adjusted weights for adding loss_weight

src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py

@@ -295,8 +295,8 @@ class CNNSupervisedTrainer_Alexnet:
         elif loss == 'cross_entropy':
             loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis)
         elif loss == 'dice_loss':
-            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)
+            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 == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py

@@ -295,8 +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':
-            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)
+            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 == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_Invariant.py

@@ -288,8 +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':
-            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)
+            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 == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_MultipleStreams.py

@@ -288,8 +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':
-            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)
+            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 == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_RNNencdec.py

@@ -288,8 +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':
-            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)
+            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 == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_RNNsearch.py

@@ -288,8 +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':
-            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)
+            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 == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_RNNtest.py

@@ -288,8 +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':
-            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)
+            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 == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_ResNeXt50.py

@@ -288,8 +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':
-            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)
+            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 == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_Show_attend_tell.py

@@ -288,8 +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':
-            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)
+            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 == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_ThreeInputCNN_M14.py

@@ -288,8 +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':
-            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)
+            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 == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py

@@ -295,8 +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':
-            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)
+            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 == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':