{
namespace machine_learning
{
-ImageClassificationDefault::ImageClassificationDefault(shared_ptr<MachineLearningConfig> config)
- : ImageClassification(config), _result()
+template<typename T>
+ImageClassificationDefault<T>::ImageClassificationDefault(shared_ptr<MachineLearningConfig> config)
+ : ImageClassification<T>(config), _result()
{}
-ImageClassificationDefault::~ImageClassificationDefault()
+template<typename T> ImageClassificationDefault<T>::~ImageClassificationDefault()
{}
-ImageClassificationResult &ImageClassificationDefault::result()
+template<typename T> ImageClassificationResult &ImageClassificationDefault<T>::result()
{
vector<string> names;
- ImageClassification::getOutputNames(names);
+ ImageClassification<T>::getOutputNames(names);
vector<float> output_vec;
// In case of image classification model, only one output tensor is used.
- ImageClassification::getOutpuTensor(names[0], output_vec);
+ ImageClassification<T>::getOutpuTensor(names[0], output_vec);
auto metaInfo = _config->getOutputMetaMap().at(names[0]);
auto decodingScore = static_pointer_cast<DecodingScore>(metaInfo->decodingTypeMap.at(DecodingType::SCORE));
return _result;
}
+template class ImageClassificationDefault<unsigned char>;
+template class ImageClassificationDefault<float>;
+
}
}