/* 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_SUBGRAPH_QUANTIZER_H_
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#define TENSORFLOW_LITE_TOOLS_OPTIMIZE_SUBGRAPH_QUANTIZER_H_
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#include "tensorflow/lite/context.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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namespace tflite {
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namespace optimize {
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namespace internal {
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// Quantizes a given subgraph, the subgraph needs to min/max information
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// present.
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//
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// Assumes that some ops like Conv and Depthwise conv are quantized by
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// per channel symmetric quantization.
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class SubgraphQuantizer {
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public:
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SubgraphQuantizer(ModelT* model, SubGraphT* subgraph,
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ErrorReporter* error_reporter)
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: model_(model), subgraph_(subgraph), error_reporter_(error_reporter) {}
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// Quantize operator at the given index.
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TfLiteStatus QuantizeOperator(int op_idx);
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private:
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// Quantizes ops with bias tensors.
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TfLiteStatus QuantizeOpWithBias(BuiltinOperator op_code, OperatorT* op);
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// Average and Max pool need special treatement. The scales are propagated
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// from inputs to outputs.
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TfLiteStatus PropagateMinMaxForAvgAndMaxPool(BuiltinOperator op_code,
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OperatorT* op);
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// Asymmetric quantizes inputs and outputs of an Softmax Op.
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// Input is quantized with the min-max range and output is hardcoded to have
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// 1/256 as scale and -128 as zero point.
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TfLiteStatus AsymmetricQuantizeSoftmax(BuiltinOperator op_code,
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OperatorT* op);
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// Asymmetric quantizes an Op with multiple inputs and outputs. E.g Add.
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TfLiteStatus AsymmetricQuantizeInputsAndOutputs(BuiltinOperator op_code,
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OperatorT* op);
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TfLiteStatus AsymmetricQuantizeTensor(BuiltinOperator op_code,
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int32_t tensor_idx);
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// Returns true if |tensor_idx| is one of the inputs in the subgraph.
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bool IsSubgraphInput(int32_t tensor_idx) const;
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ModelT* model_;
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SubGraphT* subgraph_;
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ErrorReporter* error_reporter_;
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};
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} // namespace internal
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} // namespace optimize
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} // namespace tflite
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#endif // TENSORFLOW_LITE_TOOLS_OPTIMIZE_SUBGRAPH_QUANTIZER_H_
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