def __init__(self, anchors, num_classes, match_threshold=0.7,
unmatched_threshold=0.3, rpn_batch_size_per_im=256,
rpn_fg_fraction=0.5):
"""Constructs anchor labeler to assign labels to anchors.
Args:
anchors: an instance of class Anchors.
num_classes: integer number representing number of classes in the dataset.
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.
rpn_batch_size_per_im: an integer number that represents the number of
sampled anchors per image in the first stage (region proposal network).
rpn_fg_fraction: a float number between 0 and 1 representing the fraction
of positive anchors (foreground) in the first stage.
"""
similarity_calc = region_similarity_calculator.IouSimilarity()
matcher = argmax_matcher.ArgMaxMatcher(
match_threshold,
unmatched_threshold=unmatched_threshold,
negatives_lower_than_unmatched=True,
force_match_for_each_row=True)
box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
self._target_assigner = target_assigner.TargetAssigner(
similarity_calc, matcher, box_coder)
self._anchors = anchors
self._match_threshold = match_threshold
self._unmatched_threshold = unmatched_threshold
self._rpn_batch_size_per_im = rpn_batch_size_per_im
self._rpn_fg_fraction = rpn_fg_fraction
self._num_classes = num_classes
def encode_labels(gt_boxes, gt_labels):
"""Labels anchors with ground truth inputs.
Args:
gt_boxes: A float tensor with shape [N, 4] representing groundtruth boxes.
For each row, it stores [y0, x0, y1, x1] for four corners of a box.
gt_labels: A integer tensor with shape [N, 1] representing groundtruth
classes.
Returns:
encoded_classes: a tensor with shape [num_anchors, 1].
encoded_boxes: a tensor with shape [num_anchors, 4].
num_positives: scalar tensor storing number of positives in an image.
"""
similarity_calc = region_similarity_calculator.IouSimilarity()
matcher = argmax_matcher.ArgMaxMatcher(
matched_threshold=ssd_constants.MATCH_THRESHOLD,
unmatched_threshold=ssd_constants.MATCH_THRESHOLD,
negatives_lower_than_unmatched=True,
force_match_for_each_row=True)
box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
scale_factors=ssd_constants.BOX_CODER_SCALES)
default_boxes = box_list.BoxList(
tf.convert_to_tensor(DefaultBoxes()('ltrb')))
target_boxes = box_list.BoxList(gt_boxes)
assigner = target_assigner.TargetAssigner(similarity_calc, matcher,
box_coder)
encoded_classes, _, encoded_boxes, _, matches = assigner.assign(
default_boxes, target_boxes, gt_labels)
num_matched_boxes = tf.reduce_sum(
tf.cast(tf.not_equal(matches.match_results, -1), tf.float32))
return encoded_classes, encoded_boxes, num_matched_boxes
def __init__(self, anchors, num_classes, match_threshold=0.5):
"""Constructs anchor labeler to assign labels to anchors.
Args:
anchors: an instance of class Anchors.
num_classes: integer number representing number of classes in the dataset.
match_threshold: float number between 0 and 1 representing the threshold
to assign positive labels for anchors.
"""
similarity_calc = region_similarity_calculator.IouSimilarity()
matcher = argmax_matcher.ArgMaxMatcher(
match_threshold,
unmatched_threshold=match_threshold,
negatives_lower_than_unmatched=True,
force_match_for_each_row=True)
box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
self._target_assigner = target_assigner.TargetAssigner(
similarity_calc, matcher, box_coder)
self._anchors = anchors
self._match_threshold = match_threshold
self._num_classes = num_classes
