| // |
| // Copyright © 2019-2021,2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include <CommonTestUtils.hpp> |
| |
| #include <ResolveType.hpp> |
| |
| #include <armnn/INetwork.hpp> |
| |
| #include <armnn/utility/NumericCast.hpp> |
| |
| #include <doctest/doctest.h> |
| |
| #include <vector> |
| |
| namespace |
| { |
| |
| template<armnn::DataType ArmnnTypeInput> |
| INetworkPtr CreateComparisonNetwork(const std::vector<TensorShape>& inputShapes, |
| const TensorShape& outputShape, |
| ComparisonOperation operation, |
| const float qScale = 1.0f, |
| const int32_t qOffset = 0) |
| { |
| using namespace armnn; |
| |
| INetworkPtr net(INetwork::Create()); |
| |
| ComparisonDescriptor descriptor(operation); |
| IConnectableLayer* comparisonLayer = net->AddComparisonLayer(descriptor, "comparison"); |
| |
| for (unsigned int i = 0; i < inputShapes.size(); ++i) |
| { |
| TensorInfo inputTensorInfo(inputShapes[i], ArmnnTypeInput, qScale, qOffset, true); |
| IConnectableLayer* input = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(i)); |
| Connect(input, comparisonLayer, inputTensorInfo, 0, i); |
| } |
| |
| TensorInfo outputTensorInfo(outputShape, DataType::Boolean, qScale, qOffset); |
| IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| Connect(comparisonLayer, output, outputTensorInfo, 0, 0); |
| |
| return net; |
| } |
| |
| template<armnn::DataType ArmnnInType, |
| typename TInput = armnn::ResolveType<ArmnnInType>> |
| void ComparisonSimpleEndToEnd(const std::vector<BackendId>& backends, |
| ComparisonOperation operation, |
| const std::vector<uint8_t> expectedOutput) |
| { |
| using namespace armnn; |
| |
| const std::vector<TensorShape> inputShapes{{ 2, 2, 2, 2 }, { 2, 2, 2, 2 }}; |
| const TensorShape& outputShape = { 2, 2, 2, 2 }; |
| |
| // Builds up the structure of the network |
| INetworkPtr net = CreateComparisonNetwork<ArmnnInType>(inputShapes, outputShape, operation); |
| |
| CHECK(net); |
| |
| const std::vector<TInput> input0({ 1, 1, 1, 1, 5, 5, 5, 5, |
| 3, 3, 3, 3, 4, 4, 4, 4 }); |
| |
| const std::vector<TInput> input1({ 1, 1, 1, 1, 3, 3, 3, 3, |
| 5, 5, 5, 5, 4, 4, 4, 4 }); |
| |
| std::map<int, std::vector<TInput>> inputTensorData = {{ 0, input0 }, { 1, input1 }}; |
| std::map<int, std::vector<uint8_t>> expectedOutputData = {{ 0, expectedOutput }}; |
| |
| EndToEndLayerTestImpl<ArmnnInType, DataType::Boolean>(std::move(net), inputTensorData, expectedOutputData, |
| backends); |
| } |
| |
| template<armnn::DataType ArmnnInType, |
| typename TInput = armnn::ResolveType<ArmnnInType>> |
| void ComparisonBroadcastEndToEnd(const std::vector<BackendId>& backends, |
| ComparisonOperation operation, |
| const std::vector<uint8_t> expectedOutput) |
| { |
| using namespace armnn; |
| |
| const std::vector<TensorShape> inputShapes{{ 1, 2, 2, 3 }, { 1, 1, 1, 3 }}; |
| const TensorShape& outputShape = { 1, 2, 2, 3 }; |
| |
| // Builds up the structure of the network |
| INetworkPtr net = CreateComparisonNetwork<ArmnnInType>(inputShapes, outputShape, operation); |
| |
| const std::vector<TInput> input0({ 1, 2, 3, 1, 0, 6, |
| 7, 8, 9, 10, 11, 12 }); |
| |
| const std::vector<TInput> input1({ 1, 1, 3 }); |
| |
| std::map<int, std::vector<TInput>> inputTensorData = {{ 0, input0 }, { 1, input1 }}; |
| std::map<int, std::vector<uint8_t>> expectedOutputData = {{ 0, expectedOutput }}; |
| |
| EndToEndLayerTestImpl<ArmnnInType, DataType::Boolean>(std::move(net), inputTensorData, expectedOutputData, |
| backends); |
| } |
| |
| } // anonymous namespace |