C++ (Cpp) caffe_cpu_strided_dot示例

示例#1

文件： math_functions.cpp 项目： flair2005/mmd-caffe

Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y) {
    return caffe_cpu_strided_dot(n, x, 1, y, 1);
}

示例#2

文件： math_functions.cpp 项目： fengfu-chris/caffe-twns

float caffe_cpu_dot<float>(const int n, const float* x, const float* y){
  return caffe_cpu_strided_dot(n, x, 1, y, 1);
}

示例#3

文件： math_functions.cpp 项目： fengfu-chris/caffe-twns

double caffe_cpu_dot<double>(const int n, const double* x, const double* y){
  return caffe_cpu_strided_dot(n, x, 1, y, 1);
}

示例#4

文件： normalize_layer.cpp 项目： githublsj/GPU-MTCNN

void NormalizeLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
  const Dtype* top_diff = top[0]->cpu_diff();
  const Dtype* top_data = top[0]->cpu_data();
  const Dtype* bottom_data = bottom[0]->cpu_data();
  const Dtype* square_data = squared_.cpu_data();
  const Dtype* norm_data = (top.size() == 2) ? top[1]->cpu_data() : norm_.cpu_data();
  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();

  int num = bottom[0]->num();
  int channels = bottom[0]->channels();
  int spatial_dim = bottom[0]->height() * bottom[0]->width();
  if (propagate_down[0]) {
    if (normalize_type_ == "L2") {
      for (int n = 0; n < num; ++n) {
        for (int s = 0; s < spatial_dim; s++) {
          Dtype a = caffe_cpu_strided_dot(channels, top_data + n*channels*spatial_dim + s, spatial_dim, top_diff + n*channels*spatial_dim + s, spatial_dim);
          for (int c = 0; c < channels; c++) {
            bottom_diff[(n * channels + c) * spatial_dim + s] =
              (top_diff[(n * channels + c) * spatial_dim + s] - top_data[(n * channels + c) * spatial_dim + s] * a) / norm_data[n*spatial_dim + s];
          }
        }
      }
    }
    else if (normalize_type_ == "L1") {
      for (int n = 0; n < num; ++n) {
        for (int s = 0; s < spatial_dim; s++) {
          Dtype a = caffe_cpu_strided_dot(channels, top_data + n*channels*spatial_dim + s, spatial_dim, top_diff + n*channels*spatial_dim + s, spatial_dim);
          for (int c = 0; c < channels; c++) {
            bottom_diff[(n * channels + c) * spatial_dim + s] =
              (top_diff[(n * channels + c) * spatial_dim + s] - sign(bottom_data[(n * channels + c) * spatial_dim + s]) * a) / norm_data[n*spatial_dim + s];
          }
        }
      }
    }
    else {
      NOT_IMPLEMENTED;
    }
  }

  if (bp_norm_) {
    const Dtype* norm_diff =top[1]->cpu_diff();
    if (normalize_type_ == "L2") {
      for (int n = 0; n < num; ++n) {
        for (int s = 0; s < spatial_dim; s++) {
          for (int c = 0; c < channels; c++) {
            bottom_diff[(n * channels + c) * spatial_dim + s] += norm_diff[n*spatial_dim + s] * top_data[(n * channels + c) * spatial_dim + s];
          }
        }
      }
    }
    else if (normalize_type_ == "L1") {
      for (int n = 0; n < num; ++n) {
        for (int s = 0; s < spatial_dim; s++) {
          for (int c = 0; c < channels; c++) {
            bottom_diff[(n * channels + c) * spatial_dim + s] += norm_diff[n*spatial_dim + s] * sign(bottom_data[(n * channels + c) * spatial_dim + s]);
          }
        }
      }
    }
  }
}