Merge pull request #6461 from open-cv/patch_1

fix typos and add passing by reference

cmake/Modules/FindMKL.cmake

@@ -9,7 +9,7 @@
 # This module defines the following variables:
 #
 #   MKL_FOUND            : True mkl is found
-#   MKL_INCLUDE_DIR      : unclude directory
+#   MKL_INCLUDE_DIR      : include directory
 #   MKL_LIBRARIES        : the libraries to link against.
 
 

include/caffe/net.hpp

@@ -111,9 +111,9 @@ class Net {
    *        another Net.
    */
   void CopyTrainedLayersFrom(const NetParameter& param);
-  void CopyTrainedLayersFrom(const string trained_filename);
-  void CopyTrainedLayersFromBinaryProto(const string trained_filename);
-  void CopyTrainedLayersFromHDF5(const string trained_filename);
+  void CopyTrainedLayersFrom(const string& trained_filename);
+  void CopyTrainedLayersFromBinaryProto(const string& trained_filename);
+  void CopyTrainedLayersFromHDF5(const string& trained_filename);
   /// @brief Writes the net to a proto.
   void ToProto(NetParameter* param, bool write_diff = false) const;
   /// @brief Writes the net to an HDF5 file.

include/caffe/solver.hpp

@@ -55,7 +55,7 @@ class Solver {
   // The main entry of the solver function. In default, iter will be zero. Pass
   // in a non-zero iter number to resume training for a pre-trained net.
   virtual void Solve(const char* resume_file = NULL);
-  inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
+  inline void Solve(const string& resume_file) { Solve(resume_file.c_str()); }
   void Step(int iters);
   // The Restore method simply dispatches to one of the
   // RestoreSolverStateFrom___ protected methods. You should implement these
@@ -98,7 +98,7 @@ class Solver {
   virtual void ApplyUpdate() = 0;
 
  protected:
-  string SnapshotFilename(const string extension);
+  string SnapshotFilename(const string& extension);
   string SnapshotToBinaryProto();
   string SnapshotToHDF5();
   // The test routine

include/caffe/util/signal_handler.h

@@ -8,7 +8,7 @@ namespace caffe {
 
 class SignalHandler {
  public:
-  // Contructor. Specify what action to take when a signal is received.
+  // Constructor. Specify what action to take when a signal is received.
   SignalHandler(SolverAction::Enum SIGINT_action,
                 SolverAction::Enum SIGHUP_action);
   ~SignalHandler();

python/caffe/_caffe.cpp

@@ -416,7 +416,7 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("reshape", &Net<Dtype>::Reshape)
     .def("clear_param_diffs", &Net<Dtype>::ClearParamDiffs)
     // The cast is to select a particular overload.
-    .def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
+    .def("copy_from", static_cast<void (Net<Dtype>::*)(const string&)>(
         &Net<Dtype>::CopyTrainedLayersFrom))
     .def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
     .add_property("_blob_loss_weights", bp::make_function(

src/caffe/layers/pooling_layer.cpp

@@ -145,7 +145,7 @@ void PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const int top_count = top[0]->count();
   // We'll output the mask to top[1] if it's of size >1.
   const bool use_top_mask = top.size() > 1;
-  int* mask = NULL;  // suppress warnings about uninitalized variables
+  int* mask = NULL;  // suppress warnings about uninitialized variables
   Dtype* top_mask = NULL;
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more code.

src/caffe/net.cpp

@@ -166,7 +166,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   // loss.  We can skip backward computation for blobs that don't contribute
   // to the loss.
   // Also checks if all bottom blobs don't need backward computation (possible
-  // because the skip_propagate_down param) and so we can skip bacward
+  // because the skip_propagate_down param) and so we can skip backward
   // computation for the entire layer
   set<string> blobs_under_loss;
   set<string> blobs_skip_backp;
@@ -770,7 +770,7 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
 }
 
 template <typename Dtype>
-void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
+void Net<Dtype>::CopyTrainedLayersFrom(const string& trained_filename) {
   if (H5Fis_hdf5(trained_filename.c_str())) {
     CopyTrainedLayersFromHDF5(trained_filename);
   } else {
@@ -780,14 +780,14 @@ void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
 
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFromBinaryProto(
-    const string trained_filename) {
+    const string& trained_filename) {
   NetParameter param;
   ReadNetParamsFromBinaryFileOrDie(trained_filename, &param);
   CopyTrainedLayersFrom(param);
 }
 
 template <typename Dtype>
-void Net<Dtype>::CopyTrainedLayersFromHDF5(const string trained_filename) {
+void Net<Dtype>::CopyTrainedLayersFromHDF5(const string& trained_filename) {
 #ifdef USE_HDF5
   hid_t file_hid = H5Fopen(trained_filename.c_str(), H5F_ACC_RDONLY,
                            H5P_DEFAULT);

src/caffe/proto/caffe.proto

@@ -187,7 +187,7 @@ message SolverParameter {
 
   optional int32 snapshot = 14 [default = 0]; // The snapshot interval
   // The prefix for the snapshot.
-  // If not set then is replaced by prototxt file path without extention.
+  // If not set then is replaced by prototxt file path without extension.
   // If is set to directory then is augmented by prototxt file name
   // without extention.
   optional string snapshot_prefix = 15;
@@ -248,8 +248,8 @@ message SolverParameter {
 
   // Path to caffemodel file(s) with pretrained weights to initialize finetuning.
   // Tha same as command line --weights parameter for caffe train command.
-  // If command line --weights parameter if specified, it has higher priority
-  // and owerwrites this one(s).
+  // If command line --weights parameter is specified, it has higher priority
+  // and overwrites this one(s).
   // If --snapshot command line parameter is specified, this one(s) are ignored.
   // If several model files are expected, they can be listed in a one 
   // weights parameter separated by ',' (like in a command string) or

src/caffe/solver.cpp

@@ -78,7 +78,7 @@ template <typename Dtype>
 void Solver<Dtype>::InitTrainNet() {
   const int num_train_nets = param_.has_net() + param_.has_net_param() +
       param_.has_train_net() + param_.has_train_net_param();
-  const string& field_names = "net, net_param, train_net, train_net_param";
+  const string field_names = "net, net_param, train_net, train_net_param";
   CHECK_GE(num_train_nets, 1) << "SolverParameter must specify a train net "
       << "using one of these fields: " << field_names;
   CHECK_LE(num_train_nets, 1) << "SolverParameter must not contain more than "
@@ -447,13 +447,13 @@ void Solver<Dtype>::CheckSnapshotWritePermissions() {
     } else {
       LOG(FATAL) << "Cannot write to snapshot prefix '"
           << param_.snapshot_prefix() << "'.  Make sure "
-          << "that the directory exists and is writeable.";
+          << "that the directory exists and is writable.";
     }
   }
 }
 
 template <typename Dtype>
-string Solver<Dtype>::SnapshotFilename(const string extension) {
+string Solver<Dtype>::SnapshotFilename(const string& extension) {
   return param_.snapshot_prefix() + "_iter_" + caffe::format_int(iter_)
     + extension;
 }

src/caffe/util/signal_handler.cpp

@@ -48,7 +48,7 @@ namespace {
   void UnhookHandler() {
     if (already_hooked_up) {
       struct sigaction sa;
-      // Setup the sighub handler
+      // Setup the sighup handler
       sa.sa_handler = SIG_DFL;
       // Restart the system call, if at all possible
       sa.sa_flags = SA_RESTART;