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Update global pooling

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This commit is contained in:
Liubov Batanina 2019-12-28 18:03:40 +03:00
parent cf477f7e9f
commit 752653c70b
3 changed files with 25 additions and 8 deletions
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2
modules/dnn/include/opencv2/dnn/all_layers.hpp
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@ -251,7 +251,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
CV_DEPRECATED_EXTERNAL Size kernel, stride, pad; CV_DEPRECATED_EXTERNAL Size kernel, stride, pad;
CV_DEPRECATED_EXTERNAL int pad_l, pad_t, pad_r, pad_b; CV_DEPRECATED_EXTERNAL int pad_l, pad_t, pad_r, pad_b;
bool globalPooling; bool globalPooling;
int global_axis; std::vector<bool> isGlobalPooling;
bool computeMaxIdx; bool computeMaxIdx;
String padMode; String padMode;
bool ceilMode; bool ceilMode;

28
modules/dnn/src/layers/pooling_layer.cpp
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@ -122,9 +122,17 @@ public:
} }
else else
CV_Error(Error::StsBadArg, "Cannot determine pooling type"); CV_Error(Error::StsBadArg, "Cannot determine pooling type");
setParamsFrom(params); setParamsFrom(params);
ceilMode = params.get<bool>("ceil_mode", true); ceilMode = params.get<bool>("ceil_mode", true);
global_axis = params.get<int>("global_axis", -1); if (params.has("is_global_pooling"))
{
const DictValue &global_axis = params.get("is_global_pooling");
int size = global_axis.size();
isGlobalPooling.resize(size);
for (int i = 0; i < size; i++)
isGlobalPooling[i] = global_axis.get<bool>(i);
}
spatialScale = params.get<float>("spatial_scale", 1); spatialScale = params.get<float>("spatial_scale", 1);
avePoolPaddedArea = params.get<bool>("ave_pool_padded_area", true); avePoolPaddedArea = params.get<bool>("ave_pool_padded_area", true);
} }
@ -150,8 +158,12 @@ public:
if (globalPooling) { if (globalPooling) {
kernel = Size(inp[1], inp[0]); kernel = Size(inp[1], inp[0]);
kernel_size = std::vector<size_t>(inp.begin(), inp.end()); kernel_size = std::vector<size_t>(inp.begin(), inp.end());
} else if (global_axis != -1) { } else if (!isGlobalPooling.empty()) {
kernel_size[global_axis] = inp[global_axis]; for (int i = 0; i < isGlobalPooling.size(); i++)
{
if (isGlobalPooling[i])
kernel_size[i] = inp[i];
}
kernel = Size(kernel_size[1], kernel_size[0]); kernel = Size(kernel_size[1], kernel_size[0]);
} }
@ -1041,10 +1053,14 @@ virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inp
outShape[0] = inputs[1][0]; // Number of proposals; outShape[0] = inputs[1][0]; // Number of proposals;
outShape[1] = psRoiOutChannels; outShape[1] = psRoiOutChannels;
} }
else if (global_axis != -1) else if (!isGlobalPooling.empty())
{ {
CV_Assert(global_axis >= 0 && global_axis < inpShape.size()); CV_Assert(isGlobalPooling.size() == inpShape.size());
outShape[2 + global_axis] = 1; for (int i = 0; i < isGlobalPooling.size(); i++)
{
if (isGlobalPooling[i])
outShape[2 + i] = 1;
}
} }
int numOutputs = requiredOutputs ? requiredOutputs : (type == MAX ? 2 : 1); int numOutputs = requiredOutputs ? requiredOutputs : (type == MAX ? 2 : 1);

3
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -1961,7 +1961,8 @@ void TFImporter::populateNet(Net dstNet)
CV_Assert(layer_id.find(avgName) == layer_id.end()); CV_Assert(layer_id.find(avgName) == layer_id.end());
avgLp.set("pool", "ave"); avgLp.set("pool", "ave");
// pooling kernel H x 1 // pooling kernel H x 1
avgLp.set("global_axis", 0); bool isGlobalPooling[] = {true, false};
avgLp.set("is_global_pooling", DictValue::arrayInt(&isGlobalPooling[0], 2));
avgLp.set("kernel_size", 1); avgLp.set("kernel_size", 1);
int avgId = dstNet.addLayer(avgName, "Pooling", avgLp); int avgId = dstNet.addLayer(avgName, "Pooling", avgLp);
layer_id[avgName] = avgId; layer_id[avgName] = avgId;