本文整理汇总了Python中tensorflow.contrib.image.python.ops.image_ops.bipartite_match方法的典型用法代码示例。如果您正苦于以下问题：Python image_ops.bipartite_match方法的具体用法？Python image_ops.bipartite_match怎么用？Python image_ops.bipartite_match使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.contrib.image.python.ops.image_ops的用法示例。
在下文中一共展示了image_ops.bipartite_match方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _match
# 需要导入模块: from tensorflow.contrib.image.python.ops import image_ops [as 别名]
# 或者: from tensorflow.contrib.image.python.ops.image_ops import bipartite_match [as 别名]
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 示例2: _match
# 需要导入模块: from tensorflow.contrib.image.python.ops import image_ops [as 别名]
# 或者: from tensorflow.contrib.image.python.ops.image_ops import bipartite_match [as 别名]
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 示例3: _match
# 需要导入模块: from tensorflow.contrib.image.python.ops import image_ops [as 别名]
# 或者: from tensorflow.contrib.image.python.ops.image_ops import bipartite_match [as 别名]
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 示例4: _match
# 需要导入模块: from tensorflow.contrib.image.python.ops import image_ops [as 别名]
# 或者: from tensorflow.contrib.image.python.ops.image_ops import bipartite_match [as 别名]
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 示例5: _match
# 需要导入模块: from tensorflow.contrib.image.python.ops import image_ops [as 别名]
# 或者: from tensorflow.contrib.image.python.ops.image_ops import bipartite_match [as 别名]
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