- weights are unaligned in dasiamprn sample (comes from numpy)

[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

- prevent running code over unsupported/non-tested configurations
- prevent integer div by zero

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.

* Updated UI documentation to address WUI

* Added documentation for vx_ calls

* Removed vx_store operation overload

* Doxyfile updated to enable wide UI

* Enable doxygen documentation for vx_ WUI functions

* Wide intrinsics definition rework

* core: fix SIMD C++ emulator build (supports 128-bit only)

Added OpenVINO ARM target

* Added IE ARM target

* Added OpenVINO ARM target

* Delete ARM target

* Detect ARM platform

* Changed device name in ArmPlugin

* Change ARM detection

Conv1D and Pool1D for CUDA backend

* CUDA-independent changes

* Add Conv1D and Pool1D for CUDA backend

* CUDA-independent changes

* Fix typo

* fix comment

* Update fix

* make changes more correct for pooling layer

* Minor fixes for review

* Split skip blocks

* added HDDL VPU support

* changed to return True in one line if any device connected

* dnn: use releaseHDDLPlugin()

* dnn(hddl): fix conditions

Add support for Conv1D on OpenCV backend

* Add support for Conv1D on OpenCV backend

* disable tests on other targets/backends

* Fix formatting

* Restore comment

* Remove unnecessary flag and fix test logic

* Fix perf test

* fix braces

* Fix indentation, assert check and remove unnecessary condition

* Remove unnecessary changes

* Add test cases for variable weights and bias

* dnn(conv): fallback on OpenCV+CPU instead of failures

* coding style

Tengine lite update

* update tengine

* Modify for arm32 build.

* format optimization

* add teng_ befor some tengine api

* update graph_t to teng_graph_t

* update graph_t to teng_graph_t

* Code structure optimization

* optimization

* optimization

* remove space

* update tengine url
Co-authored-by: Nliqi <qli@openailab.com>

original commit: 71f665bd

* hopefully, eliminated compile warnings, errors, as well as failure in one test

* * fixed a few typos
* decreased buffer size in some cases
* added more optimal im2row branch in the case of 1x1 convolutions
* tuned fastConv to reduce the number of passes over arrays

backport of commit 77b01deb

* Supported convolution with non-const weights

* Fix opencl blobs

* Update tests

* added depth-wise convolution; gives ~20-30% performance improvement in MobileSSD networks

* hopefully, eliminated compile warnings, errors, as well as failure in one test

* * fixed a few typos
* decreased buffer size in some cases
* added more optimal im2row branch in the case of 1x1 convolutions
* tuned fastConv to reduce the number of passes over arrays

* fix eltwise fusion segfault, more eltwise fusions, fix power fusion

* add assertion

cuda4dnn(conv): fuse eltwise with convolutions

* fuse eltwise with convolutions

* manually rebase to avoid bad git merge

cuda4dnn: optimizations for swish, mish, sigmoid, region, resize based ops, transpose, identity-conv fusion

* bunch of optimizations

* more accurate implementation for mish

* Add Tengine support .

* Modify printf to CV_LOG_WARNING

* a few minor fixes in the code

* Renew Tengine version

* Add header file for CV_LOG_WARNING

* Add #ifdef HAVE_TENGINE in tengine_graph_convolution.cpp

* remove trailing whitespace

* Remove trailing whitespace

* Modify for compile problem

* Modify some code style error

* remove whitespace

* Move some code style problem

* test

* add ios limit and build problem

* Modified as alalek suggested

* Add cmake 2.8 support

* modify cmake 3.5.1 problem

* test and set BUILD_ANDROID_PROJECTS OFF

* remove some compile error

* remove some extra code in tengine

* close test.

* Test again

* disable android.

* delete ndk version judgement

* Remove setenv() call . and add License information

* Set tengine default OFF. Close test .
Co-authored-by: NVadim Pisarevsky <vadim.pisarevsky@gmail.com>

- CMake option: OPENCV_DNN_IE_NN_BUILDER_2019

- CMake option: OPENCV_DNN_IE_NN_BUILDER_2019

cuda4dnn: fuse activations with convolutions

* fuse ReLU, ReLU6, TanH, Sigmoid with conv

* fix OpenCL errors

* improve ReLU, add power, swish and mish

* fix missing fusion entries

* fix handling of unsetAttached

* remove whole file indentation

* optimize power = 1.0, use IDENTITY instead of NONE

* handle edge case: change backend and then clear

* 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 computation of internal shapes in Deconvolution layer

* Fix computation of internal shapes

* Refactoring

Support new IE API (#15184)

* Add support OpenVINO R2 for layers

* Add Core API

* Fix tests

* Fix expectNoFallbacksFromIE for ONNX nets

* Remove deprecated API

* Remove td

* Remove TargetDevice

* Fix Async

* Add test

* Fix detectMyriadX

* Fix test

* Fix warning

* Support Deconvolution3D on IE backend

* Add test tag

* Fix tests

* Support Conv3D on OCV backend

* Add header

* Add perf tests

* Support pool3d

* Enable Resnet34_kinetics on OCV backend

* Add test

* Fix conv

* Optimize Conv2D