Open Source Computer Vision Library https://opencv.org/
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/*************************************************
USAGE:
./model_diagnostics -m <model file location>
**************************************************/
#include <opencv2/dnn.hpp>
#include <opencv2/core/utils/filesystem.hpp>
#include <opencv2/dnn/utils/debug_utils.hpp>
#include <iostream>
using namespace cv;
using namespace dnn;
static
int diagnosticsErrorCallback(int /*status*/, const char* /*func_name*/,
const char* /*err_msg*/, const char* /*file_name*/,
int /*line*/, void* /*userdata*/)
{
fflush(stdout);
fflush(stderr);
return 0;
}
static std::string checkFileExists(const std::string& fileName)
{
if (fileName.empty() || utils::fs::exists(fileName))
return fileName;
CV_Error(Error::StsObjectNotFound, "File " + fileName + " was not found! "
"Please, specify a full path to the file.");
}
std::string diagnosticKeys =
"{ model m | | Path to the model file. }"
"{ config c | | Path to the model configuration file. }"
"{ framework f | | [Optional] Name of the model framework. }";
int main( int argc, const char** argv )
{
CommandLineParser argParser(argc, argv, diagnosticKeys);
argParser.about("Use this tool to run the diagnostics of provided ONNX/TF model"
"to obtain the information about its support (supported layers).");
if (argc == 1)
{
argParser.printMessage();
return 0;
}
std::string model = checkFileExists(argParser.get<std::string>("model"));
std::string config = checkFileExists(argParser.get<std::string>("config"));
std::string frameworkId = argParser.get<std::string>("framework");
CV_Assert(!model.empty());
enableModelDiagnostics(true);
skipModelImport(true);
redirectError(diagnosticsErrorCallback, NULL);
Net ocvNet = readNet(model, config, frameworkId);
return 0;
}