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Fix padding for average pooling from TensorFlow

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This commit is contained in:
Dmitry Kurtaev
2018-01-26 16:45:25 +03:00
parent df22bafe6d
commit a2e9bfbaf4
2 changed files with 40 additions and 8 deletions
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modules/dnn/src/layers/pooling_layer.cpp
+9 -8
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@@ -228,6 +228,7 @@ public:
const Mat* src, *rois;
Mat *dst, *mask;
Size kernel, stride, pad;
String padMode;
int nstripes;
bool computeMaxIdx;
std::vector<int> ofsbuf;
@@ -238,7 +239,7 @@ public:
computeMaxIdx(0), poolingType(MAX), spatialScale(0) {}
static void run(const Mat& src, const Mat& rois, Mat& dst, Mat& mask, Size kernel,
Size stride, Size pad, int poolingType, float spatialScale,
Size stride, Size pad, String padMode, int poolingType, float spatialScale,
bool computeMaxIdx, int nstripes)
{
CV_Assert(src.isContinuous(), dst.isContinuous(),
@@ -257,6 +258,7 @@ public:
p.kernel = kernel;
p.stride = stride;
p.pad = pad;
p.padMode = padMode;
p.nstripes = nstripes;
p.computeMaxIdx = computeMaxIdx;
p.poolingType = poolingType;
@@ -336,7 +338,6 @@ public:
yend = min(ystart + kernel_h, inp_height + pad_h);
srcData = src->ptr<float>(n, c);
}
int ydelta = yend - ystart;
ystart = max(ystart, 0);
yend = min(yend, inp_height);
float *dstData = dst->ptr<float>(n, c, y0);
@@ -500,15 +501,15 @@ public:
}
else if (poolingType == AVE)
{
bool isSamePad = padMode == "SAME";
for( ; x0 < x1; x0++ )
{
int xstart = x0 * stride_w - pad_w;
int xend = min(xstart + kernel_w, inp_width + pad_w);
int xdelta = xend - xstart;
xstart = max(xstart, 0);
xend = min(xend, inp_width);
float inv_kernel_area = 1.f/(ydelta*xdelta);
float inv_kernel_area = isSamePad ? (yend - ystart) * (xend - xstart) : kernel.area();
inv_kernel_area = 1.0 / inv_kernel_area;
#if CV_SIMD128
if( xstart > 0 && x0 + 7 < x1 && (x0 + 7) * stride_w - pad_w + kernel_w < inp_width )
{
@@ -619,21 +620,21 @@ public:
{
const int nstripes = getNumThreads();
Mat rois;
PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad, type, spatialScale, computeMaxIdx, nstripes);
PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad, padMode, type, spatialScale, computeMaxIdx, nstripes);
}
void avePooling(Mat &src, Mat &dst)
{
const int nstripes = getNumThreads();
Mat rois, mask;
PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad, type, spatialScale, computeMaxIdx, nstripes);
PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad, padMode, type, spatialScale, computeMaxIdx, nstripes);
}
void roiPooling(const Mat &src, const Mat &rois, Mat &dst)
{
const int nstripes = getNumThreads();
Mat mask;
PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad, type, spatialScale, computeMaxIdx, nstripes);
PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad, padMode, type, spatialScale, computeMaxIdx, nstripes);
}
virtual Ptr<BackendNode> initMaxPoolingHalide(const std::vector<Ptr<BackendWrapper> > &inputs)