def build_cost_matrix(self, instances: Instances,
prev_instances: Instances) -> np.ndarray:
"""
Build the cost matrix for assignment problem
(https://en.wikipedia.org/wiki/Assignment_problem)
Args:
instances: D2 Instances, for current frame predictions
prev_instances: D2 Instances, for previous frame predictions
Return:
the cost matrix in numpy array
"""
assert instances is not None and prev_instances is not None
# calculate IoU of all bbox pairs
iou_all = pairwise_iou(
boxes1=instances.pred_boxes,
boxes2=self._prev_instances.pred_boxes,
)
bbox_pairs = create_prediction_pairs(
instances,
self._prev_instances,
iou_all,
threshold=self._track_iou_threshold)
# assign large cost value to make sure pair below IoU threshold won't be matched
cost_matrix = np.full((len(instances), len(prev_instances)),
LARGE_COST_VALUE)
return self.assign_cost_matrix_values(cost_matrix, bbox_pairs)
def non_maximum_regression_loss_stage1(box_reg_delta,
proposals,
box_score,
score_gap=0.2,
reg_iou=0.6,
box2box_translation=None):
device = torch.device('cuda')
loss = torch.zeros(1).to(device)
box_score_topk, box_score_index = torch.topk(box_score, k=1, dim=1)
box_score_topk, box_score_index = box_score_topk.reshape(
-1), box_score_index.reshape(-1)
proposals_max = proposals[box_score_index] ## (M)*4
proposals_ious = pairwise_iou(proposals_max, proposals)
proposals_ious[proposals_ious == 1] = 0
proposals_ious = (proposals_ious >= reg_iou).float()
if proposals_ious.sum():
vx, vy = proposals_ious.nonzero().transpose(0, 1)
proposals_reg_target = proposals_max[vx]
box_score_targets = box_score_topk[vx]
box_score_reg = box_score[vx, vy].reshape(-1)
score_att = (box_score_targets - box_score_reg) >= score_gap
proposals_need_reg = proposals[vy]
proposal_target = box2box_translation.get_deltas(
proposals_need_reg.tensor, proposals_reg_target.tensor)
reg_delta = box_reg_delta[vx, vy]
loss += weighted_smooth_l1_loss(reg_delta,
proposal_target,
beta=1,
weight=score_att.float(),
reduction='mean')
return loss
def update(self, instances: Instances) -> Instances:
"""
See BaseTracker description
"""
if instances.has("pred_keypoints"):
raise NotImplementedError("Need to add support for keypoints")
instances = self._initialize_extra_fields(instances)
if self._prev_instances is not None:
# calculate IoU of all bbox pairs
iou_all = pairwise_iou(
boxes1=instances.pred_boxes,
boxes2=self._prev_instances.pred_boxes,
)
# sort IoU in descending order
bbox_pairs = self._create_prediction_pairs(instances, iou_all)
# assign previous ID to current bbox if IoU > track_iou_threshold
self._reset_fields()
for bbox_pair in bbox_pairs:
if bbox_pair["IoU"] < self._track_iou_threshold:
continue
idx = bbox_pair["idx"]
prev_id = bbox_pair["prev_id"]
if idx in self._matched_idx or prev_id in self._matched_ID:
continue
instances.ID[idx] = prev_id
instances.ID_period[idx] = bbox_pair["prev_period"] + 1
instances.lost_frame_count[idx] = 0
self._matched_idx.add(idx)
self._matched_ID.add(prev_id)
self._untracked_prev_idx.remove(bbox_pair["prev_idx"])
instances = self._assign_new_id(instances)
instances = self._merge_untracked_instances(instances)
self._prev_instances = copy.deepcopy(instances)
return instances
def match_targets_to_proposals(self, proposal, target):
match_quality_matrix = pairwise_iou(target, proposal)
matched_idxs = self.proposal_matcher(match_quality_matrix)
# Mask RCNN needs "labels" and "masks "fields for creating the targets
target = target.copy_with_fields(["labels", "masks", "char_masks"])
# 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 non_maximum_regression_loss_stage2_ada(box_reg_delta,
proposals,
box_score,
score_gap=0.2,
reg_iou=0.6,
box2box_translation=None,
atten_mask=None):
device = torch.device('cuda')
loss = torch.zeros(1).to(device)
box_score = box_score * atten_mask
box_reg_delta = box_reg_delta.reshape(-1, 4)
num_phrase, num_boxes = box_score.shape
box_score = box_score.reshape(-1)
box_score_topk, box_score_index = torch.topk(box_score.reshape(
num_phrase, num_boxes),
k=1,
dim=1)
box_score_topk, box_score_index = box_score_topk.reshape(
-1), box_score_index.reshape(-1)
box_score_index = box_score_index + torch.arange(num_phrase).to(
device) * num_boxes
proposals_max = proposals[box_score_index] ## M*4
proposals_ious = pairwise_iou(proposals_max, proposals)
proposals_ious[proposals_ious == 1] = 0
keep_mat = torch.zeros_like(proposals_ious)
for pid in range(num_phrase):
keep_mat[pid, pid * num_boxes:(pid + 1) * num_boxes] = 1
keep_mat = keep_mat * atten_mask.reshape(-1)
proposals_ious = proposals_ious * keep_mat
proposals_ious_flag = (proposals_ious >= reg_iou).float()
if proposals_ious_flag.sum():
vx, vy = proposals_ious_flag.nonzero().transpose(0, 1)
proposals_reg_target = proposals_max[vx]
proposals_need_reg = proposals[vy]
box_score_target = box_score_topk[vx]
box_score_reg = box_score[vy]
score_att = (box_score_target - box_score_reg) >= score_gap
proposal_target = box2box_translation.get_deltas(
proposals_need_reg.tensor, proposals_reg_target.tensor)
reg_delta = box_reg_delta[vy]
loss += weighted_smooth_l1_loss(reg_delta,
proposal_target,
beta=1,
weight=score_att.float(),
reduction='mean')
return loss
def generate_attention_entropy_loss(pred_sim,
pred_topk_sim,
targets,
precomp_boxes,
s2_topk=5):
pred_sim = torch.clamp(pred_sim, min=1e-5)
pred_topk_sim = torch.clamp(pred_topk_sim, min=1e-5)
ious = pairwise_iou(targets, precomp_boxes)
atten_topk = torch.topk(pred_sim, k=s2_topk, dim=1)[1]
ious_topk = torch.gather(ious, dim=1, index=atten_topk)
gt_score = F.normalize(ious * ious.ge(0.5).float(), p=1, dim=1)
gt_score_topk = F.normalize(ious_topk * ious_topk.ge(0.5).float(),
p=1,
dim=1)
cls_loss = -(gt_score * pred_sim.log()).mean()
cls_loss_topk = -(gt_score_topk * pred_topk_sim.log()).mean()
return cls_loss, cls_loss_topk
def evaluate(self):
"""
Evaluates Referring Segmentation IoU:
"""
if self._distributed:
synchronize()
self._predictions = all_gather(self._predictions)
if not is_main_process():
return
all_prediction = {}
for p in self._predictions:
all_prediction.update(p)
else:
all_prediction = self._predictions
image_unique_ids = list(all_prediction.keys())
total_num = 0
recall_num = 0
recall_t2_num = 0
recall_t2_fusion_num = 0
num_type = {}
recall_type = {}
acc_type = {}
recall_topk_num = {5: 0, 10: 0}
point_recall_num = 0
point_recall_t2_num = 0
point_recall_fusion_t2_num = 0
for img_sent_id in image_unique_ids:
result = all_prediction[img_sent_id]
phrase_ids = result[0]
phrase_types = result[1]
pred_boxes = result[2]
pred_similarity = result[3]
targets = result[4]
precomp_boxes = result[5]
topk_pred_boxes = result[6]
topk_fusion_pred_boxes = result[8]
pred_boxes.clip()
ious = pairwise_iou(
targets, pred_boxes
) # this function will change the target_boxes into cuda mode
iou = ious.numpy().diagonal()
total_num += iou.shape[0]
recall_num += int((iou >= cfg.MODEL.VG.EVAL_THRESH).sum()) # 0.5
pred_boxes_tensor = pred_boxes.tensor
pred_center = (pred_boxes_tensor[:, :2] +
pred_boxes_tensor[:, 2:]) / 2.0
pred_center = pred_center.repeat(1, 2) ## x_c, y_c, x_c, y_c
targets_tensor = targets.tensor
fall_tensor = targets_tensor - pred_center
fall_tensor = (fall_tensor[:, :2] <= 0).float().sum(1) + (
fall_tensor[:, 2:] >= 0).float().sum(1)
point_recall_num += (fall_tensor == 4).float().numpy().sum()
topk_pred_boxes.clip()
ious_topk = pairwise_iou(targets, topk_pred_boxes)
recall_t2_num += int((ious_topk.numpy().diagonal() >
cfg.MODEL.VG.EVAL_THRESH).sum())
topk_boxes_tensor = topk_pred_boxes.tensor
pred_center = (topk_boxes_tensor[:, :2] +
topk_boxes_tensor[:, 2:]) / 2.0
pred_center = pred_center.repeat(1, 2) ## x_c, y_c, x_c, y_c
fall_tensor = targets_tensor - pred_center
fall_tensor = (fall_tensor[:, :2] <= 0).float().sum(1) + (
fall_tensor[:, 2:] >= 0).float().sum(1)
point_recall_t2_num += (fall_tensor == 4).float().numpy().sum()
topk_fusion_pred_boxes.clip()
ious_fusion_topk = pairwise_iou(targets, topk_fusion_pred_boxes)
recall_t2_fusion_num += int((ious_fusion_topk.numpy().diagonal() >
cfg.MODEL.VG.EVAL_THRESH).sum())
topk_fusion_boxes_tensor = topk_fusion_pred_boxes.tensor
pred_center = (topk_fusion_boxes_tensor[:, :2] +
topk_fusion_boxes_tensor[:, 2:]) / 2.0
pred_center = pred_center.repeat(1, 2) ## x_c, y_c, x_c, y_c
fall_tensor = targets_tensor - pred_center
fall_tensor = (fall_tensor[:, :2] <= 0).float().sum(1) + (
fall_tensor[:, 2:] >= 0).float().sum(1)
point_recall_fusion_t2_num += (
fall_tensor == 4).float().numpy().sum()
for pid, p_type in enumerate(phrase_types):
p_type = p_type[0]
num_type[p_type] = num_type.setdefault(p_type, 0) + 1
recall_type[p_type] = recall_type.setdefault(
p_type, 0) + (iou[pid] >= cfg.MODEL.VG.EVAL_THRESH)
precomp_boxes.clip()
ious_top = pairwise_iou(targets, precomp_boxes).cpu()
for k in [5, 10]:
top_k = torch.topk(pred_similarity, k=k, dim=1)[0][:, [-1]]
pred_similarity_topk = (pred_similarity >= top_k).float()
ious_top_k = (ious_top * pred_similarity_topk).numpy()
recall_topk_num[k] += int(
((ious_top_k >= cfg.MODEL.VG.EVAL_THRESH).sum(1) >
0).sum())
acc = recall_num / total_num
acc_top5 = recall_topk_num[5] / total_num
acc_top10 = recall_topk_num[10] / total_num
acc_s2 = recall_t2_num / total_num
acc_s2_fusion = recall_t2_fusion_num / total_num
point_acc = point_recall_num / total_num
point_acc_s2 = point_recall_t2_num / total_num
point_acc_s2_fusion = point_recall_fusion_t2_num / total_num
for type, type_num in num_type.items():
acc_type[type] = recall_type[type] / type_num
if self._output_dir:
PathManager.mkdirs(self._output_dir)
file_path = os.path.join(
self._output_dir,
"prediction_{}.pkl".format(str(acc).replace('.', '_')[:6]))
with PathManager.open(file_path, "wb") as f:
pickle.dump(all_prediction, f)
del all_prediction
self._logger.info(
'evaluation on {} expression instances, detailed_iou: {}'.format(
len(image_unique_ids), acc_type))
self._logger.info(
'Evaluate Pointing Accuracy: PointAcc:{}, PointAccS2:{}, PointAccS2Fusion:{}'
.format(point_acc, point_acc_s2, point_acc_s2_fusion))
results = OrderedDict({
"acc": acc,
"acc_top5": acc_top5,
"acc_top10": acc_top10,
'acc_s2': acc_s2,
'acc_s2_fusion': acc_s2_fusion
})
return results
def topk_iou_boxes(self, candidates: Boxes, targets: Boxes, k=1):
iou_matrix = pairwise_iou(candidates, targets)
_, topk_idxs = iou_matrix.topk(k, dim=0)
return candidates.tensor[topk_idxs], topk_idxs
def extend_after(self, frame_index, proposal_instances,
prior_selection_mask):
result_iou_mask = torch.zeros(prior_selection_mask.shape,
dtype=torch.bool)
if self.is_dead(frame_index):
return result_iou_mask
if not torch.any(prior_selection_mask):
return result_iou_mask
last_key_instance_index = self.last_key_instance_index[-1]
if self.config.perform_projection:
projected_last_instance = self.project_proposal_instance(
frame_index)
else:
projected_last_instance = self.proposal_instances[
last_key_instance_index]
iou_per_proposal = pairwise_iou(
proposal_instances.pred_boxes,
projected_last_instance.pred_boxes).squeeze()
result_iou_mask = (iou_per_proposal >
self.config.tubelet_iou_threshold)
selection_mask = prior_selection_mask & result_iou_mask
projection_dim = projected_last_instance.pred_boxes.tensor[:,
2:] - projected_last_instance.pred_boxes.tensor[:, :
2]
proposals_dim = proposal_instances.pred_boxes.tensor[:,
2:] - proposal_instances.pred_boxes.tensor[:, :
2]
selection_mask_rel = selection_mask_abs = selection_mask
if self.config.max_dimension_change_ratio is not None:
dim_ratios = proposals_dim / projection_dim
selection_mask_rel = torch.all(
(dim_ratios > 1 - self.config.max_dimension_change_ratio) &
(dim_ratios < 1 + self.config.max_dimension_change_ratio),
dim=1)
if self.config.max_dimension_change_abs is not None:
dim_diffs = torch.abs(proposals_dim - projection_dim)
selection_mask_abs = torch.all(
dim_diffs < self.config.max_dimension_change_abs, dim=1)
selection_mask &= (selection_mask_rel | selection_mask_abs)
if self.config.extend_class_only:
# Ignore all proposals of different classes
selection_mask &= (proposal_instances.pred_classes ==
self.config.class_index_to_detect)
if self.config.min_extension_probability is not None:
selection_mask &= (
proposal_instances.class_distributions[:, self.config.
class_index_to_detect] >
self.config.min_extension_probability)
last_index = self.frame_ids[-1]
if last_index + 1 != frame_index and self.config.min_extension_probability_after_skipped_frames is not None:
selection_mask &= (
proposal_instances.class_distributions[:, self.config.
class_index_to_detect] >
self.config.min_extension_probability_after_skipped_frames)
iou_per_proposal[~selection_mask] = 0
if not torch.any(iou_per_proposal > 0):
# Not a single proposal survived the selection process
return result_iou_mask
extension_candidate_proposal_index = int(
iou_per_proposal.argmax()) # TODO: Add class probabilities?
for j in range(last_index + 1, frame_index):
self.frame_ids.append(j)
self.proposal_instances.append(None)
self.proposal_instances_ious.append(0)
self.last_key_instance_index.append(last_key_instance_index)
self.projected_proposal_instances.append(projected_last_instance)
pi = proposal_instances[extension_candidate_proposal_index]
pi.generation_process = ["T+"] if frame_index > 0 else ["T-"]
self.frame_ids.append(frame_index)
self.proposal_instances.append(pi)
self.proposal_instances_ious.append(
iou_per_proposal[extension_candidate_proposal_index])
self.last_key_instance_index.append(len(self.last_key_instance_index))
self.projected_proposal_instances.append(projected_last_instance)
return result_iou_mask
