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Merge pull request #16034 from Quantizs:irLoadFromBuffer

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This commit is contained in:
Alexander Alekhin 2019-12-19 10:00:07 +00:00
commit 4342657762
3 changed files with 211 additions and 18 deletions
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47
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -384,7 +384,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
CV_WRAP Net(); //!< Default constructor.
CV_WRAP ~Net(); //!< Destructor frees the net only if there aren't references to the net anymore.
/** @brief Create a network from Intel's Model Optimizer intermediate representation.
/** @brief Create a network from Intel's Model Optimizer intermediate representation (IR).
* @param[in] xml XML configuration file with network's topology.
* @param[in] bin Binary file with trained weights.
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
@ -392,6 +392,25 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_WRAP static Net readFromModelOptimizer(const String& xml, const String& bin);
/** @brief Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).
* @param[in] bufferModelConfig buffer with model's configuration.
* @param[in] bufferWeights buffer with model's trained weights.
* @returns Net object.
*/
CV_WRAP static
Net readFromModelOptimizer(const std::vector<uchar>& bufferModelConfig, const std::vector<uchar>& bufferWeights);
/** @brief Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).
* @param[in] bufferModelConfigPtr buffer pointer of model's configuration.
* @param[in] bufferModelConfigSize buffer size of model's configuration.
* @param[in] bufferWeightsPtr buffer pointer of model's trained weights.
* @param[in] bufferWeightsSize buffer size of model's trained weights.
* @returns Net object.
*/
static
Net readFromModelOptimizer(const uchar* bufferModelConfigPtr, size_t bufferModelConfigSize,
const uchar* bufferWeightsPtr, size_t bufferWeightsSize);
/** Returns true if there are no layers in the network. */
CV_WRAP bool empty() const;
@ -857,7 +876,31 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
* backend.
*/
CV_EXPORTS_W Net readNetFromModelOptimizer(const String &xml, const String &bin);
CV_EXPORTS_W
Net readNetFromModelOptimizer(const String &xml, const String &bin);
/** @brief Load a network from Intel's Model Optimizer intermediate representation.
* @param[in] bufferModelConfig Buffer contains XML configuration with network's topology.
* @param[in] bufferWeights Buffer contains binary data with trained weights.
* @returns Net object.
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
* backend.
*/
CV_EXPORTS_W
Net readNetFromModelOptimizer(const std::vector<uchar>& bufferModelConfig, const std::vector<uchar>& bufferWeights);
/** @brief Load a network from Intel's Model Optimizer intermediate representation.
* @param[in] bufferModelConfigPtr Pointer to buffer which contains XML configuration with network's topology.
* @param[in] bufferModelConfigSize Binary size of XML configuration data.
* @param[in] bufferWeightsPtr Pointer to buffer which contains binary data with trained weights.
* @param[in] bufferWeightsSize Binary size of trained weights data.
* @returns Net object.
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
* backend.
*/
CV_EXPORTS
Net readNetFromModelOptimizer(const uchar* bufferModelConfigPtr, size_t bufferModelConfigSize,
const uchar* bufferWeightsPtr, size_t bufferWeightsSize);
/** @brief Reads a network model <a href="https://onnx.ai/">ONNX</a>.
* @param onnxFile path to the .onnx file with text description of the network architecture.

128
modules/dnn/src/dnn.cpp
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@ -2951,28 +2951,22 @@ struct Net::Impl
return getBlobAsync(getPinByAlias(outputName));
}
#endif // CV_CXX11
#ifdef HAVE_INF_ENGINE
static
Net createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNet);
#endif
};
Net::Net() : impl(new Net::Impl)
{
}
Net Net::readFromModelOptimizer(const String& xml, const String& bin)
#ifdef HAVE_INF_ENGINE
/*static*/
Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNet)
{
#ifndef HAVE_INF_ENGINE
CV_Error(Error::StsError, "Build OpenCV with Inference Engine to enable loading models from Model Optimizer.");
#else
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
InferenceEngine::CNNNetReader reader;
reader.ReadNetwork(xml);
reader.ReadWeights(bin);
InferenceEngine::CNNNetwork ieNet = reader.getNetwork();
#else
InferenceEngine::Core& ie = getCore();
InferenceEngine::CNNNetwork ieNet = ie.ReadNetwork(xml, bin);
#endif
CV_TRACE_FUNCTION();
std::vector<String> inputsNames;
std::vector<MatShape> inp_shapes;
@ -3051,9 +3045,95 @@ Net Net::readFromModelOptimizer(const String& xml, const String& bin)
cvNet.impl->skipInfEngineInit = true;
return cvNet;
}
#endif // HAVE_INF_ENGINE
Net Net::readFromModelOptimizer(const String& xml, const String& bin)
{
CV_TRACE_FUNCTION();
#ifndef HAVE_INF_ENGINE
CV_UNUSED(xml); CV_UNUSED(bin);
CV_Error(Error::StsError, "Build OpenCV with Inference Engine to enable loading models from Model Optimizer.");
#else
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
InferenceEngine::CNNNetReader reader;
reader.ReadNetwork(xml);
reader.ReadWeights(bin);
InferenceEngine::CNNNetwork ieNet = reader.getNetwork();
#else
InferenceEngine::Core& ie = getCore();
InferenceEngine::CNNNetwork ieNet = ie.ReadNetwork(xml, bin);
#endif
return Impl::createNetworkFromModelOptimizer(ieNet);
#endif // HAVE_INF_ENGINE
}
Net Net::readFromModelOptimizer(const std::vector<uchar>& bufferModelConfig, const std::vector<uchar>& bufferWeights)
{
CV_TRACE_FUNCTION();
CV_Assert(!bufferModelConfig.empty());
CV_Assert(!bufferWeights.empty());
return readFromModelOptimizer(bufferModelConfig.data(), bufferModelConfig.size(),
bufferWeights.data(), bufferWeights.size());
}
Net Net::readFromModelOptimizer(
const uchar* bufferModelConfigPtr, size_t bufferModelConfigSize,
const uchar* bufferWeightsPtr, size_t bufferWeightsSize
)
{
CV_TRACE_FUNCTION();
#ifndef HAVE_INF_ENGINE
CV_UNUSED(bufferModelConfigPtr); CV_UNUSED(bufferWeightsPtr);
CV_UNUSED(bufferModelConfigSize); CV_UNUSED(bufferModelConfigSize);
CV_Error(Error::StsError, "Build OpenCV with Inference Engine to enable loading models from Model Optimizer.");
#else
#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
InferenceEngine::CNNNetReader reader;
try
{
reader.ReadNetwork(bufferModelConfigPtr, bufferModelConfigSize);
InferenceEngine::TensorDesc tensorDesc(InferenceEngine::Precision::U8, { bufferWeightsSize }, InferenceEngine::Layout::C);
InferenceEngine::TBlob<uint8_t>::Ptr weightsBlobPtr(new InferenceEngine::TBlob<uint8_t>(tensorDesc));
weightsBlobPtr->allocate();
std::memcpy(weightsBlobPtr->buffer(), (uchar*)bufferWeightsPtr, bufferWeightsSize);
reader.SetWeights(weightsBlobPtr);
}
catch (const std::exception& e)
{
CV_Error(Error::StsError, std::string("DNN: IE failed to load model: ") + e.what());
}
InferenceEngine::CNNNetwork ieNet = reader.getNetwork();
#else
InferenceEngine::Core& ie = getCore();
std::string model; model.assign((char*)bufferModelConfigPtr, bufferModelConfigSize);
InferenceEngine::CNNNetwork ieNet;
try
{
InferenceEngine::TensorDesc tensorDesc(InferenceEngine::Precision::U8, { bufferWeightsSize }, InferenceEngine::Layout::C);
InferenceEngine::Blob::CPtr weights_blob = InferenceEngine::make_shared_blob<uint8_t>(tensorDesc, (uint8_t*)bufferWeightsPtr, bufferWeightsSize);
ieNet = ie.ReadNetwork(model, weights_blob);
}
catch (const std::exception& e)
{
CV_Error(Error::StsError, std::string("DNN: IE failed to load model: ") + e.what());
}
#endif
return Impl::createNetworkFromModelOptimizer(ieNet);
#endif // HAVE_INF_ENGINE
}
Net::~Net()
{
}
@ -4394,7 +4474,7 @@ Net readNet(const String& _framework, const std::vector<uchar>& bufferModel,
else if (framework == "torch")
CV_Error(Error::StsNotImplemented, "Reading Torch models from buffers");
else if (framework == "dldt")
CV_Error(Error::StsNotImplemented, "Reading Intel's Model Optimizer models from buffers");
return readNetFromModelOptimizer(bufferConfig, bufferModel);
CV_Error(Error::StsError, "Cannot determine an origin framework with a name " + framework);
}
@ -4403,5 +4483,21 @@ Net readNetFromModelOptimizer(const String &xml, const String &bin)
return Net::readFromModelOptimizer(xml, bin);
}
Net readNetFromModelOptimizer(const std::vector<uchar>& bufferCfg, const std::vector<uchar>& bufferModel)
{
return Net::readFromModelOptimizer(bufferCfg, bufferModel);
}
Net readNetFromModelOptimizer(
const uchar* bufferModelConfigPtr, size_t bufferModelConfigSize,
const uchar* bufferWeightsPtr, size_t bufferWeightsSize
)
{
return Net::readFromModelOptimizer(
bufferModelConfigPtr, bufferModelConfigSize,
bufferWeightsPtr, bufferWeightsSize
);
}
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace

54
modules/dnn/test/test_misc.cpp
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@ -637,6 +637,60 @@ TEST_P(Test_Model_Optimizer, forward_two_nets)
normAssert(ref0, ref2, 0, 0);
}
TEST_P(Test_Model_Optimizer, readFromBuffer)
{
const Backend backendId = get<0>(GetParam());
const Target targetId = get<1>(GetParam());
if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
throw SkipTestException("No support for async forward");
const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
const std::string& weightsFile = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
const std::string& modelFile = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
else
FAIL() << "Unknown backendId";
Net net1 = readNetFromModelOptimizer(modelFile, weightsFile);
net1.setPreferableBackend(backendId);
net1.setPreferableTarget(targetId);
std::vector<char> modelConfig;
readFileContent(modelFile, modelConfig);
std::vector<char> weights;
readFileContent(weightsFile, weights);
Net net2 = readNetFromModelOptimizer(
(const uchar*)modelConfig.data(), modelConfig.size(),
(const uchar*)weights.data(), weights.size()
);
net2.setPreferableBackend(backendId);
net2.setPreferableTarget(targetId);
int blobSize[] = {2, 6, 75, 113};
Mat input(4, &blobSize[0], CV_32F);
randu(input, 0, 255);
Mat ref, actual;
{
net1.setInput(input);
ref = net1.forward();
}
{
net2.setInput(input);
actual = net2.forward();
}
normAssert(ref, actual, "", 0, 0);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Model_Optimizer,
dnnBackendsAndTargetsIE()
);