* add accuracy test and performance check for matmul
  * add performance tests for transform and dotProduct
  * add test Core_TransformLargeTest for 8u version of transform

* remove raw SSE2/NEON implementation from matmul.cpp
  * use universal intrinsic instead of raw intrinsic
  * remove unused templated function
  * add v_matmuladd which multiply 3x3 matrix and add 3x1 vector
  * add v_rotate_left/right in universal intrinsic
  * suppress intrinsic on some function and platform
  * add pure SW implementation of new universal intrinsics
  * add test for new universal intrinsics

* core: prevent memory access after the end of buffer

* fix perf tests

Flush deallocation queue before calling map/unmap

- changed behavior of vec_ctf, vec_ctu, vec_cts
  in gcc and clang to make them compatible with XLC
- implemented most of missing conversion intrinsics in gcc and clang
- implemented conversions intrinsics of odd-numbered elements
- ignored gcc bug warning that caused by -Wunused-but-set-variable in rare cases
- replaced right shift with algebraic right shift for signed vectors
  to shift in the sign bit.
- added new universal intrinsics v_matmuladd, v_rotate_left/right
- avoid using floating multiply-add in RNG

MacOSX OpenCL compiler is very strict to whitespace issues

To prevent unnecessary compiler invocations

Exampls of these are gnu/kfreebsd and gnu/hurd, both available as
unofficial Debian ports.

They don't define __linux__ (as they are non-linux…) but still define
__GLIBC__, so check on that.
Signed-off-by: Mattia Rizzolo <mattia@mapreri.org>

* Update OpenCVCompilerOptimizations.cmake

Neon not supported on MSVC ARM breaking build fix

* Update OpenCVCompilerOptimizations.cmake

Whitespace

* Update intrin.hpp

Many problems in MSVC ARM builds (at least on VS2017) being fixed in this PR now.

C:\Users\Gregory\DOCUME~1\MYLIBR~1\OPENCV~3\opencv\sources\modules\core\include\opencv2/core/hal/intrin.hpp(444): error C3861: '_tzcnt_u32': identifier not found

* Update hal_replacement.hpp

Passing variadic expansion in a macro to another macro does not work properly in MSVC and a famous known workaround is hereby applied.  Discussion of it: https://stackoverflow.com/questions/5134523/msvc-doesnt-expand-va-args-correctly
Only needed the fix for ARM builds: TEGRA_ macros are used for cv_hal_ functions in the carotene library.

C:\Users\Gregory\Documents\My Libraries\opencv330\opencv\sources\modules\core\src\arithm.cpp(2378): warning C4003: not enough actual parameters for macro 'TEGRA_ADD'
C:\Users\Gregory\Documents\My Libraries\opencv330\opencv\sources\modules\core\src\arithm.cpp(2378): error C2143: syntax error: missing ')' before ','
C:\Users\Gregory\Documents\My Libraries\opencv330\opencv\sources\modules\core\src\arithm.cpp(2378): error C2059: syntax error: ')'

* Update hal_replacement.hpp

All hal_replacement's using carotene\hal\tegra_hal.hpp TEGRA_ functions as macros preprocessed by variadic macros should be changed, identical as was done in core.
C:\Users\Gregory\Documents\My Libraries\opencv330\opencv\sources\modules\imgproc\src\color.cpp(9604): warning C4003: not enough actual parameters for macro 'TEGRA_CVTBGRTOBGR'
C:\Users\Gregory\Documents\My Libraries\opencv330\opencv\sources\modules\imgproc\src\color.cpp(9604): error C2059: syntax error: '=='

* Update OpenCVCompilerOptimizations.cmake

* Update hal_replacement.hpp

* Update hal_replacement.hpp

  * replace the implementation by universal intrinsic
  * make sure no degradation happens on ARM platform

Use clFinish to gurantee commands completed, instead of waiting for events.
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>

no changes in code

The same code was repeated several time for different data types, so
it was extracted as a templated function to improve maintability and
make a code more clear.

Exception may be rasied inside the body of a copying constructor after
refcount has been increased, and beacause in the case of the exception
destrcutor is never called what causes memory leak. This commit adds a
workaround that calls the release() function before the exception is
thrown outside the contructor.

add libdnn acceleration to dnn module  (#9114)

* import libdnn code
Signed-off-by: Li Peng <peng.li@intel.com>

* add convolution layer ocl acceleration
Signed-off-by: Li Peng <peng.li@intel.com>

* add pooling layer ocl acceleration
Signed-off-by: Li Peng <peng.li@intel.com>

* add softmax layer ocl acceleration
Signed-off-by: Li Peng <peng.li@intel.com>

* add lrn layer ocl acceleration
Signed-off-by: Li Peng <peng.li@intel.com>

* add innerproduct layer ocl acceleration
Signed-off-by: Li Peng <peng.li@intel.com>

* add HAVE_OPENCL macro
Signed-off-by: Li Peng <peng.li@intel.com>

* fix for convolution ocl
Signed-off-by: Li Peng <peng.li@intel.com>

* enable getUMat() for multi-dimension Mat
Signed-off-by: Li Peng <peng.li@intel.com>

* use getUMat for ocl acceleration
Signed-off-by: Li Peng <peng.li@intel.com>

* use CV_OCL_RUN macro
Signed-off-by: Li Peng <peng.li@intel.com>

* set OPENCL target when it is available

and disable fuseLayer for OCL target for the time being
Signed-off-by: Li Peng <peng.li@intel.com>

* fix innerproduct accuracy test
Signed-off-by: Li Peng <peng.li@intel.com>

* remove trailing space
Signed-off-by: Li Peng <peng.li@intel.com>

* Fixed tensorflow demo bug.

Root cause is that tensorflow has different algorithm with libdnn
to calculate convolution output dimension.

libdnn don't calculate output dimension anymore and just use one
passed in by config.

* split gemm ocl file

split it into gemm_buffer.cl and gemm_image.cl
Signed-off-by: Li Peng <peng.li@intel.com>

* Fix compile failure
Signed-off-by: Li Peng <peng.li@intel.com>

* check env flag for auto tuning
Signed-off-by: Li Peng <peng.li@intel.com>

* switch to new ocl kernels for softmax layer
Signed-off-by: Li Peng <peng.li@intel.com>

* update softmax layer

on some platform subgroup extension may not work well,
fallback to non subgroup ocl acceleration.
Signed-off-by: Li Peng <peng.li@intel.com>

* fallback to cpu path for fc layer with multi output
Signed-off-by: Li Peng <peng.li@intel.com>

* update output message
Signed-off-by: Li Peng <peng.li@intel.com>

* update fully connected layer

fallback to gemm API if libdnn return false
Signed-off-by: Li Peng <peng.li@intel.com>

* Add ReLU OCL implementation

* disable layer fusion for now
Signed-off-by: Li Peng <peng.li@intel.com>

* Add OCL implementation for concat layer
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>

* libdnn: update license and copyrights

Also refine libdnn coding style
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>
Signed-off-by: Li Peng <peng.li@intel.com>

* DNN: Don't link OpenCL library explicitly

* DNN: Make default preferableTarget to DNN_TARGET_CPU

User should set it to DNN_TARGET_OPENCL explicitly if want to
use OpenCL acceleration.

Also don't fusion when using DNN_TARGET_OPENCL

* DNN: refine coding style

* Add getOpenCLErrorString

* DNN: Use int32_t/uint32_t instread of alias

* Use namespace ocl4dnn to include libdnn things

* remove extra copyTo in softmax ocl path
Signed-off-by: Li Peng <peng.li@intel.com>

* update ReLU layer ocl path
Signed-off-by: Li Peng <peng.li@intel.com>

* Add prefer target property for layer class

It is used to indicate the target for layer forwarding,
either the default CPU target or OCL target.
Signed-off-by: Li Peng <peng.li@intel.com>

* Add cl_event based timer for cv::ocl

* Rename libdnn to ocl4dnn
Signed-off-by: Li Peng <peng.li@intel.com>
Signed-off-by: wzw <zhiwen.wu@intel.com>

* use UMat for ocl4dnn internal buffer

Remove allocateMemory which use clCreateBuffer directly
Signed-off-by: Li Peng <peng.li@intel.com>
Signed-off-by: wzw <zhiwen.wu@intel.com>

* enable buffer gemm in ocl4dnn innerproduct
Signed-off-by: Li Peng <peng.li@intel.com>

* replace int_tp globally for ocl4dnn kernels.
Signed-off-by: wzw <zhiwen.wu@intel.com>
Signed-off-by: Li Peng <peng.li@intel.com>

* create UMat for layer params
Signed-off-by: Li Peng <peng.li@intel.com>

* update sign ocl kernel
Signed-off-by: Li Peng <peng.li@intel.com>

* update image based gemm of inner product layer
Signed-off-by: Li Peng <peng.li@intel.com>

* remove buffer gemm of inner product layer

call cv::gemm API instead
Signed-off-by: Li Peng <peng.li@intel.com>

* change ocl4dnn forward parameter to UMat
Signed-off-by: Li Peng <peng.li@intel.com>

* Refine auto-tuning mechanism.

- Use OPENCV_OCL4DNN_KERNEL_CONFIG_PATH to set cache directory
  for fine-tuned kernel configuration.
  e.g. export OPENCV_OCL4DNN_KERNEL_CONFIG_PATH=/home/tmp,
  the cache directory will be /home/tmp/spatialkernels/ on Linux.

- Define environment OPENCV_OCL4DNN_ENABLE_AUTO_TUNING to enable
  auto-tuning.

- OPENCV_OPENCL_ENABLE_PROFILING is only used to enable profiling
  for OpenCL command queue. This fix basic kernel get wrong running
  time, i.e. 0ms.

- If creating cache directory failed, disable auto-tuning.

* Detect and create cache dir on windows
Signed-off-by: Li Peng <peng.li@intel.com>

* Refine gemm like convolution kernel.
Signed-off-by: Li Peng <peng.li@intel.com>

* Fix redundant swizzleWeights calling when use cached kernel config.

* Fix "out of resource" bug when auto-tuning too many kernels.

* replace cl_mem with UMat in ocl4dnnConvSpatial class

* OCL4DNN: reduce the tuning kernel candidate.

This patch could reduce 75% of the tuning candidates with less
than 2% performance impact for the final result.
Signed-off-by: Zhigang Gong <zhigang.gong@intel.com>

* replace cl_mem with umat in ocl4dnn convolution
Signed-off-by: Li Peng <peng.li@intel.com>

* remove weight_image_ of ocl4dnn inner product

Actually it is unused in the computation
Signed-off-by: Li Peng <peng.li@intel.com>

* Various fixes for ocl4dnn

1. OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel())
2. Ptr<OCL4DNNInnerProduct<float> > innerProductOp
3. Code comments cleanup
4. ignore check on OCL cpu device
Signed-off-by: Li Peng <peng.li@intel.com>

* add build option for log softmax
Signed-off-by: Li Peng <peng.li@intel.com>

* remove unused ocl kernels in ocl4dnn
Signed-off-by: Li Peng <peng.li@intel.com>

* replace ocl4dnnSet with opencv setTo
Signed-off-by: Li Peng <peng.li@intel.com>

* replace ALIGN with cv::alignSize
Signed-off-by: Li Peng <peng.li@intel.com>

* check kernel build options
Signed-off-by: Li Peng <peng.li@intel.com>

* Handle program compilation fail properly.

* Use std::numeric_limits<float>::infinity() for large float number

* check ocl4dnn kernel compilation result
Signed-off-by: Li Peng <peng.li@intel.com>

* remove unused ctx_id
Signed-off-by: Li Peng <peng.li@intel.com>

* change clEnqueueNDRangeKernel to kernel.run()
Signed-off-by: Li Peng <peng.li@intel.com>

* change cl_mem to UMat in image based gemm
Signed-off-by: Li Peng <peng.li@intel.com>

* check intel subgroup support for lrn and pooling layer
Signed-off-by: Li Peng <peng.li@intel.com>

* Fix convolution bug if group is greater than 1
Signed-off-by: Li Peng <peng.li@intel.com>

* Set default layer preferableTarget to be DNN_TARGET_CPU
Signed-off-by: Li Peng <peng.li@intel.com>

* Add ocl perf test for convolution
Signed-off-by: Li Peng <peng.li@intel.com>

* Add more ocl accuracy test
Signed-off-by: Li Peng <peng.li@intel.com>

* replace cl_image with ocl::Image2D
Signed-off-by: Li Peng <peng.li@intel.com>

* Fix build failure in elementwise layer
Signed-off-by: Li Peng <peng.li@intel.com>

* use getUMat() to get blob data
Signed-off-by: Li Peng <peng.li@intel.com>

* replace cl_mem handle with ocl::KernelArg
Signed-off-by: Li Peng <peng.li@intel.com>

* dnn(build): don't use C++11, OPENCL_LIBRARIES fix

* dnn(ocl4dnn): remove unused OpenCL kernels

* dnn(ocl4dnn): extract OpenCL code into .cl files

* dnn(ocl4dnn): refine auto-tuning

Defaultly disable auto-tuning, set OPENCV_OCL4DNN_ENABLE_AUTO_TUNING
environment variable to enable it.

Use a set of pre-tuned configs as default config if auto-tuning is disabled.
These configs are tuned for Intel GPU with 48/72 EUs, and for googlenet,
AlexNet, ResNet-50

If default config is not suitable, use the first available kernel config
from the candidates. Candidate priority from high to low is gemm like kernel,
IDLF kernel, basick kernel.

* dnn(ocl4dnn): pooling doesn't use OpenCL subgroups

* dnn(ocl4dnn): fix perf test

OpenCV has default 3sec time limit for each performance test.
Warmup OpenCL backend outside of perf measurement loop.

* use ocl::KernelArg as much as possible
Signed-off-by: Li Peng <peng.li@intel.com>

* dnn(ocl4dnn): fix bias bug for gemm like kernel

* dnn(ocl4dnn): wrap cl_mem into UMat
Signed-off-by: Li Peng <peng.li@intel.com>

* dnn(ocl4dnn): Refine signature of kernel config

- Use more readable string as signture of kernel config
- Don't count device name and vendor in signature string
- Default kernel configurations are tuned for Intel GPU with
  24/48/72 EUs, and for googlenet, AlexNet, ResNet-50 net model.

* dnn(ocl4dnn): swap width/height in configuration

* dnn(ocl4dnn): enable configs for Intel OpenCL runtime only

* core: make configuration helper functions accessible from non-core modules

* dnn(ocl4dnn): update kernel auto-tuning behavior

Avoid unwanted creation of directories

* dnn(ocl4dnn): simplify kernel to workaround OpenCL compiler crash

* dnn(ocl4dnn): remove redundant code

* dnn(ocl4dnn): Add more clear message for simd size dismatch.

* dnn(ocl4dnn): add const to const argument
Signed-off-by: Li Peng <peng.li@intel.com>

* dnn(ocl4dnn): force compiler use a specific SIMD size for IDLF kernel

* dnn(ocl4dnn): drop unused tuneLocalSize()

* dnn(ocl4dnn): specify OpenCL queue for Timer and convolve() method

* dnn(ocl4dnn): sanitize file names used for cache

* dnn(perf): enable Network tests with OpenCL

* dnn(ocl4dnn/conv): drop computeGlobalSize()

* dnn(ocl4dnn/conv): drop unused fields

* dnn(ocl4dnn/conv): simplify ctor

* dnn(ocl4dnn/conv): refactor kernelConfig localSize=NULL

* dnn(ocl4dnn/conv): drop unsupported double / untested half types

* dnn(ocl4dnn/conv): drop unused variable

* dnn(ocl4dnn/conv): alignSize/divUp

* dnn(ocl4dnn/conv): use enum values

* dnn(ocl4dnn): drop unused innerproduct variable
Signed-off-by: Li Peng <peng.li@intel.com>

* dnn(ocl4dnn): add an generic function to check cl option support

* dnn(ocl4dnn): run softmax subgroup version kernel first
Signed-off-by: Li Peng <peng.li@intel.com>

  * remove raw NEON/SSE2 implementation as much as possible
  * replace them to universal intrinsic in InRange/Compare/AddWeighted

Prevents 10000x1 => 10000x8 transformation after getContinuousSize() call

Input arrays must be depth CV_8U and of identical size.

Manual IPP dispatcher simplification;