Merge branch 'develop' into 'master'

Adjusted BeamSearch to work with batch sizes > 1

See merge request !25

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

@@ -55,7 +55,7 @@ class SoftmaxCrossEntropyLossIgnoreIndices(gluon.loss.Loss):
             loss = -(pred * label).sum(axis=self._axis, keepdims=True)
         # ignore some indices for loss, e.g. <pad> tokens in NLP applications
         for i in self._ignore_indices:
-            loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i))
+            loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i) * mx.nd.equal(mx.nd.argmax(pred, axis=1), label))
         return loss.mean(axis=self._batch_axis, exclude=True)
 
 @mx.metric.register
@@ -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 = 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)
 
@@ -279,6 +279,7 @@ class ${tc.fileNameWithoutEnding}:
         tic = None
 
         for epoch in range(begin_epoch, begin_epoch + num_epoch):
+
             train_iter.reset()
             for batch_i, batch in enumerate(train_iter):
                 with autograd.record():
@@ -308,6 +309,7 @@ class ${tc.fileNameWithoutEnding}:
 
             tic = None
 
+
             train_test_iter.reset()
             metric = mx.metric.create(eval_metric, **eval_metric_params)
             for batch_i, batch in enumerate(train_test_iter):

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}], 1.0)]
+                    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>
@@ -55,20 +54,47 @@
 </#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)
+<#if tc.isAttentionNetwork()>
+                                candidate = (seq + [j],  newScore, attention + [attention_])
+<#else>
+                                candidate = (seq + [j],  newScore, attention + [])
+</#if>
                                 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 = [([seq[batch_entry] for seq in candidate[0]], candidate[1][batch_entry], [attention[batch_entry].expand_dims(axis=0) for attention in candidate[2]]) for candidate 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[sequenceIndex][0][seqIndex], ordered[batch_entry][sequenceIndex][0][seqIndex], dim=0) for seqIndex in range(len(newSequences[sequenceIndex][0]))],
+                                    mx.nd.concat(newSequences[sequenceIndex][1], ordered[batch_entry][sequenceIndex][1], dim=0),
+                                    [mx.nd.concat(newSequences[sequenceIndex][2][attentionIndex], ordered[batch_entry][sequenceIndex][2][attentionIndex], dim=0) for attentionIndex in range(len(newSequences[sequenceIndex][2]))])
+                                    for sequenceIndex in range(len(newSequences))]
+
+                        newSequences = [([newSequences[sequenceIndex][0][seqIndex].expand_dims(axis=1) for seqIndex in range(len(newSequences[sequenceIndex][0]))],
+                            newSequences[sequenceIndex][1].expand_dims(axis=1), [newSequences[sequenceIndex][2][attentionIndex] for attentionIndex in range(len(newSequences[sequenceIndex][2]))])
+                            for sequenceIndex in range(len(newSequences))]
+
+                        sequences = newSequences[:][:k]
 
                     for i in range(1, ${tc.getBeamSearchMaxLength(networkInstruction)}):
 <#list tc.getUnrollOutputNames(networkInstruction, "i") as outputName>
 <#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>

src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py

@@ -54,7 +54,7 @@ class SoftmaxCrossEntropyLossIgnoreIndices(gluon.loss.Loss):
             loss = -(pred * label).sum(axis=self._axis, keepdims=True)
         # ignore some indices for loss, e.g. <pad> tokens in NLP applications
         for i in self._ignore_indices:
-            loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i))
+            loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i) * mx.nd.equal(mx.nd.argmax(pred, axis=1), label))
         return loss.mean(axis=self._batch_axis, exclude=True)
 
 @mx.metric.register

src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py

@@ -54,7 +54,7 @@ class SoftmaxCrossEntropyLossIgnoreIndices(gluon.loss.Loss):
             loss = -(pred * label).sum(axis=self._axis, keepdims=True)
         # ignore some indices for loss, e.g. <pad> tokens in NLP applications
         for i in self._ignore_indices:
-            loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i))
+            loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i) * mx.nd.equal(mx.nd.argmax(pred, axis=1), label))
         return loss.mean(axis=self._batch_axis, exclude=True)
 
 @mx.metric.register

src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py

@@ -54,7 +54,7 @@ class SoftmaxCrossEntropyLossIgnoreIndices(gluon.loss.Loss):
             loss = -(pred * label).sum(axis=self._axis, keepdims=True)
         # ignore some indices for loss, e.g. <pad> tokens in NLP applications
         for i in self._ignore_indices:
-            loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i))
+            loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i) * mx.nd.equal(mx.nd.argmax(pred, axis=1), label))
         return loss.mean(axis=self._batch_axis, exclude=True)
 
 @mx.metric.register