1 files changed, 70 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/ReduceEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/ReduceEndToEndTestImpl.hpp
new file mode 100644
index 0000000000..83c59f594f
--- /dev/null
+++ b/src/backends/backendsCommon/test/ReduceEndToEndTestImpl.hpp
@@ -0,0 +1,70 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include <armnn/INetwork.hpp>
+
+#include <CommonTestUtils.hpp>
+#include <ResolveType.hpp>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+
+template<typename armnn::DataType DataType>
+armnn::INetworkPtr CreateReduceNetwork(const armnn::TensorShape& inputShape,
+                                        const armnn::TensorShape& outputShape,
+                                        const armnn::ReduceDescriptor& descriptor,
+                                        const float qScale = 1.0f,
+                                        const int32_t qOffset = 0)
+{
+    using namespace armnn;
+
+    INetworkPtr network(INetwork::Create());
+
+    TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true);
+    TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset);
+
+
+    IConnectableLayer* reduce = network->AddReduceLayer(descriptor, "reduce");
+    IConnectableLayer* input   = network->AddInputLayer(0, "input");
+    IConnectableLayer* output  = network->AddOutputLayer(0, "output");
+
+    Connect(input, reduce, inputTensorInfo, 0, 0);
+    Connect(reduce, output, outputTensorInfo, 0, 0);
+
+    return network;
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+void ReduceEndToEnd(const std::vector<armnn::BackendId>& backends)
+{
+    using namespace armnn;
+
+    const TensorShape& inputShape = { 1, 1, 1, 5 };
+    const TensorShape& outputShape = { 1, 1, 1 };
+
+    ReduceDescriptor descriptor;
+    descriptor.m_KeepDims = false;
+    descriptor.m_vAxis = { 3 };
+    descriptor.m_ReduceOperation = ReduceOperation::Sum;
+
+    INetworkPtr network = CreateReduceNetwork<ArmnnType>(inputShape, outputShape, descriptor);
+
+    CHECK(network);
+
+    std::vector<float> floatInputData({ 5.0f, 2.0f, 8.0f, 10.0f, 9.0f });
+    std::vector<float> floatOutputData({ 34.0f });
+
+    std::vector<T> inputData = armnnUtils::QuantizedVector<T>(floatInputData);
+    std::vector<T> outputData = armnnUtils::QuantizedVector<T>(floatOutputData);
+
+    std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };
+    std::map<int, std::vector<T>> expectedOutputData = { { 0, outputData } };
+
+    EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends);
+}
+} // anonymous namespace