94 lines
4.2 KiB
C++
94 lines
4.2 KiB
C++
/* Copyright 2018 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_TOOLS_OPTIMIZE_QUANTIZE_MODEL_H_
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#define TENSORFLOW_LITE_TOOLS_OPTIMIZE_QUANTIZE_MODEL_H_
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#include <memory>
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#include <unordered_set>
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#include "tensorflow/lite/context.h"
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#include "tensorflow/lite/core/api/error_reporter.h"
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#include "tensorflow/lite/model.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/util.h"
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namespace tflite {
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namespace optimize {
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// Quantizes input_model and populates the provided builder with the new model.
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// input_model is required to have min/max information populated in its
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// quantization params.
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//
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// Inputs and output types default to float instead of a quantized type.
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//
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// Note: This is a private API, subject to change.
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TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
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ModelT* input_model, ErrorReporter* error_reporter);
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// Same as above, but the types of quantized inputs and outputs are
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// configurable.
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//
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// Note: This is a private API, subject to change.
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TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
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ModelT* input_model, const TensorType& input_type,
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const TensorType& output_type,
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ErrorReporter* error_reporter);
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// Same as above, but can enable allowing float intermediate operations for ops
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// that do not yet support quantizable.
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//
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// Note: This is a private API, subject to change.
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TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
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ModelT* input_model, const TensorType& input_type,
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const TensorType& output_type, bool allow_float,
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ErrorReporter* error_reporter);
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// Same as above, but enables only quantizing an allowlist of operations,
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// specified by their operator output name.
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//
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// Note: This is a private API, subject to change.
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TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
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ModelT* input_model, const TensorType& input_type,
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const TensorType& output_type, bool allow_float,
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const std::unordered_set<string>& operator_names,
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ErrorReporter* error_reporter);
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// Same as above, but enables to provide activation type, which
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// could be TensorType_INT16 or TensorType_INT8.
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//
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// Note: This is a private API, subject to change.
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TfLiteStatus QuantizeModelAllOperators(flatbuffers::FlatBufferBuilder* builder,
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ModelT* model,
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const TensorType& input_type,
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const TensorType& output_type,
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bool allow_float,
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const TensorType& activations_type,
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ErrorReporter* error_reporter);
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// Quantizes input_model and populates the provided builder with the new model
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// with all possible input parameters.
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// All functions above call this function underneath.
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TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
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ModelT* model, const TensorType& input_type,
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const TensorType& output_type, bool allow_float,
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const std::unordered_set<string>& operator_names,
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const TensorType& activations_type,
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ErrorReporter* error_reporter);
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} // namespace optimize
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} // namespace tflite
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#endif // TENSORFLOW_LITE_TOOLS_OPTIMIZE_QUANTIZE_MODEL_H_
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