Add evaluation_loss to the estimator base class. (#16888)

* Add evaluation_loss to the estimator base class.

* Update the base estimator class to support the separate evaluation loss.

* Add evaluation loss to the base estimator class.

* Add unittest for evaluation loss in the test_evaluation function

* Update estimator.py

* Update estimator.py

python/mxnet/gluon/contrib/estimator/estimator.py

@@ -59,6 +59,9 @@ class Estimator(object):
         Trainer to apply optimizer on network parameters.
     context : Context or list of Context
         Device(s) to run the training on.
+    evaluation_loss: gluon.loss.loss
+        Loss (objective) function to calculate during evaluation. If set evaluation_loss
+        None, it will use the same loss function as self.loss
 
     """
 
@@ -85,12 +88,16 @@ def __init__(self, net,
                  metrics=None,
                  initializer=None,
                  trainer=None,
-                 context=None):
+                 context=None,
+                 evaluation_loss=None):
         self.net = net
         self.loss = self._check_loss(loss)
         self._train_metrics = _check_metrics(metrics)
         self._add_default_training_metrics()
         self._add_validation_metrics()
+        self.evaluation_loss = self.loss
+        if evaluation_loss is not None:
+            self.evaluation_loss = self._check_loss(evaluation_loss)
 
         self.logger = logging.Logger(name='Estimator', level=logging.INFO)
         self.logger.addHandler(logging.StreamHandler(sys.stdout))
@@ -228,7 +235,7 @@ def evaluate_batch(self,
         """
         data, label = self._get_data_and_label(val_batch, self.context, batch_axis)
         pred = [self.net(x) for x in data]
-        loss = [self.loss(y_hat, y) for y_hat, y in zip(pred, label)]
+        loss = [self.evaluation_loss(y_hat, y) for y_hat, y in zip(pred, label)]
         # update metrics
         for metric in val_metrics:
             if isinstance(metric, metric_loss):

tests/python/unittest/test_gluon_estimator.py

@@ -83,13 +83,15 @@ def test_validation():
     ctx = mx.cpu()
     loss = gluon.loss.L2Loss()
     acc = mx.metric.Accuracy()
+    evaluation_loss = gluon.loss.L1Loss()
     net.initialize(ctx=ctx)
     trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001})
     est = Estimator(net=net,
                     loss=loss,
                     metrics=acc,
                     trainer=trainer,
-                    context=ctx)
+                    context=ctx,
+                    evaluation_loss=evaluation_loss)
     # Input dataloader
     est.fit(train_data=dataloader,
             val_data=dataloader,