add autogen code support for accuracy (#52424)

* add autogen code support for accuracy

* fix input

paddle/fluid/operators/metrics/accuracy_op.cc

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include "paddle/fluid/framework/infershape_utils.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/phi/infermeta/ternary.h"
-
-namespace paddle {
-namespace operators {
-
-class AccuracyOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  phi::KernelKey GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const override {
-    return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(ctx, "Out"),
-                          ctx.GetPlace());
-  }
-};
-
-class AccuracyOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    // TODO(typhoonzero): support both inference value and indices.
-    AddInput("Out", "The network output of topk (inferences)");
-    AddInput("Indices", "The network output of topk (indices)");
-    AddInput("Label", "Label of the training data");
-    // TODO(typhoonzero): AddInput("Weight", ...
-    AddOutput("Accuracy", "The accuracy of current batch");
-    AddOutput("Correct", "The correct samples count of current batch");
-    AddOutput("Total", "The samples count of current batch");
-
-    AddComment(R"DOC(
-Accuracy Operator.
-
-It will print accuracy rate for classification.
-The accuracy is calculated as follows:
-
-$$accuracy = \frac{NumOfCorrectPredicts}{NumOfAllSamples}$$
-
-Both the input Out and Label can carry the LoD (Level of Details)
-information, or not. But the output only shares the LoD information
-with the input Out(Inference).
-
-)DOC");
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-// FIXME(typhoonzero): types of T is for infernece data.
-// label data is always int.
-DECLARE_INFER_SHAPE_FUNCTOR(accuracy,
-                            AccuracyInferShapeFunctor,
-                            PD_INFER_META(phi::AccuracyInferMeta));
-namespace ops = paddle::operators;
-REGISTER_OPERATOR(
-    accuracy,
-    ops::AccuracyOp,
-    ops::AccuracyOpMaker,
-    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
-    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
-    AccuracyInferShapeFunctor);

paddle/phi/api/yaml/legacy_ops.yaml

@@ -10,15 +10,6 @@
     func : abs
   backward : abs_grad
 
-- op : accuracy
-  args : (Tensor x, Tensor indices, Tensor label)
-  output : Tensor(accuracy), Tensor(correct), Tensor(total)
-  infer_meta :
-    func : AccuracyInferMeta
-  kernel :
-    func : accuracy
-    data_type : x
-
 - op : adadelta_
   args : (Tensor param, Tensor grad, Tensor avg_squared_grad, Tensor avg_squared_update, Tensor master_param, float rho, float epsilon, bool multi_precision)
   output : Tensor(param_out), Tensor(moment_out), Tensor(inf_norm_out), Tensor(master_param_out)

paddle/phi/api/yaml/op_compat.yaml

@@ -23,6 +23,12 @@
   extra :
     attrs : [bool use_mkldnn = false]
 
+- op : accuracy
+  inputs :
+    {x : Out , indices : Indices, label: Label}
+  outputs :
+    {accuracy : Accuracy, correct : Correct, total : Total}
+
 - op : acos
   inputs :
     x : X

paddle/phi/api/yaml/ops.yaml

@@ -5,6 +5,15 @@
 # are consistent and correspond one-to-one. It's forbidden that the
 # operator configured in this yaml file does not have Python API.
 
+- op : accuracy
+  args : (Tensor x, Tensor indices, Tensor label)
+  output : Tensor(accuracy), Tensor(correct), Tensor(total)
+  infer_meta :
+    func : AccuracyInferMeta
+  kernel :
+    func : accuracy
+    data_type : x
+
 - op : acos
   args : (Tensor x)
   output : Tensor