a58bb4244b3b5ffbf7ffb1994926fcbe8cfa0c1d,src/trainer.py,SamplingMultiTaskTrainer,_validate,#SamplingMultiTaskTrainer#Any#Any#Any#Any#Any#Any#Any#,515

Before Change

                    log.info("Batch %d/%d: %s", batch_num, n_val_batches, description)
                    task_info["last_log"] = time.time()
                if "labels" in batch:
                    n_examples += batch["labels"].size()[0]
                elif "targs" in batch:
                    n_examples += batch["targs"]["words"].nelement()
            assert batch_num == n_val_batches

            // Get task validation metrics and store in all_val_metrics
            task_metrics = task.get_metrics(reset=True)
            for name, value in task_metrics.items():
                all_val_metrics["%s_%s" % (task.name, name)] = value
            all_val_metrics["%s_loss" % task.name] /= batch_num  // n_val_batches
            all_val_metrics["micro_avg"] += \
                all_val_metrics[task.val_metric] * n_examples
            all_val_metrics["macro_avg"] += \
                all_val_metrics[task.val_metric]
            n_examples_overall += n_examples

            // Reset training progress
            task_info["n_batches_since_val"] = 0
            task_info["loss"] = 0

        all_val_metrics["micro_avg"] /= n_examples_overall
        all_val_metrics["macro_avg"] /= len(tasks)

        // Track per task patience

After Change

                out = self._forward(batch, task=task, for_training=False)
                loss = out["loss"]
                all_val_metrics["%s_loss" % task.name] += loss.data.cpu().numpy()
                n_examples += out["n_exs"]

                // log
                if time.time() - task_info["last_log"] > self._log_interval:
                    task_metrics = task.get_metrics()

Instances