Merge pull request #21066 from andrewerf:21052-openvino-native-onnx

Native ONNX to Inference Engine backend #21066

Resolves #21052

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or other license that is incompatible with OpenCV
- [x] The PR is proposed to proper branch
- [x] There is reference to original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [ ] The feature is well documented and sample code can be built with the project CMake

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -1021,14 +1021,14 @@ CV__DNN_INLINE_NS_BEGIN
       *                  * `*.pb` (TensorFlow, https://www.tensorflow.org/)
       *                  * `*.t7` | `*.net` (Torch, http://torch.ch/)
       *                  * `*.weights` (Darknet, https://pjreddie.com/darknet/)
-      *                  * `*.bin` (DLDT, https://software.intel.com/openvino-toolkit)
+      *                  * `*.bin` | `*.onnx` (OpenVINO, https://software.intel.com/openvino-toolkit)
       *                  * `*.onnx` (ONNX, https://onnx.ai/)
       * @param[in] config Text file contains network configuration. It could be a
       *                   file with the following extensions:
       *                  * `*.prototxt` (Caffe, http://caffe.berkeleyvision.org/)
       *                  * `*.pbtxt` (TensorFlow, https://www.tensorflow.org/)
       *                  * `*.cfg` (Darknet, https://pjreddie.com/darknet/)
-      *                  * `*.xml` (DLDT, https://software.intel.com/openvino-toolkit)
+      *                  * `*.xml` (OpenVINO, https://software.intel.com/openvino-toolkit)
       * @param[in] framework Explicit framework name tag to determine a format.
       * @returns Net object.
       *
@@ -1064,7 +1064,7 @@ CV__DNN_INLINE_NS_BEGIN
      *  backend.
      */
     CV_EXPORTS_W
-    Net readNetFromModelOptimizer(const String &xml, const String &bin);
+    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.

modules/dnn/src/dnn_read.cpp

@@ -43,9 +43,11 @@ Net readNet(const String& _model, const String& _config, const String& _framewor
             std::swap(model, config);
         return readNetFromDarknet(config, model);
     }
-    if (framework == "dldt" || modelExt == "bin" || configExt == "bin" || modelExt == "xml" || configExt == "xml")
+    if (framework == "dldt" || framework == "openvino" ||
+        modelExt == "bin" || configExt == "bin" ||
+        modelExt == "xml" || configExt == "xml")
     {
-        if (modelExt == "xml" || configExt == "bin")
+        if (modelExt == "xml" || configExt == "bin" || modelExt == "onnx")
             std::swap(model, config);
         return readNetFromModelOptimizer(config, model);
     }
@@ -68,7 +70,7 @@ Net readNet(const String& _framework, const std::vector<uchar>& bufferModel,
         return readNetFromDarknet(bufferConfig, bufferModel);
     else if (framework == "torch")
         CV_Error(Error::StsNotImplemented, "Reading Torch models from buffers");
-    else if (framework == "dldt")
+    else if (framework == "dldt" || framework == "openvino")
         return readNetFromModelOptimizer(bufferConfig, bufferModel);
     else if (framework == "tflite")
         return readNetFromTFLite(bufferModel);

modules/dnn/test/test_misc.cpp

@@ -6,6 +6,7 @@
 // Third party copyrights are property of their respective owners.
 
 #include "test_precomp.hpp"
+#include "npy_blob.hpp"
 #include <opencv2/core/ocl.hpp>
 #include <opencv2/core/opencl/ocl_defs.hpp>
 #include <opencv2/dnn/layer.details.hpp>  // CV_DNN_REGISTER_LAYER_CLASS
@@ -871,6 +872,35 @@ TEST_P(Test_Model_Optimizer, flexible_inputs)
     normAssert(ref, out, 0, 0);
 }
 
+TEST_P(Test_Model_Optimizer, readONNX)
+{
+    const Backend backendId = get<0>(GetParam());
+    const Target targetId = get<1>(GetParam());
+
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
+
+    const std::string& model = findDataFile("dnn/onnx/models/convolution.onnx");
+
+    std::vector<Net> nets = {
+        // Old API
+        readNetFromModelOptimizer(model, ""),
+        readNet("", model, "dldt"),
+        // New API
+        readNetFromModelOptimizer(model),
+        readNet(model, "", "openvino")
+    };
+
+    Mat inp = blobFromNPY(findDataFile("dnn/onnx/data/input_convolution.npy"));
+    Mat ref = blobFromNPY(findDataFile("dnn/onnx/data/output_convolution.npy"));
+
+    for (int i = 0; i < nets.size(); ++i) {
+        nets[i].setPreferableTarget(targetId);
+        nets[i].setInput(inp);
+        Mat out = nets[i].forward();
+        normAssert(out, ref, format("Index: %d", i).c_str());
+    }
+}
+
 INSTANTIATE_TEST_CASE_P(/**/, Test_Model_Optimizer,
     dnnBackendsAndTargetsIE()
 );