resolve #15618 (#16114)

* resolve #15618
Backgroud: the PR #15398 raised the box_coder op performance regression, we optimized the code via the more efficency leveraging opemmp.

paddle/fluid/operators/detection/box_coder_op.h

@@ -20,7 +20,7 @@ namespace operators {
 
 enum class BoxCodeType { kEncodeCenterSize = 0, kDecodeCenterSize = 1 };
 
-inline BoxCodeType GetBoxCodeType(const std::string& type) {
+inline BoxCodeType GetBoxCodeType(const std::string &type) {
   if (type == "encode_center_size") {
     return BoxCodeType::kEncodeCenterSize;
   } else if (type == "decode_center_size") {
@@ -32,24 +32,23 @@ inline BoxCodeType GetBoxCodeType(const std::string& type) {
 template <typename DeviceContext, typename T>
 class BoxCoderKernel : public framework::OpKernel<T> {
  public:
-  void EncodeCenterSize(const framework::Tensor* target_box,
-                        const framework::Tensor* prior_box,
-                        const framework::Tensor* prior_box_var,
+  void EncodeCenterSize(const framework::Tensor *target_box,
+                        const framework::Tensor *prior_box,
+                        const framework::Tensor *prior_box_var,
                         const bool normalized,
-                        const std::vector<float> variance, T* output) const {
+                        const std::vector<float> variance, T *output) const {
     int64_t row = target_box->dims()[0];
     int64_t col = prior_box->dims()[0];
     int64_t len = prior_box->dims()[1];
-    auto* target_box_data = target_box->data<T>();
-    auto* prior_box_data = prior_box->data<T>();
-    const T* prior_box_var_data = nullptr;
-    if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
 
 #ifdef PADDLE_WITH_MKLML
 #pragma omp parallel for collapse(2)
 #endif
     for (int64_t i = 0; i < row; ++i) {
       for (int64_t j = 0; j < col; ++j) {
+        auto *target_box_data = target_box->data<T>();
+        auto *prior_box_data = prior_box->data<T>();
+        size_t offset = i * col * len + j * len;
         T prior_box_width = prior_box_data[j * len + 2] -
                             prior_box_data[j * len] + (normalized == false);
         T prior_box_height = prior_box_data[j * len + 3] -
@@ -69,7 +68,6 @@ class BoxCoderKernel : public framework::OpKernel<T> {
                               target_box_data[i * len + 1] +
                               (normalized == false);
 
-        size_t offset = i * col * len + j * len;
         output[offset] =
             (target_box_center_x - prior_box_center_x) / prior_box_width;
         output[offset + 1] =
@@ -78,44 +76,61 @@ class BoxCoderKernel : public framework::OpKernel<T> {
             std::log(std::fabs(target_box_width / prior_box_width));
         output[offset + 3] =
             std::log(std::fabs(target_box_height / prior_box_height));
-        if (prior_box_var) {
-          int prior_var_offset = j * len;
-          output[offset] /= prior_box_var_data[prior_var_offset];
-          output[offset + 1] /= prior_box_var_data[prior_var_offset + 1];
-          output[offset + 2] /= prior_box_var_data[prior_var_offset + 2];
-          output[offset + 3] /= prior_box_var_data[prior_var_offset + 3];
-        } else if (!(variance.empty())) {
+      }
+    }
+
+    if (prior_box_var) {
+      const T *prior_box_var_data = prior_box_var->data<T>();
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for collapse(3)
+#endif
+      for (int64_t i = 0; i < row; ++i) {
+        for (int64_t j = 0; j < col; ++j) {
           for (int k = 0; k < 4; ++k) {
+            size_t offset = i * col * len + j * len;
+            int prior_var_offset = j * len;
+            output[offset + k] /= prior_box_var_data[prior_var_offset + k];
+          }
+        }
+      }
+    } else if (!(variance.empty())) {
+#ifdef PADDLE_WITH_MKLML
+#pragma omp parallel for collapse(3)
+#endif
+      for (int64_t i = 0; i < row; ++i) {
+        for (int64_t j = 0; j < col; ++j) {
+          for (int k = 0; k < 4; ++k) {
+            size_t offset = i * col * len + j * len;
             output[offset + k] /= static_cast<T>(variance[k]);
           }
         }
       }
     }
   }
+
   template <int axis, int var_size>
-  void DecodeCenterSize(const framework::Tensor* target_box,
-                        const framework::Tensor* prior_box,
-                        const framework::Tensor* prior_box_var,
+  void DecodeCenterSize(const framework::Tensor *target_box,
+                        const framework::Tensor *prior_box,
+                        const framework::Tensor *prior_box_var,
                         const bool normalized, std::vector<float> variance,
-                        T* output) const {
+                        T *output) const {
     int64_t row = target_box->dims()[0];
     int64_t col = target_box->dims()[1];
     int64_t len = target_box->dims()[2];
 
-    auto* target_box_data = target_box->data<T>();
-    auto* prior_box_data = prior_box->data<T>();
-    const T* prior_box_var_data = nullptr;
-    if (var_size == 2) prior_box_var_data = prior_box_var->data<T>();
-    int prior_box_offset = 0;
-    T var_data[4] = {1., 1., 1., 1.};
-    T* var_ptr = var_data;
 #ifdef PADDLE_WITH_MKLML
 #pragma omp parallel for collapse(2)
 #endif
     for (int64_t i = 0; i < row; ++i) {
       for (int64_t j = 0; j < col; ++j) {
+        auto *target_box_data = target_box->data<T>();
+        auto *prior_box_data = prior_box->data<T>();
+
+        T var_data[4] = {1., 1., 1., 1.};
+        T *var_ptr = var_data;
         size_t offset = i * col * len + j * len;
-        prior_box_offset = axis == 0 ? j * len : i * len;
+        int prior_box_offset = axis == 0 ? j * len : i * len;
+
         T prior_box_width = prior_box_data[prior_box_offset + 2] -
                             prior_box_data[prior_box_offset] +
                             (normalized == false);
@@ -131,10 +146,10 @@ class BoxCoderKernel : public framework::OpKernel<T> {
         T target_box_width = 0, target_box_height = 0;
         int prior_var_offset = axis == 0 ? j * len : i * len;
         if (var_size == 2) {
-          std::memcpy(var_ptr, prior_box_var_data + prior_var_offset,
+          std::memcpy(var_ptr, prior_box_var->data<T>() + prior_var_offset,
                       4 * sizeof(T));
         } else if (var_size == 1) {
-          var_ptr = reinterpret_cast<T*>(variance.data());
+          var_ptr = reinterpret_cast<T *>(variance.data());
         }
         T box_var_x = *var_ptr;
         T box_var_y = *(var_ptr + 1);
@@ -162,11 +177,11 @@ class BoxCoderKernel : public framework::OpKernel<T> {
     }
   }
 
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* prior_box = context.Input<framework::Tensor>("PriorBox");
-    auto* prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
-    auto* target_box = context.Input<framework::LoDTensor>("TargetBox");
-    auto* output_box = context.Output<framework::Tensor>("OutputBox");
+  void Compute(const framework::ExecutionContext &context) const override {
+    auto *prior_box = context.Input<framework::Tensor>("PriorBox");
+    auto *prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
+    auto *target_box = context.Input<framework::LoDTensor>("TargetBox");
+    auto *output_box = context.Output<framework::Tensor>("OutputBox");
     std::vector<float> variance = context.Attr<std::vector<float>>("variance");
     const int axis = context.Attr<int>("axis");
     if (target_box->lod().size()) {
@@ -194,7 +209,7 @@ class BoxCoderKernel : public framework::OpKernel<T> {
 
     output_box->mutable_data<T>({row, col, len}, context.GetPlace());
 
-    T* output = output_box->data<T>();
+    T *output = output_box->data<T>();
     if (code_type == BoxCodeType::kEncodeCenterSize) {
       EncodeCenterSize(target_box, prior_box, prior_box_var, normalized,
                        variance, output);