| // |
| // Copyright © 2021 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 |
| { |
| |
| armnn::INetworkPtr CreateFullyConnectedNetworkNonConstWeights(const armnn::TensorInfo& inputTensorInfo, |
| const armnn::TensorInfo& outputTensorInfo, |
| const armnn::TensorInfo& weightsTensorInfo, |
| armnn::FullyConnectedDescriptor descriptor) |
| { |
| armnn::INetworkPtr network(armnn::INetwork::Create()); |
| |
| armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input"); |
| armnn::IConnectableLayer* weightsInputLayer = network->AddInputLayer(1, "Weights_Input"); |
| armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected"); |
| armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output"); |
| |
| Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0); |
| Connect(weightsInputLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1); |
| Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0); |
| |
| return network; |
| } |
| |
| armnn::INetworkPtr CreateFullyConnectedNetworkNonConstWeightsConstBias(const armnn::TensorInfo& inputTensorInfo, |
| const armnn::TensorInfo& outputTensorInfo, |
| const armnn::TensorInfo& weightsTensorInfo, |
| const armnn::TensorInfo& biasTensorInfo, |
| const armnn::ConstTensor& biasConstantTensor, |
| armnn::FullyConnectedDescriptor descriptor) |
| { |
| armnn::INetworkPtr network(armnn::INetwork::Create()); |
| |
| armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input"); |
| armnn::IConnectableLayer* weightsInputLayer = network->AddInputLayer(1, "Weights_Input"); |
| armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biasConstantTensor, "Weights"); |
| armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected"); |
| armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output"); |
| |
| Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0); |
| Connect(weightsInputLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1); |
| Connect(biasLayer, fullyConnectedLayer, biasTensorInfo, 0, 2); |
| Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0); |
| |
| return network; |
| } |
| |
| armnn::INetworkPtr CreateFullyConnectedNetworkConstWeightsNonConstBias(const armnn::TensorInfo& inputTensorInfo, |
| const armnn::TensorInfo& outputTensorInfo, |
| const armnn::TensorInfo& weightsTensorInfo, |
| const armnn::TensorInfo& biasTensorInfo, |
| const armnn::ConstTensor& weightsConstantTensor, |
| armnn::FullyConnectedDescriptor descriptor) |
| { |
| armnn::INetworkPtr network(armnn::INetwork::Create()); |
| |
| armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input"); |
| armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weightsConstantTensor, "Weights"); |
| armnn::IConnectableLayer* biasLayer = network->AddInputLayer(2, "Bias_Input"); |
| armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected"); |
| armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output"); |
| |
| Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0); |
| Connect(weightsLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1); |
| Connect(biasLayer, fullyConnectedLayer, biasTensorInfo, 0, 2); |
| Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0); |
| |
| return network; |
| } |
| |
| armnn::INetworkPtr CreateFullyConnectedNetworkNoTensorInfoConstWeights(const armnn::TensorInfo& inputTensorInfo, |
| const armnn::TensorInfo& outputTensorInfo, |
| const armnn::ConstTensor& weightsConstantTensor, |
| armnn::FullyConnectedDescriptor descriptor) |
| { |
| armnn::INetworkPtr network(armnn::INetwork::Create()); |
| |
| armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input"); |
| armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weightsConstantTensor, "Weights"); |
| armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected"); |
| armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output"); |
| |
| Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0); |
| weightsLayer->GetOutputSlot(0).Connect(fullyConnectedLayer->GetInputSlot(1)); |
| Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0); |
| |
| return network; |
| } |
| |
| armnn::INetworkPtr CreateFullyConnectedNetworkNoConnectedWeightsExplicit(const armnn::TensorInfo& inputTensorInfo, |
| const armnn::TensorInfo& outputTensorInfo, |
| const armnn::TensorInfo& biasTensorInfo, |
| armnn::FullyConnectedDescriptor descriptor) |
| { |
| armnn::INetworkPtr network(armnn::INetwork::Create()); |
| |
| |
| ConstTensor biases; |
| |
| armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input"); |
| armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biases, "Bias_Input"); |
| armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected"); |
| armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output"); |
| |
| Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0); |
| Connect(biasLayer, fullyConnectedLayer, biasTensorInfo, 0, 2); |
| Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0); |
| |
| return network; |
| } |
| |
| armnn::INetworkPtr CreateFullyConnectedNetworkNoConnectedWeightsAndBias(const armnn::TensorInfo& inputTensorInfo, |
| const armnn::TensorInfo& outputTensorInfo, |
| armnn::FullyConnectedDescriptor descriptor) |
| { |
| armnn::INetworkPtr network(armnn::INetwork::Create()); |
| |
| armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input"); |
| armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected"); |
| armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output"); |
| |
| Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0); |
| Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0); |
| |
| return network; |
| } |
| |
| armnn::INetworkPtr CreateFullyConnectedNetworkNoConnectedBiasExplicit(const armnn::TensorInfo& inputTensorInfo, |
| const armnn::TensorInfo& outputTensorInfo, |
| const armnn::TensorInfo& weightsTensorInfo, |
| const armnn::ConstTensor& weightsConstantTensor, |
| armnn::FullyConnectedDescriptor descriptor) |
| { |
| armnn::INetworkPtr network(armnn::INetwork::Create()); |
| |
| armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input"); |
| armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weightsConstantTensor, "Weights"); |
| armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected"); |
| armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output"); |
| |
| Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0); |
| Connect(weightsLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1); |
| Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0); |
| |
| return network; |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void FullyConnectedWithDynamicWeightsEndToEnd(const std::vector<armnn::BackendId>& backends) |
| { |
| using namespace armnn; |
| |
| armnn::TensorInfo inputTensorInfo({ 1, 1, 2, 3 }, ArmnnType); |
| inputTensorInfo.SetQuantizationScale(0.1f); |
| inputTensorInfo.SetQuantizationOffset(63); |
| inputTensorInfo.SetConstant(true); |
| |
| armnn::TensorInfo outputTensorInfo({ 1, 2 }, ArmnnType); |
| outputTensorInfo.SetQuantizationScale(5.f); |
| outputTensorInfo.SetQuantizationOffset(10); |
| |
| armnn::TensorInfo weightsTensorInfo({ 2, 6 }, ArmnnType); |
| weightsTensorInfo.SetQuantizationScale(0.2f); |
| weightsTensorInfo.SetQuantizationOffset(93); |
| weightsTensorInfo.SetConstant(true); |
| |
| FullyConnectedDescriptor descriptor; |
| descriptor.m_ConstantWeights = false; |
| descriptor.m_BiasEnabled = false; |
| descriptor.m_TransposeWeightMatrix = true; |
| |
| std::vector<T> inputData { |
| -1.2f, 6.1f, -3.5f, |
| 18.8f, -5.5f, 2.9f |
| }; |
| |
| std::vector<T> weightsData { |
| -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f, |
| 23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f |
| }; |
| |
| std::vector<T> floatExpectedOutputData { |
| -107.04f, 110.f |
| }; |
| std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>(floatExpectedOutputData); |
| |
| armnn::INetworkPtr network = CreateFullyConnectedNetworkNonConstWeights(inputTensorInfo, |
| outputTensorInfo, |
| weightsTensorInfo, |
| descriptor); |
| |
| CHECK(network); |
| |
| std::map<int, std::vector<T>> inputTensorData = {{ 0, inputData }, {1, weightsData}}; |
| std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutputData }}; |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network), |
| inputTensorData, |
| expectedOutputTensorData, |
| backends, |
| 1.0f); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void FullyConnectedWithDynamicOrConstantInputsEndToEnd(const std::vector<armnn::BackendId>& backends, |
| const bool transposeWeights, |
| const bool constantWeightsOrBias) |
| { |
| unsigned int inputWidth = 1; |
| unsigned int inputHeight = 1; |
| unsigned int inputChannels = 5; |
| unsigned int inputNum = 2; |
| |
| unsigned int outputChannels = 3; |
| unsigned int outputNum = 2; |
| |
| unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; |
| unsigned int outputShape[] = { outputNum, outputChannels }; |
| unsigned int weightsShape[] = { inputChannels, outputChannels }; |
| |
| if (transposeWeights) |
| { |
| std::swap(weightsShape[0], weightsShape[1]); |
| } |
| |
| unsigned int biasShape[] = { outputChannels }; |
| |
| armnn::TensorInfo inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32, 0.0f, 0, true); |
| armnn::TensorInfo outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32); |
| armnn::TensorInfo weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::DataType::Float32, 0.0f, 0, true); |
| armnn::TensorInfo biasesDesc = armnn::TensorInfo(1, biasShape, armnn::DataType::Float32, 0.0f, 0, true); |
| |
| std::vector<float> input = |
| { |
| 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, |
| 5.0f, 4.0f, 3.0f, 2.0f, 1.0f |
| }; |
| |
| std::vector<float> weights = |
| { |
| .5f, 2.f, .5f, |
| .5f, 2.f, 1.f, |
| .5f, 2.f, 2.f, |
| .5f, 2.f, 3.f, |
| .5f, 2.f, 4.f |
| }; |
| |
| if (transposeWeights) |
| { |
| weights = |
| { |
| .5f, .5f, .5f, .5f, .5f, |
| 2.f, 2.f, 2.f, 2.f, 2.f, |
| .5f, 1.f, 2.f, 3.f, 4.f |
| }; |
| } |
| |
| std::vector<float> biasValues = std::vector<float>({10.f, 20.f, 30.f}); |
| |
| std::vector<float> expectedOutput = |
| { |
| 0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0], |
| 2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1], |
| 0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2], |
| |
| 2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0], |
| 10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1], |
| 2.5f + 4.0f + 6.0f + 6.f + 4.f + biasValues[2] |
| }; |
| |
| FullyConnectedDescriptor descriptor; |
| descriptor.m_BiasEnabled = true; |
| descriptor.m_TransposeWeightMatrix = transposeWeights; |
| descriptor.m_ConstantWeights = constantWeightsOrBias; |
| |
| if (!constantWeightsOrBias) |
| { |
| // Tests non constant weights and constant bias. |
| ConstTensor biasConstantTensor(biasesDesc, biasValues.data()); |
| |
| armnn::INetworkPtr network = CreateFullyConnectedNetworkNonConstWeightsConstBias(inputTensorInfo, |
| outputTensorInfo, |
| weightsDesc, |
| biasesDesc, |
| biasConstantTensor, |
| descriptor); |
| CHECK(network); |
| |
| std::map<int, std::vector<T>> inputTensorData = {{ 0, input }, {1, weights}}; |
| std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutput }}; |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network), |
| inputTensorData, |
| expectedOutputTensorData, |
| backends, |
| 1.0f); |
| } |
| else |
| { |
| // Tests constant weights and non constant bias. |
| ConstTensor weightsConstantTensor(weightsDesc, weights.data()); |
| |
| armnn::INetworkPtr network = CreateFullyConnectedNetworkConstWeightsNonConstBias(inputTensorInfo, |
| outputTensorInfo, |
| weightsDesc, |
| biasesDesc, |
| weightsConstantTensor, |
| descriptor); |
| CHECK(network); |
| |
| std::map<int, std::vector<T>> inputTensorData = {{ 0, input }, {2, biasValues}}; |
| std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutput }}; |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network), |
| inputTensorData, |
| expectedOutputTensorData, |
| backends, |
| 1.0f); |
| } |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void FullyConnectedErrorChecking(const std::vector<armnn::BackendId>& backends, |
| const bool explicitCheck, |
| const bool biasEnabled, |
| const bool connectedWeights, |
| const bool connectedBias, |
| const bool tensorInfoSet) |
| { |
| unsigned int inputWidth = 1; |
| unsigned int inputHeight = 1; |
| unsigned int inputChannels = 5; |
| unsigned int inputNum = 2; |
| |
| unsigned int outputChannels = 3; |
| unsigned int outputNum = 2; |
| |
| unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; |
| unsigned int outputShape[] = { outputNum, outputChannels }; |
| unsigned int weightsShape[] = { inputChannels, outputChannels }; |
| |
| unsigned int biasShape[] = { outputChannels }; |
| |
| armnn::TensorInfo inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32, 0.0f, 0, true); |
| armnn::TensorInfo outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32); |
| armnn::TensorInfo weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::DataType::Float32, 0.0f, 0, true); |
| armnn::TensorInfo biasesDesc = armnn::TensorInfo(1, biasShape, armnn::DataType::Float32, 0.0f, 0, true); |
| |
| std::vector<float> weights = |
| { |
| .5f, 2.f, .5f, |
| .5f, 2.f, 1.f, |
| .5f, 2.f, 2.f, |
| .5f, 2.f, 3.f, |
| .5f, 2.f, 4.f |
| }; |
| |
| FullyConnectedDescriptor descriptor; |
| descriptor.m_BiasEnabled = biasEnabled; |
| |
| if(explicitCheck) |
| { |
| if(!biasEnabled) |
| { |
| try |
| { |
| CreateFullyConnectedNetworkNoConnectedWeightsExplicit(inputTensorInfo, |
| outputTensorInfo, |
| biasesDesc, |
| descriptor); |
| FAIL("LayerValidationException should have been thrown"); |
| } |
| catch (const LayerValidationException& exc) |
| { |
| CHECK(strcmp(exc.what(), "Tried to connect bias to FullyConnected layer when bias is not enabled: " |
| "Failed to connect to input slot 2 on FullyConnected layer " |
| "\"Fully_Connected\" as the slot does not exist or is unavailable") == 0); |
| } |
| } |
| else if (!connectedWeights) |
| { |
| armnn::INetworkPtr network = CreateFullyConnectedNetworkNoConnectedWeightsExplicit(inputTensorInfo, |
| outputTensorInfo, |
| biasesDesc, |
| descriptor); |
| CHECK(network); |
| |
| // Create runtime in which test will run |
| IRuntime::CreationOptions options; |
| IRuntimePtr runtime(IRuntime::Create(options)); |
| |
| CHECK_THROWS_AS(Optimize(*network, backends, runtime->GetDeviceSpec()), LayerValidationException); |
| } |
| else if (!connectedBias) |
| { |
| // Tests with constant weights. |
| ConstTensor weightsConstantTensor(weightsDesc, weights.data()); |
| |
| armnn::INetworkPtr network = CreateFullyConnectedNetworkNoConnectedBiasExplicit(inputTensorInfo, |
| outputTensorInfo, |
| weightsDesc, |
| weightsConstantTensor, |
| descriptor); |
| CHECK(network); |
| |
| // Create runtime in which test will run |
| IRuntime::CreationOptions options; |
| IRuntimePtr runtime(IRuntime::Create(options)); |
| |
| CHECK_THROWS_AS(Optimize(*network, backends, runtime->GetDeviceSpec()), LayerValidationException); |
| } |
| } |
| else if(!connectedWeights && !connectedBias) |
| { |
| armnn::INetworkPtr network = CreateFullyConnectedNetworkNoConnectedWeightsAndBias(inputTensorInfo, |
| outputTensorInfo, |
| descriptor); |
| CHECK(network); |
| |
| // Create runtime in which test will run |
| IRuntime::CreationOptions options; |
| IRuntimePtr runtime(IRuntime::Create(options)); |
| |
| CHECK_THROWS_AS(Optimize(*network, backends, runtime->GetDeviceSpec()), LayerValidationException); |
| } |
| else if(!tensorInfoSet) |
| { |
| // Tests with constant weights. |
| ConstTensor weightsConstantTensor(weightsDesc, weights.data()); |
| |
| armnn::INetworkPtr network = CreateFullyConnectedNetworkNoTensorInfoConstWeights(inputTensorInfo, |
| outputTensorInfo, |
| weightsConstantTensor, |
| descriptor); |
| CHECK(network); |
| |
| // Create runtime in which test will run |
| IRuntime::CreationOptions options; |
| IRuntimePtr runtime(IRuntime::Create(options)); |
| |
| try |
| { |
| Optimize(*network, backends, runtime->GetDeviceSpec()); |
| FAIL("LayerValidationException should have been thrown"); |
| } |
| catch (const LayerValidationException& exc) |
| { |
| CHECK(strcmp(exc.what(), "Output slot TensorInfo not set on Constant layer \"Weights\"") == 0); |
| } |
| } |
| } |
| |
| } // anonymous namespace |