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139 lines
4.6 KiB
139 lines
4.6 KiB
# 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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import unittest
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import numpy as np
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from op_test import OpTest
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def bipartite_match(distance, match_indices, match_dist):
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"""Bipartite Matching algorithm.
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Arg:
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distance (numpy.array) : The distance of two entries with shape [M, N].
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match_indices (numpy.array): the matched indices from column to row
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with shape [1, N], it must be initialized to -1.
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match_dist (numpy.array): The matched distance from column to row
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with shape [1, N], it must be initialized to 0.
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"""
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match_pair = []
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row, col = distance.shape
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for i in range(row):
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for j in range(col):
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match_pair.append((i, j, distance[i][j]))
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match_sorted = sorted(match_pair, key=lambda tup: tup[2], reverse=True)
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row_indices = -1 * np.ones((row, ), dtype=np.int)
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idx = 0
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for i, j, dist in match_sorted:
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if idx >= row:
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break
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if match_indices[j] == -1 and row_indices[i] == -1 and dist > 0:
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match_indices[j] = i
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row_indices[i] = j
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match_dist[j] = dist
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idx += 1
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def argmax_match(distance, match_indices, match_dist, threshold):
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r, c = distance.shape
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for j in xrange(c):
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if match_indices[j] != -1:
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continue
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col_dist = distance[:, j]
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indices = np.argwhere(col_dist >= threshold).flatten()
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if len(indices) < 1:
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continue
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match_indices[j] = indices[np.argmax(col_dist[indices])]
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match_dist[j] = col_dist[match_indices[j]]
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def batch_bipartite_match(distance, lod, match_type=None, dist_threshold=None):
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"""Bipartite Matching algorithm for batch input.
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Arg:
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distance (numpy.array) : The distance of two entries with shape [M, N].
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lod (list of int): The offsets of each input in this batch.
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"""
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n = len(lod) - 1
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m = distance.shape[1]
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match_indices = -1 * np.ones((n, m), dtype=np.int)
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match_dist = np.zeros((n, m), dtype=np.float32)
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for i in range(len(lod) - 1):
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bipartite_match(distance[lod[i]:lod[i + 1], :], match_indices[i, :],
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match_dist[i, :])
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if match_type == 'per_prediction':
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argmax_match(distance[lod[i]:lod[i + 1], :], match_indices[i, :],
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match_dist[i, :], dist_threshold)
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return match_indices, match_dist
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class TestBipartiteMatchOpWithLoD(OpTest):
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def setUp(self):
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self.op_type = 'bipartite_match'
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lod = [[0, 5, 11, 23]]
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dist = np.random.random((23, 217)).astype('float32')
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match_indices, match_dist = batch_bipartite_match(dist, lod[0])
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self.inputs = {'DistMat': (dist, lod)}
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self.outputs = {
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'ColToRowMatchIndices': match_indices,
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'ColToRowMatchDist': match_dist,
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}
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def test_check_output(self):
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self.check_output()
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class TestBipartiteMatchOpWithoutLoD(OpTest):
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def setUp(self):
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self.op_type = 'bipartite_match'
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lod = [[0, 8]]
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dist = np.random.random((8, 17)).astype('float32')
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match_indices, match_dist = batch_bipartite_match(dist, lod[0])
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self.inputs = {'DistMat': dist}
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self.outputs = {
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'ColToRowMatchIndices': match_indices,
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'ColToRowMatchDist': match_dist,
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}
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def test_check_output(self):
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self.check_output()
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class TestBipartiteMatchOpWithPerPredictionType(OpTest):
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def setUp(self):
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self.op_type = 'bipartite_match'
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lod = [[0, 5, 11, 23]]
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dist = np.random.random((23, 237)).astype('float32')
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match_indices, match_dist = batch_bipartite_match(dist, lod[0],
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'per_prediction', 0.5)
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self.inputs = {'DistMat': (dist, lod)}
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self.outputs = {
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'ColToRowMatchIndices': match_indices,
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'ColToRowMatchDist': match_dist,
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}
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self.attrs = {
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'match_type': 'per_prediction',
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'dist_threshold': 0.5,
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}
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def test_check_output(self):
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self.check_output()
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if __name__ == '__main__':
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unittest.main()
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