def save_eval_file(opt, stats, eval_type="losses", split="dev", ext="pickle"):
if cfg.test_save:
name = "{}/{}.{}".format(
utils.make_name(opt,
prefix="garbage/{}/".format(eval_type),
is_dir=True,
eval_=True), split, ext)
else:
name = "{}/{}.{}".format(
utils.make_name(opt,
prefix="results/{}/".format(eval_type),
is_dir=True,
eval_=True), split, ext)
print("Saving {} {} to {}".format(split, eval_type, name))
if ext == "pickle":
with open(name, "wb") as f:
pickle.dump(stats, f)
elif ext == "txt":
with open(name, "w") as f:
f.write(stats)
elif ext == "json":
with open(name, "w") as f:
json.dump(stats, f)
else:
raise
def set_logger(self):
if cfg.toy:
self.logger = SummaryWriter(
utils.make_name(self.opt,
prefix="garbage/logs/",
eval_=True,
do_epoch=False))
else:
self.logger = SummaryWriter(
utils.make_name(self.opt,
prefix="logs/",
eval_=True,
do_epoch=False))
print("Logging Tensorboard Files at: {}".format(self.logger.log_dir))
def save_step(model, vocab, optimizer, opt, length, lrs):
if cfg.test_save:
name = "{}.pickle".format(
utils.make_name(opt,
prefix="garbage/models/",
is_dir=False,
eval_=True))
else:
name = "{}.pickle".format(
utils.make_name(opt, prefix="models/", is_dir=False, eval_=True))
save_checkpoint(
{
"epoch": length,
"state_dict": model.state_dict(),
"optimizer": optimizer.state_dict(),
"opt": opt,
"vocab": vocab,
"epoch_learning_rates": lrs
}, name)