def execute_validation(gpus,
exp_folder,
exp_alias,
validation_datasets,
erase_bad_validations,
restart_validations,
suppress_output=True):
"""
Args:
gpus: The gpu being used for this execution.
exp_folder: The folder this driving experiment is being executed
exp_alias: The experiment alias, file name, to be executed.
validation_datasets: Validation datasets to be deleted/restarted
erase_bad_validations:
restart_validations:
suppress_output:
Returns:
"""
validation_datasets = validation_datasets.split(
',') # Turn a string into a list of the names
create_log_folder(exp_folder)
create_exp_path(exp_folder, exp_alias)
if erase_bad_validations:
erase_wrong_plotting_summaries(exp_folder, validation_datasets)
if restart_validations:
erase_validations(exp_folder, validation_datasets)
# The difference between train and validation is the
p = multiprocessing.Process(target=validate.execute,
args=(gpus, exp_folder, exp_alias,
validation_datasets[0], suppress_output))
p.start()
# Check if the driving parameters are passed in a correct way
if args.driving_environments is not None:
for de in list(args.driving_environments):
if len(de.split('_')) < 2:
raise ValueError(
"Invalid format for the driving environments should be Suite_Town"
)
# This is the folder creation of the
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))
# The definition of parameters for driving
drive_params = {
"suppress_output": True,
"no_screen": args.no_screen,
"docker": args.docker,
"record_collisions": args.record_collisions
}
# There are two modes of execution
if args.single_process is not None:
####
# MODE 1: Single Process. Just execute a single experiment alias.
####
if args.exp is None: