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 InceptionClassifier(AssetManager assetManager)
{
labels.AddRange(ReadLabelsIntoMemory(assetManager, LABEL_FILE));
inferenceInterface = new TensorFlowInferenceInterface(assetManager, MODEL_FILE);
var operation = inferenceInterface.GraphOperation(OUTPUT_NAME);
var numClasses = operation.Output(0).Shape().Size(1);
//TODO: Confirm in Android, because my results are 1001 labels, 1008 classes
System.Console.WriteLine($"Read {labels.Count} labels, output layer size is {numClasses}");
var bmp = BitmapCreate(assetManager, "husky.png");
var results = Recognize(bmp);
}
public ImageClassifier()
{
var assets = Application.Context.Assets;
inferenceInterface = new TensorFlowInferenceInterface(assets, "model.pb");
_inputSize = (int)inferenceInterface.GraphOperation(InputName).Output(0).Shape().Size(1);
using (var sr = new StreamReader(assets.Open("labels.txt")))
{
var content = sr.ReadToEnd();
labels = content.Split('\n').Select(s => s.Trim()).Where(s => !string.IsNullOrEmpty(s)).ToList();
}
}
public async Task InitializeAsync(ModelType modelType, params string[] parameters)
{
var assets = Android.App.Application.Context.Assets;
var modelFile = $"file:///android_asset/{parameters[0]}";
var labelFile = parameters[1];
labels = new List <string>();
using var sr = new StreamReader(assets.Open(labelFile));
var content = await sr.ReadToEndAsync();
var labelStrings = content.Split(new[] { '\n' }, StringSplitOptions.RemoveEmptyEntries).Select(e => e.TrimEnd('\r'));
labels.AddRange(labelStrings);
inferenceInterface = new TensorFlowInferenceInterface(assets, modelFile);
inputSize = (int)inferenceInterface.GraphOperation(INPUT_NAME).Output(0).Shape().Size(1);
}
public async void Classification(byte[] bytes)
{
var assets = Application.Context.Assets;
var inferenceInterface = new TensorFlowInferenceInterface(assets, "model.pb");
var inputSize = (int)inferenceInterface.GraphOperation("Placeholder").Output(0).Shape().Size(1);
List <string> labels;
using (var streamReader = new StreamReader(assets.Open("labels.txt")))
{
labels = streamReader.ReadToEnd().Split('\n').Select(s => s.Trim()).Where(s => !string.IsNullOrEmpty(s))
.ToList();
}
var bitmap = await BitmapFactory.DecodeByteArrayAsync(bytes, 0, bytes.Length);
var resizedMap = Bitmap.CreateScaledBitmap(bitmap, inputSize, inputSize, false)
.Copy(Bitmap.Config.Argb8888, false);
var floatValues = new float[inputSize * inputSize * 3];
var intValues = new int[inputSize * inputSize];
resizedMap.GetPixels(intValues, 0, inputSize, 0, 0, inputSize, inputSize);
for (var i = 0; i < intValues.Length; ++i)
{
var intValue = intValues[i];
floatValues[i * 3 + 0] = (intValue & 0xFF) - 105f;
floatValues[i * 3 + 1] = ((intValue >> 8) & 0xFF) - 117f;
floatValues[i * 3 + 2] = ((intValue >> 16) & 0xFF) - 124f;
}
var operation = inferenceInterface.GraphOperation("loss");
var outputs = new float[labels.Count];
inferenceInterface.Feed("Placeholder", floatValues, 1, inputSize, inputSize, 3);
inferenceInterface.Run(new[] { "loss" }, true);
inferenceInterface.Fetch("loss", outputs);
var result = new Dictionary <string, float>();
for (var i = 0; i < labels.Count; i++)
{
var label = labels[i];
result.Add(label, outputs[i]);
}
var maxConf = 0f;
for (var i = 0; i < outputs.Length; ++i)
{
if (outputs[i] > maxConf)
{
maxConf = outputs[i];
}
}
ClassificationCompleted?.Invoke(this, new ClassificationEventArgs(result));
}
long OutputSize()
{
return(inferenceInterface.GraphOperation(OUTPUT_NAME).Output(0).Shape().Size(1));
}