def execute_train(gpu, exp_batch, exp_alias, suppress_output=True):
"""
Args:
gpu: The gpu being used for this execution.
module_name: The module name, if it is train, drive or evaluate
exp_alias: The experiment alias, file name, to be executed.
path: The path were the datasets are
Returns:
"""
create_exp_path(exp_batch, exp_alias)
p = multiprocessing.Process(target=train.execute,
args=(gpu, exp_batch, exp_alias, suppress_output))
p.start()
def test_basic_data(self):
# the town2-town01 data, try to load.
g_conf.immutable(False)
g_conf.EXPERIMENT_NAME = 'coil_icra'
create_log_folder('sample')
create_exp_path('sample', 'coil_icra')
merge_with_yaml('configs/sample/coil_icra.yaml')
set_type_of_process('train')
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
'CoILTrain')
dataset = CoILDataset(full_dataset,
transform=None,
preload_name=str(g_conf.NUMBER_OF_HOURS) +
'hours_' + g_conf.TRAIN_DATASET_NAME)
def test_town3_data(self):
# the town3 data has different names and does not have pedestrians of vehicle stop
# indications
g_conf.immutable(False)
g_conf.EXPERIMENT_NAME = 'resnet34imnet'
create_log_folder('town03')
create_exp_path('town03', 'resnet34imnet')
merge_with_yaml('configs/town03/resnet34imnet.yaml')
set_type_of_process('train')
full_dataset = os.path.join(os.environ["COIL_DATASET_PATH"],
'CoILTrainTown03')
dataset = CoILDataset(full_dataset,
transform=None,
preload_name=str(g_conf.NUMBER_OF_HOURS) +
'hours_' + g_conf.TRAIN_DATASET_NAME)
def execute_drive(gpu, exp_batch, exp_alias, exp_set_name, suppress_output=True, no_screen=False):
"""
Args:
gpu: The gpu being used for this execution.
module_name: The module name, if it is train, drive or evaluate
exp_alias: The experiment alias, file name, to be executed.
path: The path were the datasets are
Returns:
"""
create_exp_path(exp_batch, exp_alias)
p = multiprocessing.Process(target=run_drive.execute,
args=(gpu, exp_batch, exp_alias, exp_set_name,
0.2, "127.0.0.1", suppress_output,
no_screen))
p.start()
def execute_validation(gpu, exp_batch, exp_alias, dataset, architecture, suppress_output=True):
"""
Args:
gpu: The gpu being used for this execution.
module_name: The module name, if it is train, drive or evaluate
exp_alias: The experiment alias, file name, to be executed.
path: The path were the datasets are
Returns:
"""
# if module_name not in set(["train","drive","evaluate"]):
# raise ValueError("Invalid module to execute")
create_exp_path(exp_batch, exp_alias)
# The difference between train and validation is the
p = multiprocessing.Process(target=validate.execute,
args=(gpu, exp_batch, exp_alias, dataset, architecture, suppress_output))
p.start()
def execute_drive(gpu, exp_batch, exp_alias, exp_set_name, params):
"""
Args:
gpu: The gpu being used for this execution.
exp_batch: the folder this driving experiment is being executed
exp_alias: The experiment alias, file name, to be executed.
params: all the rest of parameter, if there is recording and etc.
Returns:
"""
params.update({'host': "127.0.0.1"})
create_exp_path(exp_batch, exp_alias)
p = multiprocessing.Process(target=run_drive.execute,
args=(gpu, exp_batch, exp_alias, exp_set_name,
params))
p.start()
create_log_folder(args.folder)
erase_logs(args.folder)
if args.erase_bad_validations:
erase_wrong_plotting_summaries(args.folder,
list(args.validation_datasets))
if args.restart_validations:
erase_validations(args.folder, list(args.validation_datasets))
if args.single_process is not None:
if args.exp is None:
raise ValueError(
" You should set the exp alias when using single process")
create_exp_path(args.folder, args.exp)
if args.single_process == 'train':
# TODO make without position, increases the legibility.
execute_train("0", args.folder, args.exp, False)
elif args.single_process == 'validation':
if len(args.validation_datasets) == 1:
execute_validation("0", args.folder, args.exp,
args.validation_datasets[0], args.model,
False)
else:
execute_validation("0", args.folder, args.exp,
args.validation_datasets, args.model, False)
elif args.single_process == 'drive':