update

paddle/operators/auc_op.cc

@@ -17,7 +17,7 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-class AccuracyOp : public framework::OperatorWithKernel {
+class AucOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
@@ -76,5 +76,5 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_WITHOUT_GRADIENT(auc, ops::AccuracyOp, ops::AccuracyOpMaker);
+REGISTER_OP_WITHOUT_GRADIENT(auc, ops::AucOp, ops::AucOpMaker);
 REGISTER_OP_CPU_KERNEL(auc, ops::AucKernel<paddle::platform::CPUPlace, float>);

paddle/operators/auc_op.h

@@ -23,7 +23,7 @@ namespace operators {
 using Tensor = framework::Tensor;
 
 template <typename Place, typename T>
-class AccuracyKernel : public framework::OpKernel {
+class AucKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* inference = ctx.Input<Tensor>("Inference");
@@ -45,7 +45,7 @@ class AccuracyKernel : public framework::OpKernel {
     thresholds_list[num_thresholds - 1] = 1.0f + kEpsilon;
 
     const int* inference_data = inference->data<int>();
-    const T* inference_prob_data = inference->data<T>();
+    const T* inference_prob_data = inference_prob->data<T>();
     const T* label_data = label->data<T>();
 
     size_t num_samples = inference->dims()[0];
@@ -54,17 +54,17 @@ class AccuracyKernel : public framework::OpKernel {
     // create local tensor for storing the curve: TP, FN, TN, FP
     // TODO(typhoonzero): put these tensors in Scope
     // TODO(typhoonzero): use op to caculate these values.
-    Tensor true_positive, false_positeve, true_negative, false_negative;
+    Tensor true_positive, false_positive, true_negative, false_negative;
 
     true_positive.Resize({num_thresholds});
     false_negative.Resize({num_thresholds});
     true_negative.Resize({num_thresholds});
     false_positive.Resize({num_thresholds});
 
-    int* tp_data = true_positive.mutable_data<int>();
-    int* fn_data = false_negative.mutable_data<int>();
-    int* tn_data = true_negative.mutable_data<int>();
-    int* fp_data = false_positive.mutable_data<int>();
+    int* tp_data = true_positive.mutable_data<int>(ctx.GetPlace());
+    int* fn_data = false_negative.mutable_data<int>(ctx.GetPlace());
+    int* tn_data = true_negative.mutable_data<int>(ctx.GetPlace());
+    int* fp_data = false_positive.mutable_data<int>(ctx.GetPlace());
 
     for (auto thresh = thresholds_list.begin(); thresh != thresholds_list.end();
          thresh++) {
@@ -101,13 +101,13 @@ class AccuracyKernel : public framework::OpKernel {
     tp_rate.Resize({num_thresholds});
     fp_rate.Resize({num_thresholds});
     rec_rate.Resize({num_thresholds});
-    float* tp_rate_data = tp_rate.mutable_data<float>();
-    float* fp_rate_data = fp_rate.mutable_data<float>();
-    float* rec_rate_data = rec_rate.mutable_data<float>();
+    float* tp_rate_data = tp_rate.mutable_data<float>(ctx.GetPlace());
+    float* fp_rate_data = fp_rate.mutable_data<float>(ctx.GetPlace());
+    float* rec_rate_data = rec_rate.mutable_data<float>(ctx.GetPlace());
     for (int i = 0; i < num_thresholds; i++) {
-        tp_rate_data[i] = ((float)tp_data[i + epsilon) / (tp_data[i] + fn_data[i] + epsilon);
-        fp_rate_data[i] =
-            (float)fp_data[i] / (fp_data[i] + tn_data[i] + epsilon);
+      tp_rate_data[i] =
+          ((float)tp_data[i] + epsilon) / (tp_data[i] + fn_data[i] + epsilon);
+      fp_rate_data[i] = (float)fp_data[i] / (fp_data[i] + tn_data[i] + epsilon);
       rec_rate_data[i] =
           ((float)tp_data[i] + epsilon) / (tp_data[i] + fp_data[i] + epsilon);
     }
@@ -118,7 +118,7 @@ class AccuracyKernel : public framework::OpKernel {
         auto y = (tp_rate_data[i] + tp_rate_data[i - 1]) / 2.0f;
         *auc_data = *auc_data + dx * y;
       }
-    } else if (curve = "PR") {
+    } else if (curve == "PR") {
       for (int i = 1; i < num_thresholds; i++) {
         auto dx = tp_rate_data[i] - tp_rate_data[i - 1];
         auto y = (rec_rate_data[i] + rec_rate_data[i - 1]) / 2.0f;

paddle/pybind/pybind.cc

@@ -50,6 +50,7 @@ USE_OP(cos_sim);
 USE_CPU_ONLY_OP(gather);
 USE_CPU_ONLY_OP(scatter);
 USE_OP(top_k);
+USE_CPU_ONLY_OP(auc);
 USE_OP(squared_l2_distance);
 
 namespace paddle {