From bf33b191d0cbb950d50f003f08ed3f16f0e2b92e Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Thu, 18 Jan 2018 18:41:08 +0800 Subject: [PATCH 1/4] Add bipartite matching operator and unit testing. --- paddle/operators/bipartite_match_op.cc | 178 ++++++++++++++++++ .../v2/fluid/tests/test_bipartite_match_op.py | 100 ++++++++++ 2 files changed, 278 insertions(+) create mode 100644 paddle/operators/bipartite_match_op.cc create mode 100644 python/paddle/v2/fluid/tests/test_bipartite_match_op.py diff --git a/paddle/operators/bipartite_match_op.cc b/paddle/operators/bipartite_match_op.cc new file mode 100644 index 0000000000..8dbade65a5 --- /dev/null +++ b/paddle/operators/bipartite_match_op.cc @@ -0,0 +1,178 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/framework/op_registry.h" +#include "paddle/operators/math/math_function.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; +using LoDTensor = framework::LoDTensor; + +class BipartiteMatchOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("DisMat"), + "Input(DisMat) of BipartiteMatch should not be null."); + + auto dims = ctx->GetInputDim("DisMat"); + PADDLE_ENFORCE_EQ(dims.size(), 2, "The rank of Input(DisMat) must be 2."); + + ctx->SetOutputDim("ColToRowMatchIndices", dims); + ctx->SetOutputDim("ColToRowMatchDis", dims); + } +}; + +template +class BipartiteMatchKernel : public framework::OpKernel { + public: + // The match_indices must be initialized to -1 at first. + // The match_dis must be initialized to 0 at first. + void BipartiteMatch(const Tensor& dis, int* match_indices, + T* match_dis) const { + int64_t row = dis.dims()[0]; + int64_t col = dis.dims()[1]; + auto* dis_data = dis.data(); + std::vector row_pool; + for (int i = 0; i < row; ++i) { + row_pool.push_back(i); + } + while (row_pool.size() > 0) { + int max_idx = -1; + int max_row_idx = -1; + T max_dis = -1; + for (int64_t j = 0; j < col; ++j) { + if (match_indices[j] != -1) { + continue; + } + for (int k = 0; k < row_pool.size(); ++k) { + int m = row_pool[k]; + // distance is 0 between m-th row and j-th column + if (dis_data[m * col + j] < 1e-6) { + continue; + } + if (dis_data[m * col + j] > max_dis) { + max_idx = j; + max_row_idx = m; + max_dis = dis_data[m * col + j]; + } + } + } + if (max_idx == -1) { + // Cannot find good match. + break; + } else { + PADDLE_ENFORCE_EQ(match_indices[max_idx], -1); + match_indices[max_idx] = max_row_idx; + match_dis[max_idx] = max_dis; + // Erase the row index. + row_pool.erase( + std::find(row_pool.begin(), row_pool.end(), max_row_idx)); + } + } + } + + void Compute(const framework::ExecutionContext& context) const override { + auto* dis_mat = context.Input("DisMat"); + auto* match_indices = context.Output("ColToRowMatchIndices"); + auto* match_dis = context.Output("ColToRowMatchDis"); + + auto& dev_ctx = context.device_context(); + + auto col = dis_mat->dims()[1]; + + int64_t n = dis_mat->lod().size() == 0 + ? 1 + : static_cast(dis_mat->lod().back().size() - 1); + match_indices->mutable_data({n, col}, context.GetPlace()); + match_dis->mutable_data({n, col}, context.GetPlace()); + + math::SetConstant iset; + iset(dev_ctx, match_indices, static_cast(-1)); + math::SetConstant tset; + tset(dev_ctx, match_dis, static_cast(0)); + + int* indices = match_indices->data(); + T* dis = match_dis->data(); + if (n == 1) { + BipartiteMatch(*dis_mat, indices, dis); + } else { + auto lod = dis_mat->lod().back(); + for (size_t i = 0; i < lod.size() - 1; ++i) { + Tensor one_ins = dis_mat->Slice(lod[i], lod[i + 1]); + BipartiteMatch(one_ins, indices + i * col, dis + i * col); + } + } + } +}; + +class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker { + public: + BipartiteMatchOpMaker(OpProto* proto, OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput( + "DisMat", + "(LoDTensor or Tensor) this input is a 2-D LoDTensor with shape " + "[K, M]. It is pair-wise distance matrix between the entities " + "represented by each row and each column. For example, assumed one " + "entity is A with shape [K], another entity is B with shape [M]. The " + "DisMat[i][j] is the distance between A[i] and B[j]. The bigger " + "the distance is, the more similar the pairs are. Please note, " + "This tensor can contain LoD information to represent a batch of " + "inputs. One instance of this batch can contain different numbers of " + "entities."); + AddOutput("ColToRowMatchIndices", + "(Tensor) A 2-D Tensor with shape [N, M] in int type. " + "N is the batch size. If ColToRowMatchIndices[i][j] is -1, it " + "means B[j] does not match any entity in i-th instance. " + "Otherwise, it means B[j] is matched to row " + "RowToColMatchIndices[i][j] in i-th instance. The row number of " + "i-th instance is saved in RowToColMatchIndices[i][j]."); + AddOutput("ColToRowMatchDis", + "(Tensor) A 2-D Tensor with shape [N, M] in float type. " + "N is batch size. If ColToRowMatchIndices[i][j] is -1, " + "ColToRowMatchDis[i][j] is also -1.0. Otherwise, assumed " + "RowToColMatchIndices[i][j] = d, and the row offsets of each " + "instance are called LoD. Then " + "ColToRowMatchDis[i][j] = DisMat[d+LoD[i]][j]"); + AddComment(R"DOC( +This operator is a greedy bipartite matching algorithm, which is used to +obtain the matching with the (greedy) maximum distance based on the input +distance matrix. There are two outputs to save matched indices and distance. +And this operator only calculate matched indices from column to row. +A simple description, this algothrim matched the best (maximum distance) +row entity to the column entity and the matched indices are not duplicated +in each row of ColToRowMatchIndices. If the column entity is not matched +any row entity, set -1 in ColToRowMatchIndices. + +Please note that the input DisMat can be LoDTensor (with LoD) or Tensor. +If LoDTensor with LoD, the height of ColToRowMatchIndices is batch size. +If Tensor, the height of ColToRowMatchIndices is 1. + +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OPERATOR(bipartite_match, ops::BipartiteMatchOp, + ops::BipartiteMatchOpMaker, + paddle::framework::EmptyGradOpMaker); +REGISTER_OP_CPU_KERNEL(bipartite_match, ops::BipartiteMatchKernel, + ops::BipartiteMatchKernel); diff --git a/python/paddle/v2/fluid/tests/test_bipartite_match_op.py b/python/paddle/v2/fluid/tests/test_bipartite_match_op.py new file mode 100644 index 0000000000..8f1db35d3c --- /dev/null +++ b/python/paddle/v2/fluid/tests/test_bipartite_match_op.py @@ -0,0 +1,100 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. +# +#Licensed under the Apache License, Version 2.0 (the "License"); +#you may not use this file except in compliance with the License. +#You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +#Unless required by applicable law or agreed to in writing, software +#distributed under the License is distributed on an "AS IS" BASIS, +#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +#See the License for the specific language governing permissions and +#limitations under the License. +import unittest +import numpy as np +from op_test import OpTest + + +def bipartite_match(distance, match_indices, match_dis): + """Bipartite Matching algorithm. + Arg: + distance (numpy.array) : The distance of two entries with shape [M, N]. + match_indices (numpy.array): the matched indices from column to row + with shape [1, N], it must be initialized to -1. + match_dis (numpy.array): The matched distance from column to row + with shape [1, N], it must be initialized to 0. + """ + match_pair = [] + row, col = distance.shape + for i in range(row): + for j in range(col): + match_pair.append((i, j, distance[i][j])) + + match_sorted = sorted(match_pair, key=lambda tup: tup[2], reverse=True) + + row_indices = -1 * np.ones((row, ), dtype=np.int) + + idx = 0 + for i, j, dis in match_sorted: + if idx >= row: + break + if match_indices[j] == -1 and row_indices[i] == -1 and dis > 0: + match_indices[j] = i + row_indices[i] = j + match_dis[j] = dis + idx += 1 + + +def batch_bipartite_match(distance, lod): + """Bipartite Matching algorithm for batch input. + Arg: + distance (numpy.array) : The distance of two entries with shape [M, N]. + lod (list of int): The offsets of each input in this batch. + """ + n = len(lod) - 1 + m = distance.shape[1] + match_indices = -1 * np.ones((n, m), dtype=np.int) + match_dis = np.zeros((n, m), dtype=np.float32) + for i in range(len(lod) - 1): + bipartite_match(distance[lod[i]:lod[i + 1], :], match_indices[i, :], + match_dis[i, :]) + return match_indices, match_dis + + +class TestBipartiteMatchOpForWithLoD(OpTest): + def setUp(self): + self.op_type = 'bipartite_match' + lod = [[0, 5, 11, 23]] + dis = np.random.random((23, 217)).astype('float32') + match_indices, match_dis = batch_bipartite_match(dis, lod[0]) + + self.inputs = {'DisMat': (dis, lod)} + self.outputs = { + 'ColToRowMatchIndices': (match_indices), + 'ColToRowMatchDis': (match_dis), + } + + def test_check_output(self): + self.check_output() + + +class TestBipartiteMatchOpWithoutLoD(OpTest): + def setUp(self): + self.op_type = 'bipartite_match' + lod = [[0, 8]] + dis = np.random.random((8, 17)).astype('float32') + match_indices, match_dis = batch_bipartite_match(dis, lod[0]) + + self.inputs = {'DisMat': dis} + self.outputs = { + 'ColToRowMatchIndices': (match_indices), + 'ColToRowMatchDis': (match_dis), + } + + def test_check_output(self): + self.check_output() + + +if __name__ == '__main__': + unittest.main() From 07908686d5bf63d01a313bb65d2e2c081e65db8d Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 22 Jan 2018 18:52:06 +0800 Subject: [PATCH 2/4] Update some comments and add more check. --- paddle/operators/bipartite_match_op.cc | 72 +++++++++++++++----------- 1 file changed, 42 insertions(+), 30 deletions(-) diff --git a/paddle/operators/bipartite_match_op.cc b/paddle/operators/bipartite_match_op.cc index 8dbade65a5..c2d30c7d92 100644 --- a/paddle/operators/bipartite_match_op.cc +++ b/paddle/operators/bipartite_match_op.cc @@ -21,6 +21,8 @@ namespace operators { using Tensor = framework::Tensor; using LoDTensor = framework::LoDTensor; +constexpr char kEPS = 1e-6; + class BipartiteMatchOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -41,12 +43,13 @@ template class BipartiteMatchKernel : public framework::OpKernel { public: // The match_indices must be initialized to -1 at first. - // The match_dis must be initialized to 0 at first. - void BipartiteMatch(const Tensor& dis, int* match_indices, - T* match_dis) const { - int64_t row = dis.dims()[0]; - int64_t col = dis.dims()[1]; - auto* dis_data = dis.data(); + // The match_dist must be initialized to 0 at first. + void BipartiteMatch(const Tensor& dist, int* match_indices, + T* match_dist) const { + PADDLE_ENFORCE_EQ(dist.dims().size(), 2, "The rank of dist must be 2."); + int64_t row = dist.dims()[0]; + int64_t col = dist.dims()[1]; + auto* dist_data = dist.data(); std::vector row_pool; for (int i = 0; i < row; ++i) { row_pool.push_back(i); @@ -54,7 +57,7 @@ class BipartiteMatchKernel : public framework::OpKernel { while (row_pool.size() > 0) { int max_idx = -1; int max_row_idx = -1; - T max_dis = -1; + T max_dist = -1; for (int64_t j = 0; j < col; ++j) { if (match_indices[j] != -1) { continue; @@ -62,13 +65,13 @@ class BipartiteMatchKernel : public framework::OpKernel { for (int k = 0; k < row_pool.size(); ++k) { int m = row_pool[k]; // distance is 0 between m-th row and j-th column - if (dis_data[m * col + j] < 1e-6) { + if (dist_data[m * col + j] < kEPS) { continue; } - if (dis_data[m * col + j] > max_dis) { + if (dist_data[m * col + j] > max_dist) { max_idx = j; max_row_idx = m; - max_dis = dis_data[m * col + j]; + max_dist = dist_data[m * col + j]; } } } @@ -78,7 +81,7 @@ class BipartiteMatchKernel : public framework::OpKernel { } else { PADDLE_ENFORCE_EQ(match_indices[max_idx], -1); match_indices[max_idx] = max_row_idx; - match_dis[max_idx] = max_dis; + match_dist[max_idx] = max_dist; // Erase the row index. row_pool.erase( std::find(row_pool.begin(), row_pool.end(), max_row_idx)); @@ -87,34 +90,38 @@ class BipartiteMatchKernel : public framework::OpKernel { } void Compute(const framework::ExecutionContext& context) const override { - auto* dis_mat = context.Input("DisMat"); + auto* dist_mat = context.Input("DisMat"); auto* match_indices = context.Output("ColToRowMatchIndices"); - auto* match_dis = context.Output("ColToRowMatchDis"); + auto* match_dist = context.Output("ColToRowMatchDis"); auto& dev_ctx = context.device_context(); - auto col = dis_mat->dims()[1]; + auto col = dist_mat->dims()[1]; - int64_t n = dis_mat->lod().size() == 0 + int64_t n = dist_mat->lod().size() == 0UL ? 1 - : static_cast(dis_mat->lod().back().size() - 1); + : static_cast(dist_mat->lod().back().size() - 1); + if (dist_mat->lod().size()) { + PADDLE_ENFORCE_EQ(dist_mat->lod().size(), 1UL, + "Only support 1 level of LoD."); + } match_indices->mutable_data({n, col}, context.GetPlace()); - match_dis->mutable_data({n, col}, context.GetPlace()); + match_dist->mutable_data({n, col}, context.GetPlace()); math::SetConstant iset; iset(dev_ctx, match_indices, static_cast(-1)); math::SetConstant tset; - tset(dev_ctx, match_dis, static_cast(0)); + tset(dev_ctx, match_dist, static_cast(0)); int* indices = match_indices->data(); - T* dis = match_dis->data(); + T* dist = match_dist->data(); if (n == 1) { - BipartiteMatch(*dis_mat, indices, dis); + BipartiteMatch(*dist_mat, indices, dist); } else { - auto lod = dis_mat->lod().back(); + auto lod = dist_mat->lod().back(); for (size_t i = 0; i < lod.size() - 1; ++i) { - Tensor one_ins = dis_mat->Slice(lod[i], lod[i + 1]); - BipartiteMatch(one_ins, indices + i * col, dis + i * col); + Tensor one_ins = dist_mat->Slice(lod[i], lod[i + 1]); + BipartiteMatch(one_ins, indices + i * col, dist + i * col); } } } @@ -131,7 +138,7 @@ class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker { "represented by each row and each column. For example, assumed one " "entity is A with shape [K], another entity is B with shape [M]. The " "DisMat[i][j] is the distance between A[i] and B[j]. The bigger " - "the distance is, the more similar the pairs are. Please note, " + "the distance is, the better macthing the pairs are. Please note, " "This tensor can contain LoD information to represent a batch of " "inputs. One instance of this batch can contain different numbers of " "entities."); @@ -140,20 +147,25 @@ class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker { "N is the batch size. If ColToRowMatchIndices[i][j] is -1, it " "means B[j] does not match any entity in i-th instance. " "Otherwise, it means B[j] is matched to row " - "RowToColMatchIndices[i][j] in i-th instance. The row number of " - "i-th instance is saved in RowToColMatchIndices[i][j]."); + "ColToRowMatchIndices[i][j] in i-th instance. The row number of " + "i-th instance is saved in ColToRowMatchIndices[i][j]."); AddOutput("ColToRowMatchDis", "(Tensor) A 2-D Tensor with shape [N, M] in float type. " "N is batch size. If ColToRowMatchIndices[i][j] is -1, " "ColToRowMatchDis[i][j] is also -1.0. Otherwise, assumed " - "RowToColMatchIndices[i][j] = d, and the row offsets of each " + "ColToRowMatchIndices[i][j] = d, and the row offsets of each " "instance are called LoD. Then " "ColToRowMatchDis[i][j] = DisMat[d+LoD[i]][j]"); AddComment(R"DOC( This operator is a greedy bipartite matching algorithm, which is used to -obtain the matching with the (greedy) maximum distance based on the input -distance matrix. There are two outputs to save matched indices and distance. -And this operator only calculate matched indices from column to row. +obtain the matching with the maximum distance based on the input +distance matrix. For input 2D matrix, the bipartite matching algorithm can +find the matched column for each row, also can find the matched row for +each column. And this operator only calculate matched indices from column +to row. For each instance, the number of matched indices is the number of +of columns of the input ditance matrix. + +There are two outputs to save matched indices and distance. A simple description, this algothrim matched the best (maximum distance) row entity to the column entity and the matched indices are not duplicated in each row of ColToRowMatchIndices. If the column entity is not matched From 530df1b2891413839989e2c6568f31fd9e1a1f98 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 22 Jan 2018 21:28:10 +0800 Subject: [PATCH 3/4] Fix the naming. --- paddle/operators/bipartite_match_op.cc | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/paddle/operators/bipartite_match_op.cc b/paddle/operators/bipartite_match_op.cc index c2d30c7d92..0fcff6e26d 100644 --- a/paddle/operators/bipartite_match_op.cc +++ b/paddle/operators/bipartite_match_op.cc @@ -28,11 +28,11 @@ class BipartiteMatchOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("DisMat"), - "Input(DisMat) of BipartiteMatch should not be null."); + PADDLE_ENFORCE(ctx->HasInput("DistMat"), + "Input(DistMat) of BipartiteMatch should not be null."); - auto dims = ctx->GetInputDim("DisMat"); - PADDLE_ENFORCE_EQ(dims.size(), 2, "The rank of Input(DisMat) must be 2."); + auto dims = ctx->GetInputDim("DistMat"); + PADDLE_ENFORCE_EQ(dims.size(), 2, "The rank of Input(DistMat) must be 2."); ctx->SetOutputDim("ColToRowMatchIndices", dims); ctx->SetOutputDim("ColToRowMatchDis", dims); @@ -90,7 +90,7 @@ class BipartiteMatchKernel : public framework::OpKernel { } void Compute(const framework::ExecutionContext& context) const override { - auto* dist_mat = context.Input("DisMat"); + auto* dist_mat = context.Input("DistMat"); auto* match_indices = context.Output("ColToRowMatchIndices"); auto* match_dist = context.Output("ColToRowMatchDis"); @@ -132,12 +132,12 @@ class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker { BipartiteMatchOpMaker(OpProto* proto, OpAttrChecker* op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { AddInput( - "DisMat", + "DistMat", "(LoDTensor or Tensor) this input is a 2-D LoDTensor with shape " "[K, M]. It is pair-wise distance matrix between the entities " "represented by each row and each column. For example, assumed one " "entity is A with shape [K], another entity is B with shape [M]. The " - "DisMat[i][j] is the distance between A[i] and B[j]. The bigger " + "DistMat[i][j] is the distance between A[i] and B[j]. The bigger " "the distance is, the better macthing the pairs are. Please note, " "This tensor can contain LoD information to represent a batch of " "inputs. One instance of this batch can contain different numbers of " @@ -155,7 +155,7 @@ class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker { "ColToRowMatchDis[i][j] is also -1.0. Otherwise, assumed " "ColToRowMatchIndices[i][j] = d, and the row offsets of each " "instance are called LoD. Then " - "ColToRowMatchDis[i][j] = DisMat[d+LoD[i]][j]"); + "ColToRowMatchDis[i][j] = DistMat[d+LoD[i]][j]"); AddComment(R"DOC( This operator is a greedy bipartite matching algorithm, which is used to obtain the matching with the maximum distance based on the input @@ -171,7 +171,7 @@ row entity to the column entity and the matched indices are not duplicated in each row of ColToRowMatchIndices. If the column entity is not matched any row entity, set -1 in ColToRowMatchIndices. -Please note that the input DisMat can be LoDTensor (with LoD) or Tensor. +Please note that the input DistMat can be LoDTensor (with LoD) or Tensor. If LoDTensor with LoD, the height of ColToRowMatchIndices is batch size. If Tensor, the height of ColToRowMatchIndices is 1. From e44dedf90f6b0563d6a36f58f10448e29d2ac552 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 22 Jan 2018 23:54:25 +0800 Subject: [PATCH 4/4] Fix the warning and unit test. --- paddle/operators/bipartite_match_op.cc | 2 +- python/paddle/v2/fluid/tests/test_bipartite_match_op.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/paddle/operators/bipartite_match_op.cc b/paddle/operators/bipartite_match_op.cc index 0fcff6e26d..b0f7376d27 100644 --- a/paddle/operators/bipartite_match_op.cc +++ b/paddle/operators/bipartite_match_op.cc @@ -62,7 +62,7 @@ class BipartiteMatchKernel : public framework::OpKernel { if (match_indices[j] != -1) { continue; } - for (int k = 0; k < row_pool.size(); ++k) { + for (size_t k = 0; k < row_pool.size(); ++k) { int m = row_pool[k]; // distance is 0 between m-th row and j-th column if (dist_data[m * col + j] < kEPS) { diff --git a/python/paddle/v2/fluid/tests/test_bipartite_match_op.py b/python/paddle/v2/fluid/tests/test_bipartite_match_op.py index 8f1db35d3c..34101b1da4 100644 --- a/python/paddle/v2/fluid/tests/test_bipartite_match_op.py +++ b/python/paddle/v2/fluid/tests/test_bipartite_match_op.py @@ -69,7 +69,7 @@ class TestBipartiteMatchOpForWithLoD(OpTest): dis = np.random.random((23, 217)).astype('float32') match_indices, match_dis = batch_bipartite_match(dis, lod[0]) - self.inputs = {'DisMat': (dis, lod)} + self.inputs = {'DistMat': (dis, lod)} self.outputs = { 'ColToRowMatchIndices': (match_indices), 'ColToRowMatchDis': (match_dis), @@ -86,7 +86,7 @@ class TestBipartiteMatchOpWithoutLoD(OpTest): dis = np.random.random((8, 17)).astype('float32') match_indices, match_dis = batch_bipartite_match(dis, lod[0]) - self.inputs = {'DisMat': dis} + self.inputs = {'DistMat': dis} self.outputs = { 'ColToRowMatchIndices': (match_indices), 'ColToRowMatchDis': (match_dis),