Refine doc and fix data type of metrics.

paddle/operators/precision_recall_op.cc

@@ -136,9 +136,9 @@ to compute various metrics including:
   - micro average recall
   - micro f1 score
 
-To compute the above metrics, we need to statistic counts for true positives,
+To compute the above metrics, we need to do statistics for true positives,
 false positives and false negatives. Here count of true negatives is not
-necessary, but statisticing it may provide potential usage and the cost is
+necessary, but counting it may provide potential usage and the cost is
 trivial, so the operator also provides count of true negatives.
 
 We define state as a 2-D tensor with shape [class number, 4]. Each row of a

paddle/operators/precision_recall_op.h

@@ -42,8 +42,8 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
     const int* labels_data = in1->data<int>();
     const T* weights_data = in2 ? in2->data<T>() : nullptr;
     const T* states_data = in3 ? in3->data<T>() : nullptr;
-    T* batch_metrics_data = out0->mutable_data<T>(ctx.GetPlace());
-    T* accum_metrics_data = out1->mutable_data<T>(ctx.GetPlace());
+    double* batch_metrics_data = out0->mutable_data<double>(ctx.GetPlace());
+    double* accum_metrics_data = out1->mutable_data<double>(ctx.GetPlace());
     out2->mutable_data<T>(ctx.GetPlace());
     auto accum_states = EigenMatrix<T>::From(*out2);
     accum_states.setZero();
@@ -121,7 +121,7 @@ class PrecisionRecallKernel : public framework::OpKernel<T> {
   }
 
  protected:
-  void ComputeMetrics(const T* states_data, T* metrics_data,
+  void ComputeMetrics(const T* states_data, double* metrics_data,
                       size_t state_var_num, size_t class_dim) const {
     T total_tp_count = 0;
     T total_fp_count = 0;