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我正在尝试使用 MicroMutableOpsResolver 类编译一个基于 TFLite 的微型 Arduino 草图（仅包括减少内存使用所需的操作）。

虽然在这里看到 TF lite 示例中的类似用法 - https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/micro_speech/micro_speech_test.cc

但是继续遇到下面的编译错误。

IMU_Classifier_TinyML:22:1: error: 'micro_op_resolver' does not name a type
 micro_op_resolver.AddFullyConnected();
 ^~~~~~~~~~~~~~~~~
IMU_Classifier_TinyML:23:1: error: 'micro_op_resolver' does not name a type
 micro_op_resolver.AddSoftmax();
 ^~~~~~~~~~~~~~~~~
IMU_Classifier_TinyML:24:1: error: 'micro_op_resolver' does not name a type
 micro_op_resolver.AddRelu();
 ^~~~~~~~~~~~~~~~~
Using library Arduino_LSM9DS1 at version 1.1.0 in folder: /home/balaji/Arduino/libraries/Arduino_LSM9DS1 
Using library Wire in folder: /home/balaji/.arduino15/packages/arduino/hardware/mbed/1.3.2/libraries/Wire (legacy)
Using library Arduino_TensorFlowLite at version 2.4.0-ALPHA in folder: /home/balaji/Arduino/libraries/Arduino_TensorFlowLite 
exit status 1
'micro_op_resolver' does not name a type

代码片段如下所示：

#include <Arduino_LSM9DS1.h>
#include <TensorFlowLite.h>
#include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
#include <tensorflow/lite/micro/kernels/micro_ops.h>
#include <tensorflow/lite/micro/micro_error_reporter.h>
#include <tensorflow/lite/micro/micro_interpreter.h>
#include <tensorflow/lite/schema/schema_generated.h>
#include <tensorflow/lite/version.h>

// Include the TFlite converted model header file
#include "model.h"

const float accelThreshold = 2.5;
const int numOfSamples = 119; // acceleration sample-rate

int samplesRead = numOfSamples;

tflite::MicroErrorReporter tfLiteErrorReporter;

/*Import only the required ops to reduce the memory usage*/
static tflite::MicroMutableOpResolver<3> micro_op_resolver;
micro_op_resolver.AddFullyConnected();
micro_op_resolver.AddSoftmax();
micro_op_resolver.AddRelu();

我是否缺少任何依赖项，或者这可能是由于 TF lite 版本不匹配？

4

1 回答 1

2

至少像这样的函数调用micro_op_resolver.AddFullyConnected();必须放在函数体中。这样的东西应该编译：

#include <Arduino_LSM9DS1.h>
#include <TensorFlowLite.h>
#include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
#include <tensorflow/lite/micro/kernels/micro_ops.h>
#include <tensorflow/lite/micro/micro_error_reporter.h>
#include <tensorflow/lite/micro/micro_interpreter.h>
#include <tensorflow/lite/schema/schema_generated.h>
#include <tensorflow/lite/version.h>

// Include the TFlite converted model header file
#include "model.h"

const float accelThreshold = 2.5;
const int numOfSamples = 119; // acceleration sample-rate

int samplesRead = numOfSamples;

tflite::MicroErrorReporter tfLiteErrorReporter;

/*Import only the required ops to reduce the memory usage*/
static tflite::MicroMutableOpResolver<3> micro_op_resolver;

void setup() {
  micro_op_resolver.AddFullyConnected();
  micro_op_resolver.AddSoftmax();
  micro_op_resolver.AddRelu();
}

void loop() {
  // put your main code here, to run repeatedly:

}
于 2021-02-21T21:03:47.120 回答