added metric loss accuracy_ignore_label

src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl

@@ -87,15 +87,35 @@ class DiceLoss(gluon.loss.Loss):
         diceloss = self.dice_loss(F, pred, label)
         return F.mean(loss, axis=self._batch_axis, exclude=True) + diceloss
 
+class SoftmaxCrossEntropyLossIgnoreLabel(gluon.loss.Loss):
+    def __init__(self, axis=-1, from_logits=False, weight=None,
+                 batch_axis=0, ignore_label=255, **kwargs):
+        super(SoftmaxCrossEntropyLossIgnoreLabel, self).__init__(weight, batch_axis, **kwargs)
+        self._axis = axis
+        self._from_logits = from_logits
+        self._ignore_label = ignore_label
+
+    def hybrid_forward(self, F, output, label, sample_weight=None):
+        if not self._from_logits:
+            output = F.log_softmax(output, axis=self._axis)
+
+        valid_label_map = (label != self._ignore_label)
+        loss = -(F.pick(output, label, axis=self._axis, keepdims=True) * valid_label_map )
+
+        loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight)
+        return F.sum(loss) / F.sum(valid_label_map)
+
 @mx.metric.register
 class ACCURACY_IGNORE_LABEL(mx.metric.EvalMetric):
-    def __init__(self, axis=1, ignore_label=255, name='accuracy',
+    """Ignores a label when computing accuracy.
+    """
+    def __init__(self, axis=1, metric_ignore_label=255, name='accuracy',
                  output_names=None, label_names=None):
         super(ACCURACY_IGNORE_LABEL, self).__init__(
             name, axis=axis,
             output_names=output_names, label_names=label_names)
         self.axis = axis
-        self.ignore_label = ignore_label
+        self.ignore_label = metric_ignore_label
 
     def update(self, labels, preds):
         mx.metric.check_label_shapes(labels, preds)
@@ -328,6 +348,10 @@ class ${tc.fileNameWithoutEnding}:
         elif loss == 'dice_loss':
             loss_weight = loss_params['loss_weight'] if 'loss_weight' in loss_params else None
             loss_function = DiceLoss(axis=loss_axis, weight=loss_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+        elif loss == 'softmax_cross_entropy_ignore_label':
+            loss_weight = loss_params['loss_weight'] if 'loss_weight' in loss_params else None
+            loss_ignore_label = loss_params['loss_ignore_label'] if 'loss_ignore_label' in loss_params else None
+            loss_function = SoftmaxCrossEntropyLossIgnoreLabel(axis=loss_axis, ignore_label=loss_ignore_label, weight=loss_weight, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/main/resources/templates/gluon/CNNTrainer.ftl

@@ -71,7 +71,7 @@ if __name__ == "__main__":
             'axis': ${config.evalMetric.axis},
 </#if>
 <#if (config.evalMetric.exclude)??>
-            'ignore_label': ${config.evalMetric.ignore_label},
+            'metric_ignore_label': ${config.evalMetric.metric_ignore_label},
 </#if>
         },
 </#if>