for xxYY to xx_yy

paddle/operators/detection_output_op.cc

@@ -18,8 +18,7 @@ namespace operators {
 
 class DetectionOutputOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  DetectionOutputOpMaker(framework::OpProto* proto,
-                         framework::OpAttrChecker* op_checker)
+  DetectionOutputOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("Loc",
              "(Tensor) The input tensor of detection_output operator."
@@ -86,5 +85,5 @@ REGISTER_OP_WITHOUT_GRADIENT(detection_output, ops::DetectionOutputOp,
                              ops::DetectionOutputOpMaker);
 REGISTER_OP_CPU_KERNEL(
     detection_output,
-    ops::Detection_output_Kernel<paddle::platform::CPUDeviceContext, float>,
-    ops::Detection_output_Kernel<paddle::platform::CPUDeviceContext, double>);
+    ops::DetectionOutputKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::DetectionOutputKernel<paddle::platform::CPUDeviceContext, double>);

paddle/operators/detection_output_op.cu.cc

@@ -17,5 +17,5 @@ limitations under the License. */
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
     detection_output,
-    ops::Detection_output_Kernel<paddle::platform::CUDADeviceContext, float>,
-    ops::Detection_output_Kernel<paddle::platform::CUDADeviceContext, double>);
+    ops::DetectionOutputKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::DetectionOutputKernel<paddle::platform::CUDADeviceContext, double>);

paddle/operators/detection_output_op.h

@@ -48,7 +48,7 @@ inline void transpose_fun(const framework::ExecutionContext& context,
   }
 }
 template <typename DeviceContext, typename T>
-class Detection_output_Kernel : public framework::OpKernel<T> {
+class DetectionOutputKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     const framework::Tensor* in_loc = context.Input<framework::Tensor>("Loc");
@@ -119,22 +119,22 @@ class Detection_output_Kernel : public framework::OpKernel<T> {
         size_t prior_offset = i * 8;
         size_t loc_pred_offset = n * num_priors * 4 + i * 4;
         std::vector<math::BBox<T>> prior_bbox_vec;
-        math::getBBoxFromPriorData<T>(priorbox_data + prior_offset, 1,
+        math::get_bbox_from_priorData<T>(priorbox_data + prior_offset, 1,
                                          prior_bbox_vec);
         std::vector<std::vector<T>> prior_bbox_var;
-        math::getBBoxVarFromPriorData<T>(priorbox_data + prior_offset, 1,
+        math::get_bbox_var_from_prior_data<T>(priorbox_data + prior_offset, 1,
                                               prior_bbox_var);
         std::vector<T> loc_pred_data;
         for (size_t j = 0; j < 4; ++j)
           loc_pred_data.push_back(*(loc_data + loc_pred_offset + j));
-        math::BBox<T> bbox = math::decodeBBoxWithVar<T>(
+        math::BBox<T> bbox = math::decode_bbox_with_var<T>(
             prior_bbox_vec[0], prior_bbox_var[0], loc_pred_data);
         decoded_bboxes.push_back(bbox);
       }
       all_decoded_bboxes.push_back(decoded_bboxes);
     }
     std::vector<std::map<size_t, std::vector<size_t>>> all_indices;
-    int num_kept = math::getDetectionIndices<T>(
+    int num_kept = math::get_detection_indices<T>(
         conf_data, num_priors, num_classes, background_label_id, batch_size,
         confidence_threshold, nms_top_k, nms_threshold, top_k,
         all_decoded_bboxes, &all_indices);
@@ -154,11 +154,11 @@ class Detection_output_Kernel : public framework::OpKernel<T> {
       out_cpu.mutable_data<T>(out->dims(), platform::CPUPlace());
       out_data = out_cpu.data<T>();
     }
-    math::getDetectionOutput<T>(conf_data, num_kept, num_priors, num_classes,
+    math::get_detection_output<T>(conf_data, num_kept, num_priors, num_classes,
                                   batch_size, all_indices, all_decoded_bboxes,
                                   out_data);
     if (platform::is_gpu_place(context.GetPlace())) {
-      framework::CopyFrom(out_cpu, platform::GPUPlace(),
+      framework::CopyFrom(out_cpu, platform::CUDAPlace(),
                           context.device_context(), out);
     }
   }

paddle/operators/math/detection_util.h

@@ -51,31 +51,31 @@ struct BBox {
 // KNCHW ==> NHWC
 // template <typename T>
 template <typename T>
-void getBBoxFromPriorData(const T* prior_data, const size_t num_bboxes,
+void get_bbox_from_priorData(const T* prior_data, const size_t num_bboxes,
                              std::vector<BBox<T>>& bbox_vec);
 template <typename T>
-void getBBoxVarFromPriorData(const T* prior_data, const size_t num,
+void get_bbox_var_from_prior_data(const T* prior_data, const size_t num,
                                   std::vector<std::vector<T>>& var_vec);
 template <typename T>
-BBox<T> decodeBBoxWithVar(BBox<T>& prior_bbox,
+BBox<T> decode_bbox_with_var(BBox<T>& prior_bbox,
                              const std::vector<T>& prior_bbox_var,
                              const std::vector<T>& loc_pred_data);
 template <typename T1, typename T2>
-bool sortScorePairDescend(const std::pair<T1, T2>& pair1,
+bool sort_score_pair_descend(const std::pair<T1, T2>& pair1,
                              const std::pair<T1, T2>& pair2);
 template <typename T>
-bool sortScorePairDescend(const std::pair<T, BBox<T>>& pair1,
+bool sort_score_pair_descend(const std::pair<T, BBox<T>>& pair1,
                              const std::pair<T, BBox<T>>& pair2);
 template <typename T>
-T jaccardOverlap(const BBox<T>& bbox1, const BBox<T>& bbox2);
+T jaccard_overlap(const BBox<T>& bbox1, const BBox<T>& bbox2);
 
 template <typename T>
-void applyNMSFast(const std::vector<BBox<T>>& bboxes, const T* conf_score_data,
-                  size_t class_idx, size_t top_k, T conf_threshold,
-                  T nms_threshold, size_t num_priors, size_t num_classes,
-                  std::vector<size_t>* indices);
+void apply_nms_fast(const std::vector<BBox<T>>& bboxes,
+                    const T* conf_score_data, size_t class_idx, size_t top_k,
+                    T conf_threshold, T nms_threshold, size_t num_priors,
+                    size_t num_classes, std::vector<size_t>* indices);
 template <typename T>
-int getDetectionIndices(
+int get_detection_indices(
     const T* conf_data, const size_t num_priors, const size_t num_classes,
     const size_t background_label_id, const size_t batch_size,
     const T conf_threshold, const size_t nms_top_k, const T nms_threshold,
@@ -85,13 +85,13 @@ int getDetectionIndices(
 template <typename T>
 BBox<T> clipBBox(const BBox<T>& bbox);
 template <typename T>
-void getDetectionOutput(
+void get_detection_output(
     const T* conf_data, const size_t num_kept, const size_t num_priors,
     const size_t num_classes, const size_t batch_size,
     const std::vector<std::map<size_t, std::vector<size_t>>>& all_indices,
     const std::vector<std::vector<BBox<T>>>& all_decoded_bboxes, T* out_data);
 template <typename T>
-void getBBoxFromPriorData(const T* prior_data, const size_t num_bboxes,
+void get_bbox_from_priorData(const T* prior_data, const size_t num_bboxes,
                              std::vector<BBox<T>>& bbox_vec) {
   size_t out_offset = bbox_vec.size();
   bbox_vec.resize(bbox_vec.size() + num_bboxes);
@@ -105,7 +105,7 @@ void getBBoxFromPriorData(const T* prior_data, const size_t num_bboxes,
   }
 }
 template <typename T>
-void getBBoxVarFromPriorData(const T* prior_data, const size_t num,
+void get_bbox_var_from_prior_data(const T* prior_data, const size_t num,
                                   std::vector<std::vector<T>>& var_vec) {
   size_t out_offset = var_vec.size();
   var_vec.resize(var_vec.size() + num);
@@ -119,7 +119,7 @@ void getBBoxVarFromPriorData(const T* prior_data, const size_t num,
   }
 }
 template <typename T>
-BBox<T> decodeBBoxWithVar(BBox<T>& prior_bbox,
+BBox<T> decode_bbox_with_var(BBox<T>& prior_bbox,
                              const std::vector<T>& prior_bbox_var,
                              const std::vector<T>& loc_pred_data) {
   T prior_bbox_width = prior_bbox.get_width();
@@ -147,12 +147,12 @@ BBox<T> decodeBBoxWithVar(BBox<T>& prior_bbox,
   return decoded_bbox;
 }
 template <typename T1, typename T2>
-bool sortScorePairDescend(const std::pair<T1, T2>& pair1,
+bool sort_score_pair_descend(const std::pair<T1, T2>& pair1,
                              const std::pair<T1, T2>& pair2) {
   return pair1.first > pair2.first;
 }
 template <typename T>
-T jaccardOverlap(const BBox<T>& bbox1, const BBox<T>& bbox2) {
+T jaccard_overlap(const BBox<T>& bbox1, const BBox<T>& bbox2) {
   if (bbox2.x_min > bbox1.x_max || bbox2.x_max < bbox1.x_min ||
       bbox2.y_min > bbox1.y_max || bbox2.y_max < bbox1.y_min) {
     return 0.0;
@@ -174,10 +174,10 @@ T jaccardOverlap(const BBox<T>& bbox1, const BBox<T>& bbox2) {
 }
 
 template <typename T>
-void applyNMSFast(const std::vector<BBox<T>>& bboxes, const T* conf_score_data,
-                  size_t class_idx, size_t top_k, T conf_threshold,
-                  T nms_threshold, size_t num_priors, size_t num_classes,
-                  std::vector<size_t>* indices) {
+void apply_nms_fast(const std::vector<BBox<T>>& bboxes,
+                    const T* conf_score_data, size_t class_idx, size_t top_k,
+                    T conf_threshold, T nms_threshold, size_t num_priors,
+                    size_t num_classes, std::vector<size_t>* indices) {
   std::vector<std::pair<T, size_t>> scores;
   for (size_t i = 0; i < num_priors; ++i) {
     size_t conf_offset = i * num_classes + class_idx;
@@ -185,7 +185,7 @@ void applyNMSFast(const std::vector<BBox<T>>& bboxes, const T* conf_score_data,
       scores.push_back(std::make_pair(conf_score_data[conf_offset], i));
   }
   std::stable_sort(scores.begin(), scores.end(),
-                   sortScorePairDescend<T, size_t>);
+                   sort_score_pair_descend<T, size_t>);
   if (top_k > 0 && top_k < scores.size()) scores.resize(top_k);
   while (scores.size() > 0) {
     const size_t idx = scores.front().second;
@@ -193,7 +193,7 @@ void applyNMSFast(const std::vector<BBox<T>>& bboxes, const T* conf_score_data,
     for (size_t i = 0; i < indices->size(); ++i) {
       if (keep) {
         const size_t saved_idx = (*indices)[i];
-        T overlap = jaccardOverlap<T>(bboxes[idx], bboxes[saved_idx]);
+        T overlap = jaccard_overlap<T>(bboxes[idx], bboxes[saved_idx]);
         keep = overlap <= nms_threshold;
       } else {
         break;
@@ -204,7 +204,7 @@ void applyNMSFast(const std::vector<BBox<T>>& bboxes, const T* conf_score_data,
   }
 }
 template <typename T>
-int getDetectionIndices(
+int get_detection_indices(
     const T* conf_data, const size_t num_priors, const size_t num_classes,
     const size_t background_label_id, const size_t batch_size,
     const T conf_threshold, const size_t nms_top_k, const T nms_threshold,
@@ -219,7 +219,7 @@ int getDetectionIndices(
     size_t conf_offset = n * num_priors * num_classes;
     for (size_t c = 0; c < num_classes; ++c) {
       if (c == background_label_id) continue;
-      applyNMSFast<T>(decoded_bboxes, conf_data + conf_offset, c, nms_top_k,
+      apply_nms_fast<T>(decoded_bboxes, conf_data + conf_offset, c, nms_top_k,
                         conf_threshold, nms_threshold, num_priors, num_classes,
                         &(indices[c]));
       num_detected += indices[c].size();
@@ -237,7 +237,7 @@ int getDetectionIndices(
         }
       }
       std::sort(score_index_pairs.begin(), score_index_pairs.end(),
-                sortScorePairDescend<T, std::pair<size_t, size_t>>);
+                sort_score_pair_descend<T, std::pair<size_t, size_t>>);
       score_index_pairs.resize(top_k);
       std::map<size_t, std::vector<size_t>> new_indices;
       for (size_t i = 0; i < score_index_pairs.size(); ++i) {
@@ -266,7 +266,7 @@ BBox<T> clipBBox(const BBox<T>& bbox) {
   return clipped_bbox;
 }
 template <typename T>
-void getDetectionOutput(
+void get_detection_output(
     const T* conf_data, const size_t num_kept, const size_t num_priors,
     const size_t num_classes, const size_t batch_size,
     const std::vector<std::map<size_t, std::vector<size_t>>>& all_indices,