/usr/include/caffe/layers/scale_layer.hpp is in libcaffe-cpu-dev 1.0.0~rc4-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | #ifndef CAFFE_SCALE_LAYER_HPP_
#define CAFFE_SCALE_LAYER_HPP_
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layers/bias_layer.hpp"
namespace caffe {
/**
* @brief Computes the elementwise product of two input Blobs, with the shape of
* the latter Blob "broadcast" to match the shape of the former.
* Equivalent to tiling the latter Blob, then computing the elementwise
* product. Note: for efficiency and convenience, this layer can
* additionally perform a "broadcast" sum too when `bias_term: true`
* is set.
*
* The latter, scale input may be omitted, in which case it's learned as
* parameter of the layer (as is the bias, if it is included).
*/
template <typename Dtype>
class ScaleLayer: public Layer<Dtype> {
public:
explicit ScaleLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual inline const char* type() const { return "Scale"; }
// Scale
virtual inline int MinBottomBlobs() const { return 1; }
virtual inline int MaxBottomBlobs() const { return 2; }
virtual inline int ExactNumTopBlobs() const { return 1; }
protected:
/**
* In the below shape specifications, @f$ i @f$ denotes the value of the
* `axis` field given by `this->layer_param_.scale_param().axis()`, after
* canonicalization (i.e., conversion from negative to positive index,
* if applicable).
*
* @param bottom input Blob vector (length 2)
* -# @f$ (d_0 \times ... \times
* d_i \times ... \times d_j \times ... \times d_n) @f$
* the first factor @f$ x @f$
* -# @f$ (d_i \times ... \times d_j) @f$
* the second factor @f$ y @f$
* @param top output Blob vector (length 1)
* -# @f$ (d_0 \times ... \times
* d_i \times ... \times d_j \times ... \times d_n) @f$
* the product @f$ z = x y @f$ computed after "broadcasting" y.
* Equivalent to tiling @f$ y @f$ to have the same shape as @f$ x @f$,
* then computing the elementwise product.
*/
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
shared_ptr<Layer<Dtype> > bias_layer_;
vector<Blob<Dtype>*> bias_bottom_vec_;
vector<bool> bias_propagate_down_;
int bias_param_id_;
Blob<Dtype> sum_multiplier_;
Blob<Dtype> sum_result_;
Blob<Dtype> temp_;
int axis_;
int outer_dim_, scale_dim_, inner_dim_;
};
} // namespace caffe
#endif // CAFFE_SCALE_LAYER_HPP_
|