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@ -54,7 +54,8 @@ static InferenceEngine::DataPtr wrapToInfEngineDataNode(const Mat& m, const std: |
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std::vector<size_t> reversedShape(&m.size[0], &m.size[0] + m.dims); |
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std::reverse(reversedShape.begin(), reversedShape.end()); |
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return InferenceEngine::DataPtr( |
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new InferenceEngine::Data(name, reversedShape, InferenceEngine::Precision::FP32) |
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new InferenceEngine::Data(name, reversedShape, InferenceEngine::Precision::FP32, |
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InferenceEngine::Layout::ANY) |
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); |
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} |
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@ -122,37 +123,6 @@ void InfEngineBackendNet::getOutputsInfo(InferenceEngine::OutputsDataMap &output |
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// Returns input references that aren't connected to internal outputs.
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void InfEngineBackendNet::getInputsInfo(InferenceEngine::InputsDataMap &inputs_) noexcept |
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{ |
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if (inputs.empty()) |
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{ |
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std::map<std::string, InferenceEngine::DataPtr> internalOutputs; |
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for (const auto& l : layers) |
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{ |
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for (const InferenceEngine::DataWeakPtr& ptr : l->insData) |
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{ |
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InferenceEngine::DataPtr inp(ptr); |
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if (internalOutputs.find(inp->name) == internalOutputs.end()) |
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{ |
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InferenceEngine::InputInfo::Ptr inpInfo(new InferenceEngine::InputInfo()); |
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inpInfo->setInputData(inp); |
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if (inputs.find(inp->name) == inputs.end()) |
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inputs[inp->name] = inpInfo; |
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} |
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} |
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for (const InferenceEngine::DataPtr& out : l->outData) |
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{ |
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// TODO: Replace to uniquness assertion.
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if (internalOutputs.find(out->name) == internalOutputs.end()) |
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internalOutputs[out->name] = out; |
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} |
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} |
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CV_Assert(layers.empty() || !inputs.empty()); |
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} |
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inpBlobs.clear(); |
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for (const auto& it : inputs) |
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{ |
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CV_Assert(allBlobs.find(it.first) != allBlobs.end()); |
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inpBlobs[it.first] = allBlobs[it.first]; |
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} |
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inputs_ = inputs; |
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} |
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@ -239,7 +209,31 @@ size_t InfEngineBackendNet::getBatchSize() const noexcept |
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void InfEngineBackendNet::initEngine() |
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{ |
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CV_Assert(!isInitialized()); |
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CV_Assert(!isInitialized(), !layers.empty()); |
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// Collect all external input blobs.
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std::map<std::string, InferenceEngine::DataPtr> internalOutputs; |
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for (const auto& l : layers) |
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{ |
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for (const InferenceEngine::DataWeakPtr& ptr : l->insData) |
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{ |
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InferenceEngine::DataPtr inp(ptr); |
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if (internalOutputs.find(inp->name) == internalOutputs.end()) |
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{ |
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InferenceEngine::InputInfo::Ptr inpInfo(new InferenceEngine::InputInfo()); |
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inpInfo->setInputData(inp); |
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if (inputs.find(inp->name) == inputs.end()) |
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inputs[inp->name] = inpInfo; |
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} |
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} |
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for (const InferenceEngine::DataPtr& out : l->outData) |
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{ |
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// TODO: Replace to uniquness assertion.
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if (internalOutputs.find(out->name) == internalOutputs.end()) |
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internalOutputs[out->name] = out; |
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} |
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} |
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CV_Assert(!inputs.empty()); |
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// Add all unconnected blobs to output blobs.
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InferenceEngine::OutputsDataMap unconnectedOuts; |
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@ -258,13 +252,21 @@ void InfEngineBackendNet::initEngine() |
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unconnectedOuts.erase(InferenceEngine::DataPtr(inp)->name); |
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} |
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} |
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CV_Assert(layers.empty() || !unconnectedOuts.empty()); |
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CV_Assert(!unconnectedOuts.empty()); |
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for (auto it = unconnectedOuts.begin(); it != unconnectedOuts.end(); ++it) |
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{ |
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outputs[it->first] = it->second; |
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} |
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// Set up input blobs.
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inpBlobs.clear(); |
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for (const auto& it : inputs) |
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{ |
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CV_Assert(allBlobs.find(it.first) != allBlobs.end()); |
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inpBlobs[it.first] = allBlobs[it.first]; |
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} |
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// Set up output blobs.
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outBlobs.clear(); |
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for (const auto& it : outputs) |
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@ -273,7 +275,11 @@ void InfEngineBackendNet::initEngine() |
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outBlobs[it.first] = allBlobs[it.first]; |
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} |
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#ifdef _WIN32 |
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engine = InferenceEngine::InferenceEnginePluginPtr("MKLDNNPlugin.dll"); |
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#else |
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engine = InferenceEngine::InferenceEnginePluginPtr("libMKLDNNPlugin.so"); |
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#endif // _WIN32
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InferenceEngine::ResponseDesc resp; |
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InferenceEngine::StatusCode status = engine->LoadNetwork(*this, &resp); |
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if (status != InferenceEngine::StatusCode::OK) |
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@ -305,7 +311,8 @@ void InfEngineBackendNet::forward() |
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static inline Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob) |
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{ |
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// NOTE: Inference Engine sizes are reversed.
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std::vector<int> size(blob->dims().begin(), blob->dims().end()); |
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std::vector<size_t> dims = blob->dims(); |
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std::vector<int> size(dims.begin(), dims.end()); |
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std::reverse(size.begin(), size.end()); |
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return Mat(size, CV_32F, (void*)blob->buffer()); |
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} |
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@ -313,28 +320,32 @@ static inline Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob) |
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void fuseConvWeights(const std::shared_ptr<InferenceEngine::ConvolutionLayer>& conv, |
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const Mat& w, const Mat& b) |
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{ |
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// Get convolution's weights. Clone the data because Inference Engine can host it
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// and conv->_weights->allocate() below will deallocate it.
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Mat originWeights = infEngineBlobToMat(conv->_weights).clone(); |
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// Create new weights blob.
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conv->_weights = InferenceEngine::make_shared_blob<float>( |
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InferenceEngine::Precision::FP32, conv->_weights->dims()); |
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conv->_weights->allocate(); |
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// Convolution weights have OIHW data layout.
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// (conv(I) + b1 ) * w + b2
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// w*conv(I) + b1 * w + b2
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Mat fusedWeights = infEngineBlobToMat(conv->_weights); |
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const int numChannels = fusedWeights.size[0]; |
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// Mat weights = blobs[0].reshape(1, 1);
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// Mat bias = hasBias ? blobs[1].reshape(1, 1) : Mat();
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CV_Assert(numChannels == w.total()); |
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CV_Assert(b.empty() || numChannels == b.total()); |
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for (int i = 0; i < numChannels; ++i) |
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CV_Assert(!w.empty() || !b.empty()); |
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if (!w.empty()) |
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{ |
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cv::multiply(slice(originWeights, i), w.at<float>(i), slice(fusedWeights, i)); |
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// Get convolution's weights. Clone the data because Inference Engine can host it
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// and conv->_weights->allocate() below will deallocate it.
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Mat originWeights = infEngineBlobToMat(conv->_weights).clone(); |
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// Create new weights blob.
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conv->_weights = InferenceEngine::make_shared_blob<float>( |
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InferenceEngine::Precision::FP32, conv->_weights->dims()); |
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conv->_weights->allocate(); |
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// Convolution weights have OIHW data layout.
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// (conv(I) + b1 ) * w + b2
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// w*conv(I) + b1 * w + b2
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Mat fusedWeights = infEngineBlobToMat(conv->_weights); |
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const int numChannels = fusedWeights.size[0]; |
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// Mat weights = blobs[0].reshape(1, 1);
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// Mat bias = hasBias ? blobs[1].reshape(1, 1) : Mat();
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CV_Assert(numChannels == w.total()); |
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CV_Assert(b.empty() || numChannels == b.total()); |
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for (int i = 0; i < numChannels; ++i) |
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{ |
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cv::multiply(slice(originWeights, i), w.at<float>(i), slice(fusedWeights, i)); |
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} |
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} |
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if (conv->_biases) |
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{ |
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@ -345,8 +356,10 @@ void fuseConvWeights(const std::shared_ptr<InferenceEngine::ConvolutionLayer>& c |
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InferenceEngine::Precision::FP32, conv->_biases->dims()); |
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conv->_biases->allocate(); |
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Mat fusedBiases = infEngineBlobToMat(conv->_biases); |
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originBiases.copyTo(fusedBiases); |
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cv::multiply(w.reshape(1, fusedBiases.dims, &fusedBiases.size[0]), originBiases, fusedBiases); |
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if (!w.empty()) |
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cv::multiply(w.reshape(1, fusedBiases.dims, &fusedBiases.size[0]), fusedBiases, fusedBiases); |
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if (!b.empty()) |
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cv::add(fusedBiases, b.reshape(1, fusedBiases.dims, &fusedBiases.size[0]), fusedBiases); |
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} |
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Dmitry Kurtaev
7 years ago