/usr/include/caffe/layers/accuracy_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 86 87 88 89 90 91 92 93 94 95 | #ifndef CAFFE_ACCURACY_LAYER_HPP_
#define CAFFE_ACCURACY_LAYER_HPP_
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layers/loss_layer.hpp"
namespace caffe {
/**
* @brief Computes the classification accuracy for a one-of-many
* classification task.
*/
template <typename Dtype>
class AccuracyLayer : public Layer<Dtype> {
public:
/**
* @param param provides AccuracyParameter accuracy_param,
* with AccuracyLayer options:
* - top_k (\b optional, default 1).
* Sets the maximum rank @f$ k @f$ at which a prediction is considered
* correct. For example, if @f$ k = 5 @f$, a prediction is counted
* correct if the correct label is among the top 5 predicted labels.
*/
explicit AccuracyLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual inline const char* type() const { return "Accuracy"; }
virtual inline int ExactNumBottomBlobs() const { return 2; }
// If there are two top blobs, then the second blob will contain
// accuracies per class.
virtual inline int MinTopBlobs() const { return 1; }
virtual inline int MaxTopBlobs() const { return 2; }
protected:
/**
* @param bottom input Blob vector (length 2)
* -# @f$ (N \times C \times H \times W) @f$
* the predictions @f$ x @f$, a Blob with values in
* @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
* the @f$ K = CHW @f$ classes. Each @f$ x_n @f$ is mapped to a predicted
* label @f$ \hat{l}_n @f$ given by its maximal index:
* @f$ \hat{l}_n = \arg\max\limits_k x_{nk} @f$
* -# @f$ (N \times 1 \times 1 \times 1) @f$
* the labels @f$ l @f$, an integer-valued Blob with values
* @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
* indicating the correct class label among the @f$ K @f$ classes
* @param top output Blob vector (length 1)
* -# @f$ (1 \times 1 \times 1 \times 1) @f$
* the computed accuracy: @f$
* \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \}
* @f$, where @f$
* \delta\{\mathrm{condition}\} = \left\{
* \begin{array}{lr}
* 1 & \mbox{if condition} \\
* 0 & \mbox{otherwise}
* \end{array} \right.
* @f$
*/
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
/// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
for (int i = 0; i < propagate_down.size(); ++i) {
if (propagate_down[i]) { NOT_IMPLEMENTED; }
}
}
int label_axis_, outer_num_, inner_num_;
int top_k_;
/// Whether to ignore instances with a certain label.
bool has_ignore_label_;
/// The label indicating that an instance should be ignored.
int ignore_label_;
/// Keeps counts of the number of samples per class.
Blob<Dtype> nums_buffer_;
};
} // namespace caffe
#endif // CAFFE_ACCURACY_LAYER_HPP_
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