logger.info("best OA iter now= %d" % (best_macro_OA_iter_now))
state = {
"epoch": it,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"scheduler_state": scheduler.state_dict(),
"best_macro_OA_now": best_macro_OA_now,
'best_macro_OA_iter_now':best_macro_OA_iter_now,
}
save_path = os.path.join(writer.file_writer.get_logdir(), "{}_{}_last_model.pkl".format(cfg.model.arch, cfg.data.dataloader))
torch.save(state, save_path)
if __name__ == "__main__":
cfg = args.get_argparser('configs/psr_siamdiff_polar_c3.yml')
del cfg.test
torch.backends.cudnn.benchmark = True
# generate work dir
run_id = osp.join(r'runs', cfg.model.arch + '_' + cfg.train.loss.name + '_' + cfg.train.optimizer.name)
run_id = utils.get_work_dir(run_id)
writer = SummaryWriter(log_dir=run_id)
config_fig = types.dict2fig(cfg.to_flatten_dict())
# plt.savefig(r'./tmp/ff.png')
writer.add_figure('config', config_fig, close=True)
# writer.add_hparams(types.flatten_dict_summarWriter(cfg), {'a': 'b'})
writer.flush()
# logger
logger = get_logger(run_id)
state = {
"epoch": it,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"scheduler_state": scheduler.state_dict(),
"best_macro_OA_now": best_macro_OA_now,
'best_macro_OA_iter_now': best_macro_OA_iter_now,
}
save_path = os.path.join(
writer.file_writer.get_logdir(),
"{}_{}_last_model.pkl".format(cfg.model.arch, cfg.data.dataloader))
torch.save(state, save_path)
if __name__ == "__main__":
cfg = args.get_argparser('configs/psr_siamdiff_complex_c3.yml')
del cfg.test
torch.backends.cudnn.benchmark = True
# generate work dir
run_id = osp.join(
r'runs', cfg.model.arch + '_' + cfg.train.loss.name + '_' +
cfg.train.optimizer.name)
run_id = utils.get_work_dir(run_id)
writer = SummaryWriter(log_dir=run_id)
config_fig = types.dict2fig(cfg.to_flatten_dict())
# plt.savefig(r'./tmp/ff.png')
writer.add_figure('config', config_fig, close=True)
# writer.add_hparams(types.flatten_dict_summarWriter(cfg), {'a': 'b'})
writer.flush()
state = {
"epoch": it,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"scheduler_state": scheduler.state_dict(),
"best_macro_OA_now": best_macro_OA_now,
'best_macro_OA_iter_now': best_macro_OA_iter_now,
}
save_path = os.path.join(
writer.file_writer.get_logdir(),
"{}_{}_last_model.pkl".format(cfg.model.arch, cfg.data.dataloader))
torch.save(state, save_path)
if __name__ == "__main__":
cfg = args.get_argparser('configs/psr_siamdiff_pauli.yml')
del cfg.test
torch.backends.cudnn.benchmark = True
# generate work dir
run_id = osp.join(
r'runs', cfg.model.arch + '_' + cfg.train.loss.name + '_' +
cfg.train.optimizer.name)
run_id = utils.get_work_dir(run_id)
writer = SummaryWriter(log_dir=run_id)
config_fig = types.dict2fig(cfg.to_flatten_dict())
# plt.savefig(r'./tmp/ff.png')
writer.add_figure('config', config_fig, close=True)
# writer.add_hparams(types.flatten_dict_summarWriter(cfg), {'a': 'b'})
writer.flush()
logger.debug("lost connection with user")
async def main(bus_port, browser_port, host):
serve_data_receiving = partial(serve_websocket,
listen_bus_route_data,
host,
bus_port,
ssl_context=None)
serve_browser_connection = partial(serve_websocket,
handle_browser_connection,
host,
browser_port,
ssl_context=None)
async with trio.open_nursery() as nursery:
nursery.start_soon(serve_data_receiving)
nursery.start_soon(serve_browser_connection)
if __name__ == "__main__":
args = get_argparser().parse_args()
formatter = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
logging.basicConfig(level=logging.ERROR, format=formatter)
logging_level = logging.DEBUG if args.verbose else logging.WARNING
logger.setLevel(logging_level)
with contextlib.suppress(KeyboardInterrupt):
trio.run(main, args.bus_port, args.browser_port, args.host)
state = {
"epoch": it + 1,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"scheduler_state": scheduler.state_dict(),
"best_fwIoU": best_fwIoU_now,
}
save_path = os.path.join(
writer.file_writer.get_logdir(),
"{}_{}_last_model.pkl".format(cfg['model']['arch'],
cfg['data']['dataloader']))
torch.save(state, save_path)
if __name__ == "__main__":
cfg = args.get_argparser(r'configs/tile.yml')
del cfg.test
torch.backends.cudnn.benchmark = True
# generate work dir
run_id = utils.get_work_dir(
osp.join(
r'runs', cfg.model.arch + '_' + cfg.train.loss.name + '_' +
cfg.train.optimizer.name + '_' + cfg.train.lr.name))
writer = SummaryWriter(log_dir=run_id)
config_fig = types.dict2fig(cfg.to_flatten_dict())
# plt.savefig(r'./tmp/ff.png')
writer.add_figure('config', config_fig, close=True)
# writer.add_hparams(types.flatten_dict_summarWriter(cfg), {'a': 'b'})
writer.flush()
# logger