91 lines
3.7 KiB
C++
91 lines
3.7 KiB
C++
/* Copyright 2020 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_L2NORMALIZATION_H_
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_
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#include <algorithm>
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#include <cmath>
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/common.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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inline void L2Normalization(const tflite::L2NormalizationParams& op_params,
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const RuntimeShape& input_shape,
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const float* input_data,
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const RuntimeShape& output_shape,
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float* output_data, float epsilon = 1e-6) {
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const int trailing_dim = input_shape.DimensionsCount() - 1;
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const int outer_size =
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MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape);
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const int depth =
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MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);
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for (int i = 0; i < outer_size; ++i) {
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float squared_l2_norm = 0;
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for (int c = 0; c < depth; ++c) {
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const float val = input_data[depth * i + c];
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squared_l2_norm += val * val;
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}
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float l2_norm = std::sqrt(squared_l2_norm);
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l2_norm = std::max(l2_norm, epsilon);
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for (int c = 0; c < depth; ++c) {
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output_data[depth * i + c] = input_data[depth * i + c] / l2_norm;
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}
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}
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}
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inline void L2Normalization(const tflite::L2NormalizationParams& op_params,
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const RuntimeShape& input_shape,
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const uint8_t* input_data,
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const RuntimeShape& output_shape,
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uint8_t* output_data) {
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const int trailing_dim = input_shape.DimensionsCount() - 1;
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const int depth =
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MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);
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const int outer_size =
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MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape);
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const int32_t input_zero_point = op_params.input_zero_point;
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for (int i = 0; i < outer_size; ++i) {
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int32_t square_l2_norm = 0;
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for (int c = 0; c < depth; c++) {
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int32_t diff = input_data[depth * i + c] - input_zero_point;
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square_l2_norm += diff * diff;
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}
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int32_t inv_l2norm_multiplier;
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int inv_l2norm_shift;
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GetInvSqrtQuantizedMultiplierExp(square_l2_norm, kReverseShift,
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&inv_l2norm_multiplier, &inv_l2norm_shift);
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for (int c = 0; c < depth; c++) {
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int32_t diff = input_data[depth * i + c] - input_zero_point;
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int32_t rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOneExp(
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128 * diff, inv_l2norm_multiplier, inv_l2norm_shift);
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int32_t unclamped_output_val = 128 + rescaled_diff;
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int32_t output_val =
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std::min(static_cast<int32_t>(255),
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std::max(static_cast<int32_t>(0), unclamped_output_val));
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output_data[depth * i + c] = static_cast<uint8_t>(output_val);
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}
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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_L2NORMALIZATION_H_
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