Android E/ModelDownloadManager:模型与TFLite不兼容，且应用程序未升级，请勿下载

我尝试使用Firebase ML将我的模型部署到我的Android应用程序。我遵循了以下教程：。但该模型始终无法在应用程序中下载，在日志中我有以下错误：E/ModelDownloadManager:该模型与TFLite不兼容，且该应用程序未升级，请勿下载 我用于模型下载的代码是：

private fun setupClassifier() {
        configureRemoteConfig()
        remoteConfig.fetchAndActivate()
            .addOnCompleteListener { task ->
                if (task.isSuccessful) {
                    val modelName = remoteConfig.getString("model_name")
                    val downloadTrace = firebasePerformance.newTrace("download_model")
                    downloadTrace.start()
                    downloadModel(modelName)
                        .addOnSuccessListener {
                            downloadTrace.stop()
                        }
                } else {
                    showToast("Failed to fetch model name.")
                }
            }
    }

    private fun configureRemoteConfig() {
        remoteConfig = Firebase.remoteConfig
        val configSettings = remoteConfigSettings {
            minimumFetchIntervalInSeconds = 3600
        }
        remoteConfig.setConfigSettingsAsync(configSettings)
    }

    private fun downloadModel(modelName: String): Task<Void> {
        val remoteModel = FirebaseCustomRemoteModel.Builder(modelName).build()
        val firebaseModelManager = FirebaseModelManager.getInstance()
        return firebaseModelManager
            .isModelDownloaded(remoteModel)
            .continueWithTask { task ->
                // Create update condition if model is already downloaded, otherwise create download
                // condition.
                val conditions = if (task.result != null && task.result == true) {
                    FirebaseModelDownloadConditions.Builder()
                        .requireWifi()
                        .build() // Update condition that requires wifi.
                } else {
                    FirebaseModelDownloadConditions.Builder().build() // Download condition.
                }
                firebaseModelManager.download(remoteModel, conditions)
            }
            .addOnSuccessListener {
                firebaseModelManager.getLatestModelFile(remoteModel)
                    .addOnCompleteListener {
                        val model = it.result
                        if (model == null) {
                            showToast("Failed to get model file.")
                        } else {
                            showToast("Downloaded remote model: $modelName")
                            tomatoDiseaseClassifier.initialize(model)
                        }
                    }
            }
            .addOnFailureListener {
                showToast("Model download failed for $modelName, please check your connection.")
            }
    }

我也尝试过其他型号，但我得到了同样的错误

更新 I修复了该bug，问题是firebase ml模型解释器库已被弃用，必须用firebase ml modeldownloader库替换。（）

train_datagen=ImageDataGenerator(rescale=1./255,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   validation_split=0.2,
                                   horizontal_flip=True)
test_datagen=ImageDataGenerator(rescale=1./255)
img_width,img_height =256,256
input_shape=(img_width,img_height,3)
batch_size =32

train_generator =train_datagen.flow_from_directory(train_dir,
                                                   target_size=(img_width,img_height),
                                                   batch_size=batch_size)
test_generator=test_datagen.flow_from_directory(test_dir,shuffle=True,
                                                   target_size=(img_width,img_height),
                                                   batch_size=batch_size)
validation_generator = train_datagen.flow_from_directory(
    train_dir, # same directory as training data
    target_size=(img_height, img_width),
    batch_size=batch_size)

model = Sequential()
model.add(Conv2D(32, (5, 5),input_shape=input_shape,activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, (3, 3),activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3),activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))   
model.add(Flatten())
model.add(Dense(512,activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(128,activation='relu'))          
model.add(Dense(num_classes,activation='softmax'))
model.summary()

opt=keras.optimizers.Adam(lr=0.001)
model.compile(optimizer=opt,loss='categorical_crossentropy',metrics=['accuracy'])
train=model.fit(train_generator,
                          epochs=15,
                          steps_per_epoch=train_generator.samples // batch_size,
                          validation_data=validation_generator,
                          validation_steps= validation_generator.samples// batch_size,verbose=1)

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tfmodel = converter.convert() 
tflite_models_dir = pathlib.Path("./tmp/")
tflite_models_dir.mkdir(exist_ok=True, parents=True)

tflite_model_quant_file = tflite_models_dir/"tomato_disease_quant.tflite"
tflite_model_quant_file.write_bytes(tfmodel)