From 1bd382c1d0dc7ce65b352ffb0628b5e9bb431f28 Mon Sep 17 00:00:00 2001
From: Smirnov Egor <s.e.a.98@yandex.ru>
Date: Thu, 2 Dec 2021 15:45:27 +0300
Subject: [PATCH] Add acos, acosh, asin, asinh, atan, atanh, cos, cosh, erf,
 hardswish, sin, sinh, softplus, softsign, tan layers

---
 .../dnn/include/opencv2/dnn/all_layers.hpp    |  90 +++
 modules/dnn/src/cuda/activations.cu           | 105 ++++
 modules/dnn/src/cuda/functors.hpp             | 225 +++++++
 modules/dnn/src/cuda/math.hpp                 |  84 +++
 .../dnn/src/cuda4dnn/kernels/activations.hpp  |  45 ++
 .../src/cuda4dnn/primitives/activation.hpp    | 210 +++++++
 modules/dnn/src/init.cpp                      |  15 +
 modules/dnn/src/layers/elementwise_layers.cpp | 552 ++++++++++++++++++
 .../dnn/src/onnx/onnx_graph_simplifier.cpp    |  37 ++
 modules/dnn/src/opencl/activations.cl         |  84 +++
 ...rmance_layer_filter__cuda_denylist.inl.hpp |   1 +
 ...er_filter_opencv_ocl_fp16_denylist.inl.hpp |   1 +
 ..._conformance_layer_parser_denylist.inl.hpp |  29 -
 13 files changed, 1449 insertions(+), 29 deletions(-)

diff --git a/modules/dnn/include/opencv2/dnn/all_layers.hpp b/modules/dnn/include/opencv2/dnn/all_layers.hpp
index 45935279f5..26d7a9b069 100644
--- a/modules/dnn/include/opencv2/dnn/all_layers.hpp
+++ b/modules/dnn/include/opencv2/dnn/all_layers.hpp
@@ -648,6 +648,96 @@ CV__DNN_INLINE_NS_BEGIN
         static Ptr<NotLayer> create(const LayerParams &params);
     };
 
+    class CV_EXPORTS AcosLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AcosLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AcoshLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AcoshLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AsinLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AsinLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AsinhLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AsinhLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AtanLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AtanLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS AtanhLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<AtanhLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS CosLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<CosLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS CoshLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<CoshLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS ErfLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<ErfLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS HardSwishLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<HardSwishLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SinLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SinLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SinhLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SinhLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SoftplusLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SoftplusLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS SoftsignLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<SoftsignLayer> create(const LayerParams &params);
+    };
+
+    class CV_EXPORTS TanLayer : public ActivationLayer
+    {
+    public:
+        static Ptr<TanLayer> create(const LayerParams &params);
+    };
+
     class CV_EXPORTS ActivationLayerInt8 : public ActivationLayer
     {
     public:
diff --git a/modules/dnn/src/cuda/activations.cu b/modules/dnn/src/cuda/activations.cu
index 0980b5dd46..3d99a03ae3 100644
--- a/modules/dnn/src/cuda/activations.cu
+++ b/modules/dnn/src/cuda/activations.cu
@@ -158,6 +158,81 @@ void not_k(const Stream& stream, Span<T> output, View<T> input) {
     generic_op<T, NotFunctor<T>>(stream, output, input);
 }
 
+template <class T>
+void acos(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, AcosFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void acosh(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, AcoshFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void asin(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, AsinFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void asinh(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, AsinhFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void atan(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, AtanFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void atanh(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, AtanhFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void cos(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, CosFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void cosh(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, CoshFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void erf(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, ErfFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void hardswish(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, HardSwishFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void sin(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, SinFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void sinh(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, SinhFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void softplus(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, SoftplusFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void softsign(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, SoftsignFunctor<T>>(stream, output, input);
+}
+
+template <class T>
+void tan(const Stream& stream, Span<T> output, View<T> input) {
+    generic_op<T, TanFunctor<T>>(stream, output, input);
+}
+
 template <class T>
 void abs(const Stream& stream, Span<T> output, View<T> input) {
     generic_op<T, AbsFunctor<T>>(stream, output, input);
@@ -196,6 +271,21 @@ template void log<__half>(const Stream&, Span<__half>, View<__half>);
 template void rint<__half>(const Stream&, Span<__half>, View<__half>);
 template void sqrt<__half>(const Stream&, Span<__half>, View<__half>);
 template void not_k<__half>(const Stream&, Span<__half>, View<__half>);
+template void acos<__half>(const Stream&, Span<__half>, View<__half>);
+template void acosh<__half>(const Stream&, Span<__half>, View<__half>);
+template void asin<__half>(const Stream&, Span<__half>, View<__half>);
+template void asinh<__half>(const Stream&, Span<__half>, View<__half>);
+template void atan<__half>(const Stream&, Span<__half>, View<__half>);
+template void atanh<__half>(const Stream&, Span<__half>, View<__half>);
+template void cos<__half>(const Stream&, Span<__half>, View<__half>);
+template void cosh<__half>(const Stream&, Span<__half>, View<__half>);
+template void erf<__half>(const Stream&, Span<__half>, View<__half>);
+template void hardswish<__half>(const Stream&, Span<__half>, View<__half>);
+template void sin<__half>(const Stream&, Span<__half>, View<__half>);
+template void sinh<__half>(const Stream&, Span<__half>, View<__half>);
+template void softplus<__half>(const Stream&, Span<__half>, View<__half>);
+template void softsign<__half>(const Stream&, Span<__half>, View<__half>);
+template void tan<__half>(const Stream&, Span<__half>, View<__half>);
 template void power<__half>(const Stream&, Span<__half>, View<__half>, __half, __half, __half);
 template void exp<__half>(const Stream&, Span<__half>, View<__half>, __half, __half);
 #endif
@@ -216,6 +306,21 @@ template void log<float>(const Stream&, Span<float>, View<float>);
 template void rint<float>(const Stream&, Span<float>, View<float>);
 template void sqrt<float>(const Stream&, Span<float>, View<float>);
 template void not_k<float>(const Stream&, Span<float>, View<float>);
+template void acos<float>(const Stream&, Span<float>, View<float>);
+template void acosh<float>(const Stream&, Span<float>, View<float>);
+template void asin<float>(const Stream&, Span<float>, View<float>);
+template void asinh<float>(const Stream&, Span<float>, View<float>);
+template void atan<float>(const Stream&, Span<float>, View<float>);
+template void atanh<float>(const Stream&, Span<float>, View<float>);
+template void cos<float>(const Stream&, Span<float>, View<float>);
+template void cosh<float>(const Stream&, Span<float>, View<float>);
+template void erf<float>(const Stream&, Span<float>, View<float>);
+template void hardswish<float>(const Stream&, Span<float>, View<float>);
+template void sin<float>(const Stream&, Span<float>, View<float>);
+template void sinh<float>(const Stream&, Span<float>, View<float>);
+template void softplus<float>(const Stream&, Span<float>, View<float>);
+template void softsign<float>(const Stream&, Span<float>, View<float>);
+template void tan<float>(const Stream&, Span<float>, View<float>);
 template void power<float>(const Stream&, Span<float>, View<float>, float, float, float);
 template void exp<float>(const Stream&, Span<float>, View<float>, float, float);
 
diff --git a/modules/dnn/src/cuda/functors.hpp b/modules/dnn/src/cuda/functors.hpp
index 98ae175ce8..c3d1669344 100644
--- a/modules/dnn/src/cuda/functors.hpp
+++ b/modules/dnn/src/cuda/functors.hpp
@@ -303,6 +303,231 @@ struct NotFunctor {
     }
 };
 
+template <class T>
+struct AcosFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE AcosFunctor() { }
+    CUDA4DNN_DEVICE AcosFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::acos;
+        return acos(value);
+    }
+};
+
+template <class T>
+struct AcoshFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE AcoshFunctor() { }
+    CUDA4DNN_DEVICE AcoshFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::acosh;
+        return acosh(value);
+    }
+};
+
+template <class T>
+struct AsinFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE AsinFunctor() { }
+    CUDA4DNN_DEVICE AsinFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::asin;
+        return asin(value);
+    }
+};
+
+template <class T>
+struct AsinhFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE AsinhFunctor() { }
+    CUDA4DNN_DEVICE AsinhFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::asinh;
+        return asinh(value);
+    }
+};
+
+template <class T>
+struct AtanFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE AtanFunctor() { }
+    CUDA4DNN_DEVICE AtanFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::atan;
+        return atan(value);
+    }
+};
+
+template <class T>
+struct AtanhFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE AtanhFunctor() { }
+    CUDA4DNN_DEVICE AtanhFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::atanh;
+        return atanh(value);
+    }
+};
+
+template <class T>
+struct CosFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE CosFunctor() { }
+    CUDA4DNN_DEVICE CosFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::cos;
+        return cos(value);
+    }
+};
+
+template <class T>
+struct CoshFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE CoshFunctor() { }
+    CUDA4DNN_DEVICE CoshFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::cosh;
+        return cosh(value);
+    }
+};
+
+template <class T>
+struct ErfFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE ErfFunctor() { }
+    CUDA4DNN_DEVICE ErfFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::erf;
+        return erf(value);
+    }
+};
+
+template <class T>
+struct HardSwishFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE HardSwishFunctor() { }
+    CUDA4DNN_DEVICE HardSwishFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::clamp; // saturate?
+        return value * clamp(value / static_cast<T>(6.f) + static_cast<T>(0.5f), static_cast<T>(0.f), static_cast<T>(1.f));
+    }
+};
+
+template <class T>
+struct SinFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE SinFunctor() { }
+    CUDA4DNN_DEVICE SinFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::sin;
+        return sin(value);
+    }
+};
+
+template <class T>
+struct SinhFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE SinhFunctor() { }
+    CUDA4DNN_DEVICE SinhFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::sinh;
+        return sinh(value);
+    }
+};
+
+template <class T>
+struct SoftplusFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE SoftplusFunctor() { }
+    CUDA4DNN_DEVICE SoftplusFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::log1pexp;
+        return log1pexp(value);
+    }
+};
+
+template <class T>
+struct SoftsignFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE SoftsignFunctor() { }
+    CUDA4DNN_DEVICE SoftsignFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::abs;
+        return value / (static_cast<T>(1.f) + abs(value));
+    }
+};
+
+template <class T>
+struct TanFunctor {
+    struct Params {
+        CUDA4DNN_HOST_DEVICE Params() { }
+    };
+
+    CUDA4DNN_DEVICE TanFunctor() { }
+    CUDA4DNN_DEVICE TanFunctor(const Params& params) { }
+
+    CUDA4DNN_DEVICE T operator()(T value) {
+        using csl::device::tan;
+        return tan(value);
+    }
+};
+
 template <class T>
 struct PowerFunctor {
     struct Params {
diff --git a/modules/dnn/src/cuda/math.hpp b/modules/dnn/src/cuda/math.hpp
index 0da584197d..0a312a250d 100644
--- a/modules/dnn/src/cuda/math.hpp
+++ b/modules/dnn/src/cuda/math.hpp
@@ -140,6 +140,90 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl { namespace de
     template <> inline __device__ __half rint(__half value) { return hrint(value); }
 #endif
 
+    template <class T> __device__ T acos(T value);
+    template <> inline __device__ double acos(double value) { return ::acos(value); }
+    template <> inline __device__ float acos(float value) { return acosf(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half acos(__half value) { return acosf(value); }
+#endif
+
+    template <class T> __device__ T acosh(T value);
+    template <> inline __device__ double acosh(double value) { return ::acosh(value); }
+    template <> inline __device__ float acosh(float value) { return acoshf(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half acosh(__half value) { return acoshf(value); }
+#endif
+
+    template <class T> __device__ T asin(T value);
+    template <> inline __device__ double asin(double value) { return ::asin(value); }
+    template <> inline __device__ float asin(float value) { return asinf(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half asin(__half value) { return asinf(value); }
+#endif
+
+    template <class T> __device__ T asinh(T value);
+    template <> inline __device__ double asinh(double value) { return ::asinh(value); }
+    template <> inline __device__ float asinh(float value) { return asinhf(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half asinh(__half value) { return asinhf(value); }
+#endif
+
+    template <class T> __device__ T atan(T value);
+    template <> inline __device__ double atan(double value) { return ::atan(value); }
+    template <> inline __device__ float atan(float value) { return atanf(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half atan(__half value) { return atanf(value); }
+#endif
+
+    template <class T> __device__ T atanh(T value);
+    template <> inline __device__ double atanh(double value) { return ::atanh(value); }
+    template <> inline __device__ float atanh(float value) { return atanhf(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half atanh(__half value) { return atanhf(value); }
+#endif
+
+    template <class T> __device__ T cos(T value);
+    template <> inline __device__ double cos(double value) { return ::cos(value); }
+    template <> inline __device__ float cos(float value) { return cosf(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half cos(__half value) { return hcos(value); }
+#endif
+
+    template <class T> __device__ T cosh(T value);
+    template <> inline __device__ double cosh(double value) { return ::cosh(value); }
+    template <> inline __device__ float cosh(float value) { return coshf(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half cosh(__half value) { return coshf(value); }
+#endif
+
+    template <class T> __device__ T erf(T value);
+    template <> inline __device__ double erf(double value) { return ::erf(value); }
+    template <> inline __device__ float erf(float value) { return erff(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half erf(__half value) { return erff(value); }
+#endif
+
+    template <class T> __device__ T sin(T value);
+    template <> inline __device__ double sin(double value) { return ::sin(value); }
+    template <> inline __device__ float sin(float value) { return sinf(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half sin(__half value) { return hsin(value); }
+#endif
+
+    template <class T> __device__ T sinh(T value);
+    template <> inline __device__ double sinh(double value) { return ::sinh(value); }
+    template <> inline __device__ float sinh(float value) { return sinhf(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half sinh(__half value) { return sinhf(value); }
+#endif
+
+    template <class T> __device__ T tan(T value);
+    template <> inline __device__ double tan(double value) { return ::tan(value); }
+    template <> inline __device__ float tan(float value) { return tanf(value); }
+#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ >= 530)
+    template <> inline __device__ __half tan(__half value) { return tanf(value); }
+#endif
+
     template <class T> __device__ T ceil(T value);
     template <> inline __device__ double ceil(double value) { return ::ceil(value); }
     template <> inline __device__ float ceil(float value) { return ceilf(value); }
diff --git a/modules/dnn/src/cuda4dnn/kernels/activations.hpp b/modules/dnn/src/cuda4dnn/kernels/activations.hpp
index d7c471a5ec..854bc8ac0c 100644
--- a/modules/dnn/src/cuda4dnn/kernels/activations.hpp
+++ b/modules/dnn/src/cuda4dnn/kernels/activations.hpp
@@ -60,6 +60,51 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
     template <class T>
     void not_k(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
 
+    template <class T>
+    void acos(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void acosh(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void asin(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void asinh(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void atan(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void atanh(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void cos(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void cosh(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void erf(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void hardswish(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void sin(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void sinh(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void softplus(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void softsign(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
+    template <class T>
+    void tan(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input);
+
     template <class T>
     void power(const csl::Stream& stream, csl::Span<T> output, csl::View<T> input, T exp, T scale, T shift);
 
diff --git a/modules/dnn/src/cuda4dnn/primitives/activation.hpp b/modules/dnn/src/cuda4dnn/primitives/activation.hpp
index 77a79703fe..4691996d4e 100644
--- a/modules/dnn/src/cuda4dnn/primitives/activation.hpp
+++ b/modules/dnn/src/cuda4dnn/primitives/activation.hpp
@@ -280,6 +280,216 @@ namespace cv { namespace dnn { namespace cuda4dnn {
         csl::Stream stream;
     };
 
+    template <class T>
+    class AcosOp final : public BaseOp<AcosOp, T> {
+    public:
+        AcosOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::acos<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class AcoshOp final : public BaseOp<AcoshOp, T> {
+    public:
+        AcoshOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::acosh<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class AsinOp final : public BaseOp<AsinOp, T> {
+    public:
+        AsinOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::asin<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class AsinhOp final : public BaseOp<AsinhOp, T> {
+    public:
+        AsinhOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::asinh<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class AtanOp final : public BaseOp<AtanOp, T> {
+    public:
+        AtanOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::atan<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class AtanhOp final : public BaseOp<AtanhOp, T> {
+    public:
+        AtanhOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::atanh<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class CosOp final : public BaseOp<CosOp, T> {
+    public:
+        CosOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::cos<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class CoshOp final : public BaseOp<CoshOp, T> {
+    public:
+        CoshOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::cosh<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class ErfOp final : public BaseOp<ErfOp, T> {
+    public:
+        ErfOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::erf<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class HardSwishOp final : public BaseOp<HardSwishOp, T> {
+    public:
+        HardSwishOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::hardswish<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class SinOp final : public BaseOp<SinOp, T> {
+    public:
+        SinOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::sin<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class SinhOp final : public BaseOp<SinhOp, T> {
+    public:
+        SinhOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::sinh<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class SoftplusOp final : public BaseOp<SoftplusOp, T> {
+    public:
+        SoftplusOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::softplus<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class SoftsignOp final : public BaseOp<SoftsignOp, T> {
+    public:
+        SoftsignOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::softsign<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
+    template <class T>
+    class TanOp final : public BaseOp<TanOp, T> {
+    public:
+        TanOp(csl::Stream stream_) : stream(std::move(stream_)) { }
+
+        void calculate(csl::TensorSpan<T> output, csl::TensorView<T> input) const
+        {
+            kernels::tan<T>(stream, output, input);
+        }
+
+    private:
+        csl::Stream stream;
+    };
+
     template <class T>
     class PowerOp final : public BaseOp<PowerOp, T> {
     public:
diff --git a/modules/dnn/src/init.cpp b/modules/dnn/src/init.cpp
index 443d1eaef4..89a91e17ae 100644
--- a/modules/dnn/src/init.cpp
+++ b/modules/dnn/src/init.cpp
@@ -117,6 +117,21 @@ void initializeLayerFactory()
     CV_DNN_REGISTER_LAYER_CLASS(Round,          RoundLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Sqrt,           SqrtLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Not,            NotLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Acos,           AcosLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Acosh,          AcoshLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Asin,           AsinLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Asinh,          AsinhLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Atan,           AtanLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Atanh,          AtanhLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Cos,            CosLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Cosh,           CoshLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Erf,            ErfLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(HardSwish,      HardSwishLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Sin,            SinLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Sinh,           SinhLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Softplus,       SoftplusLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Softsign,       SoftsignLayer);
+    CV_DNN_REGISTER_LAYER_CLASS(Tan,            TanLayer);
     CV_DNN_REGISTER_LAYER_CLASS(BatchNorm,      BatchNormLayer);
     CV_DNN_REGISTER_LAYER_CLASS(MaxUnpool,      MaxUnpoolLayer);
     CV_DNN_REGISTER_LAYER_CLASS(Dropout,        BlankLayer);
diff --git a/modules/dnn/src/layers/elementwise_layers.cpp b/modules/dnn/src/layers/elementwise_layers.cpp
index 7cec0d5f7b..772dfca602 100644
--- a/modules/dnn/src/layers/elementwise_layers.cpp
+++ b/modules/dnn/src/layers/elementwise_layers.cpp
@@ -76,8 +76,21 @@ using std::pow;
 using std::ceil;
 using std::floor;
 using std::log;
+using std::log1p;
 using std::sqrt;
 using std::round;
+using std::acos;
+using std::acosh;
+using std::asin;
+using std::asinh;
+using std::atan;
+using std::atanh;
+using std::cos;
+using std::cosh;
+using std::erf;
+using std::sin;
+using std::sinh;
+using std::tan;
 
 template<typename Func>
 class ElementWiseLayer : public Func::Layer
@@ -746,6 +759,20 @@ struct BaseDefaultFunctor : public BaseFunctor
     }
 #endif  // HAVE_VULKAN
 
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        CV_Error(Error::StsNotImplemented, "");
+    }
+#endif
+
+#ifdef HAVE_HALIDE
+    void attachHalide(const Halide::Expr& input, Halide::Func& top)
+    {
+        CV_Error(Error::StsNotImplemented, "");
+    }
+#endif  // HAVE_HALIDE
+
 private:
     static const char* const ocl_kernel_name;
 };
@@ -1390,6 +1417,411 @@ struct NotFunctor : public BaseDefaultFunctor<NotFunctor>
 template<>
 const char* const BaseDefaultFunctor<NotFunctor>::ocl_kernel_name = "NotForward";
 
+struct AcosFunctor : public BaseDefaultFunctor<AcosFunctor>
+{
+    typedef AcosLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return acos(x);
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::AcosOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<AcosFunctor>::ocl_kernel_name = "AcosForward";
+
+struct AcoshFunctor : public BaseDefaultFunctor<AcoshFunctor>
+{
+    typedef AcoshLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return acosh(x);
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::AcoshOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<AcoshFunctor>::ocl_kernel_name = "AcoshForward";
+
+struct AsinFunctor : public BaseDefaultFunctor<AsinFunctor>
+{
+    typedef AsinLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return asin(x);
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::AsinOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<AsinFunctor>::ocl_kernel_name = "AsinForward";
+
+struct AsinhFunctor : public BaseDefaultFunctor<AsinhFunctor>
+{
+    typedef AsinhLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return asinh(x);
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::AsinhOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<AsinhFunctor>::ocl_kernel_name = "AsinhForward";
+
+struct AtanFunctor : public BaseDefaultFunctor<AtanFunctor>
+{
+    typedef AtanLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return atan(x);
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::AtanOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<AtanFunctor>::ocl_kernel_name = "AtanForward";
+
+struct AtanhFunctor : public BaseDefaultFunctor<AtanhFunctor>
+{
+    typedef AtanhLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return atanh(x);
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::AtanhOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<AtanhFunctor>::ocl_kernel_name = "AtanhForward";
+
+struct CosFunctor : public BaseDefaultFunctor<CosFunctor>
+{
+    typedef CosLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return cos(x);
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::CosOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<CosFunctor>::ocl_kernel_name = "CosForward";
+
+struct CoshFunctor : public BaseDefaultFunctor<CoshFunctor>
+{
+    typedef CoshLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return cosh(x);
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::CoshOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<CoshFunctor>::ocl_kernel_name = "CoshForward";
+
+struct ErfFunctor : public BaseDefaultFunctor<ErfFunctor>
+{
+    typedef ErfLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return erf(x);
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::ErfOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<ErfFunctor>::ocl_kernel_name = "ErfForward";
+
+struct HardSwishFunctor : public BaseDefaultFunctor<HardSwishFunctor>
+{
+    typedef HardSwishLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return x * max(0.f, min(1.f, x / 6.f + 0.5f));
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::HardSwishOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<HardSwishFunctor>::ocl_kernel_name = "HardSwishForward";
+
+struct SinFunctor : public BaseDefaultFunctor<SinFunctor>
+{
+    typedef SinLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return sin(x);
+    }
+
+#ifdef HAVE_CUDA
+        Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+        {
+            return make_cuda_node<cuda4dnn::SinOp>(target, stream);
+        }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<SinFunctor>::ocl_kernel_name = "SinForward";
+
+struct SinhFunctor : public BaseDefaultFunctor<SinhFunctor>
+{
+    typedef SinhLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return sinh(x);
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::SinhOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<SinhFunctor>::ocl_kernel_name = "SinhForward";
+
+struct SoftplusFunctor : public BaseDefaultFunctor<SoftplusFunctor>
+{
+    typedef SoftplusLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return log1p(exp(x));
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::SoftplusOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<SoftplusFunctor>::ocl_kernel_name = "SoftplusForward";
+
+struct SoftsignFunctor : public BaseDefaultFunctor<SoftsignFunctor>
+{
+    typedef SoftsignLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return x / (1.f + abs(x));
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::SoftsignOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<SoftsignFunctor>::ocl_kernel_name = "SoftsignForward";
+
+struct TanFunctor : public BaseDefaultFunctor<TanFunctor>
+{
+    typedef TanLayer Layer;
+
+    bool supportBackend(int backendId, int)
+    {
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_CUDA;
+    }
+
+    inline float calculate(float x) const
+    {
+        return tan(x);
+    }
+
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(int target, csl::Stream stream)
+    {
+        return make_cuda_node<cuda4dnn::TanOp>(target, stream);
+    }
+#endif
+
+    int64 getFLOPSPerElement() const { return 1; }
+};
+
+template<>
+const char* const BaseDefaultFunctor<TanFunctor>::ocl_kernel_name = "TanForward";
+
 struct PowerFunctor : public BaseFunctor
 {
     typedef PowerLayer Layer;
@@ -1937,6 +2369,126 @@ Ptr<NotLayer> NotLayer::create(const LayerParams& params)
     return l;
 }
 
+Ptr<AcosLayer> AcosLayer::create(const LayerParams& params)
+{
+    Ptr<AcosLayer> l(new ElementWiseLayer<AcosFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<AcoshLayer> AcoshLayer::create(const LayerParams& params)
+{
+    Ptr<AcoshLayer> l(new ElementWiseLayer<AcoshFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<AsinLayer> AsinLayer::create(const LayerParams& params)
+{
+    Ptr<AsinLayer> l(new ElementWiseLayer<AsinFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<AsinhLayer> AsinhLayer::create(const LayerParams& params)
+{
+    Ptr<AsinhLayer> l(new ElementWiseLayer<AsinhFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<AtanLayer> AtanLayer::create(const LayerParams& params)
+{
+    Ptr<AtanLayer> l(new ElementWiseLayer<AtanFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<AtanhLayer> AtanhLayer::create(const LayerParams& params)
+{
+    Ptr<AtanhLayer> l(new ElementWiseLayer<AtanhFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<CosLayer> CosLayer::create(const LayerParams& params)
+{
+    Ptr<CosLayer> l(new ElementWiseLayer<CosFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<CoshLayer> CoshLayer::create(const LayerParams& params)
+{
+    Ptr<CoshLayer> l(new ElementWiseLayer<CoshFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<ErfLayer> ErfLayer::create(const LayerParams& params)
+{
+    Ptr<ErfLayer> l(new ElementWiseLayer<ErfFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<HardSwishLayer> HardSwishLayer::create(const LayerParams& params)
+{
+    Ptr<HardSwishLayer> l(new ElementWiseLayer<HardSwishFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<SinLayer> SinLayer::create(const LayerParams& params)
+{
+    Ptr<SinLayer> l(new ElementWiseLayer<SinFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<SinhLayer> SinhLayer::create(const LayerParams& params)
+{
+    Ptr<SinhLayer> l(new ElementWiseLayer<SinhFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<SoftplusLayer> SoftplusLayer::create(const LayerParams& params)
+{
+    Ptr<SoftplusLayer> l(new ElementWiseLayer<SoftplusFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<SoftsignLayer> SoftsignLayer::create(const LayerParams& params)
+{
+    Ptr<SoftsignLayer> l(new ElementWiseLayer<SoftsignFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
+Ptr<TanLayer> TanLayer::create(const LayerParams& params)
+{
+    Ptr<TanLayer> l(new ElementWiseLayer<TanFunctor>());
+    l->setParamsFrom(params);
+
+    return l;
+}
+
 Ptr<PowerLayer> PowerLayer::create(const LayerParams& params)
 {
     float power = params.get<float>("power", 1.0f);
diff --git a/modules/dnn/src/onnx/onnx_graph_simplifier.cpp b/modules/dnn/src/onnx/onnx_graph_simplifier.cpp
index 15ce9624c4..81a5df1a28 100644
--- a/modules/dnn/src/onnx/onnx_graph_simplifier.cpp
+++ b/modules/dnn/src/onnx/onnx_graph_simplifier.cpp
@@ -206,6 +206,42 @@ public:
     }
 };
 
+class HardSwishSubgraph : public Subgraph
+{
+public:
+    HardSwishSubgraph()
+    {
+        int input = addNodeToMatch("");
+        int hardSigmoid = addNodeToMatch("HardSigmoid", input);
+        addNodeToMatch("Mul", input, hardSigmoid);
+        setFusedNode("HardSwish", input);
+    }
+
+    virtual bool match(const Ptr<ImportGraphWrapper>& net, int nodeId,
+                       std::vector<int>& matchedNodesIds,
+                       std::vector<int>& targetNodesIds) CV_OVERRIDE
+    {
+        if (Subgraph::match(net, nodeId, matchedNodesIds, targetNodesIds))
+        {
+            Ptr<ImportNodeWrapper> hardSigmoid = net->getNode(matchedNodesIds[0]);
+            opencv_onnx::NodeProto* node = hardSigmoid.dynamicCast<ONNXNodeWrapper>()->node;
+
+            uint8_t matched = 0;
+            for (int i = 0; i < node->attribute_size(); i++)
+            {
+                opencv_onnx::AttributeProto attr = node->attribute(i);
+                if ((attr.name() == "alpha" && attr.f() == 1.f / 6.f) ||
+                    (attr.name() == "beta" && attr.f() == 0.5f))
+                {
+                    ++matched;
+                }
+            }
+            return matched == 2;
+        }
+        return false;
+    }
+};
+
 class NormalizeSubgraphBase : public Subgraph
 {
 public:
@@ -625,6 +661,7 @@ void simplifySubgraphs(opencv_onnx::GraphProto& net)
     subgraphs.push_back(makePtr<SoftMaxSubgraph>());
     subgraphs.push_back(makePtr<SoftMaxSubgraph2>());
     subgraphs.push_back(makePtr<LogSoftMaxSubgraph>());
+    subgraphs.push_back(makePtr<HardSwishSubgraph>());
     subgraphs.push_back(makePtr<NormalizeSubgraph1>());
     subgraphs.push_back(makePtr<NormalizeSubgraph2>());
     subgraphs.push_back(makePtr<NormalizeSubgraph2_2>());
diff --git a/modules/dnn/src/opencl/activations.cl b/modules/dnn/src/opencl/activations.cl
index e110160c06..02ed9345c3 100644
--- a/modules/dnn/src/opencl/activations.cl
+++ b/modules/dnn/src/opencl/activations.cl
@@ -188,3 +188,87 @@ __kernel void NotForward(const int n, __global T* in, __global T* out) {
     if(index < n)
         out[index] = floor(1.0f - in[index]);
 }
+
+__kernel void AcosForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = acos(in[index]);
+}
+
+__kernel void AcoshForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = acosh(in[index]);
+}
+
+__kernel void AsinForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = asin(in[index]);
+}
+
+__kernel void AsinhForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = asinh(in[index]);
+}
+
+__kernel void AtanForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = atan(in[index]);
+}
+
+__kernel void AtanhForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = atanh(in[index]);
+}
+
+__kernel void CosForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = cos(in[index]);
+}
+
+__kernel void CoshForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = cosh(in[index]);
+}
+
+__kernel void HardSwishForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = in[index] * max(0.f, min(1.f, in[index] / 6.f + 0.5f));
+}
+
+__kernel void SinForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = sin(in[index]);
+}
+
+__kernel void SinhForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = sinh(in[index]);
+}
+
+__kernel void SoftplusForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = log1p(exp(in[index]));
+}
+
+__kernel void SoftsignForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = in[index] / (1.f + fabs(in[index]));
+}
+
+__kernel void TanForward(const int n, __global T* in, __global T* out) {
+    int index = get_global_id(0);
+    if(index < n)
+        out[index] = tan(in[index]);
+}
diff --git a/modules/dnn/test/test_onnx_conformance_layer_filter__cuda_denylist.inl.hpp b/modules/dnn/test/test_onnx_conformance_layer_filter__cuda_denylist.inl.hpp
index 61a5843b35..0f5f387132 100644
--- a/modules/dnn/test/test_onnx_conformance_layer_filter__cuda_denylist.inl.hpp
+++ b/modules/dnn/test/test_onnx_conformance_layer_filter__cuda_denylist.inl.hpp
@@ -71,4 +71,5 @@
 "test_softmax_large_number_expanded",  // FP16 only
 "test_sub_bcast",
 "test_sub_uint8",
+"test_tan",  // FP16 only
 "test_upsample_nearest",
diff --git a/modules/dnn/test/test_onnx_conformance_layer_filter_opencv_ocl_fp16_denylist.inl.hpp b/modules/dnn/test/test_onnx_conformance_layer_filter_opencv_ocl_fp16_denylist.inl.hpp
index 573d847985..ccd1568845 100644
--- a/modules/dnn/test/test_onnx_conformance_layer_filter_opencv_ocl_fp16_denylist.inl.hpp
+++ b/modules/dnn/test/test_onnx_conformance_layer_filter_opencv_ocl_fp16_denylist.inl.hpp
@@ -19,3 +19,4 @@
 "test_split_equal_parts_1d",
 "test_split_equal_parts_2d",
 "test_split_equal_parts_default_axis",
+"test_tan",
diff --git a/modules/dnn/test/test_onnx_conformance_layer_parser_denylist.inl.hpp b/modules/dnn/test/test_onnx_conformance_layer_parser_denylist.inl.hpp
index b95cafd5d7..a69ace0d14 100644
--- a/modules/dnn/test/test_onnx_conformance_layer_parser_denylist.inl.hpp
+++ b/modules/dnn/test/test_onnx_conformance_layer_parser_denylist.inl.hpp
@@ -1,9 +1,5 @@
 // The file is autogenerated
 // Update note: execute <opencv_extra>/testdata/dnn/onnx/generate_conformance_list.py <gtest_xml_file>
-"test_acos",
-"test_acos_example",
-"test_acosh",
-"test_acosh_example",
 "test_adagrad",
 "test_adagrad_multiple",
 "test_adam",
@@ -16,14 +12,6 @@
 "test_and_bcast4v2d",
 "test_and_bcast4v3d",
 "test_and_bcast4v4d",
-"test_asin",
-"test_asin_example",
-"test_asinh",
-"test_asinh_example",
-"test_atan",
-"test_atan_example",
-"test_atanh",
-"test_atanh_example",
 "test_basic_convinteger",
 "test_batchnorm_epsilon",
 "test_batchnorm_epsilon_training_mode",
@@ -102,10 +90,6 @@
 "test_convtranspose_pad",
 "test_convtranspose_pads",
 "test_convtranspose_with_kernel",
-"test_cos",
-"test_cos_example",
-"test_cosh",
-"test_cosh_example",
 "test_cumsum_1d",
 "test_cumsum_1d_exclusive",
 "test_cumsum_1d_reverse",
@@ -138,7 +122,6 @@
 "test_einsum_transpose",
 "test_equal",
 "test_equal_bcast",
-"test_erf",
 "test_expand_dim_changed",
 "test_expand_dim_unchanged",
 "test_eyelike_populate_off_main_diagonal",
@@ -193,8 +176,6 @@
 "test_hardsigmoid",
 "test_hardsigmoid_default",
 "test_hardsigmoid_example",
-"test_hardswish",
-"test_hardswish_expanded",
 "test_identity_opt",
 "test_identity_sequence",
 "test_if",
@@ -564,10 +545,6 @@
 "test_simple_rnn_batchwise",
 "test_simple_rnn_defaults",
 "test_simple_rnn_with_initial_bias",
-"test_sin",
-"test_sin_example",
-"test_sinh",
-"test_sinh_example",
 "test_size",
 "test_size_example",
 "test_slice",
@@ -578,10 +555,6 @@
 "test_slice_neg_steps",
 "test_slice_negative_axes",
 "test_slice_start_out_of_bounds",
-"test_softplus",
-"test_softplus_example",
-"test_softsign",
-"test_softsign_example",
 "test_spacetodepth",
 "test_spacetodepth_example",
 "test_split_variable_parts_1d",
@@ -599,8 +572,6 @@
 "test_sub_example",
 "test_sum_example",
 "test_sum_two_inputs",
-"test_tan",
-"test_tan_example",
 "test_tfidfvectorizer_tf_batch_onlybigrams_skip0",
 "test_tfidfvectorizer_tf_batch_onlybigrams_skip5",
 "test_tfidfvectorizer_tf_batch_uniandbigrams_skip5",