91 lines
2.9 KiB
C++
91 lines
2.9 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.
|
|
==============================================================================*/
|
|
|
|
#include "tensorflow/lite/micro/micro_utils.h"
|
|
|
|
#include <cmath>
|
|
#include <cstdint>
|
|
#include <limits>
|
|
|
|
#include "tensorflow/lite/c/common.h"
|
|
#include "tensorflow/lite/kernels/internal/compatibility.h"
|
|
#include "tensorflow/lite/kernels/op_macros.h"
|
|
#include "tensorflow/lite/micro/memory_helpers.h"
|
|
#include "tensorflow/lite/micro/micro_log.h"
|
|
|
|
namespace tflite {
|
|
|
|
int ElementCount(const TfLiteIntArray& dims) {
|
|
int result = 1;
|
|
for (int i = 0; i < dims.size; ++i) {
|
|
result *= dims.data[i];
|
|
}
|
|
return result;
|
|
}
|
|
|
|
size_t EvalTensorBytes(const TfLiteEvalTensor* tensor) {
|
|
size_t bytes_per_element;
|
|
TFLITE_DCHECK(kTfLiteOk ==
|
|
TfLiteTypeSizeOf(tensor->type, &bytes_per_element));
|
|
return ElementCount(*tensor->dims) * bytes_per_element;
|
|
}
|
|
|
|
void SignedSymmetricPerChannelQuantize(
|
|
const float* values, TfLiteIntArray* dims, int quantized_dimension,
|
|
int8_t* quantized_values, float* scaling_factors, TfLiteType type) {
|
|
int input_size = ElementCount(*dims);
|
|
int channel_count = dims->data[quantized_dimension];
|
|
int per_channel_size = input_size / channel_count;
|
|
|
|
int stride;
|
|
int channel_stride;
|
|
|
|
int qmin = QMinFromTfLiteType(type);
|
|
int qmax = QMaxFromTfLiteType(type);
|
|
|
|
if (quantized_dimension == 0) {
|
|
stride = 1;
|
|
channel_stride = per_channel_size;
|
|
} else if (quantized_dimension == 3) {
|
|
stride = channel_count;
|
|
channel_stride = 1;
|
|
} else {
|
|
MicroPrintf("quantized dimension must be 0 or 3");
|
|
TFLITE_ABORT;
|
|
}
|
|
|
|
// Calculate scales for each channel.
|
|
for (int channel = 0; channel < channel_count; channel++) {
|
|
float min = 0;
|
|
float max = 0;
|
|
|
|
for (int i = 0; i < per_channel_size; i++) {
|
|
int idx = channel * channel_stride + i * stride;
|
|
min = fminf(min, values[idx]);
|
|
max = fmaxf(max, values[idx]);
|
|
}
|
|
scaling_factors[channel] = fmaxf(fabs(min), fabs(max)) / qmax;
|
|
for (int i = 0; i < per_channel_size; i++) {
|
|
int idx = channel * channel_stride + i * stride;
|
|
const int32_t quantized_value =
|
|
static_cast<int32_t>(roundf(values[idx] / scaling_factors[channel]));
|
|
// Clamp: just in case some odd numeric offset.
|
|
quantized_values[idx] = fminf(qmax, fmaxf(qmin + 1, quantized_value));
|
|
}
|
|
}
|
|
}
|
|
|
|
} // namespace tflite
|