss(),
cfg=args,
**args)
args['criterion'] = get_criterion(**args)
args['to_device'] = partial(to_device, args['device'])
args['to_cpu'] = partial(to_device, 'cpu')
run_trainer(**args)