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262 lines
9.4 KiB
262 lines
9.4 KiB
# Copyright 2017 The TensorFlow 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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# ==============================================================================
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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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