7a125ea5e0113ec9d10d010884cdc78419bffe6e,python/baseline/pytorch/tagger/train.py,TaggerTrainerPyTorch,_test,#TaggerTrainerPyTorch#Any#,71

Before Change

        self.model.eval()
        total_correct = 0
        total_sum = 0
        total_gold_count = 0
        total_guess_count = 0
        total_overlap_count = 0
        metrics = {}
        steps = len(ts)
        conll_output = kwargs.get("conll_output", None)
        txts = kwargs.get("txts", None)
        handle = None
        if conll_output is not None and txts is not None:
            handle = open(conll_output, "w")
        pg = create_progress_bar(steps)
        for batch_dict in pg(ts):

            inputs = self.model.make_input(batch_dict)
            y = inputs.pop("y")
            lengths = inputs["lengths"]
            ids = inputs["ids"]
            pred = self.model(inputs)
            correct, count, overlaps, golds, guesses = self.process_output(pred, y.data, lengths, ids, handle, txts)
            total_correct += correct
            total_sum += count
            total_gold_count += golds
            total_guess_count += guesses
            total_overlap_count += overlaps

        total_acc = total_correct / float(total_sum)
        // Only show the fscore if requested
        metrics["f1"] = f_score(total_overlap_count, total_gold_count, total_guess_count)
        metrics["acc"] = total_acc
        return metrics

    def _train(self, ts, **kwargs):

After Change

        total_sum = 0
        total_correct = 0

        gold_spans = []
        pred_spans = []

        metrics = {}
        steps = len(ts)
        conll_output = kwargs.get("conll_output", None)
        txts = kwargs.get("txts", None)
        handle = None
        if conll_output is not None and txts is not None:
            handle = open(conll_output, "w")
        pg = create_progress_bar(steps)
        for batch_dict in pg(ts):

            inputs = self.model.make_input(batch_dict)
            y = inputs.pop("y")
            lengths = inputs["lengths"]
            ids = inputs["ids"]
            pred = self.model(inputs)
            correct, count, golds, guesses = self.process_output(pred, y.data, lengths, ids, handle, txts)
            total_correct += correct
            total_sum += count
            gold_spans.extend(golds)
            pred_spans.extend(guesses)

        total_acc = total_correct / float(total_sum)
        metrics["acc"] = total_acc
        metrics["f1"] = span_f1(gold_spans, pred_spans)
        if self.verbose:
            // TODO: Add programmatic access to these metrics?
            conll_metrics = per_entity_f1(gold_spans, pred_spans)
            conll_metrics["acc"] = total_acc * 100
            conll_metrics["tokens"] = total_sum.item()
            logger.info(conlleval_output(conll_metrics))
        return metrics

    def _train(self, ts, **kwargs):
        self.model.train()

Instances