def __init__(self,
mix_gt_boxes: bool = True,
num_sampled_rois: int = 512,
foreground_fraction: float = 0.25,
foreground_iou_threshold: float = 0.5,
background_iou_high_threshold: float = 0.5,
background_iou_low_threshold: float = 0,
skip_subsampling: bool = False,
**kwargs):
"""Initializes a ROI sampler.
Args:
mix_gt_boxes: A `bool` of whether to mix the groundtruth boxes with
proposed ROIs.
num_sampled_rois: An `int` of the number of sampled ROIs per image.
foreground_fraction: A `float` in [0, 1], what percentage of proposed ROIs
should be sampled from the foreground boxes.
foreground_iou_threshold: A `float` that represents the IoU threshold for
a box to be considered as positive (if >= `foreground_iou_threshold`).
background_iou_high_threshold: A `float` that represents the IoU threshold
for a box to be considered as negative (if overlap in
[`background_iou_low_threshold`, `background_iou_high_threshold`]).
background_iou_low_threshold: A `float` that represents the IoU threshold
for a box to be considered as negative (if overlap in
[`background_iou_low_threshold`, `background_iou_high_threshold`])
skip_subsampling: a bool that determines if we want to skip the sampling
procedure than balances the fg/bg classes. Used for upper frcnn layers
in cascade RCNN.
**kwargs: Additional keyword arguments passed to Layer.
"""
self._config_dict = {
'mix_gt_boxes': mix_gt_boxes,
'num_sampled_rois': num_sampled_rois,
'foreground_fraction': foreground_fraction,
'foreground_iou_threshold': foreground_iou_threshold,
'background_iou_high_threshold': background_iou_high_threshold,
'background_iou_low_threshold': background_iou_low_threshold,
'skip_subsampling': skip_subsampling,
}
self._sim_calc = iou_similarity.IouSimilarity()
self._box_matcher = box_matcher.BoxMatcher(thresholds=[
background_iou_low_threshold, background_iou_high_threshold,
foreground_iou_threshold
],
indicators=[-3, -1, -2, 1])
self._target_gather = target_gather.TargetGather()
self._sampler = box_sampler.BoxSampler(num_sampled_rois,
foreground_fraction)
super(ROISampler, self).__init__(**kwargs)
def __init__(self, match_threshold=0.5, unmatched_threshold=0.5):
"""Constructs anchor labeler to assign labels to anchors.
Args:
match_threshold: a float number between 0 and 1 representing the
lower-bound threshold to assign positive labels for anchors. An anchor
with a score over the threshold is labeled positive.
unmatched_threshold: a float number between 0 and 1 representing the
upper-bound threshold to assign negative labels for anchors. An anchor
with a score below the threshold is labeled negative.
"""
self.similarity_calc = iou_similarity.IouSimilarity()
self.target_gather = target_gather.TargetGather()
self.matcher = box_matcher.BoxMatcher(
thresholds=[unmatched_threshold, match_threshold],
indicators=[-1, -2, 1],
force_match_for_each_col=True)
self.box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
def test_box_matcher_batched(self):
sim_matrix = tf.constant([[[0.04, 0, 0, 0], [0, 0, 1., 0]]],
dtype=tf.float32)
fg_threshold = 0.5
bg_thresh_hi = 0.2
bg_thresh_lo = 0.0
matcher = box_matcher.BoxMatcher(
thresholds=[bg_thresh_lo, bg_thresh_hi, fg_threshold],
indicators=[-3, -2, -1, 1])
match_indices, match_indicators = matcher(sim_matrix)
positive_matches = tf.greater_equal(match_indicators, 0)
negative_matches = tf.equal(match_indicators, -2)
self.assertAllEqual(positive_matches.numpy(), [[False, True]])
self.assertAllEqual(negative_matches.numpy(), [[True, False]])
self.assertAllEqual(match_indices.numpy(), [[0, 2]])
self.assertAllEqual(match_indicators.numpy(), [[-2, 1]])