wehub-resource-sync
90 lines
3.6 KiB
C++
90 lines
3.6 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
==============================================================================*/
|
|
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_
|
|
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_
|
|
|
|
#include <algorithm>
|
|
#include <limits>
|
|
#include <vector>
|
|
|
|
#include "tensorflow/lite/kernels/internal/common.h"
|
|
#include "tensorflow/lite/kernels/internal/compatibility.h"
|
|
#include "tensorflow/lite/kernels/internal/cppmath.h"
|
|
#include "tensorflow/lite/kernels/internal/types.h"
|
|
|
|
namespace tflite {
|
|
|
|
namespace reference_ops {
|
|
|
|
template <typename InputT, typename OutputT>
|
|
inline void AffineQuantize(const tflite::QuantizationParams& op_params,
|
|
const RuntimeShape& input_shape,
|
|
const InputT* input_data,
|
|
const RuntimeShape& output_shape,
|
|
OutputT* output_data) {
|
|
const int32_t zero_point = op_params.zero_point;
|
|
const double scale = op_params.scale;
|
|
const int flat_size = MatchingFlatSize(input_shape, output_shape);
|
|
static constexpr int32_t min_val = std::numeric_limits<OutputT>::min();
|
|
static constexpr int32_t max_val = std::numeric_limits<OutputT>::max();
|
|
|
|
for (int i = 0; i < flat_size; i++) {
|
|
const InputT val = input_data[i];
|
|
int32_t unclamped =
|
|
static_cast<int32_t>(TfLiteRound(val / static_cast<float>(scale))) +
|
|
zero_point;
|
|
int32_t clamped = std::min(std::max(unclamped, min_val), max_val);
|
|
output_data[i] = clamped;
|
|
}
|
|
}
|
|
|
|
// Quantizes per-channel.
|
|
template <typename InputT, typename OutputT>
|
|
inline void PerChannelQuantize(
|
|
const tflite::PerChannelQuantizationParams& op_params,
|
|
const RuntimeShape& input_shape, const InputT* input_data,
|
|
const RuntimeShape& output_shape, OutputT* output_data) {
|
|
// Ensure flat size is same.
|
|
MatchingFlatSize(input_shape, output_shape);
|
|
|
|
const int32_t* zero_point = op_params.zero_point;
|
|
const float* scale = op_params.scale;
|
|
const int32_t quantized_dimension = op_params.quantized_dimension;
|
|
const int32_t num_dims = input_shape.DimensionsCount();
|
|
const int32_t* dims_data = input_shape.DimsData();
|
|
std::vector<int> current_dim(num_dims, 0);
|
|
static constexpr int32_t min_val = std::numeric_limits<OutputT>::min();
|
|
static constexpr int32_t max_val = std::numeric_limits<OutputT>::max();
|
|
|
|
do {
|
|
size_t offset =
|
|
ReducedOutputOffset(num_dims, reinterpret_cast<const int*>(dims_data),
|
|
current_dim.data(), 0, nullptr);
|
|
const InputT val = input_data[offset];
|
|
const int channel = current_dim[quantized_dimension];
|
|
int32_t unclamped = static_cast<int32_t>(TfLiteRound(
|
|
val / static_cast<float>(scale[channel]))) +
|
|
zero_point[channel];
|
|
int32_t clamped = std::min(std::max(unclamped, min_val), max_val);
|
|
output_data[offset] = static_cast<OutputT>(clamped);
|
|
} while (NextIndex(num_dims, reinterpret_cast<const int*>(dims_data),
|
|
current_dim.data()));
|
|
}
|
|
|
|
} // namespace reference_ops
|
|
|
|
} // namespace tflite
|
|
#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_
|