tfm.vision.anchor.RpnAnchorLabeler

Labeler for Region Proposal Network.

tfm.vision.anchor.RpnAnchorLabeler(
    match_threshold=0.7,
    unmatched_threshold=0.3,
    rpn_batch_size_per_im=256,
    rpn_fg_fraction=0.5
)

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.

Methods

label_anchors(
    anchor_boxes, gt_boxes, gt_labels
)

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.

Returns
`score_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]. 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 * 4]. The height_l and width_l represent the dimension of bounding box regression output at l-th level.