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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <boost/test/unit_test.hpp>
#include <armnn/ArmNN.hpp>
#include <Graph.hpp>
#include <SubGraph.hpp>
#include <SubGraphSelector.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
using namespace armnn;
namespace
{
//
// this helper only works if all layers where the inputs connect to are not selected
//
SubGraph::InputSlots CreateInputsFrom(const std::vector<Layer *> & layers)
{
SubGraph::InputSlots result;
for (auto&& layer : layers)
{
for (auto&& it = layer->BeginInputSlots(); it != layer->EndInputSlots(); ++it)
{
result.push_back(&(*it));
}
}
return result;
}
//
// this helper only works if all layers where the outputs connect to are not selected
//
SubGraph::OutputSlots CreateOutputsFrom(const std::vector<Layer *> & layers)
{
SubGraph::OutputSlots result;
for (auto && layer : layers)
{
for (auto&& it = layer->BeginOutputSlots(); it != layer->EndOutputSlots(); ++it)
{
result.push_back(&(*it));
}
}
return result;
}
//
// this takes the inputs, outputs and layers as a copy and the move these copies into the
// resulting subgraph, so the pass bay value is intentional
//
SubGraphSelector::SubGraphPtr CreateSubGraphFrom(SubGraph::InputSlots inputs,
SubGraph::OutputSlots outputs,
SubGraph::Layers layers)
{
return std::make_unique<SubGraph>(std::move(inputs), std::move(outputs), std::move(layers));
}
template <typename T, typename Iterator>
std::vector<T> ToSortedArray(Iterator begin, Iterator end)
{
std::vector<T> result(begin, end);
std::sort(result.begin(), result.end());
return result;
}
template <typename T>
void CompareVectors(const std::vector<T> & result, const std::vector<T> & expected)
{
BOOST_CHECK_EQUAL_COLLECTIONS(result.begin(), result.end(), expected.begin(), expected.end());
}
void CompareSubGraphs(SubGraphSelector::SubGraphPtr & result,
SubGraphSelector::SubGraphPtr & expected)
{
// expect both to be valid subgraphs
BOOST_TEST((result.get() != nullptr));
BOOST_TEST((expected.get() != nullptr));
if (result.get() != nullptr && expected.get() != nullptr)
{
// try to detect all other obvious errors too, mainly because here
// we can get a nicer error message from boost, the collection test
// also report error for these
BOOST_TEST(result->GetInputSlots().size() == expected->GetInputSlots().size());
BOOST_TEST(result->GetOutputSlots().size() == expected->GetOutputSlots().size());
BOOST_TEST(result->GetLayers().size() == expected->GetLayers().size());
auto resultLayers = ToSortedArray<Layer *>(result->GetLayers().begin(),
result->GetLayers().end());
auto expectedLayers = ToSortedArray<Layer *>(expected->GetLayers().begin(),
expected->GetLayers().end());
CompareVectors(resultLayers, expectedLayers);
auto resultInputs = ToSortedArray<InputSlot *>(result->GetInputSlots().begin(),
result->GetInputSlots().end());
auto expectedInputs = ToSortedArray<InputSlot *>(expected->GetInputSlots().begin(),
expected->GetInputSlots().end());
CompareVectors(resultInputs, expectedInputs);
auto resultOutputs = ToSortedArray<OutputSlot *>(result->GetOutputSlots().begin(),
result->GetOutputSlots().end());
auto expectedOutputs = ToSortedArray<OutputSlot *>(expected->GetOutputSlots().begin(),
expected->GetOutputSlots().end());
CompareVectors(resultOutputs, expectedOutputs);
}
}
} // namespace <anonymous>
BOOST_AUTO_TEST_SUITE(SubGraphSelection)
BOOST_AUTO_TEST_CASE(NoSubGraphsForNoMatch)
{
Graph graph;
auto output = graph.AddLayer<OutputLayer>(0, "output");
graph.InsertNewLayer<InputLayer>(output->GetInputSlot(0), 0, "input");
SubGraphSelector::SubGraphs subGraphs =
SubGraphSelector::SelectSubGraphs(graph, [](const Layer &) { return false; });
BOOST_TEST(subGraphs.empty());
}
BOOST_AUTO_TEST_CASE(OneSubGraphsSelectedASingleMatch)
{
Graph graph;
auto output = graph.AddLayer<OutputLayer>(0, "output");
graph.InsertNewLayer<InputLayer>(output->GetInputSlot(0), 0, "input");
SubGraphSelector::SubGraphs subGraphs =
SubGraphSelector::SelectSubGraphs(
graph,
// select the output layer only
[](const Layer & l)
{
bool isOutput = l.GetNameStr().compare("output") == 0;
return isOutput;
});
BOOST_TEST(subGraphs.size() == 1);
if (subGraphs.size() == 1)
{
auto expected = CreateSubGraphFrom(CreateInputsFrom({output}),
// outputs of 'output' will be empty
CreateOutputsFrom({output}),
{output});
CompareSubGraphs(subGraphs[0], expected);
}
}
BOOST_AUTO_TEST_CASE(MultipleLayersSelectedInTheMiddle)
{
Graph graph;
auto output = graph.AddLayer<OutputLayer>(0, "output");
auto mid0 = graph.InsertNewLayer<ActivationLayer>(output->GetInputSlot(0),
ActivationDescriptor{},
"mid0");
auto mid1 = graph.InsertNewLayer<ActivationLayer>(mid0->GetInputSlot(0),
ActivationDescriptor{},
"mid1");
graph.InsertNewLayer<InputLayer>(mid1->GetInputSlot(0), 0, "input");
SubGraphSelector::SubGraphs subGraphs =
SubGraphSelector::SelectSubGraphs(
graph,
// select the middle layers only
[](const Layer & l)
{
bool toSelect = (l.GetType() == LayerType::Activation);
return toSelect;
});
BOOST_TEST(subGraphs.size() == 1);
if (subGraphs.size() == 1)
{
auto expected = CreateSubGraphFrom(CreateInputsFrom({mid1}),
CreateOutputsFrom({mid0}),
{mid1, mid0});
CompareSubGraphs(subGraphs[0], expected);
}
}
BOOST_AUTO_TEST_CASE(IslandInTheMiddle)
{
// This case represent the scenario when a non-selected X1 node placed in the middle
// of the selected M* nodes:
//
// X0 -> M1 -> M2 -> M3 -> X2
// X0 -> M4 -> X1 -> M5 -> X2
//
/*
X0
/ \
M1 M4
| |
M2 X1 < the island in the middle !
| |
M3 M5
\ /
X2
*/
// The expected result for this is that M1,M2,M3,M4 will be part of one subgraph and
// M5 will be part of another subgraph and the input and output slots in the subgraphs
// will be set accordingly.
//
Graph graph;
OriginsDescriptor mergerDescriptor(2);
auto x2 = graph.AddLayer<MergerLayer>(mergerDescriptor, "x2");
auto m3 = graph.InsertNewLayer<ActivationLayer>(x2->GetInputSlot(0),
ActivationDescriptor{},
"m3");
auto m2 = graph.InsertNewLayer<ActivationLayer>(m3->GetInputSlot(0),
ActivationDescriptor{},
"m2");
auto m1 = graph.InsertNewLayer<ActivationLayer>(m2->GetInputSlot(0),
ActivationDescriptor{},
"m1");
auto x0 = graph.InsertNewLayer<InputLayer>(m1->GetInputSlot(0), 0, "x0");
auto m5 = graph.InsertNewLayer<ActivationLayer>(x2->GetInputSlot(1),
ActivationDescriptor{},
"m5");
auto x1 = graph.InsertNewLayer<Convolution2dLayer>(m5->GetInputSlot(0),
Convolution2dDescriptor{},
"x1");
auto m4 = graph.InsertNewLayer<ActivationLayer>(x1->GetInputSlot(0),
ActivationDescriptor{},
"m4");
// Connect the other branch to the input layer
x0->GetOutputSlot(0).Connect(m4->GetInputSlot(0));
// All selected 'M*' layers will be of Activation type
SubGraphSelector::SubGraphs subGraphs =
SubGraphSelector::SelectSubGraphs(
graph,
// select the middle layers only
[](const Layer & l)
{
bool toSelect = (l.GetType() == LayerType::Activation);
return toSelect;
});
// expected results to test against
auto largerSubGraph = CreateSubGraphFrom(CreateInputsFrom({m1, m4}),
CreateOutputsFrom({m3, m4}),
{m1, m4, m2, m3});
auto smallerSubGraph = CreateSubGraphFrom(CreateInputsFrom({m5}),
CreateOutputsFrom({m5}),
{m5});
BOOST_TEST(subGraphs.size() == 2);
if (subGraphs.size() == 2)
{
// we need to have valid subgraph pointers here
BOOST_TEST((subGraphs[0] != nullptr));
BOOST_TEST((subGraphs[1] != nullptr));
if (subGraphs[0].get() != nullptr && subGraphs[1].get() != nullptr)
{
// sort the subgraphs by layer size, so it is simpler to test
std::sort(subGraphs.begin(), subGraphs.end(),
[](SubGraphSelector::SubGraphPtr & lhs, SubGraphSelector::SubGraphPtr & rhs)
{
return (lhs->GetLayers().size() < rhs->GetLayers().size());
}
);
// one subgraph needs to be size=1 and the other one is 4
BOOST_TEST(subGraphs[0]->GetLayers().size() == 1);
BOOST_TEST(subGraphs[1]->GetLayers().size() == 4);
CompareSubGraphs(subGraphs[0], smallerSubGraph);
CompareSubGraphs(subGraphs[1], largerSubGraph);
}
}
}
BOOST_AUTO_TEST_CASE(MultipleSimpleSubGraphs)
{
// This test case represents the scenario when we have two distinct subgraphs
// in a simple linear network. The selected nodes are the M* and the
// non-selected ones are the X*
//
// X1 -> M1 -> M2 -> X2 -> M3 -> X3
//
// The expected results is two subgraphs, one with {M1, M2} and another one
// with {M3}
//
Graph graph;
// the graph is constructed in reverse order
auto x3 = graph.AddLayer<OutputLayer>(0, "output");
auto m3 = graph.InsertNewLayer<ActivationLayer>(x3->GetInputSlot(0),
ActivationDescriptor{},
"m3");
auto x2 = graph.InsertNewLayer<Convolution2dLayer>(m3->GetInputSlot(0),
Convolution2dDescriptor{},
"x2");
auto m2 = graph.InsertNewLayer<ActivationLayer>(x2->GetInputSlot(0),
ActivationDescriptor{},
"m2");
auto m1 = graph.InsertNewLayer<ActivationLayer>(m2->GetInputSlot(0),
ActivationDescriptor{},
"m1");
graph.InsertNewLayer<InputLayer>(m1->GetInputSlot(0), 0, "x1");
// All selected 'M*' layers will be of Activation type
SubGraphSelector::SubGraphs subGraphs =
SubGraphSelector::SelectSubGraphs(
graph,
// select the middle layers only
[](const Layer & l)
{
bool toSelect = (l.GetType() == LayerType::Activation);
return toSelect;
});
// expected results to test against
auto largerSubGraph = CreateSubGraphFrom(CreateInputsFrom({m1}),
CreateOutputsFrom({m2}),
{m1, m2});
auto smallerSubGraph = CreateSubGraphFrom(CreateInputsFrom({m3}),
CreateOutputsFrom({m3}),
{m3});
BOOST_TEST(subGraphs.size() == 2);
if (subGraphs.size() == 2)
{
// we need to have valid subgraph pointers here
BOOST_TEST((subGraphs[0] != nullptr));
BOOST_TEST((subGraphs[1] != nullptr));
if (subGraphs[0].get() != nullptr && subGraphs[1].get() != nullptr)
{
// sort the subgraphs by layer size, so it is simpler to test
std::sort(subGraphs.begin(), subGraphs.end(),
[](SubGraphSelector::SubGraphPtr & lhs, SubGraphSelector::SubGraphPtr & rhs)
{
return (lhs->GetLayers().size() < rhs->GetLayers().size());
}
);
BOOST_TEST(subGraphs[0]->GetLayers().size() == 1);
BOOST_TEST(subGraphs[1]->GetLayers().size() == 2);
CompareSubGraphs(subGraphs[0], smallerSubGraph);
CompareSubGraphs(subGraphs[1], largerSubGraph);
}
}
}
BOOST_AUTO_TEST_CASE(SimpleLinearTest)
{
//X1 -> M1 -> M2 -> X2
//Where the input slots of M1 and the output slots of M2 are to be the sub graph boundaries.
Graph graph;
ActivationDescriptor activationDefaults;
auto layerX1 = graph.AddLayer<InputLayer>(0, "layerX1");
auto layerX2 = graph.AddLayer<OutputLayer>(0, "layerX2");
auto layerM1 = graph.AddLayer<ActivationLayer>(activationDefaults, "layerM1");
auto layerM2 = graph.AddLayer<ActivationLayer>(activationDefaults, "layerM2");
// X1
// |
// M1
// |
// M2
// |
// X2
layerX1->GetOutputSlot(0).Connect(layerM1->GetInputSlot(0));
layerM1->GetOutputSlot(0).Connect(layerM2->GetInputSlot(0));
layerM2->GetOutputSlot(0).Connect(layerX2->GetInputSlot(0));
SubGraphSelector::SubGraphs subGraphs =
SubGraphSelector::SelectSubGraphs(
graph,
// select the activation layers M1 and M2
[](const Layer & l)
{
bool toSelect = (l.GetType() == LayerType::Activation);
return toSelect;
});
BOOST_CHECK(subGraphs.size() == 1);
if(subGraphs.size() == 1)
{
auto expected = CreateSubGraphFrom(CreateInputsFrom({layerM1}),
CreateOutputsFrom({layerM2}),
{layerM1, layerM2});
CompareSubGraphs(subGraphs[0], expected);
}
}
BOOST_AUTO_TEST_CASE(MultiInputSingleOutput)
{
//X1 -> M1 -> M3 -> X3
//X2 -> M2 -> M3 -> X3
//Where the input slots of {M1, M2} and the output slots of M3 are to be the subgraph boundaries.
Graph graph;
ActivationDescriptor activationDefaults;
auto layerX1 = graph.AddLayer<InputLayer>(0, "layerX1");
auto layerX2 = graph.AddLayer<InputLayer>(1, "layerX2");
auto layerM1 = graph.AddLayer<ActivationLayer>(activationDefaults, "layerM1");
auto layerM2 = graph.AddLayer<ActivationLayer>(activationDefaults, "layerM2");
auto layerM3 = graph.AddLayer<AdditionLayer>("layerM3");
auto layerX3 = graph.AddLayer<OutputLayer>(0, "layerX3");
// X1 X2
// | |
// M1 M2
// \ |
// \ |
// \|
// M3
// |
// |
// X3
layerX1->GetOutputSlot(0).Connect(layerM1->GetInputSlot(0));
layerX2->GetOutputSlot(0).Connect(layerM2->GetInputSlot(0));
layerM1->GetOutputSlot(0).Connect(layerM3->GetInputSlot(0));
layerM2->GetOutputSlot(0).Connect(layerM3->GetInputSlot(1));
layerM3->GetOutputSlot(0).Connect(layerX3->GetInputSlot(0));
SubGraphSelector::SubGraphs subGraphs =
SubGraphSelector::SelectSubGraphs(
graph,
// select Activation and Addition Layers M1, M2 and M3
[](const Layer & l)
{
bool toSelect = (l.GetType() == LayerType::Activation
|| l.GetType() == LayerType::Addition);
return toSelect;
});
BOOST_CHECK(subGraphs.size() == 1);
if (subGraphs.size() == 1)
{
auto expected = CreateSubGraphFrom(CreateInputsFrom({layerM1, layerM2}),
CreateOutputsFrom({layerM3}),
{layerM1, layerM2, layerM3});
CompareSubGraphs(subGraphs[0], expected);
}
}
BOOST_AUTO_TEST_CASE(SingleInputMultiOutput)
{
//X1 -> M1 -> M2 -> X2
//X1 -> M1 -> M3 -> X3
//Where the input slots of M1 and the output slots of {M2, M3} are to be the subgraph boundaries.
Graph graph;
ActivationDescriptor activationDefaults;
ViewsDescriptor viewDefaults(2,4);
Layer* layerX1 = graph.AddLayer<InputLayer>(0, "layerX1");
Layer* layerM1 = graph.AddLayer<SplitterLayer>(viewDefaults, "layerM1");
Layer* layerM2 = graph.AddLayer<ActivationLayer>(activationDefaults, "layerM2");
Layer* layerM3 = graph.AddLayer<ActivationLayer>(activationDefaults, "layerM3");
Layer* layerX2 = graph.AddLayer<OutputLayer>(0, "layerX2");
Layer* layerX3 = graph.AddLayer<OutputLayer>(1, "layerX3");
// X2
// |
// M1
// /|
// / |
// / |
// M2 M3
// | |
// | |
// X2 X3
layerX1->GetOutputSlot(0).Connect(layerM1->GetInputSlot(0));
layerM1->GetOutputSlot(0).Connect(layerM2->GetInputSlot(0));
layerM1->GetOutputSlot(1).Connect(layerM3->GetInputSlot(0));
layerM2->GetOutputSlot(0).Connect(layerX2->GetInputSlot(0));
layerM3->GetOutputSlot(0).Connect(layerX3->GetInputSlot(0));
SubGraphSelector::SubGraphs subGraphs =
SubGraphSelector::SelectSubGraphs(
graph,
// select Activation and Splitter Layers M1, M2 and M3
[](const Layer & l)
{
bool toSelect = (l.GetType() == LayerType::Activation
|| l.GetType() == LayerType::Splitter);
return toSelect;
});
BOOST_CHECK(subGraphs.size() == 1);
if(subGraphs.size() == 1)
{
auto expected = CreateSubGraphFrom(CreateInputsFrom({layerM1}),
CreateOutputsFrom({layerM2, layerM3}),
{layerM1, layerM2, layerM3});
CompareSubGraphs(subGraphs[0], expected);
}
}
BOOST_AUTO_TEST_CASE(MultiInputMultiOutput)
{
// This case represents the scenario with multiple inputs and multiple outputs
//
// X1 -> M1 -> M3 -> M4 -> X3
// X2 -> M2 -> M3 -> M5 -> X4
//
// Where the input slots of {M1, M2} and the output slots of {M4, M5} are to be the subgraph
// boundaries.
Graph graph;
ActivationDescriptor activationDefaults;
OriginsDescriptor mergerDescriptor(2);
auto x1 = graph.AddLayer<InputLayer>(0, "x1");
auto x2 = graph.AddLayer<InputLayer>(1, "x2");
auto m1 = graph.AddLayer<ActivationLayer>(activationDefaults, "m1");
auto m2 = graph.AddLayer<ActivationLayer>(activationDefaults, "m2");
auto m3 = graph.AddLayer<MergerLayer>(mergerDescriptor, "m3");
auto m4 = graph.AddLayer<ActivationLayer>(activationDefaults, "m4");
auto m5 = graph.AddLayer<ActivationLayer>(activationDefaults, "m5");
auto x3 = graph.AddLayer<OutputLayer>(0, "x3");
auto x4 = graph.AddLayer<OutputLayer>(1, "x4");
x1->GetOutputSlot(0).Connect(m1->GetInputSlot(0));
x2->GetOutputSlot(0).Connect(m2->GetInputSlot(0));
m1->GetOutputSlot(0).Connect(m3->GetInputSlot(0));
m2->GetOutputSlot(0).Connect(m3->GetInputSlot(1));
m3->GetOutputSlot(0).Connect(m4->GetInputSlot(0));
m3->GetOutputSlot(0).Connect(m5->GetInputSlot(0));
m4->GetOutputSlot(0).Connect(x3->GetInputSlot(0));
m5->GetOutputSlot(0).Connect(x4->GetInputSlot(0));
SubGraphSelector::SubGraphs subGraphs =
SubGraphSelector::SelectSubGraphs(
graph,
// select Activation and Merger Layers M1, M2, M3, M4, M5
[](const Layer & l)
{
bool toSelect = (l.GetType() == LayerType::Activation
|| l.GetType() == LayerType::Merger);
return toSelect;
});
BOOST_CHECK(subGraphs.size() == 1);
if (subGraphs.size() == 1)
{
auto expected = CreateSubGraphFrom(CreateInputsFrom({m1, m2}),
CreateOutputsFrom({m4, m5}),
{m1, m2, m3, m4, m5});
CompareSubGraphs(subGraphs[0], expected);
}
}
BOOST_AUTO_TEST_SUITE_END()