def assign(
self,
anchors,
gt_boxes,
anchors_mask=None,
gt_classes=None,
matched_thresholds=None,
unmatched_thresholds=None,
):
if anchors_mask is not None:
prune_anchor_fn = lambda _: np.where(anchors_mask)[0]
else:
prune_anchor_fn = None
def similarity_fn(anchors, gt_boxes):
anchors_rbv = anchors[:, [0, 1, 3, 4, -1]]
gt_boxes_rbv = gt_boxes[:, [0, 1, 3, 4, -1]]
return self._region_similarity_calculator.compare(
anchors_rbv, gt_boxes_rbv)
def box_encoding_fn(boxes, anchors):
return self._box_coder.encode(boxes, anchors)
return create_target_np(
anchors,
gt_boxes[gt_classes == class_name],
similarity_fn,
box_encoding_fn,
prune_anchor_fn=prune_anchor_fn,
gt_classes=gt_classes[gt_classes == class_name],
matched_threshold=matched_thresholds,
unmatched_threshold=unmatched_thresholds,
positive_fraction=self._positive_fraction,
rpn_batch_size=self._sample_size,
norm_by_num_examples=False,
box_code_size=self.box_coder.code_size,
)
def assign_v2(self,
anchors_dict,
gt_boxes,
anchors_mask=None,
gt_classes=None,
gt_names=None):
def similarity_fn(anchors, gt_boxes):
anchors_rbv = anchors[:, [0, 1, 3, 4, -1]]
gt_boxes_rbv = gt_boxes[:, [0, 1, 3, 4, -1]]
return self._region_similarity_calculator.compare(
anchors_rbv, gt_boxes_rbv)
def box_encoding_fn(boxes, anchors):
return self._box_coder.encode(boxes, anchors)
targets_list = []
anchor_loc_idx = 0
for class_name, anchor_dict in anchors_dict.items():
mask = np.array([c == class_name for c in gt_names],
dtype=np.bool_)
feature_map_size = anchor_dict["anchors"].shape[:3]
num_loc = anchor_dict["anchors"].shape[-2]
if anchors_mask is not None:
anchors_mask = anchors_mask.reshape(*feature_map_size, -1)
anchors_mask_class = anchors_mask[
..., anchor_loc_idx:anchor_loc_idx + num_loc].reshape(-1)
prune_anchor_fn = lambda _: np.where(anchors_mask_class)[0]
else:
prune_anchor_fn = None
targets = create_target_np(
anchor_dict["anchors"].reshape(-1, self.box_coder.code_size),
gt_boxes[mask],
similarity_fn,
box_encoding_fn,
prune_anchor_fn=prune_anchor_fn,
gt_classes=gt_classes[mask],
matched_threshold=anchor_dict["matched_thresholds"],
unmatched_threshold=anchor_dict["unmatched_thresholds"],
positive_fraction=self._positive_fraction,
rpn_batch_size=self._sample_size,
norm_by_num_examples=False,
box_code_size=self.box_coder.code_size,
)
anchor_loc_idx += num_loc
targets_list.append(targets)
targets_dict = {
"labels": [t["labels"] for t in targets_list],
"bbox_targets": [t["bbox_targets"] for t in targets_list],
"bbox_outside_weights":
[t["bbox_outside_weights"] for t in targets_list],
}
targets_dict["bbox_targets"] = np.concatenate(
[
v.reshape(*feature_map_size, -1, self.box_coder.code_size)
for v in targets_dict["bbox_targets"]
],
axis=-2,
)
targets_dict["bbox_targets"] = targets_dict["bbox_targets"].reshape(
-1, self.box_coder.code_size)
targets_dict["labels"] = np.concatenate(
[v.reshape(*feature_map_size, -1) for v in targets_dict["labels"]],
axis=-1)
targets_dict["bbox_outside_weights"] = np.concatenate(
[
v.reshape(*feature_map_size, -1)
for v in targets_dict["bbox_outside_weights"]
],
axis=-1,
)
targets_dict["labels"] = targets_dict["labels"].reshape(-1)
targets_dict["bbox_outside_weights"] = targets_dict[
"bbox_outside_weights"].reshape(-1)
return targets_dict
