Merge pull request #14682 from l-bat:axpy_layer

* Add Axpy layer

* Fix test

* fix caffe importer

modules/dnn/src/caffe/caffe_importer.cpp

@@ -388,6 +388,27 @@ public:
                     layerParams.blobs[1].setTo(1);  // std
                 }
             }
+            else if (type == "Axpy")
+            {
+                CV_Assert_N(layer.bottom_size() == 3, layer.top_size() == 1);
+
+                std::string scaleName = name + "/scale";
+                int repetitions = layerCounter[scaleName]++;
+                if (repetitions) {
+                    scaleName += String("_") + toString(repetitions);
+                }
+
+                LayerParams scaleParams;
+                scaleParams.set("axis", 1);
+                scaleParams.set("has_bias", false);
+                int scaleId = dstNet.addLayer(scaleName, "Scale", scaleParams);
+                addInput(layer.bottom(2), scaleId, 0, dstNet);
+                addInput(layer.bottom(0), scaleId, 1, dstNet);
+                addOutput(layer, scaleId, 0);
+                net.mutable_layer(li)->set_bottom(0, layer.top(0));
+                net.mutable_layer(li)->mutable_bottom()->RemoveLast();
+                type = "Eltwise";
+            }
             else if ("ConvolutionDepthwise" == type)
             {
                 type = "Convolution";

modules/dnn/src/layers/eltwise_layer.cpp

@@ -143,7 +143,7 @@ public:
             CV_Check(dst.dims, 1 < dst.dims && dst.dims <= 4, ""); CV_CheckTypeEQ(dst.type(), CV_32FC1, ""); CV_Assert(dst.isContinuous());
             CV_Assert(coeffs.empty() || coeffs.size() == (size_t)nsrcs);
 
-            for( int i = 0; i > nsrcs; i++ )
+            for( int i = 0; i < nsrcs; i++ )
             {
                 CV_Assert(srcs[i].size == dst.size &&
                           srcs[i].type() == dst.type() &&

modules/dnn/test/test_caffe_importer.cpp

@@ -109,6 +109,49 @@ TEST(Test_Caffe, read_googlenet)
     ASSERT_FALSE(net.empty());
 }
 
+TEST_P(Test_Caffe_nets, Axpy)
+{
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE)
+        throw SkipTestException("");
+
+    String proto = _tf("axpy.prototxt");
+    Net net = readNetFromCaffe(proto);
+
+    checkBackend();
+    net.setPreferableBackend(backend);
+    net.setPreferableTarget(target);
+
+    int size[] = {1, 2, 3, 4};
+    int scale_size[] = {1, 2, 1, 1};
+    Mat scale(4, &scale_size[0], CV_32F);
+    Mat shift(4, &size[0], CV_32F);
+    Mat inp(4, &size[0], CV_32F);
+    randu(scale, -1.0f, 1.0f);
+    randu(shift, -1.0f, 1.0f);
+    randu(inp, -1.0f, 1.0f);
+
+    net.setInput(scale, "scale");
+    net.setInput(shift, "shift");
+    net.setInput(inp, "data");
+
+    Mat out = net.forward();
+
+    Mat ref(4, &size[0], inp.type());
+    for (int i = 0; i < inp.size[1]; i++) {
+        for (int h = 0; h < inp.size[2]; h++) {
+            for (int w = 0; w < inp.size[3]; w++) {
+                int idx[] = {0, i, h, w};
+                int scale_idx[] = {0, i, 0, 0};
+                ref.at<float>(idx) = inp.at<float>(idx) * scale.at<float>(scale_idx) +
+                                     shift.at<float>(idx);
+            }
+        }
+    }
+    float l1 = (target == DNN_TARGET_OPENCL_FP16) ? 2e-4 : 1e-5;
+    float lInf = (target == DNN_TARGET_OPENCL_FP16) ? 1e-3 : 1e-4;
+    normAssert(ref, out, "", l1, lInf);
+}
+
 typedef testing::TestWithParam<tuple<bool, Target> > Reproducibility_AlexNet;
 TEST_P(Reproducibility_AlexNet, Accuracy)
 {