Corrected dropout and output layer (include sigmoid). Added...

Corrected dropout and output layer (include sigmoid). Added learning_rate_decay again since it is indeed supported in Caffe2

src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/TrainParamSupportChecker.java

@@ -76,10 +76,7 @@ public class TrainParamSupportChecker implements CNNTrainVisitor {
 
     public void visit(ASTWeightDecayEntry node){}
 
-    public void visit(ASTLRDecayEntry node){
-        printUnsupportedOptimizerParam(node.getName());
-        this.unsupportedElemList.add(node.getName());
-    }
+    public void visit(ASTLRDecayEntry node){}
 
     public void visit(ASTLRPolicyEntry node){}
 

src/main/resources/templates/caffe2/CNNCreator.ftl

@@ -7,6 +7,7 @@ import logging
 import os
 import sys
 import lmdb
+
 class ${tc.fileNameWithoutEnding}:
 
     module = None
@@ -58,7 +59,7 @@ class ${tc.fileNameWithoutEnding}:
 
             return data, label, dataset_size
 
-    def create_model(self, model, data, device_opts):
+    def create_model(self, model, data, device_opts, is_test):
     	with core.DeviceScope(device_opts):
 
 ${tc.include(tc.architecture.body)}
@@ -118,7 +119,7 @@ ${tc.include(tc.architecture.body)}
     	# == Training model ==
     	train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
     	data, label, train_dataset_size = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'train_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(train_model, data, device_opts=device_opts)
+    	${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
     	self.add_training_operators(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
     	self.add_accuracy(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, eval_metric)
     	with core.DeviceScope(device_opts):
@@ -141,7 +142,7 @@ ${tc.include(tc.architecture.body)}
     	# == Testing model. ==
     	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
     	data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(test_model, data, device_opts=device_opts)
+    	${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
     	self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
     	workspace.RunNetOnce(test_model.param_init_net)
     	workspace.CreateNet(test_model.net, overwrite=True)
@@ -159,7 +160,7 @@ ${tc.include(tc.architecture.body)}
     	# == Deployment model. ==
     	# We simply need the main AddModel part.
     	deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
-    	self.create_model(deploy_model, "data", device_opts)
+    	self.create_model(deploy_model, "data", device_opts, is_test=True)
 
     	print("Saving deploy model")
     	self.save_net(self._init_net_, self._predict_net_, deploy_model)

src/main/resources/templates/caffe2/CNNTrainer.ftl

@@ -41,6 +41,8 @@ if __name__ == "__main__":
     <#elseif param == "step_size">
         <#assign paramName = "stepsize">
     <#elseif param == "gamma1">
+        <#assign paramName = "gamma1">
+    <#elseif param == "learning_rate_decay">
         <#assign paramName = "gamma">
     </#if>
         ${paramName}=${config.optimizerParams[param]}<#sep>,

src/main/resources/templates/caffe2/elements/Dropout.ftl

-    		${element.name} = mx.symbol.Dropout(data=${element.inputs[0]},
-    		    p=${element.p?c},
-    		    name="${element.name}")
+<#assign input = element.inputs[0]>
+<#assign ratio = element.p?c?string>
+    		${element.name} = brew.dropout(model, ${input}, '${element.name}', ratio=${ratio}, is_test=False)

src/main/resources/templates/caffe2/elements/Output.ftl

@@ -2,8 +2,7 @@
 <#if element.softmaxOutput>
     		${element.name} = brew.softmax(model, ${input}, '${element.name}')
 <#elseif element.logisticRegressionOutput>
-    		${element.name} = mx.symbol.LogisticRegressionOutput(data=${element.inputs[0]}, <#-- TODO: check how to adapt LogisticRegressionOutput -->
-    		    name="${element.name}")
+    		${element.name} = model.net.Sigmoid(${input}, '${element.name}')
 <#elseif element.linearRegressionOutput>
     		${element.name} = mx.symbol.LinearRegressionOutput(data=${element.inputs[0]}, <#-- TODO: check how to adapt linearRegressionOutput -->
     		    name="${element.name}")

src/test/java/de/monticore/lang/monticar/cnnarch/caffe2generator/GenerationTest.java

@@ -121,7 +121,7 @@ public class GenerationTest extends AbstractSymtabTest{
         CNNTrain2Caffe2 trainGenerator = new CNNTrain2Caffe2();
         trainGenerator.generate(Paths.get(sourcePath), "FullConfig");
 
-        assertTrue(Log.getFindings().size() == 9);
+        assertTrue(Log.getFindings().size() == 8);
         checkFilesAreEqual(
                 Paths.get("./target/generated-sources-cnnarch"),
                 Paths.get("./src/test/resources/target_code"),

src/test/resources/target_code/CNNCreator_Alexnet.py

@@ -7,6 +7,7 @@ import logging
 import os
 import sys
 import lmdb
+
 class CNNCreator_Alexnet:
 
     module = None
@@ -58,7 +59,7 @@ class CNNCreator_Alexnet:
 
             return data, label, dataset_size
 
-    def create_model(self, model, data, device_opts):
+    def create_model(self, model, data, device_opts, is_test):
     	with core.DeviceScope(device_opts):
 
     		data = data
@@ -140,15 +141,11 @@ class CNNCreator_Alexnet:
     		fc6_ = brew.fc(model, concatenate6_, 'fc6_', dim_in=256 * 6 * 6, dim_out=4096)
     		# fc6_, output shape: {[4096,1,1]}
     		relu6_ = brew.relu(model, fc6_, fc6_)
-    		dropout6_ = mx.symbol.Dropout(data=relu6_,
-    		    p=0.5,
-    		    name="dropout6_")
+    		dropout6_ = brew.dropout(model, relu6_, 'dropout6_', ratio=0.5, is_test=False)
     		fc7_ = brew.fc(model, dropout6_, 'fc7_', dim_in=4096, dim_out=4096)
     		# fc7_, output shape: {[4096,1,1]}
     		relu7_ = brew.relu(model, fc7_, fc7_)
-    		dropout7_ = mx.symbol.Dropout(data=relu7_,
-    		    p=0.5,
-    		    name="dropout7_")
+    		dropout7_ = brew.dropout(model, relu7_, 'dropout7_', ratio=0.5, is_test=False)
     		fc8_ = brew.fc(model, dropout7_, 'fc8_', dim_in=4096, dim_out=10)
     		# fc8_, output shape: {[10,1,1]}
     		predictions = brew.softmax(model, fc8_, 'predictions')
@@ -210,7 +207,7 @@ class CNNCreator_Alexnet:
     	# == Training model ==
     	train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
     	data, label, train_dataset_size = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'train_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	predictions = self.create_model(train_model, data, device_opts=device_opts)
+    	predictions = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
     	self.add_training_operators(train_model, predictions, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
     	self.add_accuracy(train_model, predictions, label, device_opts, eval_metric)
     	with core.DeviceScope(device_opts):
@@ -233,7 +230,7 @@ class CNNCreator_Alexnet:
     	# == Testing model. ==
     	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
     	data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	predictions = self.create_model(test_model, data, device_opts=device_opts)
+    	predictions = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
     	self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
     	workspace.RunNetOnce(test_model.param_init_net)
     	workspace.CreateNet(test_model.net, overwrite=True)
@@ -251,7 +248,7 @@ class CNNCreator_Alexnet:
     	# == Deployment model. ==
     	# We simply need the main AddModel part.
     	deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
-    	self.create_model(deploy_model, "data", device_opts)
+    	self.create_model(deploy_model, "data", device_opts, is_test=True)
 
     	print("Saving deploy model")
     	self.save_net(self._init_net_, self._predict_net_, deploy_model)

src/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py

@@ -7,6 +7,7 @@ import logging
 import os
 import sys
 import lmdb
+
 class CNNCreator_CifarClassifierNetwork:
 
     module = None
@@ -58,7 +59,7 @@ class CNNCreator_CifarClassifierNetwork:
 
             return data, label, dataset_size
 
-    def create_model(self, model, data, device_opts):
+    def create_model(self, model, data, device_opts, is_test):
     	with core.DeviceScope(device_opts):
 
     		data = data
@@ -231,9 +232,7 @@ class CNNCreator_CifarClassifierNetwork:
     		# globalpooling31_, output shape: {[64,1,1]}
     		fc31_ = brew.fc(model, globalpooling31_, 'fc31_', dim_in=64, dim_out=128)
     		# fc31_, output shape: {[128,1,1]}
-    		dropout31_ = mx.symbol.Dropout(data=fc31_,
-    		    p=0.5,
-    		    name="dropout31_")
+    		dropout31_ = brew.dropout(model, fc31_, 'dropout31_', ratio=0.5, is_test=False)
     		fc32_ = brew.fc(model, dropout31_, 'fc32_', dim_in=128, dim_out=10)
     		# fc32_, output shape: {[10,1,1]}
     		softmax = brew.softmax(model, fc32_, 'softmax')
@@ -295,7 +294,7 @@ class CNNCreator_CifarClassifierNetwork:
     	# == Training model ==
     	train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
     	data, label, train_dataset_size = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'train_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	softmax = self.create_model(train_model, data, device_opts=device_opts)
+    	softmax = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
     	self.add_training_operators(train_model, softmax, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
     	self.add_accuracy(train_model, softmax, label, device_opts, eval_metric)
     	with core.DeviceScope(device_opts):
@@ -318,7 +317,7 @@ class CNNCreator_CifarClassifierNetwork:
     	# == Testing model. ==
     	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
     	data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	softmax = self.create_model(test_model, data, device_opts=device_opts)
+    	softmax = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
     	self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
     	workspace.RunNetOnce(test_model.param_init_net)
     	workspace.CreateNet(test_model.net, overwrite=True)
@@ -336,7 +335,7 @@ class CNNCreator_CifarClassifierNetwork:
     	# == Deployment model. ==
     	# We simply need the main AddModel part.
     	deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
-    	self.create_model(deploy_model, "data", device_opts)
+    	self.create_model(deploy_model, "data", device_opts, is_test=True)
 
     	print("Saving deploy model")
     	self.save_net(self._init_net_, self._predict_net_, deploy_model)

src/test/resources/target_code/CNNCreator_VGG16.py

@@ -7,6 +7,7 @@ import logging
 import os
 import sys
 import lmdb
+
 class CNNCreator_VGG16:
 
     module = None
@@ -58,7 +59,7 @@ class CNNCreator_VGG16:
 
             return data, label, dataset_size
 
-    def create_model(self, model, data, device_opts):
+    def create_model(self, model, data, device_opts, is_test):
     	with core.DeviceScope(device_opts):
 
     		data = data
@@ -115,15 +116,11 @@ class CNNCreator_VGG16:
     		fc13_ = brew.fc(model, pool13_, 'fc13_', dim_in=512 * 7 * 7, dim_out=4096)
     		# fc13_, output shape: {[4096,1,1]}
     		relu14_ = brew.relu(model, fc13_, fc13_)
-    		dropout14_ = mx.symbol.Dropout(data=relu14_,
-    		    p=0.5,
-    		    name="dropout14_")
+    		dropout14_ = brew.dropout(model, relu14_, 'dropout14_', ratio=0.5, is_test=False)
     		fc14_ = brew.fc(model, dropout14_, 'fc14_', dim_in=4096, dim_out=4096)
     		# fc14_, output shape: {[4096,1,1]}
     		relu15_ = brew.relu(model, fc14_, fc14_)
-    		dropout15_ = mx.symbol.Dropout(data=relu15_,
-    		    p=0.5,
-    		    name="dropout15_")
+    		dropout15_ = brew.dropout(model, relu15_, 'dropout15_', ratio=0.5, is_test=False)
     		fc15_ = brew.fc(model, dropout15_, 'fc15_', dim_in=4096, dim_out=1000)
     		# fc15_, output shape: {[1000,1,1]}
     		predictions = brew.softmax(model, fc15_, 'predictions')
@@ -185,7 +182,7 @@ class CNNCreator_VGG16:
     	# == Training model ==
     	train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
     	data, label, train_dataset_size = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'train_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	predictions = self.create_model(train_model, data, device_opts=device_opts)
+    	predictions = self.create_model(train_model, data, device_opts=device_opts, is_test=False)
     	self.add_training_operators(train_model, predictions, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
     	self.add_accuracy(train_model, predictions, label, device_opts, eval_metric)
     	with core.DeviceScope(device_opts):
@@ -208,7 +205,7 @@ class CNNCreator_VGG16:
     	# == Testing model. ==
     	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
     	data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
-    	predictions = self.create_model(test_model, data, device_opts=device_opts)
+    	predictions = self.create_model(test_model, data, device_opts=device_opts, is_test=True)
     	self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
     	workspace.RunNetOnce(test_model.param_init_net)
     	workspace.CreateNet(test_model.net, overwrite=True)
@@ -226,7 +223,7 @@ class CNNCreator_VGG16:
     	# == Deployment model. ==
     	# We simply need the main AddModel part.
     	deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
-    	self.create_model(deploy_model, "data", device_opts)
+    	self.create_model(deploy_model, "data", device_opts, is_test=True)
 
     	print("Saving deploy model")
     	self.save_net(self._init_net_, self._predict_net_, deploy_model)

src/test/resources/target_code/CNNTrainer_fullConfig.py

@@ -21,8 +21,9 @@ if __name__ == "__main__":
         opt_type='rmsprop',
         epsilon=1.0E-6,
         weight_decay=0.01,
-        gamma=0.9,
+        gamma1=0.9,
         policy='step',
         base_learning_rate=0.001,
+        gamma=0.9,
         stepsize=1000
     )