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Merge pull request #9811 from dkurt:prelu_with_shared_channels

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This commit is contained in:
Vadim Pisarevsky 2017-10-10 09:57:51 +00:00
commit 0be1f4a573
5 changed files with 18 additions and 5 deletions
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2
modules/dnn/include/opencv2/dnn/all_layers.hpp
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@ -422,7 +422,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer
{
public:
static Ptr<ChannelsPReLULayer> create(const LayerParams& params);
static Ptr<Layer> create(const LayerParams& params);
};
class CV_EXPORTS ELULayer : public ActivationLayer

6
modules/dnn/src/caffe/caffe_importer.cpp
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@ -216,7 +216,7 @@ public:
shape.push_back((int)_shape.dim(i));
}
else
CV_Error(Error::StsError, "Unknown shape of input blob");
shape.resize(1, 1); // Is a scalar.
}
void blobFromProto(const caffe::BlobProto &pbBlob, cv::Mat &dstBlob)
@ -274,9 +274,9 @@ public:
struct BlobNote
{
BlobNote(const std::string &_name, int _layerId, int _outNum) :
name(_name.c_str()), layerId(_layerId), outNum(_outNum) {}
name(_name), layerId(_layerId), outNum(_outNum) {}
const char *name;
std::string name;
int layerId, outNum;
};

1
modules/dnn/src/init.cpp
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@ -97,6 +97,7 @@ void initializeLayerFactory()
CV_DNN_REGISTER_LAYER_CLASS(ReLU, ReLULayer);
CV_DNN_REGISTER_LAYER_CLASS(ReLU6, ReLU6Layer);
CV_DNN_REGISTER_LAYER_CLASS(ChannelsPReLU, ChannelsPReLULayer);
CV_DNN_REGISTER_LAYER_CLASS(PReLU, ChannelsPReLULayer);
CV_DNN_REGISTER_LAYER_CLASS(Sigmoid, SigmoidLayer);
CV_DNN_REGISTER_LAYER_CLASS(TanH, TanHLayer);
CV_DNN_REGISTER_LAYER_CLASS(ELU, ELULayer);

9
modules/dnn/src/layers/elementwise_layers.cpp
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@ -754,8 +754,15 @@ Ptr<PowerLayer> PowerLayer::create(const LayerParams& params)
return l;
}
Ptr<ChannelsPReLULayer> ChannelsPReLULayer::create(const LayerParams& params)
Ptr<Layer> ChannelsPReLULayer::create(const LayerParams& params)
{
CV_Assert(params.blobs.size() == 1);
if (params.blobs[0].total() == 1)
{
LayerParams reluParams = params;
reluParams.set("negative_slope", params.blobs[0].at<float>(0));
return ReLULayer::create(reluParams);
}
Ptr<ChannelsPReLULayer> l(new ElementWiseLayer<ChannelsPReLUFunctor>(ChannelsPReLUFunctor(params.blobs[0])));
l->setParamsFrom(params);

5
modules/dnn/test/test_layers.cpp
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@ -279,6 +279,11 @@ TEST(Layer_Test_Eltwise, Accuracy)
testLayerUsingCaffeModels("layer_eltwise");
}
TEST(Layer_Test_PReLU, Accuracy)
{
testLayerUsingCaffeModels("layer_prelu", DNN_TARGET_CPU, true);
}
//template<typename XMat>
//static void test_Layer_Concat()
//{