fixed style errors

diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index d8cd2e2..9151ff2 100644 (file)
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -35,11 +35,11 @@ void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK(pool_param.has_kernel_size() ||
       (pool_param.has_kernel_h() && pool_param.has_kernel_w()))
       << "For non-square filters both kernel_h and kernel_w are required.";
-  CHECK((!pool_param.has_pad() && pool_param.has_pad_h() 
+  CHECK((!pool_param.has_pad() && pool_param.has_pad_h()
       && pool_param.has_pad_w())
       || (!pool_param.has_pad_h() && !pool_param.has_pad_w()))
       << "pad is pad OR pad_h and pad_w are required.";
-  CHECK((!pool_param.has_stride() && pool_param.has_stride_h() 
+  CHECK((!pool_param.has_stride() && pool_param.has_stride_h()
       && pool_param.has_stride_w())
       || (!pool_param.has_stride_h() && !pool_param.has_stride_w()))
       << "Stride is stride OR stride_h and stride_w are required.";

diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index e38028d..a44b177 100644 (file)
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -17,7 +17,7 @@ template <typename Dtype>
 __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, const int pad_h, const int pad_w, Dtype* top_data,
     int* mask, Dtype* top_mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
@@ -55,7 +55,7 @@ template <typename Dtype>
 __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, const int pad_h, const int pad_w, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
@@ -87,7 +87,7 @@ __global__ void StoPoolForwardTrain(const int nthreads,
     const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, Dtype* rand_idx, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
@@ -128,7 +128,7 @@ __global__ void StoPoolForwardTest(const int nthreads,
     const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
@@ -175,15 +175,15 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_h_, 
-        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data, 
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
+        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data,
         mask, top_mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
         kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
@@ -195,7 +195,7 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       StoPoolForwardTrain<Dtype><<<CAFFE_GET_BLOCKS(count),
                                    CAFFE_CUDA_NUM_THREADS>>>(
           count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+          height_, width_, pooled_height_, pooled_width_, kernel_h_,
           kernel_w_, stride_h_, stride_w_,
           rand_idx_.mutable_gpu_data(), top_data);
     } else {
@@ -203,7 +203,7 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       StoPoolForwardTest<Dtype><<<CAFFE_GET_BLOCKS(count),
                                   CAFFE_CUDA_NUM_THREADS>>>(
           count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+          height_, width_, pooled_height_, pooled_width_, kernel_h_,
           kernel_w_, stride_h_, stride_w_, top_data);
     }
     break;
@@ -219,8 +219,8 @@ template <typename Dtype>
 __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
     const int* mask, const Dtype* top_mask, const int num, const int channels,
     const int height, const int width, const int pooled_height,
-    const int pooled_width, const int kernel_h, const int kernel_w, 
-    const int stride_h, const int stride_w, const int pad_h, const int pad_w, 
+    const int pooled_width, const int kernel_h, const int kernel_w,
+    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
     Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
@@ -265,8 +265,8 @@ template <typename Dtype>
 __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
-    const int stride_w, const int pad_h, const int pad_w, 
+    const int kernel_h, const int kernel_w, const int stride_h,
+    const int stride_w, const int pad_h, const int pad_w,
     Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
@@ -302,7 +302,7 @@ __global__ void StoPoolBackward(const int nthreads,
     const Dtype* rand_idx, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h, 
+    const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
@@ -354,14 +354,14 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, mask, top_mask, top[0]->num(), channels_,
         height_, width_, pooled_height_, pooled_width_,
-        kernel_h_, kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, 
+        kernel_h_, kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_,
         bottom_diff);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_h_, 
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
         kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, bottom_diff);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
@@ -369,7 +369,7 @@ void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     StoPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, rand_idx_.gpu_data(), top_diff,
         top[0]->num(), channels_, height_, width_, pooled_height_,
-        pooled_width_, kernel_h_, kernel_w_, stride_h_, stride_w_, 
+        pooled_width_, kernel_h_, kernel_w_, stride_h_, stride_w_,
         bottom_diff);
     break;
   default:

diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 651d203..bb21113 100644 (file)
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -132,7 +132,8 @@ class PoolingLayerTest : public ::testing::Test {
     // [31     9     2    22    27    20]
     // [ 8    28    33    17    10    15]
     // [30     5    34    12    14    16]
-    // [ 4    36    29    13    18    11] (this is generated by magic(6) in MATLAB)
+    // [ 4    36    29    13    18    11]
+    // (this is generated by magic(6) in MATLAB)
     for (int i = 0; i < 36 * num * channels; i += 36) {
       blob_bottom_->mutable_cpu_data()[i +  0] = 35;
       blob_bottom_->mutable_cpu_data()[i +  1] = 1;
@@ -256,7 +257,8 @@ class PoolingLayerTest : public ::testing::Test {
     // [31     9     2    22    27    20]
     // [ 8    28    33    17    10    15]
     // [30     5    34    12    14    16]
-    // [ 4    36    29    13    18    11] (this is generated by magic(6) in MATLAB)
+    // [ 4    36    29    13    18    11]
+    // (this is generated by magic(6) in MATLAB)
     for (int i = 0; i < 36 * num * channels; i += 36) {
       blob_bottom_->mutable_cpu_data()[i +  0] = 35;
       blob_bottom_->mutable_cpu_data()[i +  1] = 1;