Firebase/MLModelInterpreter 程式庫的 0.20.0 版推出了新的 getLatestModelFilePath() 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將 TensorFlow Lite Interpreter 物件例項化，以便取代 Firebase 的 ModelInterpreter 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 ModelInterpreter 遷移至
TensorFlow Lite Interpreter。
1. 更新專案依附元件
更新專案的 Podfile，加入 Firebase/MLModelInterpreter 程式庫的 0.20.0 以上版本 (或更高版本) 和 TensorFlow Lite 程式庫：
早於
Swift
pod 'Firebase/MLModelInterpreter', '0.19.0'
Objective-C
pod 'Firebase/MLModelInterpreter', '0.19.0'
晚於
Swift
pod 'Firebase/MLModelInterpreter', '~> 0.20.0'
pod 'TensorFlowLiteSwift'
Objective-C
pod 'Firebase/MLModelInterpreter', '~> 0.20.0'
pod 'TensorFlowLiteObjC'
2. 建立 TensorFlow Lite 解譯器，而非 Firebase Model 解譯器
請不要建立 Firebase ModelInterpreter，而是使用 getLatestModelFilePath() 取得裝置上模型的位置，並使用該位置建立 TensorFlow Lite Interpreter。
早於
Swift
let remoteModel = CustomRemoteModel(
name: "your_remote_model" // The name you assigned in the Firebase console.
)
interpreter = ModelInterpreter.modelInterpreter(remoteModel: remoteModel)
Objective-C
// Initialize using the name you assigned in the Firebase console.
FIRCustomRemoteModel *remoteModel =
[[FIRCustomRemoteModel alloc] initWithName:@"your_remote_model"];
interpreter = [FIRModelInterpreter modelInterpreterForRemoteModel:remoteModel];
晚於
Swift
let remoteModel = CustomRemoteModel(
name: "your_remote_model" // The name you assigned in the Firebase console.
)
ModelManager.modelManager().getLatestModelFilePath(remoteModel) { (remoteModelPath, error) in
guard error == nil, let remoteModelPath = remoteModelPath else { return }
do {
interpreter = try Interpreter(modelPath: remoteModelPath)
} catch {
// Error?
}
}
Objective-C
FIRCustomRemoteModel *remoteModel =
[[FIRCustomRemoteModel alloc] initWithName:@"your_remote_model"];
[[FIRModelManager modelManager] getLatestModelFilePath:remoteModel
completion:^(NSString * _Nullable filePath,
NSError * _Nullable error) {
if (error != nil || filePath == nil) { return; }
NSError *tfError = nil;
interpreter = [[TFLInterpreter alloc] initWithModelPath:filePath error:&tfError];
}];
3. 更新輸入和輸出準備程式碼
使用 ModelInterpreter 即可指定模型的輸入和輸出形狀
方法是在執行期間將 ModelInputOutputOptions 物件傳遞至解譯器
基礎架構
使用 TensorFlow Lite 解譯器時，您應呼叫 allocateTensors() 以
分配空間給模型的輸入和輸出內容，然後將輸入資料
輸入張量
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：
早於
Swift
let ioOptions = ModelInputOutputOptions()
do {
try ioOptions.setInputFormat(
index: 0,
type: .float32,
dimensions: [1, 224, 224, 3]
)
try ioOptions.setOutputFormat(
index: 0,
type: .float32,
dimensions: [1, 1000]
)
} catch let error as NSError {
print("Failed to set input or output format with error: \(error.localizedDescription)")
}
let inputs = ModelInputs()
do {
let inputData = Data()
// Then populate with input data.
try inputs.addInput(inputData)
} catch let error {
print("Failed to add input: \(error)")
}
interpreter.run(inputs: inputs, options: ioOptions) { outputs, error in
guard error == nil, let outputs = outputs else { return }
// Process outputs
// ...
}
Objective-C
FIRModelInputOutputOptions *ioOptions = [[FIRModelInputOutputOptions alloc] init];
NSError *error;
[ioOptions setInputFormatForIndex:0
type:FIRModelElementTypeFloat32
dimensions:@[@1, @224, @224, @3]
error:&error];
if (error != nil) { return; }
[ioOptions setOutputFormatForIndex:0
type:FIRModelElementTypeFloat32
dimensions:@[@1, @1000]
error:&error];
if (error != nil) { return; }
FIRModelInputs *inputs = [[FIRModelInputs alloc] init];
NSMutableData *inputData = [[NSMutableData alloc] initWithCapacity:0];
// Then populate with input data.
[inputs addInput:inputData error:&error];
if (error != nil) { return; }
[interpreter runWithInputs:inputs
options:ioOptions
completion:^(FIRModelOutputs * _Nullable outputs,
NSError * _Nullable error) {
if (error != nil || outputs == nil) {
return;
}
// Process outputs
// ...
}];
晚於
Swift
do {
try interpreter.allocateTensors()
let inputData = Data()
// Then populate with input data.
try interpreter.copy(inputData, toInputAt: 0)
try interpreter.invoke()
} catch let err {
print(err.localizedDescription)
}
Objective-C
NSError *error = nil;
[interpreter allocateTensorsWithError:&error];
if (error != nil) { return; }
TFLTensor *input = [interpreter inputTensorAtIndex:0 error:&error];
if (error != nil) { return; }
NSMutableData *inputData = [[NSMutableData alloc] initWithCapacity:0];
// Then populate with input data.
[input copyData:inputData error:&error];
if (error != nil) { return; }
[interpreter invokeWithError:&error];
if (error != nil) { return; }
4. 更新輸出處理程式碼
最後，請不要使用 ModelOutputs 物件的 output() 方法取得模型輸出內容，而是從轉譯器取得輸出張量，並將其資料轉換為適合用途的結構。
舉例來說，如果您要進行分類，可能會進行下列變更：
早於
Swift
let output = try? outputs.output(index: 0) as? [[NSNumber]]
let probabilities = output?[0]
guard let labelPath = Bundle.main.path(
forResource: "custom_labels",
ofType: "txt"
) else { return }
let fileContents = try? String(contentsOfFile: labelPath)
guard let labels = fileContents?.components(separatedBy: "\n") else { return }
for i in 0 ..< labels.count {
if let probability = probabilities?[i] {
print("\(labels[i]): \(probability)")
}
}
Objective-C
// Get first and only output of inference with a batch size of 1
NSError *error;
NSArray *probabilites = [outputs outputAtIndex:0 error:&error][0];
if (error != nil) { return; }
NSString *labelPath = [NSBundle.mainBundle pathForResource:@"retrained_labels"
ofType:@"txt"];
NSString *fileContents = [NSString stringWithContentsOfFile:labelPath
encoding:NSUTF8StringEncoding
error:&error];
if (error != nil || fileContents == NULL) { return; }
NSArray<NSString *> *labels = [fileContents componentsSeparatedByString:@"\n"];
for (int i = 0; i < labels.count; i++) {
NSString *label = labels[i];
NSNumber *probability = probabilites[i];
NSLog(@"%@: %f", label, probability.floatValue);
}
晚於
Swift
do {
// After calling interpreter.invoke():
let output = try interpreter.output(at: 0)
let probabilities =
UnsafeMutableBufferPointer<Float32>.allocate(capacity: 1000)
output.data.copyBytes(to: probabilities)
guard let labelPath = Bundle.main.path(
forResource: "custom_labels",
ofType: "txt"
) else { return }
let fileContents = try? String(contentsOfFile: labelPath)
guard let labels = fileContents?.components(separatedBy: "\n") else { return }
for i in labels.indices {
print("\(labels[i]): \(probabilities[i])")
}
} catch let err {
print(err.localizedDescription)
}
Objective-C
NSError *error = nil;
TFLTensor *output = [interpreter outputTensorAtIndex:0 error:&error];
if (error != nil) { return; }
NSData *outputData = [output dataWithError:&error];
if (error != nil) { return; }
Float32 probabilities[outputData.length / 4];
[outputData getBytes:&probabilities length:outputData.length];
NSString *labelPath = [NSBundle.mainBundle pathForResource:@"custom_labels"
ofType:@"txt"];
NSString *fileContents = [NSString stringWithContentsOfFile:labelPath
encoding:NSUTF8StringEncoding
error:&error];
if (error != nil || fileContents == nil) { return; }
NSArray<NSString *> *labels = [fileContents componentsSeparatedByString:@"\n"];
for (int i = 0; i < labels.count; i++) {
NSLog(@"%@: %f", labels[i], probabilities[i]);
}