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Add ONNX's padding import
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@ -96,6 +96,8 @@ Mat getMatFromTensor(opencv_onnx::TensorProto& tensor_proto)
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for (int i = 0; i < tensor_proto.dims_size(); i++) {
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for (int i = 0; i < tensor_proto.dims_size(); i++) {
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sizes.push_back(tensor_proto.dims(i));
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sizes.push_back(tensor_proto.dims(i));
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}
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}
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if (sizes.empty())
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sizes.assign(1, 1);
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if (datatype == opencv_onnx::TensorProto_DataType_FLOAT) {
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if (datatype == opencv_onnx::TensorProto_DataType_FLOAT) {
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if (!tensor_proto.float_data().empty()) {
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if (!tensor_proto.float_data().empty()) {
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@ -173,11 +175,31 @@ LayerParams ONNXImporter::getLayerParams(const opencv_onnx::NodeProto& node_prot
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}
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}
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else if(attribute_name == "pads")
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else if(attribute_name == "pads")
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{
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{
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CV_Assert(attribute_proto.ints_size() == 4);
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if (node_proto.op_type() == "Pad")
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lp.set("pad_t", saturate_cast<int32_t>(attribute_proto.ints(0)));
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{
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lp.set("pad_l", saturate_cast<int32_t>(attribute_proto.ints(1)));
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// Padding layer.
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lp.set("pad_b", saturate_cast<int32_t>(attribute_proto.ints(2)));
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// Paddings are in order begin0, begin1, .. beginN, end0, end1, ..., endN.
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lp.set("pad_r", saturate_cast<int32_t>(attribute_proto.ints(3)));
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// We need to shuffle it to begin0, end0, begin1, end1, ...
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CV_Assert(attribute_proto.ints_size() % 2 == 0);
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const int dims = attribute_proto.ints_size() / 2;
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std::vector<int32_t> paddings;
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paddings.reserve(attribute_proto.ints_size());
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for (int i = 0; i < dims; ++i)
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{
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paddings.push_back(attribute_proto.ints(i));
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paddings.push_back(attribute_proto.ints(dims + i));
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}
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lp.set("paddings", DictValue::arrayInt(&paddings[0], paddings.size()));
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}
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else
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{
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// Convolution or pooling.
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CV_Assert(attribute_proto.ints_size() == 4);
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lp.set("pad_t", saturate_cast<int32_t>(attribute_proto.ints(0)));
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lp.set("pad_l", saturate_cast<int32_t>(attribute_proto.ints(1)));
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lp.set("pad_b", saturate_cast<int32_t>(attribute_proto.ints(2)));
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lp.set("pad_r", saturate_cast<int32_t>(attribute_proto.ints(3)));
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}
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}
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}
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else if(attribute_name == "auto_pad")
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else if(attribute_name == "auto_pad")
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{
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{
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@ -543,6 +565,10 @@ void ONNXImporter::populateNet(Net dstNet)
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replaceLayerParam(layerParams, "shape", "dim");
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replaceLayerParam(layerParams, "shape", "dim");
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}
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}
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}
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}
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else if (layer_type == "Pad")
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{
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layerParams.type = "Padding";
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}
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else
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else
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{
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{
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for (int j = 0; j < node_proto.input_size(); j++) {
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for (int j = 0; j < node_proto.input_size(); j++) {
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@ -129,6 +129,11 @@ TEST_P(Test_ONNX_layers, Constant)
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testONNXModels("constant");
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testONNXModels("constant");
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}
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}
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TEST_P(Test_ONNX_layers, Padding)
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{
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testONNXModels("padding");
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}
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TEST_P(Test_ONNX_layers, MultyInputs)
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TEST_P(Test_ONNX_layers, MultyInputs)
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{
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{
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const String model = _tf("models/multy_inputs.onnx");
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const String model = _tf("models/multy_inputs.onnx");
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