fix to make it work with the latest state of TFLM

Author: dzakhar

examples/tutorial_emnist_tflm/README.md

@@ -21,7 +21,7 @@ Installation process of the following dependencies is described in [Getting Star
     * NumPy 1.16.4
     * Matplotlib
     * Jupyter Lab / Notebook
-    * tf-nightly 2.3
+    * TensorFlow 2.3
     * Keras
     * emnist 
 
@@ -41,8 +41,8 @@ Installation process of the following dependencies is described in [Getting Star
 ## Install pip requirements
 ```bash
 pip install --upgrade pip setuptools
-pip install -r requirements.txt
-python -c "import emnist; emnist.ensure_cached_data();
+pip install -r ./conversion_tutorial/requirements.txt
+python -c "import emnist; emnist.ensure_cached_data();"
 ```
 ## Generate Tensorflow Lite Micro library
 Open root directory of tensorflow in terminal (use Cygwin or MinGW terminal if you're on Windows). Run:

examples/tutorial_emnist_tflm/conversion_tutorial/requirements.txt

@@ -1,7 +1,7 @@
 numpy==1.16.4
 matplotlib
 jupyterlab==1.1.0
-tf-nightly>=2.3
+tensorflow>=2.3.0rc0
 keras>=2.2.4
 emnist
 

examples/tutorial_emnist_tflm/src/main.cc

@@ -6,7 +6,7 @@
 * the LICENSE file in the root directory of this source tree.
 *
 */
-#include "tensorflow/lite/micro/kernels/all_ops_resolver.h"
+#include "tensorflow/lite/micro/all_ops_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"
@@ -19,17 +19,17 @@
 //tensor_arena has to be 16 bytes aligned
 typedef uint8_t aligned_uint8_t __attribute__((aligned(16)));
 constexpr int kTensorArenaSize = 50 * 1024; 
-aligned_uint8_t tensor_arena[ kTensorArenaSize ] = { 0 };
+aligned_uint8_t tensor_arena[kTensorArenaSize] = {0};
 
 int main() {
     tflite::ErrorReporter* reporter = nullptr;
     tflite::MicroErrorReporter error_reporter;
     reporter = &error_reporter;
-    reporter->Report( "Run EMNIST letter recognition" );
+    reporter->Report("Run EMNIST letter recognition");
 
     //Load Model
-    const tflite::Model* model = tflite::GetModel( emnist_model_int8_tflite );
-    if( model->version() != TFLITE_SCHEMA_VERSION ) {
+    const tflite::Model* model = tflite::GetModel(emnist_model_int8_tflite);
+    if (model->version() != TFLITE_SCHEMA_VERSION) {
         reporter->Report( "Model is schema version: %d\n"
           "Supported schema version is: %d", model->version(), TFLITE_SCHEMA_VERSION );
         return 1;
@@ -39,25 +39,17 @@ int main() {
     // Add Builtins corresponding to Model layers
     // Note: If you change the model structure/layer types, you'll need to make 
     // equivalent changes to the resolver
-    tflite::MicroOpResolver<5> resolver;
-    resolver.AddBuiltin(tflite::BuiltinOperator_CONV_2D,
-      tflite::ops::micro::Register_CONV_2D(), 3);
-    resolver.AddBuiltin(tflite::BuiltinOperator_MAX_POOL_2D,
-      tflite::ops::micro::Register_MAX_POOL_2D(), 2);
-    resolver.AddBuiltin(tflite::BuiltinOperator_RESHAPE,
-      tflite::ops::micro::Register_RESHAPE());
-    resolver.AddBuiltin(tflite::BuiltinOperator_FULLY_CONNECTED,
-      tflite::ops::micro::Register_FULLY_CONNECTED(), 4);
-    resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
-      tflite::ops::micro::Register_SOFTMAX(), 2);
+    tflite::MicroMutableOpResolver<5> resolver;
+    resolver.AddConv2D();
+    resolver.AddMaxPool2D();
+    resolver.AddFullyConnected();
+    resolver.AddReshape();
+    resolver.AddSoftmax();
 
-    tflite::MicroInterpreter interpreter(
-        model, resolver, tensor_arena, kTensorArenaSize, reporter );
-    //interpreter = &static_interpreter;
+    tflite::MicroInterpreter interpreter(model, resolver, tensor_arena, kTensorArenaSize, reporter);
 
     // Allocate memory from the tensor_arena for the model's tensors.
-    //TfLiteStatus allocate_status = interpreter.AllocateTensors();
-    if( interpreter.AllocateTensors() != kTfLiteOk ) {
+    if (interpreter.AllocateTensors() != kTfLiteOk) {
         reporter->Report( "AllocateTensors() failed" );
         return 1;
     }
@@ -71,17 +63,16 @@ int main() {
     int32_t zero_point = input->params.zero_point;
 
     // Invoke interpreter for each test sample and process results
-    for (int j = 0; j < kNumSamples; j++){
+    for (int j = 0; j < kNumSamples; j++) {
         // Perform image thinning (round values to either -128 or 127)
         // Write image to input data
         for (int i = 0; i < kImageSize; i++) {
           input->data.int8[i] = (test_samples[j].image[i] <= 210) ? -128 : 127;
-         }
+        }
 
         // Run model
-        //TfLiteStatus invoke_status = interpreter.Invoke();
-        if ( interpreter.Invoke() != kTfLiteOk ) {
-            reporter->Report( "Invoke failed" );
+        if (interpreter.Invoke() != kTfLiteOk) {
+            reporter->Report("Invoke failed");
             return 1;
         }
 
@@ -97,4 +88,4 @@ int main() {
           kCategoryLabels[result], (int)(confidence * 100), status);
     }
     return 0;
-}
+}