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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <boost/test/unit_test.hpp>
#include <boost/cast.hpp>
#include <backends/WorkloadData.hpp>
#include <Graph.hpp>
#include <utility>
#include <backends/CpuTensorHandle.hpp>
#include <backends/cl/ClWorkloadFactory.hpp>
using namespace armnn;
using namespace std;
// connects two layers
void Connect(Layer* from, Layer* to, const TensorInfo& tensorInfo, unsigned int fromIndex = 0, unsigned int toIndex = 0)
{
from->GetOutputSlot(fromIndex).Connect(to->GetInputSlot(toIndex));
from->GetOutputHandler(fromIndex).SetTensorInfo(tensorInfo);
}
/////////////////////////////////////////////////////////////////////////////////////////////
// The following test are created specifically to test ReleaseConstantData() method in the Layer
// They build very simple graphs including the layer will be checked.
// Checks weights and biases before the method called and after.
/////////////////////////////////////////////////////////////////////////////////////////////
BOOST_AUTO_TEST_SUITE(LayerReleaseConstantDataTest)
BOOST_AUTO_TEST_CASE(ReleaseBatchNormalizationLayerConstantDataTest)
{
Graph graph;
ClWorkloadFactory factory;
// create the layer we're testing
BatchNormalizationDescriptor layerDesc;
layerDesc.m_Eps = 0.05f;
BatchNormalizationLayer* const layer = graph.AddLayer<BatchNormalizationLayer>(layerDesc, "layer");
armnn::TensorInfo weightInfo({3}, armnn::DataType::Float32);
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->Allocate();
layer->m_Variance->Allocate();
layer->m_Beta->Allocate();
layer->m_Gamma->Allocate();
// create extra layers
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
// connect up
armnn::TensorInfo tensorInfo({2, 3, 1, 1}, armnn::DataType::Float32);
Connect(input, layer, tensorInfo);
Connect(layer, output, tensorInfo);
// check the constants that they are not NULL
BOOST_CHECK(layer->m_Mean != nullptr);
BOOST_CHECK(layer->m_Variance != nullptr);
BOOST_CHECK(layer->m_Beta != nullptr);
BOOST_CHECK(layer->m_Gamma != nullptr);
// free up the constants..
layer->ReleaseConstantData();
// check the constants that they are NULL now
BOOST_CHECK(layer->m_Mean == nullptr);
BOOST_CHECK(layer->m_Variance == nullptr);
BOOST_CHECK(layer->m_Beta == nullptr);
BOOST_CHECK(layer->m_Gamma == nullptr);
}
BOOST_AUTO_TEST_CASE(ReleaseConvolution2dLayerConstantDataTest)
{
Graph graph;
ClWorkloadFactory factory;
// create the layer we're testing
Convolution2dDescriptor layerDesc;
layerDesc.m_PadLeft = 3;
layerDesc.m_PadRight = 3;
layerDesc.m_PadTop = 1;
layerDesc.m_PadBottom = 1;
layerDesc.m_StrideX = 2;
layerDesc.m_StrideY = 4;
layerDesc.m_BiasEnabled = true;
Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer");
layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({2, 3, 5, 3},
armnn::DataType::Float32));
layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>
(TensorInfo({2}, GetBiasDataType(armnn::DataType::Float32)));
layer->m_Weight->Allocate();
layer->m_Bias->Allocate();
// create extra layers
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
// connect up
Connect(input, layer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32));
Connect(layer, output, TensorInfo({2, 2, 2, 10}, armnn::DataType::Float32));
// check the constants that they are not NULL
BOOST_CHECK(layer->m_Weight != nullptr);
BOOST_CHECK(layer->m_Bias != nullptr);
// free up the constants..
layer->ReleaseConstantData();
// check the constants that they are NULL now
BOOST_CHECK(layer->m_Weight == nullptr);
BOOST_CHECK(layer->m_Bias == nullptr);
}
BOOST_AUTO_TEST_CASE(ReleaseDepthwiseConvolution2dLayerConstantDataTest)
{
Graph graph;
ClWorkloadFactory factory;
// create the layer we're testing
DepthwiseConvolution2dDescriptor layerDesc;
layerDesc.m_PadLeft = 3;
layerDesc.m_PadRight = 3;
layerDesc.m_PadTop = 1;
layerDesc.m_PadBottom = 1;
layerDesc.m_StrideX = 2;
layerDesc.m_StrideY = 4;
layerDesc.m_BiasEnabled = true;
DepthwiseConvolution2dLayer* const layer = graph.AddLayer<DepthwiseConvolution2dLayer>(layerDesc, "layer");
layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({3, 3, 5, 3}, DataType::Float32));
layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({9}, DataType::Float32));
layer->m_Weight->Allocate();
layer->m_Bias->Allocate();
// create extra layers
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
// connect up
Connect(input, layer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32));
Connect(layer, output, TensorInfo({2, 9, 2, 10}, armnn::DataType::Float32));
// check the constants that they are not NULL
BOOST_CHECK(layer->m_Weight != nullptr);
BOOST_CHECK(layer->m_Bias != nullptr);
// free up the constants..
layer->ReleaseConstantData();
// check the constants that they are NULL now
BOOST_CHECK(layer->m_Weight == nullptr);
BOOST_CHECK(layer->m_Bias == nullptr);
}
BOOST_AUTO_TEST_CASE(ReleaseFullyConnectedLayerConstantDataTest)
{
Graph graph;
ClWorkloadFactory factory;
// create the layer we're testing
FullyConnectedDescriptor layerDesc;
layerDesc.m_BiasEnabled = true;
layerDesc.m_TransposeWeightMatrix = true;
FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer");
float inputsQScale = 1.0f;
float outputQScale = 2.0f;
layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({7, 20},
DataType::QuantisedAsymm8, inputsQScale, 0));
layer->m_Bias = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({7},
GetBiasDataType(DataType::QuantisedAsymm8), inputsQScale));
layer->m_Weight->Allocate();
layer->m_Bias->Allocate();
// create extra layers
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
// connect up
Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType::QuantisedAsymm8, inputsQScale));
Connect(layer, output, TensorInfo({3, 7}, DataType::QuantisedAsymm8, outputQScale));
// check the constants that they are not NULL
BOOST_CHECK(layer->m_Weight != nullptr);
BOOST_CHECK(layer->m_Bias != nullptr);
// free up the constants..
layer->ReleaseConstantData();
// check the constants that they are NULL now
BOOST_CHECK(layer->m_Weight == nullptr);
BOOST_CHECK(layer->m_Bias == nullptr);
}
BOOST_AUTO_TEST_SUITE_END()
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