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Diffstat (limited to 'src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp')
-rw-r--r--src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp106
1 files changed, 53 insertions, 53 deletions
diff --git a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
index b73efbe26c..4240bb1061 100644
--- a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
+++ b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp
@@ -83,13 +83,13 @@ struct DummyLayer<armnn::BatchNormalizationLayer>
DummyLayer()
{
m_Layer = dummyGraph.AddLayer<armnn::BatchNormalizationLayer>(armnn::BatchNormalizationDescriptor(), "");
- m_Layer->m_Mean = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_Mean = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_Variance = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_Variance = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_Beta = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_Beta = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_Gamma = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_Gamma = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
}
@@ -240,9 +240,9 @@ struct DummyConvolutionLayer
desc.m_StrideX = 1;
desc.m_StrideY = 1;
m_Layer = dummyGraph.AddLayer<ConvolutionLayerType>(desc, "");
- m_Layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_Bias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_Bias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
}
@@ -278,7 +278,7 @@ struct DummyLayer<armnn::DetectionPostProcessLayer>
DummyLayer()
{
m_Layer = dummyGraph.AddLayer<armnn::DetectionPostProcessLayer>(armnn::DetectionPostProcessDescriptor(), "");
- m_Layer->m_Anchors = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_Anchors = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
}
@@ -299,30 +299,30 @@ struct DummyLstmLayer
desc.m_CifgEnabled = false;
m_Layer = dummyGraph.AddLayer<LstmLayerType>(armnn::LstmDescriptor(), "");
- m_Layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_BasicParameters.m_CellBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_CellBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_BasicParameters.m_OutputGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_OutputGateBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_CifgParameters.m_InputToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_CifgParameters.m_InputToInputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
- m_Layer->m_CifgParameters.m_InputGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_CifgParameters.m_InputGateBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
}
@@ -354,57 +354,57 @@ struct DummyQLstmLayer
m_Layer = dummyGraph.AddLayer<QLstmLayerType>(armnn::QLstmDescriptor(), "qLstm");
// Basic params
- m_Layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
- m_Layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
- m_Layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
- m_Layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
- m_Layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
- m_Layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
- m_Layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
- m_Layer->m_BasicParameters.m_CellBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_CellBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
- m_Layer->m_BasicParameters.m_OutputGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_BasicParameters.m_OutputGateBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
// CIFG optional params
- m_Layer->m_CifgParameters.m_InputToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_CifgParameters.m_InputToInputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
- m_Layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
- m_Layer->m_CifgParameters.m_InputGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_CifgParameters.m_InputGateBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
// Projection optional params
- m_Layer->m_ProjectionParameters.m_ProjectionWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_ProjectionParameters.m_ProjectionWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS8));
- m_Layer->m_ProjectionParameters.m_ProjectionBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_ProjectionParameters.m_ProjectionBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
// Peephole optional params
- m_Layer->m_PeepholeParameters.m_CellToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_PeepholeParameters.m_CellToInputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
- m_Layer->m_PeepholeParameters.m_CellToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_PeepholeParameters.m_CellToForgetWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
- m_Layer->m_PeepholeParameters.m_CellToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_PeepholeParameters.m_CellToOutputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
// Layer normalization optional params
- m_Layer->m_LayerNormParameters.m_InputLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_LayerNormParameters.m_InputLayerNormWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
- m_Layer->m_LayerNormParameters.m_ForgetLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_LayerNormParameters.m_ForgetLayerNormWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
- m_Layer->m_LayerNormParameters.m_CellLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_LayerNormParameters.m_CellLayerNormWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
- m_Layer->m_LayerNormParameters.m_OutputLayerNormWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_LayerNormParameters.m_OutputLayerNormWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QSymmS16));
}
@@ -423,31 +423,31 @@ struct DummyLayer<armnn::QuantizedLstmLayer, void>
{
m_Layer = dummyGraph.AddLayer<armnn::QuantizedLstmLayer>("");
- m_Layer->m_QuantizedLstmParameters.m_InputToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_InputToInputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
- m_Layer->m_QuantizedLstmParameters.m_InputToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_InputToForgetWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
- m_Layer->m_QuantizedLstmParameters.m_InputToCellWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_InputToCellWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
- m_Layer->m_QuantizedLstmParameters.m_InputToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_InputToOutputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
- m_Layer->m_QuantizedLstmParameters.m_RecurrentToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_RecurrentToInputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
- m_Layer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
- m_Layer->m_QuantizedLstmParameters.m_RecurrentToCellWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_RecurrentToCellWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
- m_Layer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::QAsymmU8));
- m_Layer->m_QuantizedLstmParameters.m_InputGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_InputGateBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
- m_Layer->m_QuantizedLstmParameters.m_ForgetGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_ForgetGateBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
- m_Layer->m_QuantizedLstmParameters.m_CellBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_CellBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
- m_Layer->m_QuantizedLstmParameters.m_OutputGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_QuantizedLstmParameters.m_OutputGateBias = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Signed32));
}
@@ -466,7 +466,7 @@ struct DummyLayer<armnn::FullyConnectedLayer>
{
armnn::FullyConnectedLayer::DescriptorType desc;
m_Layer = dummyGraph.AddLayer<armnn::FullyConnectedLayer>(desc, "");
- m_Layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(
+ m_Layer->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(
armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-07 02:48:00 +0000