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Open Source Computer Vision Library
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67 lines
1.9 KiB
67 lines
1.9 KiB
/************************************************* |
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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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