def get_training_stats()

in bring-your-own-container/fairseq_translation/fairseq/train_driver.py [0:0]

• 23 lines of code
• 3 McCabe index (conditional complexity)

def get_training_stats(trainer):
    stats = collections.OrderedDict()
    stats["loss"] = "{:.3f}".format(trainer.get_meter("train_loss").avg)
    if trainer.get_meter("train_nll_loss").count > 0:
        nll_loss = trainer.get_meter("train_nll_loss").avg
        stats["nll_loss"] = "{:.3f}".format(nll_loss)
    else:
        nll_loss = trainer.get_meter("train_loss").avg
    stats["ppl"] = get_perplexity(nll_loss)
    stats["wps"] = round(trainer.get_meter("wps").avg)
    stats["ups"] = "{:.1f}".format(trainer.get_meter("ups").avg)
    stats["wpb"] = round(trainer.get_meter("wpb").avg)
    stats["bsz"] = round(trainer.get_meter("bsz").avg)
    stats["num_updates"] = trainer.get_num_updates()
    stats["lr"] = trainer.get_lr()
    stats["gnorm"] = "{:.3f}".format(trainer.get_meter("gnorm").avg)
    stats["clip"] = "{:.0%}".format(trainer.get_meter("clip").avg)
    stats["oom"] = trainer.get_meter("oom").avg
    if trainer.get_meter("loss_scale") is not None:
        stats["loss_scale"] = "{:.3f}".format(trainer.get_meter("loss_scale").avg)
    stats["wall"] = round(trainer.get_meter("wall").elapsed_time)
    stats["train_wall"] = round(trainer.get_meter("train_wall").sum)
    return stats