| import glob | |
| import os | |
| from collections import OrderedDict | |
| import torch | |
| class Saver(object): | |
| def __init__(self, args): | |
| self.args = args | |
| self.directory = os.path.join("run", args.train_dataset, args.checkname) | |
| self.runs = sorted(glob.glob(os.path.join(self.directory, "experiment_*"))) | |
| run_id = int(self.runs[-1].split("_")[-1]) + 1 if self.runs else 0 | |
| self.experiment_dir = os.path.join( | |
| self.directory, "experiment_{}".format(str(run_id)) | |
| ) | |
| if not os.path.exists(self.experiment_dir): | |
| os.makedirs(self.experiment_dir) | |
| def save_checkpoint(self, state, filename="checkpoint.pth.tar"): | |
| """Saves checkpoint to disk""" | |
| filename = os.path.join(self.experiment_dir, filename) | |
| torch.save(state, filename) | |
| def save_experiment_config(self): | |
| logfile = os.path.join(self.experiment_dir, "parameters.txt") | |
| log_file = open(logfile, "w") | |
| p = OrderedDict() | |
| p["train_dataset"] = self.args.train_dataset | |
| p["lr"] = self.args.lr | |
| p["epoch"] = self.args.epochs | |
| for key, val in p.items(): | |
| log_file.write(key + ":" + str(val) + "\n") | |
| log_file.close() | |