fixed a small bug with batch sizes > 2. Added usage of attention images in Beamsearch

pom.xml

@@ -18,7 +18,7 @@
     <!-- .. SE-Libraries .................................................. -->
 
       <CNNArch.version>0.3.4-SNAPSHOT</CNNArch.version>
-      <CNNTrain.version>0.3.8-SNAPSHOT</CNNTrain.version>
+      <CNNTrain.version>0.3.9-SNAPSHOT</CNNTrain.version>
       <CNNArch2X.version>0.0.5-SNAPSHOT</CNNArch2X.version>
       <embedded-montiarc-math-opt-generator>0.1.4</embedded-montiarc-math-opt-generator>
       <EMADL2PythonWrapper.version>0.0.2-SNAPSHOT</EMADL2PythonWrapper.version>

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

@@ -30,14 +30,14 @@
                     k = ${tc.getBeamSearchWidth(networkInstruction)}
 <#list tc.getUnrollInputNames(networkInstruction, "1") as inputName>
 <#if tc.getNameWithoutIndex(inputName) == tc.outputName>
-                    sequences = [([${inputName}], mx.nd.full((batch_size, 1,), 1.0, ctx=mx_context))]
+                    sequences = [([${inputName}], mx.nd.full((batch_size, 1,), 1.0, ctx=mx_context), [mx.nd.full((batch_size, 64,), 0.0, ctx=mx_context)])]
 </#if>
 </#list>
 
                     for i in range(1, ${tc.getBeamSearchMaxLength(networkInstruction)}):
                         all_candidates = []
 
-                        for seq, score in sequences:
+                        for seq, score, attention in sequences:
 <#list tc.getUnrollInputNames(networkInstruction, "i") as inputName>
 <#if tc.getNameWithoutIndex(inputName) == tc.outputName>
                             ${inputName} = seq[-1]
@@ -45,7 +45,6 @@
 </#list>
 <#if tc.isAttentionNetwork()>
                             ${tc.join(tc.getUnrollOutputNames(networkInstruction, "i"), ", ")}, attention_ = self._networks[${networkInstruction?index}](${tc.join(tc.getUnrollInputNames(networkInstruction, "i"), ", ")})
-                            attentionList.append(attention_)
 <#else>
                             ${tc.join(tc.getUnrollOutputNames(networkInstruction, "i"), ", ")} = self._networks[${networkInstruction?index}](${tc.join(tc.getUnrollInputNames(networkInstruction, "i"), ", ")})
 </#if>
@@ -61,22 +60,25 @@
                                 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)
+<#if tc.isAttentionNetwork()>
+                                candidate = (seq + [j],  newScore, attention + [attention_])
+<#else>
+                                candidate = (seq + [j],  newScore, attention + [])
+</#if>
                                 all_candidates.append(candidate)
 
                         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]
+                            batchCandidate = [([y[batch_entry] for y in x[0]], x[1][batch_entry], [y[batch_entry].expand_dims(axis=0) for y in x[2]]) for x in all_candidates]
                             ordered[batch_entry] = sorted(batchCandidate, key=lambda tup: tup[1].asscalar())
                             if batch_entry == 0:
                                 newSequences = ordered[batch_entry]
-                            elif batch_entry < (batch_size -1):
-                                newSequences = [([mx.nd.concat(newSequences[x][0][y], ordered[batch_entry][x][0][y], dim=0) for y in range(len(newSequences[x][0]))], mx.nd.concat(newSequences[x][1], ordered[batch_entry][x][1], dim=0)) for x in range(len(newSequences))]
-                            # expand dims only once
                             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))]
+                                newSequences = [([mx.nd.concat(newSequences[x][0][y], ordered[batch_entry][x][0][y], dim=0) for y in range(len(newSequences[x][0]))], mx.nd.concat(newSequences[x][1], ordered[batch_entry][x][1], dim=0), [mx.nd.concat(newSequences[x][2][y], ordered[batch_entry][x][2][y], dim=0) for y in range(len(newSequences[x][2]))]) for x in range(len(newSequences))]
+
+                        newSequences = [([newSequences[x][0][y].expand_dims(axis=1) for y in range(len(newSequences[x][0]))], newSequences[x][1].expand_dims(axis=1), [newSequences[x][2][y] for y in range(len(newSequences[x][2]))]) for x in range(len(newSequences))]
 
                         sequences = newSequences[:][:k]
 
@@ -85,6 +87,9 @@
 <#if tc.getNameWithoutIndex(outputName) == tc.outputName>
                         ${outputName} = sequences[0][0][i]
                         outputs.append(${outputName})
+<#if tc.isAttentionNetwork()>
+                        attentionList.append(sequences[0][2][i])
+</#if>
 </#if>
 </#list>
 <#else>