2 files changed, 26 insertions, 15 deletions

diff --git a/src/armnn/test/optimizations/FoldPadTests.cpp b/src/armnn/test/optimizations/FoldPadTests.cpp
index 027b10377d..14c211f9bf 100644
--- a/src/armnn/test/optimizations/FoldPadTests.cpp
+++ b/src/armnn/test/optimizations/FoldPadTests.cpp
@@ -636,13 +636,9 @@ TEST_CASE("FoldPadLayerIntoConv2dLayer_ExecuteInferenceWithAndWithoutOptimizatio
         std::vector<float>    biasVector   = {5, 6, 7, 8};
         TensorInfo            biasInfo({4}, DataType::Float32, 0.0f, 0, true);
         ConstTensor           bias(biasInfo, biasVector);
-        Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias);
-        ARMNN_NO_DEPRECATE_WARN_BEGIN
-        IConnectableLayer* conv2dLayer = network->AddConvolution2dLayer(convDescriptor,
-                                                                        weights,
-                                                                        optionalBias,
-                                                                        "Conv2D");
-        ARMNN_NO_DEPRECATE_WARN_END
+
+        IConnectableLayer* conv2dLayer = network->AddConvolution2dLayer(convDescriptor, "Conv2D");
+
         TensorInfo outputInfo(4, outputShape, DataType::Float32);
         conv2dLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
 
@@ -653,6 +649,14 @@ TEST_CASE("FoldPadLayerIntoConv2dLayer_ExecuteInferenceWithAndWithoutOptimizatio
         padLayer->GetOutputSlot(0).Connect(conv2dLayer->GetInputSlot(0));
         conv2dLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
 
+        auto weightsLayer = network->AddConstantLayer(weights, "Weights");
+        weightsLayer->GetOutputSlot(0).SetTensorInfo(weights.GetInfo());
+        weightsLayer->GetOutputSlot(0).Connect(conv2dLayer->GetInputSlot(1));
+
+        auto biasLayer = network->AddConstantLayer(bias, "Bias");
+        biasLayer->GetOutputSlot(0).SetTensorInfo(bias.GetInfo());
+        biasLayer->GetOutputSlot(0).Connect(conv2dLayer->GetInputSlot(2));
+
         // Create ArmNN runtime
         IRuntimePtr          run              = IRuntime::Create(IRuntime::CreationOptions());    // default options
         // Optimise the network
diff --git a/src/armnn/test/optimizations/FuseBatchNormTests.cpp b/src/armnn/test/optimizations/FuseBatchNormTests.cpp
index 4a94f7889b..54cbbce89f 100644
--- a/src/armnn/test/optimizations/FuseBatchNormTests.cpp
+++ b/src/armnn/test/optimizations/FuseBatchNormTests.cpp
@@ -31,9 +31,10 @@ public:
                                              const Optional<ConstTensor> &biases,
                                              const char *name)
     {
-        ARMNN_NO_DEPRECATE_WARN_BEGIN
-        return network->AddConvolution2dLayer(descriptor, weights, biases, name);
-        ARMNN_NO_DEPRECATE_WARN_END
+        IgnoreUnused(weights);
+        IgnoreUnused(biases);
+
+        return network->AddConvolution2dLayer(descriptor, name);
     }
 
     static std::vector<IConnectableLayer*> AddConstantLayers(INetwork *network,
@@ -41,12 +42,18 @@ public:
                                                              const ConstTensor &weights,
                                                              const Optional<ConstTensor> &biases)
     {
-        IgnoreUnused(network);
-        IgnoreUnused(descriptor);
-        IgnoreUnused(weights);
-        IgnoreUnused(biases);
+        auto weightsLayer = network->AddConstantLayer(weights, "Weights");
+        weightsLayer->GetOutputSlot(0).SetTensorInfo(weights.GetInfo());
+        std::vector<IConnectableLayer*> layers = {weightsLayer};
 
-        return {};
+        if (descriptor.m_BiasEnabled)
+        {
+            auto biasLayer = network->AddConstantLayer(biases.value(), "Bias");
+            biasLayer->GetOutputSlot(0).SetTensorInfo(biases.value().GetInfo());
+            layers.emplace_back(biasLayer);
+        }
+
+        return layers;
     }
 };