從舊版自訂模型 API 遷移
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
firebase-ml-model-interpreter
程式庫 22.0.2 版推出新的 getLatestModelFile()
方法,可取得裝置上自訂模型的位置。您可以使用這個方法直接例項化 TensorFlow Lite Interpreter
物件,取代 FirebaseModelInterpreter
包裝函式。
今後建議採用這種做法。由於 TensorFlow Lite 解譯器版本不再與 Firebase 程式庫版本連結,因此您可以更彈性地升級至新版 TensorFlow Lite,或更輕鬆地使用自訂 TensorFlow Lite 建構版本。
本頁面說明如何從使用 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 firebase-ml-model-interpreter
程式庫 22.0.2 版 (或更新版本) 和 tensorflow-lite
程式庫:
之前
implementation("com.google.firebase:firebase-ml-model-interpreter:22.0.1")
晚於
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 FirebaseModelInterpreter
請勿建立 FirebaseModelInterpreter
,而是使用 getLatestModelFile()
取得模型在裝置上的位置,並用來建立 TensorFlow Lite Interpreter
。
之前
Kotlin
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);
晚於
Kotlin
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);
}
}
});
使用 FirebaseModelInterpreter
時,您可以在執行解譯器時,將 FirebaseModelInputOutputOptions
物件傳遞至解譯器,藉此指定模型的輸入和輸出形狀。
如果是 TensorFlow Lite 解譯器,您要改為為模型的輸入和輸出分配適當大小的 ByteBuffer
物件。
舉例來說,如果模型的輸入形狀為 [1 224 224 3] float
值,輸出形狀為 [1 1000] float
值,請進行下列變更:
之前
Kotlin
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
// ...
}
});
晚於
Kotlin
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,請勿使用 FirebaseModelOutputs
物件的 getOutput()
方法取得模型輸出內容,而是將 ByteBuffer
輸出內容轉換為適合您用途的結構。
舉例來說,如果您要進行分類,可以進行下列變更:
之前
Kotlin
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: $probability")
}
} catch (e: IOException) {
// File not found?
}
Java
float[][] output = result.getOutput(0);
float[] probabilities = output[0];
try {
BufferedReader reader = new BufferedReader(
new InputStreamReader(getAssets().open("custom_labels.txt")));
for (float probability : probabilities) {
String label = reader.readLine();
Log.i(TAG, String.format("%s: %1.4f", label, probability));
}
} catch (IOException e) {
// File not found?
}
晚於
Kotlin
modelOutput.rewind()
val probabilities = modelOutput.asFloatBuffer()
try {
val reader = BufferedReader(
InputStreamReader(assets.open("custom_labels.txt")))
for (i in probabilities.capacity()) {
val label: String = reader.readLine()
val probability = probabilities.get(i)
println("$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?
}
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上次更新時間:2025-07-25 (世界標準時間)。
[null,null,["上次更新時間:2025-07-25 (世界標準時間)。"],[],[],null,["\u003cbr /\u003e\n\nVersion 22.0.2 of the `firebase-ml-model-interpreter` library introduces a new\n`getLatestModelFile()` method, which gets the location on the device of custom\nmodels. You can use this method to directly instantiate a TensorFlow Lite\n`Interpreter` object, which you can use instead of the\n`FirebaseModelInterpreter` wrapper.\n\nGoing forward, this is the preferred approach. Because the TensorFlow Lite\ninterpreter version is no longer coupled with the Firebase library version, you\nhave more flexibility to upgrade to new versions of TensorFlow Lite when you\nwant, or more easily use custom TensorFlow Lite builds.\n\nThis page shows how you can migrate from using `FirebaseModelInterpreter` to the\nTensorFlow Lite `Interpreter`.\n\n1. Update project dependencies\n\nUpdate your project's dependencies to include version 22.0.2 of the\n`firebase-ml-model-interpreter` library (or newer) and the `tensorflow-lite`\nlibrary:\n\nBefore \n\n implementation(\"com.google.firebase:firebase-ml-model-interpreter:22.0.1\")\n\nAfter \n\n implementation(\"com.google.firebase:firebase-ml-model-interpreter:22.0.2\")\n implementation(\"org.tensorflow:tensorflow-lite:2.0.0\")\n\n2. Create a TensorFlow Lite interpreter instead of a FirebaseModelInterpreter\n\nInstead of creating a `FirebaseModelInterpreter`, get the model's location on\ndevice with `getLatestModelFile()` and use it to create a TensorFlow Lite\n`Interpreter`.\n\nBefore \n\nKotlin \n\n val remoteModel = FirebaseCustomRemoteModel.Builder(\"your_model\").build()\n val options = FirebaseModelInterpreterOptions.Builder(remoteModel).build()\n val interpreter = FirebaseModelInterpreter.getInstance(options)\n\nJava \n\n FirebaseCustomRemoteModel remoteModel =\n new FirebaseCustomRemoteModel.Builder(\"your_model\").build();\n FirebaseModelInterpreterOptions options =\n new FirebaseModelInterpreterOptions.Builder(remoteModel).build();\n FirebaseModelInterpreter interpreter = FirebaseModelInterpreter.getInstance(options);\n\nAfter \n\nKotlin \n\n val remoteModel = FirebaseCustomRemoteModel.Builder(\"your_model\").build()\n FirebaseModelManager.getInstance().getLatestModelFile(remoteModel)\n .addOnCompleteListener { task -\u003e\n val modelFile = task.getResult()\n if (modelFile != null) {\n // Instantiate an org.tensorflow.lite.Interpreter object.\n interpreter = Interpreter(modelFile)\n }\n }\n\nJava \n\n FirebaseCustomRemoteModel remoteModel =\n new FirebaseCustomRemoteModel.Builder(\"your_model\").build();\n FirebaseModelManager.getInstance().getLatestModelFile(remoteModel)\n .addOnCompleteListener(new OnCompleteListener\u003cFile\u003e() {\n @Override\n public void onComplete(@NonNull Task\u003cFile\u003e task) {\n File modelFile = task.getResult();\n if (modelFile != null) {\n // Instantiate an org.tensorflow.lite.Interpreter object.\n Interpreter interpreter = new Interpreter(modelFile);\n }\n }\n });\n\n3. Update input and output preparation code\n\nWith `FirebaseModelInterpreter`, you specify the model's input and output shapes\nby passing a `FirebaseModelInputOutputOptions` object to the interpreter when\nyou run it.\n\nFor the TensorFlow Lite interpreter, you instead allocate `ByteBuffer` objects\nwith the right size for your model's input and output.\n\nFor example, if your model has an input shape of \\[1 224 224 3\\] `float` values\nand an output shape of \\[1 1000\\] `float` values, make these changes:\n\nBefore \n\nKotlin \n\n val inputOutputOptions = FirebaseModelInputOutputOptions.Builder()\n .setInputFormat(0, FirebaseModelDataType.FLOAT32, intArrayOf(1, 224, 224, 3))\n .setOutputFormat(0, FirebaseModelDataType.FLOAT32, intArrayOf(1, 1000))\n .build()\n\n val input = ByteBuffer.allocateDirect(224*224*3*4).order(ByteOrder.nativeOrder())\n // Then populate with input data.\n\n val inputs = FirebaseModelInputs.Builder()\n .add(input)\n .build()\n\n interpreter.run(inputs, inputOutputOptions)\n .addOnSuccessListener { outputs -\u003e\n // ...\n }\n .addOnFailureListener {\n // Task failed with an exception.\n // ...\n }\n\nJava \n\n FirebaseModelInputOutputOptions inputOutputOptions =\n new FirebaseModelInputOutputOptions.Builder()\n .setInputFormat(0, FirebaseModelDataType.FLOAT32, new int[]{1, 224, 224, 3})\n .setOutputFormat(0, FirebaseModelDataType.FLOAT32, new int[]{1, 1000})\n .build();\n\n float[][][][] input = new float[1][224][224][3];\n // Then populate with input data.\n\n FirebaseModelInputs inputs = new FirebaseModelInputs.Builder()\n .add(input)\n .build();\n\n interpreter.run(inputs, inputOutputOptions)\n .addOnSuccessListener(\n new OnSuccessListener\u003cFirebaseModelOutputs\u003e() {\n @Override\n public void onSuccess(FirebaseModelOutputs result) {\n // ...\n }\n })\n .addOnFailureListener(\n new OnFailureListener() {\n @Override\n public void onFailure(@NonNull Exception e) {\n // Task failed with an exception\n // ...\n }\n });\n\nAfter \n\nKotlin \n\n val inBufferSize = 1 * 224 * 224 * 3 * java.lang.Float.SIZE / java.lang.Byte.SIZE\n val inputBuffer = ByteBuffer.allocateDirect(inBufferSize).order(ByteOrder.nativeOrder())\n // Then populate with input data.\n\n val outBufferSize = 1 * 1000 * java.lang.Float.SIZE / java.lang.Byte.SIZE\n val outputBuffer = ByteBuffer.allocateDirect(outBufferSize).order(ByteOrder.nativeOrder())\n\n interpreter.run(inputBuffer, outputBuffer)\n\nJava \n\n int inBufferSize = 1 * 224 * 224 * 3 * java.lang.Float.SIZE / java.lang.Byte.SIZE;\n ByteBuffer inputBuffer =\n ByteBuffer.allocateDirect(inBufferSize).order(ByteOrder.nativeOrder());\n // Then populate with input data.\n\n int outBufferSize = 1 * 1000 * java.lang.Float.SIZE / java.lang.Byte.SIZE;\n ByteBuffer outputBuffer =\n ByteBuffer.allocateDirect(outBufferSize).order(ByteOrder.nativeOrder());\n\n interpreter.run(inputBuffer, outputBuffer);\n\n4. Update output handling code\n\nFinally, instead of getting the model's output with the `FirebaseModelOutputs`\nobject's `getOutput()` method, convert the `ByteBuffer` output to whatever\nstructure is convenient for your use case.\n\nFor example, if you're doing classification, you might make changes like the\nfollowing:\n\nBefore \n\nKotlin \n\n val output = result.getOutput(0)\n val probabilities = output[0]\n try {\n val reader = BufferedReader(InputStreamReader(assets.open(\"custom_labels.txt\")))\n for (probability in probabilities) {\n val label: String = reader.readLine()\n println(\"$label: $probability\")\n }\n } catch (e: IOException) {\n // File not found?\n }\n\nJava \n\n float[][] output = result.getOutput(0);\n float[] probabilities = output[0];\n try {\n BufferedReader reader = new BufferedReader(\n new InputStreamReader(getAssets().open(\"custom_labels.txt\")));\n for (float probability : probabilities) {\n String label = reader.readLine();\n Log.i(TAG, String.format(\"%s: %1.4f\", label, probability));\n }\n } catch (IOException e) {\n // File not found?\n }\n\nAfter \n\nKotlin \n\n modelOutput.rewind()\n val probabilities = modelOutput.asFloatBuffer()\n try {\n val reader = BufferedReader(\n InputStreamReader(assets.open(\"custom_labels.txt\")))\n for (i in probabilities.capacity()) {\n val label: String = reader.readLine()\n val probability = probabilities.get(i)\n println(\"$label: $probability\")\n }\n } catch (e: IOException) {\n // File not found?\n }\n\nJava \n\n modelOutput.rewind();\n FloatBuffer probabilities = modelOutput.asFloatBuffer();\n try {\n BufferedReader reader = new BufferedReader(\n new InputStreamReader(getAssets().open(\"custom_labels.txt\")));\n for (int i = 0; i \u003c probabilities.capacity(); i++) {\n String label = reader.readLine();\n float probability = probabilities.get(i);\n Log.i(TAG, String.format(\"%s: %1.4f\", label, probability));\n }\n } catch (IOException e) {\n // File not found?\n }"]]