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https://github.com/opencv/opencv.git
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6801dd043d
fix ONNXImporter diagnostic mode layer registration issue * fix layer registration, thread unsafe access and align the behavior of DNN_DIAGNOSTICS_RUN between onnx and tf importers * move skipModelInput * print all missing layers * address TF issue
68 lines
1.9 KiB
C++
68 lines
1.9 KiB
C++
/*************************************************
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USAGE:
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./model_diagnostics -m <model file location>
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**************************************************/
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#include <opencv2/dnn.hpp>
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#include <opencv2/core/utils/filesystem.hpp>
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#include <opencv2/dnn/utils/debug_utils.hpp>
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#include <iostream>
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using namespace cv;
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using namespace dnn;
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static
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int diagnosticsErrorCallback(int /*status*/, const char* /*func_name*/,
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const char* /*err_msg*/, const char* /*file_name*/,
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int /*line*/, void* /*userdata*/)
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{
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fflush(stdout);
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fflush(stderr);
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return 0;
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}
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static std::string checkFileExists(const std::string& fileName)
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{
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if (fileName.empty() || utils::fs::exists(fileName))
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return fileName;
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CV_Error(Error::StsObjectNotFound, "File " + fileName + " was not found! "
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"Please, specify a full path to the file.");
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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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int main( int argc, const char** argv )
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{
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CommandLineParser argParser(argc, argv, diagnosticKeys);
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argParser.about("Use this tool to run the diagnostics of provided ONNX/TF model"
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"to obtain the information about its support (supported layers).");
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if (argc == 1)
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{
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argParser.printMessage();
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return 0;
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}
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std::string model = checkFileExists(argParser.get<std::string>("model"));
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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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CV_Assert(!model.empty());
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enableModelDiagnostics(true);
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skipModelImport(true);
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redirectError(diagnosticsErrorCallback, NULL);
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Net ocvNet = readNet(model, config, frameworkId);
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return 0;
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}
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