[MXNET-1396][Fit-API] Update default handler logic

ci/docker/runtime_functions.sh

@@ -1296,18 +1296,12 @@ nightly_scala_demo_test_cpu() {
     bash bin/run_im.sh
 }
 
-nightly_estimator_gpu() {
+nightly_estimator() {
     set -ex
     cd /work/mxnet/tests/nightly/estimator
     export PYTHONPATH=/work/mxnet/python/
     python test_estimator_cnn.py --type gpu
     python test_sentiment_rnn.py --type gpu
-}
-
-nightly_estimator_cpu() {
-    set -ex
-    cd /work/mxnet/tests/nightly/estimator
-    export PYTHONPATH=/work/mxnet/python/
     python test_estimator_cnn.py --type cpu
     python test_sentiment_rnn.py --type cpu
 }

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

@@ -157,6 +157,8 @@ def prepare_loss_and_metrics(self):
         Based on loss functions and training metrics in estimator
         Create metric wrappers to record loss values,
         Create copies of train loss/metric objects to record validation values
+        Returns train_metrics and val_metrics
+
         """
         if any(not hasattr(self, attribute) for attribute in
                ['train_metrics', 'val_metrics']):
@@ -165,8 +167,7 @@ def prepare_loss_and_metrics(self):
                 self.train_metrics = [Accuracy()]
             self.val_metrics = []
             for loss in self.loss:
-                self.train_metrics.append(Loss("Train " + ''.join([i for i in loss.name if not i.isdigit()])))
-                self.val_metrics.append(Loss("Validation " + ''.join([i for i in loss.name if not i.isdigit()])))
+                self.train_metrics.append(Loss(''.join([i for i in loss.name if not i.isdigit()])))
             for metric in self.train_metrics:
                 val_metric = copy.deepcopy(metric)
                 metric.name = "Train " + metric.name
@@ -231,21 +232,9 @@ def fit(self, train_data,
             from a data batch and load into contexts(devices)
         """
         self.max_epochs = epochs
-        event_handlers = event_handlers or []
-        # provide default logging handler
-        if not event_handlers:
-            train_metrics, val_metrics = self.prepare_loss_and_metrics()
-            event_handlers.append(MetricHandler(train_metrics=train_metrics))
-            if val_data:
-                event_handlers.append(ValidationHandler(val_data=val_data, eval_fn=self.evaluate,
-                                                        val_metrics=val_metrics))
-            event_handlers.append(LoggingHandler(train_metrics=train_metrics,
-                                                 val_metrics=val_metrics))
-            warnings.warn("No Event Handler specified, default %s are used. "
-                          "Please look at gluon.contrib.estimator.event_handler for more detail." %
-                          ", ".join([handler.__class__.__name__ for handler in event_handlers]))
 
-        event_handlers.sort(key=lambda handler: getattr(handler, 'rank', 0), reverse=True)
+        # provide default handlers
+        event_handlers = self._prepare_default_handlers(val_data, event_handlers)
 
         train_begin, epoch_begin, batch_begin, \
         batch_end, epoch_end, train_end = self._categorize_handlers(event_handlers)
@@ -297,6 +286,54 @@ def fit(self, train_data,
         for handler in train_end:
             handler.train_end(estimator_ref)
 
+    def _prepare_default_handlers(self, val_data, event_handlers):
+        event_handlers = event_handlers or []
+        default_handlers = []
+        train_metrics, val_metrics = self.prepare_loss_and_metrics()
+
+        if not any(isinstance(handler, MetricHandler) for handler in event_handlers):
+            event_handlers.append(MetricHandler(train_metrics=train_metrics))
+            default_handlers.append("MetricHandler")
+
+        if val_data and not any(isinstance(handler, ValidationHandler) for handler in event_handlers):
+            event_handlers.append(ValidationHandler(val_data=val_data, eval_fn=self.evaluate,
+                                                    val_metrics=val_metrics))
+            default_handlers.append("ValidationHandler")
+
+        if not any(isinstance(handler, LoggingHandler) for handler in event_handlers):
+            event_handlers.append(LoggingHandler(train_metrics=train_metrics,
+                                                 val_metrics=val_metrics))
+            default_handlers.append("LoggingHandler")
+
+        # if there is a mix of user defined event handlers and default event handlers
+        # they should have the save set of loss and metrics
+        if default_handlers:
+            msg = "You are training with the following default event handlers: %s. " \
+                  "They use loss and metrics from estimator.prepare_loss_and_metrics(). " \
+                  "Please use the same set of metrics for all your other handlers." % \
+                  ", ".join(default_handlers)
+            warnings.warn(msg)
+            references = []
+            for handler in event_handlers:
+                for attribute in dir(handler):
+                    if any(keyword in attribute for keyword in ['metric' or 'monitor']):
+                        reference = getattr(handler, attribute)
+                        if isinstance(reference, list):
+                            references += reference
+                        else:
+                            references.append(reference)
+            for metric in references:
+                if metric and metric not in train_metrics + val_metrics:
+                    msg = "We have added following default handlers for you: %s and used " \
+                          "estimator.prepare_loss_and_metrics() to pass metrics to " \
+                          "those handlers. Please use the same set of metrics " \
+                          "for all your handlers." % \
+                          ", ".join(default_handlers)
+                    raise ValueError(msg)
+
+        event_handlers.sort(key=lambda handler: getattr(handler, 'priority', 0))
+        return event_handlers
+
     def _categorize_handlers(self, event_handlers):
         """
         categorize handlers into 6 event lists to avoid calling empty methods

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

@@ -299,7 +299,7 @@ def __init__(self,
         self.save_best_only = save_best_only
         if self.save_best_only and not isinstance(self.monitor, EvalMetric):
             raise ValueError("To save best model only, please provide one of the metric objects as monitor, "
-                             "You can create these objects using estimator.prepare_loss_and_metric()")
+                             "You can get these objects using estimator.prepare_loss_and_metric()")
         self.epoch_period = epoch_period
         self.batch_period = batch_period
         self.num_batches = 0

tests/nightly/Jenkinsfile

@@ -136,22 +136,6 @@ core_logic: {
           utils.docker_run('ubuntu_nightly_cpu', 'nightly_test_javascript', false)
         }
       }
-    },
-    'Gluon estimator: GPU': {
-      node(NODE_LINUX_GPU) {
-        ws('workspace/estimator-test-gpu') {
-          utils.unpack_and_init('gpu', mx_lib)
-          utils.docker_run('ubuntu_nightly_gpu', 'nightly_estimator_gpu', true)
-        }
-      }
-    },
-    'Gluon estimator: CPU': {
-      node(NODE_LINUX_CPU) {
-        ws('workspace/estimator-test-cpu') {
-          utils.unpack_and_init('cpu', mx_lib)
-          utils.docker_run('ubuntu_nightly_cpu', 'nightly_estimator_cpu', false)
-        }
-      }
     }
   }
 }

tests/nightly/JenkinsfileForBinaries

@@ -106,6 +106,14 @@ core_logic: {
           utils.docker_run('ubuntu_nightly_gpu', 'nightly_tutorial_test_ubuntu_python3_gpu', true, '1500m')
         }
       }
+    },
+    'Gluon estimator: GPU': {
+      node(NODE_LINUX_GPU) {
+        ws('workspace/estimator-test-gpu') {
+          utils.unpack_and_init('gpu', mx_lib)
+          utils.docker_run('ubuntu_nightly_gpu', 'nightly_estimator', true)
+        }
+      }
     }
   }
 }