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87 lines
3.7 KiB
87 lines
3.7 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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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null.");
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auto x_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE_EQ(x_dims.size(), 4, "Input(X)'s rank should be 4.");
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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(ctx.Input<Tensor>("X")->type(),
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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(similarity_focus, ops::SimilarityFocusOp,
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ops::SimilarityFocusOpMaker,
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paddle::framework::EmptyGradOpMaker);
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REGISTER_OP_CPU_KERNEL(similarity_focus, ops::SimilarityFocusKernel<float>,
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ops::SimilarityFocusKernel<double>);
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