Merge pull request #14071 from barrierye/add_similarity_focus_op
Add similarity focus oprevert-14324-fix_vlog
Xin Pan
6 years ago
committed by
GitHub
commit
ff28b1ffc0
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/* 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(
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framework::ToDataType(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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@ -0,0 +1,168 @@
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/* 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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#pragma once
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#include <algorithm>
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#include <cstring>
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#include <utility>
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T>
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class SimilarityFocusKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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Tensor* out = context.Output<Tensor>("Out");
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const Tensor* x = context.Input<Tensor>("X");
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T* out_data = out->mutable_data<T>(context.GetPlace());
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const T* x_data = x->data<T>();
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int axis = context.Attr<int>("axis");
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std::vector<int> indexes = context.Attr<std::vector<int>>("indexes");
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int64_t batch_size = x->dims()[0];
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int64_t dim[4];
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for (int i = 1; i <= 3; ++i) {
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dim[i] = x->dims()[i];
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}
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if (indexes.size() < 1) {
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PADDLE_THROW("Indexes' size can not be 0.");
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}
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for (auto index : indexes) {
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if (dim[axis] < index) {
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PADDLE_THROW("Index exceeds tensor shape limit.");
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}
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}
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int64_t array_size = 1;
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for (int i = 1; i <= 3; ++i) {
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if (i != axis) {
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array_size *= dim[i];
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}
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}
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std::vector<std::pair<T, int64_t>> array(array_size);
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bool (*cmp)(std::pair<T, int64_t>, std::pair<T, int64_t>) = [](
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std::pair<T, int64_t> x, std::pair<T, int64_t> y) {
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return x.first > y.first;
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};
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int64_t (*compute_index)(int64_t*, int, int, int, int) = [](
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int64_t* dim, int d1, int d2, int d3, int d4) {
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return d1 * dim[1] * dim[2] * dim[3] + d2 * dim[2] * dim[3] +
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d3 * dim[3] + d4;
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};
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memset(out_data, 0, sizeof(T) * batch_size * dim[1] * dim[2] * dim[3]);
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for (int i = 0; i < batch_size; ++i) {
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for (auto index : indexes) {
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if (axis == 1) {
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for (int j = 0; j < dim[2]; ++j) {
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for (int k = 0; k < dim[3]; ++k) {
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array[j * dim[3] + k] = std::make_pair(
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x_data[compute_index(dim, i, index, j, k)], j * dim[3] + k);
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}
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}
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std::sort(array.begin(), array.end(), cmp);
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int tag_num = 0;
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std::vector<bool> tag2(dim[2]), tag3(dim[3]);
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for (auto x : array) {
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int idx2 = x.second / dim[3];
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int idx3 = x.second % dim[3];
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if (tag2[idx2] || tag3[idx3]) {
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continue;
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}
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tag_num++;
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tag2[idx2] = true;
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tag3[idx3] = true;
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for (int j = 0; j < dim[1]; ++j) {
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out_data[compute_index(dim, i, j, idx2, idx3)] = 1;
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}
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if (tag_num == std::min(dim[2], dim[3])) {
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break;
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}
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}
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} else if (axis == 2) {
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for (int j = 0; j < dim[1]; ++j) {
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for (int k = 0; k < dim[3]; ++k) {
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array[j * dim[3] + k] = std::make_pair(
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x_data[compute_index(dim, i, j, index, k)], j * dim[3] + k);
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}
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}
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std::sort(array.begin(), array.end(), cmp);
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int tag_num = 0;
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std::vector<bool> tag1(dim[1]), tag3(dim[3]);
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for (auto x : array) {
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int idx1 = x.second / dim[3];
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int idx3 = x.second % dim[3];
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if (tag1[idx1] || tag3[idx3]) {
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continue;
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}
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tag_num++;
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tag1[idx1] = true;
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tag3[idx3] = true;
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for (int j = 0; j < dim[2]; ++j) {
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out_data[compute_index(dim, i, idx1, j, idx3)] = 1;
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}
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if (tag_num == std::min(dim[1], dim[3])) {
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break;
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}
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}
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} else if (axis == 3) {
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for (int j = 0; j < dim[1]; ++j) {
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for (int k = 0; k < dim[2]; ++k) {
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array[j * dim[2] + k] = std::make_pair(
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x_data[compute_index(dim, i, j, k, index)], j * dim[2] + k);
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}
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}
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std::sort(array.begin(), array.end(), cmp);
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int tag_num = 0;
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std::vector<bool> tag1(dim[1]), tag2(dim[2]);
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for (auto x : array) {
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int idx1 = x.second / dim[2];
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int idx2 = x.second % dim[2];
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if (tag1[idx1] || tag2[idx2]) {
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continue;
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}
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tag_num++;
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tag1[idx1] = true;
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tag2[idx2] = true;
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for (int j = 0; j < dim[3]; ++j) {
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out_data[compute_index(dim, i, idx1, idx2, j)] = 1;
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}
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if (tag_num == std::min(dim[1], dim[2])) {
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break;
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}
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}
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} else {
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PADDLE_THROW("Axis must be 1 or 2 or 3");
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}
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}
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,217 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from op_test import OpTest
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class TestSimilarityFocusOp(OpTest):
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def setUp(self):
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self.op_type = "similarity_focus"
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batch_size = 2
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x_dim, y_dim, z_dim = 3, 2, 2
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self.inputs = {
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'X': np.array([[[[0.8, 0.1], [0.4, 0.5]], [[0.9, 0.7], [0.9, 0.9]],
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[[0.8, 0.9], [0.1, 0.2]]],
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[[[0.2, 0.5], [0.3, 0.4]], [[0.9, 0.7], [0.8, 0.4]],
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[[0.0, 0.2], [0.4, 0.7]]]]),
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}
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self.attrs = {
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'axis': 1,
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'indexes': [0],
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}
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output = None
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for batch in range(batch_size):
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res = np.zeros((1, y_dim, z_dim)).astype("float32").reshape(-1)
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for index in self.attrs['indexes']:
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channel = self.inputs['X'][batch, index, :, :].reshape(-1).copy(
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)
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tag1 = [0 for i in range(y_dim)]
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tag2 = [0 for i in range(z_dim)]
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cnt = 0
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for i in range(channel.size):
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index = channel.argmax()
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idx1 = index // z_dim
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idx2 = index % z_dim
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if tag1[idx1] + tag2[idx2] == 0:
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tag1[idx1] = 1
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tag2[idx2] = 1
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res[index] = 1
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cnt += 1
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if cnt == min(y_dim, z_dim):
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break
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channel[index] = -1
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res = res.reshape(1, y_dim, z_dim).repeat([x_dim], axis=0)
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res = res.reshape(1, x_dim, y_dim, z_dim)
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if output is not None:
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output = np.concatenate((output, res), axis=0)
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else:
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output = res
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self.outputs = {'Out': output}
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def test_check_output(self):
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self.check_output()
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class TestSimilarityFocusOp_axis1(OpTest):
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def setUp(self):
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self.op_type = "similarity_focus"
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batch_size = 3
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x_dim, y_dim, z_dim = 4, 5, 6
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self.inputs = {
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'X': np.random.random(
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(batch_size, x_dim, y_dim, z_dim)).astype("float32"),
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}
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self.attrs = {
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'axis': 1,
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'indexes': [0, 3],
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}
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output = None
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for batch in range(batch_size):
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res = np.zeros((1, y_dim, z_dim)).astype("float32").reshape(-1)
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for index in self.attrs['indexes']:
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channel = self.inputs['X'][batch, index, :, :].reshape(-1).copy(
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)
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tag1 = [0 for i in range(y_dim)]
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tag2 = [0 for i in range(z_dim)]
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cnt = 0
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for i in range(channel.size):
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index = channel.argmax()
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idx1 = index // z_dim
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idx2 = index % z_dim
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if tag1[idx1] + tag2[idx2] == 0:
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tag1[idx1] = 1
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tag2[idx2] = 1
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res[index] = 1
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cnt += 1
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if cnt == min(y_dim, z_dim):
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break
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channel[index] = -1
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res = res.reshape(1, y_dim, z_dim)
|
||||||
|
res = res.repeat([x_dim], axis=0)
|
||||||
|
res = res.reshape(1, x_dim, y_dim, z_dim)
|
||||||
|
if output is not None:
|
||||||
|
output = np.concatenate((output, res), axis=0)
|
||||||
|
else:
|
||||||
|
output = res
|
||||||
|
self.outputs = {'Out': output}
|
||||||
|
|
||||||
|
def test_check_output(self):
|
||||||
|
self.check_output()
|
||||||
|
|
||||||
|
|
||||||
|
class TestSimilarityFocusOp_axis2(OpTest):
|
||||||
|
def setUp(self):
|
||||||
|
self.op_type = "similarity_focus"
|
||||||
|
batch_size = 6
|
||||||
|
x_dim, y_dim, z_dim = 7, 8, 9
|
||||||
|
self.inputs = {
|
||||||
|
'X': np.random.random(
|
||||||
|
(batch_size, x_dim, y_dim, z_dim)).astype("float32"),
|
||||||
|
}
|
||||||
|
self.attrs = {
|
||||||
|
'axis': 2,
|
||||||
|
'indexes': [0, 3, 5],
|
||||||
|
}
|
||||||
|
|
||||||
|
output = None
|
||||||
|
for batch in range(batch_size):
|
||||||
|
res = np.zeros((x_dim, 1, z_dim)).astype("float32").reshape(-1)
|
||||||
|
for index in self.attrs['indexes']:
|
||||||
|
channel = self.inputs['X'][batch, :, index, :].reshape(-1).copy(
|
||||||
|
)
|
||||||
|
tag1 = [0 for i in range(x_dim)]
|
||||||
|
tag2 = [0 for i in range(z_dim)]
|
||||||
|
cnt = 0
|
||||||
|
for i in range(channel.size):
|
||||||
|
index = channel.argmax()
|
||||||
|
idx1 = index // z_dim
|
||||||
|
idx2 = index % z_dim
|
||||||
|
if tag1[idx1] + tag2[idx2] == 0:
|
||||||
|
tag1[idx1] = 1
|
||||||
|
tag2[idx2] = 1
|
||||||
|
res[index] = 1
|
||||||
|
cnt += 1
|
||||||
|
if cnt == min(x_dim, z_dim):
|
||||||
|
break
|
||||||
|
channel[index] = -1
|
||||||
|
res = res.reshape(x_dim, 1, z_dim)
|
||||||
|
res = res.repeat([y_dim], axis=1)
|
||||||
|
res = res.reshape(1, x_dim, y_dim, z_dim)
|
||||||
|
if output is not None:
|
||||||
|
output = np.concatenate((output, res), axis=0)
|
||||||
|
else:
|
||||||
|
output = res
|
||||||
|
self.outputs = {'Out': output}
|
||||||
|
|
||||||
|
def test_check_output(self):
|
||||||
|
self.check_output()
|
||||||
|
|
||||||
|
|
||||||
|
class TestSimilarityFocusOp_axis3(OpTest):
|
||||||
|
def setUp(self):
|
||||||
|
self.op_type = "similarity_focus"
|
||||||
|
batch_size = 64
|
||||||
|
x_dim, y_dim, z_dim = 48, 48, 13
|
||||||
|
self.inputs = {
|
||||||
|
'X': np.random.random(
|
||||||
|
(batch_size, x_dim, y_dim, z_dim)).astype("float32"),
|
||||||
|
}
|
||||||
|
self.attrs = {
|
||||||
|
'axis': 3,
|
||||||
|
'indexes': [0, 2, 7, 9],
|
||||||
|
}
|
||||||
|
|
||||||
|
output = None
|
||||||
|
for batch in range(batch_size):
|
||||||
|
res = np.zeros((x_dim, y_dim, 1)).astype("float32").reshape(-1)
|
||||||
|
for index in self.attrs['indexes']:
|
||||||
|
channel = self.inputs['X'][batch, :, :, index].reshape(-1).copy(
|
||||||
|
)
|
||||||
|
tag1 = [0 for i in range(x_dim)]
|
||||||
|
tag2 = [0 for i in range(y_dim)]
|
||||||
|
cnt = 0
|
||||||
|
for i in range(channel.size):
|
||||||
|
index = channel.argmax()
|
||||||
|
idx1 = index // y_dim
|
||||||
|
idx2 = index % y_dim
|
||||||
|
if tag1[idx1] + tag2[idx2] == 0:
|
||||||
|
tag1[idx1] = 1
|
||||||
|
tag2[idx2] = 1
|
||||||
|
res[index] = 1
|
||||||
|
cnt += 1
|
||||||
|
if cnt == min(x_dim, y_dim):
|
||||||
|
break
|
||||||
|
channel[index] = -1
|
||||||
|
res = res.reshape(x_dim, y_dim, 1)
|
||||||
|
res = res.repeat([z_dim], axis=2)
|
||||||
|
res = res.reshape(1, x_dim, y_dim, z_dim)
|
||||||
|
if output is not None:
|
||||||
|
output = np.concatenate((output, res), axis=0)
|
||||||
|
else:
|
||||||
|
output = res
|
||||||
|
self.outputs = {'Out': output}
|
||||||
|
|
||||||
|
def test_check_output(self):
|
||||||
|
self.check_output()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
unittest.main()
|
||||||
Loading…
Reference in new issue