1 files changed, 66 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp
index 0d47fd6056..dfc7bfc18e 100644
--- a/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp
+++ b/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp
@@ -110,4 +110,70 @@ void ElementwiseBinarySimpleEndToEnd(const std::vector<BackendId>& backends,
     EndToEndLayerTestImpl<ArmnnInType, ArmnnInType>(std::move(net), inputTensorData, expectedOutputData, backends);
 }
 
+template<armnn::DataType ArmnnInType,
+        typename TInput = armnn::ResolveType<ArmnnInType>>
+void ElementwiseBinarySimpleNoReshapeEndToEnd(const std::vector<BackendId>& backends,
+                                              BinaryOperation operation)
+{
+    using namespace armnn;
+
+    const float   qScale  = IsQuantizedType<TInput>() ? 0.25f : 1.0f;
+    const int32_t qOffset = IsQuantizedType<TInput>() ? 50    : 0;
+
+    const TensorShape& input1Shape  = { 2, 2, 2, 2 };
+    const TensorShape& input2Shape  = { 2, 2, 2, 2 };
+    const TensorShape& outputShape = { 2, 2, 2, 2 };
+
+    // Builds up the structure of the network
+    INetworkPtr net = CreateElementwiseBinaryNetwork<ArmnnInType>(input1Shape, input2Shape, outputShape,
+                                                                  operation, qScale, qOffset);
+
+    CHECK(net);
+
+    const std::vector<float> input1({ 1, -1, 1, 1,  5, -5, 5, 5,  -3, 3, 3, 3,  4, 4, -4, 4 });
+
+    const std::vector<float> input2({ 2, 2, 2, 2,   2, 2, 2, 2,    2, 2, 2, 2,  2, 2, 2, 2 });
+
+    std::vector<float> expectedOutput;
+    switch (operation) {
+        case armnn::BinaryOperation::Add:
+            expectedOutput = { 3, 1, 3, 3,  7, -3, 7, 7,  -1, 5, 5, 5,  6, 6, -2, 6 };
+            break;
+        case armnn::BinaryOperation::Div:
+            expectedOutput = {0.5f, -0.5f, 0.5f, 0.5f, 2.5f, -2.5f, 2.5f, 2.5f, -1.5f, 1.5f, 1.5f, 1.5f, 2, 2, -2, 2};
+            break;
+        case armnn::BinaryOperation::Maximum:
+            expectedOutput = { 2, 2, 2, 2,  5, 2, 5, 5,  2, 3, 3, 3,  4, 4, 2, 4 };
+            break;
+        case armnn::BinaryOperation::Minimum:
+            expectedOutput = { 1, -1, 1, 1,  2, -5, 2, 2,  -3, 2, 2, 2,  2, 2, -4, 2 };
+            break;
+        case armnn::BinaryOperation::Mul:
+            expectedOutput = { 2, -2, 2, 2,  10, -10, 10, 10,  -6, 6, 6, 6,  8, 8, -8, 8 };
+            break;
+        case armnn::BinaryOperation::Sub:
+            expectedOutput = { -1, -3, -1, -1,  3, -7, 3, 3,  -5, 1, 1, 1,  2, 2, -6, 2 };
+            break;
+        case armnn::BinaryOperation::SqDiff:
+            expectedOutput = { 1, 9, 1, 1,  9, 49, 9, 9, 25, 1, 1, 1,  4, 4, 36, 4  };
+            break;
+        case armnn::BinaryOperation::Power:
+            expectedOutput = { 1, 1, 1, 1, 25, 25, 25, 25,  9, 9, 9, 9,  16, 16, 16, 16 };
+            break;
+        default:
+            throw("Invalid Elementwise Binary operation");
+    }
+
+    const std::vector<float> expectedOutput_const = expectedOutput;
+    // quantize data
+    std::vector<TInput> qInput1Data     = armnnUtils::QuantizedVector<TInput>(input1, qScale, qOffset);
+    std::vector<TInput> qInput2Data     = armnnUtils::QuantizedVector<TInput>(input2, qScale, qOffset);
+    std::vector<TInput> qExpectedOutput = armnnUtils::QuantizedVector<TInput>(expectedOutput_const, qScale, qOffset);
+
+    std::map<int, std::vector<TInput>> inputTensorData    = {{ 0, qInput1Data }, { 1, qInput2Data }};
+    std::map<int, std::vector<TInput>> expectedOutputData = {{ 0, qExpectedOutput }};
+
+    EndToEndLayerTestImpl<ArmnnInType, ArmnnInType>(std::move(net), inputTensorData, expectedOutputData, backends);
+}
+
 } // anonymous namespace