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fixed some compile warnings in dnn & protobuf; improved convolution layer performance when blas is not available by parallelizing gemmCPU() function in dnn

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This commit is contained in:
Vadim Pisarevsky
2016-09-21 18:13:17 +03:00
parent b1346e5ab6
commit b59f3989f5
7 changed files with 85 additions and 25 deletions
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3
modules/dnn/3rdparty/protobuf/src/google/protobuf/descriptor.cc vendored
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@@ -223,8 +223,7 @@ struct PointerStringPairHash {
}
// Used only by MSVC and platforms where hash_map is not available.
static const size_t bucket_size = 4;
static const size_t min_buckets = 8;
enum { bucket_size = 4, min_buckets = 8 };
inline bool operator()(const PointerStringPair& a,
const PointerStringPair& b) const {
if (a.first < b.first) return true;

7
modules/dnn/3rdparty/protobuf/src/google/protobuf/stubs/strutil.cc vendored
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@@ -729,13 +729,6 @@ char *FastHex32ToBuffer(uint32 value, char* buffer) {
return InternalFastHexToBuffer(value, buffer, 8);
}
static inline char* PlaceNum(char* p, int num, char prev_sep) {
*p-- = '0' + num % 10;
*p-- = '0' + num / 10;
*p-- = prev_sep;
return p;
}
// ----------------------------------------------------------------------
// FastInt32ToBufferLeft()
// FastUInt32ToBufferLeft()

9
modules/dnn/3rdparty/protobuf/src/google/protobuf/wire_format.cc vendored
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@@ -53,15 +53,6 @@ namespace google {
namespace protobuf {
namespace internal {
namespace {
// This function turns out to be convenient when using some macros later.
inline int GetEnumNumber(const EnumValueDescriptor* descriptor) {
return descriptor->number();
}
} // anonymous namespace
// ===================================================================
bool UnknownFieldSetFieldSkipper::SkipField(

3
modules/dnn/samples/fcn_semsegm.cpp
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@@ -140,7 +140,10 @@ int main(int argc, char **argv)
//! [Set input blob]
//! [Make forward pass]
double t = (double)cv::getTickCount();
net.forward(); //compute output
t = (double)cv::getTickCount() - t;
printf("processing time: %.1fms\n", t*1000./getTickFrequency());
//! [Make forward pass]
//! [Gather output]

8
modules/dnn/src/caffe/layer_loaders.cpp
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@@ -44,7 +44,7 @@ Ptr<Layer> createLayerFromCaffe<DeconvolutionLayer>(LayerParams &params)
template<>
Ptr<Layer> createLayerFromCaffe<PoolingLayer>(LayerParams &params)
{
int type;
int type = PoolingLayer::MAX;
Size kernel, stride, pad;
bool globalPooling;
@@ -60,10 +60,6 @@ Ptr<Layer> createLayerFromCaffe<PoolingLayer>(LayerParams &params)
else
CV_Error(Error::StsBadArg, "Unknown pooling type \"" + pool + "\"");
}
else
{
type = PoolingLayer::MAX;
}
getPoolingKernelParams(params, kernel.height, kernel.width, globalPooling, pad.height, pad.width, stride.height, stride.width);
//getCaffeConvParams(params, kernel, pad, stride);
@@ -107,7 +103,7 @@ Ptr<Layer> createLayerFromCaffe<InnerProductLayer>(LayerParams &params)
template<> //LRNLayer specialization
Ptr<Layer> createLayerFromCaffe<LRNLayer>(LayerParams& params)
{
int type;
int type = -1;
String nrmType = params.get<String>("norm_region", "ACROSS_CHANNELS");
if (nrmType == "ACROSS_CHANNELS")
type = LRNLayer::CHANNEL_NRM;

2
modules/dnn/src/layers/convolution_layer.cpp
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@@ -55,7 +55,7 @@ namespace dnn
ConvolutionLayerImpl::ConvolutionLayerImpl()
{
tryUseOpenCL = true;
tryUseOpenCL = false; //true;
numOutput = -1;
group = -1;

78
modules/dnn/src/layers/op_blas.cpp
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@@ -28,8 +28,85 @@ inline void SwapRowCols(const Mat &A, int &rows, int &cols, bool isTrans)
cols = (isTrans) ? A.rows : A.cols;
}
class GEMMInvoker : public ParallelLoopBody
{
public:
GEMMInvoker(const Mat* _a, const Mat* _b, double _alpha, Mat* _c, double _beta)
{
a = _a;
b = _b;
c = _c;
alpha = _alpha;
beta = _beta;
}
void operator()(const Range& range) const
{
int mmax = a->rows;
int nmax = range.end - range.start;
int kmax = a->cols;
int m, n, k;
AutoBuffer<float> buf(nmax);
float* ptr = buf;
if( mmax %2 != 0 )
memset(ptr, 0, nmax*sizeof(ptr[0]));
for( m = 0; m < mmax; m += 2 )
{
float* dst0 = c->ptr<float>(m) + range.start;
float* dst1 = m+1 < mmax ? c->ptr<float>(m+1) + range.start : ptr;
const float* aptr0 = a->ptr<float>(m);
const float* aptr1 = m+1 < mmax ? a->ptr<float>(m+1) : aptr0;
if( beta != 1 )
{
if( beta == 0 )
for( n = 0; n < nmax; n++ )
{
dst0[n] = 0.f;
dst1[n] = 0.f;
}
else
for( n = 0; n < nmax; n++ )
{
dst0[n] *= (float)beta;
dst1[n] *= (float)beta;
}
}
for( k = 0; k < kmax; k++ )
{
float alpha0 = (float)(alpha*aptr0[k]);
float alpha1 = (float)(alpha*aptr1[k]);
const float* bptr = b->ptr<float>(k) + range.start;
for( n = 0; n < nmax; n++ )
{
float d0 = dst0[n] + alpha0*bptr[n];
float d1 = dst1[n] + alpha1*bptr[n];
dst0[n] = d0;
dst1[n] = d1;
}
}
}
}
const Mat *a, *b;
Mat* c;
double alpha, beta;
};
void gemmCPU(const Mat &A, const Mat &B, double alpha, Mat &C, double beta, int flags /*= 0*/)
{
if( C.type() == CV_32F && flags == 0 )
{
GEMMInvoker invoker(&A, &B, alpha, &C, beta);
double granularity = 10000000./((double)A.rows*A.cols);
parallel_for_(Range(0, B.cols), invoker, granularity);
}
else
{
#if HAVE_CBLAS
bool transA = static_cast<bool>(flags & GEMM_1_T);
bool transB = static_cast<bool>(flags & GEMM_2_T);
@@ -70,6 +147,7 @@ void gemmCPU(const Mat &A, const Mat &B, double alpha, Mat &C, double beta, int
#else
cv::gemm(A, B, alpha, C, beta, C, flags);
#endif
}
}
int getBlasThreads()