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#include <functional>
#include <utility>
#include <vector>
#include "caffe/layers/accuracy_layer.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
template <typename Dtype>
void AccuracyLayer<Dtype>::LayerSetUp(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
top_k_ = this->layer_param_.accuracy_param().top_k();
has_ignore_label_ =
this->layer_param_.accuracy_param().has_ignore_label();
if (has_ignore_label_) {
ignore_label_ = this->layer_param_.accuracy_param().ignore_label();
}
}
template <typename Dtype>
void AccuracyLayer<Dtype>::Reshape(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
CHECK_LE(top_k_, bottom[0]->count() / bottom[1]->count())
<< "top_k must be less than or equal to the number of classes.";
label_axis_ =
bottom[0]->CanonicalAxisIndex(this->layer_param_.accuracy_param().axis());
outer_num_ = bottom[0]->count(0, label_axis_);
inner_num_ = bottom[0]->count(label_axis_ + 1);
CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
<< "Number of labels must match number of predictions; "
<< "e.g., if label axis == 1 and prediction shape is (N, C, H, W), "
<< "label count (number of labels) must be N*H*W, "
<< "with integer values in {0, 1, ..., C-1}.";
vector<int> top_shape(0); // Accuracy is a scalar; 0 axes.
top[0]->Reshape(top_shape);
if (top.size() > 1) {
// Per-class accuracy is a vector; 1 axes.
vector<int> top_shape_per_class(1);
top_shape_per_class[0] = bottom[0]->shape(label_axis_);
top[1]->Reshape(top_shape_per_class);
nums_buffer_.Reshape(top_shape_per_class);
}
}
template <typename Dtype>
void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
Dtype accuracy = 0;
const Dtype* bottom_data = bottom[0]->cpu_data();
const Dtype* bottom_label = bottom[1]->cpu_data();
const int dim = bottom[0]->count() / outer_num_;
const int num_labels = bottom[0]->shape(label_axis_);
if (top.size() > 1) {
caffe_set(nums_buffer_.count(), Dtype(0), nums_buffer_.mutable_cpu_data());
caffe_set(top[1]->count(), Dtype(0), top[1]->mutable_cpu_data());
}
int count = 0;
for (int i = 0; i < outer_num_; ++i) {
for (int j = 0; j < inner_num_; ++j) {
const int label_value =
static_cast<int>(bottom_label[i * inner_num_ + j]);
if (has_ignore_label_ && label_value == ignore_label_) {
continue;
}
DCHECK_GE(label_value, 0);
DCHECK_LT(label_value, num_labels);
if (top.size() > 1) ++nums_buffer_.mutable_cpu_data()[label_value];
const Dtype prob_of_true_class = bottom_data[i * dim
+ label_value * inner_num_
+ j];
int num_better_predictions = -1; // true_class also counts as "better"
// Top-k accuracy
for (int k = 0; k < num_labels && num_better_predictions < top_k_; ++k) {
num_better_predictions +=
(bottom_data[i * dim + k * inner_num_ + j] >= prob_of_true_class);
}
// check if there are less than top_k_ predictions
if (num_better_predictions < top_k_) {
++accuracy;
if (top.size() > 1) ++top[1]->mutable_cpu_data()[label_value];
}
++count;
}
}
// LOG(INFO) << "Accuracy: " << accuracy;
top[0]->mutable_cpu_data()[0] = accuracy / count;
if (top.size() > 1) {
for (int i = 0; i < top[1]->count(); ++i) {
top[1]->mutable_cpu_data()[i] =
nums_buffer_.cpu_data()[i] == 0 ? 0
: top[1]->cpu_data()[i] / nums_buffer_.cpu_data()[i];
}
}
// Accuracy layer should not be used as a loss function.
}
#ifdef CPU_ONLY
STUB_GPU(AccuracyLayer);
#endif
INSTANTIATE_CLASS(AccuracyLayer);
REGISTER_LAYER_CLASS(Accuracy);
} // namespace caffe
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