Merge pull request #28750 from abhishek-gola:activation_fusion

Extended fusion for activation functions in new DNN engine #28750

After this fusion, we see following improvements in YOLO models:

| Model | Before (`ENGINE_NEW`) | After (`ENGINE_NEW`) | `ENGINE_ORT` | % Improvement (Before v/s After) |
| :--- | :--- | :--- | :--- | :--- |
| **YOLOv8n** | 18.89  ms| 12.06 ms| 12.15 ms| 36.16% |
| **YOLOv5n** | 17.12  ms| 9.29 ms| 9.23 ms| 45.73% |
| **YOLOX-S** | 38.78  ms| 25.56 ms| 25.16 ms| 34.09% |

Device details: 
      - Model name: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
### 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

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

@@ -918,6 +918,36 @@ CV__DNN_INLINE_NS_BEGIN
         static Ptr<Pad2Layer> create(const LayerParams& params);
     };
 
+    /* Activation function pointer type.
+       Used for fast, platform-optimized activation implementations.
+       @param input  pointer to input data
+       @param output pointer to output data (can be same as input for in-place)
+       @param len    number of elements
+       @param params activation-specific parameters (e.g., alpha, beta)
+    */
+    typedef void (*ActivationFunc)(const void* input, void* output,
+                                   size_t len, const float* params);
+
+    /** Activation type enumeration for dispatched activation function retrieval. */
+    enum ActivationType {
+        ACTIV_NONE = 0,
+        ACTIV_MISH,
+        ACTIV_SWISH,
+        ACTIV_SIGMOID,
+        ACTIV_TANH,
+        ACTIV_ELU,
+        ACTIV_HARDSWISH,
+        ACTIV_HARDSIGMOID,
+        ACTIV_GELU,
+        ACTIV_GELU_APPROX,
+        ACTIV_RELU,
+        ACTIV_CLIP
+    };
+
+    /** Returns a platform-optimized activation function pointer for the given type.
+        The returned function is selected via CPU dispatch for the best available ISA. */
+    CV_EXPORTS ActivationFunc getActivationFunc(int activationType);
+
     /* Activations */
     class CV_EXPORTS ActivationLayer : public Layer
     {
@@ -932,6 +962,13 @@ CV__DNN_INLINE_NS_BEGIN
                                   size_t /*outPlaneSize*/, int /*cn0*/, int /*cn1*/) const {}
         virtual void forwardSlice(const int8_t* /*src*/, const int8_t* /*lut*/, int8_t* /*dst*/, int /*len*/,
                                   size_t /*outPlaneSize*/, int /*cn0*/, int /*cn1*/) const {}
+
+        /** Returns a platform-optimized activation function pointer for this layer.
+            @return function pointer, or nullptr if not available for the given depth
+        */
+        virtual ActivationFunc getActivationFunc(int /*depth*/,
+                                                 std::vector<float>& /*activParams*/) const
+        { return nullptr; }
     };
 
     class CV_EXPORTS ReLULayer : public ActivationLayer