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# Copyright 2023 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.
"""Box matcher implementation."""
from typing import List, Tuple
import tensorflow as tf, tf_keras
class BoxMatcher:
"""Matcher based on highest value.
This class computes matches from a similarity matrix. Each column is matched
to a single row.
To support object detection target assignment this class enables setting both
positive_threshold (upper threshold) and negative_threshold (lower thresholds)
defining three categories of similarity which define whether examples are
positive, negative, or ignored, for example:
(1) thresholds=[negative_threshold, positive_threshold], and
indicators=[negative_value, ignore_value, positive_value]: The similarity
metrics below negative_threshold will be assigned with negative_value,
the metrics between negative_threshold and positive_threshold will be
assigned ignore_value, and the metrics above positive_threshold will be
assigned positive_value.
(2) thresholds=[negative_threshold, positive_threshold], and
indicators=[ignore_value, negative_value, positive_value]: The similarity
metric below negative_threshold will be assigned with ignore_value,
the metrics between negative_threshold and positive_threshold will be
assigned negative_value, and the metrics above positive_threshold will be
assigned positive_value.
"""
def __init__(self,
thresholds: List[float],
indicators: List[int],
force_match_for_each_col: bool = False):
"""Construct BoxMatcher.
Args:
thresholds: A list of thresholds to classify the matches into different
types (e.g. positive or negative or ignored match). The list needs to be
sorted, and will be prepended with -Inf and appended with +Inf.
indicators: A list of values representing match types (e.g. positive or
negative or ignored match). len(`indicators`) must equal to
len(`thresholds`) + 1.
force_match_for_each_col: If True, ensures that each column is matched to
at least one row (which is not guaranteed otherwise if the
positive_threshold is high). Defaults to False. If True, all force
matched row will be assigned to `indicators[-1]`.
Raises:
ValueError: If `threshold` not sorted,
or len(indicators) != len(threshold) + 1
"""
if not all([lo <= hi for (lo, hi) in zip(thresholds[:-1], thresholds[1:])]):
raise ValueError('`threshold` must be sorted, got {}'.format(thresholds))
self.indicators = indicators
if len(indicators) != len(thresholds) + 1:
raise ValueError('len(`indicators`) must be len(`thresholds`) + 1, got '
'indicators {}, thresholds {}'.format(
indicators, thresholds))
thresholds = thresholds[:]
thresholds.insert(0, -float('inf'))
thresholds.append(float('inf'))
self.thresholds = thresholds
self._force_match_for_each_col = force_match_for_each_col
def __call__(self,
similarity_matrix: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
"""Tries to match each column of the similarity matrix to a row.
Args:
similarity_matrix: A float tensor of shape [num_rows, num_cols] or
[batch_size, num_rows, num_cols] representing any similarity metric.
Returns:
matched_columns: An integer tensor of shape [num_rows] or [batch_size,
num_rows] storing the index of the matched column for each row.
match_indicators: An integer tensor of shape [num_rows] or [batch_size,
num_rows] storing the match type indicator (e.g. positive or negative or
ignored match).
"""
squeeze_result = False
if len(similarity_matrix.shape) == 2:
squeeze_result = True
similarity_matrix = tf.expand_dims(similarity_matrix, axis=0)
static_shape = similarity_matrix.shape.as_list()
num_rows = static_shape[1] or tf.shape(similarity_matrix)[1]
batch_size = static_shape[0] or tf.shape(similarity_matrix)[0]
def _match_when_rows_are_empty():
"""Performs matching when the rows of similarity matrix are empty.
When the rows are empty, all detections are false positives. So we return
a tensor of -1's to indicate that the rows do not match to any columns.
Returns:
matched_columns: An integer tensor of shape [num_rows] or [batch_size,
num_rows] storing the index of the matched column for each row.
match_indicators: An integer tensor of shape [num_rows] or [batch_size,
num_rows] storing the match type indicator (e.g. positive or negative
or ignored match).
"""
with tf.name_scope('empty_gt_boxes'):
matched_columns = tf.zeros([batch_size, num_rows], dtype=tf.int32)
match_indicators = -tf.ones([batch_size, num_rows], dtype=tf.int32)
return matched_columns, match_indicators
def _match_when_rows_are_non_empty():
"""Performs matching when the rows of similarity matrix are non empty.
Returns:
matched_columns: An integer tensor of shape [num_rows] or [batch_size,
num_rows] storing the index of the matched column for each row.
match_indicators: An integer tensor of shape [num_rows] or [batch_size,
num_rows] storing the match type indicator (e.g. positive or negative
or ignored match).
"""
with tf.name_scope('non_empty_gt_boxes'):
matched_columns = tf.argmax(
similarity_matrix, axis=-1, output_type=tf.int32)
# Get logical indices of ignored and unmatched columns as tf.int64
matched_vals = tf.reduce_max(similarity_matrix, axis=-1)
match_indicators = tf.zeros([batch_size, num_rows], tf.int32)
match_dtype = matched_vals.dtype
for (ind, low, high) in zip(self.indicators, self.thresholds[:-1],
self.thresholds[1:]):
low_threshold = tf.cast(low, match_dtype)
high_threshold = tf.cast(high, match_dtype)
mask = tf.logical_and(
tf.greater_equal(matched_vals, low_threshold),
tf.less(matched_vals, high_threshold))
match_indicators = self._set_values_using_indicator(
match_indicators, mask, ind)
if self._force_match_for_each_col:
# [batch_size, num_cols], for each column (groundtruth_box), find the
# best matching row (anchor).
matching_rows = tf.argmax(
input=similarity_matrix, axis=1, output_type=tf.int32)
# [batch_size, num_cols, num_rows], a transposed 0-1 mapping matrix M,
# where M[j, i] = 1 means column j is matched to row i.
column_to_row_match_mapping = tf.one_hot(
matching_rows, depth=num_rows)
# [batch_size, num_rows], for each row (anchor), find the matched
# column (groundtruth_box).
force_matched_columns = tf.argmax(
input=column_to_row_match_mapping, axis=1, output_type=tf.int32)
# [batch_size, num_rows]
force_matched_column_mask = tf.cast(
tf.reduce_max(column_to_row_match_mapping, axis=1), tf.bool)
# [batch_size, num_rows]
matched_columns = tf.where(force_matched_column_mask,
force_matched_columns, matched_columns)
match_indicators = tf.where(
force_matched_column_mask, self.indicators[-1] *
tf.ones([batch_size, num_rows], dtype=tf.int32), match_indicators)
return matched_columns, match_indicators
num_gt_boxes = similarity_matrix.shape.as_list()[-1] or tf.shape(
similarity_matrix)[-1]
matched_columns, match_indicators = tf.cond(
pred=tf.greater(num_gt_boxes, 0),
true_fn=_match_when_rows_are_non_empty,
false_fn=_match_when_rows_are_empty)
if squeeze_result:
matched_columns = tf.squeeze(matched_columns, axis=0)
match_indicators = tf.squeeze(match_indicators, axis=0)
return matched_columns, match_indicators
def _set_values_using_indicator(self, x, indicator, val):
"""Set the indicated fields of x to val.
Args:
x: tensor.
indicator: boolean with same shape as x.
val: scalar with value to set.
Returns:
modified tensor.
"""
indicator = tf.cast(indicator, x.dtype)
return tf.add(tf.multiply(x, 1 - indicator), val * indicator)
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