def execute(self) -> None:
parser = create_parser()
args = parser.parse_args()
if not args.repository:
create_parser().print_help()
sys.exit(0)
if not os.path.exists('sandbox'):
os.makedirs('sandbox')
logger.info("Downloading repository")
subprocess.Popen(cwd='./sandbox',
args=['git', 'clone', args.repository],
stderr=subprocess.STDOUT,
stdout=subprocess.DEVNULL).communicate()
logger.info("Done")
def parse_event(str: str, on_syntax_error: Any = None):
parser = create_parser(EventsLexer, EventsParser, InputStream(str), on_syntax_error)
return parser.event().value
def parse_events_from_file(filepath: str, on_syntax_error: Any = None):
parser = create_parser(EventsLexer, EventsParser, FileStream(filepath), on_syntax_error)
return parser.events().value
def parse_command(str, on_syntax_error: Any = None):
parser = create_parser(CommandsLexer, CommandsParser, InputStream(str),
on_syntax_error)
return parser.command().value
log('Hyperparameters: ' + str(parser_args), parser_args)
best_metric_score, earlystop_cnt = float('-inf'), 0
for epoch in range(1, parser_args.n_epochs+1):
log('Starting Epoch: {}'.format(epoch), parser_args)
train_losses = train(parser_args, unet, dataloader_train, epoch, optimizer, device)
_ = validate(parser_args, unet, dataloader_train, epoch, device, name='Train')
scores = validate(parser_args, unet, dataloader_val, epoch, device, name='Val')
#scheduler.step(scores['avg_pearson'])
if parser_args.earlystopping_patience <= 0: # No early stopping
if not (epoch % 10):
log('Saving model', parser_args)
save_checkpoint(unet, epoch, optimizer, wss_mean_std, os.path.join(parser_args.model_save_path, 'unet_best.pt'))
else: # Early stopping
if scores['avg_pearson'] > best_metric_score:
best_metric_score = scores['avg_pearson']
earlystop_cnt = 0
log('Saving best model', parser_args)
save_checkpoint(unet, epoch, optimizer, wss_mean_std, os.path.join(parser_args.model_save_path, 'unet_best.pt'))
else:
earlystop_cnt += 1
if earlystop_cnt >= parser_args.earlystopping_patience:
earlystop_cnt = 0
log('Early stopping at epoch %d' % epoch, parser_args)
break
if __name__ == "__main__":
PARSER_ARGS = parse_config(create_parser())
main(PARSER_ARGS)
# Create the agent.
dqn = DQN(model,
target_model_change,
gamma,
batch_size,
game.observation_space_shape,
game.action_space_size,
policy,
memory_size=replay_memory_size)
return dqn
if __name__ == '__main__':
# Get arguments.
args = create_parser().parse_args()
agent_name_prefix = args.filename_prefix
results_name_prefix = args.results_name_prefix
recording_name_prefix = args.recording_name_prefix
results_save_interval = args.results_save_interval
agent_save_interval = args.save_interval
info_interval_current = args.info_interval_current
info_interval_mean = args.info_interval_mean
target_model_change = args.target_interval
agent_path = args.agent
agent_frame_history = args.agent_history
plot_train_results = not args.no_plot
save_plots = not args.no_save_plots
plots_name_prefix = args.plot_name
render = not args.no_render
record = args.record