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Merge pull request #25120 from LaurentBerger:I25103

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Fixed ReduceMean layer behaviour #25120

### 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 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
- [] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

https://github.com/microsoft/onnxruntime/blob/a93c31e3c9971063d8dfe45a627a80cbdcf99ed9/onnxruntime/core/providers/cpu/reduction/reduction_ops.cc#L433-L443
This commit is contained in:
Laurent Berger
2024-03-04 07:36:53 +01:00
committed by GitHub
parent 500c55a808
commit 5fe3933346
2 changed files with 47 additions and 2 deletions
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modules/dnn/test/test_layers.cpp
+44
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@@ -1795,6 +1795,50 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_ShuffleChannel, Combine(
/*group*/ Values(1, 2, 3, 6), dnnBackendsAndTargets(/*with IE*/ false)
));
TEST(Layer_Test_ReduceMean, accuracy_input_0)
{
vector<int> szData = { 2, 1, 2, 1 ,2 };
std::vector<float> initData = { 0, 1, 2, 3, 4, 5, 6, 7 };
Mat inpInitA(szData, CV_32FC1, Mat(initData).data);
std::vector<float> resAxes0 = { 2, 3, 4, 5 };
std::vector<float> resAxes1 = { 0, 1, 2, 3, 4, 5, 6, 7 };
std::vector<float> resAxes2 = { 1, 2, 5, 6 };
std::vector<float> resAxes3 = { 0, 1, 2, 3, 4, 5, 6, 7 };
std::vector<float> resAxes4 = { 0.5, 2.5, 4.5, 6.5 };
std::vector < vector<float>> resReduceMean = { resAxes0, resAxes1, resAxes2, resAxes3, resAxes4 };
for (int i = 0; i < resReduceMean.size(); i++)
{
Net net;
LayerParams lp;
lp.set("keepdims", 0);
lp.type = "Reduce";
lp.set("reduce", "MEAN");
lp.name = "testReduceMean";
lp.set("axes", i);
lp.blobs.push_back(inpInitA);
net.addLayerToPrev(lp.name, lp.type, lp);
net.setInput(inpInitA);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat output = net.forward();
MatShape gt_shape;
for (int j = 0; j < szData.size(); j++)
{
if (i == j) continue;
gt_shape.push_back(szData[j]);
}
EXPECT_EQ(gt_shape, shape(output));
Mat a = output.reshape(1, output.total());
normAssert(a, Mat(resReduceMean[i]));
}
}
// Check if relu is not fused to convolution if we requested it's output
TEST(Layer_Test_Convolution, relu_fusion)
{