ld.layerInstance = Ptr<Layer>(new InfEngineBackendLayer(it.second));
ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE] = backendNode;
- cvNet.connect(0, 0, lid, 0);
+ for (int i = 0; i < inputsNames.size(); ++i)
+ cvNet.connect(0, i, lid, i);
}
cvNet.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
normAssert(outDefault, out);
}
+
+// 1. Create a .prototxt file with the following network:
+// layer {
+// type: "Input" name: "data" top: "data"
+// input_param { shape { dim: 1 dim: 2 dim: 3 } }
+// }
+// layer {
+// type: "Input" name: "second_input" top: "second_input"
+// input_param { shape { dim: 1 dim: 2 dim: 3 } }
+// }
+// layer {
+// type: "Eltwise" name: "output" top: "output"
+// bottom: "data" bottom: "second_input"
+// eltwise_param { operation: SUM }
+// }
+//
+// 2. Create a .caffemodel file using Caffe:
+//
+// import caffe
+// net = caffe.Net('/path/to/prototxt', caffe.TEST)
+// net.save('/path/to/caffemodel')
+//
+// 3. Convert using ModelOptimizer.
+TEST(Test_DLDT, two_inputs)
+{
+ Net net = readNet(_tf("net_two_inputs.xml"), _tf("net_two_inputs.bin"));
+ int inpSize[] = {1, 2, 3};
+ Mat firstInp(3, &inpSize[0], CV_32F);
+ Mat secondInp(3, &inpSize[0], CV_32F);
+ randu(firstInp, -1, 1);
+ randu(secondInp, -1, 1);
+
+ net.setInput(firstInp, "data");
+ net.setInput(secondInp, "second_input");
+ Mat out = net.forward();
+
+ normAssert(out, firstInp + secondInp);
+}
#endif // HAVE_INF_ENGINE
}} // namespace