tfm.vision.anchor.RpnAnchorLabeler

Labeler for Region Proposal Network.

Inherits From: AnchorLabeler

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

View source

label_anchors(
    anchor_boxes: Dict[str, tf.Tensor],
    gt_boxes: tf.Tensor,
    gt_labels: tf.Tensor
) -> Tuple[Dict[str, tf.Tensor], Dict[str, tf.Tensor]]

Labels anchors with ground truth inputs.

Args
anchor_boxes	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 * 4]. The height_l and width_l represent the dimension of the feature pyramid at l-th level. For each anchor box, the tensor stores [y0, x0, y1, x1] for the four corners.
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	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]. The height_l and width_l represent the dimension of class logits at l-th level.
box_targets_dict	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 * 4]. The height_l and width_l represent the dimension of bounding box regression output at l-th level.