samples: tflite-micro: update hello_world

Updates tflite-micro hello_world sample code.

Signed-off-by: Lauren Murphy <[email protected]>

Author: laurenmurphyx64

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

@@ -22,8 +22,8 @@
 #include "output_handler.h"
 #include <tensorflow/lite/micro/micro_error_reporter.h>
 #include <tensorflow/lite/micro/micro_interpreter.h>
+#include <tensorflow/lite/micro/system_setup.h>
 #include <tensorflow/lite/schema/schema_generated.h>
-#include <tensorflow/lite/version.h>
 
 /* Globals, used for compatibility with Arduino-style sketches. */
 namespace {
@@ -34,15 +34,7 @@ namespace {
 	TfLiteTensor *output = nullptr;
 	int inference_count = 0;
 
-	/* Create an area of memory to use for input, output, and intermediate arrays.
-	* Minimum arena size, at the time of writing. After allocating tensors
-	* you can retrieve this value by invoking interpreter.arena_used_bytes().
-	*/
-	const int kModelArenaSize = 2468;
-
-	/* Extra headroom for model + alignment + future interpreter changes. */
-	const int kExtraArenaSize = 560 + 16 + 100;
-	const int kTensorArenaSize = kModelArenaSize + kExtraArenaSize;
+	constexpr int kTensorArenaSize = 2000;
 	uint8_t tensor_arena[kTensorArenaSize];
 }  /* namespace */
 
@@ -63,9 +55,9 @@ void setup(void)
 	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);
+						"Model provided is schema version %d not equal "
+						"to supported version %d.",
+						model->version(), TFLITE_SCHEMA_VERSION);
 		return;
 	}
 
@@ -104,32 +96,34 @@ void loop(void)
 	 */
 	float position = static_cast < float > (inference_count) /
 			 static_cast < float > (kInferencesPerCycle);
-	float x_val = position * kXrange;
+	float x = position * kXrange;
 
-	/* Place our calculated x value in the model's input tensor */
-	input->data.f[0] = x_val;
+	/* Quantize the input from floating-point to integer */
+	int8_t x_quantized = x / input->params.scale + input->params.zero_point;
+	/* Place the quantized input in the model's input tensor */
+	input->data.int8[0] = x_quantized;
 
 	/* 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_val: %f\n",
-				     static_cast < double > (x_val));
+		TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed on x: %f\n",
+				     static_cast < double > (x));
 		return;
 	}
 
-	/* Read the predicted y value from the model's output tensor */
-	float y_val = output->data.f[0];
+	/* Obtain the quantized output from model's output tensor */
+	int8_t y_quantized = output->data.int8[0];
+	/* Dequantize the output from integer to floating-point */
+	float y = (y_quantized - output->params.zero_point) * output->params.scale;
 
 	/* Output the results. A custom HandleOutput function can be implemented
 	 * for each supported hardware target.
 	 */
-	HandleOutput(error_reporter, x_val, y_val);
+	HandleOutput(error_reporter, x, y);
 
 	/* Increment the inference_counter, and reset it if we have reached
 	 * the total number per cycle
 	 */
 	inference_count += 1;
-	if (inference_count >= kInferencesPerCycle) {
-		inference_count = 0;
-	}
+	if (inference_count >= kInferencesPerCycle) inference_count = 0;
 }