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Fixed CNNNetwork iterator for some corner cases with Memory (#4084)

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* Fixed CNNNetwork iterator for some corner cases with Memory

* Fixed tests
This commit is contained in:
Ilya Churaev
2021-02-02 16:58:51 +03:00
committed by GitHub
parent ac7c847187
commit 6a0bc80137
2 changed files with 101 additions and 10 deletions
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35
inference-engine/src/legacy_api/include/legacy/details/ie_cnn_network_iterator.hpp
View File

@@ -32,7 +32,6 @@ class INFERENCE_ENGINE_INTERNAL("Migrate to IR v10 and work with ngraph::Functio
CNNNetworkIterator {
IE_SUPPRESS_DEPRECATED_START
std::unordered_set<CNNLayer*> visited {};
std::list<CNNLayerPtr> nextLayersToVisit {};
InferenceEngine::CNNLayerPtr currentLayer = nullptr;
const ICNNNetwork* network = nullptr;
@@ -56,6 +55,7 @@ CNNNetworkIterator {
}
return consumers;
};
std::unordered_set<CNNLayer*> visited;
auto bfs = [&](const CNNLayerPtr& start_node, bool traverse_via_outputs = false) {
if (!start_node || visited.count(start_node.get())) return;
std::deque<CNNLayerPtr> q;
@@ -98,16 +98,31 @@ CNNNetworkIterator {
}
};
// First we run bfs starting from outputs that provides deterministic graph traverse
for (const auto & output : outputs) {
bfs(getCreatorLayer(output.second).lock());
// Find all outputLayers
std::vector<CNNLayerPtr> outputLayers;
const auto* networkImpl = dynamic_cast<const CNNNetworkImpl*>(network);
if (networkImpl) {
for (const auto & node : networkImpl->allLayers()) {
if (get_consumers(node.second).empty())
outputLayers.emplace_back(node.second);
}
} else {
// For backward compatibility
for (const auto& out : outputs) {
outputLayers.emplace_back(getCreatorLayer(out.second).lock());
}
}
// For cases when graph has no outputs we start bfs from inputs to ensure topological sort
for (const auto & input : inputs) {
const auto data_ptr = input.second->getInputData();
for (const auto & consumer : getInputTo(data_ptr))
bfs(consumer.second, true);
// First we run bfs starting from outputs that provides deterministic graph traverse
for (const auto & output : outputLayers) {
bfs(output);
}
if (!networkImpl) {
// For cases when graph has no outputs we start bfs from inputs to ensure topological sort
for (const auto & input : inputs) {
const auto data_ptr = input.second->getInputData();
for (const auto & consumer : getInputTo(data_ptr))
bfs(consumer.second, true);
}
}
currentLayer = nextLayersToVisit.front();
}

76
inference-engine/tests/functional/inference_engine/cnn_network/convert_ngraph_to_cnn_network_tests.cpp
View File

@@ -12,12 +12,14 @@
#include <transformations/common_optimizations/common_optimizations.hpp>
#include <legacy/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.hpp>
#include <legacy/transformations/convert_opset1_to_legacy/convert_prior_to_ie_prior.hpp>
#include <legacy/details/ie_cnn_network_iterator.hpp>
#include <transformations/opset_conversions/convert_opset2_to_opset1.hpp>
#include <transformations/opset_conversions/convert_opset3_to_opset2.hpp>
#include <transformations/convert_precision.hpp>
#include <ngraph/function.hpp>
#include <ngraph/opsets/opset1.hpp>
#include <ngraph/opsets/opset4.hpp>
#include <ngraph/opsets/opset6.hpp>
#include <ngraph_ops/convolution_ie.hpp>
#include <transformations/init_node_info.hpp>
#include <legacy/convert_function_to_cnn_network.hpp>
@@ -389,3 +391,77 @@ TEST(ConvertFunctionToCNNNetworkTests, NonUniqueNamesParametersNegative) {
EXPECT_THAT(e.what(), testing::HasSubstr(std::string("Detected two output operations with the same name:")));
}
}
TEST(ConvertFunctionToCNNNetworkTests, IteratorForMemoryLayers) {
std::shared_ptr<ngraph::Function> f(nullptr);
{
auto constReadVal = ngraph::opset6::Constant::create(ngraph::element::f32, {1, 37632}, {0});
constReadVal->set_friendly_name("const");
auto readVal = std::make_shared<ngraph::opset6::ReadValue>(constReadVal, "buffer_1");
readVal->set_friendly_name("readVal_Buf1");
auto constVarSplit1 = ngraph::opset6::Constant::create(ngraph::element::i64, {}, {1});
constVarSplit1->set_friendly_name("varSplitConst1");
auto constVarSplit2 = ngraph::opset6::Constant::create(ngraph::element::i64, {2}, {5376, 32256});
constVarSplit2->set_friendly_name("varSplitConst2");
auto varSplit = std::make_shared<ngraph::opset6::VariadicSplit>(readVal, constVarSplit1, constVarSplit2);
auto param1 = std::make_shared<ngraph::opset6::Parameter>(ngraph::element::f32, ngraph::Shape{1, 5376});
auto varConcat = std::make_shared<ngraph::opset6::Concat>(ngraph::OutputVector{varSplit->output(0), param1}, 1);
auto result = std::make_shared<ngraph::opset6::Result>(varConcat);
auto param2 = std::make_shared<ngraph::opset6::Parameter>(ngraph::element::f32, ngraph::Shape{1, 5376});
auto varConcat2 = std::make_shared<ngraph::opset6::Concat>(ngraph::OutputVector{varSplit->output(1), param2}, 1);
auto assign = std::make_shared<ngraph::opset6::Assign>(varConcat2, "buffer_1");
f = std::make_shared<ngraph::Function>(ngraph::ResultVector{result}, ngraph::SinkVector{assign}, ngraph::ParameterVector{param1, param2});
}
InferenceEngine::CNNNetwork nGraphImpl(f);
nGraphImpl = CNNNetwork(InferenceEngine::details::convertFunctionToICNNNetwork(f, nGraphImpl));
int memory_count(0);
IE_SUPPRESS_DEPRECATED_START
for (details::CNNNetworkIterator itLayer{nGraphImpl}; itLayer != details::CNNNetworkIterator(); itLayer++) {
if ((*itLayer)->type == "Memory")
memory_count++;
}
IE_SUPPRESS_DEPRECATED_END
ASSERT_EQ(2, memory_count);
}
TEST(ConvertFunctionToCNNNetworkTests, IteratorForMemoryLayers2) {
std::shared_ptr<ngraph::Function> f(nullptr);
{
auto constReadVal = ngraph::opset6::Constant::create(ngraph::element::f32, {1, 37632}, {0});
constReadVal->set_friendly_name("const");
auto readVal = std::make_shared<ngraph::opset6::ReadValue>(constReadVal, "buffer_1");
readVal->set_friendly_name("readVal_Buf1");
auto constVarSplit1 = ngraph::opset6::Constant::create(ngraph::element::i64, {}, {1});
constVarSplit1->set_friendly_name("varSplitConst1");
auto constVarSplit2 = ngraph::opset6::Constant::create(ngraph::element::i64, {2}, {5376, 32256});
constVarSplit2->set_friendly_name("varSplitConst2");
auto varSplit = std::make_shared<ngraph::opset6::VariadicSplit>(readVal, constVarSplit1, constVarSplit2);
auto param2 = std::make_shared<ngraph::opset6::Parameter>(ngraph::element::f32, ngraph::Shape{1, 5376});
auto varConcat2 = std::make_shared<ngraph::opset6::Concat>(ngraph::OutputVector{varSplit->output(1), param2}, 1);
auto assign = std::make_shared<ngraph::opset6::Assign>(varConcat2, "buffer_1");
f = std::make_shared<ngraph::Function>(ngraph::ResultVector{}, ngraph::SinkVector{assign}, ngraph::ParameterVector{param2});
}
InferenceEngine::CNNNetwork nGraphImpl(f);
nGraphImpl = CNNNetwork(InferenceEngine::details::convertFunctionToICNNNetwork(f, nGraphImpl));
int memory_count(0);
IE_SUPPRESS_DEPRECATED_START
for (details::CNNNetworkIterator itLayer{nGraphImpl}; itLayer != details::CNNNetworkIterator(); itLayer++) {
if ((*itLayer)->type == "Memory")
memory_count++;
}
IE_SUPPRESS_DEPRECATED_END
ASSERT_EQ(2, memory_count);
}