removed illegal parameter in dice_loss

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

@@ -296,9 +296,8 @@ class ${tc.fileNameWithoutEnding}:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
-            loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+            loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py

@@ -295,7 +295,6 @@ class CNNSupervisedTrainer_Alexnet:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
             loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':

src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py

@@ -295,9 +295,8 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
-            loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+            loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_Invariant.py

@@ -288,9 +288,8 @@ class CNNSupervisedTrainer_Invariant:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
-            loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+            loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_MultipleStreams.py

@@ -288,9 +288,8 @@ class CNNSupervisedTrainer_MultipleStreams:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
-            loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+            loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_RNNencdec.py

@@ -288,9 +288,8 @@ class CNNSupervisedTrainer_RNNencdec:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
-            loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+            loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_RNNsearch.py

@@ -288,9 +288,8 @@ class CNNSupervisedTrainer_RNNsearch:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
-            loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+            loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_RNNtest.py

@@ -288,9 +288,8 @@ class CNNSupervisedTrainer_RNNtest:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
-            loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+            loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_ResNeXt50.py

@@ -288,9 +288,8 @@ class CNNSupervisedTrainer_ResNeXt50:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
-            loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+            loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_Show_attend_tell.py

@@ -288,9 +288,8 @@ class CNNSupervisedTrainer_Show_attend_tell:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
-            loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+            loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_ThreeInputCNN_M14.py

@@ -288,9 +288,8 @@ class CNNSupervisedTrainer_ThreeInputCNN_M14:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
-            loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+            loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py

@@ -295,9 +295,8 @@ class CNNSupervisedTrainer_VGG16:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None
-            loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
+            loss_function = DiceLoss(axis=loss_axis, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':