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()
def execute_train_encoder(gpu, exp_batch, exp_alias, suppress_output=True, number_of_workers=12):
"""
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_encoder.execute,
args=(gpu, exp_batch, exp_alias, suppress_output, number_of_workers))
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, params):
"""
Args:
gpu: gpu being used for this execution.
exp_batch: folder this driving experiment is being executed
exp_alias: experiment alias, file name, to be executed.
exp_set_name: driving environment
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()
def execute_train(gpu,
exp_batch,
exp_alias,
suppress_output=True,
number_of_workers=12):
"""
Args:
gpu: gpu being used for this execution.
exp_batch: folder this driving experiment is being executed
exp_alias: experiment alias, file name, to be executed.
Returns:
"""
create_exp_path(exp_batch, exp_alias)
p = multiprocessing.Process(target=train.execute,
args=(gpu, exp_batch, exp_alias,
suppress_output, number_of_workers))
p.start()
def execute_validation(gpu,
exp_batch,
exp_alias,
dataset,
suppress_output=True):
"""
Args:
gpu: gpu being used for this execution.
exp_batch: folder this driving experiment is being executed
exp_alias: experiment alias, file name, to be executed.
dataset: validation dataset
Returns:
"""
create_exp_path(exp_batch, exp_alias)
p = multiprocessing.Process(target=validate.execute,
args=(gpu, exp_batch, exp_alias, dataset,
suppress_output))
p.start()
def execute_train(gpus,
exp_folder,
exp_alias,
suppress_output=True,
number_of_workers=12):
"""
Args:
gpus: The gpu being used for this execution.
exp_folder: Folder name in configs
exp_alias: The experiment alias (yaml file)
suppress_output:
number_of_workers:
Returns:
"""
create_log_folder(exp_folder)
create_exp_path(exp_folder, exp_alias)
p = multiprocessing.Process(target=train.execute,
args=(gpus, exp_folder, exp_alias,
suppress_output, number_of_workers))
p.start()
def execute_drive(gpus, exp_folder, exp_alias, exp_set_name, suppress_output,
docker, record_collisions, no_screen):
"""
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.
exp_set_name:
suppress_output:
docker:
record_collisions:
no_screen:
Returns:
"""
create_log_folder(exp_folder)
create_exp_path(exp_folder, exp_alias)
p = multiprocessing.Process(target=run_drive.execute,
args=(gpus, exp_folder, exp_alias,
exp_set_name, suppress_output, docker,
record_collisions, no_screen))
p.start()
def execute_train(gpu, exp_batch, exp_alias, suppress_output=True, number_of_workers=12, encoder_params = None):
"""
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 encoder_params:
create_exp_path(exp_batch, exp_alias + '_' + str(encoder_params['encoder_checkpoint']))
else:
create_exp_path(exp_batch, exp_alias)
p = multiprocessing.Process(target=train.execute,
args=(gpu, exp_batch, exp_alias, suppress_output, number_of_workers, encoder_params))
p.start()
def execute_validation(gpu,
exp_batch,
exp_alias,
dataset,
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)
# The difference between train and validation is the
p = multiprocessing.Process(target=validate.execute,
args=(gpu, exp_batch, exp_alias, dataset,
suppress_output))
p.start()
def execute_validation(gpu, exp_batch, exp_alias, json_file_path, suppress_output=True, encoder_params = None):
"""
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 encoder_params:
create_exp_path(exp_batch, exp_alias + '_' + str(encoder_params['encoder_checkpoint']))
else:
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, json_file_path, suppress_output, encoder_params))
p.start()
"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:
raise ValueError(
" You should set the exp alias when using single process")
create_exp_path(args.folder, args.exp)
if args.single_process == 'train':
execute_train(gpu="0",
exp_batch=args.folder,
exp_alias=args.exp,
suppress_output=False,
number_of_workers=args.number_of_workers)
elif args.single_process == 'validation':
execute_validation(gpu="0",
exp_batch=args.folder,
exp_alias=args.exp,
dataset=args.validation_datasets[0],
suppress_output=False)
from coil_core import execute_train
from coilutils.general import create_log_folder, create_exp_path, erase_logs
if __name__ == '__main__':
folder = 'cvpr'
exp = 'img_gtseg_camv_control'
create_log_folder(folder)
erase_logs(folder)
create_exp_path(folder, exp)
execute_train('0', folder, exp)
print("SUCCESSFULLY RAN TRAINING")
