lint

include/caffe/common.hpp

@@ -87,7 +87,9 @@ class Caffe {
 
   // boost RNG
   typedef boost::mt19937 random_generator_t;
-  inline static random_generator_t &rng_stream() { return Get().random_generator_; }
+  inline static random_generator_t &rng_stream() {
+    return Get().random_generator_;
+  }
 
   // Returns the mode: running on CPU or GPU.
   inline static Brew mode() { return Get().mode_; }

src/caffe/common.cpp

@@ -22,8 +22,7 @@ int64_t cluster_seedgen(void) {
 Caffe::Caffe()
     : mode_(Caffe::CPU), phase_(Caffe::TRAIN), cublas_handle_(NULL),
       curand_generator_(NULL),
-      random_generator_()
-{
+      random_generator_() {
   // Try to create a cublas handler, and report an error if failed (but we will
   // keep the program running as one might just want to run CPU code).
   if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {

src/caffe/test/test_common.cpp

@@ -36,10 +36,12 @@ TEST_F(CommonTest, TestRandSeedCPU) {
   SyncedMemory data_a(10 * sizeof(int));
   SyncedMemory data_b(10 * sizeof(int));
   Caffe::set_random_seed(1701);
-  caffe_vRngBernoulli(10, reinterpret_cast<int*>(data_a.mutable_cpu_data()), 0.5);
+  caffe_vRngBernoulli(10,
+      reinterpret_cast<int*>(data_a.mutable_cpu_data()), 0.5);
 
   Caffe::set_random_seed(1701);
-  caffe_vRngBernoulli(10, reinterpret_cast<int*>(data_b.mutable_cpu_data()), 0.5);
+  caffe_vRngBernoulli(10,
+      reinterpret_cast<int*>(data_b.mutable_cpu_data()), 0.5);
 
   for (int i = 0; i < 10; ++i) {
     EXPECT_EQ(((const int*)(data_a.cpu_data()))[i],

src/caffe/test/test_random_number_generator.cpp

@@ -1,6 +1,11 @@
+// Copyright 2014 kloudkl@github
+// Copyright 2014 Jeff Donahue
+// Copyright 2014 Alejandro Dubrovsky
+// Copyright 2014 Evan Shelhamer
+
+#include <cuda_runtime.h>
 #include <cmath>
 #include <cstring>
-#include <cuda_runtime.h>
 
 #include "gtest/gtest.h"
 #include "caffe/common.hpp"
@@ -15,8 +20,7 @@ class RandomNumberGeneratorTest : public ::testing::Test {
  public:
   virtual ~RandomNumberGeneratorTest() {}
 
-  Dtype sample_mean(const Dtype* const seqs, const size_t sample_size)
-  {
+  Dtype sample_mean(const Dtype* const seqs, const size_t sample_size) {
       double sum = 0;
       for (int i = 0; i < sample_size; ++i) {
           sum += seqs[i];
@@ -24,8 +28,7 @@ class RandomNumberGeneratorTest : public ::testing::Test {
       return sum / sample_size;
   }
 
-  Dtype sample_mean(const int* const seqs, const size_t sample_size)
-  {
+  Dtype sample_mean(const int* const seqs, const size_t sample_size) {
       Dtype sum = 0;
       for (int i = 0; i < sample_size; ++i) {
           sum += Dtype(seqs[i]);
@@ -33,9 +36,8 @@ class RandomNumberGeneratorTest : public ::testing::Test {
       return sum / sample_size;
   }
 
-  Dtype mean_bound(const Dtype std, const size_t sample_size)
-  {
-      return  std/sqrt((double)sample_size);
+  Dtype mean_bound(const Dtype std, const size_t sample_size) {
+      return  std/sqrt(static_cast<double>(sample_size));
   }
 };
 
@@ -43,6 +45,7 @@ class RandomNumberGeneratorTest : public ::testing::Test {
 typedef ::testing::Types<float, double> Dtypes;
 TYPED_TEST_CASE(RandomNumberGeneratorTest, Dtypes);
 
+
 TYPED_TEST(RandomNumberGeneratorTest, TestRngGaussian) {
   size_t sample_size = 10000;
   SyncedMemory data_a(sample_size * sizeof(TypeParam));
@@ -50,12 +53,13 @@ TYPED_TEST(RandomNumberGeneratorTest, TestRngGaussian) {
   TypeParam mu = 0;
   TypeParam sigma = 1;
   caffe_vRngGaussian(sample_size,
-      (TypeParam*)data_a.mutable_cpu_data(), mu, sigma);
+      reinterpret_cast<TypeParam*>(data_a.mutable_cpu_data()), mu, sigma);
   TypeParam true_mean = mu;
   TypeParam true_std = sigma;
   TypeParam bound = this->mean_bound(true_std, sample_size);
   TypeParam empirical_mean =
-      this->sample_mean((TypeParam*)data_a.cpu_data(), sample_size);
+      this->sample_mean(reinterpret_cast<const TypeParam*>(data_a.cpu_data()),
+          sample_size);
   EXPECT_NEAR(empirical_mean, true_mean, bound);
 }
 
@@ -67,12 +71,13 @@ TYPED_TEST(RandomNumberGeneratorTest, TestRngUniform) {
   TypeParam lower = 0;
   TypeParam upper = 1;
   caffe_vRngUniform(sample_size,
-      (TypeParam*)data_a.mutable_cpu_data(), lower, upper);
+      reinterpret_cast<TypeParam*>(data_a.mutable_cpu_data()), lower, upper);
   TypeParam true_mean = (lower + upper) / 2;
   TypeParam true_std = (upper - lower) / sqrt(12);
   TypeParam bound = this->mean_bound(true_std, sample_size);
   TypeParam empirical_mean =
-      this->sample_mean((TypeParam*)data_a.cpu_data(), sample_size);
+      this->sample_mean(reinterpret_cast<const TypeParam*>(data_a.cpu_data()),
+          sample_size);
   EXPECT_NEAR(empirical_mean, true_mean, bound);
 }
 
@@ -82,7 +87,8 @@ TYPED_TEST(RandomNumberGeneratorTest, TestRngBernoulli) {
   SyncedMemory data_a(sample_size * sizeof(int));
   Caffe::set_random_seed(1701);
   double p = 0.3;
-  caffe_vRngBernoulli(sample_size, (int*)data_a.mutable_cpu_data(), p);
+  caffe_vRngBernoulli(sample_size,
+      static_cast<int*>(data_a.mutable_cpu_data()), p);
   TypeParam true_mean = p;
   TypeParam true_std = sqrt(p * (1 - p));
   TypeParam bound = this->mean_bound(true_std, sample_size);

src/caffe/util/math_functions.cpp

@@ -1,11 +1,12 @@
 // Copyright 2013 Yangqing Jia
 // Copyright 2014 kloudkl@github
 
-#include <limits>
 #include <boost/math/special_functions/next.hpp>
 #include <boost/random.hpp>
-
 #include <cublas_v2.h>
+
+#include <limits>
+
 #include "caffe/common.hpp"
 #include "caffe/util/math_functions.hpp"