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onnx: Update to OnnxRT >= 1.13.1 API
- Replace deprecated methods - Add a check on ORT version we are compatible with. - Add clarification to the example given. - Add the url to retrieve the model mentioned in the example. Part-of: <https://gitlab.freedesktop.org/gstreamer/gstreamer/-/merge_requests/3388>
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4 changed files with 38 additions and 15 deletions
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@ -73,6 +73,7 @@ GstOnnxClient::GstOnnxClient ():session (nullptr),
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GstOnnxClient::~GstOnnxClient ()
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{
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outputNames.clear();
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delete session;
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delete[]dest;
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}
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@ -115,6 +116,10 @@ std::string GstOnnxClient::getOutputNodeName (GstMlOutputNodeFunction nodeType)
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case GST_ML_OUTPUT_NODE_FUNCTION_CLASS:
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return "label";
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break;
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case GST_ML_OUTPUT_NODE_NUMBER_OF:
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g_assert_not_reached();
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GST_WARNING("Invalid parameter");
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break;
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};
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return "";
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@ -130,9 +135,16 @@ GstMlModelInputImageFormat GstOnnxClient::getInputImageFormat (void)
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return inputImageFormat;
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}
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std::vector < const char *>GstOnnxClient::getOutputNodeNames (void)
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std::vector< const char *> GstOnnxClient::getOutputNodeNames (void)
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{
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return outputNames;
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if (!outputNames.empty() && outputNamesRaw.size() != outputNames.size()) {
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outputNamesRaw.resize(outputNames.size());
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for (size_t i = 0; i < outputNamesRaw.size(); i++) {
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outputNamesRaw[i] = outputNames[i].get();
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}
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}
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return outputNamesRaw;
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}
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void GstOnnxClient::setOutputNodeIndex (GstMlOutputNodeFunction node,
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@ -227,11 +239,13 @@ bool GstOnnxClient::createSession (std::string modelFile,
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GST_DEBUG ("Number of Output Nodes: %d", (gint) session->GetOutputCount ());
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Ort::AllocatorWithDefaultOptions allocator;
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GST_DEBUG ("Input name: %s", session->GetInputName (0, allocator));
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auto input_name = session->GetInputNameAllocated (0, allocator);
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GST_DEBUG ("Input name: %s", input_name.get());
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for (size_t i = 0; i < session->GetOutputCount (); ++i) {
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auto output_name = session->GetOutputName (i, allocator);
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outputNames.push_back (output_name);
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auto output_name = session->GetOutputNameAllocated (i, allocator);
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GST_DEBUG("Output name %lu:%s", i, output_name.get());
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outputNames.push_back (std::move(output_name));
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auto type_info = session->GetOutputTypeInfo (i);
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auto tensor_info = type_info.GetTensorTypeAndShapeInfo ();
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@ -278,7 +292,7 @@ template < typename T > std::vector < GstMlBoundingBox >
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parseDimensions (vmeta);
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Ort::AllocatorWithDefaultOptions allocator;
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auto inputName = session->GetInputName (0, allocator);
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auto inputName = session->GetInputNameAllocated (0, allocator);
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auto inputTypeInfo = session->GetInputTypeInfo (0);
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std::vector < int64_t > inputDims =
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inputTypeInfo.GetTensorTypeAndShapeInfo ().GetShape ();
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@ -366,11 +380,11 @@ template < typename T > std::vector < GstMlBoundingBox >
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std::vector < Ort::Value > inputTensors;
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inputTensors.push_back (Ort::Value::CreateTensor < uint8_t > (memoryInfo,
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dest, inputTensorSize, inputDims.data (), inputDims.size ()));
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std::vector < const char *>inputNames { inputName };
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std::vector < const char *>inputNames { inputName.get () };
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std::vector < Ort::Value > modelOutput = session->Run (Ort::RunOptions { nullptr},
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inputNames.data (),
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inputTensors.data (), 1, outputNames.data (), outputNames.size ());
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inputTensors.data (), 1, outputNamesRaw.data (), outputNamesRaw.size ());
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auto numDetections =
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modelOutput[getOutputNodeIndex
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@ -108,7 +108,8 @@ namespace GstOnnxNamespace {
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GstMlOutputNodeInfo outputNodeInfo[GST_ML_OUTPUT_NODE_NUMBER_OF];
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// !! indexed by array index
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size_t outputNodeIndexToFunction[GST_ML_OUTPUT_NODE_NUMBER_OF];
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std::vector < const char *>outputNames;
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std::vector < const char *> outputNamesRaw;
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std::vector < Ort::AllocatedStringPtr > outputNames;
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GstMlModelInputImageFormat inputImageFormat;
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bool fixedInputImageSize;
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};
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@ -55,16 +55,24 @@
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*
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* ## Example launch command:
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*
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* (note: an object detection model has 3 or 4 output nodes, but there is no naming convention
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* to indicate which node outputs the bounding box, which node outputs the label, etc.
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* So, the `onnxobjectdetector` element has properties to map each node's functionality to its
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* respective node index in the specified model )
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* (note: an object detection model has 3 or 4 output nodes, but there is no
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* naming convention to indicate which node outputs the bounding box, which
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* node outputs the label, etc. So, the `onnxobjectdetector` element has
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* properties to map each node's functionality to its respective node index in
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* the specified model. Image resolution also need to be adapted to the model.
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* The videoscale in the pipeline below will scale the image, using padding if
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* required, to 640x383 resolution required by the model.)
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*
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* model.onnx can be found here:
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* https://github.com/zoq/onnx-runtime-examples/raw/main/data/models/model.onnx
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*
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* ```
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* GST_DEBUG=objectdetector:5 gst-launch-1.0 multifilesrc \
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* location=000000088462.jpg caps=image/jpeg,framerate=\(fraction\)30/1 ! jpegdec ! \
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* videoconvert ! \
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* onnxobjectdetector \
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* videoscale ! \
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* 'video/x-raw,width=640,height=383' ! \
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* onnxobjectdetector ! \
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* box-node-index=0 \
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* class-node-index=1 \
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* score-node-index=2 \
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@ -3,7 +3,7 @@ if get_option('onnx').disabled()
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endif
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onnxrt_dep = dependency('libonnxruntime',required : get_option('onnx'))
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onnxrt_dep = dependency('libonnxruntime', version : '>= 1.13.1', required : get_option('onnx'))
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if onnxrt_dep.found()
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onnxrt_include_root = onnxrt_dep.get_variable('includedir')
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