61c6c84964
This change moves //tensorflow/contrib/lite to //tensorflow/lite in preparation for TensorFlow 2.0's deprecation of contrib/. If you refer to TF Lite build targets or headers, you will need to update them manually. If you use TF Lite from the TensorFlow python package, "tf.contrib.lite" now points to "tf.lite". Please update your imports as soon as possible. For more details, see https://groups.google.com/a/tensorflow.org/forum/#!topic/tflite/iIIXOTOFvwQ @angersson and @aselle are conducting this migration. Please contact them if you have any further questions. PiperOrigin-RevId: 219536476
75 lines
2.6 KiB
C++
75 lines
2.6 KiB
C++
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_LITE_TOCO_TFLITE_CUSTOM_OPERATOR_H_
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#define TENSORFLOW_LITE_TOCO_TFLITE_CUSTOM_OPERATOR_H_
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#include "flatbuffers/flexbuffers.h"
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#include "absl/memory/memory.h"
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#include "tensorflow/lite/toco/tflite/operator.h"
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namespace toco {
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namespace tflite {
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// Custom operators have a generic byte buffer describing their options. This
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// class provides the boilerplate code for populating those options using
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// flexbuffers. Note that most of toco's operators will likely be supported
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// as builtin operators in TF Lite.
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//
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// Template argument T must derive from ::toco::Operator.
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template <typename T>
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class CustomOperator : public BaseOperator {
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public:
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using TocoOperator = T;
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using BaseOperator::BaseOperator;
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// Populate the given flexbuffer with options obtained from the tf.mini
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// operator.
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virtual void WriteOptions(const TocoOperator& op,
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flexbuffers::Builder* fbb) const {}
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// Set options in the given tf.mini operator using values from the flexbuffer
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// map.
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virtual void ReadOptions(const flexbuffers::Map& m, TocoOperator* op) const {}
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Options Serialize(const Operator& op,
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flatbuffers::FlatBufferBuilder* builder) const override {
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flexbuffers::Builder fbb;
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fbb.Map(
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[&]() { WriteOptions(static_cast<const TocoOperator&>(op), &fbb); });
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fbb.Finish();
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return Options::Custom(builder->CreateVector(fbb.GetBuffer()));
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}
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std::unique_ptr<Operator> Deserialize(
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const BuiltinOptions* builtin_options,
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const CustomOptions* custom_options) const override {
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auto op = absl::make_unique<TocoOperator>();
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if (custom_options) {
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auto flexbuffer_map =
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flexbuffers::GetRoot(custom_options->data(), custom_options->size())
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.AsMap();
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ReadOptions(flexbuffer_map, op.get());
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}
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return std::unique_ptr<Operator>(op.release());
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}
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};
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} // namespace tflite
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} // namespace toco
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#endif // TENSORFLOW_LITE_TOCO_TFLITE_CUSTOM_OPERATOR_H_
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