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author | James Conroy <james.conroy@arm.com> | 2021-04-27 17:13:27 +0100 |
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committer | Narumol Prangnawarat <narumol.prangnawarat@arm.com> | 2021-05-06 14:40:40 +0000 |
commit | 1f58f03d82c482626b1b4673b6c0e25da4338fb5 (patch) | |
tree | e92451e00d459a2fc0d870694460f482aa4c77ae /src/armnn/test/OptimizerTests.cpp | |
parent | a7a12f5c3654da554ad6197beff0f0fc54681c92 (diff) | |
download | armnn-1f58f03d82c482626b1b4673b6c0e25da4338fb5.tar.gz |
IVGCVSW-5815 Generalise ConstCpuTensorHandle
* Generalises ConstCpuTensorHandle and inherited
classes by removing 'Cpu' from aliases.
* New renamed classes: ConstTensorHandle, TensorHandle,
ScopedTensorHandle, PassthroughTensorHandle,
ConstPassthroughTensorHandle.
Signed-off-by: James Conroy <james.conroy@arm.com>
Change-Id: I1824e0e134202735fb77051f20a7252f161dfe16
Diffstat (limited to 'src/armnn/test/OptimizerTests.cpp')
-rw-r--r-- | src/armnn/test/OptimizerTests.cpp | 56 |
1 files changed, 28 insertions, 28 deletions
diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp index 7e8a898293..d0734d83be 100644 --- a/src/armnn/test/OptimizerTests.cpp +++ b/src/armnn/test/OptimizerTests.cpp @@ -18,9 +18,9 @@ #include <armnn/utility/PolymorphicDowncast.hpp> #include <armnnUtils/FloatingPointConverter.hpp> -#include <backendsCommon/CpuTensorHandle.hpp> #include <backendsCommon/IBackendInternal.hpp> #include <backendsCommon/LayerSupportBase.hpp> +#include <backendsCommon/TensorHandle.hpp> #include <boost/test/unit_test.hpp> @@ -45,23 +45,23 @@ void CreateLSTMLayerHelper(Graph &graph, bool CifgEnabled) unsigned int numUnits = 4; unsigned int outputSize = 4; - layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits, inputSize }, DataType::Float32)); - layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits, inputSize }, DataType::Float32)); - layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits, inputSize }, DataType::Float32)); - layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits, outputSize }, DataType::Float32)); - layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits, outputSize }, DataType::Float32)); - layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits, outputSize }, DataType::Float32)); - layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<ScopedCpuTensorHandle> + layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits }, DataType::Float32)); - layer->m_BasicParameters.m_CellBias = std::make_unique<ScopedCpuTensorHandle> + layer->m_BasicParameters.m_CellBias = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits }, DataType::Float32)); - layer->m_BasicParameters.m_OutputGateBias = std::make_unique<ScopedCpuTensorHandle> + layer->m_BasicParameters.m_OutputGateBias = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits }, DataType::Float32)); layer->m_BasicParameters.m_InputToForgetWeights->Allocate(); @@ -76,11 +76,11 @@ void CreateLSTMLayerHelper(Graph &graph, bool CifgEnabled) if (!layerDesc.m_CifgEnabled) { - layer->m_CifgParameters.m_InputToInputWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_CifgParameters.m_InputToInputWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits, inputSize }, DataType::Float32)); - layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits, outputSize }, DataType::Float32)); - layer->m_CifgParameters.m_InputGateBias = std::make_unique<ScopedCpuTensorHandle> + layer->m_CifgParameters.m_InputGateBias = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits }, DataType::Float32)); layer->m_CifgParameters.m_InputToInputWeights->Allocate(); layer->m_CifgParameters.m_RecurrentToInputWeights->Allocate(); @@ -89,9 +89,9 @@ void CreateLSTMLayerHelper(Graph &graph, bool CifgEnabled) if (layerDesc.m_ProjectionEnabled) { - layer->m_ProjectionParameters.m_ProjectionWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_ProjectionParameters.m_ProjectionWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ outputSize, numUnits }, DataType::Float32)); - layer->m_ProjectionParameters.m_ProjectionBias = std::make_unique<ScopedCpuTensorHandle> + layer->m_ProjectionParameters.m_ProjectionBias = std::make_unique<ScopedTensorHandle> (TensorInfo({ outputSize }, DataType::Float32)); layer->m_ProjectionParameters.m_ProjectionWeights->Allocate(); layer->m_ProjectionParameters.m_ProjectionBias->Allocate(); @@ -101,13 +101,13 @@ void CreateLSTMLayerHelper(Graph &graph, bool CifgEnabled) { if (!layerDesc.m_CifgEnabled) { - layer->m_PeepholeParameters.m_CellToInputWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_PeepholeParameters.m_CellToInputWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits }, DataType::Float32)); layer->m_PeepholeParameters.m_CellToInputWeights->Allocate(); } - layer->m_PeepholeParameters.m_CellToForgetWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_PeepholeParameters.m_CellToForgetWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits }, DataType::Float32)); - layer->m_PeepholeParameters.m_CellToOutputWeights = std::make_unique<ScopedCpuTensorHandle> + layer->m_PeepholeParameters.m_CellToOutputWeights = std::make_unique<ScopedTensorHandle> (TensorInfo({ numUnits }, DataType::Float32)); layer->m_PeepholeParameters.m_CellToForgetWeights->Allocate(); layer->m_PeepholeParameters.m_CellToOutputWeights->Allocate(); @@ -276,7 +276,7 @@ void CreateConvolution2dGraph(Graph &graph, const unsigned int* inputShape, input->GetOutputSlot().SetTensorInfo(inputInfo); Convolution2dLayer* layer = graph.AddLayer<Convolution2dLayer>(desc, "conv2d"); - layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights); + layer->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights); layer->GetOutputSlot().SetTensorInfo(outputInfo); Layer* output = graph.AddLayer<OutputLayer>(0, "output"); @@ -326,7 +326,7 @@ void CreateDepthwiseConvolution2dGraph(Graph &graph, const unsigned int* inputSh input->GetOutputSlot().SetTensorInfo(inputInfo); DepthwiseConvolution2dLayer* layer = graph.AddLayer<DepthwiseConvolution2dLayer>(desc, "depthwiseConv2d"); - layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights); + layer->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights); layer->GetOutputSlot().SetTensorInfo(outputInfo); Layer* output = graph.AddLayer<OutputLayer>(0, "output"); @@ -529,7 +529,7 @@ BOOST_AUTO_TEST_CASE(DetectionPostProcessValidateTensorShapes) descriptor.m_MaxDetections = 3; DetectionPostProcessLayer* layer = graph.AddLayer<DetectionPostProcessLayer>(descriptor, "detectionPostProcess"); - layer->m_Anchors = std::make_unique<armnn::ScopedCpuTensorHandle>(anchors); + layer->m_Anchors = std::make_unique<armnn::ScopedTensorHandle>(anchors); layer->GetOutputSlot(0).SetTensorInfo(detectionBoxesInfo); layer->GetOutputSlot(1).SetTensorInfo(detectionScoresInfo); layer->GetOutputSlot(2).SetTensorInfo(detectionClassesInfo); @@ -571,7 +571,7 @@ BOOST_AUTO_TEST_CASE(FoldPadLayerIntoConvolution2dLayer) armnn::ConstTensor weights(armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32), weightsVector); Convolution2dLayer* conv2dLayer = graph.AddLayer<Convolution2dLayer>(convolution2dDescriptor, "conv2d"); - conv2dLayer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(weights); + conv2dLayer->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights); conv2dLayer->GetOutputSlot().SetTensorInfo(outputInfo); Layer* output = graph.AddLayer<OutputLayer>(0, "output"); @@ -1211,16 +1211,16 @@ BOOST_AUTO_TEST_CASE(OptimizeForExclusiveConnectionsFuseTest) input->GetOutputSlot().SetTensorInfo(inputInfo); conv->GetOutputSlot().SetTensorInfo(outputInfo); batchNorm->GetOutputSlot().SetTensorInfo(outputInfo); - conv->m_Weight = std::make_unique<ScopedCpuTensorHandle>(weights); - batchNorm->m_Beta = std::make_unique<ScopedCpuTensorHandle>(beta); - batchNorm->m_Gamma = std::make_unique<ScopedCpuTensorHandle>(gamma); - batchNorm->m_Mean = std::make_unique<ScopedCpuTensorHandle>(mean); - batchNorm->m_Variance = std::make_unique<ScopedCpuTensorHandle>(variance); + conv->m_Weight = std::make_unique<ScopedTensorHandle>(weights); + batchNorm->m_Beta = std::make_unique<ScopedTensorHandle>(beta); + batchNorm->m_Gamma = std::make_unique<ScopedTensorHandle>(gamma); + batchNorm->m_Mean = std::make_unique<ScopedTensorHandle>(mean); + batchNorm->m_Variance = std::make_unique<ScopedTensorHandle>(variance); if (convolution2dDescriptor.m_BiasEnabled) { std::vector<float> biasVector = { 11 }; ConstTensor bias(TensorInfo(1, outputChannelSize, DataType::Float32), biasVector); - conv->m_Bias = std::make_unique<ScopedCpuTensorHandle>(bias); + conv->m_Bias = std::make_unique<ScopedTensorHandle>(bias); } // Connect layers |