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Merge pull request #25582 from fengyuentau:dnn/dump_pbtxt
Current net exporter `dump` and `dumpToFile` exports the network structure (and its params) to a .dot file which works with `graphviz`. This is hard to use and not friendly to new user. What's worse, the produced picture is not looking pretty. dnn: better net exporter that works with netron #25582 This PR introduces new exporter `dumpToPbtxt` and uses this new exporter by default with environment variable `OPENCV_DNN_NETWORK_DUMP`. It mimics the string output of a onnx model but modified with dnn-specific changes, see below for an example.  ## Usage Call `cv::dnn::Net::dumpToPbtxt`: ```cpp TEST(DumpNet, dumpToPbtxt) { std::string path = "/path/to/model.onnx"; auto net = readNet(path); Mat input(std::vector<int>{1, 3, 640, 480}, CV_32F); net.setInput(input); net.dumpToPbtxt("yunet.pbtxt"); } ``` Set `export OPENCV_DNN_NETWORK_DUMP=1` ```cpp TEST(DumpNet, env) { std::string path = "/path/to/model.onnx"; auto net = readNet(path); Mat input(std::vector<int>{1, 3, 640, 480}, CV_32F); net.setInput(input); net.forward(); } ``` --- Note: - `pbtxt` is registered as one of the ONNX model suffix in netron. So you can see `module: ai.onnx` and such in the model. - We can get the string output of an ONNX model with the following script ```python import onnx net = onnx.load("/path/to/model.onnx") net_str = str(net) file = open("/path/to/model.pbtxt", "w") file.write(net_str) file.close() ``` ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Yuantao Feng
GitHub
parent
0044047782
commit
bc0618b688
@ -32,12 +32,36 @@ static std::string checkFileExists(const std::string& fileName)
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"Please, specify a full path to the file.");
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}
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static std::vector<int> parseShape(const std::string &shape_str) {
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std::stringstream ss(shape_str);
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std::string item;
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std::vector<std::string> items;
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while (std::getline(ss, item, ',')) {
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items.push_back(item);
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}
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std::vector<int> shape;
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for (size_t i = 0; i < items.size(); i++) {
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shape.push_back(std::stoi(items[i]));
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}
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return shape;
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}
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std::string diagnosticKeys =
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"{ model m | | Path to the model file. }"
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"{ config c | | Path to the model configuration file. }"
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"{ framework f | | [Optional] Name of the model framework. }";
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"{ framework f | | [Optional] Name of the model framework. }"
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"{ input0_name | | [Optional] Name of input0. Use with input0_shape}"
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"{ input0_shape | | [Optional] Shape of input0. Use with input0_name}"
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"{ input1_name | | [Optional] Name of input1. Use with input1_shape}"
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"{ input1_shape | | [Optional] Shape of input1. Use with input1_name}"
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"{ input2_name | | [Optional] Name of input2. Use with input2_shape}"
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"{ input2_shape | | [Optional] Shape of input2. Use with input2_name}"
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"{ input3_name | | [Optional] Name of input3. Use with input3_shape}"
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"{ input3_shape | | [Optional] Shape of input3. Use with input3_name}"
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"{ input4_name | | [Optional] Name of input4. Use with input4_shape}"
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"{ input4_shape | | [Optional] Shape of input4. Use with input4_name}";
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int main( int argc, const char** argv )
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{
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@ -55,6 +79,17 @@ int main( int argc, const char** argv )
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std::string config = checkFileExists(argParser.get<std::string>("config"));
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std::string frameworkId = argParser.get<std::string>("framework");
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std::string input0_name = argParser.get<std::string>("input0_name");
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std::string input0_shape = argParser.get<std::string>("input0_shape");
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std::string input1_name = argParser.get<std::string>("input1_name");
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std::string input1_shape = argParser.get<std::string>("input1_shape");
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std::string input2_name = argParser.get<std::string>("input2_name");
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std::string input2_shape = argParser.get<std::string>("input2_shape");
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std::string input3_name = argParser.get<std::string>("input3_name");
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std::string input3_shape = argParser.get<std::string>("input3_shape");
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std::string input4_name = argParser.get<std::string>("input4_name");
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std::string input4_shape = argParser.get<std::string>("input4_shape");
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CV_Assert(!model.empty());
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enableModelDiagnostics(true);
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@ -63,5 +98,50 @@ int main( int argc, const char** argv )
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Net ocvNet = readNet(model, config, frameworkId);
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std::vector<std::string> input_names;
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std::vector<std::vector<int>> input_shapes;
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if (!input0_name.empty() || !input0_shape.empty()) {
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CV_CheckFalse(input0_name.empty(), "input0_name cannot be empty");
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CV_CheckFalse(input0_shape.empty(), "input0_shape cannot be empty");
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input_names.push_back(input0_name);
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input_shapes.push_back(parseShape(input0_shape));
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}
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if (!input1_name.empty() || !input1_shape.empty()) {
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CV_CheckFalse(input1_name.empty(), "input1_name cannot be empty");
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CV_CheckFalse(input1_shape.empty(), "input1_shape cannot be empty");
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input_names.push_back(input1_name);
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input_shapes.push_back(parseShape(input1_shape));
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}
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if (!input2_name.empty() || !input2_shape.empty()) {
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CV_CheckFalse(input2_name.empty(), "input2_name cannot be empty");
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CV_CheckFalse(input2_shape.empty(), "input2_shape cannot be empty");
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input_names.push_back(input2_name);
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input_shapes.push_back(parseShape(input2_shape));
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}
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if (!input3_name.empty() || !input3_shape.empty()) {
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CV_CheckFalse(input3_name.empty(), "input3_name cannot be empty");
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CV_CheckFalse(input3_shape.empty(), "input3_shape cannot be empty");
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input_names.push_back(input3_name);
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input_shapes.push_back(parseShape(input3_shape));
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}
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if (!input4_name.empty() || !input4_shape.empty()) {
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CV_CheckFalse(input4_name.empty(), "input4_name cannot be empty");
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CV_CheckFalse(input4_shape.empty(), "input4_shape cannot be empty");
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input_names.push_back(input4_name);
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input_shapes.push_back(parseShape(input4_shape));
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}
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if (!input_names.empty() && !input_shapes.empty() && input_names.size() == input_shapes.size()) {
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ocvNet.setInputsNames(input_names);
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for (size_t i = 0; i < input_names.size(); i++) {
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Mat input(input_shapes[i], CV_32F);
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ocvNet.setInput(input, input_names[i]);
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}
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size_t dot_index = model.rfind('.');
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std::string graph_filename = model.substr(0, dot_index) + ".pbtxt";
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ocvNet.dumpToPbtxt(graph_filename);
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}
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return 0;
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}
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@ -518,6 +518,14 @@ CV__DNN_INLINE_NS_BEGIN
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* @see dump()
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*/
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CV_WRAP void dumpToFile(CV_WRAP_FILE_PATH const String& path);
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/** @brief Dump net structure, hyperparameters, backend, target and fusion to pbtxt file
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* @param path path to output file with .pbtxt extension
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*
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* Use Netron (https://netron.app) to open the target file to visualize the model.
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* Call method after setInput(). To see correct backend, target and fusion run after forward().
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*/
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CV_WRAP void dumpToPbtxt(CV_WRAP_FILE_PATH const String& path);
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/** @brief Adds new layer to the net.
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* @param name unique name of the adding layer.
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* @param type typename of the adding layer (type must be registered in LayerRegister).
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@ -216,6 +216,16 @@ void Net::dumpToFile(const String& path)
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file.close();
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}
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void Net::dumpToPbtxt(const String& path)
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{
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CV_TRACE_FUNCTION();
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CV_Assert(impl);
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CV_Assert(!empty());
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std::ofstream file(path.c_str());
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file << impl->dumpToPbtxt(true);
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file.close();
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}
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Ptr<Layer> Net::getLayer(int layerId) const
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{
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CV_Assert(impl);
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@ -1830,15 +1830,278 @@ string Net::Impl::dump(bool forceAllocation) const
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return out.str();
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}
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static void dumpTensorToString(std::ostringstream &out, const Mat &m, const int num_indent_spaces = 4) {
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string indent_spaces(num_indent_spaces, ' ');
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int type = 1;
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/* Check TensorProto::DataType from https://github.com/onnx/onnx/blob/main/onnx/onnx.proto */
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switch (m.type()) {
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case CV_32F: break;
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case CV_8U: type = 2; break;
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case CV_8S: type = 3; break;
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case CV_16U: type = 4; break;
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case CV_16S: type = 5; break;
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case CV_32S: type = 6; break;
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#if CV_VERSION_MAJOR > 4
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case CV_64S: type = 7; break;
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// STRING: 8
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case CV_BOOL: type = 9; break;
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#endif
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case CV_16F: type = 10; break;
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case CV_64F: type = 11; break;
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#if CV_VERSION_MAJOR > 4
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case CV_32U: type = 12; break;
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case CV_64U: type = 13; break;
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// COMPLEX64: 14
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// COMPLEX128: 15
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case CV_16BF: type = 16; break;
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#endif
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default: CV_Error(Error::StsUnsupportedFormat, "Type of mat is not supported");
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}
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const auto &mshape = shape(m);
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out << indent_spaces << "type {\n"
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<< indent_spaces << " tensor_type {\n"
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<< indent_spaces << " elem_type: " << type << "\n";
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out << indent_spaces << " shape {\n";
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for (size_t i = 0; i < mshape.size(); i++) {
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out << indent_spaces << format(" dim { dim_value: %d }\n", mshape[i]);
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}
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out << indent_spaces << " }\n" // shape{}
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<< indent_spaces << " }\n" // tensor_type{}
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<< indent_spaces << "}\n"; // type{}
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}
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static void dumpParamToString(std::ostringstream &out, const std::string &key, const DictValue &value, const int num_indent_spaces = 2) {
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std::string indent_spaces(num_indent_spaces, ' ');
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out << indent_spaces << "attribute {\n"
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<< indent_spaces << format(" name: \"%s\"\n", key.c_str());
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if (value.size() == 1) {
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if (value.isString()) {
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out << indent_spaces << format(" type: STRING\n")
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<< indent_spaces << format(" s: \"%s\"\n", value.getStringValue(0).c_str());
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} else if (value.isInt()) {
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out << indent_spaces << format(" type: INT\n")
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<< indent_spaces << format(" i: %d\n", value.getIntValue(0));
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} else if (value.isReal()) {
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out << indent_spaces << format(" type: FLOAT\n")
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<< indent_spaces << format(" f: %f\n", value.getRealValue(0));
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} else {
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out << indent_spaces << format(" type: UNKNOWN-SCALAR\n");
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}
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} else {
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if (value.isString()) {
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out << indent_spaces << format(" type: STRINGS\n");
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} else if (value.isInt()) {
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out << indent_spaces << format(" type: INTS\n");
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} else if (value.isReal()) {
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out << indent_spaces << format(" type: FLOATS\n");
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} else {
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out << indent_spaces << format(" type: UNKNOWN-ARRAY\n");
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}
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for (int i = 0; i < value.size(); i++) {
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if (value.isString()) {
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out << indent_spaces << format(" strings: \"%s\"\n", value.getStringValue(i).c_str());
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} else if (value.isInt()) {
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out << indent_spaces << format(" ints: %d\n", value.getIntValue(i));
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} else if (value.isReal()) {
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out << indent_spaces << format(" floats: %f\n", value.getRealValue());
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}
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}
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}
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out << indent_spaces << "}\n"; // attribute{}
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}
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static void dumpLayerToString(std::ostringstream &out,
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const std::vector<std::string> &inputs,
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const std::vector<std::string> &outputs,
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const std::string &name,
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const std::string &op_type,
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const LayerParams ¶ms,
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const std::string &backend_name,
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const std::string &target_name,
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const int num_indent_spaces = 2) {
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std::string indent_spaces(num_indent_spaces, ' ');
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for (size_t i = 0; i < inputs.size(); i++) {
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out << indent_spaces << format("input: \"%s\"\n", inputs[i].c_str());
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}
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for (size_t i = 0; i < outputs.size(); i++) {
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out << indent_spaces << format("output: \"%s\"\n", outputs[i].c_str());
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}
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if (!name.empty()) {
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out << indent_spaces << format("name: \"%s\"\n", name.c_str());
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}
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if (!op_type.empty()) {
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out << indent_spaces << format("op_type: \"%s\"\n", op_type.c_str());
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}
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if (!params.name.empty()) {
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for (auto param_iter = params.begin(); param_iter != params.end(); param_iter++) {
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auto key = param_iter->first;
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auto value = param_iter->second;
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dumpParamToString(out, key, value, num_indent_spaces);
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}
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}
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if (!backend_name.empty()) {
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DictValue dvb(backend_name);
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dumpParamToString(out, "Backend", dvb, num_indent_spaces);
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}
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if (!target_name.empty()) {
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DictValue dvt(target_name);
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dumpParamToString(out, "Target", dvt, num_indent_spaces);
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}
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}
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string Net::Impl::dumpToPbtxt(bool forceAllocation) const {
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if (forceAllocation && !netWasAllocated) {
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const_cast<Net::Impl*>(this)->setUpNet();
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}
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std::ostringstream out;
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const std::map<int, LayerData> &map = layers;
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std::map<String, Mat*> value_info;
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Backend prefBackend = (Backend)preferableBackend;
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Target prefTarget = (Target)preferableTarget;
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auto GetBackendName = [] (int backendId) {
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std::string backend = "Unknown";
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switch (backendId) {
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case DNN_BACKEND_DEFAULT: backend = "DEFAULT"; break;
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#if CV_VERSION_MAJOR <= 4
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case DNN_BACKEND_HALIDE: backend = "HALIDE"; break;
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#endif
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case DNN_BACKEND_INFERENCE_ENGINE: // fallthru
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case DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019: // fallthru
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case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH: backend = "OpenVINO"; break;
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case DNN_BACKEND_OPENCV: backend = "OCV"; break;
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case DNN_BACKEND_VKCOM: backend = "VULKAN"; break;
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case DNN_BACKEND_CUDA: backend = "CUDA"; break;
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case DNN_BACKEND_WEBNN: backend = "WEBNN"; break;
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case DNN_BACKEND_TIMVX: backend = "TIMVX"; break;
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case DNN_BACKEND_CANN: backend = "CANN"; break;
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}
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return backend;
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};
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auto GetTargetName = [] (int targetId) {
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std::string target = "Unknown";
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switch (targetId) {
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case DNN_TARGET_CPU: target = "CPU"; break;
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case DNN_TARGET_OPENCL: target = "OCL"; break;
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case DNN_TARGET_OPENCL_FP16: target = "OCL_FP16"; break;
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case DNN_TARGET_MYRIAD: target = "MYRIAD"; break;
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case DNN_TARGET_VULKAN: target = "VULKAN"; break;
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case DNN_TARGET_FPGA: target = "FPGA"; break;
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case DNN_TARGET_CUDA: target = "CUDA"; break;
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case DNN_TARGET_CUDA_FP16: target = "CUDA_FP16"; break;
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case DNN_TARGET_HDDL: target = "HDDL"; break;
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case DNN_TARGET_NPU: target = "NPU"; break;
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case DNN_TARGET_CPU_FP16: target = "CPU_FP16"; break;
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}
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return target;
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};
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const int num_indent_spaces = 2;
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std::string indent_spaces(num_indent_spaces, ' ');
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out << "producer_name: \"opencv dnn\"\n"
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<< "producer_version: \"" << getVersionString() << "\"\n"
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<< "graph {\n";
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// Add nodes, inputs and outputs
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for (std::map<int, LayerData>::const_iterator iter = map.begin(); iter != map.end(); iter++) {
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auto &ld = iter->second;
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if (ld.id == 0) {
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for (int i = 0; i < ld.outputBlobs.size(); i++) {
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const auto &name = netInputLayer->outNames.empty() ? cv::format("%s_%d", ld.name.c_str(), i) : netInputLayer->outNames[i];
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out << indent_spaces << "input {\n"
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<< indent_spaces << format(" name: \"%s\"\n", name.c_str());
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// Add shape
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if (!ld.outputBlobs.empty()) {
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dumpTensorToString(out, ld.outputBlobs[i], num_indent_spaces + 2);
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}
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out << indent_spaces << "}\n"; // input{}
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}
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} else if (ld.consumers.size() == 0) {
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out << indent_spaces << "output {\n"
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<< indent_spaces << format(" name: \"%s\"\n", ld.name.c_str());
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// Add shape
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if (!ld.outputBlobs.empty()) {
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dumpTensorToString(out, ld.outputBlobs.front(), num_indent_spaces + 2);
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}
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out << indent_spaces << "}\n"; // output{}
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} else {
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out << indent_spaces << "node {\n";
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const auto &name = ld.name;
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const auto &op_type = "cv::dnn::" + ld.type;
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std::vector<std::string> inputs, outputs;
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// Collect names of inputs
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for (size_t i = 0; i < ld.inputBlobsId.size(); i++) {
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int lid = ld.inputBlobsId[i].lid;
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int oid = ld.inputBlobsId[i].oid;
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std::string name;
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if (lid == 0) {
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name = netInputLayer->outNames.empty() ? cv::format("%s_%d", ld.name.c_str(), oid) : netInputLayer->outNames[oid];
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} else {
|
||||
name = format("%s_output%d", map.find(lid)->second.name.c_str(), oid);
|
||||
if (!ld.inputBlobs.empty()) {
|
||||
value_info.insert({name, ld.inputBlobs[i]});
|
||||
}
|
||||
}
|
||||
inputs.push_back(name);
|
||||
}
|
||||
// Collect names of outputs
|
||||
for (size_t i = 0; i < ld.consumers.size(); i++) {
|
||||
int lid = ld.consumers[i].lid;
|
||||
const auto &layer_output_layer = map.find(lid)->second;
|
||||
std::string name;
|
||||
if (layer_output_layer.consumers.size() == 0) {
|
||||
name = layer_output_layer.name;
|
||||
} else {
|
||||
name = format("%s_output%zu", ld.name.c_str(), i);
|
||||
}
|
||||
outputs.push_back(name);
|
||||
}
|
||||
const auto ¶ms = ld.params;
|
||||
// Collect backend and target
|
||||
const Backend backend = ld.backendNodes.find(prefBackend) == ld.backendNodes.end() ? DNN_BACKEND_OPENCV : prefBackend;
|
||||
const std::string backend_name = GetBackendName(backend);
|
||||
const Target target = ld.layerInstance.empty() ? DNN_TARGET_CPU : (Target)(ld.layerInstance->preferableTarget);
|
||||
const std::string target_name = GetTargetName(target);
|
||||
dumpLayerToString(out, inputs, outputs, name, op_type, params, backend_name, target_name, num_indent_spaces + 2);
|
||||
out << indent_spaces << "}\n"; // node{}
|
||||
}
|
||||
}
|
||||
// Add value_info
|
||||
for (std::map<String, Mat*>::const_iterator iter = value_info.begin(); iter != value_info.end(); iter++) {
|
||||
out << indent_spaces << "value_info {\n"
|
||||
<< indent_spaces << format(" name: \"%s\"\n", iter->first.c_str());
|
||||
dumpTensorToString(out, *(iter->second), num_indent_spaces + 2);
|
||||
out << indent_spaces << "}\n"; // value_info{}
|
||||
}
|
||||
out << "}\n"; // graph{}
|
||||
|
||||
// Add preferable backend and target as metadata
|
||||
out << "metadata_props {\n";
|
||||
out << indent_spaces << format(" key: \"%s\"", "Preferable Backend")
|
||||
<< indent_spaces << format(" value: \"%s\"", GetBackendName(prefBackend).c_str());
|
||||
out << "}\n"; // metadata_props{}
|
||||
out << "metadata_props {\n";
|
||||
out << indent_spaces << format(" key: \"%s\"", "Preferable Target")
|
||||
<< indent_spaces << format(" value: \"%s\"", GetTargetName(prefTarget).c_str());
|
||||
out << "}\n"; // metadata_props{}
|
||||
|
||||
return out.str();
|
||||
}
|
||||
|
||||
void Net::Impl::dumpNetworkToFile() const
|
||||
{
|
||||
#ifndef OPENCV_DNN_DISABLE_NETWORK_AUTO_DUMP
|
||||
string dumpFileNameBase = getDumpFileNameBase();
|
||||
string dumpFileName = dumpFileNameBase + ".dot";
|
||||
string dumpFileName = dumpFileNameBase + ".pbtxt";
|
||||
try
|
||||
{
|
||||
string dumpStr = dump();
|
||||
string dumpStr = dumpToPbtxt();
|
||||
std::ofstream out(dumpFileName.c_str(), std::ios::out | std::ios::binary);
|
||||
out << dumpStr;
|
||||
}
|
||||
|
||||
@ -278,6 +278,7 @@ struct Net::Impl : public detail::NetImplBase
|
||||
AsyncArray getBlobAsync(String outputName);
|
||||
|
||||
string dump(bool forceAllocation = false) const;
|
||||
string dumpToPbtxt(bool forceAllocation = false) const;
|
||||
|
||||
void dumpNetworkToFile() const;
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user