Fixed tests

src/test/resources/target_code/gluon/CNNPredictor_mnist_mnistClassifier_net.h

@@ -44,7 +44,7 @@ public:
         MXPredGetOutputShape(handle, output_index, &shape, &shape_len);
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
-        assert(out_predictions_.size() == 1 || size == out_predictions_.size());
+        assert(size == out_predictions_.size());
         MXPredGetOutput(handle, output_index, &(out_predictions_[0]), out_predictions_.size());
 
     }

src/test/resources/target_code/gluon/CNNSupervisedTrainer_mnist_mnistClassifier_net.py

@@ -189,6 +189,7 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
               context='gpu',
               checkpoint_period=5,
               save_attention_image=False,
+              use_teacher_forcing=False,
               normalize=True):
         if context == 'gpu':
             mx_context = mx.gpu()
@@ -241,12 +242,12 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
 
         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
-        #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)
+        ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
-            ignore_indices = [2]
+            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
+        if 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()
@@ -288,6 +289,8 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                     predictions_ = mx.nd.zeros((train_batch_size, 10,), ctx=mx_context)
 
 
+                    nd.waitall()
+
                     lossList = []
 
                     predictions_ = self._networks[0](image_)
@@ -329,6 +332,8 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                     predictions_ = mx.nd.zeros((test_batch_size, 10,), ctx=mx_context)
 
 
+                    nd.waitall()
+
                     outputs = []
                     attentionList=[]
                     predictions_ = self._networks[0](image_)
@@ -341,19 +346,29 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                         logging.getLogger('matplotlib').setLevel(logging.ERROR)
 
                         plt.clf()
-                        fig = plt.figure(figsize=(10,10))
+                        fig = plt.figure(figsize=(15,15))
                         max_length = len(labels)-1
 
                         if(os.path.isfile('src/test/resources/training_data/Show_attend_tell/dict.pkl')):
                             with open('src/test/resources/training_data/Show_attend_tell/dict.pkl', 'rb') as f:
                                 dict = pickle.load(f)
 
+
+                        ax = fig.add_subplot(max_length//3, max_length//4, 1)
+                        ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
+
                         for l in range(max_length):
                             attention = attentionList[l]
                             attention = mx.nd.slice_axis(attention, axis=0, begin=0, end=1)
                             attention = mx.nd.squeeze(attention)
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
-                            ax = fig.add_subplot(max_length//3, max_length//4, l+1)
+                            ax = fig.add_subplot(max_length//3, max_length//4, l+2)
+                            if dict[int(labels[l+1][0].asscalar())] == "<end>":
+                                ax.set_title(".")
+                                img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
+                                ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
+                                break
+                            else:
                                 ax.set_title(dict[int(labels[l+1][0].asscalar())])
                                 img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
                                 ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
@@ -387,6 +402,8 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                     predictions_ = mx.nd.zeros((test_batch_size, 10,), ctx=mx_context)
 
 
+                    nd.waitall()
+
                     outputs = []
                     attentionList=[]
                     predictions_ = self._networks[0](image_)
@@ -396,16 +413,25 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
 
                     if save_attention_image == "True":
                         plt.clf()
-                        fig = plt.figure(figsize=(10,10))
+                        fig = plt.figure(figsize=(15,15))
                         max_length = len(labels)-1
 
+
+                        ax = fig.add_subplot(max_length//3, max_length//4, 1)
+                        ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
+
                         for l in range(max_length):
                             attention = attentionList[l]
-                            attention = mx.nd.slice_axis(attention, axis=2, begin=0, end=1)
                             attention = mx.nd.slice_axis(attention, axis=0, begin=0, end=1)
                             attention = mx.nd.squeeze(attention)
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
-                            ax = fig.add_subplot(max_length//3, max_length//4, l+1)
+                            ax = fig.add_subplot(max_length//3, max_length//4, l+2)
+                            if dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())] == "<end>":
+                                ax.set_title(".")
+                                img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
+                                ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
+                                break
+                            else:
                                 ax.set_title(dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())])
                                 img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
                                 ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())

src/test/resources/target_code/gluon/reinforcementModel/cartpole/CNNPredictor_cartpole_master_dqn.h

@@ -44,7 +44,7 @@ public:
         MXPredGetOutputShape(handle, output_index, &shape, &shape_len);
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
-        assert(out_qvalues_.size() == 1 || size == out_qvalues_.size());
+        assert(size == out_qvalues_.size());
         MXPredGetOutput(handle, output_index, &(out_qvalues_[0]), out_qvalues_.size());
 
     }

src/test/resources/target_code/gluon/reinforcementModel/mountaincar/CNNPredictor_mountaincar_master_actor.h

@@ -44,7 +44,7 @@ public:
         MXPredGetOutputShape(handle, output_index, &shape, &shape_len);
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
-        assert(out_action_.size() == 1 || size == out_action_.size());
+        assert(size == out_action_.size());
         MXPredGetOutput(handle, output_index, &(out_action_[0]), out_action_.size());
 
     }

src/test/resources/target_code/gluon/reinforcementModel/torcs/CNNPredictor_torcs_agent_torcsAgent_dqn.h

@@ -44,7 +44,7 @@ public:
         MXPredGetOutputShape(handle, output_index, &shape, &shape_len);
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
-        assert(out_qvalues_.size() == 1 || size == out_qvalues_.size());
+        assert(size == out_qvalues_.size());
         MXPredGetOutput(handle, output_index, &(out_qvalues_[0]), out_qvalues_.size());
 
     }

src/test/resources/target_code/gluon/reinforcementModel/torcs_td3/CNNPredictor_torcs_agent_torcsAgent_actor.h

@@ -44,7 +44,7 @@ public:
         MXPredGetOutputShape(handle, output_index, &shape, &shape_len);
         size = 1;
         for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
-        assert(out_commands_.size() == 1 || size == out_commands_.size());
+        assert(size == out_commands_.size());
         MXPredGetOutput(handle, output_index, &(out_commands_[0]), out_commands_.size());
 
     }