diff options
Diffstat (limited to 'src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp | 170 |
1 files changed, 166 insertions, 4 deletions
diff --git a/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp index 923d6f3641..af6b56852a 100644 --- a/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp @@ -28,10 +28,7 @@ armnn::INetworkPtr CreateFullyConnectedNetworkNonConstWeights(const armnn::Tenso armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input"); armnn::IConnectableLayer* weightsInputLayer = network->AddInputLayer(1, "Weights_Input"); - armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, - armnn::EmptyOptional(), - armnn::EmptyOptional(), - "Fully_Connected"); + armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected"); armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output"); Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0); @@ -41,6 +38,52 @@ armnn::INetworkPtr CreateFullyConnectedNetworkNonConstWeights(const armnn::Tenso 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; +} + template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> void FullyConnectedWithDynamicWeightsEndToEnd(const std::vector<armnn::BackendId>& backends) { @@ -94,4 +137,123 @@ void FullyConnectedWithDynamicWeightsEndToEnd(const std::vector<armnn::BackendId 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); + armnn::TensorInfo outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32); + armnn::TensorInfo weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::DataType::Float32); + armnn::TensorInfo biasesDesc = armnn::TensorInfo(1, biasShape, armnn::DataType::Float32); + + 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); + } +} + } // anonymous namespace |