Merge pull request #49114 from advaitjain:remove-unused-apis

PiperOrigin-RevId: 373228316 Change-Id: I0bb38514b89c96b99cde21b70e500976123842cf
tensorflow · May 11, 2021 · cf5ddf6 · cf5ddf6
2 parents d148e19 + 52cd43c
commit cf5ddf6
Show file tree

Hide file tree

Showing 3 changed files with 0 additions and 101 deletions.
diff --git a/tensorflow/lite/micro/micro_interpreter.cc b/tensorflow/lite/micro/micro_interpreter.cc
@@ -305,18 +305,6 @@ TfLiteTensor* MicroInterpreter::output(size_t index) {
   return output_tensors_[index];
 }
 
-TfLiteTensor* MicroInterpreter::tensor(size_t index) {
-  const size_t length = tensors_size();
-  if (index >= length) {
-    TF_LITE_REPORT_ERROR(error_reporter_,
-                         "Tensor index %d out of range (length is %d)", index,
-                         length);
-    return nullptr;
-  }
-  return allocator_.AllocatePersistentTfLiteTensor(model_, eval_tensors_,
-                                                   index);
-}
-
 TfLiteStatus MicroInterpreter::ResetVariableTensors() {
   for (size_t i = 0; i < subgraph_->tensors()->size(); ++i) {
     auto* tensor = subgraph_->tensors()->Get(i);

diff --git a/tensorflow/lite/micro/micro_interpreter.h b/tensorflow/lite/micro/micro_interpreter.h
@@ -68,18 +68,6 @@ class MicroInterpreter {
   // TODO(b/149795762): Add this to the TfLiteStatus enum.
   TfLiteStatus Invoke();
 
-  size_t tensors_size() const { return context_.tensors_size; }
-  TfLiteTensor* tensor(size_t tensor_index);
-  template <class T>
-  T* typed_tensor(int tensor_index) {
-    if (TfLiteTensor* tensor_ptr = tensor(tensor_index)) {
-      if (tensor_ptr->type == typeToTfLiteType<T>()) {
-        return GetTensorData<T>(tensor_ptr);
-      }
-    }
-    return nullptr;
-  }
-
   TfLiteTensor* input(size_t index);
   size_t inputs_size() const { return subgraph_->inputs()->Length(); }
   const flatbuffers::Vector<int32_t>& inputs() const {

diff --git a/tensorflow/lite/micro/micro_interpreter_test.cc b/tensorflow/lite/micro/micro_interpreter_test.cc
@@ -74,7 +74,6 @@ TF_LITE_MICRO_TEST(TestInterpreter) {
     TF_LITE_MICRO_EXPECT_LE(interpreter.arena_used_bytes(), 928 + 100);
     TF_LITE_MICRO_EXPECT_EQ(static_cast<size_t>(1), interpreter.inputs_size());
     TF_LITE_MICRO_EXPECT_EQ(static_cast<size_t>(2), interpreter.outputs_size());
-    TF_LITE_MICRO_EXPECT_EQ(static_cast<size_t>(4), interpreter.tensors_size());
 
     TfLiteTensor* input = interpreter.input(0);
     TF_LITE_MICRO_EXPECT_NE(nullptr, input);
@@ -251,81 +250,6 @@ TF_LITE_MICRO_TEST(TestKernelMemoryPlanning) {
   }
 }
 
-TF_LITE_MICRO_TEST(TestVariableTensorReset) {
-  const tflite::Model* model = tflite::testing::GetComplexMockModel();
-  TF_LITE_MICRO_EXPECT_NE(nullptr, model);
-
-  tflite::AllOpsResolver op_resolver = tflite::testing::GetOpResolver();
-
-  constexpr size_t allocator_buffer_size =
-      3072 /* optimal arena size at the time of writting. */ +
-      16 /* alignment */ + 100 /* some headroom */;
-  uint8_t allocator_buffer[allocator_buffer_size];
-  tflite::MicroInterpreter interpreter(model, op_resolver, allocator_buffer,
-                                       allocator_buffer_size,
-                                       tflite::GetMicroErrorReporter());
-  TF_LITE_MICRO_EXPECT_EQ(interpreter.AllocateTensors(), kTfLiteOk);
-  TF_LITE_MICRO_EXPECT_LE(interpreter.arena_used_bytes(), 2096 + 100);
-  TF_LITE_MICRO_EXPECT_EQ(static_cast<size_t>(1), interpreter.inputs_size());
-  TF_LITE_MICRO_EXPECT_EQ(static_cast<size_t>(1), interpreter.outputs_size());
-
-  // Assign hard-code values:
-  for (size_t i = 0; i < interpreter.tensors_size(); ++i) {
-    TfLiteTensor* cur_tensor = interpreter.tensor(i);
-    int buffer_length = tflite::ElementCount(*cur_tensor->dims);
-    // Assign all buffers to non-zero values. Variable tensors will be assigned
-    // 2 here and will be verified that they have been reset after the API call.
-    int buffer_value = cur_tensor->is_variable ? 2 : 1;
-    switch (cur_tensor->type) {
-      case kTfLiteInt32: {
-        int32_t* buffer = tflite::GetTensorData<int32_t>(cur_tensor);
-        for (int j = 0; j < buffer_length; ++j) {
-          buffer[j] = static_cast<int32_t>(buffer_value);
-        }
-        break;
-      }
-      case kTfLiteUInt8: {
-        uint8_t* buffer = tflite::GetTensorData<uint8_t>(cur_tensor);
-        for (int j = 0; j < buffer_length; ++j) {
-          buffer[j] = static_cast<uint8_t>(buffer_value);
-        }
-        break;
-      }
-      default:
-        TF_LITE_MICRO_FAIL("Unsupported dtype");
-    }
-  }
-
-  interpreter.ResetVariableTensors();
-
-  // Ensure only variable tensors have been reset to zero:
-  for (size_t i = 0; i < interpreter.tensors_size(); ++i) {
-    TfLiteTensor* cur_tensor = interpreter.tensor(i);
-    int buffer_length = tflite::ElementCount(*cur_tensor->dims);
-    // Variable tensors should be zero (not the value assigned in the for loop
-    // above).
-    int buffer_value = cur_tensor->is_variable ? 0 : 1;
-    switch (cur_tensor->type) {
-      case kTfLiteInt32: {
-        int32_t* buffer = tflite::GetTensorData<int32_t>(cur_tensor);
-        for (int j = 0; j < buffer_length; ++j) {
-          TF_LITE_MICRO_EXPECT_EQ(buffer_value, buffer[j]);
-        }
-        break;
-      }
-      case kTfLiteUInt8: {
-        uint8_t* buffer = tflite::GetTensorData<uint8_t>(cur_tensor);
-        for (int j = 0; j < buffer_length; ++j) {
-          TF_LITE_MICRO_EXPECT_EQ(buffer_value, buffer[j]);
-        }
-        break;
-      }
-      default:
-        TF_LITE_MICRO_FAIL("Unsupported dtype");
-    }
-  }
-}
-
 // The interpreter initialization requires multiple steps and this test case
 // ensures that simply creating and destructing an interpreter object is ok.
 // b/147830765 has one example of a change that caused trouble for this simple
@@ -508,7 +432,6 @@ TF_LITE_MICRO_TEST(TestInterpreterMultipleInputs) {
 
     TF_LITE_MICRO_EXPECT_EQ(static_cast<size_t>(3), interpreter.inputs_size());
     TF_LITE_MICRO_EXPECT_EQ(static_cast<size_t>(1), interpreter.outputs_size());
-    TF_LITE_MICRO_EXPECT_EQ(static_cast<size_t>(4), interpreter.tensors_size());
 
     TfLiteTensor* input = interpreter.input(0);
     TF_LITE_MICRO_EXPECT_NE(nullptr, input);