Merge pull request #12775 from radomsak:radomsak_dnn_fix_caffe_importer_reused_layers

modules/dnn/src/caffe/caffe_importer.cpp

@@ -278,11 +278,13 @@ public:
         int li;
         for (li = 0; li != netBinary.layer_size(); li++)
         {
-            if (netBinary.layer(li).name() == name)
+            const caffe::LayerParameter& binLayer = netBinary.layer(li);
+            // Break if the layer name is the same and the blobs are not cleared
+            if (binLayer.name() == name && binLayer.blobs_size() != 0)
                 break;
         }
 
-        if (li == netBinary.layer_size() || netBinary.layer(li).blobs_size() == 0)
+        if (li == netBinary.layer_size())
             return;
 
         caffe::LayerParameter* binLayer = netBinary.mutable_layer(li);

modules/dnn/test/test_caffe_importer.cpp

@@ -454,6 +454,28 @@ TEST(Test_Caffe, multiple_inputs)
     normAssert(out, first_image + second_image);
 }
 
+TEST(Test_Caffe, shared_weights)
+{
+  const string proto = findDataFile("dnn/layers/shared_weights.prototxt", false);
+  const string model = findDataFile("dnn/layers/shared_weights.caffemodel", false);
+
+  Net net = readNetFromCaffe(proto, model);
+
+  Mat input_1 = (Mat_<float>(2, 2) << 0., 2., 4., 6.);
+  Mat input_2 = (Mat_<float>(2, 2) << 1., 3., 5., 7.);
+
+  Mat blob_1 = blobFromImage(input_1);
+  Mat blob_2 = blobFromImage(input_2);
+
+  net.setInput(blob_1, "input_1");
+  net.setInput(blob_2, "input_2");
+
+  Mat sum = net.forward();
+
+  EXPECT_EQ(sum.at<float>(0,0), 12.);
+  EXPECT_EQ(sum.at<float>(0,1), 16.);
+}
+
 typedef testing::TestWithParam<tuple<std::string, Target> > opencv_face_detector;
 TEST_P(opencv_face_detector, Accuracy)
 {