struct LayerData
{
- LayerData() : id(-1), flag(0) {}
+ LayerData() : id(-1), skip(false), flag(0) {}
LayerData(int _id, const String &_name, const String &_type, LayerParams &_params)
: id(_id), name(_name), type(_type), params(_params), skip(false), flag(0)
{
int poolingType;
float spatialScale;
- PoolingInvoker() : src(0), rois(0), dst(0), mask(0), nstripes(0),
+ PoolingInvoker() : src(0), rois(0), dst(0), mask(0), avePoolPaddedArea(false), nstripes(0),
computeMaxIdx(0), poolingType(MAX), spatialScale(0) {}
static void run(const Mat& src, const Mat& rois, Mat& dst, Mat& mask, Size kernel,
tsize_t scanlength = TIFFScanlineSize(tif);
tdata_t buf = _TIFFmalloc(scanlength);
float* data;
+ bool result = true;
for (uint32 row = 0; row < img_height; row++)
{
if (TIFFReadScanline(tif, buf, row) != 1)
{
- close();
- return false;
+ result = false;
+ break;
}
data=(float*)buf;
for (uint32 i=0; i<img_width; i++)
_TIFFfree(buf);
close();
- return true;
+ return result;
}
//////////////////////////////////////////////////////////////////////////////////////////
double varTmp;
int index;
public:
- SimulatedAnnealingANN_MLP(ml::ANN_MLP& x, const Ptr<ml::TrainData>& d) : nn(x), data(d)
+ SimulatedAnnealingANN_MLP(ml::ANN_MLP& x, const Ptr<ml::TrainData>& d) : nn(x), data(d), varTmp(0.0), index(0)
{
initVarMap();
}