def test_random_hparams_deterministic(self, algorithm_name, dataset_name):
"""Test that hparams_registry.random_hparams is deterministic"""
a = hparams_registry.random_hparams(algorithm_name, dataset_name, 0)
b = hparams_registry.random_hparams(algorithm_name, dataset_name, 0)
self.assertEqual(a.keys(), b.keys())
for key in a.keys():
self.assertEqual(a[key], b[key], key)
print("\tTorchvision: {}".format(torchvision.__version__))
print("\tCUDA: {}".format(torch.version.cuda))
print("\tCUDNN: {}".format(torch.backends.cudnn.version()))
print("\tNumPy: {}".format(np.__version__))
print("\tPIL: {}".format(PIL.__version__))
print('Args:')
for k, v in sorted(vars(args).items()):
print('\t{}: {}'.format(k, v))
if args.hparams_seed == 0:
hparams = hparams_registry.default_hparams(args.algorithm,
args.dataset)
else:
hparams = hparams_registry.random_hparams(
args.algorithm, args.dataset,
misc.seed_hash(args.hparams_seed, args.trial_seed))
if args.hparams:
hparams.update(json.loads(args.hparams))
print('HParams:')
for k, v in sorted(hparams.items()):
print('\t{}: {}'.format(k, v))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if torch.cuda.is_available():
