79 lines
2.9 KiB
C++
79 lines
2.9 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_DEQUANTIZE_H_
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_
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#include <limits.h>
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#include <vector>
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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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// Dequantizes into a float without rounding.
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template <typename InputT, typename OutputT>
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inline void Dequantize(const tflite::DequantizationParams& 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, OutputT* output_data) {
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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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for (int i = 0; i < flat_size; i++) {
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const int32 val = input_data[i];
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const OutputT result = static_cast<OutputT>(scale * (val - zero_point));
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output_data[i] = result;
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}
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}
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// Dequantizes per-channel quantized tensor to float.
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template <typename T>
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inline void PerChannelDequantize(
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const tflite::PerChannelDequantizationParams& op_params,
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const RuntimeShape& input_shape, const T* input_data,
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const RuntimeShape& output_shape, float* output_data) {
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// Ensure flat size is same.
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MatchingFlatSize(input_shape, output_shape);
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const int32* zero_point = op_params.zero_point;
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const float* scale = op_params.scale;
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const int32 quantized_dimension = op_params.quantized_dimension;
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const int32 num_dims = input_shape.DimensionsCount();
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const int32* dims_data = input_shape.DimsData();
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std::vector<int> current_dim(num_dims, 0);
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do {
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size_t offset =
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ReducedOutputOffset(num_dims, reinterpret_cast<const int*>(dims_data),
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current_dim.data(), 0, nullptr);
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const int channel = current_dim[quantized_dimension];
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const int32 val = input_data[offset];
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const float result =
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static_cast<float>(scale[channel] * (val - zero_point[channel]));
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output_data[offset] = result;
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} while (NextIndex(num_dims, reinterpret_cast<const int*>(dims_data),
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current_dim.data()));
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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_DEQUANTIZE_H_
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