text: #include "local_layer.h"
  #include "utils.h"
  #include "im2col.h"
  #include "col2im.h"
  #include "blas.h"
  #include "gemm.h"
  #include <stdio.h>
  #include <time.h>
  
  int local_out_height(local_layer l)
  {
      int h = l.h;
      if (!l.pad) h -= l.size;
      else h -= 1;
      return h/l.stride + 1;
  }
  
  int local_out_width(local_layer l)
  {
      int w = l.w;
      if (!l.pad) w -= l.size;
      else w -= 1;
      return w/l.stride + 1;
  }
  
  local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
  {
      int i;
      local_layer l;
      memset(&l, 0, sizeof(l));
      l.type = LOCAL;
  
      l.h = h;
      l.w = w;
      l.c = c;
      l.n = n;
      l.batch = batch;
      l.stride = stride;
      l.size = size;
      l.pad = pad;
  
      int out_h = local_out_height(l);
      int out_w = local_out_width(l);
      int locations = out_h*out_w;
      l.out_h = out_h;
      l.out_w = out_w;
      l.out_c = n;
      l.outputs = l.out_h * l.out_w * l.out_c;
      l.inputs = l.w * l.h * l.c;
  
      l.weights = (float*)calloc(c*n*size*size*locations, sizeof(float));
      l.weight_updates = (float*)calloc(c*n*size*size*locations, sizeof(float));
  
      l.biases = (float*)calloc(l.outputs, sizeof(float));
      l.bias_updates = (float*)calloc(l.outputs, sizeof(float));
  
      // float scale = 1./sqrt(size*size*c);
      float scale = sqrt(2./(size*size*c));
      for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1,1);
  
      l.output = (float*)calloc(l.batch*out_h * out_w * n, sizeof(float));
      l.delta  = (float*)calloc(l.batch*out_h * out_w * n, sizeof(float));
  
      l.workspace_size = out_h*out_w*size*size*c;
      
      l.forward = forward_local_layer;
      l.backward = backward_local_layer;
      l.update = update_local_layer;
  
  #ifdef GPU
      l.forward_gpu = forward_local_layer_gpu;
      l.backward_gpu = backward_local_layer_gpu;
      l.update_gpu = update_local_layer_gpu;
  
      l.weights_gpu = cuda_make_array(l.weights, c*n*size*size*locations);
      l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size*locations);
  
      l.biases_gpu = cuda_make_array(l.biases, l.outputs);
      l.bias_updates_gpu = cuda_make_array(l.bias_updates, l.outputs);
  
      l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
      l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
  
  #endif
      l.activation = activation;
  
      fprintf(stderr, "Local Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
  
      return l;
  }
  
  void forward_local_layer(const local_layer l, network net)
  {
      int out_h = local_out_height(l);
      int out_w = local_out_width(l);
      int i, j;
      int locations = out_h * out_w;
  
      for(i = 0; i < l.batch; ++i){
          copy_cpu(l.outputs, l.biases, 1, l.output + i*l.outputs, 1);
      }
  
      for(i = 0; i < l.batch; ++i){
          float *input = net.input + i*l.w*l.h*l.c;
          im2col_cpu(input, l.c, l.h, l.w, 
                  l.size, l.stride, l.pad, net.workspace);
          float *output = l.output + i*l.outputs;
          for(j = 0; j < locations; ++j){
              float *a = l.weights + j*l.size*l.size*l.c*l.n;
              float *b = net.workspace + j;
              float *c = output + j;
  
              int m = l.n;
              int n = 1;
              int k = l.size*l.size*l.c;
  
              gemm(0,0,m,n,k,1,a,k,b,locations,1,c,locations);
          }
      }
      activate_array(l.output, l.outputs*l.batch, l.activation);
  }
  
  void backward_local_layer(local_layer l, network net)
  {
      int i, j;
      int locations = l.out_w*l.out_h;
  
      gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
  
      for(i = 0; i < l.batch; ++i){
          axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1);
      }
  
      for(i = 0; i < l.batch; ++i){
          float *input = net.input + i*l.w*l.h*l.c;
          im2col_cpu(input, l.c, l.h, l.w, 
                  l.size, l.stride, l.pad, net.workspace);
  
          for(j = 0; j < locations; ++j){ 
              float *a = l.delta + i*l.outputs + j;
              float *b = net.workspace + j;
              float *c = l.weight_updates + j*l.size*l.size*l.c*l.n;
              int m = l.n;
              int n = l.size*l.size*l.c;
              int k = 1;
  
              gemm(0,1,m,n,k,1,a,locations,b,locations,1,c,n);
          }
  
          if(net.delta){
              for(j = 0; j < locations; ++j){ 
                  float *a = l.weights + j*l.size*l.size*l.c*l.n;
                  float *b = l.delta + i*l.outputs + j;
                  float *c = net.workspace + j;
  
                  int m = l.size*l.size*l.c;
                  int n = 1;
                  int k = l.n;
  
                  gemm(1,0,m,n,k,1,a,m,b,locations,0,c,locations);
              }
  
              col2im_cpu(net.workspace, l.c,  l.h,  l.w,  l.size,  l.stride, l.pad, net.delta+i*l.c*l.h*l.w);
          }
      }
  }
  
  void update_local_layer(local_layer l, update_args a)
  {
      float learning_rate = a.learning_rate*l.learning_rate_scale;
      float momentum = a.momentum;
      float decay = a.decay;
      int batch = a.batch;
  
      int locations = l.out_w*l.out_h;
      int size = l.size*l.size*l.c*l.n*locations;
      axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
      scal_cpu(l.outputs, momentum, l.bias_updates, 1);
  
      axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1);
      axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
      scal_cpu(size, momentum, l.weight_updates, 1);
  }
  
  #ifdef GPU
  
  void forward_local_layer_gpu(const local_layer l, network net)
  {
      int out_h = local_out_height(l);
      int out_w = local_out_width(l);
      int i, j;
      int locations = out_h * out_w;
  
      for(i = 0; i < l.batch; ++i){
          copy_gpu(l.outputs, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1);
      }
  
      for(i = 0; i < l.batch; ++i){
          float *input = net.input_gpu + i*l.w*l.h*l.c;
          im2col_gpu(input, l.c, l.h, l.w, 
                  l.size, l.stride, l.pad, net.workspace);
          float *output = l.output_gpu + i*l.outputs;
          for(j = 0; j < locations; ++j){
              float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n;
              float *b = net.workspace + j;
              float *c = output + j;
  
              int m = l.n;
              int n = 1;
              int k = l.size*l.size*l.c;
  
              gemm_gpu(0,0,m,n,k,1,a,k,b,locations,1,c,locations);
          }
      }
      activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation);
  }
  
  void backward_local_layer_gpu(local_layer l, network net)
  {
      int i, j;
      int locations = l.out_w*l.out_h;
  
      gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
      for(i = 0; i < l.batch; ++i){
          axpy_gpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1);
      }
  
      for(i = 0; i < l.batch; ++i){
          float *input = net.input_gpu + i*l.w*l.h*l.c;
          im2col_gpu(input, l.c, l.h, l.w, 
                  l.size, l.stride, l.pad, net.workspace);
  
          for(j = 0; j < locations; ++j){ 
              float *a = l.delta_gpu + i*l.outputs + j;
              float *b = net.workspace + j;
              float *c = l.weight_updates_gpu + j*l.size*l.size*l.c*l.n;
              int m = l.n;
              int n = l.size*l.size*l.c;
              int k = 1;
  
              gemm_gpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n);
          }
  
          if(net.delta_gpu){
              for(j = 0; j < locations; ++j){ 
                  float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n;
                  float *b = l.delta_gpu + i*l.outputs + j;
                  float *c = net.workspace + j;
  
                  int m = l.size*l.size*l.c;
                  int n = 1;
                  int k = l.n;
  
                  gemm_gpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations);
              }
  
              col2im_gpu(net.workspace, l.c,  l.h,  l.w,  l.size,  l.stride, l.pad, net.delta_gpu+i*l.c*l.h*l.w);
          }
      }
  }
  
  void update_local_layer_gpu(local_layer l, update_args a)
  {
      float learning_rate = a.learning_rate*l.learning_rate_scale;
      float momentum = a.momentum;
      float decay = a.decay;
      int batch = a.batch;
  
      int locations = l.out_w*l.out_h;
      int size = l.size*l.size*l.c*l.n*locations;
      axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
      scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1);
  
      axpy_gpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
      axpy_gpu(size, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
      scal_gpu(size, momentum, l.weight_updates_gpu, 1);
  }
  
  void pull_local_layer(local_layer l)
  {
      int locations = l.out_w*l.out_h;
      int size = l.size*l.size*l.c*l.n*locations;
      cuda_pull_array(l.weights_gpu, l.weights, size);
      cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
  }
  
  void push_local_layer(local_layer l)
  {
      int locations = l.out_w*l.out_h;
      int size = l.size*l.size*l.c*l.n*locations;
      cuda_push_array(l.weights_gpu, l.weights, size);
      cuda_push_array(l.biases_gpu, l.biases, l.outputs);
  }
  #endif
  