def _run(self,
testproblem=None,
initializations=None,
hyperparams=None,
batch_size=None,
num_epochs=None,
random_seed=None,
data_dir=None,
output_dir=None,
weight_decay=None,
no_logs=None,
train_log_interval=None,
print_train_iter=None,
tb_log=None,
tb_log_dir=None,
**training_params):
# Creates a backup copy of the initial parameters. Users might change the dicts during training.
hyperparams_before_training = deepcopy(hyperparams)
training_params_before_training = deepcopy(training_params)
batch_size = self._use_default_batch_size_if_missing(
testproblem, batch_size)
num_epochs = self._use_default_num_epochs_if_missing(
testproblem, num_epochs)
if data_dir is not None:
global_config.set_data_dir(data_dir)
run_directory, file_name = self.generate_output_directory_name(
testproblem, initializations, batch_size, num_epochs, weight_decay,
random_seed, output_dir, hyperparams, **training_params)
if tb_log:
if tb_log_dir == 'none':
print(
'Tensorboard logging: No tb_log_dir specified, using settings folder {0:s} as default.'
.format(run_directory))
os.makedirs(run_directory, exist_ok=True)
tb_log_dir = run_directory
tproblem = self.create_testproblem(testproblem, initializations,
batch_size, weight_decay,
random_seed)
output = self.training(tproblem, hyperparams, num_epochs,
print_train_iter, train_log_interval, tb_log,
tb_log_dir, **training_params)
output = self._post_process_output(output, testproblem,
initializations, batch_size,
num_epochs, random_seed,
weight_decay,
hyperparams_before_training,
**training_params_before_training)
if not no_logs:
os.makedirs(run_directory, exist_ok=True)
self.write_output(output, run_directory, file_name)
return output
"""
Reduced and unreduced forward pass using only one forward throught the model.
"""
from test_forward import set_up_problem
from backobs.integration import integrate_individual_loss
from backpack import backpack, extensions
from deepobs.config import set_data_dir
from deepobs.pytorch.testproblems import fmnist_2c2d, mnist_logreg, quadratic_deep
if __name__ == "__main__":
use_backpack = False
set_data_dir("~/tmp/data_deepobs")
batch_size = 20
tp_classes = [
mnist_logreg,
fmnist_2c2d,
quadratic_deep,
]
for tp_cls in tp_classes:
for use_backpack in [
False,
True,
]:
losses = []
accuracies = []
def fix_deepobs_data_dir():
"""Fix DeepOBS' data directory to one path avoid multiple dataset copies."""
DIR = "~/tmp/data_deepobs"
set_data_dir(os.path.expanduser(DIR))
