refactoring: lossAxis to loss_axis

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

@@ -254,17 +254,17 @@ class ${tc.fileNameWithoutEnding}:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py

@@ -253,17 +253,17 @@ class CNNSupervisedTrainer_Alexnet:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py

@@ -253,17 +253,17 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_Invariant.py

@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_Invariant:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_MultipleStreams.py

@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_MultipleStreams:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_RNNencdec.py

@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_RNNencdec:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_RNNsearch.py

@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_RNNsearch:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_RNNtest.py

@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_RNNtest:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_ResNeXt50.py

@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_ResNeXt50:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_Show_attend_tell.py

@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_Show_attend_tell:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_ThreeInputCNN_M14.py

@@ -246,17 +246,17 @@ class CNNSupervisedTrainer_ThreeInputCNN_M14:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py

@@ -253,17 +253,17 @@ class CNNSupervisedTrainer_VGG16:
         margin = loss_params['margin'] if 'margin' in loss_params else 1.0
         sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
         ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
-        lossAxis = loss_params['lossAxis'] if 'lossAxis' in loss_params else -1
+        loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
             loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
         elif loss == 'cross_entropy':
-            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':