import sys sys.path.insert(0, '../../filling_level/vggish/') # nopep8 from main import Config, run_kfold, get_cmd_args from time import localtime, strftime # import argparse if __name__ == "__main__": # Reproduce the best experiment # if True, will use the pre-trained model and make predictions, if False, will train the model use_pretrained = True exp_name = 200903163404 cfg = Config() cfg.load_from(path=f'./predictions/{exp_name}/cfg.txt') # replacing the time with the old_time + current_time such that there is no collision if use_pretrained: cfg.init_time = exp_name else: cfg.init_time = f'{cfg.init_time}_{strftime("%y%m%d%H%M%S", localtime())}' # Expected average of Best Metrics on Each Valid Set: 0.912957 @ 200903163404 run_kfold(cfg, use_pretrained, get_cmd_args().predict_on_private) # Experiment with other parameters # cfg = Config() # cfg.assign_variable('task', 'ftype') # cfg.assign_variable('output_dim', 4) # cfg.assign_variable('model_type', 'GRU') # cfg.assign_variable('bi_dir', False) # cfg.assign_variable('device', 'cuda:0') # cfg.assign_variable('data_root', '../../filling_level/vggish/vggish_features') # cfg.assign_variable('batch_size', 64) # cfg.assign_variable('input_dim', 128)