fixed small bug in bleu computation

src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py

@@ -115,7 +115,8 @@ class BLEU(mx.metric.EvalMetric):
                     i *= 2
                     match_counts = 1 / i
 
-                precisions[n] = match_counts / counts
+                if (match_counts / counts) > 0:
+                    precisions[n] = match_counts / counts
 
         bleu = self._get_brevity_penalty() * math.exp(sum(map(math.log, precisions)) / self.N)
 
@@ -320,7 +321,7 @@ class CNNSupervisedTrainer_Alexnet:
             train_test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(train_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)
@@ -354,7 +355,7 @@ class CNNSupervisedTrainer_Alexnet:
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
                             ax = fig.add_subplot(max_length//3, max_length//4, l+1)
                             ax.set_title(dict[int(labels[l+1][0].asscalar())])
-                            img = ax.imshow(train_images[0+test_batch_size*(batch_i)])
+                            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())
 
 
@@ -378,7 +379,7 @@ class CNNSupervisedTrainer_Alexnet:
             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)
@@ -406,7 +407,7 @@ class CNNSupervisedTrainer_Alexnet:
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
                             ax = fig.add_subplot(max_length//3, max_length//4, l+1)
                             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)])
+                            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/CNNSupervisedTrainer_CifarClassifierNetwork.py

@@ -115,7 +115,8 @@ class BLEU(mx.metric.EvalMetric):
                     i *= 2
                     match_counts = 1 / i
 
-                precisions[n] = match_counts / counts
+                if (match_counts / counts) > 0:
+                    precisions[n] = match_counts / counts
 
         bleu = self._get_brevity_penalty() * math.exp(sum(map(math.log, precisions)) / self.N)
 
@@ -320,7 +321,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
             train_test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(train_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)
@@ -354,7 +355,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
                             ax = fig.add_subplot(max_length//3, max_length//4, l+1)
                             ax.set_title(dict[int(labels[l+1][0].asscalar())])
-                            img = ax.imshow(train_images[0+test_batch_size*(batch_i)])
+                            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())
 
 
@@ -378,7 +379,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
             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)
@@ -406,7 +407,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
                             ax = fig.add_subplot(max_length//3, max_length//4, l+1)
                             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)])
+                            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/CNNSupervisedTrainer_VGG16.py

@@ -115,7 +115,8 @@ class BLEU(mx.metric.EvalMetric):
                     i *= 2
                     match_counts = 1 / i
 
-                precisions[n] = match_counts / counts
+                if (match_counts / counts) > 0:
+                    precisions[n] = match_counts / counts
 
         bleu = self._get_brevity_penalty() * math.exp(sum(map(math.log, precisions)) / self.N)
 
@@ -320,7 +321,7 @@ class CNNSupervisedTrainer_VGG16:
             train_test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(train_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)
@@ -354,7 +355,7 @@ class CNNSupervisedTrainer_VGG16:
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
                             ax = fig.add_subplot(max_length//3, max_length//4, l+1)
                             ax.set_title(dict[int(labels[l+1][0].asscalar())])
-                            img = ax.imshow(train_images[0+test_batch_size*(batch_i)])
+                            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())
 
 
@@ -378,7 +379,7 @@ class CNNSupervisedTrainer_VGG16:
             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)
@@ -406,7 +407,7 @@ class CNNSupervisedTrainer_VGG16:
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
                             ax = fig.add_subplot(max_length//3, max_length//4, l+1)
                             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)])
+                            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())