问题

I have a tflite model and the input signature of the TFLite model is 'shape_signature': array([ -1, 12000, 1]. I have tested with random data with shape [1,1200,1] and model runs without any error.

prediction shape is also (1,1200,1)

Now I want to do this in android

I tried this in android but i'm getting this error

Node number 6 (RESHAPE) failed to prepare.```

<details>
<summary>英文:</summary>

I have a tflite model and the input signature of the TFLite model is &#39;shape_signature&#39;: array([   -1, 12000,     1]. I have tested with random data with shape [1,1200,1] and model runs without any error.

prediction shape is also (1,1200,1)

https://colab.research.google.com/gist/pjpratik/bd48804cc8d40239812079b5a249aac3/60367.ipynb#scrollTo=9qc4EpLTUw0v

Now I want to do this in android

I tried this in android but i&#39;m getting this error

private fun applyModel() {
val inputFloatArray = Array(1) { Array(inputAudioData.size) { FloatArray(1) } } //1,1200,1

    val outputFloatArray = inputFloatArray //Attempt 1
    val outputFloatArray = FloatArray(1200) //Attempt 2
    val outputFloatArray = FloatArray(1) //Attempt 3
    val outputFloatArray = Array(1) { Array(inputAudioData.size) { FloatArray(1) } } //Attempt 4

    Log.d("tflite", "Model input data: ${inputFloatArray.toString()}")

    tflite!!.run(inputFloatArray, outputFloatArray)

    Log.d("tflite", "Model output data: ${outputFloatArray.toString()}")
}

java.lang.IllegalStateException: Internal error: Unexpected failure when preparing tensor allocations: tensorflow/lite/kernels/reshape.cc:85 num_input_elements != num_output_elements (1200 != 0)
Node number 6 (RESHAPE) failed to prepare.
at org.tensorflow.lite.NativeInterpreterWrapper.allocateTensors(Native Method)
at org.tensorflow.lite.NativeInterpreterWrapper.allocateTensorsIfNeeded(NativeInterpreterWrapper.java:308)
at org.tensorflow.lite.NativeInterpreterWrapper.run(NativeInterpreterWrapper.java:248)
at org.tensorflow.lite.InterpreterImpl.runForMultipleInputsOutputs(InterpreterImpl.java:101)
at org.tensorflow.lite.Interpreter.runForMultipleInputsOutputs(Interpreter.java:77)
at org.tensorflow.lite.InterpreterImpl.run(InterpreterImpl.java:94)
```

答案1

得分: 2

这似乎更像是调试的一部分，你需要检查模型期望的输入类型以及生成什么类型的数据。

你可以尝试将数据分块，然后尝试在数据的一部分上进行操作，下面显示了我在 dtln_aec_128_1.tflite 上应用的方式。

你可以按照以下实现并尝试在你的数据集上运行它

https://github.com/breizhn/DTLN-aec/tree/main/pretrained_models

private fun applyModel(data: ShortArray): ShortArray {
    val chunkSize = 257 // 可以根据你的模型测试而变化，可以是 1200
    val outputData = ShortArray(chunkSize)

    val inputArray = Array(1) { Array(1) { FloatArray(chunkSize) } }

    for (i in 0 until chunkSize)
        inputArray[0][0][i] = data[i].toFloat()

    val outputArray = Array(1) { Array(1) { FloatArray(chunkSize) } }

    tflite!!.run(inputArray, outputArray)

    Log.d("tflite output", "Model direct output ${outputArray[0][0].joinToString(" ")}")

    val outBuffer = FloatArray(chunkSize)

    for (i in 0 until chunkSize)
        outBuffer[i] = (outputArray[0][0][i]).toFloat()

    for (i in 0 until chunkSize)
        outputData[i] = outBuffer[i].toInt().toShort()

    return outputData
}

加载模型的部分可以这样做

@Throws(IOException::class)
private fun loadModelFile(activity: Activity): MappedByteBuffer? {
    val fileDescriptor: AssetFileDescriptor = activity.assets.openFd("dtln_aec_128_1.tflite")
    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)
}

在 app/build.gradle 中添加：

implementation 'org.tensorflow:tensorflow-lite:2.12.0'

在 buildTypes{} 的下方添加：

aaptOptions {
    noCompress "dtln_aec_128_1.tflite"
}

并将其复制到 assets 文件夹中。

private fun applyModel(data: ShortArray): ShortArray {
        val chunkSize = 257 // can be depending on your model test, can be 1200
        val outputData = ShortArray(chunkSize)

        val inputArray = Array(1) { Array(1) { FloatArray(chunkSize) } }

        for (i in 0 until chunkSize)
            inputArray[0][0][i] = data[i].toFloat()

        val outputArray = Array(1) { Array(1) { FloatArray(chunkSize) } }

        tflite!!.run(inputArray, outputArray)

        Log.d("tflite output", "Model direct output ${outputArray[0][0].joinToString(" ")}")


        val outBuffer = FloatArray(chunkSize)

        for (i in 0 until chunkSize)
            outBuffer[i] = (outputArray[0][0][i]).toFloat()

        for (i in 0 until chunkSize)
            outputData[i] = outBuffer[i].toInt()
        .toShort()

        return outputData
    }

@Throws(IOException::class)
    private fun loadModelFile(activity: Activity): MappedByteBuffer? {
        val fileDescriptor: AssetFileDescriptor = activity.assets.openFd("dtln_aec_128_1.tflite")
        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, declared length)
    }

implementation 'org.tensorflow:tensorflow-lite:2.12.0'

aaptOptions {
        noCompress "dtln_aec_128_1.tflite"
    }