changed .ftl templates to work with Unrolls

_gitignore

-target
-nppBackup
-.project
-.settings
-.classpath
-.idea
-.git
-
-*.iml
-

_gitlab-ci.yml

-#
-#
-#  ******************************************************************************
-#  MontiCAR Modeling Family, www.se-rwth.de
-#  Copyright (c) 2017, Software Engineering Group at RWTH Aachen,
-#  All rights reserved.
-#
-#  This project is free software; you can redistribute it and/or
-#  modify it under the terms of the GNU Lesser General Public
-#  License as published by the Free Software Foundation; either
-#  version 3.0 of the License, or (at your option) any later version.
-#  This library is distributed in the hope that it will be useful,
-#  but WITHOUT ANY WARRANTY; without even the implied warranty of
-#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-#  Lesser General Public License for more details.
-#
-#  You should have received a copy of the GNU Lesser General Public
-#  License along with this project. If not, see <http://www.gnu.org/licenses/>.
-# *******************************************************************************
-#
-
-stages:
-- windows
-- linux
-
-masterJobLinux:
-  stage: linux
-  image: maven:3-jdk-8
-  script: 
-  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  clean deploy --settings settings.xml
-  - cat target/site/jacoco/index.html
-  - mvn package sonar:sonar -s settings.xml
-  only:
-  - master
-  
-masterJobWindows:
-  stage: windows
-  script:
-  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  clean install --settings settings.xml
-  tags:
-  - Windows10
-
-BranchJobLinux:
-  stage: linux
-  image: maven:3-jdk-8
-  script: 
-  - mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B  clean install --settings settings.xml
-  - cat target/site/jacoco/index.html
-  except:
-  - master

_travis.yml

-script:
-- git checkout ${TRAVIS_BRANCH}
-- mvn clean install cobertura:cobertura org.eluder.coveralls:coveralls-maven-plugin:report --settings "settings.xml"
-after_success:
-- if [ "${TRAVIS_BRANCH}" == "master" ]; then mvn -Dorg.slf4j.simpleLogger.log.org.apache.maven.cli.transfer.Slf4jMavenTransferListener=warn -B deploy --debug --settings "./settings.xml"; fi

src/main/java/de/monticore/lang/monticar/cnnarch/gluongenerator/CNNArch2GluonTemplateController.java

@@ -40,7 +40,6 @@ public class CNNArch2GluonTemplateController extends CNNArchTemplateController {
     }
 
     public void include(String relativePath, String templateWithoutFileEnding, Writer writer, NetDefinitionMode netDefinitionMode){
-        System.err.println("include called. templateName: " + templateWithoutFileEnding);
         String templatePath = relativePath + templateWithoutFileEnding + FTL_FILE_ENDING;
         Map<String, Object> ftlContext = new HashMap<>();
         ftlContext.put(TEMPLATE_CONTROLLER_KEY, this);

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

 import mxnet as mx
 import logging
 import os
+
 <#list tc.architecture.streams as stream>
 <#if stream.isTrainable()>
 from CNNNet_${tc.fullArchitectureName} import Net_${stream?index}
 </#if>
 </#list>
 
+<#list tc.architecture.unrolls as unroll>
+<#if unroll.isTrainable()>
+from CNNNet_${tc.fullArchitectureName} import Net_${unroll?index}
+</#if>
+</#list>
+
 class ${tc.fileNameWithoutEnding}:
     _model_dir_ = "model/${tc.componentName}/"
     _model_prefix_ = "model"
@@ -58,6 +65,15 @@ class ${tc.fileNameWithoutEnding}:
         self.networks[${stream?index}].hybridize()
         self.networks[${stream?index}](<#list tc.getStreamInputDimensions(stream) as dimensions>mx.nd.zeros((${tc.join(dimensions, ",")},), ctx=context)<#sep>, </#list>)
 </#if>
+</#list>
+
+<#list tc.architecture.unrolls as unroll>
+<#if unroll.isTrainable()>
+        self.networks[${unroll?index}] = Net_${unroll?index}(data_mean=data_mean, data_std=data_std)
+        self.networks[${unroll?index}].collect_params().initialize(self.weight_initializer, ctx=context)
+        self.networks[${unroll?index}].hybridize()
+        self.networks[${unroll?index}](<#list tc.getUnrollInputDimensions(unroll) as dimensions>mx.nd.zeros((${tc.join(dimensions, ",")},), ctx=context)<#sep>, </#list>)
+</#if>
 </#list>
 
         if not os.path.exists(self._model_dir_):

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

@@ -94,6 +94,22 @@ ${tc.include(stream, "FORWARD_FUNCTION")}
 </#if>
 </#list>
 
+<#list tc.architecture.unrolls as unroll>
+<#if unroll.isTrainable()>
+class Net_${unroll?index}(gluon.HybridBlock):
+    def __init__(self, data_mean=None, data_std=None, **kwargs):
+        super(Net_${unroll?index}, self).__init__(**kwargs)
+        self.last_layers = {}
+        with self.name_scope():
+${tc.include(unroll, "ARCHITECTURE_DEFINITION")}
+
+    def hybrid_forward(self, F, ${tc.join(tc.getUnrollInputNames(unroll), ", ")}):
+${tc.include(unroll, "FORWARD_FUNCTION")}
+        return ${tc.join(tc.getUnrollOutputNames(unroll), ", ")}
+
+</#if>
+</#list>
+
 <#list tc.architecture.unrolls as unroll>
 <#if unroll.isTrainable()>
 class Net_${unroll?index}(gluon.HybridBlock):

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

@@ -116,4 +116,112 @@ public:
 </#if>
 </#list>
 
+<#list tc.architecture.unrolls as unroll>
+<#if unroll.isTrainable()>
+class ${tc.fileNameWithoutEnding}_${unroll?index}{
+public:
+    const std::string json_file = "model/${tc.componentName}/model_${unroll?index}_newest-symbol.json";
+    const std::string param_file = "model/${tc.componentName}/model_${unroll?index}_newest-0000.params";
+    const std::vector<std::string> input_keys = {
+<#if tc.getUnrollInputNames(unroll)?size == 1>
+        "data"
+<#else>
+        <#list tc.getUnrollInputNames(unroll) as variable>"data${variable?index}"<#sep>, </#list>
+</#if>
+    };
+    const std::vector<std::vector<mx_uint>> input_shapes = {<#list tc.getUnrollInputDimensions(unroll) as dimensions>{${tc.join(dimensions, ", ")}}<#sep>, </#list>};
+    const bool use_gpu = false;
+
+    PredictorHandle handle;
+
+    explicit ${tc.fileNameWithoutEnding}_${unroll?index}(){
+        init(json_file, param_file, input_keys, input_shapes, use_gpu);
+    }
+
+    ~${tc.fileNameWithoutEnding}_${unroll?index}(){
+        if(handle) MXPredFree(handle);
+    }
+
+    void predict(${tc.join(tc.getUnrollInputNames(unroll), ", ", "const std::vector<float> &in_", "")},
+                 ${tc.join(tc.getUnrollOutputNames(unroll), ", ", "std::vector<float> &out_", "")}){
+<#list tc.getUnrollInputNames(unroll) as variable>
+        MXPredSetInput(handle, input_keys[${variable?index}].c_str(), in_${variable}.data(), static_cast<mx_uint>(in_${variable}.size()));
+</#list>
+
+        MXPredForward(handle);
+
+        mx_uint output_index;
+        mx_uint *shape = 0;
+        mx_uint shape_len;
+        size_t size;
+
+<#list tc.getUnrollOutputNames(unroll) as variable>
+        output_index = ${variable?index?c};
+        MXPredGetOutputShape(handle, output_index, &shape, &shape_len);
+        size = 1;
+        for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
+        assert(size == out_${variable}.size());
+        MXPredGetOutput(handle, ${variable?index?c}, &(out_${variable}[0]), out_${variable}.size());
+
+</#list>
+    }
+
+    void init(const std::string &json_file,
+              const std::string &param_file,
+              const std::vector<std::string> &input_keys,
+              const std::vector<std::vector<mx_uint>> &input_shapes,
+              const bool &use_gpu){
+
+        BufferFile json_data(json_file);
+        BufferFile param_data(param_file);
+
+        int dev_type = use_gpu ? 2 : 1;
+        int dev_id = 0;
+
+        if (json_data.GetLength() == 0 ||
+            param_data.GetLength() == 0) {
+            std::exit(-1);
+        }
+
+        const mx_uint num_input_nodes = input_keys.size();
+
+        const char* input_keys_ptr[num_input_nodes];
+        for(mx_uint i = 0; i < num_input_nodes; i++){
+            input_keys_ptr[i] = input_keys[i].c_str();
+        }
+
+        mx_uint shape_data_size = 0;
+        mx_uint input_shape_indptr[input_shapes.size() + 1];
+        input_shape_indptr[0] = 0;
+        for(mx_uint i = 0; i < input_shapes.size(); i++){
+            shape_data_size += input_shapes[i].size();
+            input_shape_indptr[i+1] = shape_data_size;
+        }
+
+        mx_uint input_shape_data[shape_data_size];
+        mx_uint index = 0;
+        for(mx_uint i = 0; i < input_shapes.size(); i++){
+            for(mx_uint j = 0; j < input_shapes[i].size(); j++){
+                input_shape_data[index] = input_shapes[i][j];
+                index++;
+            }
+        }
+
+        MXPredCreate(static_cast<const char*>(json_data.GetBuffer()),
+                     static_cast<const char*>(param_data.GetBuffer()),
+                     static_cast<size_t>(param_data.GetLength()),
+                     dev_type,
+                     dev_id,
+                     num_input_nodes,
+                     input_keys_ptr,
+                     input_shape_indptr,
+                     input_shape_data,
+                     &handle);
+        assert(handle);
+    }
+};
+</#if>
+</#list>
+
+
 #endif // ${tc.fileNameWithoutEnding?upper_case}

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

@@ -16,6 +16,14 @@ ${tc.include(stream, "CPP_INLINE")}
 </#if>
 </#list>
 
+<#list tc.architecture.unrolls as unroll>
+<#if unroll.isTrainable()>
+    _predictor_${unroll?index}_.predict(${tc.join(tc.getUnrollInputNames(unroll), ", ")}, ${tc.join(tc.getUnrollOutputNames(unroll), ", ")});
+<#else>
+${tc.include(unroll, "CPP_INLINE")}
+</#if>
+</#list>
+
 <#list tc.architecture.outputs as output>
 <#assign shape = output.ioDeclaration.type.dimensions>
 <#if shape?size == 1>

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

@@ -11,4 +11,12 @@
 <#else>
 ${tc.include(stream, "PYTHON_INLINE")}
 </#if>
+</#list>
+
+<#list tc.architecture.unrolls as unroll>
+<#if unroll.isTrainable()>
+                    ${tc.join(tc.getUnrollOutputNames(unroll), ", ")} = self._networks[${unroll?index}](${tc.join(tc.getUnrollInputNames(unroll), ", ")})
+<#else>
+${tc.include(unroll, "PYTHON_INLINE")}
+</#if>
 </#list>
\ No newline at end of file

src/test/java/de/monticore/lang/monticar/cnnarch/gluongenerator/GenerationTest.java

@@ -80,27 +80,7 @@ public class GenerationTest extends AbstractSymtabTest {
         CNNArch2GluonCli.main(args);
         assertTrue(Log.getFindings().isEmpty());
 
-        /*checkFilesAreEqual(
-                Paths.get("./target/generated-sources-cnnarch"),
-                Paths.get("./src/test/resources/target_code"),
-                Arrays.asList(
-                        "CNNCreator_Alexnet.py",
-                        "CNNNet_Alexnet.py",
-                        "CNNDataLoader_Alexnet.py",
-                        "CNNSupervisedTrainer_Alexnet.py",
-                        "CNNPredictor_Alexnet.h",
-                        "execute_Alexnet"));*/
-    }
-
-
-    @Test
-    public void testRNNencdecGeneration() throws IOException, TemplateException {
-        Log.getFindings().clear();
-        String[] args = {"-m", "src/test/resources/valid_tests", "-r", "RNNencdec", "-o", "./target/generated-sources-cnnarch/"};
-        CNNArch2GluonCli.main(args);
-//        assertTrue(Log.getFindings().isEmpty());
-
-        /*checkFilesAreEqual(
+        checkFilesAreEqual(
                 Paths.get("./target/generated-sources-cnnarch"),
                 Paths.get("./src/test/resources/target_code"),
                 Arrays.asList(
@@ -109,7 +89,7 @@ public class GenerationTest extends AbstractSymtabTest {
                         "CNNDataLoader_Alexnet.py",
                         "CNNSupervisedTrainer_Alexnet.py",
                         "CNNPredictor_Alexnet.h",
-                        "execute_Alexnet"));*/
+                        "execute_Alexnet"));
     }
 
     @Test
@@ -171,6 +151,14 @@ public class GenerationTest extends AbstractSymtabTest {
         assertTrue(Log.getFindings().isEmpty());
     }
 
+    @Test
+    public void testRNNencdec() throws IOException, TemplateException {
+        Log.getFindings().clear();
+        String[] args = {"-m", "src/test/resources/valid_tests", "-r", "RNNencdec", "-o", "./target/generated-sources-cnnarch/"};
+        CNNArch2GluonCli.main(args);
+        assertTrue(Log.getFindings().isEmpty());
+    }
+
     @Test
     public void testFullCfgGeneration() throws IOException, TemplateException {
         Log.getFindings().clear();

src/test/resources/target_code/CNNCreator_Alexnet.py

 import mxnet as mx
 import logging
 import os
+
 from CNNNet_Alexnet import Net_0
 
+
 class CNNCreator_Alexnet:
     _model_dir_ = "model/Alexnet/"
     _model_prefix_ = "model"
@@ -52,6 +54,7 @@ class CNNCreator_Alexnet:
         self.networks[0].hybridize()
         self.networks[0](mx.nd.zeros((1,3,224,224,), ctx=context))
 
+
         if not os.path.exists(self._model_dir_):
             os.makedirs(self._model_dir_)
 

src/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py

 import mxnet as mx
 import logging
 import os
+
 from CNNNet_CifarClassifierNetwork import Net_0
 
+
 class CNNCreator_CifarClassifierNetwork:
     _model_dir_ = "model/CifarClassifierNetwork/"
     _model_prefix_ = "model"
@@ -52,6 +54,7 @@ class CNNCreator_CifarClassifierNetwork:
         self.networks[0].hybridize()
         self.networks[0](mx.nd.zeros((1,3,32,32,), ctx=context))
 
+
         if not os.path.exists(self._model_dir_):
             os.makedirs(self._model_dir_)
 

src/test/resources/target_code/CNNCreator_VGG16.py

 import mxnet as mx
 import logging
 import os
+
 from CNNNet_VGG16 import Net_0
 
+
 class CNNCreator_VGG16:
     _model_dir_ = "model/VGG16/"
     _model_prefix_ = "model"
@@ -52,6 +54,7 @@ class CNNCreator_VGG16:
         self.networks[0].hybridize()
         self.networks[0](mx.nd.zeros((1,3,224,224,), ctx=context))
 
+
         if not os.path.exists(self._model_dir_):
             os.makedirs(self._model_dir_)
 

src/test/resources/target_code/CNNNet_Alexnet.py

@@ -275,3 +275,5 @@ class Net_0(gluon.HybridBlock):
 
         return predictions_
 
+
+

src/test/resources/target_code/CNNNet_CifarClassifierNetwork.py

@@ -468,3 +468,5 @@ class Net_0(gluon.HybridBlock):
 
         return softmax_
 
+
+

src/test/resources/target_code/CNNNet_VGG16.py

@@ -295,3 +295,5 @@ class Net_0(gluon.HybridBlock):
 
         return predictions_
 
+
+

src/test/resources/target_code/CNNPredictor_Alexnet.h

@@ -104,4 +104,6 @@ public:
     }
 };
 
+
+
 #endif // CNNPREDICTOR_ALEXNET

src/test/resources/target_code/CNNPredictor_CifarClassifierNetwork.h

@@ -104,4 +104,6 @@ public:
     }
 };
 
+
+
 #endif // CNNPREDICTOR_CIFARCLASSIFIERNETWORK

src/test/resources/target_code/CNNPredictor_VGG16.h

@@ -104,4 +104,6 @@ public:
     }
 };
 
+
+
 #endif // CNNPREDICTOR_VGG16

src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py

@@ -140,6 +140,7 @@ class CNNSupervisedTrainer_Alexnet:
 
                     predictions_ = self._networks[0](data_)
 
+
                     loss = \
                         loss_function(predictions_, predictions_label)
 
@@ -172,11 +173,12 @@ class CNNSupervisedTrainer_Alexnet:
                     batch.label[0].as_in_context(mx_context)
                 ]
 
-                if True:
+                if True: 
                     predictions_ = mx.nd.zeros((10,), ctx=mx_context)
 
                     predictions_ = self._networks[0](data_)
 
+
                 predictions = [
                     mx.nd.argmax(predictions_, axis=1)
                 ]
@@ -193,11 +195,12 @@ class CNNSupervisedTrainer_Alexnet:
                     batch.label[0].as_in_context(mx_context)
                 ]
 
-                if True:
+                if True: 
                     predictions_ = mx.nd.zeros((10,), ctx=mx_context)
 
                     predictions_ = self._networks[0](data_)
 
+
                 predictions = [
                     mx.nd.argmax(predictions_, axis=1)
                 ]

src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py

@@ -140,6 +140,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
 
                     softmax_ = self._networks[0](data_)
 
+
                     loss = \
                         loss_function(softmax_, softmax_label)
 
@@ -172,11 +173,12 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
                     batch.label[0].as_in_context(mx_context)
                 ]
 
-                if True:
+                if True: 
                     softmax_ = mx.nd.zeros((10,), ctx=mx_context)
 
                     softmax_ = self._networks[0](data_)
 
+
                 predictions = [
                     mx.nd.argmax(softmax_, axis=1)
                 ]
@@ -193,11 +195,12 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
                     batch.label[0].as_in_context(mx_context)
                 ]
 
-                if True:
+                if True: 
                     softmax_ = mx.nd.zeros((10,), ctx=mx_context)
 
                     softmax_ = self._networks[0](data_)
 
+
                 predictions = [
                     mx.nd.argmax(softmax_, axis=1)
                 ]

src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py

@@ -140,6 +140,7 @@ class CNNSupervisedTrainer_VGG16:
 
                     predictions_ = self._networks[0](data_)
 
+
                     loss = \
                         loss_function(predictions_, predictions_label)
 
@@ -172,11 +173,12 @@ class CNNSupervisedTrainer_VGG16:
                     batch.label[0].as_in_context(mx_context)
                 ]
 
-                if True:
+                if True: 
                     predictions_ = mx.nd.zeros((1000,), ctx=mx_context)
 
                     predictions_ = self._networks[0](data_)
 
+
                 predictions = [
                     mx.nd.argmax(predictions_, axis=1)
                 ]
@@ -193,11 +195,12 @@ class CNNSupervisedTrainer_VGG16:
                     batch.label[0].as_in_context(mx_context)
                 ]
 
-                if True:
+                if True: 
                     predictions_ = mx.nd.zeros((1000,), ctx=mx_context)
 
                     predictions_ = self._networks[0](data_)
 
+
                 predictions = [
                     mx.nd.argmax(predictions_, axis=1)
                 ]

src/test/resources/target_code/execute_Alexnet

@@ -3,4 +3,5 @@
 
     _predictor_0_.predict(data_, predictions_);
 
+
     predictions = CNNTranslator::translateToCol(predictions_, std::vector<size_t> {10});

src/test/resources/target_code/execute_CifarClassifierNetwork

@@ -3,4 +3,5 @@
 
     _predictor_0_.predict(data_, softmax_);
 
+
     softmax = CNNTranslator::translateToCol(softmax_, std::vector<size_t> {10});

src/test/resources/target_code/execute_VGG16

@@ -3,4 +3,5 @@
 
     _predictor_0_.predict(data_, predictions_);
 
+
     predictions = CNNTranslator::translateToCol(predictions_, std::vector<size_t> {1000});