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- # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
- #
- # 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.
- # ==============================================================================
-
- """Bipartite matcher implementation."""
-
- import tensorflow as tf
-
- from tensorflow.contrib.image.python.ops import image_ops
- from object_detection.core import matcher
-
-
- class GreedyBipartiteMatcher(matcher.Matcher):
- """Wraps a Tensorflow greedy bipartite matcher."""
-
- def __init__(self, use_matmul_gather=False):
- """Constructs a Matcher.
-
- Args:
- use_matmul_gather: Force constructed match objects to use matrix
- multiplication based gather instead of standard tf.gather.
- (Default: False).
- """
- super(GreedyBipartiteMatcher, self).__init__(
- use_matmul_gather=use_matmul_gather)
-
- def _match(self, similarity_matrix, valid_rows):
- """Bipartite matches a collection rows and columns. A greedy bi-partite.
-
- TODO(rathodv): Add num_valid_columns options to match only that many columns
- with all the rows.
-
- Args:
- similarity_matrix: Float tensor of shape [N, M] with pairwise similarity
- where higher values mean more similar.
- valid_rows: A boolean tensor of shape [N] indicating the rows that are
- valid.
-
- Returns:
- match_results: int32 tensor of shape [M] with match_results[i]=-1
- meaning that column i is not matched and otherwise that it is matched to
- row match_results[i].
- """
- valid_row_sim_matrix = tf.gather(similarity_matrix,
- tf.squeeze(tf.where(valid_rows), axis=-1))
- invalid_row_sim_matrix = tf.gather(
- similarity_matrix,
- tf.squeeze(tf.where(tf.logical_not(valid_rows)), axis=-1))
- similarity_matrix = tf.concat(
- [valid_row_sim_matrix, invalid_row_sim_matrix], axis=0)
- # Convert similarity matrix to distance matrix as tf.image.bipartite tries
- # to find minimum distance matches.
- distance_matrix = -1 * similarity_matrix
- num_valid_rows = tf.reduce_sum(tf.to_float(valid_rows))
- _, match_results = image_ops.bipartite_match(
- distance_matrix, num_valid_rows=num_valid_rows)
- match_results = tf.reshape(match_results, [-1])
- match_results = tf.cast(match_results, tf.int32)
- return match_results
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