firebase-ml-model-interpreter
程式庫 22.0.2 版引進了全新功能
getLatestModelFile()
方法,可取得自訂裝置上的位置資訊
我們來看評估分類模型成效時
的喚回度和精確度指標您可以使用這個方法直接將 TensorFlow Lite 執行個體化
Interpreter
物件,您可以使用該物件取代
FirebaseModelInterpreter
包裝函式。
這也是我們建議日後的方法。由於 TensorFlow Lite 翻譯版本不再與 Firebase 程式庫版本結合, 也能更靈活地升級至新版 TensorFlow Lite 或更輕鬆地自訂 TensorFlow Lite 版本
本頁說明如何從使用 FirebaseModelInterpreter
遷移至
TensorFlow Lite Interpreter
。
1. 更新專案依附元件
更新專案的依附元件,加入 22.0.2 版
firebase-ml-model-interpreter
程式庫 (或更新版本) 和 tensorflow-lite
程式庫:
更新前
implementation("com.google.firebase:firebase-ml-model-interpreter:22.0.1")
更新後
implementation("com.google.firebase:firebase-ml-model-interpreter:22.0.2")
implementation("org.tensorflow:tensorflow-lite:2.0.0")
2. 建立 TensorFlow Lite 解譯器,而非 FirebaseModelTranslateer
請在以下位置取得模型的位置,而不要建立 FirebaseModelInterpreter
並用來建立 TensorFlow LitegetLatestModelFile()
Interpreter
。
更新前
Kotlin+KTX
val remoteModel = FirebaseCustomRemoteModel.Builder("your_model").build()
val options = FirebaseModelInterpreterOptions.Builder(remoteModel).build()
val interpreter = FirebaseModelInterpreter.getInstance(options)
Java
FirebaseCustomRemoteModel remoteModel =
new FirebaseCustomRemoteModel.Builder("your_model").build();
FirebaseModelInterpreterOptions options =
new FirebaseModelInterpreterOptions.Builder(remoteModel).build();
FirebaseModelInterpreter interpreter = FirebaseModelInterpreter.getInstance(options);
更新後
Kotlin+KTX
val remoteModel = FirebaseCustomRemoteModel.Builder("your_model").build()
FirebaseModelManager.getInstance().getLatestModelFile(remoteModel)
.addOnCompleteListener { task ->
val modelFile = task.getResult()
if (modelFile != null) {
// Instantiate an org.tensorflow.lite.Interpreter object.
interpreter = Interpreter(modelFile)
}
}
Java
FirebaseCustomRemoteModel remoteModel =
new FirebaseCustomRemoteModel.Builder("your_model").build();
FirebaseModelManager.getInstance().getLatestModelFile(remoteModel)
.addOnCompleteListener(new OnCompleteListener<File>() {
@Override
public void onComplete(@NonNull Task<File> task) {
File modelFile = task.getResult();
if (modelFile != null) {
// Instantiate an org.tensorflow.lite.Interpreter object.
Interpreter interpreter = new Interpreter(modelFile);
}
}
});
3. 更新輸入和輸出準備程式碼
使用 FirebaseModelInterpreter
即可指定模型的輸入和輸出形狀
方法是將 FirebaseModelInputOutputOptions
物件傳遞至解譯器
每個階段執行時
使用 TensorFlow Lite 解譯器,請改為分配 ByteBuffer
物件
為模型輸入和輸出正確大小
舉例來說,如果模型的輸入形狀為 [1 224 224 3] float
值
以及 [1 1000] float
值的輸出形狀,請進行以下變更:
更新前
Kotlin+KTX
val inputOutputOptions = FirebaseModelInputOutputOptions.Builder()
.setInputFormat(0, FirebaseModelDataType.FLOAT32, intArrayOf(1, 224, 224, 3))
.setOutputFormat(0, FirebaseModelDataType.FLOAT32, intArrayOf(1, 1000))
.build()
val input = ByteBuffer.allocateDirect(224*224*3*4).order(ByteOrder.nativeOrder())
// Then populate with input data.
val inputs = FirebaseModelInputs.Builder()
.add(input)
.build()
interpreter.run(inputs, inputOutputOptions)
.addOnSuccessListener { outputs ->
// ...
}
.addOnFailureListener {
// Task failed with an exception.
// ...
}
Java
FirebaseModelInputOutputOptions inputOutputOptions =
new FirebaseModelInputOutputOptions.Builder()
.setInputFormat(0, FirebaseModelDataType.FLOAT32, new int[]{1, 224, 224, 3})
.setOutputFormat(0, FirebaseModelDataType.FLOAT32, new int[]{1, 1000})
.build();
float[][][][] input = new float[1][224][224][3];
// Then populate with input data.
FirebaseModelInputs inputs = new FirebaseModelInputs.Builder()
.add(input)
.build();
interpreter.run(inputs, inputOutputOptions)
.addOnSuccessListener(
new OnSuccessListener<FirebaseModelOutputs>() {
@Override
public void onSuccess(FirebaseModelOutputs result) {
// ...
}
})
.addOnFailureListener(
new OnFailureListener() {
@Override
public void onFailure(@NonNull Exception e) {
// Task failed with an exception
// ...
}
});
更新後
Kotlin+KTX
val inBufferSize = 1 * 224 * 224 * 3 * java.lang.Float.SIZE / java.lang.Byte.SIZE
val inputBuffer = ByteBuffer.allocateDirect(inBufferSize).order(ByteOrder.nativeOrder())
// Then populate with input data.
val outBufferSize = 1 * 1000 * java.lang.Float.SIZE / java.lang.Byte.SIZE
val outputBuffer = ByteBuffer.allocateDirect(outBufferSize).order(ByteOrder.nativeOrder())
interpreter.run(inputBuffer, outputBuffer)
Java
int inBufferSize = 1 * 224 * 224 * 3 * java.lang.Float.SIZE / java.lang.Byte.SIZE;
ByteBuffer inputBuffer =
ByteBuffer.allocateDirect(inBufferSize).order(ByteOrder.nativeOrder());
// Then populate with input data.
int outBufferSize = 1 * 1000 * java.lang.Float.SIZE / java.lang.Byte.SIZE;
ByteBuffer outputBuffer =
ByteBuffer.allocateDirect(outBufferSize).order(ByteOrder.nativeOrder());
interpreter.run(inputBuffer, outputBuffer);
4. 更新輸出處理程式碼
最後,而不是使用 FirebaseModelOutputs
取得模型的輸出內容
物件的 getOutput()
方法,將 ByteBuffer
輸出內容轉換為
方便您達成目標
舉例來說,如果要分類,您可能會進行 包括:
更新前
Kotlin+KTX
val output = result.getOutput(0)
val probabilities = output[0]
try {
val reader = BufferedReader(InputStreamReader(assets.open("custom_labels.txt")))
for (probability in probabilities) {
val label: String = reader.readLine()
println("$label: $probability")
}
} catch (e: IOException) {
// File not found?
}
Java
float[][] output = result.getOutput(0);
float[] probabilities = output[0];
try {
BufferedReader reader = new BufferedReader(
new InputStreamReader(getAssets().open("custom_labels.txt")));
for (float probability : probabilities) {
String label = reader.readLine();
Log.i(TAG, String.format("%s: %1.4f", label, probability));
}
} catch (IOException e) {
// File not found?
}
更新後
Kotlin+KTX
modelOutput.rewind()
val probabilities = modelOutput.asFloatBuffer()
try {
val reader = BufferedReader(
InputStreamReader(assets.open("custom_labels.txt")))
for (i in probabilities.capacity()) {
val label: String = reader.readLine()
val probability = probabilities.get(i)
println("$label: $probability")
}
} catch (e: IOException) {
// File not found?
}
Java
modelOutput.rewind();
FloatBuffer probabilities = modelOutput.asFloatBuffer();
try {
BufferedReader reader = new BufferedReader(
new InputStreamReader(getAssets().open("custom_labels.txt")));
for (int i = 0; i < probabilities.capacity(); i++) {
String label = reader.readLine();
float probability = probabilities.get(i);
Log.i(TAG, String.format("%s: %1.4f", label, probability));
}
} catch (IOException e) {
// File not found?
}