tfm.vision.anchor.AnchorLabeler

Labeler for dense object detector.

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.

Methods

label_anchors

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Labels anchors with ground truth inputs.

Args
anchor_boxes A float tensor with shape [N, 4] representing anchor boxes. For each row, it stores [y0, x0, y1, x1] for four corners of a box.
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.
gt_attributes If not None, a dict of (name, gt_attribute) pairs. gt_attribute is a float tensor with shape [N, attribute_size] representing groundtruth attributes.
gt_weights If not None, a float tensor with shape [N] representing groundtruth weights.

Returns
cls_targets_dict ordered dictionary with keys [min_level, min_level+1, ..., max_level]. The values are tensor with shape [height_l, width_l, num_anchors_per_location]. The height_l and width_l represent the dimension of class logits at l-th level.
box_targets_dict ordered dictionary with keys [min_level, min_level+1, ..., max_level]. The values are tensor with shape [height_l, width_l, num_anchors_per_location * 4]. The height_l and width_l represent the dimension of bounding box regression output at l-th level.
attribute_targets_dict a dict with (name, attribute_targets) pairs. Each attribute_targets represents an ordered dictionary with keys [min_level, min_level+1, ..., max_level]. The values are tensor with shape [height_l, width_l, num_anchors_per_location * attribute_size]. The height_l and width_l represent the dimension of attribute prediction output at l-th level.
cls_weights A flattened Tensor with shape [batch_size, num_anchors], that serves as masking / sample weight for classification loss. Its value is 1.0 for positive and negative matched anchors, and 0.0 for ignored anchors.
box_weights A flattened Tensor with shape [batch_size, num_anchors], that serves as masking / sample weight for regression loss. Its value is 1.0 for positive matched anchors, and 0.0 for negative and ignored anchors.