include relu6 ocl kernel and layer fusion support
Signed-off-by: NLi Peng <peng.li@intel.com>

Found via `codespell`

* Added the imagesFromBlob method to the dnn module.

* Rewritten imagesFromBlob based on first dkurt comments

* Updated code with getPlane()

* Modify comment of imagesFromBlob() in dnn module

* modified comments, removed useless assertions & added OutputArrayOfArray

* replaced tabs with whitespaces & put vectorOfChannels instantiation outside the loop

* Changed pre-commit.sample to pre-commit in .git/hooks/

* Added a test for imagesFromBlob in test_misc.cpp (dnn)

* Changed nbOfImages, robustified test with cv::randu, modified assertion

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

* Intel Inference Engine deep learning backend.

* OpenFace network using Inference Engine backend

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

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

* UMat blobs are wrapped

* Replace getUMat and getMat at OpenCLBackendWrapper

replaces REUSE_DNN_MEMORY compile-time option

Add a flag to disable memory reusing in dnn module.

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

Implementation of bit-exact resize. Internal calls to linear resize updated to use bit-exact version. (#9468)

Also, fix bug when use OPENCL target but no OpenCL runtime
Signed-off-by: NWu Zhiwen <zhiwen.wu@intel.com>

It allows umat buffers used in net forward interface
Signed-off-by: NLi Peng <peng.li@intel.com>

Signed-off-by: NWu Zhiwen <zhiwen.wu@intel.com>

Add layer forward interface with InputArrayOfArrays and
OutputArrayOfArrays parameters, it allows UMat buffer to be
processed and transferred in the layers.
Signed-off-by: NLi Peng <peng.li@intel.com>

Fixed Concat optimization.

ocl4dnn supports following fusion styles:
Conv + [BN] + [Scale] + [ReLU/PReLU]
Signed-off-by: NWu Zhiwen <zhiwen.wu@intel.com>

To avoid problem with reduced stack size of inner threads.

add libdnn acceleration to dnn module  (#9114)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

* update fully connected layer

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

* Add ReLU OCL implementation

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

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

* libdnn: update license and copyrights

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

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

* Add cl_event based timer for cv::ocl

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

* use UMat for ocl4dnn internal buffer

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

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

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

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

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

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

* remove buffer gemm of inner product layer

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

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

* Refine gemm like convolution kernel.
Signed-off-by: NLi 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: NZhigang Gong <zhigang.gong@intel.com>

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

* remove weight_image_ of ocl4dnn inner product

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

fixed problem in concat layer by disabling memory re-use in layers with multiple inputs

trying to fix the tests when Halide is used to run deep nets

another attempt to fix Halide tests

see if the Halide tests will pass with concat layer fusion turned off

trying to fix failures in halide tests; another try

one more experiment to make halide_concat & halide_enet tests pass

continue attempts to fix halide tests

moving on

uncomment parallel concat layer

seemingly fixed failures in Halide tests and re-enabled concat layer fusion; thanks to dkurt for the patch