* Simulated Annealing for ANN_MLP training method
* EXPECT_LT
* just to test new data
* manage RNG
* Try again
* Just run buildbot with new data
* try to understand
* Test layer
* New data- new test
* Force RNG in backprop
* Use Impl to avoid virtual method
* reset all weights
* try to solve ABI
* retry
* ABI solved?
* till problem with dynamic_cast
* Something is wrong
* Solved?
* disable backprop test
* remove ANN_MLP_ANNEALImpl
* Disable weight in varmap
* Add example for SimulatedAnnealing
* Fix issue #10114
Convert table change
From:
CV_8U -> HALF
CV_8S -> HALF
CV_16U -> UINT
CV_16S -> UINT
CV_32S -> UINT
CV_32F -> FLOAT
To:
CV_8U -> HALF
CV_8S -> HALF
CV_16U -> UINT
CV_16S -> FLOAT
CV_32S -> FLOAT loss precision
CV_32F -> FLOAT
Signed integer can't be presented well with UINT. Even adjust bias, CV16S and CV32S will be confused when load from exr file.
Also fix CV_8S negative value incorrect bug
* EXR import and export
imread() from EXR returns CV_32F only
imwrite() accepts CV_32 cv::Mat only and stores FLOAT images by default. Add imwrite() flag to store in HALF format.
* fix compiling error
* clean up
* fix EXR import issues
* remove raw SSE2/NEON implementation from convert.cpp
* remove raw implementation from Cvt_SIMD
* remove raw implementation from cvtScale_SIMD
* remove raw implementation from cvtScaleAbs_SIMD
* remove duplicated implementation cvt_<float, short>
* remove duplicated implementation cvtScale_<short, short, float>
* add "from double" version of Cvt_SIMD
* modify the condition of test ConvertScaleAbs
* Update convert.cpp
fixed crash in cvtScaleAbs(8s=>8u)
* fixed compile error on Win32
* fixed several test failures because of accuracy loss in cvtScale(int=>int)
* fixed NEON implementation of v_cvt_f64(int=>double) intrinsic
* another attempt to fix test failures
* keep trying to fix the test failures and just introduced compile warnings
* fixed one remaining test (subtractScalar)
- 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
* Add test that fails
* Fix integer pointPolygonTest for large coordinate values
* Review fixes:
- change type from long long to int64
- move test code to test_contours.cpp, and make it C++98 compliant
* Hopefully fix compiler error by using push_back instead of emplace_back
* fixed OpenCL functions on Mac, so that the tests pass
* fixed compile warnings; temporarily disabled OCL branch of TV L1 optical flow on mac
* fixed other few warnings on macos
'WITH_' variables is intended to enable CMake scripts with some autodetection logic.
'WITH_' can be off, but components is really enabled via command-line options
with proper variables setup (including 'HAVE_').
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)
Tests are usually lauched from source directory, so additional unnecessary
files should be eliminated.
Alternative ways (command line):
- python -B ...
- PYTHONDONTWRITEBYTECODE=1 python ...
* 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
Fix GDAL image decoding color problems identified by issue #10089, by: (#10093)
* Fix GDAL image decoding color problems identified by issue #10089, by:
Fixing CV_8UC1 symbol, which should be CV_8UC3 for RGB GDAL color table images.
Fixing image.ptr<VecX>(row,col)[] to be (*image.ptr<VecX>(row,col))[] to correctly access VecX array elements, as ptr<VecX>() returns a pointer to the VecX, not the first element of VecX. This fixes the color problem with color table gif images, and avoids out-of-bounds memory access.
Respecting the color identification of raster bands provided by the GDAL image driver, and produce BGR or BGRA images. Note that color bands of images using the HSL, CMY, CMYK, or YCbCr color space are ignored, rather than converting them to BGR.
* When reading image files using the GDAL decoder, exit with an error if a color band is encountered that isn't used (eg. from CMYK or YCbCbr), rather than silently ignoring the band's data.
Fix build with FFmpeg master. Some deprecated APIs have been removed. (#10011)
* Fix build with FFmpeg master.
* ffmpeg: update AVFMT_RAWPICTURE support removal
Incorrect type of negative_slope result in this bug.
Also and OCL test for darknet to validate this patch.
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>
convolution kernel use default queue to run, so that ocl::Timer
, to measure the kernel run time, should use the default queue too.
Also remove useless parameter for convolve()
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>
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>
Add layer forward interface with InputArrayOfArrays and
OutputArrayOfArrays parameters, it allows UMat buffer to be
processed and transferred in the layers.
Signed-off-by: Li Peng <peng.li@intel.com>
* Adding constants to json file
* adding sub-module to constants name
* adding namespace to functions
* adding namespace to classes
* remove namespace from methods
* static methods
* python signatures generation
* python: more fixes for signatures generation
Python names existence can be checked via command:
python -c "import cv2 as cv; print(repr(<py_name>))"
* Error in the documentation for cv::getRectSubPix. #9788
The function name is corrected to GetRectSubPix since, it uses the notation
of src, dst and center. Also added the number of channel assertion criteria.
* Error in the documentation for cv::getRectSubPix. #9788
Replace dst with patch in the formula, reverted function name to
getRectSubPix, removed BorderTypes comment line due to no explicit call
to the function found.
* Error in the documentation for cv::getRectSubPix. #9788
Replace dst with patch in the formula, reverted function name to
getRectSubPix, removed BorderTypes comment line due to no explicit call
to the function found.
- 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
getViewerPose() doesn't modify an object of the class so it can be
made const. It also makes this method consistent with other getters
in the class as they are defined as const.
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
These two typdefs are not compiled when BUILD_opencv_dnn is set to
false, however there are other modules that uses these typedef so
it may cause build errors. Moving typedef to the python module
ensures they are always defined.
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.
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.
The entire AssetsLibrary framework is deprecated since iOS 8.0. The code
used in the camera example code can use UIKit to save videos to the
camera instead, which allows to avoid linking with PhotoKit instead to
prevent increasing the iOS deployment target.
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>
