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Merge pull request #24196 from dkurt:ov_backend_cleanups

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Use ngraph::Output in OpenVINO backend wrapper #24196

### Pull Request Readiness Checklist

resolves https://github.com/opencv/opencv/issues/24102

* Use `ngraph::Output<ngraph::Node>>` insead of `std::shared_ptr<ngraph::Node>` as a backend wrapper. It lets access to multi-output nodes: https://github.com/opencv/opencv/blob/588ddf1b181aa7243144b27d65fc7690fb89e344/modules/dnn/src/net_openvino.cpp#L501-L504
* All layers can be customizable with OpenVINO >= 2022.1. nGraph reference code used for default layer implementation does not required CPU plugin also (might be tested by commenting CPU plugin at `/opt/intel/openvino/runtime/lib/intel64/plugins.xml`).
* Correct inference if only intermediate blobs requested.


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 another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Dmitry Kurtaev
2023-09-05 18:08:28 +03:00
committed by GitHub
parent 2c53e3f53d
commit 178fdbbda8
26 changed files with 136 additions and 95 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/test/test_halide_layers.cpp
+10 -1
View File
@@ -425,6 +425,13 @@ TEST_P(FullyConnected, Accuracy)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
}
#endif
// https://github.com/openvinotoolkit/openvino/issues/19436
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL_FP16 && batch == 16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2023000000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL && batch == 16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
#endif
Mat weights(outChannels, inChannels * inSize.height * inSize.width, CV_32F);
randu(weights, -1.0f, 1.0f);
@@ -454,11 +461,13 @@ TEST_P(FullyConnected, Accuracy)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.01;
if (INF_ENGINE_VER_MAJOR_GE(2023000000))
lInf = 0.016;
}
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL)
{
l1 = 5e-3;
lInf = 7e-3;
lInf = INF_ENGINE_VER_MAJOR_GE(2023000000) ? 0.016 : 7e-3;
}
#endif
if (targetId == DNN_TARGET_CUDA_FP16)
modules/dnn/test/test_tflite_importer.cpp
+1 -8
View File
@@ -157,14 +157,7 @@ TEST_P(Test_TFLite, max_unpooling)
net.setInput(input);
std::vector<std::vector<Mat> > outs;
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
// TODO: seems like a bug with a retrieving intermediate tensors
net.forward(outs, {"conv2d_transpose_4", "p_re_lu_1", "max_pooling_with_argmax2d", "conv2d_86", "max_unpooling2d_2"});
outs.erase(outs.begin());
}
else {
net.forward(outs, {"p_re_lu_1", "max_pooling_with_argmax2d", "conv2d_86", "max_unpooling2d_2"});
}
net.forward(outs, {"p_re_lu_1", "max_pooling_with_argmax2d", "conv2d_86", "max_unpooling2d_2"});
ASSERT_EQ(outs.size(), 4);
ASSERT_EQ(outs[0].size(), 1);