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Merge pull request #10867 from dkurt:dnn_fix_ave_pooling_area

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Alexander Alekhin
2018-02-16 11:17:32 +00:00
parent 8ffcc76011 f8d0d6365e
commit 0e4eed0ba1
5 changed files with 44 additions and 8 deletions
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modules/dnn/src/layers/pooling_layer.cpp
+10 -8
View File
@@ -106,6 +106,7 @@ public:
setParamsFrom(params);
ceilMode = params.get<bool>("ceil_mode", true);
spatialScale = params.get<float>("spatial_scale", 1);
avePoolPaddedArea = params.get<bool>("ave_pool_padded_area", true);
}
#ifdef HAVE_OPENCL
@@ -259,7 +260,7 @@ public:
const Mat* src, *rois;
Mat *dst, *mask;
Size kernel, stride, pad;
String padMode;
bool avePoolPaddedArea;
int nstripes;
bool computeMaxIdx;
std::vector<int> ofsbuf;
@@ -270,7 +271,7 @@ 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, String padMode, int poolingType, float spatialScale,
Size stride, Size pad, bool avePoolPaddedArea, int poolingType, float spatialScale,
bool computeMaxIdx, int nstripes)
{
CV_Assert(src.isContinuous(), dst.isContinuous(),
@@ -289,7 +290,7 @@ public:
p.kernel = kernel;
p.stride = stride;
p.pad = pad;
p.padMode = padMode;
p.avePoolPaddedArea = avePoolPaddedArea;
p.nstripes = nstripes;
p.computeMaxIdx = computeMaxIdx;
p.poolingType = poolingType;
@@ -369,6 +370,7 @@ 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);
@@ -532,14 +534,14 @@ 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 = isSamePad ? (yend - ystart) * (xend - xstart) : kernel.area();
float inv_kernel_area = avePoolPaddedArea ? xdelta * ydelta : ((yend - ystart) * (xend - xstart));
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 )
@@ -651,21 +653,21 @@ public:
{
const int nstripes = getNumThreads();
Mat rois;
PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad, padMode, type, spatialScale, computeMaxIdx, nstripes);
PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad, avePoolPaddedArea, 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, padMode, type, spatialScale, computeMaxIdx, nstripes);
PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad, avePoolPaddedArea, 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, padMode, type, spatialScale, computeMaxIdx, nstripes);
PoolingInvoker::run(src, rois, dst, mask, kernel, stride, pad, avePoolPaddedArea, type, spatialScale, computeMaxIdx, nstripes);
}
virtual Ptr<BackendNode> initMaxPoolingHalide(const std::vector<Ptr<BackendWrapper> > &inputs)
modules/dnn/src/tensorflow/tf_importer.cpp
+1
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@@ -1078,6 +1078,7 @@ void TFImporter::populateNet(Net dstNet)
else if (type == "AvgPool")
{
layerParams.set("pool", "ave");
layerParams.set("ave_pool_padded_area", false);
setKSize(layerParams, layer);
setStrides(layerParams, layer);
modules/dnn/src/torch/torch_importer.cpp
+4
View File
@@ -559,7 +559,11 @@ struct TorchImporter
layerParams.set("indices_blob_id", tensorParams["indices"].first);
}
if (nnName == "SpatialAveragePooling")
{
layerParams.set("pool", "AVE");
layerParams.set("ave_pool_padded_area", scalarParams.has("count_include_pad") &&
scalarParams.get<bool>("count_include_pad"));
}
convertTorchKernelsParams(scalarParams, layerParams);
CV_Assert(scalarParams.has("ceil_mode"));