Merge branch 'master' of github.com:Yangqing/caffe

Conflicts:
	Makefile

5 files changed, 52 insertions, 3 deletions

diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 178607f4..b663cb2c 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -33,6 +33,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return new EuclideanLossLayer<Dtype>(param);
   } else if (type == "im2col") {
     return new Im2colLayer<Dtype>(param);
+  } else if (type == "infogain_loss") {
+    return new InfogainLossLayer<Dtype>(param);
   } else if (type == "innerproduct") {
     return new InnerProductLayer<Dtype>(param);
   } else if (type == "lrn") {
diff --git a/src/caffe/layers/loss_layer.cu b/src/caffe/layers/loss_layer.cu
index 0a6f5ee9..ac05ba41 100644
--- a/src/caffe/layers/loss_layer.cu
+++ b/src/caffe/layers/loss_layer.cu
@@ -6,6 +6,7 @@
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/util/io.hpp"
 
 using std::max;
 
@@ -17,7 +18,7 @@ template <typename Dtype>
 void MultinomialLogisticLossLayer<Dtype>::SetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   CHECK_EQ(bottom.size(), 2) << "Loss Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 0) << "Loss Layer takes no as output.";
+  CHECK_EQ(top->size(), 0) << "Loss Layer takes no output.";
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
   CHECK_EQ(bottom[1]->channels(), 1);
@@ -50,6 +51,49 @@ Dtype MultinomialLogisticLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>
 
 
 template <typename Dtype>
+void InfogainLossLayer<Dtype>::SetUp(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  CHECK_EQ(bottom.size(), 2) << "Loss Layer takes two blobs as input.";
+  CHECK_EQ(top->size(), 0) << "Loss Layer takes no 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);
+  BlobProto blob_proto;
+  ReadProtoFromBinaryFile(this->layer_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>::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);
+  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], kLOG_THRESHOLD);
+      loss -= infogain_mat[label * dim + j] * log(prob);
+      bottom_diff[i * dim + j] = - infogain_mat[label * dim + j] / prob / num;
+    }
+  }
+  return loss / num;
+}
+
+
+template <typename Dtype>
 void EuclideanLossLayer<Dtype>::SetUp(
   const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   CHECK_EQ(bottom.size(), 2) << "Loss Layer takes two blobs as input.";
@@ -122,6 +166,7 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 }
 
 INSTANTIATE_CLASS(MultinomialLogisticLossLayer);
+INSTANTIATE_CLASS(InfogainLossLayer);
 INSTANTIATE_CLASS(EuclideanLossLayer);
 INSTANTIATE_CLASS(AccuracyLayer);
 
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 1cbb4abe..4fd326cb 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -120,7 +120,8 @@ __global__ void StoPoolForwardTest(const int nthreads,
     int hend = min(hstart + ksize, height);
     int wstart = pw * stride;
     int wend = min(wstart + ksize, width);
-    Dtype cumsum = 0.;
+    // We set cumsum to be 0 to avoid divide-by-zero problems
+    Dtype cumsum = FLT_MIN;
     Dtype cumvalues = 0.;
     bottom_data += (n * channels + c) * height * width;
     // First pass: get sum
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index dec42036..3266064d 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -287,7 +287,7 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
       DLOG(INFO) << "Ignoring source layer " << source_layer_name;
       continue;
     }
-    DLOG(INFO) << "Loading source layer " << source_layer_name;
+    LOG(INFO) << "Copying source layer " << source_layer_name;
     vector<shared_ptr<Blob<Dtype> > >& target_blobs =
         layers_[target_layer_id]->blobs();
     CHECK_EQ(target_blobs.size(), source_layer.blobs_size())
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index c4682f52..ced807ae 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -7,6 +7,7 @@
 #include <google/protobuf/io/coded_stream.h>
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
 
 #include <algorithm>
 #include <string>