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Merge pull request #24610 from jimmylaw21:dnn-onnx-add-group-norm-layer

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dnn onnx: add group norm layer #24610

dnn onnx: add group norm layer

Todo:

- [x] speed up by multi-threading
- [x] add perf
- [x] add backend: OpenVINO
- [x] add backend: CUDA
- [x] add backend: OpenCL (no fp16)
- [ ] add backend: CANN

### 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
- [ ] 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

Co-authored-by: fengyuentau <yuantao.feng@opencv.org.cn>
This commit is contained in:
jimmylaw21
2024-01-12 20:13:26 +08:00
committed by GitHub
parent 97c418ab86
commit a7fa1e6f4b
13 changed files with 486 additions and 1 deletions
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modules/dnn/perf/perf_layer.cpp
+61 -1
View File
@@ -795,6 +795,66 @@ PERF_TEST_P_(Layer_Attention, VisionTransformer) {
test_layer({1, 197, 768}, {768, 768, 768}, 12);
}
struct Layer_GroupNorm : public TestBaseWithParam<tuple<Backend, Target> >
{
void test_layer(const std::vector<int>& x_shape, int num_groups)
{
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
Mat x(x_shape, CV_32FC1);
Mat scale(x_shape[1], 1, CV_32FC1);
Mat b(x_shape[1], 1, CV_32FC1);
randu(x, 0.f, 1.f);
randu(scale, 0.f, 1.f);
randu(b, 0.f, 1.f);
Net net;
LayerParams lp;
lp.type = "GroupNormalization";
lp.name = "testLayer";
lp.set("num_groups", num_groups);
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 0);
net.connect(0, 1, id, 1);
net.connect(0, 2, id, 2);
// warmup
{
std::vector<String> inpNames{"x", "scale", "b"};
net.setInputsNames(inpNames);
net.setInput(x, inpNames[0]);
net.setInput(scale, inpNames[1]);
net.setInput(b, inpNames[2]);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
}
TEST_CYCLE()
{
Mat res = net.forward();
}
SANITY_CHECK_NOTHING();
}
int N = 2;
int C = 64;
int H = 180;
int W = 240;
int num_groups = 16;
};
PERF_TEST_P_(Layer_GroupNorm, GroupNorm)
{
test_layer({N, C, H, W}, num_groups);
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Slice, dnnBackendsAndTargets(false, false));
INSTANTIATE_TEST_CASE_P(/**/, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
#ifdef HAVE_CUDA
@@ -807,7 +867,7 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_LayerNormExpanded, testing::Values(std::make
INSTANTIATE_TEST_CASE_P(/**/, Layer_GatherElements, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_InstanceNorm, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_Attention, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_GroupNorm, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
typedef TestBaseWithParam<tuple<Vec4i, int, bool, tuple<Backend, Target> > > Layer_FullyConnected;
PERF_TEST_P_(Layer_FullyConnected, fc)