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
*/
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;
* 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.
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;
for (auto& it : ieNet.getInputsInfo())
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()
{
}
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);
}
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
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()
);
projects / platform / upstream / opencv.git / commitdiff