diff options
Diffstat (limited to 'src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp | 67 |
1 files changed, 66 insertions, 1 deletions
diff --git a/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp index dfc7bfc18e..5138e496e8 100644 --- a/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/ElementwiseBinaryEndToEndTestImpl.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2023 Arm Ltd and contributors. All rights reserved. +// Copyright © 2023-2024 Arm Ltd and contributors. All rights reserved. // SPDX-License-Identifier: MIT // #pragma once @@ -110,6 +110,71 @@ 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 ElementwiseBinarySimple3DEndToEnd(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 }; + const TensorShape& input2Shape = { 2, 2 }; + const TensorShape& outputShape = { 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 }); + + const std::vector<float> input2({ 2, 2, 2, 2 }); + std::vector<float> expectedOutput; + switch (operation) { + case armnn::BinaryOperation::Add: + expectedOutput = { 3, 1, 3, 3 }; + break; + case armnn::BinaryOperation::Div: + expectedOutput = {0.5f, -0.5f, 0.5f, 0.5f }; + break; + case armnn::BinaryOperation::Maximum: + expectedOutput = { 2, 2, 2, 2 }; + break; + case armnn::BinaryOperation::Minimum: + expectedOutput = { 1, -1, 1, 1 }; + break; + case armnn::BinaryOperation::Mul: + expectedOutput = { 2, -2, 2, 2 }; + break; + case armnn::BinaryOperation::Sub: + expectedOutput = { -1, -3, -1, -1 }; + break; + case armnn::BinaryOperation::SqDiff: + expectedOutput = { 1, 9, 1, 1 }; + break; + case armnn::BinaryOperation::Power: + expectedOutput = { 1, 1, 1, 1 }; + 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); +} + template<armnn::DataType ArmnnInType, typename TInput = armnn::ResolveType<ArmnnInType>> void ElementwiseBinarySimpleNoReshapeEndToEnd(const std::vector<BackendId>& backends, |