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Merge pull request #28855 from omrope79:wechat-fix-layers

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Add Prior Box Layer to support the WeChat QR model #28855

OpenCV extra: https://github.com/opencv/opencv_extra/pull/1349

### Pull Request Readiness Checklist

This PR is part 1 of the split from the original PR: WeChatQR-fix conversion Caffe to ONNX #28746.
It focuses on adding the PriorBox layer and its related unit tests. The WeChatQR specific changes will be submitted in a separate PR.
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:
omrope79
2026-04-27 20:42:58 +05:30
committed by GitHub
parent 0ba385abe4
commit 237be03b7e
3 changed files with 59 additions and 1 deletions
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modules/dnn/test/test_onnx_importer.cpp
+30
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@@ -1179,6 +1179,11 @@ TEST_P(Test_ONNX_layers, Resize_HumanSeg)
testONNXModels("resize_humanseg");
}
TEST_P(Test_ONNX_layers, Resample)
{
testONNXModels("nearest", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, Div)
{
const String model = _tf("models/div.onnx");
@@ -2293,6 +2298,31 @@ TEST_P(Test_ONNX_layers, QLinearSoftmax)
testONNXModels("qlinearsoftmax_v13", npy, 0.002, 0.002);
}
TEST_P(Test_ONNX_layers, PriorBox_ONNX)
{
Net net = readNetFromONNX(_tf("models/prior_box.onnx"));
ASSERT_FALSE(net.empty());
int inp_size[] = {1, 3, 10, 10};
int shape_size[] = {1, 2, 3, 4};
Mat inp(4, inp_size, CV_32F, Scalar(0));
Mat shape(4, shape_size, CV_32F, Scalar(0));
net.setInput(inp, "input_0");
net.setInput(shape, "input_1");
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat out = net.forward();
Mat ref = blobFromNPY(_tf("data/output_prior_box.npy"));
double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-3 : 1e-5;
double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-3 : 1e-4;
if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 7e-5;
lInf = 0.0005;
}
normAssert(out, ref, "", l1, lInf);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets());
class Test_ONNX_nets : public Test_ONNX_layers