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//
// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <armnn/INetwork.hpp>
#include <GraphUtils.hpp>
#include <TestUtils.hpp>
#include <doctest/doctest.h>
using namespace armnn;
TEST_SUITE("GpuFsaOptimizedNetwork")
{
TEST_CASE("SingleConv2dSupportedOptimizedNetwork")
{
IRuntime::CreationOptions options;
IRuntimePtr runtime(IRuntime::Create(options));
INetworkPtr network(INetwork::Create());
TensorInfo inputInfo({ 1, 5, 5, 1 }, DataType::Float32);
TensorInfo outputInfo({ 1, 3, 3, 1 }, DataType::Float32);
TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32, 0.0f, 0, true);
TensorInfo biasesInfo({ 1 }, DataType::Float32, 0.0f, 0, true);
Convolution2dDescriptor desc;
desc.m_BiasEnabled = true;
desc.m_DataLayout = DataLayout::NHWC;
auto inputLayer = network->AddInputLayer(0, "input");
auto weightLayer = network->AddConstantLayer(ConstTensor(weightsInfo, nullptr), "weights");
auto biasLayer = network->AddConstantLayer(ConstTensor(biasesInfo, nullptr), "bias");
auto convLayer = network->AddConvolution2dLayer(desc, "conv2d");
auto outputLayer = network->AddOutputLayer(1, "output");
inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
weightLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(1));
weightLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
biasLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(2));
biasLayer->GetOutputSlot(0).SetTensorInfo(biasesInfo);
convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
std::vector<BackendId> backends = { "GpuFsa" };
OptimizerOptions optimizedOptions;
IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec(), optimizedOptions);
CHECK(optNet);
Graph& graph = GetGraphForTesting(optNet.get());
// Check graph layer sequence to ensure that the network has been replaced with a PreCompiledLayer
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<PreCompiledLayer>,
&IsLayerOfType<OutputLayer>));
}
TEST_CASE("TwoConv2dSupportedOptimizedNetwork")
{
IRuntime::CreationOptions options;
IRuntimePtr runtime(IRuntime::Create(options));
INetworkPtr network(INetwork::Create());
TensorInfo inputInfo({ 1, 5, 5, 1 }, DataType::Float32);
TensorInfo intermediateInfo({ 1, 3, 3, 1 }, DataType::Float32);
TensorInfo outputInfo({ 1, 1, 1, 1 }, DataType::Float32);
TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32, 0.0f, 0, true);
TensorInfo biasesInfo({ 1 }, DataType::Float32, 0.0f, 0, true);
Convolution2dDescriptor desc;
desc.m_BiasEnabled = true;
desc.m_DataLayout = DataLayout::NHWC;
auto inputLayer = network->AddInputLayer(0, "input");
auto weightLayer1 = network->AddConstantLayer(ConstTensor(weightsInfo, nullptr), "weights");
auto biasLayer1 = network->AddConstantLayer(ConstTensor(biasesInfo, nullptr), "bias");
auto convLayer1 = network->AddConvolution2dLayer(desc, "conv2d");
auto weightLayer2 = network->AddConstantLayer(ConstTensor(weightsInfo, nullptr), "weights");
auto biasLayer2 = network->AddConstantLayer(ConstTensor(biasesInfo, nullptr), "bias");
auto convLayer2 = network->AddConvolution2dLayer(desc, "conv2d");
auto outputLayer = network->AddOutputLayer(0, "output");
inputLayer->GetOutputSlot(0).Connect(convLayer1->GetInputSlot(0));
inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
weightLayer1->GetOutputSlot(0).Connect(convLayer1->GetInputSlot(1));
weightLayer1->GetOutputSlot(0).SetTensorInfo(weightsInfo);
biasLayer1->GetOutputSlot(0).Connect(convLayer1->GetInputSlot(2));
biasLayer1->GetOutputSlot(0).SetTensorInfo(biasesInfo);
convLayer1->GetOutputSlot(0).Connect(convLayer2->GetInputSlot(0));
convLayer1->GetOutputSlot(0).SetTensorInfo(intermediateInfo);
weightLayer2->GetOutputSlot(0).Connect(convLayer2->GetInputSlot(1));
weightLayer2->GetOutputSlot(0).SetTensorInfo(weightsInfo);
biasLayer2->GetOutputSlot(0).Connect(convLayer2->GetInputSlot(2));
biasLayer2->GetOutputSlot(0).SetTensorInfo(biasesInfo);
convLayer2->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
convLayer2->GetOutputSlot(0).SetTensorInfo(outputInfo);
std::vector<BackendId> backends = { "GpuFsa" };
OptimizerOptions optimizedOptions;
IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec(), optimizedOptions);
CHECK(optNet);
Graph& graph = GetGraphForTesting(optNet.get());
// Check graph layer sequence to ensure that the network has been replaced with a PreCompiledLayer
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<ConstantLayer>,
&IsLayerOfType<PreCompiledLayer>,
&IsLayerOfType<PreCompiledLayer>,
&IsLayerOfType<OutputLayer>));
}
}
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