56 lines
2.1 KiB
C++
56 lines
2.1 KiB
C++
/* Copyright 2019 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_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_
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#include <algorithm>
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#include <limits>
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#include "tensorflow/lite/kernels/internal/common.h"
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#include "tensorflow/lite/kernels/internal/compatibility.h"
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#include "tensorflow/lite/kernels/internal/cppmath.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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namespace tflite {
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namespace reference_ops {
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template <typename InputT, typename OutputT>
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inline void AffineQuantize(const tflite::QuantizationParams& op_params,
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const RuntimeShape& input_shape,
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const InputT* input_data,
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const RuntimeShape& output_shape,
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OutputT* output_data) {
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const int32 zero_point = op_params.zero_point;
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const double scale = op_params.scale;
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const int flat_size = MatchingFlatSize(input_shape, output_shape);
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static constexpr int32 min_val = std::numeric_limits<OutputT>::min();
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static constexpr int32 max_val = std::numeric_limits<OutputT>::max();
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for (int i = 0; i < flat_size; i++) {
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const InputT val = input_data[i];
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int32 unclamped =
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static_cast<int32>(TfLiteRound(val / static_cast<float>(scale))) +
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zero_point;
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int32 clamped = std::min(std::max(unclamped, min_val), max_val);
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output_data[i] = clamped;
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}
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}
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} // namespace reference_ops
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} // namespace tflite
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#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_
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