/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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/operators/accuracy_op.h"

namespace paddle {
namespace operators {

class AccuracyOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext *ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("Out"),
                   "Input (Out) of accuracy op should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Indices"),
                   "Input (Indices) of accuracy op should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("Label"),
                   "Input (Label) of accuracy op should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("Accuracy"),
                   "Output (Accuracy) of AccuracyOp should not be null.");

    auto inference_dim = ctx->GetInputDim("Out");
    auto label_dim = ctx->GetInputDim("Label");
    // Assume indices has same shape as inference, because
    // it's the output of topk.

    PADDLE_ENFORCE_EQ(label_dim.size(), 2, "label's rank must be 2.");
    PADDLE_ENFORCE_EQ(label_dim[1], 1, "label's second dimension must be 1");
    PADDLE_ENFORCE_EQ(inference_dim[0], label_dim[0],
                      "the inference tensor's num_rows must be"
                      " the same as label.");

    ctx->SetOutputDim("Accuracy", {1});
    ctx->ShareLoD("Out", /*->*/ "Accuracy");
  }

 protected:
  // IndicateDataType
  framework::DataType IndicateDataType(
      const framework::ExecutionContext &ctx) const override {
    return framework::ToDataType(ctx.Input<Tensor>("Out")->type());
  }
};

class AccuracyOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  AccuracyOpMaker(framework::OpProto *proto,
                  framework::OpAttrChecker *op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    // TODO(typhoonzero): support both inference value and indices.
    AddInput("Out", "The network output of topk (inferences)");
    AddInput("Indices", "The the network output of topk (indices)");
    AddInput("Label", "Label of the training data");
    // TODO(typhoonzero): AddInput("Weight", ...
    AddOutput("Accuracy", "The accuracy 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

namespace ops = paddle::operators;
REGISTER_OPERATOR(accuracy, ops::AccuracyOp, ops::AccuracyOpMaker,
                  paddle::framework::EmptyGradOpMaker);
// FIXME(typhoonzero): types of T is for infernece data.
// label data is always int.
REGISTER_OP_CPU_KERNEL(accuracy,
                       ops::AccuracyKernel<paddle::platform::CPUPlace, float>,
                       ops::AccuracyKernel<paddle::platform::CPUPlace, double>);