Table Of Contents
Table Of Contents

Source code for gluoncv.nn.matcher

# pylint: disable=arguments-differ
"""Matchers for target assignment.
Matchers are commonly used in object-detection for anchor-groundtruth matching.
The matching process is a prerequisite to training target assignment.
Matching is usually not required during testing.
"""
from __future__ import absolute_import
from mxnet import gluon
from mxnet.gluon import nn


[docs]class CompositeMatcher(gluon.HybridBlock): """A Matcher that combines multiple strategies. Parameters ---------- matchers : list of Matcher Matcher is a Block/HybridBlock used to match two groups of boxes """ def __init__(self, matchers): super(CompositeMatcher, self).__init__() assert len(matchers) > 0, "At least one matcher required." for matcher in matchers: assert isinstance(matcher, (gluon.Block, gluon.HybridBlock)) self._matchers = nn.HybridSequential() for m in matchers: self._matchers.add(m)
[docs] def hybrid_forward(self, F, x): matches = [matcher(x) for matcher in self._matchers] return self._compose_matches(F, matches)
def _compose_matches(self, F, matches): """Given multiple match results, compose the final match results. The order of matches matters. Only the unmatched(-1s) in the current state will be substituted with the matching in the rest matches. Parameters ---------- matches : list of NDArrays N match results, each is an output of a different Matcher Returns ------- one match results as (B, N, M) NDArray """ result = matches[0] for match in matches[1:]: result = F.where(result > -0.5, result, match) return result
[docs]class BipartiteMatcher(gluon.HybridBlock): """A Matcher implementing bipartite matching strategy. Parameters ---------- threshold : float Threshold used to ignore invalid paddings is_ascend : bool Whether sort matching order in ascending order. Default is False. eps : float Epsilon for floating number comparison share_max : bool, default is True The maximum overlap between anchor/gt is shared by multiple ground truths. We recommend Fast(er)-RCNN series to use ``True``, while for SSD, it should defaults to ``False`` for better result. """ def __init__(self, threshold=1e-12, is_ascend=False, eps=1e-12, share_max=True): super(BipartiteMatcher, self).__init__() self._threshold = threshold self._is_ascend = is_ascend self._eps = eps self._share_max = share_max
[docs] def hybrid_forward(self, F, x): """BipartiteMatching Parameters: ---------- x : NDArray or Symbol IOU overlaps with shape (N, M), batching is supported. """ match = F.contrib.bipartite_matching(x, threshold=self._threshold, is_ascend=self._is_ascend) # make sure if iou(a, y) == iou(b, y), then b should also be a good match # otherwise positive/negative samples are confusing # potential argmax and max pargmax = x.argmax(axis=-1, keepdims=True) # (B, num_anchor, 1) maxs = x.max(axis=-2, keepdims=True) # (B, 1, num_gt) if self._share_max: mask = F.broadcast_greater_equal(x + self._eps, maxs) # (B, num_anchor, num_gt) mask = F.sum(mask, axis=-1, keepdims=True) # (B, num_anchor, 1) else: pmax = F.pick(x, pargmax, axis=-1, keepdims=True) # (B, num_anchor, 1) mask = F.broadcast_greater_equal(pmax + self._eps, maxs) # (B, num_anchor, num_gt) mask = F.pick(mask, pargmax, axis=-1, keepdims=True) # (B, num_anchor, 1) new_match = F.where(mask > 0, pargmax, F.ones_like(pargmax) * -1) result = F.where(match[0] < 0, new_match.squeeze(axis=-1), match[0]) return result
[docs]class MaximumMatcher(gluon.HybridBlock): """A Matcher implementing maximum matching strategy. Parameters ---------- threshold : float Matching threshold. """ def __init__(self, threshold): super(MaximumMatcher, self).__init__() self._threshold = threshold
[docs] def hybrid_forward(self, F, x): argmax = F.argmax(x, axis=-1) match = F.where(F.pick(x, argmax, axis=-1) >= self._threshold, argmax, F.ones_like(argmax) * -1) return match