# generate work dir
run_id = osp.join(
r'./runs', cfg['model']['arch'] + '_' + cfg['train']['loss']['name'] +
'_' + cfg['train']['optimizer']['name'] + '_' +
cfg['train']['lr_schedule']['name'] + '_')
all_runs = glob.glob(run_id + '*')
all_runs = natsort.natsorted(all_runs)
if all_runs:
run_id_cnt = re.search('_\d+', all_runs[-1])
run_id_cnt = int(run_id_cnt.group(0)[1:])
run_id = run_id + str(run_id_cnt + 1)
else:
run_id = run_id + '0'
writer = SummaryWriter(log_dir=run_id)
config_fig = types.dict2fig(cfg)
# 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)
# logger.info('Let the games begin')
# print('-'*100)
# print('RUNDIR: {}'.format(logdir))
logger.info(f'RUNDIR: {run_id}')
shutil.copy(args.config, run_id)
train(cfg, writer, logger, run_id)
'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)
# print('-'*100)
logger.info(f'RUNDIR: {run_id}')
logger.info(f'using config file: {cfg.config_file}')
shutil.copy(cfg.config_file, run_id)
train(cfg, writer, logger)
logger.info(f'RUNDIR:{run_id}')