Question

我想弄清楚是否有可能使用tensorflow lite在android环境中从特定图层获取输出。目前，我知道使用：'interpreter.run（）'可以获取“标准”输出，但这不是我想要的。感谢您的任何建议。

Answer 1

@Simo我将在此处编写此问题的解决方法。如何将要保存的模型部分保存为.tflite文件。让我解释一下自己。而不是在下面做并保存整个模型：

# WHOLE MODEL
tflite_model = tf.keras.models.load_model('face_recog.weights.best.hdf5')
converter = tf.lite.TFLiteConverter.from_keras_model(tflite_model)
tflite_save = converter.convert()
open("face_recog.tflite", "wb").write(tflite_save)

您可以打印keras模型的各层：

print([layer.name for layer in keras_model.layers])
Output:
['anchor', 'positive', 'negative', 'model', 'lambda']

print([layer.name for layer in keras_model.get_layer('model').layers])
Output:  
['input_1', 'Conv1_pad', 'Conv1', 'bn_Conv1', 'Conv1_relu', 'expanded_conv_depthwise', 'expanded_conv_depthwise_BN', 'expanded_conv_depthwise_relu', 'expanded_conv_project', 'expanded_conv_project_BN', 'block_1_expand', 'block_1_expand_BN', 'block_1_expand_relu', 'block_1_pad', 'block_1_depthwise', 'block_1_depthwise_BN', 'block_1_depthwise_relu', 'block_1_project', 'block_1_project_BN', 'block_2_expand', 'block_2_expand_BN', 'block_2_expand_relu', 'block_2_depthwise', 'block_2_depthwise_BN', 'block_2_depthwise_relu', 'block_2_project', 'block_2_project_BN', 'block_2_add', 'block_3_expand', 'block_3_expand_BN', 'block_3_expand_relu', 'block_3_pad', 'block_3_depthwise', 'block_3_depthwise_BN', 'block_3_depthwise_relu', 'block_3_project', 'block_3_project_BN', 'block_4_expand', 'block_4_expand_BN', 'block_4_expand_relu', 'block_4_depthwise', 'block_4_depthwise_BN', 'block_4_depthwise_relu', 'block_4_project', 'block_4_project_BN', 'block_4_add', 'block_5_expand', 'block_5_expand_BN', 'block_5_expand_relu', 'block_5_depthwise', 'block_5_depthwise_BN', 'block_5_depthwise_relu', 'block_5_project', 'block_5_project_BN', 'block_5_add', 'block_6_expand', 'block_6_expand_BN', 'block_6_expand_relu', 'block_6_pad', 'block_6_depthwise', 'block_6_depthwise_BN', 'block_6_depthwise_relu', 'block_6_project', 'block_6_project_BN', 'block_7_expand', 'block_7_expand_BN', 'block_7_expand_relu', 'block_7_depthwise', 'block_7_depthwise_BN', 'block_7_depthwise_relu', 'block_7_project', 'block_7_project_BN', 'block_7_add', 'block_8_expand', 'block_8_expand_BN', 'block_8_expand_relu', 'block_8_depthwise', 'block_8_depthwise_BN', 'block_8_depthwise_relu', 'block_8_project', 'block_8_project_BN', 'block_8_add', 'block_9_expand', 'block_9_expand_BN', 'block_9_expand_relu', 'block_9_depthwise', 'block_9_depthwise_BN', 'block_9_depthwise_relu', 'block_9_project', 'block_9_project_BN', 'block_9_add', 'block_10_expand', 'block_10_expand_BN', 'block_10_expand_relu', 'block_10_depthwise', 'block_10_depthwise_BN', 'block_10_depthwise_relu', 'block_10_project', 'block_10_project_BN', 'block_11_expand', 'block_11_expand_BN', 'block_11_expand_relu', 'block_11_depthwise', 'block_11_depthwise_BN', 'block_11_depthwise_relu', 'block_11_project', 'block_11_project_BN', 'block_11_add', 'block_12_expand', 'block_12_expand_BN', 'block_12_expand_relu', 'block_12_depthwise', 'block_12_depthwise_BN', 'block_12_depthwise_relu', 'block_12_project', 'block_12_project_BN', 'block_12_add', 'block_13_expand', 'block_13_expand_BN', 'block_13_expand_relu', 'block_13_pad', 'block_13_depthwise', 'block_13_depthwise_BN', 'block_13_depthwise_relu', 'block_13_project', 'block_13_project_BN', 'block_14_expand', 'block_14_expand_BN', 'block_14_expand_relu', 'block_14_depthwise', 'block_14_depthwise_BN', 'block_14_depthwise_relu', 'block_14_project', 'block_14_project_BN', 'block_14_add', 'block_15_expand', 'block_15_expand_BN', 'block_15_expand_relu', 'block_15_depthwise', 'block_15_depthwise_BN', 'block_15_depthwise_relu', 'block_15_project', 'block_15_project_BN', 'block_15_add', 'block_16_expand', 'block_16_expand_BN', 'block_16_expand_relu', 'block_16_depthwise', 'block_16_depthwise_BN', 'block_16_depthwise_relu', 'block_16_project', 'block_16_project_BN', 'Conv_1', 'Conv_1_bn', 'out_relu', 'global_average_pooling2d', 'predictions', 'dense', 'dense_1']

然后您可以从模型中获取所需的任何层并将其保存到.tflite：

# PART OF MODEL
tflite_model = tf.keras.models.load_model('face_recog.weights.best.hdf5')
converter = tf.lite.TFLiteConverter.from_keras_model(tflite_model.get_layer('model'))
tflite_save = converter.convert()
open("face_recog.tflite", "wb").write(tflite_save)

因此，使用上述代码，.tflite文件将具有输入张量=“ input_1”和输出=“ dense_1”

然后在android内部，您必须使用特定层“模型”的输入，并且您将获得特定形状的输出，就像在python中打印输出详细信息时一样：

interpreter = tf.lite.Interpreter('face_recog.tflite')
print(interpreter.get_output_details())
interpreter.get_tensor_details()

Android部分：

// Initialize interpreter
@Throws(IOException::class)
private suspend fun initializeInterpreter(app: Application) = withContext(Dispatchers.IO) {
    // Load the TF Lite model from asset folder and initialize TF Lite Interpreter without NNAPI enabled.
    val assetManager = app.assets
    val model = loadModelFile(assetManager, "face_recog_model_layer.tflite")
    val options = Interpreter.Options()
    options.setUseNNAPI(false)
    interpreter = Interpreter(model, options)
    // Reads type and shape of input and output tensors, respectively.
    val imageTensorIndex = 0
    val imageShape: IntArray =
        interpreter.getInputTensor(imageTensorIndex).shape() 
    Log.i("INPUT_TENSOR_WHOLE", Arrays.toString(imageShape))
    val imageDataType: DataType =
        interpreter.getInputTensor(imageTensorIndex).dataType()
    Log.i("INPUT_DATA_TYPE", imageDataType.toString())
    val probabilityTensorIndex = 0
    val probabilityShape: IntArray =
        interpreter.getOutputTensor(probabilityTensorIndex).shape()
    Log.i("OUTPUT_TENSOR_SHAPE", Arrays.toString(probabilityShape))
    val probabilityDataType: DataType =
        interpreter.getOutputTensor(probabilityTensorIndex).dataType()
    Log.i("OUTPUT_DATA_TYPE", probabilityDataType.toString())
    Log.i(TAG, "Initialized TFLite interpreter.")

}

@Throws(IOException::class)
private fun loadModelFile(assetManager: AssetManager, filename: String): MappedByteBuffer {
    val fileDescriptor = assetManager.openFd(filename)
    val inputStream = FileInputStream(fileDescriptor.fileDescriptor)
    val fileChannel = inputStream.channel
    val startOffset = fileDescriptor.startOffset
    val declaredLength = fileDescriptor.declaredLength
    return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength)
}

我希望这会对某人有所帮助。当然，如果您还需要其他标签，请标记我：）

如何从特定图层获取输出张量？

1 个答案: