samples: tflite-micro: update samples to use MicroPrintf

tflite-micro now uses MicroPrintf instead of MicroErrorReporter. Update
the samples to use this function instead.

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

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

@@ -20,14 +20,13 @@
 #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;
@@ -41,23 +40,14 @@ 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.
-	 * 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;
 	}
 
@@ -68,13 +58,13 @@ void setup(void)
 
 	/* 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 +96,7 @@ 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 +108,7 @@ 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/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/src/inference_process.cpp

@@ -8,7 +8,7 @@
 
 #include <tensorflow/lite/micro/all_ops_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>
@@ -119,10 +119,8 @@ bool InferenceProcess::runJob(InferenceJob &job)
 
 	/* Create the TFL micro interpreter */
 	tflite::AllOpsResolver resolver;
-	tflite::MicroErrorReporter errorReporter;
 
-	tflite::MicroInterpreter interpreter(model, resolver, tensorArena, tensorArenaSize,
-					     &errorReporter);
+	tflite::MicroInterpreter interpreter(model, resolver, tensorArena, tensorArenaSize);
 
 	/* Allocate tensors */
 	TfLiteStatus allocate_status = interpreter.AllocateTensors();