IntenseWebs/openvino
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

it is duplicate of PR: #656, but without test modification (#794)

Browse Source
This commit is contained in:
Daria Mityagina 2020-06-05 19:57:03 +03:00 committed by GitHub
parent 7f09b54af8
commit 807f85f93f
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 29 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
inference-engine/cmake/vpu_dependencies.cmake
View File

@ -19,7 +19,7 @@ set(VPU_SUPPORTED_FIRMWARES usb-ma2450 usb-ma2x8x pcie-ma248x)
# Default packages
#
set(FIRMWARE_PACKAGE_VERSION 1187)
set(FIRMWARE_PACKAGE_VERSION 1201)
set(VPU_CLC_MA2X8X_VERSION "movi-cltools-20.02.0")
#

35
inference-engine/src/vpu/graph_transformer/src/stages/rnn.cpp
View File

@ -61,6 +61,7 @@ private:
void initialCheckImpl() const override {
IE_ASSERT(numInputs() == 5);
IE_ASSERT(numOutputs() > 0);
IE_ASSERT(numOutputs() < 4);
assertAllInputsOutputsTypes(this, DataType::FP16, DataType::FP16);
}
@ -73,6 +74,7 @@ private:
serializer.append(nCells);
serializer.append(nBatches);
serializer.append(static_cast<int>(useCellState));
serializer.append(static_cast<int>(outputEdges().size()));
}
void serializeDataImpl(BlobSerializer& serializer) const override {
@ -118,7 +120,7 @@ static void RNNRelayout(
void FrontEnd::parseRNN(const Model& model, const ie::CNNLayerPtr& _layer, const DataVector &inputs, const DataVector &outputs) const {
IE_ASSERT(inputs.size() == 3);
IE_ASSERT(outputs.size() == 1);
IE_ASSERT(outputs.size() <= 3);
auto layer = std::dynamic_pointer_cast<ie::RNNSequenceLayer>(_layer);
IE_ASSERT(layer != nullptr);
@ -128,16 +130,30 @@ void FrontEnd::parseRNN(const Model& model, const ie::CNNLayerPtr& _layer, const
Data weights, biases;
std::tie(weights, biases) = getWeightsAndBiases(model, layer);
size_t nCells = inputs[0]->desc().dim(Dim::H);
size_t nBatches = inputs[0]->desc().dim(Dim::C);
size_t nCells = 0;
size_t nBatches = 0;
size_t inputSize = inputs[0]->desc().dim(Dim::W);
if (layer->axis == 1) {
nCells = inputs[0]->desc().dim(Dim::H);
nBatches = inputs[0]->desc().dim(Dim::C);
} else if (layer->axis == 0) {
nCells = inputs[0]->desc().dim(Dim::C);
nBatches = inputs[0]->desc().dim(Dim::H);
} else if (layer->axis == 2) {
nCells = inputs[0]->desc().dim(Dim::W);
nBatches = inputs[0]->desc().dim(Dim::C);
inputSize = inputs[0]->desc().dim(Dim::H);
}
IE_ASSERT(nCells >= 1);
IE_ASSERT(nBatches >= 1);
size_t inputSize = inputs[0]->desc().dim(Dim::W);
IE_ASSERT(inputSize == inputs[0]->desc().totalDimSize() / nCells / nBatches);
size_t stateSize = outputs[0]->desc().totalDimSize() / nCells / nBatches;
size_t cellStateSize = inputs[2]->desc().totalDimSize() / nBatches;
IE_ASSERT(stateSize == cellStateSize);
size_t weightsSize = weights->desc().totalDimSize();
@ -147,7 +163,7 @@ void FrontEnd::parseRNN(const Model& model, const ie::CNNLayerPtr& _layer, const
IE_ASSERT(stateSize * ngates == biasesSize);
/* weights repacking */
const auto generator = [&weights, stateSize, inputSize, ngates](const ie::Blob::Ptr& blob) {
const auto generator = [&weights, stateSize, inputSize, ngates, outputs](const ie::Blob::Ptr& blob) {
auto newWeightsPtr = blob->buffer().as<fp16_t*>();
auto content = weights->content();
@ -158,22 +174,27 @@ void FrontEnd::parseRNN(const Model& model, const ie::CNNLayerPtr& _layer, const
RNNRelayout(
origWeights,
newWeightsPtr,
newWeightsPtr + ngates * stateSize * inputSize,
ngates,
stateSize,
inputSize);
};
auto newWeights = model->addConstData(_layer->name + "@weights", weights->desc(), generator);
auto outputData = outputs;
if (outputs.size() == 1) {
outputData.push_back(model->addFakeData());
}
auto stateCellFinal = model->addFakeData();
auto stage = model->addNewStage<LSTMCellStage>(
layer->name,
StageType::LSTMCell,
layer,
{inputs[0], inputs[1], inputs[2], newWeights, biases},
{outputs[0], stateCellFinal});
outputData);
if (nCells > 1)
model->addTempBuffer(stage, DataDesc({stateSize}));