adding some basic linting - prepping support for multiple models being loaded in by the app.

.gitignore

@@ -45,3 +45,5 @@ app.*.map.json
 /android/app/debug
 /android/app/profile
 /android/app/release
+/assets/mobilenetv2_gpu.tflite
+/assets/mobilenetv2_gpu.tflite

analysis_options.yaml

@@ -22,8 +22,8 @@ linter:
   # `// ignore_for_file: name_of_lint` syntax on the line or in the file
   # producing the lint.
   rules:
-    # avoid_print: false  # Uncomment to disable the `avoid_print` rule
+     avoid_print: true  # Uncomment to disable the `avoid_print` rule
-    # prefer_single_quotes: true  # Uncomment to enable the `prefer_single_quotes` rule
+     prefer_single_quotes: true  # Uncomment to enable the `prefer_single_quotes` rule
 
 # Additional information about this file can be found at
 # https://dart.dev/guides/language/analysis-options

android/app/src/main/AndroidManifest.xml

@@ -25,7 +25,7 @@
                 <category android:name="android.intent.category.LAUNCHER"/>
             </intent-filter>
         </activity>
-        <!-- Don't delete the meta-data below.
+        <!-- Don't delete the meta-outputs below.
              This is used by the Flutter tool to generate GeneratedPluginRegistrant.java -->
         <meta-data
             android:name="flutterEmbedding"

lib/main.dart

@@ -12,7 +12,7 @@ class MyApp extends StatelessWidget {
   // This widget is the root of your application.
   @override
   Widget build(BuildContext context) {
-    logger.i("Building main app");
+    logger.i('Building main app');
     return MaterialApp(
       title: 'Tensordex',
       theme: ThemeData(

lib/tflite/classifier.dart

@@ -1,21 +1,23 @@
+import 'dart:math';
+
 import 'package:collection/collection.dart';
 import 'package:image/image.dart' as image_lib;
 import 'package:tflite_flutter/tflite_flutter.dart';
 import 'package:tflite_flutter_helper/tflite_flutter_helper.dart';
 
+import 'model/outputs/recognition.dart';
 import '../utils/logger.dart';
-import 'data/recognition.dart';
+import 'model/outputs/stats.dart';
-import 'data/stats.dart';
 
 /// Classifier
 class Classifier {
-  static const String modelFileName = "efficientnet_v2s.tflite";
+  static const String modelFileName = 'efficientnet_v2s.tflite';
   static const int inputSize = 224;
 
   /// [ImageProcessor] used to pre-process the image
   ImageProcessor? imageProcessor;
 
-  ///Tensor image to move image data into
+  ///Tensor image to move image outputs into
   late TensorImage _inputImage;
 
   /// Instance of Interpreter
@@ -30,55 +32,50 @@ class Classifier {
   late List<String> _labels;
   int classifierCreationStart = -1;
 
-  Classifier({
+  Classifier(
-    Interpreter? interpreter,
+    Interpreter interpreter, {
     List<String>? labels,
   }) {
-    loadModel(interpreter: interpreter);
+    loadModel(interpreter);
     loadLabels(labels: labels);
   }
 
   /// Loads interpreter from asset
-  void loadModel({Interpreter? interpreter}) async {
+  void loadModel(Interpreter interpreter) async {
     try {
-      _interpreter = interpreter ??
+      _interpreter = interpreter;
-          await Interpreter.fromAsset(
-            modelFileName,
-            options: InterpreterOptions()..threads = 8,
-          );
       var outputTensor = _interpreter.getOutputTensor(0);
       var outputShape = outputTensor.shape;
       _outputType = outputTensor.type;
       var inputTensor = _interpreter.getInputTensor(0);
-      // var intputShape = inputTensor.shape;
       _inputType = inputTensor.type;
       _inputImage = TensorImage(_inputType);
       _outputBuffer = TensorBuffer.createFixedSize(outputShape, _outputType);
       _outputProcessor =
           TensorProcessorBuilder().add(NormalizeOp(0, 1)).build();
     } catch (e) {
-      logger.e("Error while creating interpreter: ", e);
+      logger.e('Error while creating interpreter: ', e);
     }
   }
 
   /// Loads labels from assets
   void loadLabels({List<String>? labels}) async {
     try {
-      _labels = labels ?? await FileUtil.loadLabels("assets/labels.txt");
+      _labels = labels ?? await FileUtil.loadLabels('assets/labels.txt');
     } catch (e) {
-      logger.e("Error while loading labels: $e");
+      logger.e('Error while loading labels: $e');
     }
   }
 
   /// Pre-process the image
   TensorImage? getProcessedImage(TensorImage? inputImage) {
-    // padSize = max(inputImage.height, inputImage.width);
+    int cropSize = min(_inputImage.height, _inputImage.width);
     if (inputImage != null) {
       imageProcessor ??= ImageProcessorBuilder()
-          .add(ResizeWithCropOrPadOp(224, 224))
+          .add(ResizeWithCropOrPadOp(cropSize, cropSize))
           .add(ResizeOp(inputSize, inputSize, ResizeMethod.BILINEAR))
           .add(NormalizeOp(0, 1))
-          // .add(NormalizeOp(127.5, 127.5))
+          // .add(NormalizeOp(127.5, 127.5)) // photo vs quant normalization
           .build();
       return imageProcessor?.process(inputImage);
     }
@@ -102,8 +99,8 @@ class Classifier {
         .toList();
     var endTime = DateTime.now().millisecondsSinceEpoch;
     return {
-      "recognitions": predictions,
+      'recognitions': predictions,
-      "stats": Stats(
+      'stats': Stats(
         totalTime: endTime - preProcStart,
         preProcessingTime: inferenceStart - preProcStart,
         inferenceTime: postProcStart - inferenceStart,

lib/tflite/ml_isolate.dart

@@ -11,7 +11,7 @@ class IsolateBase {
 }
 
 class MLIsolate extends IsolateBase {
-  static const String debugIsolate = "MLIsolate";
+  static const String debugIsolate = 'MLIsolate';
   late SendPort _sendPort;
 
   SendPort get sendPort => _sendPort;
@@ -34,19 +34,18 @@ class MLIsolate extends IsolateBase {
       var converted = ImageUtils.convertCameraImage(cameraImage);
       if (converted != null) {
         Classifier classifier = Classifier(
-            interpreter:
+            Interpreter.fromAddress(mlIsolateData.interpreterAddress),
-                Interpreter.fromAddress(mlIsolateData.interpreterAddress),
             labels: mlIsolateData.labels);
         var result = classifier.predict(converted);
         mlIsolateData.responsePort?.send(result);
       } else {
-        mlIsolateData.responsePort?.send({"response": "not working yet"});
+        mlIsolateData.responsePort?.send({'response': 'not working yet'});
       }
     }
   }
 }
 
-/// Bundles data to pass between Isolate
+/// Bundles outputs to pass between Isolate
 class MLIsolateData {
   CameraImage cameraImage;
   int interpreterAddress;

lib/tflite/model/configuration.dart

@@ -0,0 +1,16 @@
+import 'package:tflite_flutter/tflite_flutter.dart';
+import 'constants.dart';
+
+class ModelConfiguration{
+  String name;
+  late List<InterpreterOptions> interpreters;
+
+  ModelConfiguration(this.name){
+    interpreters = name.contains('gpu') ? ModelConstants.gpuInterpreterList : ModelConstants.cpuInterpreterList;
+  }
+
+  @override
+  String toString() {
+    return 'ModelConfiguration(name: $name, interpreters: $interpreters)';
+  }
+}

lib/tflite/model/constants.dart

@@ -0,0 +1,10 @@
+import 'package:tflite_flutter/tflite_flutter.dart';
+
+
+class ModelConstants {
+  static final InterpreterOptions _npuConfig = InterpreterOptions()..threads = 8..useNnApiForAndroid = true..useMetalDelegateForIOS = true;
+  static final InterpreterOptions _cpuConfig = InterpreterOptions()..threads = 8;
+  static final List<InterpreterOptions> gpuInterpreterList = [_npuConfig, _cpuConfig];
+  static final List<InterpreterOptions> cpuInterpreterList = [_cpuConfig];
+}
+

lib/widgets/poke_finder.dart

@@ -1,16 +1,19 @@
+import 'dart:convert';
 import 'dart:isolate';
 
 import 'package:camera/camera.dart';
 import 'package:flutter/material.dart';
+import 'package:flutter/services.dart';
 import 'package:tensordex_mobile/tflite/ml_isolate.dart';
+import 'package:tensordex_mobile/tflite/model/configuration.dart';
+import 'package:tensordex_mobile/tflite/model/outputs/stats.dart';
 import 'package:tflite_flutter/tflite_flutter.dart';
 
 import '../tflite/classifier.dart';
+import '../tflite/model/outputs/recognition.dart';
 import '../utils/logger.dart';
-import '../tflite/data/recognition.dart';
-import '../tflite/data/stats.dart';
 
-/// [PokeFinder] sends each frame for inference
+
 class PokeFinder extends StatefulWidget {
   /// Callback to pass results after inference to [HomeView]
   final Function(List<Recognition> recognitions) resultsCallback;
@@ -28,17 +31,20 @@ class PokeFinder extends StatefulWidget {
 }
 
 class _PokeFinderState extends State<PokeFinder> with WidgetsBindingObserver {
-  late List<CameraDescription> cameras;
-  late CameraController cameraController;
-  late MLIsolate _mlIsolate;
-
   /// true when inference is ongoing
   bool predicting = false;
   bool _cameraInitialized = false;
   bool _classifierInitialized = false;
 
+  //cameras
+  late List<CameraDescription> cameras;
+  late CameraController cameraController;
+
+  //ml variables
   late Interpreter interpreter;
   late Classifier classifier;
+  late MLIsolate _mlIsolate;
+  late List<ModelConfiguration> modelConfigurations;
 
   @override
   void initState() {
@@ -55,19 +61,34 @@ class _PokeFinderState extends State<PokeFinder> with WidgetsBindingObserver {
     predicting = false;
   }
 
+  Future<List<String>> getModelFiles() async {
+    final manifestContent = await rootBundle.loadString('AssetManifest.jsn');
+    final Map<String, dynamic> manifestMap = json.decode(manifestContent);
+    return manifestMap.keys
+        .where((String key) => key.contains('.tflite'))
+        .map((String key) => key.substring(7))
+        .toList();
+  }
+
   void initializeModel() async {
-    var interpreterOptions = InterpreterOptions()..threads = 8;
+    var modelFiles = await getModelFiles();
-    interpreter = await Interpreter.fromAsset('efficientnet_v2s.tflite',
+    var modelConfigurations =
-        options: interpreterOptions);
+        modelFiles.map((e) => ModelConfiguration(e)).toList();
-    classifier = Classifier(interpreter: interpreter);
+    var currentConfig = modelConfigurations[0];
+    logger.i(modelFiles);
+    interpreter = await createInterpreter(currentConfig);
+    classifier = Classifier(interpreter);
     _classifierInitialized = true;
   }
 
+  Future<Interpreter> createInterpreter(ModelConfiguration config) async {
+    return await Interpreter.fromAsset(config.name,
+        options: config.interpreters[0]);
+  }
+
   /// Initializes the camera by setting [cameraController]
   void initializeCamera() async {
     cameras = await availableCameras();
-
-    // cameras[0] for rear-camera
     cameraController =
         CameraController(cameras[0], ResolutionPreset.low, enableAudio: false);
 
@@ -94,11 +115,11 @@ class _PokeFinderState extends State<PokeFinder> with WidgetsBindingObserver {
       var results = await inference(MLIsolateData(
           cameraImage, classifier.interpreter.address, classifier.labels));
 
-      if (results.containsKey("recognitions")) {
+      if (results.containsKey('recognitions')) {
-        widget.resultsCallback(results["recognitions"]);
+        widget.resultsCallback(results['recognitions']);
       }
-      if (results.containsKey("stats")) {
+      if (results.containsKey('stats')) {
-        widget.statsCallback(results["stats"]);
+        widget.statsCallback(results['stats']);
       }
       logger.i(results);
 

lib/widgets/results.dart

@@ -1,7 +1,7 @@
 import 'package:flutter/material.dart';
 import 'package:tensordex_mobile/widgets/poke_finder.dart';
-import 'package:tensordex_mobile/tflite/data/recognition.dart';
+import '../tflite/model/outputs/recognition.dart';
-import 'package:tensordex_mobile/tflite/data/stats.dart';
+import '../tflite/model/outputs/stats.dart';
 
 
 /// [PokeFinder] sends each frame for inference

lib/widgets/tensordex_home.dart

@@ -1,10 +1,10 @@
 import 'package:flutter/material.dart';
+import 'package:tensordex_mobile/tflite/model/outputs/recognition.dart';
+import 'package:tensordex_mobile/tflite/model/outputs/stats.dart';
 import 'package:tensordex_mobile/widgets/poke_finder.dart';
 import 'package:tensordex_mobile/widgets/results.dart';
 
 import '../utils/logger.dart';
-import '../tflite/data/recognition.dart';
-import '../tflite/data/stats.dart';
 
 class TensordexHome extends StatefulWidget {
   const TensordexHome({Key? key, required this.title}) : super(key: key);
@@ -22,7 +22,7 @@ class TensordexHome extends StatefulWidget {
 
 class _TensordexHomeState extends State<TensordexHome> {
   /// Results from the image classifier
-  List<Recognition> results = [Recognition(1, "NOTHING DETECTED", .5)];
+  List<Recognition> results = [Recognition(1, 'NOTHING DETECTED', .5)];
   Stats stats = Stats();
 
   /// Scaffold Key
@@ -30,7 +30,7 @@ class _TensordexHomeState extends State<TensordexHome> {
 
   void _incrementCounter() {
     setState(() {
-      logger.d("Counter Incremented!");
+      logger.d('Counter Incremented!');
     });
   }