detectron2/modeling/matcher.py

# Copyright (c) Facebook, Inc. and its affiliates.
from typing import List
import torch

from detectron2.layers import nonzero_tuple


# TODO: the name is too general
class Matcher:
    """
    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 matched to zero or more predicted elements.

    The matching is determined by the MxN match_quality_matrix, that characterizes
    how well each (ground-truth, prediction)-pair match each other. For example,
    if the elements are boxes, this matrix may contain box intersection-over-union
    overlap values.

    The matcher returns (a) a vector of length N containing the index of the
    ground-truth element m in [0, M) that matches to prediction n in [0, N).
    (b) a vector of length N containing the labels for each prediction.
    """

    def __init__(
        self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False
    ):
        """
        Args:
            thresholds (list): a list of thresholds used to stratify predictions
                into levels.
            labels (list): a list of values to label predictions belonging at
                each level. A label can be one of {-1, 0, 1} signifying
                {ignore, negative class, positive class}, respectively.
            allow_low_quality_matches (bool): if True, produce additional matches
                for predictions with maximum match quality lower than high_threshold.
                See set_low_quality_matches_ for more details.

            For example,
                thresholds = [0.3, 0.5]
                labels = [0, -1, 1]
                All predictions with iou < 0.3 will be marked with 0 and
                thus will be considered as false positives while training.
                All predictions with 0.3 <= iou < 0.5 will be marked with -1 and
                thus will be ignored.
                All predictions with 0.5 <= iou will be marked with 1 and
                thus will be considered as true positives.
        """
        # Add -inf and +inf to first and last position in thresholds
        thresholds = thresholds[:]
        assert thresholds[0] > 0
        thresholds.insert(0, -float("inf"))
        thresholds.append(float("inf"))
        # Currently torchscript does not support all + generator
        assert all([low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])])
        assert all([l in [-1, 0, 1] for l in labels])
        assert len(labels) == len(thresholds) - 1
        self.thresholds = thresholds
        self.labels = labels
        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. All elements must be >= 0 (due to the us of `torch.nonzero`
                for selecting indices in :meth:`set_low_quality_matches_`).

        Returns:
            matches (Tensor[int64]): a vector of length N, where matches[i] is a matched
                ground-truth index in [0, M)
            match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates
                whether a prediction is a true or false positive or ignored
        """
        assert match_quality_matrix.dim() == 2
        if match_quality_matrix.numel() == 0:
            default_matches = match_quality_matrix.new_full(
                (match_quality_matrix.size(1),), 0, dtype=torch.int64
            )
            # When no gt boxes exist, we define IOU = 0 and therefore set labels
            # to `self.labels[0]`, which usually defaults to background class 0
            # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds
            default_match_labels = match_quality_matrix.new_full(
                (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8
            )
            return default_matches, default_match_labels

        assert torch.all(match_quality_matrix >= 0)

        # 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)

        match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)

        for l, low, high in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):
            low_high = (matched_vals >= low) & (matched_vals < high)
            match_labels[low_high] = l

        if self.allow_low_quality_matches:
            self.set_low_quality_matches_(match_labels, match_quality_matrix)

        return matches, match_labels

    def set_low_quality_matches_(self, match_labels, match_quality_matrix):
        """
        Produce additional matches for predictions that have only low-quality matches.
        Specifically, for each ground-truth G 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 G.

        This function implements the RPN assignment case (i) in Sec. 3.1.2 of
        :paper:`Faster R-CNN`.
        """
        # For each gt, find the prediction with which it has highest quality
        highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
        # Find the highest quality match available, even if it is low, including ties.
        # Note that the matches qualities must be positive due to the use of
        # `torch.nonzero`.
        _, pred_inds_with_highest_quality = nonzero_tuple(
            match_quality_matrix == highest_quality_foreach_gt[:, None]
        )
        # If an anchor was labeled positive only due to a low-quality match
        # with gt_A, but it has larger overlap with gt_B, it's matched index will still be gt_B.
        # This follows the implementation in Detectron, and is found to have no significant impact.
        match_labels[pred_inds_with_highest_quality] = 1