dnn: disable warning when loading a fp16 model #23853

### 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

match

Build DNN without Protobuf

DNN module can be built without Protobuf for Darknet, TFLite, OpenVINO, Torch (not PyTorch) models.

```
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DBUILD_LIST=dnn \
    -DWITH_PROTOBUF=OFF \
    -DWITH_OPENCL=OFF

7.1M    lib/libopencv_dnn.so.4.7.0
```


```
cmake \
    -DCMAKE_BUILD_TYPE=Release \
    -DBUILD_LIST=dnn \
    -DWITH_OPENCL=OFF

3.9M    lib/libopencv_dnn.so.4.7.0
```

### 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

Fix identifying initializers in ONNX graph simplification #23296

Fixes https://github.com/opencv/opencv/issues/23295

### 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

Add GELU layer for vision transformers

* add gelu and gelu approximation

* drop setKernelParams

dnn: add layer normalization for vision transformers

* add layer norm onnx parser, impl and tests

* add onnx graph simplifier for layer norm expanded

* handle the case when constants are of type Initializer

* add test case for layer norm expanded with initializers

* use CV_Assert & CV_CheckType in place of CV_Assert_N; use forward_fallback for OCL_FP16

* use const ref / ref in parameters of invoker::run; extract inner const if from nested loop; use size_t in place of ull

* template hasBias

* remove trailing whitespace

* use pointer parameter with null check; move normSize division & mean_square division outside of loop; use std::max to ensure positive value before std::sqrt

* refactor implementation, optimize parallel_for

* disable layer norm expanded

* remove the removal of layer norm optional outputs

Add acos, acosh, asin, asinh, atan, atanh, cos, cosh, erf, hardswish, sin, sinh, softplus, softsign, tan layers

dnn : int8 quantized layers support in onnx importer

* added quantized layers support in onnx importer

* added more cases in eltwise node, some more checks

* added tests for quantized nodes

* relax thresholds for failed tests, address review comments

* refactoring based on review comments

* added support for unsupported cases and pre-quantized resnet50 test

* relax thresholds due to int8 resize layer

Add Normalize subgraph, fix Slice, Mul and Expand

* Add Normalize subgraph, support for starts<0 and axis<0 in Slice, Mul broadcasting in the middle and fix Expand's unsqueeze

* remove todos

* remove range-based for loop

* address review comments

* change >> to > > in template

* fix indexation

* fix expand that does nothing

ONNX diagnostic tool

* Final

* Add forgotten Normalize layer to the set of supported types

* ONNX diagnostic tool corrections

* Fixed CI test warnings

* Added code minor corrections
Co-authored-by: NSergey Slashchinin <sergei.slashchinin@xperience.ai>

Fix loading issue for Faster RCNN model from #16783

* Add a reproducer with multi-output Gather

* Fix an issue with ONNX graph simplifier

* fix build

* Move checks to correct class

* Minor changes for better code appearence

* Added ReduceSum to ONNX importer

* Fix comments

* Fix Mul

* Added ONNX BatchNorm subgraph

* Move removing constant inputs to addConstantNodesForInitializers

* Added initializers to ONNXGraphWrapper

* Supported FullyConnected layer with two inputs

* Skipped test

* Fix conditions

* Added OpenCL support

* Supported ReduceMean3D

* Supported Expand layer

* Fix warning

* Added Normalize subgraph

* refactoring

* Used addLayer

* Fix check

* Used addLayer

* Skip failed test

* Added normalize1 subgraph

* Fix comments