Spaces:
Sleeping
Sleeping
File size: 5,347 Bytes
749745d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class Matcher(object):
"""
This class assigns to each predicted "element" (e.g., a box) a ground-truth
element. Each predicted element will have exactly zero or one matches; each
ground-truth element may be assigned to zero or more predicted elements.
Matching is based on the MxN match_quality_matrix, that characterizes how well
each (ground-truth, predicted)-pair match. For example, if the elements are
boxes, the matrix may contain box IoU overlap values.
The matcher returns a tensor of size N containing the index of the ground-truth
element m that matches to prediction n. If there is no match, a negative value
is returned.
"""
BELOW_LOW_THRESHOLD = -1
BETWEEN_THRESHOLDS = -2
def __init__(self, high_threshold, low_threshold, allow_low_quality_matches=False):
"""
Args:
high_threshold (float): quality values greater than or equal to
this value are candidate matches.
low_threshold (float): a lower quality threshold used to stratify
matches into three levels:
1) matches >= high_threshold
2) BETWEEN_THRESHOLDS matches in [low_threshold, high_threshold)
3) BELOW_LOW_THRESHOLD matches in [0, low_threshold)
allow_low_quality_matches (bool): if True, produce additional matches
for predictions that have only low-quality match candidates. See
set_low_quality_matches_ for more details.
"""
assert low_threshold <= high_threshold
self.high_threshold = high_threshold
self.low_threshold = low_threshold
self.allow_low_quality_matches = allow_low_quality_matches
def __call__(self, match_quality_matrix):
"""
Args:
match_quality_matrix (Tensor[float]): an MxN tensor, containing the
pairwise quality between M ground-truth elements and N predicted elements.
Returns:
matches (Tensor[int64]): an N tensor where N[i] is a matched gt in
[0, M - 1] or a negative value indicating that prediction i could not
be matched.
"""
if match_quality_matrix.numel() == 0:
# empty targets or proposals not supported during training
if match_quality_matrix.shape[0] == 0:
# raise ValueError(
# "No ground-truth boxes available for one of the images "
# "during training")
length = match_quality_matrix.size(1)
device = match_quality_matrix.device
return torch.ones(length, dtype=torch.int64, device=device) * -1
else:
raise ValueError("No proposal boxes available for one of the images " "during training")
# match_quality_matrix is M (gt) x N (predicted)
# Max over gt elements (dim 0) to find best gt candidate for each prediction
matched_vals, matches = match_quality_matrix.max(dim=0)
if self.allow_low_quality_matches:
all_matches = matches.clone()
# Assign candidate matches with low quality to negative (unassigned) values
below_low_threshold = matched_vals < self.low_threshold
between_thresholds = (matched_vals >= self.low_threshold) & (matched_vals < self.high_threshold)
matches[below_low_threshold] = Matcher.BELOW_LOW_THRESHOLD
matches[between_thresholds] = Matcher.BETWEEN_THRESHOLDS
if self.allow_low_quality_matches:
self.set_low_quality_matches_(matches, all_matches, match_quality_matrix)
return matches
def set_low_quality_matches_(self, matches, all_matches, match_quality_matrix):
"""
Produce additional matches for predictions that have only low-quality matches.
Specifically, for each ground-truth find the set of predictions that have
maximum overlap with it (including ties); for each prediction in that set, if
it is unmatched, then match it to the ground-truth with which it has the highest
quality value.
"""
# For each gt, find the prediction with which it has highest quality
highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
# Find highest quality match available, even if it is low, including ties
gt_pred_pairs_of_highest_quality = torch.nonzero(match_quality_matrix == highest_quality_foreach_gt[:, None])
# Example gt_pred_pairs_of_highest_quality:
# tensor([[ 0, 39796],
# [ 1, 32055],
# [ 1, 32070],
# [ 2, 39190],
# [ 2, 40255],
# [ 3, 40390],
# [ 3, 41455],
# [ 4, 45470],
# [ 5, 45325],
# [ 5, 46390]])
# Each row is a (gt index, prediction index)
# Note how gt items 1, 2, 3, and 5 each have two ties
pred_inds_to_update = gt_pred_pairs_of_highest_quality[:, 1]
matches[pred_inds_to_update] = all_matches[pred_inds_to_update]
|