def forward(self, locations, box_cls, box_regression, centerness, proposal_embed, proposal_margin, pixel_embed, image_sizes, targets):
"""
Arguments:
anchors: list[list[BoxList]]
box_cls: list[tensor]
box_regression: list[tensor]
image_sizes: list[(h, w)]
Returns:
boxlists (list[BoxList]): the post-processed anchors, after
applying box decoding and NMS
"""
sampled_boxes = []
for i, (l, o, b, c) in enumerate(zip(locations, box_cls, box_regression, centerness)):
em = proposal_embed[i]
mar = proposal_margin[i]
if self.fix_margin:
mar = torch.ones_like(mar) * self.init_margin
sampled_boxes.append(
self.forward_for_single_feature_map(
l, o, b, c, em, mar, image_sizes, i
)
)
boxlists = list(zip(*sampled_boxes))
boxlists = [cat_boxlist(boxlist) for boxlist in boxlists]
boxlists = self.select_over_all_levels(boxlists)
# resize pixel embedding for higher resolution
N, dim, m_h, m_w = pixel_embed.shape
o_h = m_h * self.mask_scale_factor
o_w = m_w * self.mask_scale_factor
pixel_embed = interpolate(pixel_embed, size=(o_h, o_w), mode='bilinear', align_corners=False)
boxlists = self.forward_for_mask(boxlists, pixel_embed)
return boxlists
def forward(self, anchors, objectness, box_regression, targets=None):
"""
Arguments:
anchors: list[list[BoxList]]
objectness: list[tensor]
box_regression: list[tensor]
Returns:
boxlists (list[BoxList]): the post-processed anchors, after
applying box decoding and NMS
"""
sampled_boxes = []
num_levels = len(objectness)
anchors = list(zip(*anchors))
for a, o, b in zip(anchors, objectness, box_regression):
sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))
boxlists = list(zip(*sampled_boxes))
boxlists = [cat_boxlist(boxlist) for boxlist in boxlists]
if num_levels > 1:
boxlists = self.select_over_all_levels(boxlists)
# append ground-truth bboxes to proposals
if self.training and targets is not None:
boxlists = self.add_gt_proposals(boxlists, targets)
return boxlists
def forward(self, locations, box_cls, box_regression, centerness, box_mask,
image_sizes):
"""
Arguments:
anchors: list[list[BoxList]]
box_cls: list[tensor]
box_regression: list[tensor]
box_mask: list[tensor]
image_sizes: list[(h, w)]
Returns:
boxlists (list[BoxList]): the post-processed anchors, after
applying box decoding and NMS
"""
sampled_boxes = []
for _, (l, o, b, c, m) in enumerate(
zip(locations, box_cls, box_regression, centerness, box_mask)):
sampled_boxes.append(
self.forward_for_single_feature_map(l, o, b, c, m,
image_sizes))
boxlists = list(zip(*sampled_boxes))
boxlists = [cat_boxlist(boxlist) for boxlist in boxlists]
boxlists = self.select_over_all_levels(boxlists)
return boxlists
def forward(self, locations, box_cls, box_regression, centerness, image_sizes, benchmark=False, timers=None):
"""
Arguments:
anchors: list[list[BoxList]]
box_cls: list[tensor]
box_regression: list[tensor]
image_sizes: list[(h, w)]
Returns:
boxlists (list[BoxList]): the post-processed anchors, after
applying box decoding and NMS
"""
if benchmark and timers is not None:
torch.cuda.synchronize()
timers[2].tic()
sampled_boxes = []
for _, (l, o, b, c) in enumerate(zip(locations, box_cls, box_regression, centerness)):
sampled_boxes.append(
self.forward_for_single_feature_map(
l, o, b, c, image_sizes
)
)
if benchmark and timers is not None:
torch.cuda.synchronize()
timers[2].toc()
timers[3].tic()
boxlists = list(zip(*sampled_boxes))
boxlists = [cat_boxlist(boxlist) for boxlist in boxlists]
if not self.bbox_aug_enabled:
boxlists = self.select_over_all_levels(boxlists)
if benchmark and timers is not None:
torch.cuda.synchronize()
timers[3].toc()
return boxlists
def select_over_all_levels(self, boxlists):
num_images = len(boxlists)
results = []
for i in range(num_images):
scores = boxlists[i].get_field("scores")
labels = boxlists[i].get_field("labels")
diagonals = boxlists[i].get_field("diagonals")
boxes = boxlists[i].bbox
boxlist = boxlists[i]
result = []
# skip the background
for j in tqdm(range(1, self.num_classes)):
inds = (labels == j).nonzero().view(-1)
scores_j = scores[inds]
boxes_j = boxes[inds, :].view(-1, 4)
diagonals_j = diagonals[inds, :].view(-1, 16)
boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
boxlist_for_class.add_field("scores", scores_j)
boxlist_for_class.add_field("diagonals", diagonals_j)
if scores_j.size()[0] != 0:
boxlist_for_class = diagonal_nms(boxlist_for_class,
self.nms_thresh,
self.nms_topk,
score_field="scores")
num_labels = len(boxlist_for_class)
boxlist_for_class.add_field(
"labels",
torch.full((num_labels, ),
j,
dtype=torch.int64,
device=scores.device))
result.append(boxlist_for_class)
result = cat_boxlist(result)
# new_result = all_class_nms(result, self.nms_thresh)
number_of_detections = len(result)
print('Number of detections in this image {}'.format(
number_of_detections))
# Limit to max_per_image detections **over all classes**
if number_of_detections > self.fpn_post_nms_top_n > 0:
cls_scores = result.get_field("scores")
image_thresh, _ = torch.kthvalue(
cls_scores.cpu(),
number_of_detections - self.fpn_post_nms_top_n + 1)
# image_thresh = torch.as_tensor(0.5)
keep = cls_scores >= image_thresh.item()
keep = torch.nonzero(keep).squeeze(1)
result = result[keep]
result = all_class_nms(result, self.nms_thresh)
results.append(result)
return results
def cat_boxlist_with_keypoints(boxlists):
assert all(boxlist.has_field("keypoints") for boxlist in boxlists)
kp = [boxlist.get_field("keypoints").keypoints for boxlist in boxlists]
kp = cat(kp, 0)
fields = boxlists[0].get_fields()
fields = [field for field in fields if field != "keypoints"]
boxlists = [boxlist.copy_with_fields(fields) for boxlist in boxlists]
boxlists = cat_boxlist(boxlists)
boxlists.add_field("keypoints", kp)
return boxlists
def __call__(self, box_regression, centerness, targets, anchors):
labels, reg_targets = self.prepare_targets(targets, anchors)
N = len(labels)
box_regression_flatten = self.concat_box_prediction_layers(
box_regression)
centerness_flatten = [
ct.permute(0, 2, 3, 1).reshape(N, -1, 1) for ct in centerness
]
centerness_flatten = torch.cat(centerness_flatten, dim=1).reshape(-1)
labels_flatten = torch.cat(labels, dim=0)
reg_targets_flatten = torch.cat(reg_targets, dim=0)
anchors_flatten = torch.cat([
cat_boxlist(anchors_per_image).bbox
for anchors_per_image in anchors
],
dim=0)
pos_inds = torch.nonzero(labels_flatten > 0).squeeze(1)
num_gpus = get_num_gpus()
total_num_pos = reduce_sum(pos_inds.new_tensor([pos_inds.numel()
])).item()
num_pos_avg_per_gpu = max(total_num_pos / float(num_gpus), 1.0)
box_regression_flatten = box_regression_flatten[pos_inds]
reg_targets_flatten = reg_targets_flatten[pos_inds]
anchors_flatten = anchors_flatten[pos_inds]
centerness_flatten = centerness_flatten[pos_inds]
centerness_targets = self.compute_centerness_targets(
reg_targets_flatten, anchors_flatten)
sum_centerness_targets_avg_per_gpu = reduce_sum(
centerness_targets.sum()).item() / float(num_gpus)
if pos_inds.numel() > 0:
reg_loss = self.GIoULoss(
box_regression_flatten,
reg_targets_flatten,
anchors_flatten,
weight=centerness_targets) / sum_centerness_targets_avg_per_gpu
centerness_loss = self.centerness_loss_func(
centerness_flatten, centerness_targets) / num_pos_avg_per_gpu
else:
reg_loss = box_regression_flatten.sum()
centerness_loss = centerness_flatten.sum()
return reg_loss * self.cfg.MODEL.ATSS.REG_LOSS_WEIGHT, centerness_loss
def select_over_all_levels(self, boxlists):
num_images = len(boxlists)
results = []
for i in range(num_images):
scores = boxlists[i].get_field("scores")
labels = boxlists[i].get_field("labels")
masks = boxlists[i].get_field("mask")
boxes = boxlists[i].bbox
boxlist = boxlists[i]
result = []
# skip the background
for j in range(1, self.num_classes):
inds = (labels == j).nonzero().view(-1)
scores_j = scores[inds]
boxes_j = boxes[inds, :].view(-1, 4)
boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
boxlist_for_class.add_field("scores", scores_j)
boxlist_for_class, keep = boxlist_nms_with_keep(
boxlist_for_class, self.nms_thresh, score_field="scores")
num_labels = len(boxlist_for_class)
boxlist_for_class.add_field(
"labels",
torch.full((num_labels, ),
j,
dtype=torch.int64,
device=scores.device))
boxlist_for_class.add_field("mask", masks[inds[keep], :, :, :])
result.append(boxlist_for_class)
result = cat_boxlist(result)
number_of_detections = len(result)
# Limit to max_per_image detections **over all classes**
if number_of_detections > self.fpn_post_nms_top_n > 0:
cls_scores = result.get_field("scores")
image_thresh, _ = torch.kthvalue(
cls_scores.cpu(),
number_of_detections - self.fpn_post_nms_top_n + 1)
keep = cls_scores >= image_thresh.item()
keep = torch.nonzero(keep).squeeze(1)
result = result[keep]
results.append(result)
return results
def __call__(self, anchors, objectness, box_regression, targets):
"""
Arguments:
anchors (list[BoxList])
objectness (list[Tensor])
box_regression (list[Tensor])
targets (list[BoxList])
Returns:
objectness_loss (Tensor)
box_loss (Tensor
"""
anchors = [
cat_boxlist(anchors_per_image) for anchors_per_image in anchors
]
labels, regression_targets = self.prepare_targets(anchors, targets)
sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
sampled_pos_inds = torch.nonzero(torch.cat(sampled_pos_inds,
dim=0)).squeeze(1)
sampled_neg_inds = torch.nonzero(torch.cat(sampled_neg_inds,
dim=0)).squeeze(1)
sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)
objectness, box_regression = \
concat_box_prediction_layers(objectness, box_regression)
objectness = objectness.squeeze()
labels = torch.cat(labels, dim=0)
regression_targets = torch.cat(regression_targets, dim=0)
box_loss = smooth_l1_loss(
box_regression[sampled_pos_inds],
regression_targets[sampled_pos_inds],
beta=1.0 / 9,
size_average=False,
) / (sampled_inds.numel())
# box_loss = self.box_reg_loss_func(box_regression[sampled_pos_inds],
# regression_targets[sampled_pos_inds]) / (sampled_inds.numel())
objectness_loss = F.binary_cross_entropy_with_logits(
objectness[sampled_inds], labels[sampled_inds])
return objectness_loss, box_loss
def merge_result_from_multi_scales(boxlists, nms_type='nms', vote_thresh=0.65):
num_images = len(boxlists)
results = []
for i in range(num_images):
scores = boxlists[i].get_field("scores")
labels = boxlists[i].get_field("labels")
boxes = boxlists[i].bbox
boxlist = boxlists[i]
result = []
# skip the background
for j in range(1, cfg.MODEL.RETINANET.NUM_CLASSES):
inds = (labels == j).nonzero().view(-1)
scores_j = scores[inds]
boxes_j = boxes[inds, :].view(-1, 4)
boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
boxlist_for_class.add_field("scores", scores_j)
boxlist_for_class = boxlist_nms(boxlist_for_class,
cfg.MODEL.ATSS.NMS_TH,
score_field="scores",
nms_type=nms_type,
vote_thresh=vote_thresh)
num_labels = len(boxlist_for_class)
boxlist_for_class.add_field(
"labels",
torch.full((num_labels, ),
j,
dtype=torch.int64,
device=scores.device))
result.append(boxlist_for_class)
result = cat_boxlist(result)
number_of_detections = len(result)
# Limit to max_per_image detections **over all classes**
if number_of_detections > cfg.MODEL.ATSS.PRE_NMS_TOP_N > 0:
cls_scores = result.get_field("scores")
image_thresh, _ = torch.kthvalue(
cls_scores.cpu(),
number_of_detections - cfg.MODEL.ATSS.PRE_NMS_TOP_N + 1)
keep = cls_scores >= image_thresh.item()
keep = torch.nonzero(keep).squeeze(1)
result = result[keep]
results.append(result)
return results
def filter_results(self, boxlist, num_classes):
"""Returns bounding-box detection results by thresholding on scores and
applying non-maximum suppression (NMS).
"""
# unwrap the boxlist to avoid additional overhead.
# if we had multi-class NMS, we could perform this directly on the boxlist
boxes = boxlist.bbox.reshape(-1, num_classes * 4)
scores = boxlist.get_field("scores").reshape(-1, num_classes)
device = scores.device
result = []
# Apply threshold on detection probabilities and apply NMS
# Skip j = 0, because it's the background class
inds_all = scores > self.score_thresh
for j in range(1, num_classes):
inds = inds_all[:, j].nonzero().squeeze(1)
scores_j = scores[inds, j]
boxes_j = boxes[inds, j * 4:(j + 1) * 4]
boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
boxlist_for_class.add_field("scores", scores_j)
boxlist_for_class = boxlist_nms(boxlist_for_class, self.nms)
num_labels = len(boxlist_for_class)
boxlist_for_class.add_field(
"labels",
torch.full((num_labels, ), j, dtype=torch.int64,
device=device))
result.append(boxlist_for_class)
result = cat_boxlist(result)
number_of_detections = len(result)
# Limit to max_per_image detections **over all classes**
if number_of_detections > self.detections_per_img > 0:
cls_scores = result.get_field("scores")
image_thresh, _ = torch.kthvalue(
cls_scores.cpu(),
number_of_detections - self.detections_per_img + 1)
keep = cls_scores >= image_thresh.item()
keep = torch.nonzero(keep).squeeze(1)
result = result[keep]
return result
def __call__(self, anchors, box_cls, box_regression, targets):
"""
Arguments:
anchors (list[BoxList])
box_cls (list[Tensor])
box_regression (list[Tensor])
targets (list[BoxList])
Returns:
retinanet_cls_loss (Tensor)
retinanet_regression_loss (Tensor
"""
anchors = [
cat_boxlist(anchors_per_image) for anchors_per_image in anchors
]
labels, regression_targets = self.prepare_targets(anchors, targets)
N = len(labels)
box_cls, box_regression = \
concat_box_prediction_layers(box_cls, box_regression)
labels = torch.cat(labels, dim=0)
regression_targets = torch.cat(regression_targets, dim=0)
pos_inds = torch.nonzero(labels > 0).squeeze(1)
retinanet_regression_loss = smooth_l1_loss(
box_regression[pos_inds],
regression_targets[pos_inds],
beta=self.bbox_reg_beta,
size_average=False,
) / (max(1,
pos_inds.numel() * self.regress_norm))
labels = labels.int()
retinanet_cls_loss = self.box_cls_loss_func(
box_cls, labels) / (pos_inds.numel() + N)
return retinanet_cls_loss, retinanet_regression_loss
def add_gt_proposals(self, proposals, targets):
"""
Arguments:
proposals: list[BoxList]
targets: list[BoxList]
"""
# Get the device we're operating on
device = proposals[0].bbox.device
gt_boxes = [target.copy_with_fields([]) for target in targets]
# later cat of bbox requires all fields to be present for all bbox
# so we need to add a dummy for objectness that's missing
for gt_box in gt_boxes:
gt_box.add_field("objectness",
torch.ones(len(gt_box), device=device))
proposals = [
cat_boxlist((proposal, gt_box))
for proposal, gt_box in zip(proposals, gt_boxes)
]
return proposals
def __call__(self, anchors, objectness, box_regression, targets):
"""
Arguments:
anchors (list[BoxList])
objectness (list[Tensor])
box_regression (list[Tensor])
targets (list[BoxList])
Returns:
objectness_loss (Tensor)
box_loss (Tensor
"""
print_log = (self.num_call % 100) == 0
self.num_call += 1
if print_log:
all_anchor_sizes_each_pyramid = [[len(a) for a in anchors_per_image]
for anchors_per_image in anchors]
anchor_boxes = [cat_boxlist(anchors_per_image) for anchors_per_image in anchors]
all_num_anchor_per_level = [[len(a) for a in anchors_per_image] for anchors_per_image in anchors]
labels, regression_targets = self.prepare_targets(
anchor_boxes, targets, all_num_anchor_per_level)
if print_log:
with torch.no_grad():
all_kind_to_num = get_kind_to_num_info(labels, all_anchor_sizes_each_pyramid)
for kind_to_num in all_kind_to_num:
for k in kind_to_num:
kind_to_num[k] /= 1. * len(targets)
from qd.qd_common import print_table
print_table(all_kind_to_num)
#if self.all_kind_to_num is None:
#self.all_kind_to_num = all_kind_to_num
#else:
#for kind_to_num, self_kind_to_num in zip(all_kind_to_num, self.all_kind_to_num):
#for kind, num in kind_to_num.items():
#self_kind_to_num[kind] += num
#print_table(self.all_kind_to_num)
sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
sampled_pos_inds = torch.nonzero(torch.cat(sampled_pos_inds, dim=0),
as_tuple=False).squeeze(1)
sampled_neg_inds = torch.nonzero(torch.cat(sampled_neg_inds, dim=0),
as_tuple=False).squeeze(1)
sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)
objectness, box_regression = \
concat_box_prediction_layers(objectness, box_regression)
objectness = objectness.squeeze()
labels = torch.cat(labels, dim=0)
regression_targets = torch.cat(regression_targets, dim=0)
box_loss = smooth_l1_loss(
box_regression[sampled_pos_inds],
regression_targets[sampled_pos_inds],
beta=1.0 / 9,
size_average=False,
) / (sampled_inds.numel())
objectness_loss = F.binary_cross_entropy_with_logits(
objectness[sampled_inds], labels[sampled_inds]
)
return objectness_loss, box_loss
def prepare_targets(self, targets, anchors):
cls_labels = []
reg_targets = []
for im_i in range(len(targets)):
targets_per_im = targets[im_i]
assert targets_per_im.mode == "xyxy"
bboxes_per_im = targets_per_im.bbox
labels_per_im = targets_per_im.get_field("labels")
anchors_per_im = cat_boxlist(anchors[im_i])
num_gt = bboxes_per_im.shape[0]
if self.cfg.MODEL.ATSS.POSITIVE_TYPE == 'SSC':
object_sizes_of_interest = [[-1, 64], [64, 128], [128, 256], [256, 512], [512, INF]]
area_per_im = targets_per_im.area()
expanded_object_sizes_of_interest = []
points = []
for l, anchors_per_level in enumerate(anchors[im_i]):
anchors_per_level = anchors_per_level.bbox
anchors_cx_per_level = (anchors_per_level[:, 2] + anchors_per_level[:, 0]) / 2.0
anchors_cy_per_level = (anchors_per_level[:, 3] + anchors_per_level[:, 1]) / 2.0
points_per_level = torch.stack((anchors_cx_per_level, anchors_cy_per_level), dim=1)
points.append(points_per_level)
object_sizes_of_interest_per_level = \
points_per_level.new_tensor(object_sizes_of_interest[l])
expanded_object_sizes_of_interest.append(
object_sizes_of_interest_per_level[None].expand(len(points_per_level), -1)
)
expanded_object_sizes_of_interest = torch.cat(expanded_object_sizes_of_interest, dim=0)
points = torch.cat(points, dim=0)
xs, ys = points[:, 0], points[:, 1]
l = xs[:, None] - bboxes_per_im[:, 0][None]
t = ys[:, None] - bboxes_per_im[:, 1][None]
r = bboxes_per_im[:, 2][None] - xs[:, None]
b = bboxes_per_im[:, 3][None] - ys[:, None]
reg_targets_per_im = torch.stack([l, t, r, b], dim=2)
is_in_boxes = reg_targets_per_im.min(dim=2)[0] > 0.01
max_reg_targets_per_im = reg_targets_per_im.max(dim=2)[0]
is_cared_in_the_level = \
(max_reg_targets_per_im >= expanded_object_sizes_of_interest[:, [0]]) & \
(max_reg_targets_per_im <= expanded_object_sizes_of_interest[:, [1]])
locations_to_gt_area = area_per_im[None].repeat(len(points), 1)
locations_to_gt_area[is_in_boxes == 0] = INF
locations_to_gt_area[is_cared_in_the_level == 0] = INF
locations_to_min_area, locations_to_gt_inds = locations_to_gt_area.min(dim=1)
cls_labels_per_im = labels_per_im[locations_to_gt_inds]
cls_labels_per_im[locations_to_min_area == INF] = 0
matched_gts = bboxes_per_im[locations_to_gt_inds]
elif self.cfg.MODEL.ATSS.POSITIVE_TYPE == 'ATSS':
num_anchors_per_loc = len(self.cfg.MODEL.ATSS.ASPECT_RATIOS) * self.cfg.MODEL.ATSS.SCALES_PER_OCTAVE
num_anchors_per_level = [len(anchors_per_level.bbox) for anchors_per_level in anchors[im_i]]
ious = boxlist_iou(anchors_per_im, targets_per_im)
gt_cx = (bboxes_per_im[:, 2] + bboxes_per_im[:, 0]) / 2.0
gt_cy = (bboxes_per_im[:, 3] + bboxes_per_im[:, 1]) / 2.0
gt_points = torch.stack((gt_cx, gt_cy), dim=1)
anchors_cx_per_im = (anchors_per_im.bbox[:, 2] + anchors_per_im.bbox[:, 0]) / 2.0
anchors_cy_per_im = (anchors_per_im.bbox[:, 3] + anchors_per_im.bbox[:, 1]) / 2.0
anchor_points = torch.stack((anchors_cx_per_im, anchors_cy_per_im), dim=1)
distances = (anchor_points[:, None, :] - gt_points[None, :, :]).pow(2).sum(-1).sqrt()
# Selecting candidates based on the center distance between anchor box and object
candidate_idxs = []
star_idx = 0
for level, anchors_per_level in enumerate(anchors[im_i]):
end_idx = star_idx + num_anchors_per_level[level]
distances_per_level = distances[star_idx:end_idx, :]
topk = min(self.cfg.MODEL.ATSS.TOPK * num_anchors_per_loc, num_anchors_per_level[level])
_, topk_idxs_per_level = distances_per_level.topk(topk, dim=0, largest=False)
candidate_idxs.append(topk_idxs_per_level + star_idx)
star_idx = end_idx
candidate_idxs = torch.cat(candidate_idxs, dim=0)
# Using the sum of mean and standard deviation as the IoU threshold to select final positive samples
candidate_ious = ious[candidate_idxs, torch.arange(num_gt)]
iou_mean_per_gt = candidate_ious.mean(0)
iou_std_per_gt = candidate_ious.std(0)
iou_thresh_per_gt = iou_mean_per_gt + iou_std_per_gt
is_pos = candidate_ious >= iou_thresh_per_gt[None, :]
# Limiting the final positive samples’ center to object
anchor_num = anchors_cx_per_im.shape[0]
for ng in range(num_gt):
candidate_idxs[:, ng] += ng * anchor_num
e_anchors_cx = anchors_cx_per_im.view(1, -1).expand(num_gt, anchor_num).contiguous().view(-1)
e_anchors_cy = anchors_cy_per_im.view(1, -1).expand(num_gt, anchor_num).contiguous().view(-1)
candidate_idxs = candidate_idxs.view(-1)
l = e_anchors_cx[candidate_idxs].view(-1, num_gt) - bboxes_per_im[:, 0]
t = e_anchors_cy[candidate_idxs].view(-1, num_gt) - bboxes_per_im[:, 1]
r = bboxes_per_im[:, 2] - e_anchors_cx[candidate_idxs].view(-1, num_gt)
b = bboxes_per_im[:, 3] - e_anchors_cy[candidate_idxs].view(-1, num_gt)
is_in_gts = torch.stack([l, t, r, b], dim=1).min(dim=1)[0] > 0.01
is_pos = is_pos & is_in_gts
# if an anchor box is assigned to multiple gts, the one with the highest IoU will be selected.
ious_inf = torch.full_like(ious, -INF).t().contiguous().view(-1)
index = candidate_idxs.view(-1)[is_pos.view(-1)]
ious_inf[index] = ious.t().contiguous().view(-1)[index]
ious_inf = ious_inf.view(num_gt, -1).t()
anchors_to_gt_values, anchors_to_gt_indexs = ious_inf.max(dim=1)
cls_labels_per_im = labels_per_im[anchors_to_gt_indexs]
cls_labels_per_im[anchors_to_gt_values == -INF] = 0
matched_gts = bboxes_per_im[anchors_to_gt_indexs]
elif self.cfg.MODEL.ATSS.POSITIVE_TYPE == 'TOPK':
gt_cx = (bboxes_per_im[:, 2] + bboxes_per_im[:, 0]) / 2.0
gt_cy = (bboxes_per_im[:, 3] + bboxes_per_im[:, 1]) / 2.0
gt_points = torch.stack((gt_cx, gt_cy), dim=1)
anchors_cx_per_im = (anchors_per_im.bbox[:, 2] + anchors_per_im.bbox[:, 0]) / 2.0
anchors_cy_per_im = (anchors_per_im.bbox[:, 3] + anchors_per_im.bbox[:, 1]) / 2.0
anchor_points = torch.stack((anchors_cx_per_im, anchors_cy_per_im), dim=1)
distances = (anchor_points[:, None, :] - gt_points[None, :, :]).pow(2).sum(-1).sqrt()
distances = distances / distances.max() / 1000
ious = boxlist_iou(anchors_per_im, targets_per_im)
is_pos = ious * False
for ng in range(num_gt):
_, topk_idxs = (ious[:, ng] - distances[:, ng]).topk(self.cfg.MODEL.ATSS.TOPK, dim=0)
l = anchors_cx_per_im[topk_idxs] - bboxes_per_im[ng, 0]
t = anchors_cy_per_im[topk_idxs] - bboxes_per_im[ng, 1]
r = bboxes_per_im[ng, 2] - anchors_cx_per_im[topk_idxs]
b = bboxes_per_im[ng, 3] - anchors_cy_per_im[topk_idxs]
is_in_gt = torch.stack([l, t, r, b], dim=1).min(dim=1)[0] > 0.01
is_pos[topk_idxs[is_in_gt == 1], ng] = True
ious[is_pos == 0] = -INF
anchors_to_gt_values, anchors_to_gt_indexs = ious.max(dim=1)
cls_labels_per_im = labels_per_im[anchors_to_gt_indexs]
cls_labels_per_im[anchors_to_gt_values == -INF] = 0
matched_gts = bboxes_per_im[anchors_to_gt_indexs]
elif self.cfg.MODEL.ATSS.POSITIVE_TYPE == 'IoU':
match_quality_matrix = boxlist_iou(targets_per_im, anchors_per_im)
matched_idxs = self.matcher(match_quality_matrix)
targets_per_im = targets_per_im.copy_with_fields(['labels'])
matched_targets = targets_per_im[matched_idxs.clamp(min=0)]
cls_labels_per_im = matched_targets.get_field("labels")
cls_labels_per_im = cls_labels_per_im.to(dtype=torch.float32)
# Background (negative examples)
bg_indices = matched_idxs == Matcher.BELOW_LOW_THRESHOLD
cls_labels_per_im[bg_indices] = 0
# discard indices that are between thresholds
inds_to_discard = matched_idxs == Matcher.BETWEEN_THRESHOLDS
cls_labels_per_im[inds_to_discard] = -1
matched_gts = matched_targets.bbox
# Limiting positive samples’ center to object
# in order to filter out poor positives and use the centerness branch
pos_idxs = torch.nonzero(cls_labels_per_im > 0).squeeze(1)
pos_anchors_cx = (anchors_per_im.bbox[pos_idxs, 2] + anchors_per_im.bbox[pos_idxs, 0]) / 2.0
pos_anchors_cy = (anchors_per_im.bbox[pos_idxs, 3] + anchors_per_im.bbox[pos_idxs, 1]) / 2.0
l = pos_anchors_cx - matched_gts[pos_idxs, 0]
t = pos_anchors_cy - matched_gts[pos_idxs, 1]
r = matched_gts[pos_idxs, 2] - pos_anchors_cx
b = matched_gts[pos_idxs, 3] - pos_anchors_cy
is_in_gts = torch.stack([l, t, r, b], dim=1).min(dim=1)[0] > 0.01
cls_labels_per_im[pos_idxs[is_in_gts == 0]] = -1
else:
raise NotImplementedError
reg_targets_per_im = self.box_coder.encode(matched_gts, anchors_per_im.bbox)
cls_labels.append(cls_labels_per_im)
reg_targets.append(reg_targets_per_im)
return cls_labels, reg_targets
