C++ linter

Makefile

@@ -32,6 +32,16 @@ TOOL_SRCS := $(shell find tools -name "*.cpp")
 EXAMPLE_SRCS := $(shell find examples -name "*.cpp")
 # PROTO_SRCS are the protocol buffer definitions
 PROTO_SRCS := $(wildcard src/$(PROJECT)/proto/*.proto)
+# NONGEN_CXX_SRCS includes all source/header files except those generated by
+# proto.
+NONGEN_CXX_SRCS := $(shell find \
+	src/$(PROJECT) \
+	include/$(PROJECT) \
+	python/$(PROJECT) \
+	matlab/$(PROJECT) \
+	examples \
+	tools \
+	-regex ".*\.\(cpp\|hpp\|cu\|cuh\)")
 # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
 PY$(PROJECT)_SRC := python/$(PROJECT)/py$(PROJECT).cpp
 PY$(PROJECT)_SO := python/$(PROJECT)/py$(PROJECT).so
@@ -110,6 +120,9 @@ init:
 linecount: clean
 	cloc --read-lang-def=$(PROJECT).cloc src/$(PROJECT)/
 
+lint:
+	./scripts/cpp_lint.py $(NONGEN_CXX_SRCS)
+
 test: init $(TEST_BINS)
 
 tools: init $(TOOL_BINS)

examples/cifar/convert_cifar_data.cpp

@@ -12,25 +12,23 @@
 #include <leveldb/db.h>
 
 #include <stdint.h>
-#include <iostream>
-#include <fstream>
+#include <fstream>  // NOLINT(readability/streams)
 #include <string>
 
 #include "caffe/proto/caffe.pb.h"
 
 using std::string;
 
+const int kCIFARSize = 32;
+const int kCIFARImageNBytes = 3072;
+const int kCIFARBatchSize = 10000;
+const int kCIFARTrainBatches = 5;
 
-const int kCIFAR_SIZE=32;
-const int kCIFAR_IMAGE_NBYTES=3072;
-const int kCIFAR_BATCHSIZE=10000;
-const int kCIFAR_TRAIN_BATCHES=5;
-
-void read_image(std::ifstream& file, int* label, char* buffer) {
+void read_image(std::ifstream* file, int* label, char* buffer) {
   char label_char;
-  file.read(&label_char, 1);
+  file->read(&label_char, 1);
   *label = label_char;
-  file.read(buffer, kCIFAR_IMAGE_NBYTES);
+  file->read(buffer, kCIFARImageNBytes);
   return;
 }
 
@@ -41,32 +39,33 @@ void convert_dataset(const string& input_folder, const string& output_folder) {
   options.error_if_exists = true;
   // Data buffer
   int label;
-  char str_buffer[kCIFAR_IMAGE_NBYTES];
+  char str_buffer[kCIFARImageNBytes];
   string value;
   caffe::Datum datum;
   datum.set_channels(3);
-  datum.set_height(kCIFAR_SIZE);
-  datum.set_width(kCIFAR_SIZE);
+  datum.set_height(kCIFARSize);
+  datum.set_width(kCIFARSize);
 
   LOG(INFO) << "Writing Training data";
   leveldb::DB* train_db;
   leveldb::Status status;
   status = leveldb::DB::Open(options, output_folder + "/cifar-train-leveldb",
       &train_db);
   CHECK(status.ok()) << "Failed to open leveldb.";
-  for (int fileid = 0; fileid < kCIFAR_TRAIN_BATCHES; ++fileid) {
+  for (int fileid = 0; fileid < kCIFARTrainBatches; ++fileid) {
     // Open files
     LOG(INFO) << "Training Batch " << fileid + 1;
-    sprintf(str_buffer, "/data_batch_%d.bin", fileid + 1);
+    snprintf(str_buffer, kCIFARImageNBytes, "/data_batch_%d.bin", fileid + 1);
     std::ifstream data_file((input_folder + str_buffer).c_str(),
         std::ios::in | std::ios::binary);
     CHECK(data_file) << "Unable to open train file #" << fileid + 1;
-    for (int itemid = 0; itemid < kCIFAR_BATCHSIZE; ++itemid) {
-      read_image(data_file, &label, str_buffer);
+    for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) {
+      read_image(&data_file, &label, str_buffer);
       datum.set_label(label);
-      datum.set_data(str_buffer, kCIFAR_IMAGE_NBYTES);
+      datum.set_data(str_buffer, kCIFARImageNBytes);
       datum.SerializeToString(&value);
-      sprintf(str_buffer, "%05d", fileid * kCIFAR_BATCHSIZE + itemid);
+      snprintf(str_buffer, kCIFARImageNBytes, "%05d",
+          fileid * kCIFARBatchSize + itemid);
       train_db->Put(leveldb::WriteOptions(), string(str_buffer), value);
     }
   }
@@ -79,20 +78,20 @@ void convert_dataset(const string& input_folder, const string& output_folder) {
   std::ifstream data_file((input_folder + "/test_batch.bin").c_str(),
       std::ios::in | std::ios::binary);
   CHECK(data_file) << "Unable to open test file.";
-  for (int itemid = 0; itemid < kCIFAR_BATCHSIZE; ++itemid) {
-    read_image(data_file, &label, str_buffer);
+  for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) {
+    read_image(&data_file, &label, str_buffer);
     datum.set_label(label);
-    datum.set_data(str_buffer, kCIFAR_IMAGE_NBYTES);
+    datum.set_data(str_buffer, kCIFARImageNBytes);
     datum.SerializeToString(&value);
-    sprintf(str_buffer, "%05d", itemid);
+    snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
     test_db->Put(leveldb::WriteOptions(), string(str_buffer), value);
   }
 
   delete train_db;
   delete test_db;
 }
 
-int main (int argc, char** argv) {
+int main(int argc, char** argv) {
   if (argc != 3) {
     printf("This script converts the CIFAR dataset to the leveldb format used\n"
            "by caffe to perform classification.\n"

examples/lenet/convert_mnist_data.cpp

@@ -12,14 +12,13 @@
 #include <leveldb/db.h>
 
 #include <stdint.h>
-#include <iostream>
-#include <fstream>
+#include <fstream>  // NOLINT(readability/streams)
+#include <string>
 
 #include "caffe/proto/caffe.pb.h"
 
-uint32_t swap_endian( uint32_t val )
-{
-    val = ((val << 8) & 0xFF00FF00 ) | ((val >> 8) & 0xFF00FF );
+uint32_t swap_endian(uint32_t val) {
+    val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF);
     return (val << 16) | (val >> 16);
 }
 
@@ -37,20 +36,20 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   uint32_t rows;
   uint32_t cols;
 
-  image_file.read((char*)(&magic), 4);
+  image_file.read(reinterpret_cast<char*>(&magic), 4);
   magic = swap_endian(magic);
   CHECK_EQ(magic, 2051) << "Incorrect image file magic.";
-  label_file.read((char*)(&magic), 4);
+  label_file.read(reinterpret_cast<char*>(&magic), 4);
   magic = swap_endian(magic);
   CHECK_EQ(magic, 2049) << "Incorrect label file magic.";
-  image_file.read((char*)(&num_items), 4);
+  image_file.read(reinterpret_cast<char*>(&num_items), 4);
   num_items = swap_endian(num_items);
-  label_file.read((char*)(&num_labels), 4);
+  label_file.read(reinterpret_cast<char*>(&num_labels), 4);
   num_labels = swap_endian(num_labels);
   CHECK_EQ(num_items, num_labels);
-  image_file.read((char*)(&rows), 4);
+  image_file.read(reinterpret_cast<char*>(&rows), 4);
   rows = swap_endian(rows);
-  image_file.read((char*)(&cols), 4);
+  image_file.read(reinterpret_cast<char*>(&cols), 4);
   cols = swap_endian(cols);
 
   // Open leveldb
@@ -65,7 +64,8 @@ void convert_dataset(const char* image_filename, const char* label_filename,
 
   char label;
   char* pixels = new char[rows * cols];
-  char key[10];
+  const int kMaxKeyLength = 10;
+  char key[kMaxKeyLength];
   std::string value;
 
   caffe::Datum datum;
@@ -80,15 +80,15 @@ void convert_dataset(const char* image_filename, const char* label_filename,
     datum.set_data(pixels, rows*cols);
     datum.set_label(label);
     datum.SerializeToString(&value);
-    sprintf(key, "%08d", itemid);
+    snprintf(key, kMaxKeyLength, "%08d", itemid);
     db->Put(leveldb::WriteOptions(), std::string(key), value);
   }
 
   delete db;
   delete pixels;
 }
 
-int main (int argc, char** argv) {
+int main(int argc, char** argv) {
   if (argc != 4) {
     printf("This script converts the MNIST dataset to the leveldb format used\n"
            "by caffe to perform classification.\n"

include/caffe/filler.hpp

@@ -42,7 +42,7 @@ class ConstantFiller : public Filler<Dtype> {
     for (int i = 0; i < count; ++i) {
       data[i] = value;
     }
-  };
+  }
 };
 
 template <typename Dtype>

include/caffe/layer.hpp

@@ -67,7 +67,7 @@ class Layer {
       vector<Blob<Dtype>*>* top) {
     // LOG(WARNING) << "Using CPU code as backup.";
     Forward_cpu(bottom, top);
-  };
+  }
 
   // Backward functions: the backward function will compute the gradients for
   // any parameters and also for the bottom blobs if propagate_down is true.
@@ -80,7 +80,7 @@ class Layer {
       vector<Blob<Dtype>*>* bottom) {
     // LOG(WARNING) << "Using CPU code as backup.";
     return Backward_cpu(top, propagate_down, bottom);
-  };
+  }
 
   DISABLE_COPY_AND_ASSIGN(Layer);
 };  // class Layer
@@ -101,7 +101,7 @@ inline void Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
   default:
     LOG(FATAL) << "Unknown caffe mode.";
   }
-};
+}
 
 template <typename Dtype>
 inline Dtype Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
@@ -115,7 +115,7 @@ inline Dtype Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
   default:
     LOG(FATAL) << "Unknown caffe mode.";
   }
-};
+}
 
 template <typename Dtype>
 void Layer<Dtype>::ToProto(LayerParameter* param, bool write_diff) {

include/caffe/net.hpp

@@ -22,8 +22,8 @@ namespace caffe {
 template <typename Dtype>
 class Net {
  public:
-  Net(const NetParameter& param);
-  Net(const string& param_file);
+  explicit Net(const NetParameter& param);
+  explicit Net(const string& param_file);
   virtual ~Net() {}
 
   // Initialize a network with the network parameter.

include/caffe/solver.hpp

@@ -3,6 +3,7 @@
 #ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_
 #define CAFFE_OPTIMIZATION_SOLVER_HPP_
 
+#include <string>
 #include <vector>
 
 namespace caffe {
@@ -66,6 +67,6 @@ class SGDSolver : public Solver<Dtype> {
 };
 
 
-}  // namspace caffe
+}  // namespace caffe
 
 #endif  // CAFFE_OPTIMIZATION_SOLVER_HPP_

include/caffe/util/im2col.hpp

@@ -7,23 +7,23 @@ namespace caffe {
 
 template <typename Dtype>
 void im2col_cpu(const Dtype* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad, const int stride,
-    Dtype* data_col);
+    const int height, const int width, const int ksize, const int pad,
+    const int stride, Dtype* data_col);
 
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad, const int stride,
-    Dtype* data_im);
+    const int height, const int width, const int psize, const int pad,
+    const int stride, Dtype* data_im);
 
 template <typename Dtype>
 void im2col_gpu(const Dtype* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad, const int stride,
-    Dtype* data_col);
+    const int height, const int width, const int ksize, const int pad,
+    const int stride, Dtype* data_col);
 
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad, const int stride,
-    Dtype* data_im);
+    const int height, const int width, const int psize, const int pad,
+    const int stride, Dtype* data_im);
 
 }  // namespace caffe
 

include/caffe/util/insert_splits.hpp

@@ -3,6 +3,8 @@
 #ifndef _CAFFE_UTIL_INSERT_SPLITS_HPP_
 #define _CAFFE_UTIL_INSERT_SPLITS_HPP_
 
+#include <string>
+
 #include "caffe/proto/caffe.pb.h"
 
 using std::pair;

include/caffe/util/io.hpp

@@ -3,16 +3,15 @@
 #ifndef CAFFE_UTIL_IO_H_
 #define CAFFE_UTIL_IO_H_
 
-#include <google/protobuf/message.h>
+#include <string>
 
-#include <boost/scoped_ptr.hpp>
+#include "google/protobuf/message.h"
 #include "hdf5.h"
 #include "hdf5_hl.h"
+#include "caffe/proto/caffe.pb.h"
 
-#include <string>
-
+#include "boost/scoped_ptr.hpp"
 #include "caffe/blob.hpp"
-#include "caffe/proto/caffe.pb.h"
 
 using std::string;
 using ::google::protobuf::Message;

include/caffe/vision_layers.hpp

@@ -3,14 +3,13 @@
 #ifndef CAFFE_VISION_LAYERS_HPP_
 #define CAFFE_VISION_LAYERS_HPP_
 
-#include <leveldb/db.h>
-#include <pthread.h>
-#include <boost/scoped_ptr.hpp>
+#include <vector>
 
+#include "leveldb/db.h"
+#include "pthread.h"
+#include "boost/scoped_ptr.hpp"
 #include "hdf5.h"
 
-#include <vector>
-
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"