a4f90c8e8420db451da188fcb50d7b801a5c6ff1,parlai/agents/fairseq/fairseq.py,FairseqAgent,report,#FairseqAgent#,546

Before Change

        // These are the metrics we"ll pass up the way, and their new names
        train_metrics = {"train_loss", "ups", "wps", "gnorm", "clip"}
        valid_metrics = {"valid_loss"}

        metrics = train_metrics if self.is_training else valid_metrics

        m = {k: self.trainer.meters[k].avg for k in metrics}

        // additionally output perplexity. note that fairseq models use base 2
        // in cross_entropy:
        // github.com/pytorch/fairseq/blob/master/fairseq/criterions/cross_entropy.py//L55
        if "train_loss" in m:
            m["train_ppl"] = np.exp2(m["train_loss"])
        if "valid_loss" in m:
            m["ppl"] = np.exp2(m["valid_loss"])

        for k, v in m.items():
            // clean up: rounds to sigfigs and converts tensors to floats
            m[k] = round_sigfigs(v, 4)

After Change

                output[k] = self.trainer.meters[k].avg

        // for display purposes
        output = {k: round_sigfigs(v, 4) for k, v in output.items()}
        return output

    def reset_metrics(self):
        Reset metrics calculated by the model back to zero.

Instances