updated tests

src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py

@@ -253,16 +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
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, 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(sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py

@@ -253,16 +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
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, 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(sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNSupervisedTrainer_Invariant.py

@@ -246,16 +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
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, 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(sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':
@@ -425,7 +426,7 @@ class CNNSupervisedTrainer_Invariant:
             test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(test_iter):
-                if True: 
+                if True:
                     labels = [batch.label[i].as_in_context(mx_context) for i in range(3)]
 
                     data_0_ = batch.data[0].as_in_context(mx_context)

src/test/resources/target_code/CNNSupervisedTrainer_MultipleStreams.py

@@ -246,16 +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
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, 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(sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':
@@ -417,7 +418,7 @@ class CNNSupervisedTrainer_MultipleStreams:
             test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(test_iter):
-                if True: 
+                if True:
                     labels = [batch.label[i].as_in_context(mx_context) for i in range(2)]
 
                     data_0_ = batch.data[0].as_in_context(mx_context)

src/test/resources/target_code/CNNSupervisedTrainer_RNNencdec.py

@@ -246,16 +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
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, 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(sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':
@@ -481,7 +482,7 @@ class CNNSupervisedTrainer_RNNencdec:
             test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(test_iter):
-                if True: 
+                if True:
                     labels = [batch.label[i].as_in_context(mx_context) for i in range(30)]
 
                     source_ = batch.data[0].as_in_context(mx_context)

src/test/resources/target_code/CNNSupervisedTrainer_RNNsearch.py

@@ -246,16 +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
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, 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(sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':
@@ -479,7 +480,7 @@ class CNNSupervisedTrainer_RNNsearch:
             test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(test_iter):
-                if True: 
+                if True:
                     labels = [batch.label[i].as_in_context(mx_context) for i in range(30)]
 
                     source_ = batch.data[0].as_in_context(mx_context)

src/test/resources/target_code/CNNSupervisedTrainer_RNNtest.py

@@ -246,16 +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
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, 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(sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':
@@ -458,7 +459,7 @@ class CNNSupervisedTrainer_RNNtest:
             test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(test_iter):
-                if True: 
+                if True:
                     labels = [batch.label[i].as_in_context(mx_context) for i in range(5)]
 
                     source_ = batch.data[0].as_in_context(mx_context)

src/test/resources/target_code/CNNSupervisedTrainer_ResNeXt50.py

@@ -246,16 +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
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, 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(sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':
@@ -407,7 +408,7 @@ class CNNSupervisedTrainer_ResNeXt50:
             test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(test_iter):
-                if True: 
+                if True:
                     labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
 
                     data_ = batch.data[0].as_in_context(mx_context)

src/test/resources/target_code/CNNSupervisedTrainer_Show_attend_tell.py

@@ -246,16 +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
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, 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(sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':
@@ -473,7 +474,7 @@ class CNNSupervisedTrainer_Show_attend_tell:
             test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(test_iter):
-                if True: 
+                if True:
                     labels = [batch.label[i].as_in_context(mx_context) for i in range(25)]
 
                     images_ = batch.data[0].as_in_context(mx_context)

src/test/resources/target_code/CNNSupervisedTrainer_ThreeInputCNN_M14.py

@@ -246,16 +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
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, 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(sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':
@@ -411,7 +412,7 @@ class CNNSupervisedTrainer_ThreeInputCNN_M14:
             test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(test_iter):
-                if True: 
+                if True:
                     labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
 
                     data_0_ = batch.data[0].as_in_context(mx_context)

src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py

@@ -253,16 +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
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=lossAxis, 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(sparse_label=sparseLabel)
+            loss_function = CrossEntropyLoss(axis=lossAxis, sparse_label=sparseLabel)
         elif loss == 'l2':
             loss_function = mx.gluon.loss.L2Loss()
         elif loss == 'l1':

src/test/resources/target_code/CNNTrainer_fullConfig.py

@@ -30,6 +30,7 @@ if __name__ == "__main__":
         loss='softmax_cross_entropy',
         loss_params={
             'sparse_label': True,
+            'loss_axis': -1,
             'from_logits': False},
         optimizer='rmsprop',
         optimizer_params={

src/test/resources/valid_tests/FullConfig.cnnt

@@ -5,6 +5,7 @@ configuration FullConfig{
      load_checkpoint : true
      eval_metric : mse
      loss: softmax_cross_entropy{
+        loss_axis: -1
         sparse_label: true
         from_logits: false
      }