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Custom layers for deep learning networks (#11129)

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* Custom deep learning layers support

* Stack custom deep learning layers
This commit is contained in:
Dmitry Kurtaev
2018-04-24 14:59:59 +03:00
committed by Vadim Pisarevsky
parent 909a25571e
commit 4ec456f0a0
19 changed files with 928 additions and 146 deletions
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modules/dnn/test/test_misc.cpp
+62
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@@ -7,6 +7,8 @@
#include "test_precomp.hpp"
#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
namespace opencv_test { namespace {
TEST(blobFromImage_4ch, Regression)
@@ -75,4 +77,64 @@ TEST(readNet, Regression)
EXPECT_FALSE(net.empty());
}
class FirstCustomLayer CV_FINAL : public Layer
{
public:
FirstCustomLayer(const LayerParams &params) : Layer(params) {}
static Ptr<Layer> create(LayerParams& params)
{
return Ptr<Layer>(new FirstCustomLayer(params));
}
virtual void forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE {}
virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat>& internals) CV_OVERRIDE
{
outputs[0].setTo(1);
}
};
class SecondCustomLayer CV_FINAL : public Layer
{
public:
SecondCustomLayer(const LayerParams &params) : Layer(params) {}
static Ptr<Layer> create(LayerParams& params)
{
return Ptr<Layer>(new SecondCustomLayer(params));
}
virtual void forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE {}
virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat>& internals) CV_OVERRIDE
{
outputs[0].setTo(2);
}
};
TEST(LayerFactory, custom_layers)
{
LayerParams lp;
lp.name = "name";
lp.type = "CustomType";
Mat inp(1, 1, CV_32FC1);
for (int i = 0; i < 3; ++i)
{
if (i == 0) { CV_DNN_REGISTER_LAYER_CLASS(CustomType, FirstCustomLayer); }
else if (i == 1) { CV_DNN_REGISTER_LAYER_CLASS(CustomType, SecondCustomLayer); }
else if (i == 2) { LayerFactory::unregisterLayer("CustomType"); }
Net net;
net.addLayerToPrev(lp.name, lp.type, lp);
net.setInput(inp);
Mat output = net.forward();
if (i == 0) EXPECT_EQ(output.at<float>(0), 1);
else if (i == 1) EXPECT_EQ(output.at<float>(0), 2);
else if (i == 2) EXPECT_EQ(output.at<float>(0), 1);
}
LayerFactory::unregisterLayer("CustomType");
}
}} // namespace