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Remove unused MicroInterpreter::tensor() API. #49114

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May 11, 2021
Merged

Remove unused MicroInterpreter::tensor() API. #49114

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12 changes: 0 additions & 12 deletions tensorflow/lite/micro/micro_interpreter.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -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);
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12 changes: 0 additions & 12 deletions tensorflow/lite/micro/micro_interpreter.h
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Expand Up @@ -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 {
Expand Down
77 changes: 0 additions & 77 deletions tensorflow/lite/micro/micro_interpreter_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -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);
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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);
Expand Down