Added Backend Agnostic fusion in DNN - #29656
# What This Adds
A way for any layer, on any backend, to absorb trailing per-element math — with the pass knowing nothing about either layer and holding no list of op types.
- **Description**: `LayerMath` (one layer's math) → `AdjacencyGraph` (hash-consed chain)
- **Contract**: two `bool` virtuals on `Layer`
- **CPU implementations**: three, behind that contract
Pointwise fusion is the first consumer, not the point.
## The Problem
```cpp
ActivationLayer* activ = dynamic_cast<ActivationLayer*>(layer_ptr);
Conv2Layer* conv = getLayer<Conv2Layer>(newprog, conv_layer_idx);
if (conv) conv->fuseActivation(layer);
```
Guest type, host type, method name all hardcoded in the pass. `Clip` never fused (fails the cast), `Gemm` never fused (no virtual), no 2-op chain fused at all, and no backend could fuse anything without a CPU-specific method.
## Results
| Model | 5.x | this PR | speedup |
|-----------------------|----------|----------|---------|
| MPHand | 2.34 ms | 1.03 ms | 2.27 |
| EfficientNet | 9.82 ms | 5.00 ms | 1.96 |
| MPPose | 5.14 ms | 2.81 ms | 1.83 |
| MobileNet_SSD_v1_ONNX | 12.58 ms | 8.38 ms | 1.50 |
| BlazeFace | 0.95 ms | 0.77 ms | 1.23 |
| DenseNet_121 | 20.56 ms | 19.08 ms | 1.08 |
| MobileNetv2_ONNX | 2.07 ms | 1.99 ms | 1.04 |
| MobileViT_XS | 6.33 ms | 6.11 ms | 1.04 |
| YuNet_320 | 1.33 ms | 1.27 ms | 1.04 |
| PPOCRv3 | 43.05 ms | 42.15 ms | 1.02 |
| BERT | 9.78 ms | 9.68 ms | 1.01 |
| BEiT_Base_Patch16_224 | 27.48 ms | 27.08 ms | 1.01 |
| DeiT_Tiny_Patch16_224 | 4.74 ms | 4.72 ms | 1.01 |
| MPPalm | 1.88 ms | 1.85 ms | 1.01 |
| SSD | 50.19 ms | 49.82 ms | 1.01 |
| YOLOv4_tiny | 7.10 ms | 7.03 ms | 1.01 |
### 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
This PR is about Introducing cuDNN JIT support for the DNN Module
### 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
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
fixed Dynamic quantized linear layer error #29386
### 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
Savya Sanchi Sharma