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Merge pull request #11835 from dkurt:dnn_tf_two_inputs
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commit
e87425f047
@ -376,6 +376,8 @@ private:
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// and may be used to build the network using binary format only as a weights storage.
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// This approach is similar to Caffe's `.prorotxt` and `.caffemodel`.
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tensorflow::GraphDef netTxt;
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std::vector<String> netInputsNames;
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};
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TFImporter::TFImporter(const char *model, const char *config)
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@ -443,7 +445,14 @@ void TFImporter::connect(const std::map<String, int>& layers_name_id_map, Net& n
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std::map<String, int>::const_iterator it = layers_name_id_map.find(outPin.name);
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if (it == layers_name_id_map.end())
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CV_Error(Error::StsError, "Input layer not found: " + outPin.name);
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network.connect(it->second, outPin.blobIndex, input_layer_id, input_blob_id);
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std::vector<String>::iterator inpNameIt = std::find(netInputsNames.begin(), netInputsNames.end(), outPin.name);
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int blobIndex;
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if (inpNameIt == netInputsNames.end())
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blobIndex = outPin.blobIndex;
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else
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blobIndex = inpNameIt - netInputsNames.begin();
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network.connect(it->second, blobIndex, input_layer_id, input_blob_id);
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}
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void TFImporter::connectToAllBlobs(const std::map<String, int>& layer_id, Net& network, const Pin& outPin,
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@ -845,7 +854,7 @@ void TFImporter::populateNet(Net dstNet)
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Pin inp = parsePin(layer.input(ii));
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if (layer_id.find(inp.name) == layer_id.end())
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CV_Error(Error::StsError, "Input layer not found: " + inp.name);
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dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii);
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connect(layer_id, dstNet, inp, id, ii);
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}
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}
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}
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@ -1099,7 +1108,7 @@ void TFImporter::populateNet(Net dstNet)
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Pin inp = parsePin(layer.input(ii));
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if (layer_id.find(inp.name) == layer_id.end())
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CV_Error(Error::StsError, "Input layer not found: " + inp.name);
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dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii - from);
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connect(layer_id, dstNet, inp, id, ii - from);
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}
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}
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else if (type == "MaxPool")
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@ -1131,10 +1140,12 @@ void TFImporter::populateNet(Net dstNet)
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}
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else if (type == "Placeholder")
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{
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std::vector<String> netInputs(1);
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netInputs[0] = name;
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layer_id[name] = 0;
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dstNet.setInputsNames(netInputs);
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if (!hasLayerAttr(layer, "dtype") ||
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getLayerAttr(layer, "dtype").type() != tensorflow::DT_BOOL) // If input is not a train/test flag.
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{
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netInputsNames.push_back(name);
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layer_id[name] = 0;
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}
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}
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else if (type == "Split") {
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// TODO: determining axis index remapping by input dimensions order of input blob
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@ -1272,7 +1283,7 @@ void TFImporter::populateNet(Net dstNet)
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Pin inp = parsePin(layer.input(ii));
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if (layer_id.find(inp.name) == layer_id.end())
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CV_Error(Error::StsError, "Input layer not found: " + inp.name);
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dstNet.connect(layer_id.at(inp.name), inp.blobIndex, id, ii);
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connect(layer_id, dstNet, inp, id, ii);
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}
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}
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}
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@ -1790,6 +1801,7 @@ void TFImporter::populateNet(Net dstNet)
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}
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}
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}
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dstNet.setInputsNames(netInputsNames);
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}
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} // namespace
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@ -441,4 +441,20 @@ TEST(Test_TensorFlow, resize_bilinear)
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runTensorFlowNet("resize_bilinear_factor");
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}
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TEST(Test_TensorFlow, two_inputs)
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{
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Net net = readNet(path("two_inputs_net.pbtxt"));
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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Mat firstInput(2, 3, CV_32FC1), secondInput(2, 3, CV_32FC1);
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randu(firstInput, -1, 1);
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randu(secondInput, -1, 1);
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net.setInput(firstInput, "first_input");
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net.setInput(secondInput, "second_input");
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Mat out = net.forward();
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normAssert(out, firstInput + secondInput);
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}
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}
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