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-rw-r--r--src/armnn/test/CreateWorkload.hpp85
1 files changed, 73 insertions, 12 deletions
diff --git a/src/armnn/test/CreateWorkload.hpp b/src/armnn/test/CreateWorkload.hpp
index b07e3b80a5..759ada97cd 100644
--- a/src/armnn/test/CreateWorkload.hpp
+++ b/src/armnn/test/CreateWorkload.hpp
@@ -1193,7 +1193,7 @@ std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWorkloadTest(armnn::
{
// Creates the layer we're testing.
FullyConnectedDescriptor layerDesc;
- layerDesc.m_BiasEnabled = true;
+ layerDesc.m_BiasEnabled = false;
layerDesc.m_TransposeWeightMatrix = true;
FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer");
@@ -1201,17 +1201,24 @@ std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWorkloadTest(armnn::
float inputsQScale = DataType == armnn::DataType::QAsymmU8 ? 1.0f : 0.0;
float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 0.0;
+ // As optimization isn't run member variables need to be updated.
layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({7, 20}, DataType, inputsQScale, 0));
- layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo({7}, GetBiasDataType(DataType), inputsQScale));
layer->m_Weight->Allocate();
- layer->m_Bias->Allocate();
+
+ armnn::TensorInfo weightsTensorInfo({7, 20}, DataType, inputsQScale);
+ weightsTensorInfo.SetConstant();
// Creates extra layers.
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
+ auto const weights = graph.AddLayer<ConstantLayer>("weights");
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
+ weights->m_LayerOutput = std::make_unique<ScopedTensorHandle>(weightsTensorInfo);
+ weights->m_LayerOutput->Allocate();
+
// Connects up.
- Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale));
+ Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale), 0, 0);
+ Connect(weights, layer, weightsTensorInfo, 0, 1);
Connect(layer, output, TensorInfo({3, 7}, DataType, outputQScale));
CreateTensorHandles(graph, factory);
@@ -1219,13 +1226,10 @@ std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWorkloadTest(armnn::
auto workload = MakeAndCheckWorkload<FullyConnectedWorkload>(*layer, factory);
FullyConnectedQueueDescriptor queueDescriptor = workload->GetData();
- CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true);
CHECK(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true);
- CHECK(queueDescriptor.m_Inputs.size() == 1);
+ CHECK(queueDescriptor.m_Inputs.size() == 2);
CHECK(queueDescriptor.m_Outputs.size() == 1);
- CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({7, 20}, DataType, inputsQScale)));
- CHECK((queueDescriptor.m_Bias->GetTensorInfo() == TensorInfo({7}, GetBiasDataType(DataType), inputsQScale)));
// Returns so we can do extra, backend-specific tests.
return workload;
@@ -1246,11 +1250,17 @@ std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWithBlobWorkloadTest
float inputsQScale = DataType == armnn::DataType::QAsymmU8 ? 1.0f : 0.0;
float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 0.0;
+ // As optimization isn't run member variables need to be updated.
layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({7, 20}, DataType, inputsQScale, 0));
layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo({7}, GetBiasDataType(DataType), inputsQScale));
layer->m_Weight->Allocate();
layer->m_Bias->Allocate();
+ armnn::TensorInfo weightsTensorInfo({7, 20}, DataType, inputsQScale);
+ armnn::TensorInfo biasesTensorInfo({7}, GetBiasDataType(DataType), inputsQScale);
+ weightsTensorInfo.SetConstant();
+ biasesTensorInfo.SetConstant();
+
auto activationDesc = std::make_shared<ActivationDescriptor>();
activationDesc->m_A = 10.0f;
activationDesc->m_B = 5.0f;
@@ -1267,10 +1277,19 @@ std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWithBlobWorkloadTest
// Creates extra layers.
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
+ auto const weights = graph.AddLayer<ConstantLayer>("weights");
+ auto const biases = graph.AddLayer<ConstantLayer>("biases");
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
+ weights->m_LayerOutput = std::make_unique<ScopedTensorHandle>(weightsTensorInfo);
+ weights->m_LayerOutput->Allocate();
+ biases->m_LayerOutput = std::make_unique<ScopedTensorHandle>(biasesTensorInfo);
+ biases->m_LayerOutput->Allocate();
+
// Connects up.
- Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale));
+ Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale), 0, 0);
+ Connect(weights, layer, weightsTensorInfo, 0, 1);
+ Connect(biases, layer, biasesTensorInfo, 0, 2);
Connect(layer, output, TensorInfo({3, 7}, DataType, outputQScale));
CreateTensorHandles(graph, factory);
@@ -1290,10 +1309,52 @@ std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWithBlobWorkloadTest
CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true);
CHECK(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true);
- CHECK(queueDescriptor.m_Inputs.size() == 1);
+ CHECK(queueDescriptor.m_Inputs.size() == 3);
+ CHECK(queueDescriptor.m_Outputs.size() == 1);
+
+ // Returns so we can do extra, backend-specific tests.
+ return workload;
+}
+
+template <typename FullyConnectedWorkload, armnn::DataType DataType>
+std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWorkloadWeightsBiasesAsInputsTest
+ (armnn::IWorkloadFactory& factory,
+ armnn::Graph& graph)
+{
+ // Creates the layer we're testing.
+ FullyConnectedDescriptor layerDesc;
+ layerDesc.m_BiasEnabled = true;
+ layerDesc.m_TransposeWeightMatrix = true;
+ layerDesc.m_ConstantWeights = false;
+
+ FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer");
+
+ float inputsQScale = DataType == armnn::DataType::QAsymmU8 ? 1.0f : 0.0;
+ float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 0.0;
+
+ // Creates extra layers with weights and biases as input layers.
+ Layer* const input = graph.AddLayer<InputLayer>(1, "input");
+ Layer* const weights = graph.AddLayer<InputLayer>(2, "weights");
+ Layer* const biases = graph.AddLayer<InputLayer>(3, "biases");
+ Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
+
+ // Connects up.
+ Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale), 0, 0);
+ Connect(weights, layer, TensorInfo({7, 20}, DataType, inputsQScale), 0, 1);
+ Connect(biases, layer, TensorInfo({7}, GetBiasDataType(DataType), inputsQScale), 0, 2);
+ Connect(layer, output, TensorInfo({3, 7}, DataType, outputQScale));
+ CreateTensorHandles(graph, factory);
+
+ // Makes the workload and checks it.
+ auto workload = MakeAndCheckWorkload<FullyConnectedWorkload>(*layer, factory);
+
+ FullyConnectedQueueDescriptor queueDescriptor = workload->GetData();
+
+ CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true);
+ CHECK(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true);
+ CHECK(queueDescriptor.m_Parameters.m_ConstantWeights == false);
+ CHECK(queueDescriptor.m_Inputs.size() == 3);
CHECK(queueDescriptor.m_Outputs.size() == 1);
- CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({7, 20}, DataType, inputsQScale)));
- CHECK((queueDescriptor.m_Bias->GetTensorInfo() == TensorInfo({7}, GetBiasDataType(DataType), inputsQScale)));
// Returns so we can do extra, backend-specific tests.
return workload;
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-17 07:17:43 +0000