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// Copyright 2021 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#ifndef COMPONENTS_OPTIMIZATION_GUIDE_CORE_BASE_MODEL_EXECUTOR_H_
#define COMPONENTS_OPTIMIZATION_GUIDE_CORE_BASE_MODEL_EXECUTOR_H_
#include "components/optimization_guide/core/base_model_executor_helpers.h"
#include "components/optimization_guide/core/execution_status.h"
#include "components/optimization_guide/core/tflite_model_executor.h"
#include "components/optimization_guide/core/tflite_op_resolver.h"
#include "third_party/tflite_support/src/tensorflow_lite_support/cc/task/core/base_task_api.h"
namespace optimization_guide {
// An ModelExecutor that executes models with arbitrary
// input and output types. Note that callers will need to give an implementation
// of this class to a |ModelHandler|, whereas the
// handle is the actual class that calling code would own and call into.
template <class OutputType, class... InputTypes>
class BaseModelExecutor : public TFLiteModelExecutor<OutputType, InputTypes...>,
public InferenceDelegate<OutputType, InputTypes...> {
public:
using ModelExecutionTask =
tflite::task::core::BaseTaskApi<OutputType, InputTypes...>;
BaseModelExecutor() = default;
~BaseModelExecutor() override = default;
BaseModelExecutor(const BaseModelExecutor&) = delete;
BaseModelExecutor& operator=(const BaseModelExecutor&) = delete;
protected:
absl::optional<OutputType> Execute(ModelExecutionTask* execution_task,
ExecutionStatus* out_status,
InputTypes... args) override {
return static_cast<GenericModelExecutionTask<OutputType, InputTypes...>*>(
execution_task)
->Execute(out_status, args...);
}
std::unique_ptr<ModelExecutionTask> BuildModelExecutionTask(
base::MemoryMappedFile* model_file,
ExecutionStatus* out_status) override {
std::unique_ptr<tflite::task::core::TfLiteEngine> tflite_engine =
std::make_unique<tflite::task::core::TfLiteEngine>(
std::make_unique<TFLiteOpResolver>());
absl::Status model_load_status = tflite_engine->BuildModelFromFlatBuffer(
reinterpret_cast<const char*>(model_file->data()),
model_file->length());
if (!model_load_status.ok()) {
DLOG(ERROR) << "Failed to load model: " << model_load_status.ToString();
*out_status = ExecutionStatus::kErrorModelFileNotValid;
return nullptr;
}
absl::Status interpreter_status =
tflite_engine->InitInterpreter(tflite::proto::ComputeSettings(),
/*num_threads=*/1);
if (!interpreter_status.ok()) {
DLOG(ERROR) << "Failed to initialize model interpreter: "
<< interpreter_status.ToString();
*out_status = ExecutionStatus::kErrorUnknown;
return nullptr;
}
return std::make_unique<
GenericModelExecutionTask<OutputType, InputTypes...>>(
std::move(tflite_engine), this);
}
// InferenceDelegate:
absl::Status Preprocess(const std::vector<TfLiteTensor*>& input_tensors,
InputTypes... input) override = 0;
OutputType Postprocess(
const std::vector<const TfLiteTensor*>& output_tensors) override = 0;
};
} // namespace optimization_guide
#endif // COMPONENTS_OPTIMIZATION_GUIDE_CORE_BASE_MODEL_EXECUTOR_H_
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