samples: tflite-micro: update samples for latest tflite-micro

tflite-micro now uses MicroPrintf instead of MicroErrorReporter. Update
the samples to use this function instead. AllOpsResolver is now removed
from tflite-micro. AllOpsResolver was also removed in the latest
tflite-micro. Use MicroMutableOpResolver and only include the kernels
used instead.

Signed-off-by: Ryan McClelland <ryanmcclelland@meta.com>

samples/modules/tflite-micro/hello_world/prj.conf

@@ -13,5 +13,6 @@
 # limitations under the License.
 # ==============================================================================s
 CONFIG_CPP=y
+CONFIG_STD_CPP17=y
 CONFIG_TENSORFLOW_LITE_MICRO=y
 CONFIG_MAIN_STACK_SIZE=2048

samples/modules/tflite-micro/hello_world/src/main_functions.cpp

@@ -16,18 +16,17 @@
 
 #include "main_functions.h"
 
-#include <tensorflow/lite/micro/all_ops_resolver.h>
+#include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
 #include "constants.h"
 #include "model.hpp"
 #include "output_handler.hpp"
-#include <tensorflow/lite/micro/micro_error_reporter.h>
+#include <tensorflow/lite/micro/micro_log.h>
 #include <tensorflow/lite/micro/micro_interpreter.h>
 #include <tensorflow/lite/micro/system_setup.h>
 #include <tensorflow/lite/schema/schema_generated.h>
 
 /* Globals, used for compatibility with Arduino-style sketches. */
 namespace {
- tflite::ErrorReporter *error_reporter = nullptr;
  const tflite::Model *model = nullptr;
  tflite::MicroInterpreter *interpreter = nullptr;
  TfLiteTensor *input = nullptr;
@@ -41,40 +40,32 @@ namespace {
 /* The name of this function is important for Arduino compatibility. */
 void setup(void)
 {
- /* Set up logging. Google style is to avoid globals or statics because of
- * lifetime uncertainty, but since this has a trivial destructor it's okay.
- * NOLINTNEXTLINE(runtime-global-variables)
- */
- static tflite::MicroErrorReporter micro_error_reporter;
-
- error_reporter = &micro_error_reporter;
-
  /* Map the model into a usable data structure. This doesn't involve any
  * copying or parsing, it's a very lightweight operation.
  */
  model = tflite::GetModel(g_model);
  if (model->version() != TFLITE_SCHEMA_VERSION) {
- TF_LITE_REPORT_ERROR(error_reporter,
- "Model provided is schema version %d not equal "
- "to supported version %d.",
- model->version(), TFLITE_SCHEMA_VERSION);
+ MicroPrintf("Model provided is schema version %d not equal "
+ "to supported version %d.",
+ model->version(), TFLITE_SCHEMA_VERSION);
  return;
  }
 
- /* This pulls in all the operation implementations we need.
+ /* This pulls in the operation implementations we need.
  * NOLINTNEXTLINE(runtime-global-variables)
  */
- static tflite::AllOpsResolver resolver;
+ static tflite::MicroMutableOpResolver <1> resolver;
+ resolver.AddFullyConnected();
 
  /* Build an interpreter to run the model with. */
  static tflite::MicroInterpreter static_interpreter(
- model, resolver, tensor_arena, kTensorArenaSize, error_reporter);
+ model, resolver, tensor_arena, kTensorArenaSize);
  interpreter = &static_interpreter;
 
  /* Allocate memory from the tensor_arena for the model's tensors. */
  TfLiteStatus allocate_status = interpreter->AllocateTensors();
  if (allocate_status != kTfLiteOk) {
- TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
+ MicroPrintf("AllocateTensors() failed");
  return;
  }
 
@@ -106,8 +97,7 @@ void loop(void)
  /* Run inference, and report any error */
  TfLiteStatus invoke_status = interpreter->Invoke();
  if (invoke_status != kTfLiteOk) {
- TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed on x: %f\n",
- static_cast < double > (x));
+ MicroPrintf("Invoke failed on x: %f\n", static_cast < double > (x));
  return;
  }
 
@@ -119,7 +109,7 @@ void loop(void)
  /* Output the results. A custom HandleOutput function can be implemented
  * for each supported hardware target.
  */
- HandleOutput(error_reporter, x, y);
+ HandleOutput(x, y);
 
  /* Increment the inference_counter, and reset it if we have reached
  * the total number per cycle

samples/modules/tflite-micro/hello_world/src/output_handler.cpp

@@ -16,11 +16,10 @@
 
 #include "output_handler.hpp"
 
-void HandleOutput(tflite::ErrorReporter *error_reporter, float x_value,
- float y_value)
+void HandleOutput(float x_value, float y_value)
 {
  /* Log the current X and Y values */
- TF_LITE_REPORT_ERROR(error_reporter, "x_value: %f, y_value: %f\n",
-  static_cast < double > (x_value),
-  static_cast < double > (y_value));
+ MicroPrintf("x_value: %f, y_value: %f\n",
+ static_cast < double > (x_value),
+ static_cast < double > (y_value));
 }

samples/modules/tflite-micro/hello_world/src/output_handler.hpp

@@ -18,10 +18,9 @@
 #define TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_OUTPUT_HANDLER_H_
 
 #include <tensorflow/lite/c/common.h>
-#include <tensorflow/lite/micro/micro_error_reporter.h>
+#include <tensorflow/lite/micro/micro_log.h>
 
 /* Called by the main loop to produce some output based on the x and y values */
-void HandleOutput(tflite::ErrorReporter *error_reporter, float x_value,
- float y_value);
+void HandleOutput(float x_value, float y_value);
 
 #endif /* TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_OUTPUT_HANDLER_H_ */

samples/modules/tflite-micro/magic_wand/prj.conf

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 CONFIG_CPP=y
+CONFIG_STD_CPP17=y
 CONFIG_NEWLIB_LIBC_FLOAT_PRINTF=y
 CONFIG_SENSOR=y
 CONFIG_NETWORKING=n

samples/modules/tflite-micro/magic_wand/src/accelerometer_handler.cpp

@@ -33,34 +33,32 @@ float bufz[BUFLEN] = { 0.0f };
 
 bool initial = true;
 
-TfLiteStatus SetupAccelerometer(tflite::ErrorReporter *error_reporter)
+TfLiteStatus SetupAccelerometer()
 {
  if (!device_is_ready(sensor)) {
  printk("%s: device not ready.\n", sensor->name);
  return kTfLiteApplicationError;
  }
 
  if (sensor == NULL) {
- TF_LITE_REPORT_ERROR(error_reporter,
- "Failed to get accelerometer, name: %s\n",
- sensor->name);
+ MicroPrintf("Failed to get accelerometer, name: %s\n",
+ sensor->name);
  } else {
- TF_LITE_REPORT_ERROR(error_reporter, "Got accelerometer, name: %s\n",
-  sensor->name);
+ MicroPrintf("Got accelerometer, name: %s\n",
+ sensor->name);
  }
  return kTfLiteOk;
 }
 
-bool ReadAccelerometer(tflite::ErrorReporter *error_reporter, float *input,
- int length)
+bool ReadAccelerometer(float *input, int length)
 {
  int rc;
  struct sensor_value accel[3];
  int samples_count;
 
  rc = sensor_sample_fetch(sensor);
  if (rc < 0) {
- TF_LITE_REPORT_ERROR(error_reporter, "Fetch failed\n");
+ MicroPrintf("Fetch failed\n");
  return false;
  }
  /* Skip if there is no data */
@@ -72,7 +70,7 @@ bool ReadAccelerometer(tflite::ErrorReporter *error_reporter, float *input,
  for (int i = 0; i < samples_count; i++) {
  rc = sensor_channel_get(sensor, SENSOR_CHAN_ACCEL_XYZ, accel);
  if (rc < 0) {
- TF_LITE_REPORT_ERROR(error_reporter, "ERROR: Update failed: %d\n", rc);
+ MicroPrintf("ERROR: Update failed: %d\n", rc);
  return false;
  }
  bufx[begin_index] = (float)sensor_value_to_double(&accel[0]);

samples/modules/tflite-micro/magic_wand/src/accelerometer_handler.hpp

@@ -20,11 +20,10 @@
 #define kChannelNumber 3
 
 #include <tensorflow/lite/c/c_api_types.h>
-#include <tensorflow/lite/micro/micro_error_reporter.h>
+#include <tensorflow/lite/micro/micro_log.h>
 
 extern int begin_index;
-extern TfLiteStatus SetupAccelerometer(tflite::ErrorReporter *error_reporter);
-extern bool ReadAccelerometer(tflite::ErrorReporter *error_reporter,
- float *input, int length);
+extern TfLiteStatus SetupAccelerometer();
+extern bool ReadAccelerometer(float *input, int length);
 
 #endif /* TENSORFLOW_LITE_MICRO_EXAMPLES_MAGIC_WAND_ACCELEROMETER_HANDLER_H_ */

samples/modules/tflite-micro/magic_wand/src/main_functions.cpp

@@ -21,14 +21,13 @@
 #include "gesture_predictor.hpp"
 #include "magic_wand_model_data.hpp"
 #include "output_handler.hpp"
-#include <tensorflow/lite/micro/micro_error_reporter.h>
+#include <tensorflow/lite/micro/micro_log.h>
 #include <tensorflow/lite/micro/micro_interpreter.h>
 #include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
 #include <tensorflow/lite/schema/schema_generated.h>
 
 /* Globals, used for compatibility with Arduino-style sketches. */
 namespace {
- tflite::ErrorReporter *error_reporter = nullptr;
  const tflite::Model *model = nullptr;
  tflite::MicroInterpreter *interpreter = nullptr;
  TfLiteTensor *model_input = nullptr;
@@ -45,22 +44,14 @@ namespace {
 /* The name of this function is important for Arduino compatibility. */
 void setup(void)
 {
- /* Set up logging. Google style is to avoid globals or statics because of
- * lifetime uncertainty, but since this has a trivial destructor it's okay.
- */
- static tflite::MicroErrorReporter micro_error_reporter; /* NOLINT */
-
- error_reporter = &micro_error_reporter;
-
  /* Map the model into a usable data structure. This doesn't involve any
  * copying or parsing, it's a very lightweight operation.
  */
  model = tflite::GetModel(g_magic_wand_model_data);
  if (model->version() != TFLITE_SCHEMA_VERSION) {
- TF_LITE_REPORT_ERROR(error_reporter,
- "Model provided is schema version %d not equal "
- "to supported version %d.",
- model->version(), TFLITE_SCHEMA_VERSION);
+ MicroPrintf("Model provided is schema version %d not equal "
+ "to supported version %d.",
+ model->version(), TFLITE_SCHEMA_VERSION);
  return;
  }
 
@@ -79,7 +70,7 @@ void setup(void)
 
  /* Build an interpreter to run the model with. */
  static tflite::MicroInterpreter static_interpreter(
- model, micro_op_resolver, tensor_arena, kTensorArenaSize, error_reporter);
+ model, micro_op_resolver, tensor_arena, kTensorArenaSize);
  interpreter = &static_interpreter;
 
  /* Allocate memory from the tensor_arena for the model's tensors. */
@@ -91,24 +82,23 @@ void setup(void)
  (model_input->dims->data[1] != 128) ||
  (model_input->dims->data[2] != kChannelNumber) ||
  (model_input->type != kTfLiteFloat32)) {
- TF_LITE_REPORT_ERROR(error_reporter,
- "Bad input tensor parameters in model");
+ MicroPrintf("Bad input tensor parameters in model");
  return;
  }
 
  input_length = model_input->bytes / sizeof(float);
 
- TfLiteStatus setup_status = SetupAccelerometer(error_reporter);
+ TfLiteStatus setup_status = SetupAccelerometer();
  if (setup_status != kTfLiteOk) {
- TF_LITE_REPORT_ERROR(error_reporter, "Set up failed\n");
+ MicroPrintf("Set up failed\n");
  }
 }
 
 void loop(void)
 {
  /* Attempt to read new data from the accelerometer. */
  bool got_data =
- ReadAccelerometer(error_reporter, model_input->data.f, input_length);
+ ReadAccelerometer(model_input->data.f, input_length);
 
  /* If there was no new data, wait until next time. */
  if (!got_data) {
@@ -118,13 +108,12 @@ void loop(void)
  /* Run inference, and report any error */
  TfLiteStatus invoke_status = interpreter->Invoke();
  if (invoke_status != kTfLiteOk) {
- TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed on index: %d\n",
- begin_index);
+ MicroPrintf("Invoke failed on index: %d\n", begin_index);
  return;
  }
  /* Analyze the results to obtain a prediction */
  int gesture_index = PredictGesture(interpreter->output(0)->data.f);
 
  /* Produce an output */
- HandleOutput(error_reporter, gesture_index);
+ HandleOutput(gesture_index);
 }

samples/modules/tflite-micro/magic_wand/src/output_handler.cpp

@@ -16,24 +16,21 @@
 
 #include "output_handler.hpp"
 
-void HandleOutput(tflite::ErrorReporter *error_reporter, int kind)
+void HandleOutput(int kind)
 {
  /* light (red: wing, blue: ring, green: slope) */
  if (kind == 0) {
- TF_LITE_REPORT_ERROR(
- error_reporter,
+ MicroPrintf(
  "WING:\n\r* * *\n\r * * * "
  "*\n\r * * * *\n\r * * * *\n\r * * "
  "* *\n\r * *\n\r");
  } else if (kind == 1) {
- TF_LITE_REPORT_ERROR(
- error_reporter,
+ MicroPrintf(
  "RING:\n\r *\n\r * *\n\r * *\n\r "
  " * *\n\r * *\n\r * *\n\r "
  " *\n\r");
  } else if (kind == 2) {
- TF_LITE_REPORT_ERROR(
- error_reporter,
+ MicroPrintf(
  "SLOPE:\n\r *\n\r *\n\r *\n\r *\n\r "
  "*\n\r *\n\r *\n\r * * * * * * * *\n\r");
  }

samples/modules/tflite-micro/magic_wand/src/output_handler.hpp

@@ -18,8 +18,8 @@
 #define TENSORFLOW_LITE_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_
 
 #include <tensorflow/lite/c/common.h>
-#include <tensorflow/lite/micro/micro_error_reporter.h>
+#include <tensorflow/lite/micro/micro_log.h>
 
-void HandleOutput(tflite::ErrorReporter *error_reporter, int kind);
+void HandleOutput(int kind);
 
 #endif /* TENSORFLOW_LITE_MICRO_EXAMPLES_MAGIC_WAND_OUTPUT_HANDLER_H_ */

samples/modules/tflite-micro/tflm_ethosu/prj.conf

@@ -1,6 +1,7 @@
 #application default configuration
 # include TFLM based on CMSIS NN optimization and ETHOSU acceleration
 CONFIG_CPP=y
+CONFIG_STD_CPP17=y
 CONFIG_TENSORFLOW_LITE_MICRO=y
 CONFIG_ARM_ETHOS_U=y
 CONFIG_HEAP_MEM_POOL_SIZE=16384

samples/modules/tflite-micro/tflm_ethosu/src/inference_process.cpp

@@ -6,9 +6,9 @@
 
 #include "inference_process.hpp"
 
-#include <tensorflow/lite/micro/all_ops_resolver.h>
+#include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
 #include <tensorflow/lite/micro/cortex_m_generic/debug_log_callback.h>
-#include <tensorflow/lite/micro/micro_error_reporter.h>
+#include <tensorflow/lite/micro/micro_log.h>
 #include <tensorflow/lite/micro/micro_interpreter.h>
 #include <tensorflow/lite/micro/micro_profiler.h>
 #include <tensorflow/lite/schema/schema_generated.h>
@@ -118,11 +118,10 @@ bool InferenceProcess::runJob(InferenceJob &job)
  }
 
  /* Create the TFL micro interpreter */
- tflite::AllOpsResolver resolver;
- tflite::MicroErrorReporter errorReporter;
+ tflite::MicroMutableOpResolver <1> resolver;
+ resolver.AddEthosU();
 
- tflite::MicroInterpreter interpreter(model, resolver, tensorArena, tensorArenaSize,
- &errorReporter);
+ tflite::MicroInterpreter interpreter(model, resolver, tensorArena, tensorArenaSize);
 
  /* Allocate tensors */
  TfLiteStatus allocate_status = interpreter.AllocateTensors();