* cann backend impl v1

* cann backend impl v2: use opencv parsers to build models for cann

* adjust fc according to the new transA and transB

* put cann net in cann backend node and reuse forwardLayer

* use fork() to create a child process and compile cann model

* remove legacy code

* remove debug code

* fall bcak to CPU backend if there is one layer not supoorted by CANN backend

* fix netInput forward

- this mode is not supported by 22828

* Add Loongson Advanced SIMD Extension support: -DCPU_BASELINE=LASX
* Add resize.lasx.cpp for Loongson SIMD acceleration
* Add imgwarp.lasx.cpp for Loongson SIMD acceleration
* Add LASX acceleration support for dnn/conv
* Add CV_PAUSE(v) for Loongarch
* Set LASX by default on Loongarch64
* LoongArch: tune test threshold for Core/HAL.mat_decomp/15
Co-authored-by: Nshengwenxue <shengwenxue@loongson.cn>

Add per_tensor_quantize to int8 quantize

* add per_tensor_quantize to dnn int8 module.

* change api flag from perTensor to perChannel, and recognize quantize type and onnx importer.

* change the default to hpp

dnn: TIM-VX NPU backend support

* Add TimVX NPU backend for DNN module.

* use official branch from tim-vx repo; fix detecting viv sdk
Co-authored-by: Nfytao <yuantao.feng@outlook.com>

* dnn: LSTM optimisation

This uses the AVX-optimised fastGEMM1T for matrix multiplications where available, instead of the standard cv::gemm.

fastGEMM1T is already used by the fully-connected layer.  This commit involves two minor modifications:
 - Use unaligned access.  I don't believe this involves any performance hit in on modern CPUs (Nehalem and Bulldozer onwards) in the case where the address is actually aligned.
 - Allow for weight matrices where the number of columns is not a multiple of 8.

I have not enabled AVX-512 as I don't have an AVX-512 CPU to test on.

* Fix warning about initialisation order

* Remove C++11 syntax

* Fix build when AVX(2) is not available

In this case the CV_TRY_X macros are defined to 0, rather than being undefined.

* Minor changes as requested:

 - Don't check hardware support for AVX(2) when dispatch is disabled for these
 - Add braces

* Fix out-of-bounds access in fully connected layer

The old tail handling in fastGEMM1T implicitly rounded vecsize up to the next multiple of 8, and the fully connected layer implements padding up to the next multiple of 8 to cope with this.  The new tail handling does not round the vecsize upwards like this but it does require that the vecsize is at least 8.  To adapt to the new tail handling, the fully connected layer now rounds vecsize itself at the same time as adding the padding(which makes more sense anyway).

This also means that the fully connected layer always passes a vecsize of at least 8 to fastGEMM1T, which fixes the out-of-bounds access problems.

* Improve tail mask handling

 - Use static array for generating tail masks (as requested)
 - Apply tail mask to the weights as well as the input vectors to prevent spurious propagation of NaNs/Infs

* Revert whitespace change

* Improve readability of conditions for using AVX

* dnn(lstm): minor coding style changes, replaced left aligned load

[GSoC] OpenCV.js: Accelerate OpenCV.js DNN via WebNN

* Add WebNN backend for OpenCV DNN Module

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Add WebNN head files into OpenCV 3rd partiy files

Create webnn.hpp

update cmake

Complete README and add OpenCVDetectWebNN.cmake file

add webnn.cpp

Modify webnn.cpp

Can successfully compile the codes for creating a MLContext

Update webnn.cpp

Update README.md

Update README.md

Update README.md

Update README.md

Update cmake files and

update README.md

Update OpenCVDetectWebNN.cmake and README.md

Update OpenCVDetectWebNN.cmake

Fix OpenCVDetectWebNN.cmake and update README.md

Add source webnn_cpp.cpp and libary libwebnn_proc.so

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

update dnn.cpp

update op_webnn

update op_webnn

Update op_webnn.hpp

update op_webnn.cpp & hpp

Update op_webnn.hpp

Update op_webnn

update the skeleton

Update op_webnn.cpp

Update op_webnn

Update op_webnn.cpp

Update op_webnn.cpp

Update op_webnn.hpp

update op_webnn

update op_webnn

Solved the problems of released variables.

Fixed the bugs in op_webnn.cpp

Implement op_webnn

Implement Relu by WebNN API

Update dnn.cpp for better test

Update elementwise_layers.cpp

Implement ReLU6

Update elementwise_layers.cpp

Implement SoftMax using WebNN API

Implement Reshape by WebNN API

Implement PermuteLayer by WebNN API

Implement PoolingLayer using WebNN API

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Implement poolingLayer by WebNN API and add more detailed logs

Update dnn.cpp

Update dnn.cpp

Remove redundant codes and add more logs for poolingLayer

Add more logs in the pooling layer implementation

Fix the indent issue and resolve the compiling issue

Fix the build problems

Fix the build issue

FIx the build issue

Update dnn.cpp

Update dnn.cpp

* Fix the build issue

* Implement BatchNorm Layer by WebNN API

* Update convolution_layer.cpp

This is a temporary file for Conv2d layer implementation

* Integrate some general functions into op_webnn.cpp&hpp

* Update const_layer.cpp

* Update convolution_layer.cpp

Still have some bugs that should be fixed.

* Update conv2d layer and fc layer

still have some problems to be fixed.

* update constLayer, conv layer, fc layer

There are still some bugs to be fixed.

* Fix the build issue

* Update concat_layer.cpp

Still have some bugs to be fixed.

* Update conv2d layer, fully connected layer and const layer

* Update convolution_layer.cpp

* Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron)

* Delete bib19450.aux

* Add WebNN backend for OpenCV DNN Module

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Add WebNN head files into OpenCV 3rd partiy files

Create webnn.hpp

update cmake

Complete README and add OpenCVDetectWebNN.cmake file

add webnn.cpp

Modify webnn.cpp

Can successfully compile the codes for creating a MLContext

Update webnn.cpp

Update README.md

Update README.md

Update README.md

Update README.md

Update cmake files and

update README.md

Update OpenCVDetectWebNN.cmake and README.md

Update OpenCVDetectWebNN.cmake

Fix OpenCVDetectWebNN.cmake and update README.md

Add source webnn_cpp.cpp and libary libwebnn_proc.so

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

Update dnn.cpp

update dnn.cpp

update op_webnn

update op_webnn

Update op_webnn.hpp

update op_webnn.cpp & hpp

Update op_webnn.hpp

Update op_webnn

update the skeleton

Update op_webnn.cpp

Update op_webnn

Update op_webnn.cpp

Update op_webnn.cpp

Update op_webnn.hpp

update op_webnn

update op_webnn

Solved the problems of released variables.

Fixed the bugs in op_webnn.cpp

Implement op_webnn

Implement Relu by WebNN API

Update dnn.cpp for better test

Update elementwise_layers.cpp

Implement ReLU6

Update elementwise_layers.cpp

Implement SoftMax using WebNN API

Implement Reshape by WebNN API

Implement PermuteLayer by WebNN API

Implement PoolingLayer using WebNN API

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Update pooling_layer.cpp

Implement poolingLayer by WebNN API and add more detailed logs

Update dnn.cpp

Update dnn.cpp

Remove redundant codes and add more logs for poolingLayer

Add more logs in the pooling layer implementation

Fix the indent issue and resolve the compiling issue

Fix the build problems

Fix the build issue

FIx the build issue

Update dnn.cpp

Update dnn.cpp

* Fix the build issue

* Implement BatchNorm Layer by WebNN API

* Update convolution_layer.cpp

This is a temporary file for Conv2d layer implementation

* Integrate some general functions into op_webnn.cpp&hpp

* Update const_layer.cpp

* Update convolution_layer.cpp

Still have some bugs that should be fixed.

* Update conv2d layer and fc layer

still have some problems to be fixed.

* update constLayer, conv layer, fc layer

There are still some bugs to be fixed.

* Update conv2d layer, fully connected layer and const layer

* Update convolution_layer.cpp

* Add OpenCV.js DNN module WebNN Backend (both using webnn-polyfill and electron)

* Update dnn.cpp

* Fix Error in dnn.cpp

* Resolve duplication in conditions in convolution_layer.cpp

* Fixed the issues in the comments

* Fix building issue

* Update tutorial

* Fixed comments

* Address the comments

* Update CMakeLists.txt

* Offer more accurate perf test on native

* Add better perf tests for both native and web

* Modify per tests for better results

* Use more latest version of Electron

* Support latest WebNN Clamp op

* Add definition of HAVE_WEBNN macro

* Support group convolution

* Implement Scale_layer using WebNN

* Add Softmax option for native classification example

* Fix comments

* Fix comments

Optimization of DNN using native RISC-V vector intrinsics.

* Use RVV to optimize fastGEMM (FP32) in DNN.

* Use RVV to optimize fastGEMM1T in DNN.

* Use RVV to optimize fastConv in DNN.

* Use RVV to optimize fastDepthwiseConv in DNN.

* Vectorize tails using vl.

* Use "vl" instead of scalar to handle small block in fastConv.

* Fix memory access out of bound in "fastGEMM1T".

* Remove setvl.

* Remove useless initialization.

* Use loop unrolling to handle tail part instead of switch.

* Added ReduceSum to ONNX importer

* Fix comments

* Fix Mul

* 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

- CMake option: OPENCV_DNN_IE_NN_BUILDER_2019

- CMake option: OPENCV_DNN_IE_NN_BUILDER_2019

* Support nGraph

* Fix resize

CUDA backend for the DNN module

* stub cuda4dnn design

* minor fixes for tests and doxygen

* add csl public api directory to module headers

* add low-level CSL components

* add high-level CSL components

* integrate csl::Tensor into backbone code

* switch to CPU iff unsupported; otherwise, fail on error

* add fully connected layer

* add softmax layer

* add activation layers

* support arbitary rank TensorDescriptor

* pass input wrappers to `initCUDA()`

* add 1d/2d/3d-convolution

* add pooling layer

* reorganize and refactor code

* fixes for gcc, clang and doxygen; remove cxx14/17 code

* add blank_layer

* add LRN layer

* add rounding modes for pooling layer

* split tensor.hpp into tensor.hpp and tensor_ops.hpp

* add concat layer

* add scale layer

* add batch normalization layer

* split math.cu into activations.cu and math.hpp

* add eltwise layer

* add flatten layer

* add tensor transform api

* add asymmetric padding support for convolution layer

* add reshape layer

* fix rebase issues

* add permute layer

* add padding support for concat layer

* refactor and reorganize code

* add normalize layer

* optimize bias addition in scale layer

* add prior box layer

* fix and optimize normalize layer

* add asymmetric padding support for pooling layer

* add event API

* improve pooling performance for some padding scenarios

* avoid over-allocation of compute resources to kernels

* improve prior box performance

* enable layer fusion

* add const layer

* add resize layer

* add slice layer

* add padding layer

* add deconvolution layer

* fix channelwise  ReLU initialization

* add vector traits

* add vectorized versions of relu, clipped_relu, power

* add vectorized concat kernels

* improve concat_with_offsets performance

* vectorize scale and bias kernels

* add support for multi-billion element tensors

* vectorize prior box kernels

* fix address alignment check

* improve bias addition performance of conv/deconv/fc layers

* restructure code for supporting multiple targets

* add DNN_TARGET_CUDA_FP64

* add DNN_TARGET_FP16

* improve vectorization

* add region layer

* improve tensor API, add dynamic ranks

1. use ManagedPtr instead of a Tensor in backend wrapper
2. add new methods to tensor classes
  - size_range: computes the combined size of for a given axis range
  - tensor span/view can be constructed from a raw pointer and shape
3. the tensor classes can change their rank at runtime (previously rank was fixed at compile-time)
4. remove device code from tensor classes (as they are unused)
5. enforce strict conditions on tensor class APIs to improve debugging ability

* fix parametric relu activation

* add squeeze/unsqueeze tensor API

* add reorg layer

* optimize permute and enable 2d permute

* enable 1d and 2d slice

* add split layer

* add shuffle channel layer

* allow tensors of different ranks in reshape primitive

* patch SliceOp to allow Crop Layer

* allow extra shape inputs in reshape layer

* use `std::move_backward` instead of `std::move` for insert in resizable_static_array

* improve workspace management

* add spatial LRN

* add nms (cpu) to region layer

* add max pooling with argmax ( and a fix to limits.hpp)

* add max unpooling layer

* rename DNN_TARGET_CUDA_FP32 to DNN_TARGET_CUDA

* update supportBackend to be more rigorous

* remove stray include from preventing non-cuda build

* include op_cuda.hpp outside condition #if

* refactoring, fixes and many optimizations

* drop DNN_TARGET_CUDA_FP64

* fix gcc errors

* increase max. tensor rank limit to six

* add Interp layer

* drop custom layers; use BackendNode

* vectorize activation kernels

* fixes for gcc

* remove wrong assertion

* fix broken assertion in unpooling primitive

* fix build errors in non-CUDA build

* completely remove workspace from public API

* fix permute layer

* enable accuracy and perf. tests for DNN_TARGET_CUDA

* add asynchronous forward

* vectorize eltwise ops

* vectorize fill kernel

* fixes for gcc

* remove CSL headers from public API

* remove csl header source group from cmake

* update min. cudnn version in cmake

* add numerically stable FP32 log1pexp

* refactor code

* add FP16 specialization to cudnn based tensor addition

* vectorize scale1 and bias1 + minor refactoring

* fix doxygen build

* fix invalid alignment assertion

* clear backend wrappers before allocateLayers

* ignore memory lock failures

* do not allocate internal blobs

* integrate NVTX

* add numerically stable half precision log1pexp

* fix indentation, following coding style,  improve docs

* remove accidental modification of IE code

* Revert "add asynchronous forward"

This reverts commit 1154b9da9da07e9b52f8a81bdcea48cf31c56f70.

* [cmake] throw error for unsupported CC versions

* fix rebase issues

* add more docs, refactor code, fix bugs

* minor refactoring and fixes

* resolve warnings/errors from clang

* remove haveCUDA() checks from supportBackend()

* remove NVTX integration

* changes based on review comments

* avoid exception when no CUDA device is present

* add color code for CUDA in Net::dump

* Fix IE FullyConnected layer

* Fix MyriadX

* Remove Inference Engine 2018R3 and 2018R4

* Fix 2018R5

especially in CPU only processing

* Remove isIntel check from deep learning layers

* Remove fp16->fp32 fallbacks where it's not necessary

* Fix Kernel::run to prevent localsize > globalsize

* Remove a forward method in dnn::Layer

* Add a test

* Fix tests

* Mark multiple dnn::Layer::finalize methods as deprecated

* Replace back dnn's inputBlobs to vector of pointers

* Remove Layer::forward_fallback from CV_OCL_RUN scopes

* optimize ocl kernel enqueue in fc layer
Signed-off-by: NLi Peng <peng.li@intel.com>

* use CV_LOG_INFO in convolution auto tuning
Signed-off-by: NLi Peng <peng.li@intel.com>

* update convolution IDLF kernel

extend parameter tuning range, also cleanup
ocl kernel implementation
Signed-off-by: NLi Peng <peng.li@intel.com>

* update in-memory convolution cache config

fp16 and fp32 cache config are stored separately
Signed-off-by: NLi Peng <peng.li@intel.com>

Signed-off-by: NLi Peng <peng.li@intel.com>