135 lines
4.8 KiB
C++
135 lines
4.8 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_MICRO_MICRO_UTILS_H_
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#define TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_
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#include <algorithm>
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#include <cmath>
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#include <cstdint>
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#include "tensorflow/lite/c/common.h"
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namespace tflite {
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// Returns number of elements in the shape array.
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int ElementCount(const TfLiteIntArray& dims);
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// Converts a float value into a quantized value. Note that large values (close
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// to max int and min int) may see significant error due to a lack of floating
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// point granularity for large values.
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template <typename T>
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T FloatToQuantizedType(const float value, const float scale, int zero_point) {
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int32_t result = round(value / scale) + zero_point;
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result =
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std::max(static_cast<int32_t>(std::numeric_limits<T>::min()), result);
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result =
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std::min(static_cast<int32_t>(std::numeric_limits<T>::max()), result);
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return result;
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}
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template <typename T>
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T FloatToSymmetricQuantizedType(const float value, const float scale) {
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int32_t result = round(value / scale);
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result =
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std::max(static_cast<int32_t>(std::numeric_limits<T>::min() + 1), result);
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result =
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std::min(static_cast<int32_t>(std::numeric_limits<T>::max()), result);
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return result;
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}
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// Helper methods to quantize arrays of floats to the desired format.
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//
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// There are several key flavors of quantization in TfLite:
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// asymmetric symmetric per channel
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// int8_t | X | X | X |
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// uint8_t | X | X | |
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// int16_t | X | | |
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// int32_t | | X | X |
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//
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// The per-op quantization spec can be found here:
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// https://www.tensorflow.org/lite/performance/quantization_spec
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template <typename T>
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void Quantize(const float* input, T* output, int num_elements, float scale,
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int zero_point) {
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for (int i = 0; i < num_elements; i++) {
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output[i] = FloatToQuantizedType<T>(input[i], scale, zero_point);
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}
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}
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template <typename T>
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void SymmetricQuantize(const float* input, T* output, int num_elements,
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float scale) {
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for (int i = 0; i < num_elements; i++) {
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output[i] = FloatToSymmetricQuantizedType<T>(input[i], scale);
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}
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}
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template <typename T>
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void SymmetricPerChannelQuantize(const float* input, T* output,
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int num_elements, int num_channels,
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float* scales) {
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int elements_per_channel = num_elements / num_channels;
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for (int i = 0; i < num_channels; i++) {
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for (int j = 0; j < elements_per_channel; j++) {
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output[i * elements_per_channel + j] = FloatToSymmetricQuantizedType<T>(
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input[i * elements_per_channel + j], scales[i]);
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}
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}
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}
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void SignedSymmetricPerChannelQuantize(const float* values,
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TfLiteIntArray* dims,
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int quantized_dimension,
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int8_t* quantized_values,
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float* scaling_factor);
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// Quantizes inputs based on the values provided, choosing the smallest range
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// which includes all input values.
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template <typename T>
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void SymmetricQuantizeCalculateScales(const float* values, TfLiteIntArray* dims,
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T* output, float* scale) {
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int input_size = ElementCount(*dims);
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float min = 0;
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float max = 0;
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for (int i = 0; i < input_size; i++) {
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min = fminf(min, values[i]);
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max = fmaxf(max, values[i]);
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}
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*scale = fmaxf(std::abs(min), std::abs(max)) / std::numeric_limits<T>::max();
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for (int i = 0; i < input_size; i++) {
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const int32_t quantized_value =
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static_cast<int32_t>(roundf(values[i] / *scale));
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// Clamp: just in case some odd numeric offset.
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quantized_value = fminf(std::numeric_limits<T>::max(), quantized_value);
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quantized_value = fmaxf(std::numeric_limits<T>::min() + 1, quantized_value);
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output[i] = quantized_value;
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}
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}
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template <typename T>
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void Dequantize(const T* values, const int size, const float scale,
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int zero_point, float* dequantized_values) {
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for (int i = 0; i < size; ++i) {
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dequantized_values[i] = (values[i] - zero_point) * scale;
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}
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}
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} // namespace tflite
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#endif // TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_
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