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99 lines
4.1 KiB
99 lines
4.1 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/edit_distance_op.h"
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namespace paddle {
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namespace operators {
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class EditDistanceOp : 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("Hyps"), "Input(Hyps) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasInput("Refs"), "Input(Refs) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) shouldn't be null.");
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auto hyp_dims = ctx->GetInputDim("Hyps");
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auto ref_dims = ctx->GetInputDim("Refs");
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PADDLE_ENFORCE(hyp_dims.size() == 2 && hyp_dims[1] == 1,
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"Input(Hyps) must be a 2-D LoDTensor with the 2nd dimension "
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"equal to 1.");
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PADDLE_ENFORCE(ref_dims.size() == 2 && ref_dims[1] == 1,
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"Input(Refs) must be a 2-D LoDTensor with the 2nd dimension "
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"equal to 1.");
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ctx->SetOutputDim("Out", ctx->GetInputDim("Refs"));
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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(framework::proto::DataType::FP32,
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ctx.device_context());
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}
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};
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class EditDistanceOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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EditDistanceOpMaker(OpProto *proto, OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Hyps",
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"(2-D LoDTensor<int>, 2nd dim. equal to 1) "
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"The indices for hypothesis strings.");
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AddInput("Refs",
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"(2-D LoDTensor<int>, 2nd dim. equal to 1) "
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"The indices for reference strings.");
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AddAttr<bool>("normalized",
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"(bool, default false) Indicated whether to normalize "
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"the edit distance by the length of reference string.")
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.SetDefault(false);
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AddOutput("Out",
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"(2-D Tensor with shape [`batch_size` x 1]) "
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"The output edit distances of EditDistance operator.");
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AddComment(R"DOC(
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EditDistance operator computes the edit distances between a batch of hypothesis
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strings and their references.
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Edit distance, also called Levenshtein distance, measures how dissimilar two strings
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are by counting the minimum number of operations to transform one string into anthor.
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Here the operations include insertion, deletion, and substitution. For example,
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given hypothesis string A = "kitten" and reference B = "sitting", the edit distance
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is 3 for A will be transformed into B at least after two substitutions and one
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insertion:
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"kitten" -> "sitten" -> "sittin" -> "sitting"
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Input(Hyps) is a LoDTensor consisting of all the hypothesis strings with the total
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number denoted by `batch_size`, and the separation is specified by the LoD information.
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And the `batch_size` reference strings are arranged in order in the same way in the
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LoDTensor Input(Refs).
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Output(Out) contains the `batch_size` results and each stands for the edit stance
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for a pair of strings respectively. If Attr(normalized) is true, the edit distance
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will be divided by the length of reference string.
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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(edit_distance, ops::EditDistanceOp, ops::EditDistanceOpMaker,
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paddle::framework::EmptyGradOpMaker);
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REGISTER_OP_CPU_KERNEL(
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edit_distance, ops::EditDistanceKernel<paddle::platform::CPUPlace, float>);
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