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paddle/operators/precision_recall_op.cc

@@ -22,7 +22,6 @@ class PrecisionRecallOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    // may contains weights and StatesInfo
     PADDLE_ENFORCE(ctx->HasInput("Predictions"),
                    "Input(Predictions) should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Labels"),
@@ -108,11 +107,54 @@ class PrecisionRecallOpMaker : public framework::OpProtoAndCheckerMaker {
              "provided, current state will be accumulated to this state and "
              "the accumulation state will be as the output state.")
         .AsDispensable();
-    AddOutput("BatchMetrics", "");
-    AddOutput("AccumMetrics", "");
-    AddOutput("AccumStatesInfo", "");
+    AddOutput("BatchMetrics",
+              "(Tensor, default Tensor<float>), a 1-D tensor with shape {6}."
+              "This output tensor contains metrics for current batch data."
+              "The layout is [macro average precision, macro average recall, "
+              "macro f1 score, micro average precision, micro average recall, "
+              "micro f1 score]");
+    AddOutput("AccumMetrics",
+              "(Tensor, default Tensor<float>), a 1-D tensor with shape {6}."
+              "This output tensor contains metrics for accumulated data."
+              "The layout is [macro average precision, macro average recall, "
+              "macro f1 score, micro average precision, micro average recall, "
+              "micro f1 score]");
+    AddOutput("AccumStatesInfo",
+              "(Tensor, default Tensor<float>), a 2-D tensor with shape D x 4, "
+              "where D is equal to class number. This output tensor contains "
+              "accumulated state variables used to compute metrics. The layout "
+              "for each class is [true positives, false positives, "
+              "true negatives, false negatives].");
 
     AddComment(R"DOC(
+When given 'Input(Predictions)' and 'Input(Labels)', this operator can be used
+to compute various metrics including:
+  - macro average precision
+  - macro average recall
+  - macro f1 score
+  - micro average precision
+  - micro average recall
+  - micro f1 score
+
+To compute the above metrics, we need to statistic counts 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
+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
+state contains statistic variables for corresponding class. Layout of each row
+is: TP(true positives), FP(false positives), TN(true negatives),
+FN(false negatives). If 'Input(Weights)' provided, TP, FP, TN, FN will be
+calculated by given weight instead of instance count.
+
+This operator also supports metrics computing for cross-batch situation. To
+achieve this, 'Input(StatesInfo)' should be provided. State of current batch
+data will be accumulated to 'Input(StatesInfo)' and 'Output(AccumStatesInfo)'
+is the accumulation state.
+
+'Output(BatchMetrics)' is metrics of current batch data while
+'Output(AccumStatesInfo)' is metrics of accumulation data.
+
 )DOC");
   }
 };