Fix 'unused variable' compiler error when building for ARM Cortex M4 in release mode

PiperOrigin-RevId: 323071869
Change-Id: I894b62b4c333838d45e556856f4bfef4f5d98a9c

tensorflow/lite/micro/memory_helpers.cc

@@ -131,7 +131,7 @@ TfLiteStatus AllocateOutputDimensionsFromInput(TfLiteContext* context,
   input = input1->dims->size > input2->dims->size ? input1 : input2;
   TF_LITE_ENSURE(context, output->type == input->type);
 
-  size_t size;
+  size_t size = 0;
   TfLiteTypeSizeOf(input->type, &size);
   const int dimensions_count = tflite::GetTensorShape(input).DimensionsCount();
   for (int i = 0; i < dimensions_count; i++) {

tensorflow/lite/micro/micro_allocator.cc

@@ -97,7 +97,9 @@ TfLiteStatus CheckOfflinePlannedOffsets(const Model* model,
         int version = metadata_buffer[0];
         int subgraph_idx = metadata_buffer[1];
         const int nbr_offline_offsets = metadata_buffer[2];
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
         int* offline_planner_offsets = (int*)&metadata_buffer[3];
+#endif
 
         TF_LITE_REPORT_ERROR(error_reporter, "==== Model metadata info: =====");
         TF_LITE_REPORT_ERROR(error_reporter,

tensorflow/lite/micro/micro_interpreter.cc

@@ -68,11 +68,13 @@ void* ContextHelper::GetScratchBuffer(TfLiteContext* ctx, int buffer_idx) {
 
 void ContextHelper::ReportOpError(struct TfLiteContext* context,
                                   const char* format, ...) {
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
   ContextHelper* helper = static_cast<ContextHelper*>(context->impl_);
   va_list args;
   va_start(args, format);
   TF_LITE_REPORT_ERROR(helper->error_reporter_, format, args);
   va_end(args);
+#endif
 }
 
 TfLiteTensor* ContextHelper::GetTensor(const struct TfLiteContext* context,

tensorflow/lite/micro/micro_optional_debug_tools.cc

@@ -117,6 +117,7 @@ const char* AllocTypeName(TfLiteAllocationType type) {
 // Helper function to print model flatbuffer data. This function is not called
 // by default. Hence it's not linked in to the final binary code.
 void PrintModelData(const Model* model, ErrorReporter* error_reporter) {
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
   auto* subgraphs = model->subgraphs();
   const SubGraph* subgraph = (*subgraphs)[0];
   const flatbuffers::Vector<flatbuffers::Offset<Tensor>>* tensors =
@@ -139,6 +140,7 @@ void PrintModelData(const Model* model, ErrorReporter* error_reporter) {
         error_reporter, "Tensor index: %d arena tensor %d size %d ", i,
         !array_size && !flatbuffer_tensor.is_variable(), tensor_size);
   }
+#endif
 }
 
 // Prints a dump of what tensors and what nodes are in the interpreter.

tensorflow/lite/micro/micro_profiler.cc

@@ -33,9 +33,10 @@ uint32_t MicroProfiler::BeginEvent(const char* tag, EventType event_type,
 }
 
 void MicroProfiler::EndEvent(uint32_t event_handle) {
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
   int32_t end_time = GetCurrentTimeTicks();
   TF_LITE_REPORT_ERROR(reporter_, "%s took %d cycles\n", event_tag_,
                        end_time - start_time_);
+#endif
 }
-
 }  // namespace tflite

tensorflow/lite/micro/recording_micro_allocator.cc

@@ -104,6 +104,7 @@ void RecordingMicroAllocator::PrintAllocations() const {
 void RecordingMicroAllocator::PrintRecordedAllocation(
     RecordedAllocationType allocation_type, const char* allocation_name,
     const char* allocation_description) const {
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
   RecordedAllocation allocation = GetRecordedAllocation(allocation_type);
   TF_LITE_REPORT_ERROR(
       error_reporter(),
@@ -111,6 +112,7 @@ void RecordingMicroAllocator::PrintRecordedAllocation(
       "(requested %d bytes for %d %s)",
       allocation_name, allocation.used_bytes, allocation.requested_bytes,
       allocation.count, allocation_description);
+#endif
 }
 
 TfLiteStatus RecordingMicroAllocator::AllocateNodeAndRegistrations(

tensorflow/lite/micro/simple_memory_allocator.cc

@@ -78,11 +78,13 @@ uint8_t* SimpleMemoryAllocator::AllocateFromTail(size_t size,
                                                  size_t alignment) {
   uint8_t* const aligned_result = AlignPointerDown(tail_ - size, alignment);
   if (aligned_result < head_) {
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
     const size_t missing_memory = head_ - aligned_result;
     TF_LITE_REPORT_ERROR(
         error_reporter_,
         "Failed to allocate memory. Requested: %u, available %u, missing: %u",
         size, size - missing_memory, missing_memory);
+#endif
     return nullptr;
   }
   tail_ = aligned_result;

tensorflow/lite/micro/test_helpers.cc

@@ -812,11 +812,13 @@ int TestStrcmp(const char* a, const char* b) {
 
 // Wrapper to forward kernel errors to the interpreter's error reporter.
 void ReportOpError(struct TfLiteContext* context, const char* format, ...) {
+#ifndef TF_LITE_STRIP_ERROR_STRINGS
   ErrorReporter* error_reporter = static_cast<ErrorReporter*>(context->impl_);
   va_list args;
   va_start(args, format);
   TF_LITE_REPORT_ERROR(error_reporter, format, args);
   va_end(args);
+#endif
 }
 
 // Create a TfLiteIntArray from an array of ints.  The first element in the