Split all loss layers into own .cpp files

1 files changed, 1 insertions, 229 deletions

diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 3fc34a6d..1efb6235 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -14,8 +14,6 @@ using std::max;
 
 namespace caffe {
 
-const float kLOG_THRESHOLD = 1e-20;
-
 template <typename Dtype>
 void LossLayer<Dtype>::SetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
@@ -26,232 +24,6 @@ void LossLayer<Dtype>::SetUp(
   FurtherSetUp(bottom, top);
 }
 
-template <typename Dtype>
-void LossLayer<Dtype>::FurtherSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  // Nothing to do
-}
-
-template <typename Dtype>
-void MultinomialLogisticLossLayer<Dtype>::FurtherSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom[1]->channels(), 1);
-  CHECK_EQ(bottom[1]->height(), 1);
-  CHECK_EQ(bottom[1]->width(), 1);
-}
-
-template <typename Dtype>
-Dtype MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  const Dtype* bottom_label = bottom[1]->cpu_data();
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
-  Dtype loss = 0;
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
-    loss -= log(prob);
-  }
-  return loss / num;
-}
-
-template <typename Dtype>
-void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  const Dtype* bottom_label = (*bottom)[1]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  int num = (*bottom)[0]->num();
-  int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-  memset(bottom_diff, 0, sizeof(Dtype) * (*bottom)[0]->count());
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
-    bottom_diff[i * dim + label] = -1. / prob / num;
-  }
-}
-
-
-template <typename Dtype>
-void InfogainLossLayer<Dtype>::FurtherSetUp(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom[1]->channels(), 1);
-  CHECK_EQ(bottom[1]->height(), 1);
-  CHECK_EQ(bottom[1]->width(), 1);
-
-  BlobProto blob_proto;
-  ReadProtoFromBinaryFile(
-    this->layer_param_.infogain_loss_param().source(), &blob_proto);
-  infogain_.FromProto(blob_proto);
-  CHECK_EQ(infogain_.num(), 1);
-  CHECK_EQ(infogain_.channels(), 1);
-  CHECK_EQ(infogain_.height(), infogain_.width());
-}
-
-
-template <typename Dtype>
-Dtype InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  const Dtype* bottom_label = bottom[1]->cpu_data();
-  const Dtype* infogain_mat = infogain_.cpu_data();
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
-  CHECK_EQ(infogain_.height(), dim);
-  Dtype loss = 0;
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    for (int j = 0; j < dim; ++j) {
-      Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
-      loss -= infogain_mat[label * dim + j] * log(prob);
-    }
-  }
-  return loss / num;
-}
-
-template <typename Dtype>
-void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
-    vector<Blob<Dtype>*>* bottom) {
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  const Dtype* bottom_label = (*bottom)[1]->cpu_data();
-  const Dtype* infogain_mat = infogain_.cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  int num = (*bottom)[0]->num();
-  int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-  CHECK_EQ(infogain_.height(), dim);
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    for (int j = 0; j < dim; ++j) {
-      Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
-      bottom_diff[i * dim + j] = - infogain_mat[label * dim + j] / prob / num;
-    }
-  }
-}
-
-
-template <typename Dtype>
-void EuclideanLossLayer<Dtype>::FurtherSetUp(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
-  CHECK_EQ(bottom[0]->height(), bottom[1]->height());
-  CHECK_EQ(bottom[0]->width(), bottom[1]->width());
-  difference_.Reshape(bottom[0]->num(), bottom[0]->channels(),
-      bottom[0]->height(), bottom[0]->width());
-}
-
-template <typename Dtype>
-Dtype EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  int count = bottom[0]->count();
-  int num = bottom[0]->num();
-  caffe_sub(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(),
-      difference_.mutable_cpu_data());
-  Dtype loss = caffe_cpu_dot(
-      count, difference_.cpu_data(), difference_.cpu_data()) / num / Dtype(2);
-  return loss;
-}
-
-template <typename Dtype>
-void EuclideanLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  int count = (*bottom)[0]->count();
-  int num = (*bottom)[0]->num();
-  // Compute the gradient
-  caffe_cpu_axpby(count, Dtype(1) / num, difference_.cpu_data(), Dtype(0),
-      (*bottom)[0]->mutable_cpu_diff());
-}
-
-template <typename Dtype>
-Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-  const Dtype* label = bottom[1]->cpu_data();
-  int num = bottom[0]->num();
-  int count = bottom[0]->count();
-  int dim = count / num;
-
-  caffe_copy(count, bottom_data, bottom_diff);
-  for (int i = 0; i < num; ++i) {
-    bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
-  }
-  for (int i = 0; i < num; ++i) {
-    for (int j = 0; j < dim; ++j) {
-      bottom_diff[i * dim + j] = max(Dtype(0), 1 + bottom_diff[i * dim + j]);
-    }
-  }
-  return caffe_cpu_asum(count, bottom_diff) / num;
-}
-
-template <typename Dtype>
-void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  const Dtype* label = (*bottom)[1]->cpu_data();
-  int num = (*bottom)[0]->num();
-  int count = (*bottom)[0]->count();
-  int dim = count / num;
-
-  caffe_cpu_sign(count, bottom_diff, bottom_diff);
-  for (int i = 0; i < num; ++i) {
-    bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
-  }
-  caffe_scal(count, Dtype(1. / num), bottom_diff);
-}
-
-template <typename Dtype>
-void AccuracyLayer<Dtype>::SetUp(
-  const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 2) << "Accuracy Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 1) << "Accuracy Layer takes 1 output.";
-  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
-      << "The data and label should have the same number.";
-  CHECK_EQ(bottom[1]->channels(), 1);
-  CHECK_EQ(bottom[1]->height(), 1);
-  CHECK_EQ(bottom[1]->width(), 1);
-  (*top)[0]->Reshape(1, 2, 1, 1);
-}
-
-template <typename Dtype>
-Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-    vector<Blob<Dtype>*>* top) {
-  Dtype accuracy = 0;
-  Dtype logprob = 0;
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  const Dtype* bottom_label = bottom[1]->cpu_data();
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
-  for (int i = 0; i < num; ++i) {
-    // Accuracy
-    Dtype maxval = -FLT_MAX;
-    int max_id = 0;
-    for (int j = 0; j < dim; ++j) {
-      if (bottom_data[i * dim + j] > maxval) {
-        maxval = bottom_data[i * dim + j];
-        max_id = j;
-      }
-    }
-    if (max_id == static_cast<int>(bottom_label[i])) {
-      ++accuracy;
-    }
-    Dtype prob = max(bottom_data[i * dim + static_cast<int>(bottom_label[i])],
-                     Dtype(kLOG_THRESHOLD));
-    logprob -= log(prob);
-  }
-  // LOG(INFO) << "Accuracy: " << accuracy;
-  (*top)[0]->mutable_cpu_data()[0] = accuracy / num;
-  (*top)[0]->mutable_cpu_data()[1] = logprob / num;
-  // Accuracy layer should not be used as a loss function.
-  return Dtype(0);
-}
-
-INSTANTIATE_CLASS(MultinomialLogisticLossLayer);
-INSTANTIATE_CLASS(InfogainLossLayer);
-INSTANTIATE_CLASS(EuclideanLossLayer);
-INSTANTIATE_CLASS(HingeLossLayer);
-INSTANTIATE_CLASS(AccuracyLayer);
+INSTANTIATE_CLASS(LossLayer);
 
 }  // namespace caffe