Splitting source files between CUDA and CPU code.

1 files changed, 59 insertions, 0 deletions

diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
new file mode 100644
index 00000000..2ec73080
--- /dev/null
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -0,0 +1,59 @@
+// Copyright 2013 Yangqing Jia
+
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+#include "caffe/util/math_functions.hpp"
+
+using std::max;
+
+namespace caffe {
+
+template <typename Dtype>
+void SoftmaxWithLossLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  CHECK_EQ(bottom.size(), 2) << "SoftmaxLoss Layer takes two blobs as input.";
+  CHECK_EQ(top->size(), 0) << "SoftmaxLoss Layer takes no blob as output.";
+  softmax_bottom_vec_.clear();
+  softmax_bottom_vec_.push_back(bottom[0]);
+  softmax_top_vec_.push_back(&prob_);
+  softmax_layer_->SetUp(softmax_bottom_vec_, &softmax_top_vec_);
+}
+
+template <typename Dtype>
+void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  // The forward pass computes the softmax prob values.
+  softmax_bottom_vec_[0] = bottom[0];
+  softmax_layer_->Forward(softmax_bottom_vec_, &softmax_top_vec_);
+}
+
+template <typename Dtype>
+Dtype SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const bool propagate_down,
+    vector<Blob<Dtype>*>* bottom) {
+  // First, compute the diff
+  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  const Dtype* prob_data = prob_.cpu_data();
+  memcpy(bottom_diff, prob_data, sizeof(Dtype) * prob_.count());
+  const Dtype* label = (*bottom)[1]->cpu_data();
+  int num = prob_.num();
+  int dim = prob_.count() / num;
+  Dtype loss = 0;
+  for (int i = 0; i < num; ++i) {
+    bottom_diff[i * dim + static_cast<int>(label[i])] -= 1;
+    loss += -log(max(prob_data[i * dim + static_cast<int>(label[i])], Dtype(FLT_MIN)));
+  }
+  // Scale down gradient
+  caffe_scal(prob_.count(), Dtype(1) / num, bottom_diff);
+  return loss / num;
+}
+
+
+INSTANTIATE_CLASS(SoftmaxWithLossLayer);
+
+
+}  // namespace caffe