some namespace cleaning.

diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index bff0c4e..c8caf5a 100644 (file)
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -1,6 +1,7 @@
 // The main caffe test code. Your test cpp code should include this hpp
 // to allow a main function to be compiled into the binary.
 
+#include "caffe/caffe.hpp"
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
@@ -15,7 +16,7 @@ using caffe::CAFFE_TEST_CUDA_PROP;
 
 int main(int argc, char** argv) {
   ::testing::InitGoogleTest(&argc, argv);
-  ::google::InitGoogleLogging(argv[0]);
+  caffe::GlobalInit(&argc, &argv);
 #ifndef CPU_ONLY
   // Before starting testing, let's first print out a few cuda defice info.
   int device;

diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
index da121fa..9954835 100644 (file)
--- a/src/caffe/test/test_dummy_data_layer.cpp
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -10,9 +10,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-using std::stringstream;
-
 namespace caffe {
 
 template <typename Dtype>

diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index c828223..2e8f096 100644 (file)
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -11,9 +11,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-using std::vector;
-
 namespace caffe {
 
 template<typename TypeParam>

diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index ed6fed8..41a3a83 100644 (file)
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -13,8 +13,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-
 namespace caffe {
 
 template <typename TypeParam>

diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index f46a0e8..52e7f1f 100644 (file)
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -11,8 +11,6 @@
 
 #include "caffe/test/test_caffe_main.hpp"
 
-using std::string;
-
 namespace caffe {
 
 class PaddingLayerUpgradeTest : public ::testing::Test {

diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 20f1ff8..6adde8b 100644 (file)
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -11,7 +11,6 @@
 
 using caffe::Datum;
 using caffe::BlobProto;
-using std::string;
 using std::max;
 
 int main(int argc, char** argv) {
@@ -22,9 +21,9 @@ int main(int argc, char** argv) {
     return 1;
   }
 
-  string db_backend = "lmdb";
+  std::string db_backend = "lmdb";
   if (argc == 4) {
-    db_backend = string(argv[3]);
+    db_backend = std::string(argv[3]);
   }
 
   // leveldb
@@ -94,7 +93,7 @@ int main(int argc, char** argv) {
     for (it->SeekToFirst(); it->Valid(); it->Next()) {
       // just a dummy operation
       datum.ParseFromString(it->value().ToString());
-      const string& data = datum.data();
+      const std::string& data = datum.data();
       size_in_datum = std::max<int>(datum.data().size(),
           datum.float_data_size());
       CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
@@ -120,7 +119,7 @@ int main(int argc, char** argv) {
     do {
       // just a dummy operation
       datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
-      const string& data = datum.data();
+      const std::string& data = datum.data();
       size_in_datum = std::max<int>(datum.data().size(),
           datum.float_data_size());
       CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<

diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 4ab93fd..7c8c1da 100644 (file)
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -27,7 +27,6 @@
 
 using namespace caffe;  // NOLINT(build/namespaces)
 using std::pair;
-using std::string;
 
 DEFINE_bool(gray, false,
     "When this option is on, treat images as grayscale ones");
@@ -62,8 +61,8 @@ int main(int argc, char** argv) {
   bool is_color = !FLAGS_gray;
   bool check_size = FLAGS_check_size;
   std::ifstream infile(argv[2]);
-  std::vector<std::pair<string, int> > lines;
-  string filename;
+  std::vector<std::pair<std::string, int> > lines;
+  std::string filename;
   int label;
   while (infile >> filename >> label) {
     lines.push_back(std::make_pair(filename, label));
@@ -75,7 +74,7 @@ int main(int argc, char** argv) {
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
 
-  const string& db_backend = FLAGS_backend;
+  const std::string& db_backend = FLAGS_backend;
   const char* db_path = argv[3];
 
   int resize_height = std::max<int>(0, FLAGS_resize_height);
@@ -121,7 +120,7 @@ int main(int argc, char** argv) {
   }
 
   // Storing to db
-  string root_folder(argv[1]);
+  std::string root_folder(argv[1]);
   Datum datum;
   int count = 0;
   const int kMaxKeyLength = 256;
@@ -139,7 +138,7 @@ int main(int argc, char** argv) {
         data_size = datum.channels() * datum.height() * datum.width();
         data_size_initialized = true;
       } else {
-        const string& data = datum.data();
+        const std::string& data = datum.data();
         CHECK_EQ(data.size(), data_size) << "Incorrect data field size "
             << data.size();
       }
@@ -147,9 +146,9 @@ int main(int argc, char** argv) {
     // sequential
     snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
         lines[line_id].first.c_str());
-    string value;
+    std::string value;
     datum.SerializeToString(&value);
-    string keystr(key_cstr);
+    std::string keystr(key_cstr);
 
     // Put in db
     if (db_backend == "leveldb") {  // leveldb

diff --git a/tools/dump_network.cpp b/tools/dump_network.cpp
index 90895fd..9cb996e 100644 (file)
--- a/tools/dump_network.cpp
+++ b/tools/dump_network.cpp
@@ -22,7 +22,12 @@
 #include "caffe/solver.hpp"
 #include "caffe/util/io.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
+using boost::shared_ptr;
+using caffe::Blob;
+using caffe::BlobProto;
+using caffe::Caffe;
+using caffe::Net;
+using caffe::NetParameter;
 
 int main(int argc, char** argv) {
   Caffe::set_mode(Caffe::GPU);
@@ -37,7 +42,7 @@ int main(int argc, char** argv) {
   }
   caffe_net->CopyTrainedLayersFrom(argv[2]);
 
-  vector<Blob<float>* > input_vec;
+  std::vector<Blob<float>* > input_vec;
   shared_ptr<Blob<float> > input_blob(new Blob<float>());
   if (strcmp(argv[3], "none") != 0) {
     BlobProto input_blob_proto;
@@ -46,7 +51,7 @@ int main(int argc, char** argv) {
     input_vec.push_back(input_blob.get());
   }
 
-  string output_prefix(argv[4]);
+  std::string output_prefix(argv[4]);
   // Run the network without training.
   LOG(ERROR) << "Performing Forward";
   caffe_net->Forward(input_vec);
@@ -62,8 +67,8 @@ int main(int argc, char** argv) {
   }
   // Now, let's dump all the layers
 
-  const vector<string>& blob_names = caffe_net->blob_names();
-  const vector<shared_ptr<Blob<float> > >& blobs = caffe_net->blobs();
+  const std::vector<std::string>& blob_names = caffe_net->blob_names();
+  const std::vector<shared_ptr<Blob<float> > >& blobs = caffe_net->blobs();
   for (int blobid = 0; blobid < caffe_net->blobs().size(); ++blobid) {
     // Serialize blob
     LOG(ERROR) << "Dumping " << blob_names[blobid];

diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 9b0288a..299b311 100644 (file)
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -14,7 +14,11 @@
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
+using boost::shared_ptr;
+using caffe::Blob;
+using caffe::Caffe;
+using caffe::Datum;
+using caffe::Net;
 
 template<typename Dtype>
 int feature_extraction_pipeline(int argc, char** argv);
@@ -62,7 +66,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
   Caffe::set_phase(Caffe::TEST);
 
   arg_pos = 0;  // the name of the executable
-  string pretrained_binary_proto(argv[++arg_pos]);
+  std::string pretrained_binary_proto(argv[++arg_pos]);
 
   // Expected prototxt contains at least one data layer such as
   //  the layer data_layer_name and one feature blob such as the
@@ -91,17 +95,17 @@ int feature_extraction_pipeline(int argc, char** argv) {
      top: "fc7"
    }
    */
-  string feature_extraction_proto(argv[++arg_pos]);
+  std::string feature_extraction_proto(argv[++arg_pos]);
   shared_ptr<Net<Dtype> > feature_extraction_net(
       new Net<Dtype>(feature_extraction_proto));
   feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
 
-  string extract_feature_blob_names(argv[++arg_pos]);
-  vector<string> blob_names;
+  std::string extract_feature_blob_names(argv[++arg_pos]);
+  std::vector<std::string> blob_names;
   boost::split(blob_names, extract_feature_blob_names, boost::is_any_of(","));
 
-  string save_feature_leveldb_names(argv[++arg_pos]);
-  vector<string> leveldb_names;
+  std::string save_feature_leveldb_names(argv[++arg_pos]);
+  std::vector<std::string> leveldb_names;
   boost::split(leveldb_names, save_feature_leveldb_names,
                boost::is_any_of(","));
   CHECK_EQ(blob_names.size(), leveldb_names.size()) <<
@@ -118,7 +122,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
   options.error_if_exists = true;
   options.create_if_missing = true;
   options.write_buffer_size = 268435456;
-  vector<shared_ptr<leveldb::DB> > feature_dbs;
+  std::vector<shared_ptr<leveldb::DB> > feature_dbs;
   for (size_t i = 0; i < num_features; ++i) {
     LOG(INFO)<< "Opening leveldb " << leveldb_names[i];
     leveldb::DB* db;
@@ -134,13 +138,13 @@ int feature_extraction_pipeline(int argc, char** argv) {
   LOG(ERROR)<< "Extacting Features";
 
   Datum datum;
-  vector<shared_ptr<leveldb::WriteBatch> > feature_batches(
+  std::vector<shared_ptr<leveldb::WriteBatch> > feature_batches(
       num_features,
       shared_ptr<leveldb::WriteBatch>(new leveldb::WriteBatch()));
   const int kMaxKeyStrLength = 100;
   char key_str[kMaxKeyStrLength];
-  vector<Blob<float>*> input_vec;
-  vector<int> image_indices(num_features, 0);
+  std::vector<Blob<float>*> input_vec;
+  std::vector<int> image_indices(num_features, 0);
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
     feature_extraction_net->Forward(input_vec);
     for (int i = 0; i < num_features; ++i) {
@@ -160,10 +164,10 @@ int feature_extraction_pipeline(int argc, char** argv) {
         for (int d = 0; d < dim_features; ++d) {
           datum.add_float_data(feature_blob_data[d]);
         }
-        string value;
+        std::string value;
         datum.SerializeToString(&value);
         snprintf(key_str, kMaxKeyStrLength, "%d", image_indices[i]);
-        feature_batches[i]->Put(string(key_str), value);
+        feature_batches[i]->Put(std::string(key_str), value);
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
           feature_dbs[i]->Write(leveldb::WriteOptions(),