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104 lines
4.0 KiB
104 lines
4.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/accuracy_op.h"
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namespace paddle {
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namespace operators {
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class AccuracyOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Out"),
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"Input (Out) of accuracy op should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Indices"),
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"Input (Indices) of accuracy op should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Label"),
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"Input (Label) of accuracy op should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Accuracy"),
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"Output (Accuracy) of AccuracyOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Correct"),
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"Output (Correct) of AccuracyOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Total"),
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"Output (Total) of AccuracyOp should not be null.");
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auto inference_dim = ctx->GetInputDim("Out");
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auto label_dim = ctx->GetInputDim("Label");
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// Assume indices has same shape as inference, because
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// it's the output of topk.
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PADDLE_ENFORCE_EQ(label_dim.size(), 2, "label's rank must be 2.");
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PADDLE_ENFORCE_EQ(label_dim[1], 1, "label's second dimension must be 1");
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PADDLE_ENFORCE_EQ(inference_dim[0], label_dim[0],
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"the inference tensor's num_rows must be"
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" the same as label.");
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ctx->SetOutputDim("Accuracy", {1});
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ctx->SetOutputDim("Correct", {1});
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ctx->SetOutputDim("Total", {1});
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ctx->ShareLoD("Out", /*->*/ "Accuracy");
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<Tensor>("Out")->type()),
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ctx.GetPlace());
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}
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};
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class AccuracyOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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AccuracyOpMaker(OpProto *proto, OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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// TODO(typhoonzero): support both inference value and indices.
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AddInput("Out", "The network output of topk (inferences)");
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AddInput("Indices", "The the network output of topk (indices)");
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AddInput("Label", "Label of the training data");
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// TODO(typhoonzero): AddInput("Weight", ...
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AddOutput("Accuracy", "The accuracy of current batch");
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AddOutput("Correct", "The correct samples count of current batch");
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AddOutput("Total", "The samples count of current batch");
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AddComment(R"DOC(
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Accuracy Operator.
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It will print accuracy rate for classification.
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The accuracy is calculated as follows:
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$$accuracy = \frac{NumOfCorrectPredicts}{NumOfAllSamples}$$
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Both the input Out and Label can carry the LoD (Level of Details)
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information, or not. But the output only shares the LoD information
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with the input Out(Inference).
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(accuracy, ops::AccuracyOp, ops::AccuracyOpMaker,
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paddle::framework::EmptyGradOpMaker);
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// FIXME(typhoonzero): types of T is for infernece data.
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// label data is always int.
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REGISTER_OP_CPU_KERNEL(accuracy,
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ops::AccuracyKernel<paddle::platform::CPUPlace, float>,
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ops::AccuracyKernel<paddle::platform::CPUPlace, double>);
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