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							94 lines
						
					
					
						
							3.9 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/similarity_focus_op.h"
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namespace paddle {
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namespace operators {
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class SimilarityFocusOpMaker : public framework::OpProtoAndCheckerMaker {
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 public:
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  void Make() override {
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    AddInput("X",
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             "(Tensor, default Tensor<float>), a 4-D tensor with shape,"
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             " [BatchSize, X, Y, Z]");
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    AddOutput("Out",
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              "(Tensor, default Tensor<float>), the similarity focus mask"
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              " with the same shape of input X.");
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    AddAttr<int>("axis",
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                 "(int32), indicating the dimension to be select. It can"
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                 " only be 1, 2, or 3.");
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    AddAttr<std::vector<int>>("indexes",
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                              "(std::vector<int32>), indicating the indexes"
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                              " of the selected dimension.");
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    AddComment(R"DOC(
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SimilarityFocus Operator.
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Generate a similarity focus mask with the same shape of input using the following method:
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1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding 
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   to the axis according to the indexes. For example, if axis=1 and indexes=[a], 
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   it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X 
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   is (BatchSize, A, B, C), the shape of tensor T is (BatchSize, B, C).
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2. For each index, find the largest numbers in the tensor T, so that the same 
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   row and same column has at most one number(what it means is that if the 
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   largest number has been found in the i-th row and the j-th column, then 
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   the numbers in the i-th row or j-th column will be skipped. And then the 
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   next largest number will be selected from the remaining numbers. Obviously 
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   there will be min(B, C) numbers), and mark the corresponding position of the 
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   3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for 
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   each index.
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3. Broadcast the 3-D similarity focus mask to the same shape of input X.
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Refer to `Similarity Focus Layer <http://www.aclweb.org/anthology/N16-1108>`_
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)DOC");
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  }
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};
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class SimilarityFocusOp : 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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    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SimilarityFocus");
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    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SimilarityFocus");
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    auto x_dims = ctx->GetInputDim("X");
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    PADDLE_ENFORCE_EQ(
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        x_dims.size(), 4,
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        platform::errors::InvalidArgument(
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            "The dimension size of Input(X) be 4, but received %d.",
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            x_dims.size()));
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    ctx->SetOutputDim("Out", x_dims);
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    ctx->ShareLoD("X", /*->*/ "Out");
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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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        OperatorWithKernel::IndicateVarDataType(ctx, "X"),
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        platform::CPUPlace());
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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(
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    similarity_focus, ops::SimilarityFocusOp, ops::SimilarityFocusOpMaker,
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    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
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    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OP_CPU_KERNEL(similarity_focus, ops::SimilarityFocusKernel<float>,
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                       ops::SimilarityFocusKernel<double>);
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