Python tensorflow.contrib.image.python.ops.image_ops.bipartite_match() Examples
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code examples of tensorflow.contrib.image.python.ops.image_ops.bipartite_match().
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Example #1
| Source File: bipartite_matcher.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #2
| Source File: bipartite_matcher.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
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
Example #3
| Source File: bipartite_matcher.py From mtl-ssl with Apache License 2.0 | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #4
| Source File: bipartite_matcher.py From motion-rcnn with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #5
| Source File: bipartite_matcher.py From models with Apache License 2.0 | 5 votes |
|
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.cast(valid_rows, dtype=tf.float32))
_, 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
Example #6
| Source File: bipartite_matcher.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
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
Example #7
| Source File: bipartite_matcher.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
|
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
Example #8
| Source File: bipartite_matcher.py From object_detection_with_tensorflow with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #9
| Source File: bipartite_matcher.py From object_detection_with_tensorflow with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #10
| Source File: bipartite_matcher.py From Elphas with Apache License 2.0 | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #11
| Source File: bipartite_matcher.py From MBMD with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #12
| Source File: bipartite_matcher.py From object_detection_kitti with Apache License 2.0 | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #13
| Source File: bipartite_matcher.py From hands-detection with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #14
| Source File: bipartite_matcher.py From moveo_ros with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #15
| Source File: bipartite_matcher.py From DOTA_models with Apache License 2.0 | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #16
| Source File: bipartite_matcher.py From ros_tensorflow with Apache License 2.0 | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #17
| Source File: bipartite_matcher.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #18
| Source File: bipartite_matcher.py From tensorflow with BSD 2-Clause "Simplified" License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #19
| Source File: bipartite_matcher.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #20
| Source File: bipartite_matcher.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #21
| Source File: bipartite_matcher.py From yolo_v2 with Apache License 2.0 | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #22
| Source File: bipartite_matcher.py From HereIsWally with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #23
| Source File: bipartite_matcher.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #24
| Source File: bipartite_matcher.py From cartoonify with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #25
| Source File: bipartite_matcher.py From Person-Detection-and-Tracking with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #26
| Source File: bipartite_matcher.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
Example #27
| Source File: bipartite_matcher.py From vehicle_counting_tensorflow with MIT License | 5 votes |
|
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
Example #28
| Source File: bipartite_matcher.py From object_detector_app with MIT License | 5 votes |
|
def _match(self, similarity_matrix, num_valid_rows=-1):
"""Bipartite matches a collection rows and columns. A greedy bi-partite.
TODO: 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.
num_valid_rows: A scalar or a 1-D tensor with one element describing the
number of valid rows of similarity_matrix to consider for the bipartite
matching. If set to be negative, then all rows from similarity_matrix
are used.
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].
"""
# Convert similarity matrix to distance matrix as tf.image.bipartite tries
# to find minimum distance matches.
distance_matrix = -1 * similarity_matrix
_, match_results = image_ops.bipartite_match(
distance_matrix, num_valid_rows)
match_results = tf.reshape(match_results, [-1])
match_results = tf.cast(match_results, tf.int32)
return match_results
