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"""Matcher interface and Match class. |
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This module defines the Matcher interface and the Match object. The job of the |
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matcher is to match row and column indices based on the similarity matrix and |
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other optional parameters. Each column is matched to at most one row. There |
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are three possibilities for the matching: |
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1) match: A column matches a row. |
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2) no_match: A column does not match any row. |
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3) ignore: A column that is neither 'match' nor no_match. |
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The ignore case is regularly encountered in object detection: when an anchor has |
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a relatively small overlap with a ground-truth box, one neither wants to |
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consider this box a positive example (match) nor a negative example (no match). |
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The Match class is used to store the match results and it provides simple apis |
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to query the results. |
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""" |
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from abc import ABCMeta |
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from abc import abstractmethod |
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import tensorflow as tf |
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from object_detection.utils import ops |
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class Match(object): |
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"""Class to store results from the matcher. |
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This class is used to store the results from the matcher. It provides |
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convenient methods to query the matching results. |
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""" |
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def __init__(self, match_results, use_matmul_gather=False): |
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"""Constructs a Match object. |
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Args: |
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match_results: Integer tensor of shape [N] with (1) match_results[i]>=0, |
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meaning that column i is matched with row match_results[i]. |
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(2) match_results[i]=-1, meaning that column i is not matched. |
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(3) match_results[i]=-2, meaning that column i is ignored. |
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use_matmul_gather: Use matrix multiplication based gather instead of |
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standard tf.gather. (Default: False). |
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Raises: |
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ValueError: if match_results does not have rank 1 or is not an |
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integer int32 scalar tensor |
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""" |
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if match_results.shape.ndims != 1: |
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raise ValueError('match_results should have rank 1') |
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if match_results.dtype != tf.int32: |
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raise ValueError('match_results should be an int32 or int64 scalar ' |
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'tensor') |
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self._match_results = match_results |
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self._gather_op = tf.gather |
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if use_matmul_gather: |
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self._gather_op = ops.matmul_gather_on_zeroth_axis |
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@property |
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def match_results(self): |
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"""The accessor for match results. |
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Returns: |
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the tensor which encodes the match results. |
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""" |
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return self._match_results |
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def matched_column_indices(self): |
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"""Returns column indices that match to some row. |
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The indices returned by this op are always sorted in increasing order. |
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Returns: |
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column_indices: int32 tensor of shape [K] with column indices. |
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""" |
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return self._reshape_and_cast(tf.where(tf.greater(self._match_results, -1))) |
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def matched_column_indicator(self): |
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"""Returns column indices that are matched. |
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Returns: |
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column_indices: int32 tensor of shape [K] with column indices. |
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""" |
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return tf.greater_equal(self._match_results, 0) |
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def num_matched_columns(self): |
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"""Returns number (int32 scalar tensor) of matched columns.""" |
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return tf.size(self.matched_column_indices()) |
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def unmatched_column_indices(self): |
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"""Returns column indices that do not match any row. |
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The indices returned by this op are always sorted in increasing order. |
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Returns: |
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column_indices: int32 tensor of shape [K] with column indices. |
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""" |
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return self._reshape_and_cast(tf.where(tf.equal(self._match_results, -1))) |
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def unmatched_column_indicator(self): |
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"""Returns column indices that are unmatched. |
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Returns: |
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column_indices: int32 tensor of shape [K] with column indices. |
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""" |
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return tf.equal(self._match_results, -1) |
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def num_unmatched_columns(self): |
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"""Returns number (int32 scalar tensor) of unmatched columns.""" |
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return tf.size(self.unmatched_column_indices()) |
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def ignored_column_indices(self): |
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"""Returns column indices that are ignored (neither Matched nor Unmatched). |
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The indices returned by this op are always sorted in increasing order. |
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Returns: |
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column_indices: int32 tensor of shape [K] with column indices. |
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""" |
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return self._reshape_and_cast(tf.where(self.ignored_column_indicator())) |
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def ignored_column_indicator(self): |
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"""Returns boolean column indicator where True means the colum is ignored. |
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Returns: |
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column_indicator: boolean vector which is True for all ignored column |
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indices. |
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""" |
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return tf.equal(self._match_results, -2) |
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def num_ignored_columns(self): |
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"""Returns number (int32 scalar tensor) of matched columns.""" |
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return tf.size(self.ignored_column_indices()) |
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def unmatched_or_ignored_column_indices(self): |
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"""Returns column indices that are unmatched or ignored. |
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The indices returned by this op are always sorted in increasing order. |
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Returns: |
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column_indices: int32 tensor of shape [K] with column indices. |
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""" |
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return self._reshape_and_cast(tf.where(tf.greater(0, self._match_results))) |
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def matched_row_indices(self): |
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"""Returns row indices that match some column. |
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The indices returned by this op are ordered so as to be in correspondence |
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with the output of matched_column_indicator(). For example if |
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self.matched_column_indicator() is [0,2], and self.matched_row_indices() is |
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[7, 3], then we know that column 0 was matched to row 7 and column 2 was |
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matched to row 3. |
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Returns: |
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row_indices: int32 tensor of shape [K] with row indices. |
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""" |
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return self._reshape_and_cast( |
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self._gather_op(self._match_results, self.matched_column_indices())) |
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def _reshape_and_cast(self, t): |
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return tf.cast(tf.reshape(t, [-1]), tf.int32) |
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def gather_based_on_match(self, input_tensor, unmatched_value, |
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ignored_value): |
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"""Gathers elements from `input_tensor` based on match results. |
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For columns that are matched to a row, gathered_tensor[col] is set to |
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input_tensor[match_results[col]]. For columns that are unmatched, |
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gathered_tensor[col] is set to unmatched_value. Finally, for columns that |
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are ignored gathered_tensor[col] is set to ignored_value. |
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Note that the input_tensor.shape[1:] must match with unmatched_value.shape |
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and ignored_value.shape |
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Args: |
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input_tensor: Tensor to gather values from. |
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unmatched_value: Constant tensor value for unmatched columns. |
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ignored_value: Constant tensor value for ignored columns. |
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Returns: |
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gathered_tensor: A tensor containing values gathered from input_tensor. |
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The shape of the gathered tensor is [match_results.shape[0]] + |
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input_tensor.shape[1:]. |
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""" |
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input_tensor = tf.concat( |
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[tf.stack([ignored_value, unmatched_value]), |
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tf.to_float(input_tensor)], |
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axis=0) |
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gather_indices = tf.maximum(self.match_results + 2, 0) |
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gathered_tensor = self._gather_op(input_tensor, gather_indices) |
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return gathered_tensor |
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class Matcher(object): |
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"""Abstract base class for matcher. |
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""" |
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__metaclass__ = ABCMeta |
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def __init__(self, use_matmul_gather=False): |
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"""Constructs a Matcher. |
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Args: |
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use_matmul_gather: Force constructed match objects to use matrix |
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multiplication based gather instead of standard tf.gather. |
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(Default: False). |
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""" |
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self._use_matmul_gather = use_matmul_gather |
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def match(self, similarity_matrix, valid_rows=None, scope=None): |
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"""Computes matches among row and column indices and returns the result. |
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Computes matches among the row and column indices based on the similarity |
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matrix and optional arguments. |
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Args: |
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similarity_matrix: Float tensor of shape [N, M] with pairwise similarity |
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where higher value means more similar. |
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valid_rows: A boolean tensor of shape [N] indicating the rows that are |
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valid for matching. |
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scope: Op scope name. Defaults to 'Match' if None. |
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Returns: |
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A Match object with the results of matching. |
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""" |
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with tf.name_scope(scope, 'Match') as scope: |
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if valid_rows is None: |
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valid_rows = tf.ones(tf.shape(similarity_matrix)[0], dtype=tf.bool) |
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return Match(self._match(similarity_matrix, valid_rows), |
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self._use_matmul_gather) |
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@abstractmethod |
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def _match(self, similarity_matrix, valid_rows): |
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"""Method to be overridden by implementations. |
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Args: |
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similarity_matrix: Float tensor of shape [N, M] with pairwise similarity |
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where higher value means more similar. |
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valid_rows: A boolean tensor of shape [N] indicating the rows that are |
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valid for matching. |
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Returns: |
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match_results: Integer tensor of shape [M]: match_results[i]>=0 means |
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that column i is matched to row match_results[i], match_results[i]=-1 |
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means that the column is not matched. match_results[i]=-2 means that |
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the column is ignored (usually this happens when there is a very weak |
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match which one neither wants as positive nor negative example). |
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""" |
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pass |
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