internal void Init(string modelName, string labelsFileName, ModelType modelType)
{
_modelType = modelType;
try
{
var assets = Android.App.Application.Context.Assets;
using (var sr = new StreamReader(File.Exists(labelsFileName)? File.OpenRead(labelsFileName) : assets.Open(labelsFileName)))
{
var content = sr.ReadToEnd();
_labels = content.Split('\n').Select(s => s.Trim()).Where(s => !string.IsNullOrEmpty(s)).ToList();
}
_inferenceInterface = new TensorFlowInferenceInterface(assets, modelName);
InputSize = Convert.ToInt32(_inferenceInterface.GraphOperation(InputName).Output(0).Shape().Size(1));
var iter = _inferenceInterface.Graph().Operations();
while (iter.HasNext && !_hasNormalizationLayer)
{
var op = iter.Next() as Operation;
if (op.Name().Contains(DataNormLayerPrefix))
{
_hasNormalizationLayer = true;
}
}
}
catch (Exception ex)
{
throw new ImageClassifierException("Failed to load the model - check the inner exception for more details", ex);
}
}
public void LoadModel(string path)
{
var name = Path.GetFileNameWithoutExtension(path);
path = name + ".pb";
if (path.Equals(LastModelLoaded))
{
return;
}
var info = CorePackage.Entity.Type.Resource.Instance.Directory + name + ".txt";
List <string> lines = new List <string>();
using (StreamReader sr = new StreamReader(info))
{
while (sr.Peek() >= 0)
{
lines.Add(sr.ReadLine());
}
}
_inputName = lines[0];
_outputName = lines[1];
var shape = lines[2].Replace(" ", "").Replace("(", "").Replace(")", "").Split(",");
_inputSize = int.Parse(shape[1]);
_lastDim = int.Parse(shape[3]);
CorePackage.Entity.Type.Resource.Instance.Directory = Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData) + "/";
var modelFile = File.Open(CorePackage.Entity.Type.Resource.Instance.Directory + path, FileMode.Open);
try
{
//var assets = Android.App.Application.Context.Assets;
//using (var sr = new StreamReader(assets.Open(labelsFileName)))
//{
// var content = sr.ReadToEnd();
// _labels = content.Split('\n').Select(s => s.Trim()).Where(s => !string.IsNullOrEmpty(s)).ToList();
//}
_inferenceInterface = new TensorFlowInferenceInterface(modelFile);
LastModelLoaded = path;
var iter = _inferenceInterface.Graph().Operations();
while (iter.HasNext && !_hasNormalizationLayer)
{
var op = iter.Next() as Operation;
if (op.Name().Contains(DataNormLayerPrefix))
{
_hasNormalizationLayer = true;
}
}
}
catch (Exception ex)
{
throw new RuntimeException("Failed to load the model - check the inner exception for more details" + ex.Message);
}
}