[Enhancement] Load openvino model from memory (#2027)

* fix cpu docker (different openvino ver for python & sdk)

* load openvino model from memory

* fix lint

Author: irexyc

csrc/mmdeploy/net/openvino/openvino_net.cpp

@@ -77,30 +77,18 @@ Result<void> OpenVINONet::Init(const Value& args) {
   auto model = context["model"].get<Model>();
   OUTCOME_TRY(auto config, model.GetModelConfig(name));
 
-  // TODO: read network with stream
-  // save xml and bin to temp file
-  auto tmp_dir = fs::temp_directory_path();
-  std::string tmp_xml = (tmp_dir / fs::path("tmp.xml")).string();
-  std::string tmp_bin = (tmp_dir / fs::path("tmp.bin")).string();
   OUTCOME_TRY(auto raw_xml, model.ReadFile(config.net));
   OUTCOME_TRY(auto raw_bin, model.ReadFile(config.weights));
-
-  try {
-    std::ofstream xml_out(tmp_xml, std::ios::binary);
-    xml_out << raw_xml;
-    xml_out.close();
-    std::ofstream bin_out(tmp_bin, std::ios::binary);
-    bin_out << raw_bin;
-    bin_out.close();
-  } catch (const std::exception& e) {
-    MMDEPLOY_ERROR("unhandled exception when creating tmp xml/bin: {}", e.what());
-    return Status(eFail);
-  }
+  auto ov_tensor = InferenceEngine::TensorDesc(InferenceEngine::Precision::U8, {raw_bin.size()},
+                                               InferenceEngine::Layout::C);
+  auto ov_blob = InferenceEngine::make_shared_blob<uint8_t>(ov_tensor);
+  ov_blob->allocate();
+  memcpy(ov_blob->buffer(), raw_bin.data(), ov_blob->byteSize());
 
   try {
     // create cnnnetwork
     core_ = InferenceEngine::Core();
-    network_ = core_.ReadNetwork(tmp_xml, tmp_bin);
+    network_ = core_.ReadNetwork(raw_xml, std::move(ov_blob));
 
     // set input tensor
     InferenceEngine::InputsDataMap input_info = network_.getInputsInfo();

docker/CPU/Dockerfile

@@ -1,4 +1,4 @@
-FROM openvino/ubuntu18_dev:2021.4.2
+FROM openvino/ubuntu20_dev:2022.3.0
 ARG PYTHON_VERSION=3.8
 ARG TORCH_VERSION=1.10.0
 ARG TORCHVISION_VERSION=0.11.0
@@ -57,7 +57,7 @@ RUN if [ ${USE_SRC_INSIDE} == true ] ; \
 RUN /opt/conda/bin/pip install torch==${TORCH_VERSION}+cpu torchvision==${TORCHVISION_VERSION}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html \
     && /opt/conda/bin/pip install --no-cache-dir openmim
 
-RUN /opt/conda/bin/mim install --no-cache-dir "mmcv"${MMCV_VERSION} onnxruntime==${ONNXRUNTIME_VERSION} openvino-dev mmengine${MMENGINE_VERSION}
+RUN /opt/conda/bin/mim install --no-cache-dir "mmcv"${MMCV_VERSION} onnxruntime==${ONNXRUNTIME_VERSION} openvino-dev==2022.3.0 mmengine${MMENGINE_VERSION}
 
 ENV PATH /opt/conda/bin:$PATH
 WORKDIR /root/workspace
@@ -100,14 +100,14 @@ RUN git clone -b main https://github.com/open-mmlab/mmdeploy.git &&\
     /opt/conda/bin/mim install -e .
 
 ### build SDK
-ENV LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:/opt/intel/openvino/deployment_tools/ngraph/lib:/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64:${LD_LIBRARY_PATH}"
+ENV LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:${LD_LIBRARY_PATH}"
 RUN cd mmdeploy && rm -rf build/CM* && mkdir -p build && cd build && cmake .. \
     -DMMDEPLOY_BUILD_SDK=ON \
     -DMMDEPLOY_BUILD_EXAMPLES=ON \
-    -DCMAKE_CXX_COMPILER=g++-7 \
+    -DCMAKE_CXX_COMPILER=g++-9 \
     -DONNXRUNTIME_DIR=${ONNXRUNTIME_DIR} \
     -Dncnn_DIR=/root/workspace/ncnn/build/install/lib/cmake/ncnn \
-    -DInferenceEngine_DIR=/opt/intel/openvino/deployment_tools/inference_engine/share \
+    -DInferenceEngine_DIR=/opt/intel/openvino/runtime/cmake \
     -DMMDEPLOY_TARGET_DEVICES=cpu \
     -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
     -DMMDEPLOY_TARGET_BACKENDS="ort;ncnn;openvino" \