made batch sizes > 1 possible for Beamsearch

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

@@ -214,7 +214,7 @@ class ${tc.fileNameWithoutEnding}:
             del optimizer_params['learning_rate_decay']
 
         train_batch_size = batch_size
-        test_batch_size = ${tc.hasUnrollInstructions()?then('1', 'batch_size')}
+        test_batch_size = ${tc.hasUnrollInstructions()?then('batch_size', 'batch_size')}
 
         train_iter, train_test_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(train_batch_size, test_batch_size)
 

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

@@ -30,7 +30,7 @@
                     k = ${tc.getBeamSearchWidth(networkInstruction)}
 <#list tc.getUnrollInputNames(networkInstruction, "1") as inputName>
 <#if tc.getNameWithoutIndex(inputName) == tc.outputName>
-                    sequences = [([${inputName}], 1.0)]
+                    sequences = [([${inputName}], mx.nd.full((batch_size, 1,), 1.0, ctx=mx_context))]
 </#if>
 </#list>
 
@@ -55,14 +55,27 @@
 </#if>
 </#list>
 
-                            topk = out.topk(k=k)[0]
+                            topk = out.topk(k=k)
 
-                            for j in topk:
-                                candidate = (seq + [mx.nd.full((1, 1,), j, ctx=mx_context)], score * out[0][j].asscalar())
+                            for top_index in range(len(topk[0])):
+                                j = mx.nd.slice_axis(topk, axis=1, begin=top_index, end=top_index+1)
+                                currentScore = mx.nd.slice_axis(out, axis=1, begin=top_index, end=top_index+1)
+                                newScore = mx.nd.expand_dims(score.squeeze() * currentScore.squeeze(), axis=1)
+                                candidate = (seq + [j],  newScore)
                                 all_candidates.append(candidate)
 
-                        ordered = sorted(all_candidates, key=lambda tup: tup[1])
-                        sequences = ordered[:k]
+                        ordered = []
+                        newSequences = []
+                        for batch_entry in range(batch_size):
+                            ordered.append([])
+                            batchCandidate = [([y[batch_entry] for y in x[0]], x[1][batch_entry]) for x in all_candidates]
+                            ordered[batch_entry] = sorted(batchCandidate, key=lambda tup: tup[1].asscalar())
+                            if batch_entry == 0:
+                                newSequences = ordered[batch_entry]
+                            else:
+                                newSequences = [([mx.nd.concat(newSequences[x][0][y], ordered[batch_entry][x][0][y], dim=0).expand_dims(axis=1) for y in range(len(newSequences[x][0]))], mx.nd.concat(newSequences[x][1], ordered[batch_entry][x][1], dim=0).expand_dims(axis=1)) for x in range(len(newSequences))]
+
+                        sequences = newSequences[:][:k]
 
                     for i in range(1, ${tc.getBeamSearchMaxLength(networkInstruction)}):
 <#list tc.getUnrollOutputNames(networkInstruction, "i") as outputName>