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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "LayersFwd.hpp"
#include <boost/test/unit_test.hpp>
BOOST_AUTO_TEST_SUITE(Optimizer)
using namespace armnn;
// This unit test needs the reference backend, it's not available if the reference backend is not built
#if defined(ARMNNREF_ENABLED)
BOOST_AUTO_TEST_CASE(Fuse_batchNorm_into_Conv2D_Float32_Test)
{
// Define layers information
Convolution2dDescriptor convolution2dDescriptor;
convolution2dDescriptor.m_BiasEnabled = false;
convolution2dDescriptor.m_DataLayout = DataLayout::NHWC;
convolution2dDescriptor.m_StrideX = 1;
convolution2dDescriptor.m_StrideY = 1;
BatchNormalizationDescriptor batchNormDescriptor;
batchNormDescriptor.m_DataLayout = DataLayout::NHWC;
const unsigned int inputDimensionSizes[] = {1, 4, 4, 3}; // NHWCin
const unsigned int weightsDimensionSizes[] = {4, 2, 2, 3}; // CoutHWCin
const unsigned int outputDimensionSizes[] = {1, 3, 3, 4}; // NHWCout
const unsigned int outputChannelSize[] = {outputDimensionSizes[3]}; // Cout
TensorInfo inputInfo (4, inputDimensionSizes, DataType::Float32);
TensorInfo outputInfo(4, outputDimensionSizes, DataType::Float32);
std::vector<float> weightsVector = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
11, 12, 13, 14, 15, 16, 17, 18, 19, 110, 111, 112,
21, 22, 23, 24, 25, 26, 27, 28, 29, 210, 211, 212,
31, 32, 33, 34, 35, 36, 37, 38, 39, 310, 311, 312};
TensorInfo weightsInfo(4, weightsDimensionSizes, DataType::Float32);
ConstTensor weights (weightsInfo, weightsVector);
std::vector<float> biasVector = {3.3f, 3.2f, 3.1f, 3.0f};
TensorInfo biasInfo(1, outputChannelSize, DataType::Float32);
ConstTensor bias (biasInfo, biasVector);
Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias);
std::vector<float> betaVector = {0.0f, 0.2f, 0.3f, 0.4f};
std::vector<float> gammaVector = {0.5f, 0.6f, 0.7f, 0.8f};
std::vector<float> meanVector = {0.1f, 0.2f, 0.3f, 0.4f};
std::vector<float> varianceVector = {1.0f, 1.1f, 1.2f, 1.3f};
ConstTensor beta (TensorInfo(1, outputChannelSize, DataType::Float32), betaVector);
ConstTensor gamma (TensorInfo(1, outputChannelSize, DataType::Float32), gammaVector);
ConstTensor mean (TensorInfo(1, outputChannelSize, DataType::Float32), meanVector);
ConstTensor variance(TensorInfo(1, outputChannelSize, DataType::Float32), varianceVector);
auto inputSize = inputDimensionSizes[0]*inputDimensionSizes[1]*inputDimensionSizes[2]*inputDimensionSizes[3];
auto outputSize = outputDimensionSizes[0]*outputDimensionSizes[1]*outputDimensionSizes[2]*outputDimensionSizes[3];
// FIRST NETWORK: Fused
// Construct ArmNN network
NetworkId networkIdentifier;
INetworkPtr network = INetwork::Create();
IConnectableLayer *inputLayer = network->AddInputLayer(0);
IConnectableLayer *convLayer = network->AddConvolution2dLayer(convolution2dDescriptor,
weights,
optionalBias,
"convolution");
IConnectableLayer *batchNormLayer = network->AddBatchNormalizationLayer(batchNormDescriptor,
mean,
variance,
beta,
gamma,
"batchNorm");
IConnectableLayer *outputLayer = network->AddOutputLayer(0);
inputLayer ->GetOutputSlot(0).Connect(convLayer ->GetInputSlot(0));
convLayer ->GetOutputSlot(0).Connect(batchNormLayer->GetInputSlot(0));
batchNormLayer ->GetOutputSlot(0).Connect(outputLayer ->GetInputSlot(0));
//Set the tensors in the network.
inputLayer ->GetOutputSlot(0).SetTensorInfo(inputInfo);
convLayer ->GetOutputSlot(0).SetTensorInfo(outputInfo);
batchNormLayer ->GetOutputSlot(0).SetTensorInfo(outputInfo);
// Create ArmNN runtime
IRuntime::CreationOptions options; // default options
IRuntimePtr run = IRuntime::Create(options);
// Optimise ArmNN network
IOptimizedNetworkPtr optNet = Optimize(*network, {Compute::CpuRef}, run->GetDeviceSpec());
// Load graph into runtime
BOOST_TEST(run->LoadNetwork(networkIdentifier, std::move(optNet)) == Status::Success);
//Creates structures for inputs and outputs.
std::vector<float> inputData(inputSize, 128);
std::vector<float> outputData(outputSize);
InputTensors inputTensors {{0, ConstTensor(run->GetInputTensorInfo (networkIdentifier, 0), inputData.data())}};
OutputTensors outputTensors{{0, Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}};
// Execute network
run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
// SECOND NETWORK: NotFused
// Construct ArmNN network
NetworkId networkIdentifierNotFused;
INetworkPtr networkNotFused = INetwork::Create();
IConnectableLayer *inputLayerNotFused = networkNotFused->AddInputLayer(0);
IConnectableLayer *convLayerNotFused = networkNotFused->AddConvolution2dLayer(convolution2dDescriptor,
weights,
optionalBias,
"convolution");
IConnectableLayer *batchNormLayerNotFused = networkNotFused->AddBatchNormalizationLayer(batchNormDescriptor,
mean,
variance,
beta,
gamma,
"batchNorm");
IConnectableLayer *outputLayerNotFused = networkNotFused->AddOutputLayer(0);
IConnectableLayer *output2LayerNotFused = networkNotFused->AddOutputLayer(1);
inputLayerNotFused ->GetOutputSlot(0).Connect(convLayerNotFused ->GetInputSlot(0));
convLayerNotFused ->GetOutputSlot(0).Connect(batchNormLayerNotFused->GetInputSlot(0));
batchNormLayerNotFused ->GetOutputSlot(0).Connect(outputLayerNotFused ->GetInputSlot(0));
convLayerNotFused ->GetOutputSlot(0).Connect(output2LayerNotFused ->GetInputSlot(0));
//Set the tensors in the network.
inputLayerNotFused ->GetOutputSlot(0).SetTensorInfo(inputInfo);
convLayerNotFused ->GetOutputSlot(0).SetTensorInfo(outputInfo);
batchNormLayerNotFused ->GetOutputSlot(0).SetTensorInfo(outputInfo);
// Create ArmNN runtime
IRuntimePtr runNotFused = IRuntime::Create(options);
// Optimise ArmNN network
IOptimizedNetworkPtr optNetNotFused = Optimize(*networkNotFused, {Compute::CpuRef}, runNotFused->GetDeviceSpec());
// Load graph into runtime
BOOST_TEST(runNotFused->LoadNetwork(networkIdentifierNotFused, std::move(optNetNotFused)) == Status::Success);
//Creates structures for inputs and outputs.
std::vector<float> inputDataNotFused(inputSize, 128);
std::vector<float> outputDataNotFused(outputSize);
std::vector<float> outputData2NotFused(outputSize);
InputTensors inputTensorsNotFused{
{0, ConstTensor(runNotFused->GetInputTensorInfo(networkIdentifierNotFused, 0), inputDataNotFused.data())}};
OutputTensors outputTensorsNotFused{
{0, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 0), outputDataNotFused.data())},
{1, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 1), outputData2NotFused.data())}};
// Execute network
runNotFused->EnqueueWorkload(networkIdentifierNotFused, inputTensorsNotFused, outputTensorsNotFused);
// Check the output of the fused-convolution matches with the output of the batchNormm in the "NotFused" network
for (unsigned int n = 0; n < outputData.size(); ++n)
{
BOOST_CHECK_CLOSE(outputData[n], outputDataNotFused[n], 0.001);
}
}
#endif
BOOST_AUTO_TEST_SUITE_END()
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