def main():
args = argopts.parse_arguments()
socket = (args.interface, args.port)
# Initialize Logging settings for Provenance, basically telling
# the "Provenance" logger to redirect to the specified logfile AND
# to the UI
logger_controller = LoggingController(logfile="logs/provenance.log",
level=args.verbose)
logger = logging.getLogger("Provenance")
# Initialize Backend
try:
if args.threading:
server = ThreadedProvenanceServer(server_address=socket,
discovery=args.discovery,
whitelist=args.whitelist,
backup_dir="backups",
restore=args.restore)
# Start a thread with the server -- that thread will then start one
# more thread for each request
server_thread = threading.Thread(target=server.serve_forever)
# Exit the server thread when the main thread terminates
server_thread.daemon = True
server_thread.start()
else:
# We don't want to use this
sys.exit(0)
# server = ProvenanceServer(socket, ProvenanceClientHandler, args)
# server.serve_forever()
except KeyboardInterrupt:
sys.exit(0)
# Initialize Controllers for model-ui interactions
ui_controller = UIController()
model_controller = ModelController(server)
logger.critical(f"Server starting on {socket[0]}:{socket[1]}.")
# Initialize Frontend
if args.ui == "cli":
ProvenanceCLI.model = model_controller
ProvenanceCLI.logger = logger_controller
ProvenanceCLI.ui = ui_controller
ProvenanceCLI.run()
output_vis_path / f"{base_name}_gt.obj",
level=1)
return {'loss': 0}
def train_implicit_refinement(args):
seed_everything(args.seed)
checkpoint_callback = ModelCheckpoint(filepath=os.path.join(
"runs", args.experiment, 'checkpoints'),
save_top_k=-1,
verbose=False,
period=args.save_epoch)
model = ImplicitRefinementTrainer(args)
trainer = Trainer(gpus=[args.gpu],
num_sanity_val_steps=args.sanity_steps,
checkpoint_callback=checkpoint_callback,
max_epochs=args.max_epoch,
limit_val_batches=args.val_check_percent,
val_check_interval=min(args.val_check_interval, 1.0),
check_val_every_n_epoch=max(1, args.val_check_interval),
resume_from_checkpoint=args.resume,
logger=None,
benchmark=True)
trainer.fit(model)
if __name__ == '__main__':
_args = arguments.parse_arguments()
train_implicit_refinement(_args)
#!/usr/bin/env python
## This script marshals the other parts of the parser
from util.reader import read
from util.arguments import parse_arguments
import util.statistics as statistics
if __name__ == "__main__":
args = parse_arguments()
csv_data = read(args.filename)
environ = {}
# process options which set flags for other modules
if args.numeric_ids:
environ["numeric_ids"] = True
else:
environ["numeric_ids"] = False
# process mutators, according to which ones were selected
if args.condense_java:
from mutators import condense_java
condense_java.mutate(csv_data, environ)
if args.condense_ms:
from mutators import condense_ms
condense_ms.mutate(csv_data, environ)
if args.select_adobe:
from mutators import select_adobe
select_adobe.mutate(csv_data, args.select_adobe, environ)
#!/usr/bin/env python
## This script marshals the other parts of the parser
from util.reader import read
from util.arguments import parse_arguments
import util.statistics as statistics
if __name__ == '__main__':
args = parse_arguments()
csv_data = read(args.filename)
environ = {}
# process options which set flags for other modules
if args.numeric_ids:
environ['numeric_ids'] = True
else:
environ['numeric_ids'] = False
if args.level:
environ['level'] = args.level
# process mutators, according to which ones were selected
if args.condense_java:
from mutators import condense_java
condense_java.mutate(csv_data, environ)
if args.condense_ms:
from mutators import condense_ms
condense_ms.mutate(csv_data, environ)
if args.select_adobe:
from mutators import select_adobe
select_adobe.mutate(csv_data, args.select_adobe, environ)
# level and plugin_list don't really make sense together,