text: #include "blas.h"
  
  #include <math.h>
  #include <assert.h>
  #include <float.h>
  #include <stdio.h>
  #include <stdlib.h>
  #include <string.h>
  void reorg_cpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out)
  {
      int b,i,j,k;
      int out_c = c/(stride*stride);
  
      for(b = 0; b < batch; ++b){
          for(k = 0; k < c; ++k){
              for(j = 0; j < h; ++j){
                  for(i = 0; i < w; ++i){
                      int in_index  = i + w*(j + h*(k + c*b));
                      int c2 = k % out_c;
                      int offset = k / out_c;
                      int w2 = i*stride + offset % stride;
                      int h2 = j*stride + offset / stride;
                      int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b));
                      if(forward) out[out_index] = x[in_index];
                      else out[in_index] = x[out_index];
                  }
              }
          }
      }
  }
  
  void flatten(float *x, int size, int layers, int batch, int forward)
  {
      float *swap = calloc(size*layers*batch, sizeof(float));
      int i,c,b;
      for(b = 0; b < batch; ++b){
          for(c = 0; c < layers; ++c){
              for(i = 0; i < size; ++i){
                  int i1 = b*layers*size + c*size + i;
                  int i2 = b*layers*size + i*layers + c;
                  if (forward) swap[i2] = x[i1];
                  else swap[i1] = x[i2];
              }
          }
      }
      memcpy(x, swap, size*layers*batch*sizeof(float));
      free(swap);
  }
  
  void weighted_sum_cpu(float *a, float *b, float *s, int n, float *c)
  {
      int i;
      for(i = 0; i < n; ++i){
          c[i] = s[i]*a[i] + (1-s[i])*(b ? b[i] : 0);
      }
  }
  
  void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out)
  {
      int stride = w1/w2;
      int sample = w2/w1;
      assert(stride == h1/h2);
      assert(sample == h2/h1);
      if(stride < 1) stride = 1;
      if(sample < 1) sample = 1;
      int minw = (w1 < w2) ? w1 : w2;
      int minh = (h1 < h2) ? h1 : h2;
      int minc = (c1 < c2) ? c1 : c2;
  
      int i,j,k,b;
      for(b = 0; b < batch; ++b){
          for(k = 0; k < minc; ++k){
              for(j = 0; j < minh; ++j){
                  for(i = 0; i < minw; ++i){
                      int out_index = i*sample + w2*(j*sample + h2*(k + c2*b));
                      int add_index = i*stride + w1*(j*stride + h1*(k + c1*b));
                      out[out_index] += add[add_index];
                  }
              }
          }
      }
  }
  
  void mean_cpu(float *x, int batch, int filters, int spatial, float *mean)
  {
      float scale = 1./(batch * spatial);
      int i,j,k;
      for(i = 0; i < filters; ++i){
          mean[i] = 0;
          for(j = 0; j < batch; ++j){
              for(k = 0; k < spatial; ++k){
                  int index = j*filters*spatial + i*spatial + k;
                  mean[i] += x[index];
              }
          }
          mean[i] *= scale;
      }
  }
  
  void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
  {
      float scale = 1./(batch * spatial - 1);
      int i,j,k;
      for(i = 0; i < filters; ++i){
          variance[i] = 0;
          for(j = 0; j < batch; ++j){
              for(k = 0; k < spatial; ++k){
                  int index = j*filters*spatial + i*spatial + k;
                  variance[i] += pow((x[index] - mean[i]), 2);
              }
          }
          variance[i] *= scale;
      }
  }
  
  void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial)
  {
      int b, f, i;
      for(b = 0; b < batch; ++b){
          for(f = 0; f < filters; ++f){
              for(i = 0; i < spatial; ++i){
                  int index = b*filters*spatial + f*spatial + i;
                  x[index] = (x[index] - mean[f])/(sqrt(variance[f]) + .000001f);
              }
          }
      }
  }
  
  void const_cpu(int N, float ALPHA, float *X, int INCX)
  {
      int i;
      for(i = 0; i < N; ++i) X[i*INCX] = ALPHA;
  }
  
  void mul_cpu(int N, float *X, int INCX, float *Y, int INCY)
  {
      int i;
      for(i = 0; i < N; ++i) Y[i*INCY] *= X[i*INCX];
  }
  
  void pow_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
  {
      int i;
      for(i = 0; i < N; ++i) Y[i*INCY] = pow(X[i*INCX], ALPHA);
  }
  
  void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
  {
      int i;
      for(i = 0; i < N; ++i) Y[i*INCY] += ALPHA*X[i*INCX];
  }
  
  void scal_cpu(int N, float ALPHA, float *X, int INCX)
  {
      int i;
      for(i = 0; i < N; ++i) X[i*INCX] *= ALPHA;
  }
  
  void fill_cpu(int N, float ALPHA, float *X, int INCX)
  {
      int i;
      for(i = 0; i < N; ++i) X[i*INCX] = ALPHA;
  }
  
  void copy_cpu(int N, float *X, int INCX, float *Y, int INCY)
  {
      int i;
      for(i = 0; i < N; ++i) Y[i*INCY] = X[i*INCX];
  }
  
  void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error)
  {
      int i;
      for(i = 0; i < n; ++i){
          float diff = truth[i] - pred[i];
          float abs_val = fabs(diff);
          if(abs_val < 1) {
              error[i] = diff * diff;
              delta[i] = diff;
          }
          else {
              error[i] = 2*abs_val - 1;
              delta[i] = (diff < 0) ? 1 : -1;
          }
      }
  }
  
  void l1_cpu(int n, float *pred, float *truth, float *delta, float *error)
  {
      int i;
      for(i = 0; i < n; ++i){
          float diff = truth[i] - pred[i];
          error[i] = fabs(diff);
          delta[i] = diff > 0 ? 1 : -1;
      }
  }
  
  void l2_cpu(int n, float *pred, float *truth, float *delta, float *error)
  {
      int i;
      for(i = 0; i < n; ++i){
          float diff = truth[i] - pred[i];
          error[i] = diff * diff;
          delta[i] = diff;
      }
  }
  
  float dot_cpu(int N, float *X, int INCX, float *Y, int INCY)
  {
      int i;
      float dot = 0;
      for(i = 0; i < N; ++i) dot += X[i*INCX] * Y[i*INCY];
      return dot;
  }
  
  void softmax(float *input, int n, float temp, int stride, float *output)
  {
      int i;
      float sum = 0;
      float largest = -FLT_MAX;
      for(i = 0; i < n; ++i){
          if(input[i*stride] > largest) largest = input[i*stride];
      }
      for(i = 0; i < n; ++i){
          float e = exp(input[i*stride]/temp - largest/temp);
          sum += e;
          output[i*stride] = e;
      }
      for(i = 0; i < n; ++i){
          output[i*stride] /= sum;
      }
  }
  
  
  void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output)
  {
      int g, b;
      for(b = 0; b < batch; ++b){
          for(g = 0; g < groups; ++g){
              softmax(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset);
          }
      }
  }
  
  