def make_args_list(n_trials, dataset_names, algorithms, n_hparams_from,
n_hparams, steps, data_dir, task, holdout_fraction,
single_test_envs, hparams):
args_list = []
for trial_seed in range(n_trials):
for dataset in dataset_names:
for algorithm in algorithms:
if single_test_envs:
all_test_envs = [
[i] for i in range(datasets.num_environments(dataset))
]
else:
all_test_envs = all_test_env_combinations(
datasets.num_environments(dataset))
for test_envs in all_test_envs:
for hparams_seed in range(n_hparams_from, n_hparams):
train_args = {}
train_args['dataset'] = dataset
train_args['algorithm'] = algorithm
train_args['test_envs'] = test_envs
train_args['holdout_fraction'] = holdout_fraction
train_args['hparams_seed'] = hparams_seed
train_args['data_dir'] = data_dir
train_args['task'] = task
train_args['trial_seed'] = trial_seed
train_args['seed'] = misc.seed_hash(
dataset, algorithm, test_envs, hparams_seed,
trial_seed)
if steps is not None:
train_args['steps'] = steps
if hparams is not None:
train_args['hparams'] = hparams
args_list.append(train_args)
return args_list
def make_args_list(n_trials, dataset_names, algorithms, n_hparams, steps,
data_dir, hparams):
args_list = []
for trial_seed in range(n_trials):
for dataset in dataset_names:
for algorithm in algorithms:
all_test_envs = all_test_env_combinations(
datasets.NUM_ENVIRONMENTS[dataset])
for test_envs in all_test_envs:
for hparams_seed in range(n_hparams):
train_args = {}
train_args['dataset'] = dataset
train_args['algorithm'] = algorithm
train_args['test_envs'] = test_envs
train_args['hparams_seed'] = hparams_seed
train_args['data_dir'] = data_dir
train_args['trial_seed'] = trial_seed
train_args['seed'] = misc.seed_hash(
dataset, algorithm, test_envs, hparams_seed,
trial_seed)
if steps is not None:
train_args['steps'] = steps
if hparams is not None:
train_args['hparams'] = hparams
args_list.append(train_args)
return args_list
def _hparam(name, default_val, random_val_fn):
"""Define a hyperparameter. random_val_fn takes a RandomState and
returns a random hyperparameter value."""
assert(name not in hparams)
random_state = np.random.RandomState(
misc.seed_hash(random_seed, name)
)
hparams[name] = (default_val, random_val_fn(random_state))
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():
device = "cuda"
uda = []
for tfm_idx, env in enumerate(envs):
if env_i in args.test_envs:
out, in_ = misc_aug.split_dataset(
env, int(len(env) * args.holdout_fraction),
misc_aug.seed_hash(args.trial_seed, env_i))
outs_.append(out)
ins_.append(in_)
in_val, in_ = misc_aug.split_dataset(
in_, int(len(in_) * args.holdout_fraction),
misc_aug.seed_hash(args.trial_seed, env_i))
in_vals.append(in_val)
else: # traain_env
out, in_ = misc.split_dataset(
env, int(len(env) * args.holdout_fraction),
misc.seed_hash(args.trial_seed, env_i))
in_val, in_ = misc_aug.split_dataset(
in_, int(len(in_) * args.holdout_fraction),
misc_aug.seed_hash(args.trial_seed, env_i))
in_vals.append(in_val)
if tfm_idx == 0:
ins_.append(
in_
) #append only the datasets without our out_augs as the in splits
outs_.append(
out
) #make sure no augmentation on out for train env val val
if env_i in args.test_envs:
uda, in_ = misc_aug.split_dataset(
in_, int(len(in_) * args.uda_holdout_fraction),
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:
# seed_hash(hparams_seed, 0) is for backwards-compatibility
hparams = hparams_registry.random_hparams(
args.algorithm, args.dataset, misc.seed_hash(args.hparams_seed, 0))
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.trial_seed)
np.random.seed(args.trial_seed)
torch.manual_seed(args.trial_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if torch.cuda.is_available():
device = "cuda"
