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path: root/src/armnn/test/OptimizerTests.cpp
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Diffstat (limited to 'src/armnn/test/OptimizerTests.cpp')
-rw-r--r--src/armnn/test/OptimizerTests.cpp56
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
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-01 12:16:36 +0000