opt: 优化 ReduceMeanObj 构造器实现

Signed-off-by: YdrMaster <ydrml@hotmail.com>

include/operators/reduce_mean.h

@@ -7,7 +7,7 @@ namespace infini {
  *
  */
 class ReduceMeanObj : public OperatorObj {
-    set<int> axis; // axis to reduce
+    set<int> axes; // axis to reduce
     bool keepDims;
 
   public:
@@ -17,11 +17,11 @@ class ReduceMeanObj : public OperatorObj {
      * @param graph The computation graph that this operator belongs to.
      * @param input The input tensor.
      * @param output The output tensor.
-     * @param axis Axes to reduce.
+     * @param axes Axes to reduce.
      * @param keepDims Keep the reduced dimensions or not.
      */
     ReduceMeanObj(GraphObj *graph, Tensor input, Tensor output,
-                  const optional<const vector<int>> &axis,
+                  const optional<const vector<int>> &axes,
                   bool keepDims = true);
     OP_CLONE(ReduceMeanObj);
     optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;

src/operators/reduce_mean.cc

@@ -2,27 +2,27 @@
 
 namespace infini {
 ReduceMeanObj::ReduceMeanObj(GraphObj *graph, Tensor input, Tensor output,
-                             const optional<const vector<int>> &_axis,
+                             const optional<const vector<int>> &_axes,
                              bool keepDims)
     : OperatorObj(OpType::ReduceMean, {input}, {output}), keepDims(keepDims) {
-
+    const auto size = input->getDims().size();
-    if (_axis != std::nullopt) {
+    if (_axes) {
-        IT_ASSERT((*_axis).size() <= input->getDims().size());
+        // TODO 不需要这个，但需要处理负数，一对相反数应该不能同时出现。
-        for (size_t j = 0; j < (*_axis).size(); ++j) {
+        // IT_ASSERT((*_axes).size() <= input->getDims().size());
-            int idx = (*_axis)[j];
+        for (auto idx : *_axes) {
             if (idx < 0)
                 IT_TODO_HALT();
-            IT_ASSERT((size_t)idx < input->getDims().size());
+            IT_ASSERT((size_t)idx < size);
-            axis.emplace(idx);
+            axes.emplace(idx);
         }
     } else
-        for (size_t i = 0; i < input->getDims().size(); ++i)
+        for (size_t i = 0; i < size; ++i)
-            axis.emplace(i);
+            axes.emplace(i);
     IT_ASSERT(checkValid(graph));
 }
 
 bool ReduceMeanObj::isReduced(int idx) const {
-    return axis.find(idx) != axis.end();
+    return axes.find(idx) != axes.end();
 }
 
 optional<vector<Shape>>
@@ -31,7 +31,7 @@ ReduceMeanObj::inferShape(const TensorVec &inputs) const {
 
     if (keepDims) {
         Shape ret = dims;
-        for (auto it : axis)
+        for (auto it : axes)
             ret[it] = 1;
         return {{ret}};
     } else {
@@ -55,14 +55,14 @@ std::string ReduceMeanObj::toString() const {
 
     std::string axisstr;
     axisstr.append("[");
-    for (auto d : axis) {
+    for (auto d : axes) {
         axisstr.append(std::to_string(d));
         axisstr.append(",");
     }
-    if (!axis.empty())
+    if (!axes.empty())
         axisstr.pop_back();
     axisstr.append("]");
-    os << "axis=" << axisstr << ",";
+    os << "axes=" << axisstr << ",";
     os << "keepDims=" << keepDims << ",";
     os << "input=" << inputs[0]->getGuid() << ",";
     os << "output=" << outputs[0]->getGuid() << ")";
@@ -73,13 +73,13 @@ vector<int> ReduceMeanObj::getWorkloadVector() const {
     vector<int> ret = inputs[0]->getDims();
     ret.emplace(ret.begin(), enum_to_underlying(type));
     ret.emplace_back((int)keepDims);
-    ret.insert(ret.end(), axis.begin(), axis.end());
+    ret.insert(ret.end(), axes.begin(), axes.end());
     return ret;
 }
 
 vector<int> ReduceMeanObj::getOpAttrVector() const {
     vector<int> ret = {enum_to_underlying(type), (int)keepDims};
-    ret.insert(ret.end(), axis.begin(), axis.end());
+    ret.insert(ret.end(), axes.begin(), axes.end());
     return ret;
 }
 } // namespace infini