[GNA] Security fixes for 2021.2 (#3167)
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@ -391,7 +391,7 @@ void SubstituteSoftSignPass::run() {
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// pattern matched - lets substitute
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gnalog() << "SoftSign subgraph found consits of: \n"
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<< "\t" << abs->name << "\n";
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if (addition == nullptr) gnalog() << "\t" << addition->name << "\n";
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if (addition != nullptr) gnalog() << "\t" << addition->name << "\n";
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gnalog() << "\t" << mul->name << "\n"
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<< std::endl;
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@ -402,6 +402,7 @@ void SubstituteSoftSignPass::run() {
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CNNLayerPtr activationLayer =
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std::make_shared<GenericLayer>(LayerParams({layerName, "SoftSign", Precision::FP32}));
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IE_ASSERT(activationLayer != nullptr);
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auto activationLayerWithQuant = quantized ?
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InferenceEngine::injectData<QuantizedLayerParams>(activationLayer) :
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activationLayer;
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@ -483,6 +484,7 @@ void SubstitutePReluPass::run() {
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// sum
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auto sum = getNext(negate);
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IE_ASSERT(sum != nullptr);
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if (!LayerInfo(sum).isEltwiseSum()) continue;
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if (sum->insData.size() != 2
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|| sum->insData[0].lock() == nullptr
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@ -922,6 +924,7 @@ void InsertConcatAligningFilterPass::run() {
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if (!info.isConcat()) continue;
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size_t offset = 0;
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auto concatLayer = info.as<ConcatLayer*>();
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IE_ASSERT(concatLayer != nullptr);
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for (auto input_idx = 0; input_idx != concatLayer->insData.size(); input_idx++) {
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auto getLayerByIndex = [&concatLayer](int idx) {
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@ -1237,6 +1240,8 @@ void EltwiseSplitOverChannelsPass::run() {
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continue;
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}
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auto masterEltwise = std::dynamic_pointer_cast<EltwiseLayer>(l);
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IE_ASSERT(masterEltwise != nullptr);
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if (l->outData.size() != 1) {
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THROW_GNA_LAYER_EXCEPTION(l) << "number of outputs expected to be 1";
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}
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@ -1339,6 +1344,7 @@ void SubstituteScaleShiftBroadCastPass::run() {
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}
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auto scaleShift = layerInfo.as<ScaleShiftLayer*>();
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IE_ASSERT(scaleShift != nullptr);
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auto insData = scaleShift->insData.front().lock();
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if (!insData) {
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@ -74,8 +74,7 @@ class PWLMatcher : public ::testing::MatcherInterface<const gna_nnet_type_t*> {
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};
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DnnActivationType detectPwlType(gna_nnet_layer_t *layer) const {
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intel_dnn_component_t comp;
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intel_dnn_component_t comp{};
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comp.ptr_outputs = layer->pOutputs;
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comp.num_columns_in = layer->nInputColumns;
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comp.num_rows_in = layer->nInputRows;
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