1 files changed, 83 insertions, 0 deletions
diff --git a/src/armnn/test/QuantizerTest.cpp b/src/armnn/test/QuantizerTest.cpp
index 56b1497967..fbafbd8f1e 100644
--- a/src/armnn/test/QuantizerTest.cpp
+++ b/src/armnn/test/QuantizerTest.cpp
@@ -92,5 +92,88 @@ BOOST_AUTO_TEST_CASE(QuantizeAddition)
     VisitLayersTopologically(quantizedNetwork.get(), validator);
 }
 
+BOOST_AUTO_TEST_CASE(QuantizeBatchNorm)
+{
+
+    class TestQuantization : public LayerVisitorBase<VisitorThrowingPolicy>
+    {
+    public:
+        virtual void VisitBatchNormalizationLayer(const IConnectableLayer* layer,
+                                                  const BatchNormalizationDescriptor& desc,
+                                                  const ConstTensor& mean,
+                                                  const ConstTensor& variance,
+                                                  const ConstTensor& beta,
+                                                  const ConstTensor& gamma,
+                                                  const char* name = nullptr)
+        {
+            TensorInfo info = layer->GetOutputSlot(0).GetTensorInfo();
+
+            BOOST_TEST((info.GetDataType() == DataType::QuantisedAsymm8));
+
+            BOOST_TEST((info.GetQuantizationOffset() == 128));
+
+            // Based off current static value [-15.0f, 15.0f]
+            BOOST_CHECK_CLOSE(info.GetQuantizationScale(), 30.0f/255.0f, 0.000001f );
+
+            //Test constants
+            BOOST_TEST((mean.GetInfo().GetDataType() == DataType::QuantisedAsymm8));
+            BOOST_TEST((variance.GetInfo().GetDataType() == DataType::QuantisedAsymm8));
+            BOOST_TEST((beta.GetInfo().GetDataType() == DataType::QuantisedAsymm8));
+            BOOST_TEST((gamma.GetInfo().GetDataType() == DataType::QuantisedAsymm8));
+
+            BOOST_CHECK_CLOSE(mean.GetInfo().GetQuantizationScale(), 3.0f/255.0f, 0.000001f);
+            BOOST_CHECK_CLOSE(variance.GetInfo().GetQuantizationScale(), 3.0f/255.0f, 0.000001f);
+            BOOST_CHECK_CLOSE(beta.GetInfo().GetQuantizationScale(), 3.0f/255.0f, 0.000001f);
+            BOOST_CHECK_CLOSE(gamma.GetInfo().GetQuantizationScale(), 3.0f/255.0f, 0.000001f);
+
+            BOOST_TEST((mean.GetInfo().GetQuantizationOffset() == 85));
+        }
+
+        virtual void VisitInputLayer(const IConnectableLayer* layer,
+                                     LayerBindingId id,
+                                     const char* name = nullptr)
+        {}
+
+        virtual void VisitOutputLayer(const IConnectableLayer* layer,
+                                      LayerBindingId id,
+                                      const char* name = nullptr)
+        {}
+    };
+
+    auto network = INetwork::Create();
+
+    TensorShape shape{3U};
+    TensorInfo info(shape, DataType::Float32);
+
+    std::vector<float> meanData{-1.0f, 1.5f, 2.0f};
+    std::vector<float> varData{-1.0f, 1.5f, 2.0f};
+    std::vector<float> betaData{-1.0f, 1.5f, 2.0f};
+    std::vector<float> gammaData{-1.0f, 1.5f, 2.0f};
+
+    ConstTensor mean(info, meanData);
+    ConstTensor var(info, varData);
+    ConstTensor beta(info, betaData);
+    ConstTensor gamma(info, gammaData);
+
+    BatchNormalizationDescriptor desc;
+
+    // Add the layers
+    IConnectableLayer* input0 = network->AddInputLayer(0);
+    IConnectableLayer* batchNorm = network->AddBatchNormalizationLayer(desc, mean, var, beta, gamma);
+    IConnectableLayer* output = network->AddOutputLayer(1);
+
+    // Establish connections
+    input0->GetOutputSlot(0).Connect(batchNorm->GetInputSlot(0));
+    batchNorm->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+    //Set TensorInfo
+    input0->GetOutputSlot(0).SetTensorInfo(info);
+    batchNorm->GetOutputSlot(0).SetTensorInfo(info);
+
+    auto quantizedNetwork = INetworkQuantizer::Create(network.get())->ExportNetwork();
+    TestQuantization validator;
+    VisitLayersTopologically(quantizedNetwork.get(), validator);
+}
+
 BOOST_AUTO_TEST_SUITE_END()
 } //namespace armnn