'_z_') if args.el_datasets != "" else None args.ed_val_datasets = [int(x) for x in args.ed_val_datasets.split('_')] args.el_val_datasets = [int(x) for x in args.el_val_datasets.split('_')] args.gm_bucketing_pempos = [ int(x) for x in args.gm_bucketing_pempos.split('_') ] if args.gm_bucketing_pempos else [] print(args) return args, train_args if __name__ == "__main__": args, train_args = _parse_args() # TODO do this argument transfering in the load_train_args instead of train.py, evaluate.py, ensemble_evaluate.py train_args.checkpoint_model_num = args.checkpoint_model_num train_args.entity_extension = args.entity_extension train.args = args args.batch_size = train_args.batch_size printPredictions = None if args.print_predictions: from evaluation.print_predictions import PrintPredictions printPredictions = PrintPredictions( config.base_folder + "data/tfrecords/" + args.experiment_name + "/", args.predictions_folder, args.entity_extension, args.gm_bucketing_pempos, args.print_global_voters, args.print_global_pairwise_scores) from model.util import Tee tee = Tee(args.output_folder + 'evaluate-log.txt', 'a') print("train_args:\n", train_args) evaluate()
print(train_args) return args, train_args def terminate(): tee.close() if args.build_entity_universe: buildEntityUniverse.flush_entity_universe() else: print("from_myspans_to_given_spans_map_errors:", nnprocessing.from_myspans_to_given_spans_map_errors) if __name__ == "__main__": args, train_args = _parse_args() print(args) print(train_args) if args.build_entity_universe: buildEntityUniverse = BuildEntityUniverse() else: nnprocessing = NNProcessing(train_args, args) server = HTTPServer(('localhost', 5555), GetHandler) print('Starting server at http://localhost:5555') from model.util import Tee tee = Tee('server.txt', 'w') try: server.serve_forever() except KeyboardInterrupt: terminate() exit(0)
args.el_datasets = None return args def log_args(filepath): with open(filepath, "w") as fout: attrs = vars(args) # {'kids': 0, 'name': 'Dog', 'color': 'Spotted', 'age': 10, 'legs': 2, 'smell': 'Alot'} fout.write('\n'.join("%s: %s" % item for item in attrs.items())) with open(args.output_folder + "train_args.pickle", 'wb') as handle: pickle.dump(args, handle) def terminate(): tee.close() with open(args.output_folder + "train_args.pickle", 'wb') as handle: pickle.dump(args, handle) if __name__ == "__main__": args = _parse_args() print(args) log_args(args.output_folder + "train_args.txt") from model.util import Tee tee = Tee(args.output_folder + 'log.txt', 'a') try: train() except KeyboardInterrupt: terminate()
def log_args(filepath): with open(filepath, "w") as fout: attrs = vars(args) # {'kids': 0, 'name': 'Dog', 'color': 'Spotted', 'age': 10, 'legs': 2, 'smell': 'Alot'} fout.write('\n'.join("%s: %s" % item for item in attrs.items())) with open(args.output_folder+"train_args.pickle", 'wb') as handle: pickle.dump(args, handle) def terminate(): tee.close() with open(args.output_folder+"train_args.pickle", 'wb') as handle: pickle.dump(args, handle) if __name__ == "__main__": args = _parse_args() log_args(args.output_folder+"train_args.txt") from model.util import Tee tee = Tee(args.output_folder+'train-log.txt', 'a') print(args) try: train() except KeyboardInterrupt: terminate()