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_target_gather_unbatched(self):
gt_boxes = tf.constant([
[0, 0, 5, 5],
[0, 5, 5, 10],
[5, 0, 10, 5],
[5, 5, 10, 10],
],
dtype=tf.float32)
gt_classes = tf.constant([[2], [10], [3], [-1]], dtype=tf.int32)
labeler = target_gather.TargetGather()
match_indices = tf.constant([0, 2], dtype=tf.int32)
match_indicators = tf.constant([-2, 1])
mask = tf.less_equal(match_indicators, 0)
cls_mask = tf.expand_dims(mask, -1)
matched_gt_classes = labeler(gt_classes, match_indices, cls_mask)
box_mask = tf.tile(cls_mask, [1, 4])
matched_gt_boxes = labeler(gt_boxes, match_indices, box_mask)
self.assertAllEqual(matched_gt_classes.numpy(), [[0], [3]])
self.assertAllClose(matched_gt_boxes.numpy(),
[[0, 0, 0, 0], [5, 0, 10, 5]])