Merge pull request #24378 from fengyuentau:instance_norm

dnn onnx: add instance norm layer #24378

Resolves https://github.com/opencv/opencv/issues/24377
Relates https://github.com/opencv/opencv/pull/24092#discussion_r1349841644

| Perf | multi-thread | single-thread |
| - | - | - |
| x: [2, 64, 180, 240] | 3.95ms | 11.12ms |

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 (will be done via https://github.com/opencv/opencv/pull/24462)


### 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
- [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

```
force_builders=Linux OpenCL,Win64 OpenCL,Custom
buildworker:Custom=linux-4
build_image:Custom=ubuntu:18.04
modules_filter:Custom=none
disable_ipp:Custom=ON
```

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

@@ -1166,6 +1166,13 @@ CV__DNN_INLINE_NS_BEGIN
         static Ptr<ExpandLayer> create(const LayerParams &params);
     };
 
+    class CV_EXPORTS InstanceNormLayer : public Layer {
+    public:
+        float epsilon;
+
+        static Ptr<InstanceNormLayer> create(const LayerParams &params);
+    };
+
 //! @}
 //! @}
 CV__DNN_INLINE_NS_END

modules/dnn/perf/perf_layer.cpp

@@ -683,6 +683,62 @@ PERF_TEST_P_(Layer_GatherElements, GatherElements)
     test_layer({2700, 1, 2914}, {2700, 1, 81}, 2);
 }
 
+struct Layer_InstanceNorm : public TestBaseWithParam<tuple<Backend, Target> >
+{
+    void test_layer(const std::vector<int>& x_shape)
+    {
+        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 = "InstanceNormalization";
+        lp.name = "testLayer";
+        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;
+};
+
+PERF_TEST_P_(Layer_InstanceNorm, InstanceNorm)
+{
+    test_layer({N, C, H, W});
+}
+
 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
@@ -693,6 +749,7 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_ScatterND, testing::Values(std::make_tuple(D
 INSTANTIATE_TEST_CASE_P(/**/, Layer_LayerNorm, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
 INSTANTIATE_TEST_CASE_P(/**/, Layer_LayerNormExpanded, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
 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)));
 
 
 typedef TestBaseWithParam<tuple<Vec4i, int, bool, tuple<Backend, Target> > > Layer_FullyConnected;

modules/dnn/src/cuda/mvn.cu

@@ -66,6 +66,17 @@ namespace raw {
             output[idx] = (static_cast<float>(input[idx]) - means[outer_idx]) * scale[outer_idx];
         }
     }
+
+    template <class T>
+    __global__ void normalize_mean_variance_channelwise(Span<T> output, View<T> input, View<T> scale, View<T> bias, View<float> means, View<float> stdev, size_type inner_size, size_type C) {
+        for (auto idx : grid_stride_range(output.size())) {
+            const index_type outer_idx = idx / inner_size;
+            const index_type c = outer_idx % C;
+            auto s = static_cast<float>(scale[c]) * stdev[outer_idx];
+            auto b = static_cast<float>(bias[c]);
+            output[idx] = (static_cast<float>(input[idx]) - means[outer_idx]) * s + b;
+        }
+    }
 }
 
 template <class T>
@@ -142,4 +153,21 @@ template void normalize_mean_variance(const Stream&, Span<__half>, View<__half>,
 #endif
 template void normalize_mean_variance(const Stream&, Span<float>, View<float>, View<float>, View<float>, std::size_t);
 
+template <class T>
+void normalize_mean_variance_channelwise(const Stream& stream, Span<T> output, View<T> input, View<T> scale, View<T> bias, View<float> means, View<float> stdev, std::size_t inner_size, std::size_t C)
+{
+    CV_Assert(input.size() == output.size());
+    CV_Assert(input.size() / inner_size == means.size());
+    CV_Assert(means.size() == stdev.size());
+
+    auto kernel = raw::normalize_mean_variance_channelwise<T>;
+    auto policy = make_policy(kernel, output.size(), 0, stream);
+    launch_kernel(kernel, policy, output, input, scale, bias, means, stdev, inner_size, C);
+}
+
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+template void normalize_mean_variance_channelwise(const Stream&, Span<__half> /*output*/, View<__half> /*input*/, View<__half> /*scale*/, View<__half> /*bias*/, View<float> /*means*/, View<float> /*stdev*/, std::size_t, std::size_t);
+#endif
+template void normalize_mean_variance_channelwise(const Stream&, Span<float> /*output*/, View<float> /*input*/, View<float> /*scale*/, View<float> /*bias*/, View<float> /*means*/, View<float> /*stdev*/, std::size_t, std::size_t);
+
 }}}} /* namespace cv::dnn::cuda4dnn::kernels */

modules/dnn/src/cuda4dnn/kernels/mvn.hpp

@@ -26,6 +26,9 @@ void normalize_mean(const csl::Stream& stream, csl::Span<T> output, csl::View<T>
 template <class T>
 void normalize_mean_variance(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input, csl::View<float> means, csl::View<float> scale, std::size_t inner_size);
 
+template <class T>
+void normalize_mean_variance_channelwise(const csl::Stream &stream, csl::Span<T> output, csl::View<T> input, csl::View<T> scale, csl::View<T> bias, csl::View<float> means, csl::View<float> stdev, std::size_t inner_size, std::size_t C);
+
 }}}} /* namespace cv::dnn::cuda4dnn::kernels */
 
 #endif /* OPENCV_DNN_SRC_CUDA4DNN_KERNELS_MVN_HPP */

modules/dnn/src/cuda4dnn/primitives/instance_norm.hpp

@@ -0,0 +1,86 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#ifndef OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_INSTANCE_NORM_HPP
+#define OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_INSTANCE_NORM_HPP
+
+#include "../../op_cuda.hpp"
+
+#include "../csl/stream.hpp"
+#include "../csl/span.hpp"
+#include "../csl/tensor.hpp"
+#include "../csl/workspace.hpp"
+
+#include "../kernels/fill_copy.hpp"
+#include "../kernels/mvn.hpp"
+
+#include <opencv2/core.hpp>
+
+#include <cstddef>
+#include <vector>
+#include <utility>
+
+namespace cv { namespace dnn { namespace cuda4dnn {
+
+    template <class T>
+    class InstanceNormOp final : public CUDABackendNode {
+     public:
+        using wrapper_type = GetCUDABackendWrapperType<T>;
+
+        InstanceNormOp(csl::Stream stream_, float epsilon_, size_t loops)
+            : stream(std::move(stream_)), epsilon(epsilon_) {
+            csl::WorkspaceBuilder builder;
+            builder.require<float>(loops);
+            builder.require<float>(loops);
+            scratch_mem_in_bytes = builder.required_workspace_size();
+        }
+
+        void forward(const std::vector<cv::Ptr<BackendWrapper>>& inputs,
+                     const std::vector<cv::Ptr<BackendWrapper>>& outputs,
+                     csl::Workspace& workspace) override {
+            auto input_wrapper = inputs[0].dynamicCast<wrapper_type>();
+            auto scale_wrapper = inputs[1].dynamicCast<wrapper_type>();
+            auto bias_wrapper = inputs[2].dynamicCast<wrapper_type>();
+
+            auto input = input_wrapper->getView();
+            auto scale = scale_wrapper->getView();
+            auto bias = bias_wrapper->getView();
+
+            auto output_wrapper = outputs[0].dynamicCast<wrapper_type>();
+            auto output = output_wrapper->getSpan();
+
+            auto C = input.get_axis_size(1);
+            auto loops = input.size_range(0, 2);
+            auto norm_size = input.size_range(2, input.rank());
+            if (norm_size == 1) {
+                kernels::fill<T>(stream, output, 0.f);
+                return;
+            } else {
+                auto ws_allocator = csl::WorkspaceAllocator(workspace);
+
+                auto mean = ws_allocator.get_span<float>(loops);
+                kernels::fill<float>(stream, mean, 0.f);
+
+                auto stdev = ws_allocator.get_span<float>(loops);
+                kernels::fill<float>(stream, stdev, 0.f);
+
+                kernels::reduce_mean_sqr_sum<T>(stream, mean, stdev, input, norm_size);
+                kernels::compute_normalization_scale(stream, stdev, mean, stdev, norm_size, epsilon);
+                kernels::normalize_mean_variance_channelwise<T>(stream, output, input, scale, bias, mean, stdev, norm_size, C);
+            }
+        }
+
+        std::size_t get_workspace_memory_in_bytes() const noexcept override { return scratch_mem_in_bytes; }
+
+     private:
+        csl::Stream stream;
+
+        float epsilon;
+
+        std::size_t scratch_mem_in_bytes;
+    };
+
+}}} // cv::dnn::cuda4dnn
+
+#endif // OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_INSTANCE_NORM_HPP

modules/dnn/src/init.cpp

@@ -160,6 +160,7 @@ void initializeLayerFactory()
     CV_DNN_REGISTER_LAYER_CLASS(GatherElements, GatherElementsLayer);
     CV_DNN_REGISTER_LAYER_CLASS(LayerNormalization, LayerNormLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Expand,         ExpandLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(InstanceNormalization, InstanceNormLayer);
 
     CV_DNN_REGISTER_LAYER_CLASS(Crop,           CropLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Eltwise,        EltwiseLayer);

modules/dnn/src/layers/cpu_kernels/fast_norm.cpp

@@ -118,10 +118,11 @@ void fastNorm(const Mat &input, const Mat &scale, const Mat &bias, Mat &output,
 
 void fastNormChannel(const Mat &input, const Mat &scale, const Mat &bias, Mat &output, float epsilon) {
     const auto input_shape = shape(input);
+    size_t N = input_shape[0], C = input_shape[1];
     CV_CheckEQ(scale.total(), bias.total(), "fastNormChannel: scale and bias should have the same shape");
+    CV_CheckEQ(scale.total(), C, "fastNormChannel: scale should be a 1d tensor and match the channel of input");
     CV_CheckGE(input.dims, 3, "fastNormChannel: input dimension >= 3");
 
-    size_t N = input_shape[0], C = input_shape[1];
     size_t loops = N * C,
            norm_size = static_cast<size_t>(total(input_shape, 2));
     float inv_norm_size = 1.0 / norm_size;
@@ -147,9 +148,9 @@ void fastNormChannel(const Mat &input, const Mat &scale, const Mat &bias, Mat &o
             float inv_stdev = 1.f / mean_square;
 
             size_t c = i % C;
-            float s = scale_data[c], b = bias_data[c];
+            float s = scale_data[c] * inv_stdev, b = bias_data[c];
             for (size_t j = 0; j < norm_size; j++) {
-                y[j] = s * (x[j] - mean) * inv_stdev + b;
+                y[j] = s * (x[j] - mean) + b;
             }
         }
     };

modules/dnn/src/layers/instance_norm_layer.cpp

@@ -0,0 +1,231 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+#include "../precomp.hpp"
+#include <opencv2/dnn/shape_utils.hpp>
+#include "./cpu_kernels/fast_norm.hpp"
+
+// OpenVINO backend
+#include "../op_inf_engine.hpp"
+#include "../ie_ngraph.hpp"
+
+// CUDA backend
+#include "../op_cuda.hpp"
+#ifdef HAVE_CUDA
+#include "../cuda4dnn/primitives/instance_norm.hpp"
+using namespace cv::dnn::cuda4dnn;
+#endif
+
+// OpenCL backend
+#ifdef HAVE_OPENCL
+#include "../ocl4dnn/include/math_functions.hpp"
+#include "opencl_kernels_dnn.hpp"
+#endif
+
+namespace cv { namespace dnn {
+
+// https://github.com/onnx/onnx/blob/main/docs/Operators.md#InstanceNormalization
+class InstanceNormLayerImpl CV_FINAL : public InstanceNormLayer {
+public:
+    InstanceNormLayerImpl(const LayerParams &params) {
+        setParamsFrom(params);
+
+        epsilon = params.get<float>("epsilon", 1e-5);
+    }
+
+    virtual bool supportBackend(int backendId) CV_OVERRIDE {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
+        return backendId == DNN_BACKEND_OPENCV ||
+               backendId == DNN_BACKEND_CUDA;
+    }
+
+    bool getMemoryShapes(const std::vector<MatShape> &inputs,
+                         const int requiredOutputs,
+                         std::vector<MatShape> &outputs,
+                         std::vector<MatShape> &internals) const CV_OVERRIDE {
+        const auto &input = inputs[0];
+        const auto &scale = inputs[1];
+        const auto &bias = inputs[2];
+        CV_CheckGE(input.size(), static_cast<size_t>(3), "DNN/InstanceNorm: input dimension >= 3 is required");
+
+        int C = input[1];
+        int scale_dim = std::accumulate(scale.begin(), scale.end(), 1, std::multiplies<int>());
+        CV_CheckEQ(scale_dim, C, "DNN/InstanceNorm: scale must be a 1d tensor and match the channel of input");
+        int bias_dim = std::accumulate(bias.begin(), bias.end(), 1, std::multiplies<int>());
+        CV_CheckEQ(bias_dim, C, "DNN/InstanceNorm: bias must be a 1d tensor and match the channel of input");
+
+        outputs.assign(1, inputs[0]);
+        return false;
+    }
+
+    void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE {
+        CV_TRACE_FUNCTION();
+        CV_TRACE_ARG_VALUE(name, "name", name.c_str());
+
+        CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
+                   forward_ocl(inputs_arr, outputs_arr, internals_arr))
+
+        if (inputs_arr.depth() == CV_16S)
+        {
+            forward_fallback(inputs_arr, outputs_arr, internals_arr);
+            return;
+        }
+
+        std::vector<Mat> inputs, outputs;
+        inputs_arr.getMatVector(inputs);
+        outputs_arr.getMatVector(outputs);
+
+        const auto &input = inputs[0];
+        const auto &scale = inputs[1];
+        const auto &bias = inputs[2];
+
+        fastNormChannel(input, scale, bias, outputs[0], epsilon);
+    }
+
+#ifdef HAVE_OPENCL
+    bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_) {
+        std::vector<UMat> inputs;
+        std::vector<UMat> outputs;
+
+        inputs_.getUMatVector(inputs);
+        outputs_.getUMatVector(outputs);
+
+        const auto &input = inputs[0], &scale = inputs[1], &bias = inputs[2];
+        auto &output = outputs[0];
+
+        const auto input_shape = shape(input);
+        size_t N = input_shape[0], C = input_shape[1],
+               loops = N * C, norm_size = static_cast<size_t>(total(input_shape, 2));
+        float inv_norm_size = 1.f / norm_size;
+
+        // no fp16 support
+        if (input.depth() == CV_16S) {
+            return false;
+        }
+
+        String base_opts = format(" -DT=float -DT4=float4 -Dconvert_T=convert_float4");
+
+        // Calculate mean
+        UMat one = UMat::ones(norm_size, 1, CV_32F);
+        UMat mean = UMat(loops, 1, CV_32F);
+        UMat mean_square = UMat(loops, 1, CV_32F);
+        UMat tmp = UMat(loops, norm_size, CV_32F);
+        bool ret = ocl4dnn::ocl4dnnGEMV<float>(ocl4dnn::CblasNoTrans, loops, norm_size, inv_norm_size,
+                                               input, 0, one, 0, 0.f, mean, 0);
+        if (!ret) {
+            return false;
+        }
+        // Calculate mean_square
+        int num_vector = (norm_size % 8 == 0) ? 8 : ((norm_size % 4 == 0) ? 4 : 1);
+        size_t global[] = {loops, static_cast<size_t>(norm_size / num_vector)};
+        String build_opt = format(" -DNUM=%d", num_vector) + base_opts;
+        String mean_square_kernel_name = format("calc_mean%d", num_vector);
+        ocl::Kernel mean_square_kernel(mean_square_kernel_name.c_str(), ocl::dnn::mvn_oclsrc, build_opt + " -DKERNEL_MEAN");
+        if (mean_square_kernel.empty()) {
+            return false;
+        }
+        mean_square_kernel.set(0, ocl::KernelArg::PtrReadOnly(input));
+        mean_square_kernel.set(1, (int)loops);
+        mean_square_kernel.set(2, (int)norm_size);
+        mean_square_kernel.set(3, ocl::KernelArg::PtrReadOnly(mean));
+        mean_square_kernel.set(4, ocl::KernelArg::PtrWriteOnly(tmp));
+        ret = mean_square_kernel.run(2, global, NULL, false);
+        if (!ret) {
+            return false;
+        }
+        ret = ocl4dnn::ocl4dnnGEMV<float>(ocl4dnn::CblasNoTrans, loops, norm_size, inv_norm_size,
+                                          tmp, 0, one, 0, 0.f, mean_square, 0);
+        if (!ret) {
+            return false;
+        }
+        // Calculate instance norm: output = scale * (x - mean) / sqrt(var + eps) + bias
+        String mvn_kernel_name = format("mvn%d", num_vector);
+        build_opt += " -DNORM_VARIANCE -DFUSE_BATCH_NORM -DKERNEL_MVN";
+        ocl::Kernel mvn_kernel(mvn_kernel_name.c_str(), ocl::dnn::mvn_oclsrc, build_opt);
+        if (mvn_kernel.empty()) {
+            return false;
+        }
+        mvn_kernel.set(0, ocl::KernelArg::PtrReadOnly(input));
+        mvn_kernel.set(1, (int)loops);
+        mvn_kernel.set(2, (int)norm_size);
+        mvn_kernel.set(3, (float)epsilon);
+        mvn_kernel.set(4, ocl::KernelArg::PtrReadOnly(mean));
+        mvn_kernel.set(5, ocl::KernelArg::PtrReadOnly(mean_square));
+        mvn_kernel.set(6, ocl::KernelArg::PtrReadOnly(scale));
+        mvn_kernel.set(7, ocl::KernelArg::PtrReadOnly(bias));
+        mvn_kernel.set(8, (int)C);
+        mvn_kernel.set(9, (float)0.f);
+        mvn_kernel.set(10, ocl::KernelArg::PtrWriteOnly(output));
+        ret = mvn_kernel.run(2, global, NULL, false);
+        if (!ret) {
+            return false;
+        }
+
+        return true;
+    }
+#endif
+
+#ifdef HAVE_DNN_NGRAPH
+    virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
+                                        const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE {
+        // onnx to openvino convertion: https://github.com/openvinotoolkit/openvino/blob/2023.1.0/src/frontends/onnx/frontend/src/op/instance_norm.cpp
+
+        auto ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
+        const auto &input_shape = ieInpNode.get_shape();
+        std::shared_ptr<ngraph::Node> mvn, result;
+
+        // mvn
+#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2021_2)
+        // https://docs.openvino.ai/2021.4/api/ngraph_python_api/_autosummary/ngraph.opset3.mvn.html?highlight=mvn#ngraph.opset3.mvn
+        bool across_channels = false;
+        bool normalize_variance = true;
+        mvn = std::make_shared<ngraph::op::MVN>(ieInpNode, across_channels, normalize_variance, epsilon);
+#else
+        // https://docs.openvino.ai/2023.1/openvino_docs_ops_normalization_MVN_6.html
+        std::vector<int64_t> axes_v(input_shape.size() - 2);
+        std::iota(axes_v.begin(), axes_v.end(), 2); // {2, 3, ...} for nd input tensor, n>=3
+        auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_v.size()}, axes_v.data());
+        bool normalize_variance = true;
+        mvn = std::make_shared<ngraph::op::v6::MVN>(ieInpNode, axes, normalize_variance, epsilon, ngraph::op::MVNEpsMode::INSIDE_SQRT);
+#endif
+
+        // instance norm = scale * mvn + bias
+        auto scale = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
+        std::vector<int64_t> shared_shape_v(input_shape.size(), 1);
+        shared_shape_v[1] = -1;
+        auto shared_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{shared_shape_v.size()}, shared_shape_v.data());
+        scale  = std::make_shared<ngraph::op::v1::Reshape>(scale, shared_shape, true);
+        result = std::make_shared<ngraph::op::v1::Multiply>(mvn, scale);
+        auto bias = nodes[2].dynamicCast<InfEngineNgraphNode>()->node;
+        bias  = std::make_shared<ngraph::op::v1::Reshape>(bias, shared_shape, true);
+        result = std::make_shared<ngraph::op::v1::Add>(result, bias);
+
+        return Ptr<BackendNode>(new InfEngineNgraphNode(result));
+    }
+#endif // HAVE_DNN_NGRAPH
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(void *context_,
+                              const std::vector<Ptr<BackendWrapper>>& inputs,
+                              const std::vector<Ptr<BackendWrapper>>& outputs) override {
+        auto context = reinterpret_cast<csl::CSLContext*>(context_);
+
+        auto input_wrapper = inputs[0].dynamicCast<CUDABackendWrapper>();
+        auto input_shape = input_wrapper->getShape();
+        size_t loops = static_cast<size_t>(total(input_shape, 0, 2));
+
+        return make_cuda_node<cuda4dnn::InstanceNormOp>(preferableTarget, std::move(context->stream), epsilon, loops);
+    }
+#endif // HAVE_CUDA
+
+};
+
+Ptr<InstanceNormLayer> InstanceNormLayer::create(const LayerParams &params) {
+    return Ptr<InstanceNormLayer>(new InstanceNormLayerImpl(params));
+}
+
+}} // cv::dnn

modules/dnn/src/onnx/onnx_importer.cpp

@@ -1844,45 +1844,44 @@ void ONNXImporter::parseLRN(LayerParams& layerParams, const opencv_onnx::NodePro
     addLayer(layerParams, node_proto);
 }
 
-void ONNXImporter::parseInstanceNormalization(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
+void ONNXImporter::parseInstanceNormalization(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto) {
-{
+    int num_inputs = node_proto.input_size();
-    opencv_onnx::NodeProto node_proto = node_proto_;
+    CV_CheckEQ(num_inputs, 3, "DNN/ONNXImporter - InstanceNorm: three inputs are required");
-    if (node_proto.input_size() != 3)
-        CV_Error(Error::StsNotImplemented,
-                 "Expected input, scale, bias");
 
-    layerParams.blobs.resize(4);
+    bool found_input = constBlobs.find(node_proto.input(0)) != constBlobs.end();
-    layerParams.blobs[2] = getBlob(node_proto, 1);  // weightData
+    bool found_scale = constBlobs.find(node_proto.input(1)) != constBlobs.end();
-    layerParams.blobs[3] = getBlob(node_proto, 2);  // biasData
+    bool found_bias = constBlobs.find(node_proto.input(2)) != constBlobs.end();
-    layerParams.set("has_bias", true);
-    layerParams.set("has_weight", true);
 
-    // Get number of channels in input
+    if (found_input && found_scale && found_bias) {
-    int size = layerParams.blobs[2].total();
+        std::vector<Mat> inputs, output;
-    layerParams.blobs[0] = Mat::zeros(size, 1, CV_32F); // mean
-    layerParams.blobs[1] = Mat::ones(size, 1, CV_32F); // std
 
-    LayerParams mvnParams;
+        Mat input = getBlob(node_proto, 0);
-    mvnParams.name = layerParams.name + "/MVN";
+        Mat scale = getBlob(node_proto, 1);
-    mvnParams.type = "MVN";
+        Mat bias = getBlob(node_proto, 2);
-    mvnParams.set("eps", layerParams.get<float>("epsilon"));
+        inputs.push_back(input);
-    layerParams.erase("epsilon");
+        inputs.push_back(scale);
+        inputs.push_back(bias);
 
-    //Create MVN layer
+        runLayer(layerParams, inputs, output);
-    int id = dstNet.addLayer(mvnParams.name, mvnParams.type, mvnParams);
+        addConstant(node_proto.output(0), output[0]);
-    //Connect to input
+    } else {
-    IterLayerId_t layerId = layer_id.find(node_proto.input(0));
+        auto add_const_node = [&] (int i) {
-    CV_Assert(layerId != layer_id.end());
+            LayerParams const_params;
-    dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
+            const_params.name = node_proto.input(i);
-    //Add shape
+            const_params.type = "Const";
-    layer_id.insert(std::make_pair(mvnParams.name, LayerInfo(id, 0)));
+            Mat blob = getBlob(node_proto, i);
-    outShapes[mvnParams.name] = outShapes[node_proto.input(0)];
+            const_params.blobs.push_back(blob);
 
-    //Replace Batch Norm's input to MVN
+            opencv_onnx::NodeProto proto;
-    node_proto.set_input(0, mvnParams.name);
+            proto.add_output(const_params.name);
-    layerParams.type = "BatchNorm";
+            addLayer(const_params, proto);
+        };
+        if (found_input && layer_id.find(node_proto.input(0)) == layer_id.end()) { add_const_node(0); }
+        if (found_scale && layer_id.find(node_proto.input(1)) == layer_id.end()) { add_const_node(1); }
+        if (found_bias && layer_id.find(node_proto.input(2)) == layer_id.end()) { add_const_node(2); }
         addLayer(layerParams, node_proto);
     }
+}
 
 void ONNXImporter::parseBatchNormalization(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
 {

modules/dnn/test/test_onnx_conformance_layer_parser_denylist.inl.hpp

@@ -159,8 +159,6 @@
 "test_if",
 "test_if_opt",
 "test_if_seq",
-"test_instancenorm_epsilon",
-"test_instancenorm_example",
 "test_isinf",
 "test_isinf_negative",
 "test_isinf_positive",