Add logging metrics to console.

combo/training/tensorboard_writer.py

+import logging
 from typing import Dict, Optional, List
 
 import torch
@@ -5,6 +6,8 @@ from allennlp import models, common
 from allennlp.data import dataloader
 from allennlp.training import optimizers
 
+logger = logging.getLogger(__name__)
+
 
 class NullTensorboardWriter(common.FromParams):
 
@@ -50,13 +53,36 @@ class NullTensorboardWriter(common.FromParams):
         pass
 
     def log_metrics(
-            self,
-            train_metrics: dict,
-            val_metrics: dict = None,
-            epoch: int = None,
-            log_to_console: bool = False,
+        self,
+        train_metrics: dict,
+        val_metrics: dict = None,
+        epoch: int = None,
+        log_to_console: bool = False,
     ) -> None:
-        pass
+        metric_names = set(train_metrics.keys())
+        if val_metrics is not None:
+            metric_names.update(val_metrics.keys())
+        val_metrics = val_metrics or {}
+
+        if log_to_console:
+            dual_message_template = "%s |  %8.3f  |  %8.3f"
+            no_val_message_template = "%s |  %8.3f  |  %8s"
+            no_train_message_template = "%s |  %8s  |  %8.3f"
+            header_template = "%s |  %-10s"
+            name_length = max(len(x) for x in metric_names)
+            logger.info(header_template, "Training".rjust(name_length + 13), "Validation")
+
+            for name in metric_names:
+                train_metric = train_metrics.get(name)
+                val_metric = val_metrics.get(name)
+                if val_metric is not None and train_metric is not None:
+                    logger.info(
+                        dual_message_template, name.ljust(name_length), train_metric, val_metric
+                    )
+                elif val_metric is not None:
+                    logger.info(no_train_message_template, name.ljust(name_length), "N/A", val_metric)
+                elif train_metric is not None:
+                    logger.info(no_val_message_template, name.ljust(name_length), train_metric, "N/A")
 
     def enable_activation_logging(self, model: models.Model) -> None:
         pass