Intrinsics must be effective, so don't declare FP16 type/operations if there is no native support.
- CV_FP16: supports load/store into/from float32
- CV_SIMD_FP16: declares FP16 types and native FP16 operations
for some big negative values less than -INT_MAX+32767 the sign of the numbers is lost due to overflow that leads to
incorrect saturation to MAX value, instead of zero.
The issue is not reproduced with CV_ENABLED_INTRINSICS=OFF
* 1. changed static const __m128/256 to const __m128/256 to avoid wierd instructions and calls inserted by compiler.
2. added universal intrinsics that wrap MOVNTPS and other such (non-temporary or "no cache" store) instructions. v_store_interleave() and v_store() got respective flags/overloaded variants
3. rewrote split & merge to use the "no cache" store instructions. It resulted in dramatic performance improvement when processing big arrays
* hopefully, fixed some test failures where 4-channel v_store_interleave() is used
* added missing implementation of the new universal intrinsics (v_store_aligned_nocache() etc.)
* fixed silly typo in the new intrinsics in intrin_vsx.hpp
* still trying to fix VSX compiler errors
* still trying to fix VSX compiler errors
* still trying to fix VSX compiler errors
* still trying to fix VSX compiler errors
* fixed/updated v_load_deinterleave and v_store_interleave intrinsics; modified split() and merge() functions to use those intrinsics
* fixed a few compile errors and bug in v_load_deinterleave(ptr, v_uint32x4& a, v_uint32x4& b)
* fixed few more compile errors
* core:OE-27 prepare universal intrinsics to expand (#11022)
* core:OE-27 prepare universal intrinsics to expand (#11022)
* core: Add universal intrinsics for AVX2
* updated implementation of wide univ. intrinsics; converted several OpenCV HAL functions: sqrt, invsqrt, magnitude, phase, exp to the wide universal intrinsics.
* converted log to universal intrinsics; cleaned up the code a bit; added v_lut_deinterleave intrinsics.
* core: Add universal intrinsics for AVX2
* fixed multiple compile errors
* fixed many more compile errors and hopefully some test failures
* fixed some more compile errors
* temporarily disabled IPP to debug exp & log; hopefully fixed Doxygen complains
* fixed some more compile errors
* fixed v_store(short*, v_float16&) signatures
* trying to fix the test failures on Linux
* fixed some issues found by alalek
* restored IPP optimization after the patch with AVX wide intrinsics has been properly tested
* restored IPP optimization after the patch with AVX wide intrinsics has been properly tested
- 'if' logic is moved into templates.
- removed unnecessary cv::Mat objects creation.
- fixed inv() test (invA * A == eye)
- added more Matx tests to cover all defined template specializations
fixes handling of empty matrices in some functions (#11634)
* a part of PR #11416 by Yuki Takehara
* moved the empty mat check in Mat::copyTo()
* fixed some test failures
* make sure that the matrix with more than INT_MAX elements is marked as non-continuous, and thus all the pixel-wise functions process it correctly (i.e. row-by-row, not as a single row, where integer overflow may occur when computing the total number of elements)
* Issue 11242 intrinsics v_extract, v_rotate improvement, branch 3.4, without C++11 (remove type restrictions for SSE2, use PALIGNR on SSSE3, compile to no-op when imm is 0 or nlanes).
* fix whitespace
* Fix#11242 (NEON intrinsics v_rotate...) branch 3.4
Separate macro expansion OPENCV_HAL_IMPL_NEON_SHIFT_OP for bitwise shifts for integers, from macro expansion OPENCV_HAL_IMPL_NEON_ROTATE for lane rotations. Bitwise shifts do not apply to floats, but lane-rotations can apply to both.
* fix whitespace
* Fix#11242 compile error (VSX intrinsics v_rotate(a)) branch 3.4 no-c++11
* Fix CV_Asserts with negation of strings
{!"string"} causes some compilers to throw a warning.
The value of the string is not that important -- it's only for printing
the assertion message.
Replace these calls with:
CV_Error(Error::StsError, "string")
to suppress the warning.
* remove unnecessary 'break' after CV_Error()
* use universal intrinsic instead of raw intrinsic
* add 2 channels de-interleave on x86 platform
* add v_int32x4 version of v_muladd
* add accumulate version of v_dotprod based on the commit from seiko2plus on bf1852d
* remove some verify check in performance test
* avoid the out of boundary access and keep the performance
* remove unnecessary defines from vsx_utils
* fix v_load_expand, load lower 64bit
* use vec_ld, vec_st with alignment load/store on all types except 64bit
* map v_extract to v_rotate_right
* update license header
* enable VSX by default on clang since #11167
To avoid compilation of this code:
- buf = 0;
This code can be received after refactoring of 1D cv::Mat to cv::AutoBuffer.
- "cv_mat = 0" calls setTo().
- cv::AutoBuffer calls "allocate(0)" - this is wrong.
* Make <array> #ifdef true for MSVC
I think MSVC had `std::array` for quite a while (possibly going back as far as VS 2012, but it's definitely there in 2015 and 2017. So I think `_MSC_VER` `1900` is a safe bet. Probably `1800` and maybe even `1700` could work as well but I can't test that locally.
* fix test
* Update BufferReader documentation with some example code
* Add warning to BufferPool doc regarding deallocation of StackAllocator
* Added a sample code that satisfies LIFO rule for StackAllocator
OpenCV pthreads-based implementation changes:
- rework worker threads pool, allow to execute job by the main thread too
- rework synchronization scheme (wait for job completion, threads 'pong' answer is not required)
- allow "active wait" (spin) by worker threads and by the main thread
- use _mm_pause() during active wait (support for Hyper-Threading technology)
- use sched_yield() to avoid preemption of still working other workers
- don't use getTickCount()
- optional builtin thread pool profiler (disabled by compilation flag)
UMatData locks are not mapped on real locks (they are mapped to some "pre-initialized" pool).
Concurrent execution of these statements may lead to deadlock:
- a.copyTo(b) from thread 1
- c.copyTo(d) from thread 2
where:
- 'a' and 'd' are mapped to single lock "A".
- 'b' and 'c' are mapped to single lock "B".
Workaround is to process locks with strict order.
The opencv infrastructure mostly has the basics for supporting avx512 math functions,
but it wasn't hooked up (likely due to lack of users)
In order to compile the DNN functions for AVX512, a few things need to be hooked up
and this patch does that
Signed-off-by: Arjan van de Ven <arjan@linux.intel.com>
- don't store ProgramSource in compiled Programs (resolved problem with "source" buffers lifetime)
- completelly remove Program::read/write methods implementation:
- replaced with method to query RAW OpenCL binary without any "custom" data
- deprecate Program::getPrefix() methods
If there are no OpenCL/UMat methods calls from application.
OpenCL subsystem is initialized:
- haveOpenCL() is called from application
- useOpenCL() is called from application
- access to OpenCL allocator: UMat is created (empty UMat is ignored) or UMat <-> Mat conversions are called
Don't call OpenCL functions if OPENCV_OPENCL_RUNTIME=disabled
(independent from OpenCL linkage type)
* 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
When elements are 64 bits, the vec_st_interleave()/vec_ld_deinterleave()
doesn't interleave 4 elements correctly.
For vec_st_interleave(), following is saved into mem:
a0 b0 a1 b1 c0 d0 c1 d1
-> we expected:
a0 b0 c0 d0 a1 b1 c1 d1
for vec_ld_deinterleave(), following is loaded into a b c d for memory
string { 1 2 3 4 5 6 7 8 }:
a: 1 3
b: 2 4
c: 5 7
d: 6 8
-> we expected:
a: 1 5
b: 2 6
c: 3 7
d: 4 8
This patch corrects this behavior.
Signed-off-by: Simon Guo <wei.guo.simon@gmail.com>
- 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
* 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
The original template based mat ptr for indexing is not implemented,
add the similar implementation as uchar type, but cast to
user-defined type from the uchar pointer.
Adds fitEllipseDirect to imgproc: The Direct least square (Direct) method by Fitzgibbon1999.
New Tests are included for the methods.
fitEllipseAMS Tests
fitEllipseDirect Tests
Comparative examples are added to fitEllipse.cpp in Samples.
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>
Added forkfour Latex command to math js support.
Split cv::norm documentation between the cv::norm and its overload, to make things clearer
Corrected some typos and cleaned up grammar.
Result is clearer documentation for the norms.
Work pending...
This adds the possibility to use multi-channel masks for the functions
cv::mean, cv::meanStdDev and the method Mat::setTo. The tests have now a
probability to use multi-channel masks for operations that support them.
This also includes Mat::copyTo, which supported multi-channel masks
before, but there was no test confirming this.
This function is the counterpart of "Context::getProg".
With this function, users have chance to unload a program
from global run-time cached programs, and save resource.
- Optimizations set change. Now IPP integrations will provide code for SSE42, AVX2 and AVX512 (SKX) CPUs only. For HW below SSE42 IPP code is disabled.
- Performance regressions fixes for IPP code paths;
- cv::boxFilter integration improvement;
- cv::filter2D integration improvement;
Add constructors taking initializer_list for some of OpenCV data types (#9034)
* Add a constructor taking initializer_list for Matx
* Add a constructor taking initializer list for Mat and Mat_
* Add one more method to initialize Mat to the corresponding tutorial
* Add a note how to initialize Matx
* CV_CXX_11->CV_CXX11
BufferPoolController has a non virtual protected destructor (which is legitimate)
However, Visual Studio sees this as a bug, if you enable more warnings, like below
```
add_compile_options(/W3) # level 3 warnings
add_compile_options(/we4265) # warning about missing virtual destructors
```
This is a proposition in order to silence this warning.
See https://github.com/ivsgroup/boost_warnings_minimal_demo for a demo of the same problem
with boost/exception.hpp
Remove unnecessary Non-ASCII characters from source code (#9075)
* Remove unnecessary Non-ASCII characters from source code
Remove unnecessary Non-ASCII characters and replace them with ASCII
characters
* Remove dashes in the @param statement
Remove dashes and place single space in the @param statement to keep
coding style
* misc: more fixes for non-ASCII symbols
* misc: fix non-ASCII symbol in CMake file
* another round of dnn optimization:
* increased malloc alignment across OpenCV from 16 to 64 bytes to make it AVX2 and even AVX-512 friendly
* improved SIMD optimization of pooling layer, optimized average pooling
* cleaned up convolution layer implementation
* made activation layer "attacheable" to all other layers, including fully connected and addition layer.
* fixed bug in the fusion algorithm: "LayerData::consumers" should not be cleared, because it desctibes the topology.
* greatly optimized permutation layer, which improved SSD performance
* parallelized element-wise binary/ternary/... ops (sum, prod, max)
* also, added missing copyrights to many of the layer implementation files
* temporarily disabled (again) the check for intermediate blobs consistency; fixed warnings from various builders
Parallelize Canny with custom gradient (#8694)
* New Canny implementation. Restructuring code in parallelCanny class. Align mag buffer and map.
* Fix warnings.
* Missing SIMD check added.
* Replaced local trailingZeros in contours.cpp. Use alignSize in canny.cpp
* Fix warnings in alignSize and allocate just minimum extra columns.
* Fix another warning in map.create.
* Exchange for loop by do loop to avoid double check at the beginning.
Define extra SIMD CANNY_CHECK to avoid unnecessary continue.
* Correct the existing documented T-API functions to match the doxygen format.
* docs: fix comments style
* T-API documentation: minor formatting changes
Updated integrations for:
cv::split
cv::merge
cv::insertChannel
cv::extractChannel
cv::Mat::convertTo - now with scaled conversions support
cv::LUT - disabled due to performance issues
Mat::copyTo
Mat::setTo
cv::flip
cv::copyMakeBorder - currently disabled
cv::polarToCart
cv::pow - ipp pow function was removed due to performance issues
cv::hal::magnitude32f/64f - disabled for <= SSE42, poor performance
cv::countNonZero
cv::minMaxIdx
cv::norm
cv::canny - new integration. Disabled for threaded;
cv::cornerHarris
cv::boxFilter
cv::bilateralFilter
cv::integral
Add support for std::array<T, N> (#8535)
* Add support for std::array<T, N>
* Add std::array<Mat, N> support
* Remove UMat constructor with std::array parameter
Gemm kernels for Intel GPU (#8104)
* Fix an issue with Kernel object reset release when consecutive Kernel::run calls
Kernel::run launch OCL gpu kernels and set a event callback function
to decreate the ref count of UMat or remove UMat when the lauched workloads
are completed. However, for some OCL kernels requires multiple call of
Kernel::run function with some kernel parameter changes (e.g., input
and output buffer offset) to get the final computation result.
In the case, the current implementation requires unnecessary
synchronization and cleanupMat.
This fix requires the user to specify whether there will be more work or not.
If there is no remaining computation, the Kernel::run will reset the
kernel object
Signed-off-by: Woo, Insoo <insoo.woo@intel.com>
* GEMM kernel optimization for Intel GEN
The optimized kernels uses cl_intel_subgroups extension for better
performance.
Note: This optimized kernels will be part of ISAAC in a code generation
way under MIT license.
Signed-off-by: Woo, Insoo <insoo.woo@intel.com>
* Fix API compatibility error
This patch fixes a OCV API compatibility error. The error was reported
due to the interface changes of Kernel::run. To resolve the issue,
An overloaded function of Kernel::run is added. It take a flag indicating
whether there are more work to be done with the kernel object without
releasing resources related to it.
Signed-off-by: Woo, Insoo <insoo.woo@intel.com>
* Renaming intel_gpu_gemm.cpp to intel_gpu_gemm.inl.hpp
Signed-off-by: Woo, Insoo <insoo.woo@intel.com>
* Revert "Fix API compatibility error"
This reverts commit 2ef427db91.
Conflicts:
modules/core/src/intel_gpu_gemm.inl.hpp
* Revert "Fix an issue with Kernel object reset release when consecutive Kernel::run calls"
This reverts commit cc7f9f5469.
* Fix the case of uninitialization D
When C is null and beta is non-zero, D is used without initialization.
This resloves the issue
Signed-off-by: Woo, Insoo <insoo.woo@intel.com>
* fix potential output error due to 0 * nan
Signed-off-by: Woo, Insoo <insoo.woo@intel.com>
* whitespace fix, eliminate non-ASCII symbols
* fix build warning
