def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
"""Assign gt to bboxes.
This method assign a gt bbox to every bbox (proposal/anchor), each bbox
will be assigned with -2, -1, or a positive number. -2 means don't care,
-1 means negative sample, positive number is the index of
assigned gt.
The assignment is done in following steps, the order matters.
1. assign every bbox to -2
2. assign proposals whose iou with all gts < neg_iou_thr to -1
3. for each bbox, if the iou with its nearest gt >= pos_iou_thr,
assign it to that bbox
4. for each gt bbox, assign its nearest proposals (may be more than
one) to itself
Args:
bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
labelled as `ignored`, e.g., crowd boxes in COCO.
gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
Returns:
:obj:`AssignResult`: The assign result.
"""
if bboxes.shape[0] == 0 or gt_bboxes.shape[0] == 0:
raise ValueError('No gt or bboxes')
bboxes = bboxes[:, :4]
overlaps = boxlist_iou(gt_bboxes, bboxes)
if (self.ignore_iof_thr > 0) and (gt_bboxes_ignore is not None) and (
gt_bboxes_ignore.numel() > 0):
if self.ignore_wrt_candidates:
ignore_overlaps = boxlist_iou(bboxes,
gt_bboxes_ignore,
mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
else:
ignore_overlaps = boxlist_iou(gt_bboxes_ignore,
bboxes,
mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
return assign_result
def match_targets_to_proposals(self, proposal, target):
match_quality_matrix = boxlist_iou(target, proposal)
matched_idxs = self.proposal_matcher(match_quality_matrix)
# Keypoint RCNN needs "labels" and "keypoints "fields for creating the targets
target = target.copy_with_fields(["labels", "keypoints"])
# get the targets corresponding GT for each proposal
# NB: need to clamp the indices because we can have a single
# GT in the image, and matched_idxs can be -2, which goes
# out of bounds
matched_targets = target[matched_idxs.clamp(min=0)]
matched_targets.add_field("matched_idxs", matched_idxs)
return matched_targets
def match_targets_to_anchors(self, anchor, target, copied_fields=[]):
match_quality_matrix = boxlist_iou(target, anchor)
matched_idxs = self.proposal_matcher(match_quality_matrix)
# RPN doesn't need any fields from target for creating the labels, so clear them all
target = target.copy_with_fields(copied_fields)
# get the targets corresponding GT for each anchor
# NB: need to clamp the indices because we can have a single GT in the image,
# and matched_idxs can be -2, which goes out of bounds
# matched_targets = target[matched_idxs.clamp(min=0)]
if self.use_negative_samples and len(target) == 0:
matched_targets = target
else:
matched_targets = target[matched_idxs.clamp(min=0)]
matched_targets.add_field("matched_idxs", matched_idxs)
return matched_targets
def match_targets_to_proposals(self, proposal, target):
match_quality_matrix = boxlist_iou(target, proposal)
matched_idxs = self.proposal_matcher(match_quality_matrix)
# target = target.copy_with_fields("labels")
# matched_targets = target[matched_idxs.clamp(min=0)]
if self.use_negative_samples and len(target) == 0:
matched_targets = target
matched_targets.add_field("labels", matched_idxs.clamp(min=1,
max=1))
else:
# Fast RCNN only need "labels" field for selecting the targets
target = target.copy_with_fields("labels")
# get the targets corresponding GT for each proposal
# NB: need to clamp the indices because we can have a single GT in the image,
# and matched_idxs can be -2, which goes out of bounds
matched_targets = target[matched_idxs.clamp(min=0)]
matched_targets.add_field("matched_idxs", matched_idxs)
return matched_targets
def evaluate_box_proposals(
predictions, dataset, thresholds=None, area="all", limit=None
):
"""Evaluate detection proposal recall metrics. This function is a much
faster alternative to the official COCO API recall evaluation code. However,
it produces slightly different results.
"""
# Record max overlap value for each gt box
# Return vector of overlap values
areas = {
"all": 0,
"small": 1,
"medium": 2,
"large": 3,
"96-128": 4,
"128-256": 5,
"256-512": 6,
"512-inf": 7,
}
area_ranges = [
[0 ** 2, 1e5 ** 2], # all
[0 ** 2, 32 ** 2], # small
[32 ** 2, 96 ** 2], # medium
[96 ** 2, 1e5 ** 2], # large
[96 ** 2, 128 ** 2], # 96-128
[128 ** 2, 256 ** 2], # 128-256
[256 ** 2, 512 ** 2], # 256-512
[512 ** 2, 1e5 ** 2],
] # 512-inf
assert area in areas, "Unknown area range: {}".format(area)
area_range = area_ranges[areas[area]]
gt_overlaps = []
num_pos = 0
for image_id, prediction in enumerate(predictions):
original_id = dataset.id_to_img_map[image_id]
# TODO replace with get_img_info?
img_info = dataset.get_img_info(image_id)
image_width = img_info["width"]
image_height = img_info["height"]
prediction = prediction.resize((image_width, image_height))
# sort predictions in descending order
# TODO maybe remove this and make it explicit in the documentation
inds = prediction.get_field("scores").sort(descending=True)[1]
prediction = prediction[inds]
ann_ids = dataset.coco.getAnnIds(imgIds=original_id)
anno = dataset.coco.loadAnns(ann_ids)
gt_boxes = [obj["bbox"] for obj in anno if obj["iscrowd"] == 0]
gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes
gt_boxes = BoxList(gt_boxes, (image_width, image_height), mode="xywh").convert(
"xyxy"
)
gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0])
if len(gt_boxes) == 0:
continue
valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1])
gt_boxes = gt_boxes[valid_gt_inds]
num_pos += len(gt_boxes)
if len(gt_boxes) == 0:
continue
if len(prediction) == 0:
continue
if limit is not None and len(prediction) > limit:
prediction = prediction[:limit]
overlaps = boxlist_iou(prediction, gt_boxes)
_gt_overlaps = torch.zeros(len(gt_boxes))
for j in range(min(len(prediction), len(gt_boxes))):
# find which proposal box maximally covers each gt box
# and get the iou amount of coverage for each gt box
max_overlaps, argmax_overlaps = overlaps.max(dim=0)
# find which gt box is 'best' covered (i.e. 'best' = most iou)
gt_ovr, gt_ind = max_overlaps.max(dim=0)
assert gt_ovr >= 0
# find the proposal box that covers the best covered gt box
box_ind = argmax_overlaps[gt_ind]
# record the iou coverage of this gt box
_gt_overlaps[j] = overlaps[box_ind, gt_ind]
assert _gt_overlaps[j] == gt_ovr
# mark the proposal box and the gt box as used
overlaps[box_ind, :] = -1
overlaps[:, gt_ind] = -1
# append recorded iou coverage level
gt_overlaps.append(_gt_overlaps)
gt_overlaps = torch.cat(gt_overlaps, dim=0)
gt_overlaps, _ = torch.sort(gt_overlaps)
if thresholds is None:
step = 0.05
thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)
recalls = torch.zeros_like(thresholds)
# compute recall for each iou threshold
for i, t in enumerate(thresholds):
recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)
# ar = 2 * np.trapz(recalls, thresholds)
ar = recalls.mean()
return {
"ar": ar,
"recalls": recalls,
"thresholds": thresholds,
"gt_overlaps": gt_overlaps,
"num_pos": num_pos,
}
def calc_detection_voc_prec_rec(gt_boxlists, pred_boxlists, iou_thresh=0.5):
"""Calculate precision and recall based on evaluation code of PASCAL VOC.
This function calculates precision and recall of
predicted bounding boxes obtained from a dataset which has :math:`N`
images.
The code is based on the evaluation code used in PASCAL VOC Challenge.
"""
n_pos = defaultdict(int)
score = defaultdict(list)
match = defaultdict(list)
for gt_boxlist, pred_boxlist in zip(gt_boxlists, pred_boxlists):
pred_bbox = pred_boxlist.bbox.numpy()
pred_label = pred_boxlist.get_field("labels").numpy()
pred_score = pred_boxlist.get_field("scores").numpy()
gt_bbox = gt_boxlist.bbox.numpy()
gt_label = gt_boxlist.get_field("labels").numpy()
gt_difficult = gt_boxlist.get_field("difficult").numpy()
for l in np.unique(np.concatenate((pred_label, gt_label)).astype(int)):
pred_mask_l = pred_label == l
pred_bbox_l = pred_bbox[pred_mask_l]
pred_score_l = pred_score[pred_mask_l]
# sort by score
order = pred_score_l.argsort()[::-1]
pred_bbox_l = pred_bbox_l[order]
pred_score_l = pred_score_l[order]
gt_mask_l = gt_label == l
gt_bbox_l = gt_bbox[gt_mask_l]
gt_difficult_l = gt_difficult[gt_mask_l]
n_pos[l] += np.logical_not(gt_difficult_l).sum()
score[l].extend(pred_score_l)
if len(pred_bbox_l) == 0:
continue
if len(gt_bbox_l) == 0:
match[l].extend((0,) * pred_bbox_l.shape[0])
continue
# VOC evaluation follows integer typed bounding boxes.
pred_bbox_l = pred_bbox_l.copy()
pred_bbox_l[:, 2:] += 1
gt_bbox_l = gt_bbox_l.copy()
gt_bbox_l[:, 2:] += 1
iou = boxlist_iou(
BoxList(pred_bbox_l, gt_boxlist.size),
BoxList(gt_bbox_l, gt_boxlist.size),
).numpy()
gt_index = iou.argmax(axis=1)
# set -1 if there is no matching ground truth
gt_index[iou.max(axis=1) < iou_thresh] = -1
del iou
selec = np.zeros(gt_bbox_l.shape[0], dtype=bool)
for gt_idx in gt_index:
if gt_idx >= 0:
if gt_difficult_l[gt_idx]:
match[l].append(-1)
else:
if not selec[gt_idx]:
match[l].append(1)
else:
match[l].append(0)
selec[gt_idx] = True
else:
match[l].append(0)
n_fg_class = max(n_pos.keys()) + 1
prec = [None] * n_fg_class
rec = [None] * n_fg_class
for l in n_pos.keys():
score_l = np.array(score[l])
match_l = np.array(match[l], dtype=np.int8)
order = score_l.argsort()[::-1]
match_l = match_l[order]
tp = np.cumsum(match_l == 1)
fp = np.cumsum(match_l == 0)
# If an element of fp + tp is 0,
# the corresponding element of prec[l] is nan.
prec[l] = tp / (fp + tp)
# If n_pos[l] is 0, rec[l] is None.
if n_pos[l] > 0:
rec[l] = tp / n_pos[l]
return prec, rec
def assign(self,
approxs,
squares,
approxs_per_octave,
gt_bboxes,
gt_bboxes_ignore=None,
gt_labels=None):
"""Assign gt to approxs.
This method assign a gt bbox to each group of approxs (bboxes),
each group of approxs is represent by a base approx (bbox) and
will be assigned with -2, -1, or a positive number.
-2 means don't care, -1 means negative sample,
positive number is the index of assigned gt.
The assignment is done in following steps, the order matters.
1. assign every bbox to -2
2. use the max IoU of each group of approxs to assign
2. assign proposals whose iou with all gts < neg_iou_thr to -1
3. for each bbox, if the iou with its nearest gt >= pos_iou_thr,
assign it to that bbox
4. for each gt bbox, assign its nearest proposals (may be more than
one) to itself
Args:
approxs (Tensor): Bounding boxes to be assigned,
shape(approxs_per_octave*n, 4).
squares (Tensor): Base Bounding boxes to be assigned,
shape(n, 4).
approxs_per_octave (int): number of approxs per octave
gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
labelled as `ignored`, e.g., crowd boxes in COCO.
gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
Returns:
:obj:`AssignResult`: The assign result.
"""
if squares.shape[0] == 0 or gt_bboxes.shape[0] == 0:
raise ValueError('No gt or approxs')
num_squares = squares.size(0)
num_gts = gt_bboxes.size(0)
# re-organize anchors by approxs_per_octave x num_squares
approxs = torch.transpose(
approxs.view(num_squares, approxs_per_octave, 4), 0,
1).contiguous().view(-1, 4)
all_overlaps = boxlist_iou(approxs, gt_bboxes)
overlaps, _ = all_overlaps.view(approxs_per_octave, num_squares,
num_gts).max(dim=0)
overlaps = torch.transpose(overlaps, 0, 1)
bboxes = squares[:, :4]
if (self.ignore_iof_thr > 0) and (gt_bboxes_ignore is not None) and (
gt_bboxes_ignore.numel() > 0):
if self.ignore_wrt_candidates:
ignore_overlaps = boxlist_iou(bboxes,
gt_bboxes_ignore,
mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
else:
ignore_overlaps = bbox_overlaps(gt_bboxes_ignore,
bboxes,
mode='iof')
ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
return assign_result
