1 files changed, 77 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/ElementwiseUnaryEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/ElementwiseUnaryEndToEndTestImpl.hpp
new file mode 100644
index 0000000000..4c93735bc8
--- /dev/null
+++ b/src/backends/backendsCommon/test/ElementwiseUnaryEndToEndTestImpl.hpp
@@ -0,0 +1,77 @@
+//
+// Copyright © 2019 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#pragma once
+
+#include "CommonTestUtils.hpp"
+
+#include <ResolveType.hpp>
+
+#include <armnn/INetwork.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+#include <vector>
+
+namespace
+{
+
+template<armnn::DataType ArmnnTypeInput>
+INetworkPtr CreateElementwiseUnaryNetwork(const TensorShape& inputShape,
+                                          const TensorShape& outputShape,
+                                          UnaryOperation operation,
+                                          const float qScale = 1.0f,
+                                          const int32_t qOffset = 0)
+{
+    using namespace armnn;
+
+    INetworkPtr net(INetwork::Create());
+
+    ElementwiseUnaryDescriptor descriptor(operation);
+    IConnectableLayer* elementwiseUnaryLayer = net->AddElementwiseUnaryLayer(descriptor, "elementwiseUnary");
+
+    TensorInfo inputTensorInfo(inputShape, ArmnnTypeInput, qScale, qOffset);
+    IConnectableLayer* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(0));
+    Connect(input, elementwiseUnaryLayer, inputTensorInfo, 0, 0);
+
+    TensorInfo outputTensorInfo(outputShape, ArmnnTypeInput, qScale, qOffset);
+    IConnectableLayer* output = net->AddOutputLayer(0, "output");
+    Connect(elementwiseUnaryLayer, output, outputTensorInfo, 0, 0);
+
+    return net;
+}
+
+template<armnn::DataType ArmnnInType,
+         typename TInput = armnn::ResolveType<ArmnnInType>>
+void ElementwiseUnarySimpleEndToEnd(const std::vector<BackendId>& backends,
+                                    UnaryOperation operation,
+                                    const std::vector<float> expectedOutput)
+{
+    using namespace armnn;
+
+    const float   qScale  = IsQuantizedType<TInput>() ? 0.25f : 1.0f;
+    const int32_t qOffset = IsQuantizedType<TInput>() ? 50    : 0;
+
+    const TensorShape& inputShape  = { 2, 2, 2, 2 };
+    const TensorShape& outputShape = { 2, 2, 2, 2 };
+
+    // Builds up the structure of the network
+    INetworkPtr net = CreateElementwiseUnaryNetwork<ArmnnInType>(inputShape, outputShape, operation, qScale, qOffset);
+
+    BOOST_TEST_CHECKPOINT("create a network");
+
+    const std::vector<float> input({ 1, -1, 1, 1,  5, -5, 5, 5,
+                                       -3, 3, 3, 3,  4, 4, -4, 4 });
+
+    // quantize data
+    std::vector<TInput> qInputData      = armnnUtils::QuantizedVector<TInput>(input, qScale, qOffset);
+    std::vector<TInput> qExpectedOutput = armnnUtils::QuantizedVector<TInput>(expectedOutput, qScale, qOffset);
+
+    std::map<int, std::vector<TInput>> inputTensorData    = {{ 0, qInputData }};
+    std::map<int, std::vector<TInput>> expectedOutputData = {{ 0, qExpectedOutput }};
+
+    EndToEndLayerTestImpl<ArmnnInType, ArmnnInType>(move(net), inputTensorData, expectedOutputData, backends);
+}
+
+} // anonymous namespace