Added Spatial Attention Module in Darknet Importer

diff --git a/modules/dnn/src/darknet/darknet_io.cpp b/modules/dnn/src/darknet/darknet_io.cpp
index e3c978a8c0cf5e4a30ed46626523c7eaa50cad66..4915538ff7150b1bb67d411d86be932e484ce7f9 100644 (file)
--- a/modules/dnn/src/darknet/darknet_io.cpp
+++ b/modules/dnn/src/darknet/darknet_io.cpp
@@ -558,6 +558,29 @@ namespace cv {
                     fused_layer_names.push_back(last_layer);
                 }
 
+                void setSAM(int from)
+                {
+                    cv::dnn::LayerParams eltwise_param;
+                    eltwise_param.name = "SAM-name";
+                    eltwise_param.type = "Eltwise";
+
+                    eltwise_param.set<std::string>("operation", "prod");
+                    eltwise_param.set<std::string>("output_channels_mode", "same");
+
+                    darknet::LayerParameter lp;
+                    std::string layer_name = cv::format("sam_%d", layer_id);
+                    lp.layer_name = layer_name;
+                    lp.layer_type = eltwise_param.type;
+                    lp.layerParams = eltwise_param;
+                    lp.bottom_indexes.push_back(last_layer);
+                    lp.bottom_indexes.push_back(fused_layer_names.at(from));
+                    last_layer = layer_name;
+                    net->layers.push_back(lp);
+
+                    layer_id++;
+                    fused_layer_names.push_back(last_layer);
+                }
+
                 void setUpsample(int scaleFactor)
                 {
                     cv::dnn::LayerParams param;
@@ -837,6 +860,14 @@ namespace cv {
                         from = from < 0 ? from + layers_counter : from;
                         setParams.setScaleChannels(from);
                     }
+                    else if (layer_type == "sam")
+                    {
+                        std::string bottom_layer = getParam<std::string>(layer_params, "from", "");
+                        CV_Assert(!bottom_layer.empty());
+                        int from = std::atoi(bottom_layer.c_str());
+                        from = from < 0 ? from + layers_counter : from;
+                        setParams.setSAM(from);
+                    }
                     else if (layer_type == "upsample")
                     {
                         int scaleFactor = getParam<int>(layer_params, "stride", 1);

diff --git a/modules/dnn/test/test_darknet_importer.cpp b/modules/dnn/test/test_darknet_importer.cpp
index 00638f83c50d33214cbc340372f05bb98d66dc81..8a633fa56605d67070f3caffdc21c7ff66958221 100644 (file)
--- a/modules/dnn/test/test_darknet_importer.cpp
+++ b/modules/dnn/test/test_darknet_importer.cpp
@@ -770,6 +770,11 @@ TEST_P(Test_Darknet_layers, relu)
     testDarknetLayer("relu");
 }
 
+TEST_P(Test_Darknet_layers, sam)
+{
+    testDarknetLayer("sam", true);
+}
+
 INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_layers, dnnBackendsAndTargets());
 
 }} // namespace