Function init_experiment

project_utils/general_utils.py:123–181

(args, runner_name=None, exp_id=None)

def init_experiment(args, runner_name=None, exp_id=None):

    args.cuda = torch.cuda.is_available()

    # Get filepath of calling script
    if runner_name is None:
        runner_name = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))).split(".")[-2:]

    root_dir = os.path.join(args.exp_root, *runner_name)

    if not os.path.exists(root_dir):
        os.makedirs(root_dir)

    # Either generate a unique experiment ID, or use one which is passed
    if exp_id is None:

        # Unique identifier for experiment
        now = '({:02d}.{:02d}.{}_|_'.format(datetime.now().day, datetime.now().month, datetime.now().year) + \
              datetime.now().strftime("%S.%f")[:-3] + ')'

        log_dir = os.path.join(root_dir, 'log', now)
        while os.path.exists(log_dir):
            now = '({:02d}.{:02d}.{}_|_'.format(datetime.now().day, datetime.now().month, datetime.now().year) + \
                  datetime.now().strftime("%S.%f")[:-3] + ')'

            log_dir = os.path.join(root_dir, 'log', now)

    else:

        log_dir = os.path.join(root_dir, 'log', f'{exp_id}')

    if not os.path.exists(log_dir):
        os.makedirs(log_dir)
    args.log_dir = log_dir

    # Instantiate directory to save models to
    model_root_dir = os.path.join(args.log_dir, 'checkpoints')
    if not os.path.exists(model_root_dir):
        os.mkdir(model_root_dir)

    args.model_dir = model_root_dir
    args.model_path = os.path.join(args.model_dir, 'model.pt')

    print(f'Experiment saved to: {args.log_dir}')

    args.writer = SummaryWriter(log_dir=args.log_dir)

    hparam_dict = {}

    for k, v in vars(args).items():
        if isinstance(v, (int, float, str, bool, torch.Tensor)):
            hparam_dict[k] = v

    args.writer.add_hparams(hparam_dict=hparam_dict, metric_dict={})

    print(runner_name)
    print(args)