opencv/modules/core/src/umatrix.cpp
pengli e340ff9c3a Merge pull request #9114 from pengli:dnn_rebase
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>
2017-10-02 15:38:00 +03:00

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C++

/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, Itseez Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "opencl_kernels_core.hpp"
///////////////////////////////// UMat implementation ///////////////////////////////
namespace cv {
// forward decls, implementation is below in this file
void setSize(UMat& m, int _dims, const int* _sz, const size_t* _steps,
bool autoSteps = false);
void updateContinuityFlag(UMat& m);
void finalizeHdr(UMat& m);
// it should be a prime number for the best hash function
enum { UMAT_NLOCKS = 31 };
static Mutex umatLocks[UMAT_NLOCKS];
UMatData::UMatData(const MatAllocator* allocator)
{
prevAllocator = currAllocator = allocator;
urefcount = refcount = mapcount = 0;
data = origdata = 0;
size = 0;
flags = 0;
handle = 0;
userdata = 0;
allocatorFlags_ = 0;
originalUMatData = NULL;
}
UMatData::~UMatData()
{
prevAllocator = currAllocator = 0;
urefcount = refcount = 0;
CV_Assert(mapcount == 0);
data = origdata = 0;
size = 0;
flags = 0;
handle = 0;
userdata = 0;
allocatorFlags_ = 0;
if (originalUMatData)
{
UMatData* u = originalUMatData;
CV_XADD(&(u->urefcount), -1);
CV_XADD(&(u->refcount), -1);
bool showWarn = false;
if (u->refcount == 0)
{
if (u->urefcount > 0)
showWarn = true;
// simulate Mat::deallocate
if (u->mapcount != 0)
{
(u->currAllocator ? u->currAllocator : /* TODO allocator ? allocator :*/ Mat::getDefaultAllocator())->unmap(u);
}
else
{
// we don't do "map", so we can't do "unmap"
}
}
if (u->refcount == 0 && u->urefcount == 0) // oops, we need to free resources
{
showWarn = true;
// simulate UMat::deallocate
u->currAllocator->deallocate(u);
}
#ifndef NDEBUG
if (showWarn)
{
static int warn_message_showed = 0;
if (warn_message_showed++ < 100)
{
fflush(stdout);
fprintf(stderr, "\n! OPENCV warning: getUMat()/getMat() call chain possible problem."
"\n! Base object is dead, while nested/derived object is still alive or processed."
"\n! Please check lifetime of UMat/Mat objects!\n");
fflush(stderr);
}
}
#else
(void)showWarn;
#endif
originalUMatData = NULL;
}
}
void UMatData::lock()
{
umatLocks[(size_t)(void*)this % UMAT_NLOCKS].lock();
}
void UMatData::unlock()
{
umatLocks[(size_t)(void*)this % UMAT_NLOCKS].unlock();
}
MatAllocator* UMat::getStdAllocator()
{
#ifdef HAVE_OPENCL
if( ocl::haveOpenCL() && ocl::useOpenCL() )
return ocl::getOpenCLAllocator();
#endif
return Mat::getDefaultAllocator();
}
void swap( UMat& a, UMat& b )
{
std::swap(a.flags, b.flags);
std::swap(a.dims, b.dims);
std::swap(a.rows, b.rows);
std::swap(a.cols, b.cols);
std::swap(a.allocator, b.allocator);
std::swap(a.u, b.u);
std::swap(a.offset, b.offset);
std::swap(a.size.p, b.size.p);
std::swap(a.step.p, b.step.p);
std::swap(a.step.buf[0], b.step.buf[0]);
std::swap(a.step.buf[1], b.step.buf[1]);
if( a.step.p == b.step.buf )
{
a.step.p = a.step.buf;
a.size.p = &a.rows;
}
if( b.step.p == a.step.buf )
{
b.step.p = b.step.buf;
b.size.p = &b.rows;
}
}
void setSize( UMat& m, int _dims, const int* _sz,
const size_t* _steps, bool autoSteps )
{
CV_Assert( 0 <= _dims && _dims <= CV_MAX_DIM );
if( m.dims != _dims )
{
if( m.step.p != m.step.buf )
{
fastFree(m.step.p);
m.step.p = m.step.buf;
m.size.p = &m.rows;
}
if( _dims > 2 )
{
m.step.p = (size_t*)fastMalloc(_dims*sizeof(m.step.p[0]) + (_dims+1)*sizeof(m.size.p[0]));
m.size.p = (int*)(m.step.p + _dims) + 1;
m.size.p[-1] = _dims;
m.rows = m.cols = -1;
}
}
m.dims = _dims;
if( !_sz )
return;
size_t esz = CV_ELEM_SIZE(m.flags), total = esz;
int i;
for( i = _dims-1; i >= 0; i-- )
{
int s = _sz[i];
CV_Assert( s >= 0 );
m.size.p[i] = s;
if( _steps )
m.step.p[i] = i < _dims-1 ? _steps[i] : esz;
else if( autoSteps )
{
m.step.p[i] = total;
int64 total1 = (int64)total*s;
if( (uint64)total1 != (size_t)total1 )
CV_Error( CV_StsOutOfRange, "The total matrix size does not fit to \"size_t\" type" );
total = (size_t)total1;
}
}
if( _dims == 1 )
{
m.dims = 2;
m.cols = 1;
m.step[1] = esz;
}
}
void updateContinuityFlag(UMat& m)
{
int i, j;
for( i = 0; i < m.dims; i++ )
{
if( m.size[i] > 1 )
break;
}
for( j = m.dims-1; j > i; j-- )
{
if( m.step[j]*m.size[j] < m.step[j-1] )
break;
}
uint64 total = (uint64)m.step[0]*m.size[0];
if( j <= i && total == (size_t)total )
m.flags |= UMat::CONTINUOUS_FLAG;
else
m.flags &= ~UMat::CONTINUOUS_FLAG;
}
void finalizeHdr(UMat& m)
{
updateContinuityFlag(m);
int d = m.dims;
if( d > 2 )
m.rows = m.cols = -1;
}
UMat Mat::getUMat(int accessFlags, UMatUsageFlags usageFlags) const
{
UMat hdr;
if(!data)
return hdr;
if (data != datastart)
{
Size wholeSize;
Point ofs;
locateROI(wholeSize, ofs);
Size sz(cols, rows);
if (ofs.x != 0 || ofs.y != 0)
{
Mat src = *this;
int dtop = ofs.y;
int dbottom = wholeSize.height - src.rows - ofs.y;
int dleft = ofs.x;
int dright = wholeSize.width - src.cols - ofs.x;
src.adjustROI(dtop, dbottom, dleft, dright);
return src.getUMat(accessFlags, usageFlags)(cv::Rect(ofs.x, ofs.y, sz.width, sz.height));
}
}
CV_Assert(data == datastart);
accessFlags |= ACCESS_RW;
UMatData* new_u = NULL;
{
MatAllocator *a = allocator, *a0 = getDefaultAllocator();
if(!a)
a = a0;
new_u = a->allocate(dims, size.p, type(), data, step.p, accessFlags, usageFlags);
}
bool allocated = false;
try
{
allocated = UMat::getStdAllocator()->allocate(new_u, accessFlags, usageFlags);
}
catch (const cv::Exception& e)
{
fprintf(stderr, "Exception: %s\n", e.what());
}
if (!allocated)
{
allocated = getDefaultAllocator()->allocate(new_u, accessFlags, usageFlags);
CV_Assert(allocated);
}
if (u != NULL)
{
#ifdef HAVE_OPENCL
if (ocl::useOpenCL() && new_u->currAllocator == ocl::getOpenCLAllocator())
{
CV_Assert(new_u->tempUMat());
}
#endif
new_u->originalUMatData = u;
CV_XADD(&(u->refcount), 1);
CV_XADD(&(u->urefcount), 1);
}
hdr.flags = flags;
setSize(hdr, dims, size.p, step.p);
finalizeHdr(hdr);
hdr.u = new_u;
hdr.offset = 0; //data - datastart;
hdr.addref();
return hdr;
}
void UMat::create(int d, const int* _sizes, int _type, UMatUsageFlags _usageFlags)
{
this->usageFlags = _usageFlags;
int i;
CV_Assert(0 <= d && d <= CV_MAX_DIM && _sizes);
_type = CV_MAT_TYPE(_type);
if( u && (d == dims || (d == 1 && dims <= 2)) && _type == type() )
{
if( d == 2 && rows == _sizes[0] && cols == _sizes[1] )
return;
for( i = 0; i < d; i++ )
if( size[i] != _sizes[i] )
break;
if( i == d && (d > 1 || size[1] == 1))
return;
}
int _sizes_backup[CV_MAX_DIM]; // #5991
if (_sizes == (this->size.p))
{
for(i = 0; i < d; i++ )
_sizes_backup[i] = _sizes[i];
_sizes = _sizes_backup;
}
release();
if( d == 0 )
return;
flags = (_type & CV_MAT_TYPE_MASK) | MAGIC_VAL;
setSize(*this, d, _sizes, 0, true);
offset = 0;
if( total() > 0 )
{
MatAllocator *a = allocator, *a0 = getStdAllocator();
if (!a)
{
a = a0;
a0 = Mat::getDefaultAllocator();
}
try
{
u = a->allocate(dims, size, _type, 0, step.p, 0, usageFlags);
CV_Assert(u != 0);
}
catch(...)
{
if(a != a0)
u = a0->allocate(dims, size, _type, 0, step.p, 0, usageFlags);
CV_Assert(u != 0);
}
CV_Assert( step[dims-1] == (size_t)CV_ELEM_SIZE(flags) );
}
finalizeHdr(*this);
addref();
}
void UMat::create(const std::vector<int>& _sizes, int _type, UMatUsageFlags _usageFlags)
{
create((int)_sizes.size(), _sizes.data(), _type, _usageFlags);
}
void UMat::copySize(const UMat& m)
{
setSize(*this, m.dims, 0, 0);
for( int i = 0; i < dims; i++ )
{
size[i] = m.size[i];
step[i] = m.step[i];
}
}
UMat::~UMat()
{
release();
if( step.p != step.buf )
fastFree(step.p);
}
void UMat::deallocate()
{
UMatData* u_ = u;
u = NULL;
u_->currAllocator->deallocate(u_);
}
UMat::UMat(const UMat& m, const Range& _rowRange, const Range& _colRange)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(USAGE_DEFAULT), u(0), offset(0), size(&rows)
{
CV_Assert( m.dims >= 2 );
if( m.dims > 2 )
{
AutoBuffer<Range> rs(m.dims);
rs[0] = _rowRange;
rs[1] = _colRange;
for( int i = 2; i < m.dims; i++ )
rs[i] = Range::all();
*this = m(rs);
return;
}
*this = m;
if( _rowRange != Range::all() && _rowRange != Range(0,rows) )
{
CV_Assert( 0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows );
rows = _rowRange.size();
offset += step*_rowRange.start;
flags |= SUBMATRIX_FLAG;
}
if( _colRange != Range::all() && _colRange != Range(0,cols) )
{
CV_Assert( 0 <= _colRange.start && _colRange.start <= _colRange.end && _colRange.end <= m.cols );
cols = _colRange.size();
offset += _colRange.start*elemSize();
flags &= cols < m.cols ? ~CONTINUOUS_FLAG : -1;
flags |= SUBMATRIX_FLAG;
}
if( rows == 1 )
flags |= CONTINUOUS_FLAG;
if( rows <= 0 || cols <= 0 )
{
release();
rows = cols = 0;
}
}
UMat::UMat(const UMat& m, const Rect& roi)
: flags(m.flags), dims(2), rows(roi.height), cols(roi.width),
allocator(m.allocator), usageFlags(m.usageFlags), u(m.u), offset(m.offset + roi.y*m.step[0]), size(&rows)
{
CV_Assert( m.dims <= 2 );
flags &= roi.width < m.cols ? ~CONTINUOUS_FLAG : -1;
flags |= roi.height == 1 ? CONTINUOUS_FLAG : 0;
size_t esz = CV_ELEM_SIZE(flags);
offset += roi.x*esz;
CV_Assert( 0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols &&
0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows );
if( u )
CV_XADD(&(u->urefcount), 1);
if( roi.width < m.cols || roi.height < m.rows )
flags |= SUBMATRIX_FLAG;
step[0] = m.step[0]; step[1] = esz;
if( rows <= 0 || cols <= 0 )
{
release();
rows = cols = 0;
}
}
UMat::UMat(const UMat& m, const Range* ranges)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(USAGE_DEFAULT), u(0), offset(0), size(&rows)
{
int i, d = m.dims;
CV_Assert(ranges);
for( i = 0; i < d; i++ )
{
Range r = ranges[i];
CV_Assert( r == Range::all() || (0 <= r.start && r.start < r.end && r.end <= m.size[i]) );
}
*this = m;
for( i = 0; i < d; i++ )
{
Range r = ranges[i];
if( r != Range::all() && r != Range(0, size.p[i]))
{
size.p[i] = r.end - r.start;
offset += r.start*step.p[i];
flags |= SUBMATRIX_FLAG;
}
}
updateContinuityFlag(*this);
}
UMat::UMat(const UMat& m, const std::vector<Range>& ranges)
: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(USAGE_DEFAULT), u(0), offset(0), size(&rows)
{
int i, d = m.dims;
CV_Assert((int)ranges.size() == d);
for (i = 0; i < d; i++)
{
Range r = ranges[i];
CV_Assert(r == Range::all() || (0 <= r.start && r.start < r.end && r.end <= m.size[i]));
}
*this = m;
for (i = 0; i < d; i++)
{
Range r = ranges[i];
if (r != Range::all() && r != Range(0, size.p[i]))
{
size.p[i] = r.end - r.start;
offset += r.start*step.p[i];
flags |= SUBMATRIX_FLAG;
}
}
updateContinuityFlag(*this);
}
UMat UMat::diag(int d) const
{
CV_Assert( dims <= 2 );
UMat m = *this;
size_t esz = elemSize();
int len;
if( d >= 0 )
{
len = std::min(cols - d, rows);
m.offset += esz*d;
}
else
{
len = std::min(rows + d, cols);
m.offset -= step[0]*d;
}
CV_DbgAssert( len > 0 );
m.size[0] = m.rows = len;
m.size[1] = m.cols = 1;
m.step[0] += (len > 1 ? esz : 0);
if( m.rows > 1 )
m.flags &= ~CONTINUOUS_FLAG;
else
m.flags |= CONTINUOUS_FLAG;
if( size() != Size(1,1) )
m.flags |= SUBMATRIX_FLAG;
return m;
}
void UMat::locateROI( Size& wholeSize, Point& ofs ) const
{
CV_Assert( dims <= 2 && step[0] > 0 );
size_t esz = elemSize(), minstep;
ptrdiff_t delta1 = (ptrdiff_t)offset, delta2 = (ptrdiff_t)u->size;
if( delta1 == 0 )
ofs.x = ofs.y = 0;
else
{
ofs.y = (int)(delta1/step[0]);
ofs.x = (int)((delta1 - step[0]*ofs.y)/esz);
CV_DbgAssert( offset == (size_t)(ofs.y*step[0] + ofs.x*esz) );
}
minstep = (ofs.x + cols)*esz;
wholeSize.height = (int)((delta2 - minstep)/step[0] + 1);
wholeSize.height = std::max(wholeSize.height, ofs.y + rows);
wholeSize.width = (int)((delta2 - step*(wholeSize.height-1))/esz);
wholeSize.width = std::max(wholeSize.width, ofs.x + cols);
}
UMat& UMat::adjustROI( int dtop, int dbottom, int dleft, int dright )
{
CV_Assert( dims <= 2 && step[0] > 0 );
Size wholeSize; Point ofs;
size_t esz = elemSize();
locateROI( wholeSize, ofs );
int row1 = std::min(std::max(ofs.y - dtop, 0), wholeSize.height), row2 = std::max(0, std::min(ofs.y + rows + dbottom, wholeSize.height));
int col1 = std::min(std::max(ofs.x - dleft, 0), wholeSize.width), col2 = std::max(0, std::min(ofs.x + cols + dright, wholeSize.width));
if(row1 > row2)
std::swap(row1, row2);
if(col1 > col2)
std::swap(col1, col2);
offset += (row1 - ofs.y)*step + (col1 - ofs.x)*esz;
rows = row2 - row1; cols = col2 - col1;
size.p[0] = rows; size.p[1] = cols;
if( esz*cols == step[0] || rows == 1 )
flags |= CONTINUOUS_FLAG;
else
flags &= ~CONTINUOUS_FLAG;
return *this;
}
UMat UMat::reshape(int new_cn, int new_rows) const
{
int cn = channels();
UMat hdr = *this;
if( dims > 2 && new_rows == 0 && new_cn != 0 && size[dims-1]*cn % new_cn == 0 )
{
hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn-1) << CV_CN_SHIFT);
hdr.step[dims-1] = CV_ELEM_SIZE(hdr.flags);
hdr.size[dims-1] = hdr.size[dims-1]*cn / new_cn;
return hdr;
}
CV_Assert( dims <= 2 );
if( new_cn == 0 )
new_cn = cn;
int total_width = cols * cn;
if( (new_cn > total_width || total_width % new_cn != 0) && new_rows == 0 )
new_rows = rows * total_width / new_cn;
if( new_rows != 0 && new_rows != rows )
{
int total_size = total_width * rows;
if( !isContinuous() )
CV_Error( CV_BadStep,
"The matrix is not continuous, thus its number of rows can not be changed" );
if( (unsigned)new_rows > (unsigned)total_size )
CV_Error( CV_StsOutOfRange, "Bad new number of rows" );
total_width = total_size / new_rows;
if( total_width * new_rows != total_size )
CV_Error( CV_StsBadArg, "The total number of matrix elements "
"is not divisible by the new number of rows" );
hdr.rows = new_rows;
hdr.step[0] = total_width * elemSize1();
}
int new_width = total_width / new_cn;
if( new_width * new_cn != total_width )
CV_Error( CV_BadNumChannels,
"The total width is not divisible by the new number of channels" );
hdr.cols = new_width;
hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((new_cn-1) << CV_CN_SHIFT);
hdr.step[1] = CV_ELEM_SIZE(hdr.flags);
return hdr;
}
UMat UMat::diag(const UMat& d)
{
CV_Assert( d.cols == 1 || d.rows == 1 );
int len = d.rows + d.cols - 1;
UMat m(len, len, d.type(), Scalar(0));
UMat md = m.diag();
if( d.cols == 1 )
d.copyTo(md);
else
transpose(d, md);
return m;
}
int UMat::checkVector(int _elemChannels, int _depth, bool _requireContinuous) const
{
return (depth() == _depth || _depth <= 0) &&
(isContinuous() || !_requireContinuous) &&
((dims == 2 && (((rows == 1 || cols == 1) && channels() == _elemChannels) ||
(cols == _elemChannels && channels() == 1))) ||
(dims == 3 && channels() == 1 && size.p[2] == _elemChannels && (size.p[0] == 1 || size.p[1] == 1) &&
(isContinuous() || step.p[1] == step.p[2]*size.p[2])))
? (int)(total()*channels()/_elemChannels) : -1;
}
UMat UMat::reshape(int _cn, int _newndims, const int* _newsz) const
{
if(_newndims == dims)
{
if(_newsz == 0)
return reshape(_cn);
if(_newndims == 2)
return reshape(_cn, _newsz[0]);
}
if (isContinuous())
{
CV_Assert(_cn >= 0 && _newndims > 0 && _newndims <= CV_MAX_DIM && _newsz);
if (_cn == 0)
_cn = this->channels();
else
CV_Assert(_cn <= CV_CN_MAX);
size_t total_elem1_ref = this->total() * this->channels();
size_t total_elem1 = _cn;
AutoBuffer<int, 4> newsz_buf( (size_t)_newndims );
for (int i = 0; i < _newndims; i++)
{
CV_Assert(_newsz[i] >= 0);
if (_newsz[i] > 0)
newsz_buf[i] = _newsz[i];
else if (i < dims)
newsz_buf[i] = this->size[i];
else
CV_Error(CV_StsOutOfRange, "Copy dimension (which has zero size) is not present in source matrix");
total_elem1 *= (size_t)newsz_buf[i];
}
if (total_elem1 != total_elem1_ref)
CV_Error(CV_StsUnmatchedSizes, "Requested and source matrices have different count of elements");
UMat hdr = *this;
hdr.flags = (hdr.flags & ~CV_MAT_CN_MASK) | ((_cn-1) << CV_CN_SHIFT);
setSize(hdr, _newndims, (int*)newsz_buf, NULL, true);
return hdr;
}
CV_Error(CV_StsNotImplemented, "Reshaping of n-dimensional non-continuous matrices is not supported yet");
// TBD
return UMat();
}
Mat UMat::getMat(int accessFlags) const
{
if(!u)
return Mat();
// TODO Support ACCESS_READ (ACCESS_WRITE) without unnecessary data transfers
accessFlags |= ACCESS_RW;
UMatDataAutoLock autolock(u);
if(CV_XADD(&u->refcount, 1) == 0)
u->currAllocator->map(u, accessFlags);
if (u->data != 0)
{
Mat hdr(dims, size.p, type(), u->data + offset, step.p);
hdr.flags = flags;
hdr.u = u;
hdr.datastart = u->data;
hdr.data = u->data + offset;
hdr.datalimit = hdr.dataend = u->data + u->size;
return hdr;
}
else
{
CV_XADD(&u->refcount, -1);
CV_Assert(u->data != 0 && "Error mapping of UMat to host memory.");
return Mat();
}
}
void* UMat::handle(int accessFlags) const
{
if( !u )
return 0;
CV_Assert(u->refcount == 0);
CV_Assert(!u->deviceCopyObsolete() || u->copyOnMap());
if (u->deviceCopyObsolete())
{
u->currAllocator->unmap(u);
}
if ((accessFlags & ACCESS_WRITE) != 0)
u->markHostCopyObsolete(true);
return u->handle;
}
void UMat::ndoffset(size_t* ofs) const
{
// offset = step[0]*ofs[0] + step[1]*ofs[1] + step[2]*ofs[2] + ...;
size_t val = offset;
for( int i = 0; i < dims; i++ )
{
size_t s = step.p[i];
ofs[i] = val / s;
val -= ofs[i]*s;
}
}
void UMat::copyTo(OutputArray _dst) const
{
CV_INSTRUMENT_REGION()
int dtype = _dst.type();
if( _dst.fixedType() && dtype != type() )
{
CV_Assert( channels() == CV_MAT_CN(dtype) );
convertTo( _dst, dtype );
return;
}
if( empty() )
{
_dst.release();
return;
}
size_t i, sz[CV_MAX_DIM] = {0}, srcofs[CV_MAX_DIM], dstofs[CV_MAX_DIM], esz = elemSize();
for( i = 0; i < (size_t)dims; i++ )
sz[i] = size.p[i];
sz[dims-1] *= esz;
ndoffset(srcofs);
srcofs[dims-1] *= esz;
_dst.create( dims, size.p, type() );
if( _dst.isUMat() )
{
UMat dst = _dst.getUMat();
CV_Assert(dst.u);
if( u == dst.u && dst.offset == offset )
return;
if (u->currAllocator == dst.u->currAllocator)
{
dst.ndoffset(dstofs);
dstofs[dims-1] *= esz;
u->currAllocator->copy(u, dst.u, dims, sz, srcofs, step.p, dstofs, dst.step.p, false);
return;
}
}
Mat dst = _dst.getMat();
u->currAllocator->download(u, dst.ptr(), dims, sz, srcofs, step.p, dst.step.p);
}
void UMat::copyTo(OutputArray _dst, InputArray _mask) const
{
CV_INSTRUMENT_REGION()
if( _mask.empty() )
{
copyTo(_dst);
return;
}
#ifdef HAVE_OPENCL
int cn = channels(), mtype = _mask.type(), mdepth = CV_MAT_DEPTH(mtype), mcn = CV_MAT_CN(mtype);
CV_Assert( mdepth == CV_8U && (mcn == 1 || mcn == cn) );
if (ocl::useOpenCL() && _dst.isUMat() && dims <= 2)
{
UMatData * prevu = _dst.getUMat().u;
_dst.create( dims, size, type() );
UMat dst = _dst.getUMat();
bool haveDstUninit = false;
if( prevu != dst.u ) // do not leave dst uninitialized
haveDstUninit = true;
String opts = format("-D COPY_TO_MASK -D T1=%s -D scn=%d -D mcn=%d%s",
ocl::memopTypeToStr(depth()), cn, mcn,
haveDstUninit ? " -D HAVE_DST_UNINIT" : "");
ocl::Kernel k("copyToMask", ocl::core::copyset_oclsrc, opts);
if (!k.empty())
{
k.args(ocl::KernelArg::ReadOnlyNoSize(*this),
ocl::KernelArg::ReadOnlyNoSize(_mask.getUMat()),
haveDstUninit ? ocl::KernelArg::WriteOnly(dst) :
ocl::KernelArg::ReadWrite(dst));
size_t globalsize[2] = { (size_t)cols, (size_t)rows };
if (k.run(2, globalsize, NULL, false))
{
CV_IMPL_ADD(CV_IMPL_OCL);
return;
}
}
}
#endif
Mat src = getMat(ACCESS_READ);
src.copyTo(_dst, _mask);
}
void UMat::convertTo(OutputArray _dst, int _type, double alpha, double beta) const
{
CV_INSTRUMENT_REGION()
bool noScale = std::fabs(alpha - 1) < DBL_EPSILON && std::fabs(beta) < DBL_EPSILON;
int stype = type(), cn = CV_MAT_CN(stype);
if( _type < 0 )
_type = _dst.fixedType() ? _dst.type() : stype;
else
_type = CV_MAKETYPE(CV_MAT_DEPTH(_type), cn);
int sdepth = CV_MAT_DEPTH(stype), ddepth = CV_MAT_DEPTH(_type);
if( sdepth == ddepth && noScale )
{
copyTo(_dst);
return;
}
#ifdef HAVE_OPENCL
bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
bool needDouble = sdepth == CV_64F || ddepth == CV_64F;
if( dims <= 2 && cn && _dst.isUMat() && ocl::useOpenCL() &&
((needDouble && doubleSupport) || !needDouble) )
{
int wdepth = std::max(CV_32F, sdepth), rowsPerWI = 4;
char cvt[2][40];
ocl::Kernel k("convertTo", ocl::core::convert_oclsrc,
format("-D srcT=%s -D WT=%s -D dstT=%s -D convertToWT=%s -D convertToDT=%s%s%s",
ocl::typeToStr(sdepth), ocl::typeToStr(wdepth), ocl::typeToStr(ddepth),
ocl::convertTypeStr(sdepth, wdepth, 1, cvt[0]),
ocl::convertTypeStr(wdepth, ddepth, 1, cvt[1]),
doubleSupport ? " -D DOUBLE_SUPPORT" : "", noScale ? " -D NO_SCALE" : ""));
if (!k.empty())
{
UMat src = *this;
_dst.create( size(), _type );
UMat dst = _dst.getUMat();
float alphaf = (float)alpha, betaf = (float)beta;
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
dstarg = ocl::KernelArg::WriteOnly(dst, cn);
if (noScale)
k.args(srcarg, dstarg, rowsPerWI);
else if (wdepth == CV_32F)
k.args(srcarg, dstarg, alphaf, betaf, rowsPerWI);
else
k.args(srcarg, dstarg, alpha, beta, rowsPerWI);
size_t globalsize[2] = { (size_t)dst.cols * cn, ((size_t)dst.rows + rowsPerWI - 1) / rowsPerWI };
if (k.run(2, globalsize, NULL, false))
{
CV_IMPL_ADD(CV_IMPL_OCL);
return;
}
}
}
#endif
UMat src = *this; // Fake reference to itself.
// Resolves issue 8693 in case of src == dst.
Mat m = getMat(ACCESS_READ);
m.convertTo(_dst, _type, alpha, beta);
}
UMat& UMat::setTo(InputArray _value, InputArray _mask)
{
CV_INSTRUMENT_REGION()
bool haveMask = !_mask.empty();
#ifdef HAVE_OPENCL
int tp = type(), cn = CV_MAT_CN(tp), d = CV_MAT_DEPTH(tp);
if( dims <= 2 && cn <= 4 && CV_MAT_DEPTH(tp) < CV_64F && ocl::useOpenCL() )
{
Mat value = _value.getMat();
CV_Assert( checkScalar(value, type(), _value.kind(), _InputArray::UMAT) );
int kercn = haveMask || cn == 3 ? cn : std::max(cn, ocl::predictOptimalVectorWidth(*this)),
kertp = CV_MAKE_TYPE(d, kercn);
double buf[16] = { 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0 };
convertAndUnrollScalar(value, tp, (uchar *)buf, kercn / cn);
int scalarcn = kercn == 3 ? 4 : kercn, rowsPerWI = ocl::Device::getDefault().isIntel() ? 4 : 1;
String opts = format("-D dstT=%s -D rowsPerWI=%d -D dstST=%s -D dstT1=%s -D cn=%d",
ocl::memopTypeToStr(kertp), rowsPerWI,
ocl::memopTypeToStr(CV_MAKETYPE(d, scalarcn)),
ocl::memopTypeToStr(d), kercn);
ocl::Kernel setK(haveMask ? "setMask" : "set", ocl::core::copyset_oclsrc, opts);
if( !setK.empty() )
{
ocl::KernelArg scalararg(ocl::KernelArg::CONSTANT, 0, 0, 0, buf, CV_ELEM_SIZE(d) * scalarcn);
UMat mask;
if( haveMask )
{
mask = _mask.getUMat();
CV_Assert( mask.size() == size() && mask.type() == CV_8UC1 );
ocl::KernelArg maskarg = ocl::KernelArg::ReadOnlyNoSize(mask),
dstarg = ocl::KernelArg::ReadWrite(*this);
setK.args(maskarg, dstarg, scalararg);
}
else
{
ocl::KernelArg dstarg = ocl::KernelArg::WriteOnly(*this, cn, kercn);
setK.args(dstarg, scalararg);
}
size_t globalsize[] = { (size_t)cols * cn / kercn, ((size_t)rows + rowsPerWI - 1) / rowsPerWI };
if( setK.run(2, globalsize, NULL, false) )
{
CV_IMPL_ADD(CV_IMPL_OCL);
return *this;
}
}
}
#endif
Mat m = getMat(haveMask ? ACCESS_RW : ACCESS_WRITE);
m.setTo(_value, _mask);
return *this;
}
UMat& UMat::operator = (const Scalar& s)
{
setTo(s);
return *this;
}
UMat UMat::t() const
{
UMat m;
transpose(*this, m);
return m;
}
UMat UMat::inv(int method) const
{
UMat m;
invert(*this, m, method);
return m;
}
UMat UMat::mul(InputArray m, double scale) const
{
UMat dst;
multiply(*this, m, dst, scale);
return dst;
}
#ifdef HAVE_OPENCL
static bool ocl_dot( InputArray _src1, InputArray _src2, double & res )
{
UMat src1 = _src1.getUMat().reshape(1), src2 = _src2.getUMat().reshape(1);
int type = src1.type(), depth = CV_MAT_DEPTH(type),
kercn = ocl::predictOptimalVectorWidth(src1, src2);
bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
if ( !doubleSupport && depth == CV_64F )
return false;
int dbsize = ocl::Device::getDefault().maxComputeUnits();
size_t wgs = ocl::Device::getDefault().maxWorkGroupSize();
int ddepth = std::max(CV_32F, depth);
int wgs2_aligned = 1;
while (wgs2_aligned < (int)wgs)
wgs2_aligned <<= 1;
wgs2_aligned >>= 1;
char cvt[40];
ocl::Kernel k("reduce", ocl::core::reduce_oclsrc,
format("-D srcT=%s -D srcT1=%s -D dstT=%s -D dstTK=%s -D ddepth=%d -D convertToDT=%s -D OP_DOT "
"-D WGS=%d -D WGS2_ALIGNED=%d%s%s%s -D kercn=%d",
ocl::typeToStr(CV_MAKE_TYPE(depth, kercn)), ocl::typeToStr(depth),
ocl::typeToStr(ddepth), ocl::typeToStr(CV_MAKE_TYPE(ddepth, kercn)),
ddepth, ocl::convertTypeStr(depth, ddepth, kercn, cvt),
(int)wgs, wgs2_aligned, doubleSupport ? " -D DOUBLE_SUPPORT" : "",
_src1.isContinuous() ? " -D HAVE_SRC_CONT" : "",
_src2.isContinuous() ? " -D HAVE_SRC2_CONT" : "", kercn));
if (k.empty())
return false;
UMat db(1, dbsize, ddepth);
ocl::KernelArg src1arg = ocl::KernelArg::ReadOnlyNoSize(src1),
src2arg = ocl::KernelArg::ReadOnlyNoSize(src2),
dbarg = ocl::KernelArg::PtrWriteOnly(db);
k.args(src1arg, src1.cols, (int)src1.total(), dbsize, dbarg, src2arg);
size_t globalsize = dbsize * wgs;
if (k.run(1, &globalsize, &wgs, false))
{
res = sum(db.getMat(ACCESS_READ))[0];
return true;
}
return false;
}
#endif
double UMat::dot(InputArray m) const
{
CV_INSTRUMENT_REGION()
CV_Assert(m.sameSize(*this) && m.type() == type());
#ifdef HAVE_OPENCL
double r = 0;
CV_OCL_RUN_(dims <= 2, ocl_dot(*this, m, r), r)
#endif
return getMat(ACCESS_READ).dot(m);
}
UMat UMat::zeros(int rows, int cols, int type)
{
return UMat(rows, cols, type, Scalar::all(0));
}
UMat UMat::zeros(Size size, int type)
{
return UMat(size, type, Scalar::all(0));
}
UMat UMat::zeros(int ndims, const int* sz, int type)
{
return UMat(ndims, sz, type, Scalar::all(0));
}
UMat UMat::ones(int rows, int cols, int type)
{
return UMat::ones(Size(cols, rows), type);
}
UMat UMat::ones(Size size, int type)
{
return UMat(size, type, Scalar(1));
}
UMat UMat::ones(int ndims, const int* sz, int type)
{
return UMat(ndims, sz, type, Scalar(1));
}
UMat UMat::eye(int rows, int cols, int type)
{
return UMat::eye(Size(cols, rows), type);
}
UMat UMat::eye(Size size, int type)
{
UMat m(size, type);
setIdentity(m);
return m;
}
}
/* End of file. */