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// Copyright 2024 The Chromium Authors
// 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_SIGNATURE_MODEL_EXECUTOR_H_
#define COMPONENTS_OPTIMIZATION_GUIDE_CORE_SIGNATURE_MODEL_EXECUTOR_H_
#include <optional>
#include "base/notreached.h"
#include "components/optimization_guide/core/base_model_executor.h"
#include "components/optimization_guide/core/execution_status.h"
#include "third_party/tflite/src/tensorflow/lite/signature_runner.h"
namespace optimization_guide {
// An ModelExecutor that uses TfLite Signatures for inference.
// This works exactly like the BaseModelExecutor except that the input and
// output tensors are available in the form of a map instead of a vector.
template <class OutputType, class InputType>
class SignatureModelExecutor : public BaseModelExecutor<OutputType, InputType> {
public:
using ModelExecutionTask =
tflite::task::core::BaseTaskApi<OutputType, InputType>;
SignatureModelExecutor() = default;
~SignatureModelExecutor() override = default;
SignatureModelExecutor(const SignatureModelExecutor&) = delete;
SignatureModelExecutor& operator=(const SignatureModelExecutor&) = delete;
protected:
virtual bool Preprocess(
const std::map<std::string, TfLiteTensor*>& input_tensors_map,
InputType input) = 0;
virtual std::optional<OutputType> Postprocess(
const std::map<std::string, const TfLiteTensor*>& output_tensors_map) = 0;
virtual const char* GetSignature() = 0;
// optimization_guide::BaseModelExecutor:
bool Preprocess(const std::vector<TfLiteTensor*>& input_tensors,
const InputType& input) override {
NOTREACHED();
return false;
}
std::optional<OutputType> Postprocess(
const std::vector<const TfLiteTensor*>& output_tensors) override {
NOTREACHED();
return std::nullopt;
}
std::optional<OutputType> Execute(ModelExecutionTask* execution_task,
ExecutionStatus* out_status,
InputType input) override {
tflite::SignatureRunner* signature_runner =
static_cast<GenericModelExecutionTask<OutputType, InputType>*>(
execution_task)
->GetSignatureRunner(GetSignature());
if (!signature_runner) {
*out_status = ExecutionStatus::kErrorModelFileNotValid;
return std::nullopt;
}
Preprocess(GetTensorsMappedByInputNames(signature_runner), input);
TfLiteStatus status = signature_runner->Invoke();
if (status == kTfLiteCancelled) {
*out_status = ExecutionStatus::kErrorCancelled;
return std::nullopt;
}
if (status != kTfLiteOk) {
*out_status = ExecutionStatus::kErrorUnknown;
return std::nullopt;
}
*out_status = ExecutionStatus::kSuccess;
return Postprocess(GetTensorsMappedByOutputNames(signature_runner));
}
private:
std::map<std::string, TfLiteTensor*> GetTensorsMappedByInputNames(
tflite::SignatureRunner* signature_runner) {
auto input_keys = signature_runner->input_names();
std::map<std::string, TfLiteTensor*> input_tensors;
for (const char* input : input_keys) {
input_tensors[input] = signature_runner->input_tensor(input);
}
return input_tensors;
}
std::map<std::string, const TfLiteTensor*> GetTensorsMappedByOutputNames(
tflite::SignatureRunner* signature_runner) {
auto output_keys = signature_runner->output_names();
std::map<std::string, const TfLiteTensor*> output_tensors;
for (const char* output : output_keys) {
const TfLiteTensor* output_tensor =
signature_runner->output_tensor(output);
output_tensors[output] = output_tensor;
}
return output_tensors;
}
};
} // namespace optimization_guide
#endif // COMPONENTS_OPTIMIZATION_GUIDE_CORE_SIGNATURE_MODEL_EXECUTOR_H_
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