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author | Nikhil Raj <nikhil.raj@arm.com> | 2022-12-05 11:24:35 +0000 |
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committer | Nikhil Raj Arm <nikhil.raj@arm.com> | 2022-12-23 10:28:30 +0000 |
commit | 9a33946fd0d5e14be6f957b5a985438fa69684d6 (patch) | |
tree | 07b93ffccc31c2183567fda3523d79a82510745b /src/backends/backendsCommon/test/MultiplicationEndToEndTestImpl.hpp | |
parent | 259adafd6840c612c2eb64653290cbe2cfa7cd8e (diff) | |
download | armnn-9a33946fd0d5e14be6f957b5a985438fa69684d6.tar.gz |
IVGCVSW-7172 Add ElementwiseBinary (Subtraction & Multiplication) support to TOSA Reference Backend
* Removed AdditionOperator and moved to new ElementwiseBinaryOperator.
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I8ce20f7575d68334aadcd176827bca3db53d0052
Diffstat (limited to 'src/backends/backendsCommon/test/MultiplicationEndToEndTestImpl.hpp')
-rw-r--r-- | src/backends/backendsCommon/test/MultiplicationEndToEndTestImpl.hpp | 96 |
1 files changed, 96 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/MultiplicationEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/MultiplicationEndToEndTestImpl.hpp new file mode 100644 index 0000000000..40442e2d47 --- /dev/null +++ b/src/backends/backendsCommon/test/MultiplicationEndToEndTestImpl.hpp @@ -0,0 +1,96 @@ +// +// Copyright © 2022 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 CreateMultiplicationNetwork(const armnn::TensorShape& inputXShape, + const armnn::TensorShape& inputYShape, + const armnn::TensorShape& outputShape, + const float qScale = 1.0f, + const int32_t qOffset = 0) +{ + using namespace armnn; + + INetworkPtr network(INetwork::Create()); + + TensorInfo inputXTensorInfo(inputXShape, DataType, qScale, qOffset, true); + TensorInfo inputYTensorInfo(inputYShape, DataType, qScale, qOffset, true); + + TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset); + + + IConnectableLayer* multiplication = network->AddMultiplicationLayer("multiplication"); + IConnectableLayer* inputX = network->AddInputLayer(0, "inputX"); + IConnectableLayer* inputY = network->AddInputLayer(1, "inputY"); + IConnectableLayer* output = network->AddOutputLayer(0, "output"); + + Connect(inputX, multiplication, inputXTensorInfo, 0, 0); + Connect(inputY, multiplication, inputYTensorInfo, 0, 1); + Connect(multiplication, output, outputTensorInfo, 0, 0); + + return network; +} + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +void MultiplicationEndToEnd(const std::vector<armnn::BackendId>& backends) +{ + using namespace armnn; + + const TensorShape& inputXShape = { 2, 2 }; + const TensorShape& inputYShape = { 2, 2 }; + const TensorShape& outputShape = { 2, 2 }; + + INetworkPtr network = CreateMultiplicationNetwork<ArmnnType>(inputXShape, inputYShape, outputShape); + + CHECK(network); + + std::vector<T> inputXData{ 1, 2, 3, 4 }; + std::vector<T> inputYData{ 5, 2, 6, 3 }; + std::vector<T> expectedOutput{ 5, 4, 18, 12 }; + + std::map<int, std::vector<T>> inputTensorData = {{ 0, inputXData }, {1, inputYData}}; + std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } }; + + EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); +} + +template<armnn::DataType ArmnnType> +void MultiplicationEndToEndFloat16(const std::vector<armnn::BackendId>& backends) +{ + using namespace armnn; + using namespace half_float::literal; + using Half = half_float::half; + + const TensorShape& inputXShape = { 2, 2 }; + const TensorShape& inputYShape = { 2, 2 }; + const TensorShape& outputShape = { 2, 2 }; + + INetworkPtr network = CreateMultiplicationNetwork<ArmnnType>(inputXShape, inputYShape, outputShape); + CHECK(network); + + std::vector<Half> inputXData{ 1._h, 2._h, + 3._h, 4._h }; + std::vector<Half> inputYData{ 1._h, 2._h, + 3._h, 4._h }; + std::vector<Half> expectedOutput{ 1._h, 4._h, + 9._h, 16._h }; + + std::map<int, std::vector<Half>> inputTensorData = {{ 0, inputXData }, { 1, inputYData }}; + std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } }; + + EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); +} + +} // anonymous namespace |