tensorflow/lite/kernels/test_util.cc

/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/lite/kernels/test_util.h"

#include "tensorflow/core/platform/logging.h"
#include "tensorflow/lite/delegates/nnapi/nnapi_delegate.h"
#include "tensorflow/lite/version.h"

namespace tflite {

using ::testing::FloatNear;
using ::testing::Matcher;

namespace {

// Whether to enable (global) use of NNAPI. Note that this will typically
// be set via a command-line flag.
static bool force_use_nnapi = false;

TfLiteDelegate* TestNnApiDelegate() {
  static TfLiteDelegate* delegate = [] {
    StatefulNnApiDelegate::Options options;
    // In Android Q, the NNAPI delegate avoids delegation if the only device
    // is the reference CPU. However, for testing purposes, we still want
    // delegation coverage, so force use of this reference path.
    options.accelerator_name = "nnapi-reference";
    return new StatefulNnApiDelegate(options);
  }();
  return delegate;
}

}  // namespace

std::vector<Matcher<float>> ArrayFloatNear(const std::vector<float>& values,
                                           float max_abs_error) {
  std::vector<Matcher<float>> matchers;
  matchers.reserve(values.size());
  for (const float& v : values) {
    matchers.emplace_back(FloatNear(v, max_abs_error));
  }
  return matchers;
}

std::vector<Matcher<std::complex<float>>> ArrayComplex64Near(
    const std::vector<std::complex<float>>& values, float max_abs_error) {
  std::vector<Matcher<std::complex<float>>> matchers;
  matchers.reserve(values.size());
  for (const std::complex<float>& v : values) {
    matchers.emplace_back(
        AllOf(::testing::Property(&std::complex<float>::real,
                                  FloatNear(v.real(), max_abs_error)),
              ::testing::Property(&std::complex<float>::imag,
                                  FloatNear(v.imag(), max_abs_error))));
  }
  return matchers;
}

int SingleOpModel::AddInput(const TensorData& t, bool is_variable) {
  int id = 0;
  if (t.per_channel_quantization) {
    id = AddTensorPerChannelQuant(t);
  } else {
    id = AddTensor<float>(t, {}, is_variable);
  }
  inputs_.push_back(id);
  return id;
}

int SingleOpModel::AddNullInput() {
  int id = kOptionalTensor;
  inputs_.push_back(id);
  return id;
}

int SingleOpModel::AddOutput(const TensorData& t) {
  int id = AddTensor<float>(t, {});
  outputs_.push_back(id);
  return id;
}

void SingleOpModel::SetBuiltinOp(BuiltinOperator type,
                                 BuiltinOptions builtin_options_type,
                                 flatbuffers::Offset<void> builtin_options) {
  opcodes_.push_back(CreateOperatorCode(builder_, type, 0));
  operators_.push_back(CreateOperator(
      builder_, /*opcode_index=*/0, builder_.CreateVector<int32_t>(inputs_),
      builder_.CreateVector<int32_t>(outputs_), builtin_options_type,
      builtin_options,
      /*custom_options=*/0, CustomOptionsFormat_FLEXBUFFERS));
}

void SingleOpModel::SetCustomOp(
    const string& name, const std::vector<uint8_t>& custom_option,
    const std::function<TfLiteRegistration*()>& registration) {
  custom_registrations_[name] = registration;
  opcodes_.push_back(
      CreateOperatorCodeDirect(builder_, BuiltinOperator_CUSTOM, name.data()));
  operators_.push_back(CreateOperator(
      builder_, /*opcode_index=*/0, builder_.CreateVector<int32_t>(inputs_),
      builder_.CreateVector<int32_t>(outputs_), BuiltinOptions_NONE, 0,
      builder_.CreateVector<uint8_t>(custom_option),
      CustomOptionsFormat_FLEXBUFFERS));
}

void SingleOpModel::BuildInterpreter(std::vector<std::vector<int>> input_shapes,
                                     int num_threads,
                                     bool allow_fp32_relax_to_fp16) {
  auto opcodes = builder_.CreateVector(opcodes_);
  auto operators = builder_.CreateVector(operators_);
  auto tensors = builder_.CreateVector(tensors_);
  auto inputs = builder_.CreateVector<int32_t>(inputs_);
  auto outputs = builder_.CreateVector<int32_t>(outputs_);
  // Create a single subgraph
  std::vector<flatbuffers::Offset<SubGraph>> subgraphs;
  auto subgraph = CreateSubGraph(builder_, tensors, inputs, outputs, operators);
  subgraphs.push_back(subgraph);
  auto subgraphs_flatbuffer = builder_.CreateVector(subgraphs);

  auto buffers = builder_.CreateVector(buffers_);
  auto description = builder_.CreateString("programmatic model");
  builder_.Finish(CreateModel(builder_, TFLITE_SCHEMA_VERSION, opcodes,
                              subgraphs_flatbuffer, description, buffers));

  auto* model = GetModel(builder_.GetBufferPointer());

  if (!resolver_) {
    auto resolver = new ops::builtin::BuiltinOpResolver();
    for (const auto& reg : custom_registrations_) {
      resolver->AddCustom(reg.first.data(), reg.second());
    }
    resolver_ = std::unique_ptr<OpResolver>(resolver);
  }
  CHECK(InterpreterBuilder(model, *resolver_)(&interpreter_, num_threads) ==
        kTfLiteOk);

  CHECK(interpreter_ != nullptr);

  for (size_t i = 0; i < input_shapes.size(); ++i) {
    const int input_idx = interpreter_->inputs()[i];
    if (input_idx == kOptionalTensor) continue;
    const auto& shape = input_shapes[i];
    if (shape.empty()) continue;
    CHECK(interpreter_->ResizeInputTensor(input_idx, shape) == kTfLiteOk);
  }

  interpreter_->SetAllowFp16PrecisionForFp32(allow_fp32_relax_to_fp16);

  CHECK(interpreter_->AllocateTensors() == kTfLiteOk)
      << "Cannot allocate tensors";
  interpreter_->ResetVariableTensors();

  if (force_use_nnapi) {
    // TODO(b/124505407): Check the result and fail accordingly.
    interpreter_->ModifyGraphWithDelegate(TestNnApiDelegate());
  }

  // Modify delegate with function.
  if (apply_delegate_fn_) {
    apply_delegate_fn_(interpreter_.get());
  }
}

void SingleOpModel::Invoke() { ASSERT_EQ(interpreter_->Invoke(), kTfLiteOk); }

TfLiteStatus SingleOpModel::InvokeUnchecked() { return interpreter_->Invoke(); }

void SingleOpModel::BuildInterpreter(
    std::vector<std::vector<int>> input_shapes) {
  BuildInterpreter(input_shapes, /*num_threads=*/-1,
                   /*allow_fp32_relax_to_fp16=*/false);
}

void SingleOpModel::BuildInterpreter(std::vector<std::vector<int>> input_shapes,
                                     int num_threads) {
  BuildInterpreter(input_shapes, num_threads,
                   /*allow_fp32_relax_to_fp16=*/false);
}

void SingleOpModel::BuildInterpreter(std::vector<std::vector<int>> input_shapes,
                                     bool allow_fp32_relax_to_fp16) {
  BuildInterpreter(input_shapes, /*num_threads=*/-1, allow_fp32_relax_to_fp16);
}

// static
void SingleOpModel::SetForceUseNnapi(bool use_nnapi) {
  force_use_nnapi = use_nnapi;
}

int32_t SingleOpModel::GetTensorSize(int index) const {
  TfLiteTensor* t = interpreter_->tensor(index);
  CHECK(t);
  int total_size = 1;
  for (int i = 0; i < t->dims->size; ++i) {
    total_size *= t->dims->data[i];
  }
  return total_size;
}

template <>
std::vector<string> SingleOpModel::ExtractVector(int index) const {
  TfLiteTensor* tensor_ptr = interpreter_->tensor(index);
  CHECK(tensor_ptr != nullptr);
  const int num_strings = GetStringCount(tensor_ptr);
  std::vector<string> result;
  result.reserve(num_strings);
  for (int i = 0; i < num_strings; ++i) {
    const auto str = GetString(tensor_ptr, i);
    result.emplace_back(str.str, str.len);
  }
  return result;
}
}  // namespace tflite