mindspore2022/mindspore/lite/nnacl/fp32/batchnorm.c

/**
 * Copyright 2020 Huawei Technologies Co., Ltd
 *
 * 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 "nnacl/fp32/batchnorm.h"
#include <math.h>
#include "nnacl/batchnorm_parameter.h"
#include "nnacl/op_base.h"
#include "nnacl/errorcode.h"

void BatchNormFp32(const void *input, const void *mean, const void *variance, BatchNormParameter *param, int task_id,
                   void *output) {
  int units_per_thread = UP_DIV(param->unit_, param->op_parameter_.thread_num_);
  int completed_units = task_id * units_per_thread;
  int cur_unit = MSMIN(units_per_thread, param->unit_ - completed_units);
  int cur_offset = completed_units * param->channel_;

  for (int i = 0; i < cur_unit; i++) {
    for (int c = 0; c < param->channel_; c++) {
      float variance_sqrt = sqrt(((const float *)variance)[c] + param->epsilon_);
      ((float *)output)[cur_offset + c] =
        (((const float *)input)[cur_offset + c] - ((const float *)mean)[c]) / variance_sqrt;
    }
    cur_offset += param->channel_;
  }
}

void FusedBatchNormFp32(const void *input, const void *scale, const void *offset, const void *mean,
                        const void *variance, BatchNormParameter *param, int task_id, void *output) {
  int units_per_thread = UP_DIV(param->unit_, param->op_parameter_.thread_num_);
  int completed_units = task_id * units_per_thread;
  int cur_unit = MSMIN(units_per_thread, param->unit_ - completed_units);
  int cur_offset = completed_units * param->channel_;

  for (int i = 0; i < cur_unit; i++) {
    for (int c = 0; c < param->channel_; c++) {
      float variance_sqrt = sqrt(((const float *)variance)[c] + param->epsilon_);
      float norm_val = (((const float *)input)[cur_offset + c] - ((const float *)mean)[c]) / variance_sqrt;
      ((float *)output)[cur_offset + c] = norm_val * ((const float *)scale)[c] + ((const float *)offset)[c];
    }
    cur_offset += param->channel_;
  }
}

void FusedBatchNormFp32MeanVar(const float *input, float *run_mean, float *run_var, BatchNormParameter *param,
                               float *save_mean, float *save_var) {
  float N = (float)param->unit_;
  for (int i = 0; i < param->unit_; i++) {
    for (int c = 0; c < param->channel_; c++) {
      int idx = i * param->channel_ + c;
      run_mean[c] += input[idx];
      run_var[c] += input[idx] * input[idx];
    }
  }
  const float VN = (N > 1.0f) ? (N - 1.0f) : 1.0f;
  for (int c = 0; c < param->channel_; c++) {
    run_mean[c] = run_mean[c] / N;
    run_var[c] = run_var[c] / VN - run_mean[c] * run_mean[c];
    save_mean[c] = param->momentum_ * save_mean[c] + (1 - param->momentum_) * run_mean[c];
    const float var = run_var[c];
    save_var[c] = param->momentum_ * save_var[c] + (1 - param->momentum_) * var;
  }
}