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-rw-r--r--src/armnn/test/CreateWorkload.hpp116
1 files changed, 58 insertions, 58 deletions
diff --git a/src/armnn/test/CreateWorkload.hpp b/src/armnn/test/CreateWorkload.hpp
index 3ea2c35061..12623e62a0 100644
--- a/src/armnn/test/CreateWorkload.hpp
+++ b/src/armnn/test/CreateWorkload.hpp
@@ -14,9 +14,9 @@
#include <armnn/utility/IgnoreUnused.hpp>
#include <armnn/utility/PolymorphicDowncast.hpp>
+#include <backendsCommon/TensorHandle.hpp>
#include <backendsCommon/WorkloadData.hpp>
#include <backendsCommon/WorkloadFactory.hpp>
-#include <backendsCommon/CpuTensorHandle.hpp>
#include <boost/test/unit_test.hpp>
@@ -353,10 +353,10 @@ std::unique_ptr<BatchNormalizationWorkloadType> CreateBatchNormalizationWorkload
BatchNormalizationLayer* const layer = graph.AddLayer<BatchNormalizationLayer>(layerDesc, "layer");
armnn::TensorInfo weightInfo({3}, DataType);
- layer->m_Mean = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
- layer->m_Variance = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
- layer->m_Beta = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
- layer->m_Gamma = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
+ layer->m_Mean = std::make_unique<ScopedTensorHandle>(weightInfo);
+ layer->m_Variance = std::make_unique<ScopedTensorHandle>(weightInfo);
+ layer->m_Beta = std::make_unique<ScopedTensorHandle>(weightInfo);
+ layer->m_Gamma = std::make_unique<ScopedTensorHandle>(weightInfo);
layer->m_Mean->Allocate();
layer->m_Variance->Allocate();
layer->m_Beta->Allocate();
@@ -411,10 +411,10 @@ std::unique_ptr<BatchNormalizationWorkloadType> CreateBatchNormalizationWithBlob
BatchNormalizationLayer* const layer = graph.AddLayer<BatchNormalizationLayer>(layerDesc, "layer");
armnn::TensorInfo weightInfo({3}, DataType);
- layer->m_Mean = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
- layer->m_Variance = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
- layer->m_Beta = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
- layer->m_Gamma = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
+ layer->m_Mean = std::make_unique<ScopedTensorHandle>(weightInfo);
+ layer->m_Variance = std::make_unique<ScopedTensorHandle>(weightInfo);
+ layer->m_Beta = std::make_unique<ScopedTensorHandle>(weightInfo);
+ layer->m_Gamma = std::make_unique<ScopedTensorHandle>(weightInfo);
layer->m_Mean->Allocate();
layer->m_Variance->Allocate();
layer->m_Beta->Allocate();
@@ -492,8 +492,8 @@ std::unique_ptr<Convolution2dWorkload> CreateConvolution2dWorkloadTest(armnn::IW
TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 8, 16} : TensorShape{2, 8, 16, 3};
TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 2, 2, 10} : TensorShape{2, 2, 10, 2};
- layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo(weightShape, DataType));
- layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({2}, GetBiasDataType(DataType)));
+ layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo(weightShape, DataType));
+ layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo({2}, GetBiasDataType(DataType)));
layer->m_Weight->Allocate();
layer->m_Bias->Allocate();
@@ -555,8 +555,8 @@ std::unique_ptr<Convolution2dWorkload> CreateConvolution2dFusedActivationWithBlo
TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 8, 16} : TensorShape{2, 8, 16, 3};
TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 2, 2, 10} : TensorShape{2, 2, 10, 2};
- layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo(weightShape, DataType));
- layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({2}, GetBiasDataType(DataType)));
+ layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo(weightShape, DataType));
+ layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo({2}, GetBiasDataType(DataType)));
layer->m_Weight->Allocate();
layer->m_Bias->Allocate();
@@ -639,8 +639,8 @@ std::unique_ptr<Convolution2dWorkload> CreateConvolution2dWorkloadFastMathTest(a
TensorShape inputShape = TensorShape{1, 32, 149, 149};
TensorShape outputShape = TensorShape{1, 32, 147, 147};
- layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo(weightShape, DataType));
- layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({2}, GetBiasDataType(DataType)));
+ layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo(weightShape, DataType));
+ layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo({2}, GetBiasDataType(DataType)));
layer->m_Weight->Allocate();
layer->m_Bias->Allocate();
@@ -692,23 +692,23 @@ std::unique_ptr<LstmWorkload> CreateLstmWorkloadTest(armnn::IWorkloadFactory& fa
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();
@@ -724,9 +724,9 @@ std::unique_ptr<LstmWorkload> CreateLstmWorkloadTest(armnn::IWorkloadFactory& fa
if (layerDesc.m_PeepholeEnabled)
{
- 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();
@@ -814,27 +814,27 @@ std::unique_ptr<QuantizedLstmWorkload> CreateQuantizedLstmWorkloadTest(armnn::IW
// Weights and bias
layer->m_QuantizedLstmParameters.m_InputToInputWeights =
- std::make_unique<ScopedCpuTensorHandle>(inputWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
layer->m_QuantizedLstmParameters.m_InputToForgetWeights =
- std::make_unique<ScopedCpuTensorHandle>(inputWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
layer->m_QuantizedLstmParameters.m_InputToCellWeights =
- std::make_unique<ScopedCpuTensorHandle>(inputWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
layer->m_QuantizedLstmParameters.m_InputToOutputWeights =
- std::make_unique<ScopedCpuTensorHandle>(inputWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
layer->m_QuantizedLstmParameters.m_RecurrentToInputWeights =
- std::make_unique<ScopedCpuTensorHandle>(recurrentWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
layer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights =
- std::make_unique<ScopedCpuTensorHandle>(recurrentWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
layer->m_QuantizedLstmParameters.m_RecurrentToCellWeights =
- std::make_unique<ScopedCpuTensorHandle>(recurrentWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
layer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights =
- std::make_unique<ScopedCpuTensorHandle>(recurrentWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
- layer->m_QuantizedLstmParameters.m_InputGateBias = std::make_unique<ScopedCpuTensorHandle>(biasInfo);
- layer->m_QuantizedLstmParameters.m_ForgetGateBias = std::make_unique<ScopedCpuTensorHandle>(biasInfo);
- layer->m_QuantizedLstmParameters.m_CellBias = std::make_unique<ScopedCpuTensorHandle>(biasInfo);
- layer->m_QuantizedLstmParameters.m_OutputGateBias = std::make_unique<ScopedCpuTensorHandle>(biasInfo);
+ layer->m_QuantizedLstmParameters.m_InputGateBias = std::make_unique<ScopedTensorHandle>(biasInfo);
+ layer->m_QuantizedLstmParameters.m_ForgetGateBias = std::make_unique<ScopedTensorHandle>(biasInfo);
+ layer->m_QuantizedLstmParameters.m_CellBias = std::make_unique<ScopedTensorHandle>(biasInfo);
+ layer->m_QuantizedLstmParameters.m_OutputGateBias = std::make_unique<ScopedTensorHandle>(biasInfo);
// Allocate weights and bias
layer->m_QuantizedLstmParameters.m_InputToInputWeights->Allocate();
@@ -977,27 +977,27 @@ std::unique_ptr<QLstmWorkload> CreateQLstmWorkloadTest(armnn::IWorkloadFactory&
armnn::TensorInfo layerNormWeightsInfo({numUnits}, armnn::DataType::QSymmS16, layerNormScale, layerNormOffset);
// Create and allocate tensors
- layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<ScopedCpuTensorHandle>(inputWeightsInfo);
- layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<ScopedCpuTensorHandle>(inputWeightsInfo);
- layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<ScopedCpuTensorHandle>(inputWeightsInfo);
+ layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
+ layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
+ layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<ScopedTensorHandle>(inputWeightsInfo);
layer->m_BasicParameters.m_RecurrentToForgetWeights =
- std::make_unique<ScopedCpuTensorHandle>(recurrentWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
layer->m_BasicParameters.m_RecurrentToCellWeights =
- std::make_unique<ScopedCpuTensorHandle>(recurrentWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
layer->m_BasicParameters.m_RecurrentToOutputWeights =
- std::make_unique<ScopedCpuTensorHandle>(recurrentWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo);
- layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<ScopedCpuTensorHandle>(biasInfo);
- layer->m_BasicParameters.m_CellBias = std::make_unique<ScopedCpuTensorHandle>(biasInfo);
- layer->m_BasicParameters.m_OutputGateBias = std::make_unique<ScopedCpuTensorHandle>(biasInfo);
+ layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<ScopedTensorHandle>(biasInfo);
+ layer->m_BasicParameters.m_CellBias = std::make_unique<ScopedTensorHandle>(biasInfo);
+ layer->m_BasicParameters.m_OutputGateBias = std::make_unique<ScopedTensorHandle>(biasInfo);
layer->m_LayerNormParameters.m_ForgetLayerNormWeights =
- std::make_unique<ScopedCpuTensorHandle>(layerNormWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(layerNormWeightsInfo);
layer->m_LayerNormParameters.m_CellLayerNormWeights =
- std::make_unique<ScopedCpuTensorHandle>(layerNormWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(layerNormWeightsInfo);
layer->m_LayerNormParameters.m_OutputLayerNormWeights =
- std::make_unique<ScopedCpuTensorHandle>(layerNormWeightsInfo);
+ std::make_unique<ScopedTensorHandle>(layerNormWeightsInfo);
layer->m_BasicParameters.m_InputToForgetWeights->Allocate();
layer->m_BasicParameters.m_InputToCellWeights->Allocate();
@@ -1093,8 +1093,8 @@ std::unique_ptr<Convolution2dWorkload> CreateDirectConvolution2dWorkloadTest(arm
float inputsQScale = DataType == armnn::DataType::QAsymmU8 ? 1.0f : 0.0;
float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 0.0;
- layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({ 2, 3, 3, 3 }, DataType, inputsQScale));
- layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>
+ layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({ 2, 3, 3, 3 }, DataType, inputsQScale));
+ layer->m_Bias = std::make_unique<ScopedTensorHandle>
(TensorInfo({2}, GetBiasDataType(DataType), inputsQScale));
layer->m_Weight->Allocate();
layer->m_Bias->Allocate();
@@ -1148,7 +1148,7 @@ std::unique_ptr<DepthwiseConvolution2dFloat32Workload> CreateDepthwiseConvolutio
DepthwiseConvolution2dLayer* const layer = graph.AddLayer<DepthwiseConvolution2dLayer>(layerDesc, "layer");
- layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({1, 2, 4, 4}, DataType)); // [ M, I, H, W ]
+ layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({1, 2, 4, 4}, DataType)); // [ M, I, H, W ]
layer->m_Weight->Allocate();
// Creates extra layers.
@@ -1200,8 +1200,8 @@ 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;
- layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({7, 20}, DataType, inputsQScale, 0));
- layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({7}, GetBiasDataType(DataType), inputsQScale));
+ 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();
@@ -1245,8 +1245,8 @@ 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;
- layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({7, 20}, DataType, inputsQScale, 0));
- layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({7}, GetBiasDataType(DataType), inputsQScale));
+ 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();
@@ -2108,7 +2108,7 @@ std::unique_ptr<ConstantWorkload> CreateConstantWorkloadTest(armnn::IWorkloadFac
armnn::TensorInfo outputTensorInfo(outputShape, DataType);
auto constant = graph.AddLayer<ConstantLayer>("constant");
- constant->m_LayerOutput = std::make_unique<ScopedCpuTensorHandle>(outputTensorInfo);
+ constant->m_LayerOutput = std::make_unique<ScopedTensorHandle>(outputTensorInfo);
BOOST_TEST_CHECKPOINT("created constant layer");
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-01 12:01:15 +0000