def forward(
self,
features, # type: Dict[str, Tensor]
proposals, # type: List[Tensor]
image_shapes, # type: List[Tuple[int, int]]
targets=None # type: Optional[List[Dict[str, Tensor]]]
):
# type: (...) -> Tuple[List[Dict[str, Tensor]], Dict[str, Tensor]]
"""
Arguments:
features (List[Tensor])
proposals (List[Tensor[N, 4]])
image_shapes (List[Tuple[H, W]])
targets (List[Dict])
"""
if targets is not None:
for t in targets:
# TODO: https://github.com/pytorch/pytorch/issues/26731
floating_point_types = (torch.float, torch.double, torch.half)
assert t[
"boxes"].dtype in floating_point_types, 'target boxes must of float type'
assert t[
"labels"].dtype == torch.int64, 'target labels must of int64 type'
if self.training:
proposals, matched_idxs, labels, regression_targets, data_sbj, data_obj, data_rlp = self.select_training_samples(
proposals, targets)
# faster_rcnn branch
box_features = self.box_roi_pool(features, proposals, image_shapes)
box_features = self.box_head(box_features)
class_logits, box_regression = self.box_predictor(box_features)
# predicate branch
sbj_feat = self.box_roi_pool(features, data_sbj["proposals"],
image_shapes)
sbj_feat = self.box_head(sbj_feat)
obj_feat = self.box_roi_pool(features, data_obj["proposals"],
image_shapes)
obj_feat = self.box_head(obj_feat)
rel_feat = self.box_roi_pool(features, data_rlp["proposals"],
image_shapes)
rel_feat = self.rlp_head(rel_feat)
concat_feat = torch.cat((sbj_feat, rel_feat, obj_feat), dim=1)
sbj_cls_scores, obj_cls_scores, rlp_cls_scores = \
self.RelDN(concat_feat, sbj_feat, obj_feat)
result = torch.jit.annotate(List[Dict[str, torch.Tensor]], [])
losses = {}
assert labels is not None and regression_targets is not None
loss_cls_sbj, accuracy_cls_sbj = reldn_losses(
sbj_cls_scores, data_sbj["labels"])
loss_cls_obj, accuracy_cls_obj = reldn_losses(
obj_cls_scores, data_obj['labels'])
loss_cls_rlp, accuracy_cls_rlp = reldn_losses(
rlp_cls_scores, data_rlp['labels'])
loss_classifier, loss_box_reg = fastrcnn_loss(
class_logits, box_regression, labels, regression_targets)
losses = {
"loss_classifier": loss_classifier,
"loss_box_reg": loss_box_reg,
"loss_sbj": loss_cls_sbj,
"acc_sbj": accuracy_cls_sbj.item(),
"loss_obj": loss_cls_obj,
"acc_obj": accuracy_cls_obj.item(),
"loss_rlp": loss_cls_rlp,
"acc_rlp": accuracy_cls_rlp.item()
}
else:
labels = None
regression_targets = None
matched_idxs = None
result = []
# faster_rcnn branch
box_features = self.box_roi_pool(features, proposals, image_shapes)
box_features = self.box_head(box_features)
class_logits, box_regression = self.box_predictor(box_features)
boxes, scores, labels = self.postprocess_detections(
class_logits, box_regression, proposals, image_shapes)
num_images = len(boxes)
all_sbj_boxes = []
all_obj_boxes = []
all_rlp_boxes = []
all_shapes = []
for img_id in range(num_images):
sbj_inds = np.repeat(np.arange(boxes[img_id].shape[0]),
boxes[img_id].shape[0])
obj_inds = np.tile(np.arange(boxes[img_id].shape[0]),
boxes[img_id].shape[0])
sbj_inds, obj_inds = self.remove_self_pairs(sbj_inds, obj_inds)
sbj_boxes = boxes[img_id][sbj_inds]
obj_boxes = boxes[img_id][obj_inds]
rlp_boxes = box_utils.boxes_union(sbj_boxes, obj_boxes)
all_sbj_boxes.append(sbj_boxes)
all_obj_boxes.append(obj_boxes)
all_rlp_boxes.append(rlp_boxes)
all_shapes.append(rlp_boxes.shape[0])
# predicate branch
sbj_feat = self.box_roi_pool(features, all_sbj_boxes, image_shapes)
sbj_feat = self.box_head(sbj_feat)
obj_feat = self.box_roi_pool(features, all_obj_boxes, image_shapes)
obj_feat = self.box_head(obj_feat)
rel_feat = self.box_roi_pool(features, all_rlp_boxes, image_shapes)
rel_feat = self.rlp_head(rel_feat)
concat_feat = torch.cat((sbj_feat, rel_feat, obj_feat), dim=1)
sbj_cls_scores, obj_cls_scores, rlp_cls_scores = \
self.RelDN(concat_feat, sbj_feat, obj_feat)
sbj_cls_scores_list, obj_cls_scores_list, rlp_cls_scores_list = \
sbj_cls_scores.split(all_shapes), obj_cls_scores.split(
all_shapes), rlp_cls_scores.split(all_shapes)
for i, _ in enumerate(sbj_cls_scores_list):
_, sbj_indices = torch.max(sbj_cls_scores_list[i], dim=1)
_, obj_indices = torch.max(obj_cls_scores_list[i], dim=1)
rel_scores, rel_indices = torch.max(rlp_cls_scores_list[i],
dim=1)
# filter "unknown"
mask = rel_indices > 0
rel_scores = rel_scores[mask]
predicates = rel_indices[mask]
subjects = sbj_indices[mask]
objects = obj_indices[mask]
sbj_boxes = all_sbj_boxes[i][mask]
obj_boxes = all_obj_boxes[i][mask]
rlp_boxes = all_rlp_boxes[i][mask]
score_mask = rel_scores > 0.4
result = [{
"sbj_boxes": sbj_boxes[score_mask],
"obj_boxes": obj_boxes[score_mask],
'sbj_labels': subjects[score_mask],
'obj_labels': objects[score_mask],
'predicates': predicates[score_mask],
}]
# result = [{"sbj_boxes": sbj_boxes,
# "obj_boxes": obj_boxes,
# 'sbj_labels': subjects,
# 'obj_labels': objects,
# 'predicates': predicates,
# }]
losses = {}
return result, losses
def select_training_samples(
self,
proposals, # type: List[Tensor]
# type: Optional[List[Dict[str, Tensor]]]
targets):
# type: (...) -> Tuple[List[Tensor], List[Tensor], List[Tensor], List[Tensor]]
self.check_targets(targets)
assert targets is not None
dtype = proposals[0].dtype
device = proposals[0].device
# shape --> list of [Tensor of size 10,2,4]
gt_boxes = [t["boxes"].to(dtype) for t in targets]
# shape --> list of [Tensor of size 10,2]
gt_labels = [t["labels"] for t in targets]
gt_preds = [t["preds"] for t in targets]
# append ground-truth bboxes to propos
proposals = self.add_gt_proposals(proposals, gt_boxes)
# get matching gt indices for each proposal
matched_idxs, labels = self.assign_targets_to_proposals(
proposals, gt_boxes, gt_labels, assign_to="all")
# sample a fixed proportion of positive-negative proposals
sampled_inds = self.subsample(labels, sample_for="all") # size 512
all_proposals = proposals.copy()
matched_gt_boxes = []
num_images = len(proposals)
for img_id in range(num_images):
img_sampled_inds = sampled_inds[img_id]
all_proposals[img_id] = proposals[img_id][img_sampled_inds]
labels[img_id] = labels[img_id][img_sampled_inds]
matched_idxs[img_id] = matched_idxs[img_id][img_sampled_inds]
gt_boxes_in_image = gt_boxes[img_id].view(-1, 4)
if gt_boxes_in_image.numel() == 0:
gt_boxes_in_image = torch.zeros((1, 4),
dtype=dtype,
device=device)
matched_gt_boxes.append(gt_boxes_in_image[matched_idxs[img_id]])
regression_targets = self.box_coder.encode(matched_gt_boxes,
all_proposals)
# get matching gt indices for each proposal
_, sbj_labels = self.assign_targets_to_proposals(proposals,
gt_boxes,
gt_labels,
assign_to="subject")
sampled_inds = self.subsample(
sbj_labels, sample_for="subject") # 64 --> 32 pos, 32 neg
sbj_proposals = proposals.copy()
for img_id in range(num_images):
img_sampled_inds = sampled_inds[img_id]
sbj_proposals[img_id] = proposals[img_id][img_sampled_inds]
sbj_labels[img_id] = sbj_labels[img_id][img_sampled_inds]
pos_sbj_labels, pos_sbj_proposals = self.extract_positive_proposals(
sbj_labels, sbj_proposals)
# get matching gt indices for each proposal
_, obj_labels = self.assign_targets_to_proposals(proposals,
gt_boxes,
gt_labels,
assign_to="objects")
sampled_inds = self.subsample(
obj_labels, sample_for="object") # 64 --> 32 pos, 32 neg
obj_proposals = proposals.copy()
for img_id in range(num_images):
img_sampled_inds = sampled_inds[img_id]
obj_proposals[img_id] = proposals[img_id][img_sampled_inds]
obj_labels[img_id] = obj_labels[img_id][img_sampled_inds]
pos_obj_labels, pos_obj_proposals = self.extract_positive_proposals(
obj_labels, obj_proposals)
# prepare relation proposals
rlp_proposals = []
for img_id in range(num_images):
sbj_shape = pos_sbj_labels[img_id].shape[0]
obj_shape = pos_obj_labels[img_id].shape[0]
sbj_inds = np.repeat(np.arange(sbj_shape), obj_shape)
obj_inds = np.tile(np.arange(obj_shape), sbj_shape)
pos_sbj_labels[img_id] = pos_sbj_labels[img_id][sbj_inds]
pos_obj_labels[img_id] = pos_obj_labels[img_id][obj_inds]
pos_sbj_proposals[img_id] = pos_sbj_proposals[img_id][sbj_inds]
pos_obj_proposals[img_id] = pos_obj_proposals[img_id][obj_inds]
rlp_proposals.append(
box_utils.boxes_union(pos_obj_proposals[img_id],
pos_sbj_proposals[img_id]))
# assign gt_predicate to relation proposals
rlp_labels = self.assign_pred_to_rlp_proposals(pos_sbj_proposals,
pos_obj_proposals,
gt_boxes, gt_labels,
gt_preds)
# 128 --> 64 pos, 64 neg)
sampled_inds = self.subsample(rlp_labels, sample_for="rel")
for img_id in range(num_images):
img_sampled_inds = sampled_inds[img_id]
pos_sbj_proposals[img_id] = pos_sbj_proposals[img_id][
img_sampled_inds]
pos_obj_proposals[img_id] = pos_obj_proposals[img_id][
img_sampled_inds]
rlp_proposals[img_id] = rlp_proposals[img_id][img_sampled_inds]
pos_sbj_labels[
img_id] = pos_sbj_labels[img_id][img_sampled_inds] - 1
pos_obj_labels[
img_id] = pos_obj_labels[img_id][img_sampled_inds] - 1
rlp_labels[img_id] = rlp_labels[img_id][img_sampled_inds]
data_sbj = {'proposals': pos_sbj_proposals, 'labels': pos_sbj_labels}
data_obj = {'proposals': pos_obj_proposals, 'labels': pos_obj_labels}
data_rlp = {'proposals': rlp_proposals, 'labels': rlp_labels}
return all_proposals, matched_idxs, labels, regression_targets, data_sbj, data_obj, data_rlp
def eval_rel_results(all_results, output_dir, do_val):
if do_val:
if cfg.TEST.DATASETS[0].find('vg') >= 0:
prd_k_set = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20)
elif cfg.TEST.DATASETS[0].find('vrd') >= 0:
prd_k_set = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 70)
else:
prd_k_set = (1, 2, 3, 4, 5, 6, 7, 8, 9)
if cfg.TEST.DATASETS[0].find('vg') >= 0:
eval_sets = (False, )
else:
eval_sets = (False, True)
for phrdet in eval_sets:
eval_metric = 'phrdet' if phrdet else 'reldet'
print('{}:'.format(eval_metric))
for prd_k in prd_k_set:
print('prd_k = {}:'.format(prd_k))
recalls = {20: 0, 50: 0, 100: 0}
all_gt_cnt = 0
topk_dets = []
for im_i, res in enumerate(tqdm(all_results)):
# in oi_all_rel some images have no dets
if res['prd_scores'] is None:
det_boxes_s_top = np.zeros((0, 4), dtype=np.float32)
det_boxes_o_top = np.zeros((0, 4), dtype=np.float32)
det_labels_s_top = np.zeros(0, dtype=np.int32)
det_labels_p_top = np.zeros(0, dtype=np.int32)
det_labels_o_top = np.zeros(0, dtype=np.int32)
det_scores_top = np.zeros(0, dtype=np.float32)
else:
det_boxes_sbj = res['sbj_boxes'] # (#num_rel, 4)
det_boxes_obj = res['obj_boxes'] # (#num_rel, 4)
det_labels_sbj = res['sbj_labels'] # (#num_rel,)
det_labels_obj = res['obj_labels'] # (#num_rel,)
det_scores_sbj = res['sbj_scores'] # (#num_rel,)
det_scores_obj = res['obj_scores'] # (#num_rel,)
det_scores_prd = res['prd_scores'][:, 1:]
det_labels_prd = np.argsort(-det_scores_prd, axis=1)
det_scores_prd = -np.sort(-det_scores_prd, axis=1)
det_scores_so = det_scores_sbj * det_scores_obj
det_scores_spo = det_scores_so[:,
None] * det_scores_prd[:, :
prd_k]
# det_scores_spo = det_scores_prd[:, :prd_k]
det_scores_inds = argsort_desc(det_scores_spo)[:topk]
det_scores_top = det_scores_spo[det_scores_inds[:, 0],
det_scores_inds[:, 1]]
det_boxes_so_top = np.hstack(
(det_boxes_sbj[det_scores_inds[:, 0]],
det_boxes_obj[det_scores_inds[:, 0]]))
det_labels_p_top = det_labels_prd[det_scores_inds[:,
0],
det_scores_inds[:,
1]]
det_labels_spo_top = np.vstack(
(det_labels_sbj[det_scores_inds[:, 0]],
det_labels_p_top,
det_labels_obj[det_scores_inds[:,
0]])).transpose()
det_boxes_s_top = det_boxes_so_top[:, :4]
det_boxes_o_top = det_boxes_so_top[:, 4:]
det_labels_s_top = det_labels_spo_top[:, 0]
det_labels_p_top = det_labels_spo_top[:, 1]
det_labels_o_top = det_labels_spo_top[:, 2]
topk_dets.append(
dict(image=res['image'],
det_boxes_s_top=det_boxes_s_top,
det_boxes_o_top=det_boxes_o_top,
det_labels_s_top=det_labels_s_top,
det_labels_p_top=det_labels_p_top,
det_labels_o_top=det_labels_o_top,
det_scores_top=det_scores_top))
gt_boxes_sbj = res['gt_sbj_boxes'] # (#num_gt, 4)
gt_boxes_obj = res['gt_obj_boxes'] # (#num_gt, 4)
gt_labels_sbj = res['gt_sbj_labels'] # (#num_gt,)
gt_labels_obj = res['gt_obj_labels'] # (#num_gt,)
gt_labels_prd = res['gt_prd_labels'] # (#num_gt,)
gt_boxes_so = np.hstack((gt_boxes_sbj, gt_boxes_obj))
gt_labels_spo = np.vstack((gt_labels_sbj, gt_labels_prd,
gt_labels_obj)).transpose()
# Compute recall. It's most efficient to match once and then do recall after
# det_boxes_so_top is (#num_rel, 8)
# det_labels_spo_top is (#num_rel, 3)
if phrdet:
det_boxes_r_top = boxes_union(det_boxes_s_top,
det_boxes_o_top)
gt_boxes_r = boxes_union(gt_boxes_sbj, gt_boxes_obj)
pred_to_gt = _compute_pred_matches(gt_labels_spo,
det_labels_spo_top,
gt_boxes_r,
det_boxes_r_top,
phrdet=phrdet)
else:
pred_to_gt = _compute_pred_matches(gt_labels_spo,
det_labels_spo_top,
gt_boxes_so,
det_boxes_so_top,
phrdet=phrdet)
all_gt_cnt += gt_labels_spo.shape[0]
for k in recalls:
if len(pred_to_gt):
match = reduce(np.union1d, pred_to_gt[:k])
else:
match = []
recalls[k] += len(match)
topk_dets[-1].update(
dict(gt_boxes_sbj=gt_boxes_sbj,
gt_boxes_obj=gt_boxes_obj,
gt_labels_sbj=gt_labels_sbj,
gt_labels_obj=gt_labels_obj,
gt_labels_prd=gt_labels_prd))
for k in recalls:
recalls[k] = float(
recalls[k]) / (float(all_gt_cnt) + 1e-12)
print_stats(recalls)
else:
prd_k = 2
topk_dets = []
for im_i, res in enumerate(tqdm(all_results)):
# in oi_all_rel some images have no dets
if res['prd_scores'] is None:
det_boxes_s_top = np.zeros((0, 4), dtype=np.float32)
det_boxes_o_top = np.zeros((0, 4), dtype=np.float32)
det_labels_s_top = np.zeros(0, dtype=np.int32)
det_labels_p_top = np.zeros(0, dtype=np.int32)
det_labels_o_top = np.zeros(0, dtype=np.int32)
det_scores_top = np.zeros(0, dtype=np.float32)
else:
det_boxes_sbj = res['sbj_boxes'] # (#num_rel, 4)
det_boxes_obj = res['obj_boxes'] # (#num_rel, 4)
det_labels_sbj = res['sbj_labels'] # (#num_rel,)
det_labels_obj = res['obj_labels'] # (#num_rel,)
det_scores_sbj = res['sbj_scores'] # (#num_rel,)
det_scores_obj = res['obj_scores'] # (#num_rel,)
det_scores_prd = res['prd_scores'][:, 1:]
det_labels_prd = np.argsort(-det_scores_prd, axis=1)
det_scores_prd = -np.sort(-det_scores_prd, axis=1)
det_scores_so = det_scores_sbj * det_scores_obj
det_scores_spo = det_scores_so[:, None] * det_scores_prd[:, :
prd_k]
# det_scores_spo = det_scores_prd[:, :prd_k]
det_scores_inds = argsort_desc(det_scores_spo)[:topk]
det_scores_top = det_scores_spo[det_scores_inds[:, 0],
det_scores_inds[:, 1]]
det_boxes_so_top = np.hstack(
(det_boxes_sbj[det_scores_inds[:, 0]],
det_boxes_obj[det_scores_inds[:, 0]]))
det_labels_p_top = det_labels_prd[det_scores_inds[:, 0],
det_scores_inds[:, 1]]
det_labels_spo_top = np.vstack(
(det_labels_sbj[det_scores_inds[:, 0]], det_labels_p_top,
det_labels_obj[det_scores_inds[:, 0]])).transpose()
det_boxes_s_top = det_boxes_so_top[:, :4]
det_boxes_o_top = det_boxes_so_top[:, 4:]
det_labels_s_top = det_labels_spo_top[:, 0]
det_labels_p_top = det_labels_spo_top[:, 1]
det_labels_o_top = det_labels_spo_top[:, 2]
topk_dets.append(
dict(image=res['image'],
det_boxes_s_top=det_boxes_s_top,
det_boxes_o_top=det_boxes_o_top,
det_labels_s_top=det_labels_s_top,
det_labels_p_top=det_labels_p_top,
det_labels_o_top=det_labels_o_top,
det_scores_top=det_scores_top))
print('Saving topk dets...')
topk_dets_f = os.path.join(output_dir, 'rel_detections_topk.pkl')
with open(topk_dets_f, 'wb') as f:
pickle.dump(topk_dets, f, pickle.HIGHEST_PROTOCOL)
logger.info('topk_dets size: {}'.format(len(topk_dets)))
print('Done.')
def eval_rel_results(all_results, output_dir, do_val):
if cfg.TEST.DATASETS[0].find('vg') >= 0:
prd_k_set = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20)
elif cfg.TEST.DATASETS[0].find('vrd') >= 0:
prd_k_set = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 70)
else:
prd_k_set = (1, 2, 3, 4, 5, 6, 7, 8, 9)
if cfg.TEST.DATASETS[0].find('vg') >= 0:
eval_sets = (False, )
else:
eval_sets = (False, True)
prd_k_set = (1, 2)
for phrdet in eval_sets:
eval_metric = 'phrdet' if phrdet else 'reldet'
print('{}:'.format(eval_metric))
for prd_k in prd_k_set:
print('prd_k = {}:'.format(prd_k))
recalls = {20: 0, 50: 0, 100: 0}
if do_val:
all_gt_cnt = 0
topk_dets = []
for im_i, res in enumerate(tqdm(all_results)):
# in oi_all_rel some images have no dets
if res['prd_scores'] is None:
det_boxes_s_top = np.zeros((0, 4), dtype=np.float32)
det_boxes_o_top = np.zeros((0, 4), dtype=np.float32)
det_labels_s_top = np.zeros(0, dtype=np.int32)
det_labels_p_top = np.zeros(0, dtype=np.int32)
det_labels_o_top = np.zeros(0, dtype=np.int32)
det_scores_top = np.zeros(0, dtype=np.float32)
else:
out_dict = {}
det_boxes_sbj = res['sbj_boxes'] # (#num_rel, 4)
#print('det_boxes_sbj:')
#print(det_boxes_sbj)
out_dict['det_boxes_sbj'] = det_boxes_sbj
det_boxes_obj = res['obj_boxes'] # (#num_rel, 4)
#print('det_boxes_obj:')
#print(det_boxes_obj)
out_dict['det_boxes_obj'] = det_boxes_obj
det_labels_sbj = res['sbj_labels'] # (#num_rel,)
#print('det_labels_sbj:')
#print(det_labels_sbj)
out_dict['det_labels_sbj'] = det_labels_sbj
det_labels_obj = res['obj_labels'] # (#num_rel,)
#print('det_labels_obj:')
#print(det_labels_obj)
out_dict['det_labels_obj'] = det_labels_obj
det_scores_sbj = res['sbj_scores'] # (#num_rel,)
#print('det_scores_sbj:')
#print(det_scores_sbj)
out_dict['det_scores_sbj'] = det_scores_sbj
det_scores_obj = res['obj_scores'] # (#num_rel,)
#print('det_scores_obj:')
#print(det_scores_obj)
out_dict['det_scores_obj'] = det_scores_obj
det_scores_prd = res['prd_scores'][:, 1:]
#print('det_scores_prd:')
#print(det_scores_prd)
out_dict['det_scores_prd'] = det_scores_prd
det_labels_prd = np.argsort(-det_scores_prd, axis=1)
det_scores_prd = -np.sort(-det_scores_prd, axis=1)
det_scores_so = det_scores_sbj * det_scores_obj
det_scores_spo = det_scores_so[:,
None] * det_scores_prd[:, :
prd_k]
# det_scores_spo = det_scores_prd[:, :prd_k]
det_scores_inds = argsort_desc(det_scores_spo)[:topk]
det_scores_top = det_scores_spo[det_scores_inds[:, 0],
det_scores_inds[:, 1]]
det_boxes_so_top = np.hstack(
(det_boxes_sbj[det_scores_inds[:, 0]],
det_boxes_obj[det_scores_inds[:, 0]]))
det_labels_p_top = det_labels_prd[det_scores_inds[:, 0],
det_scores_inds[:, 1]]
det_labels_spo_top = np.vstack(
(det_labels_sbj[det_scores_inds[:,
0]], det_labels_p_top,
det_labels_obj[det_scores_inds[:, 0]])).transpose()
# cand_inds = np.where(det_scores_top > cfg.TEST.SPO_SCORE_THRESH)[0]
# det_boxes_so_top = det_boxes_so_top[cand_inds]
# det_labels_spo_top = det_labels_spo_top[cand_inds]
# det_scores_top = det_scores_top[cand_inds]
det_boxes_s_top = det_boxes_so_top[:, :4]
det_boxes_o_top = det_boxes_so_top[:, 4:]
det_labels_s_top = det_labels_spo_top[:, 0]
det_labels_p_top = det_labels_spo_top[:, 1]
det_labels_o_top = det_labels_spo_top[:, 2]
out_dict = {}
out_dict['boxes_s_top'] = det_boxes_s_top
out_dict['boxes_o_top'] = det_boxes_o_top
out_dict['labels_s_top'] = det_labels_s_top
out_dict['labels_p_top'] = det_labels_p_top
out_dict['labels_o_top'] = det_labels_o_top
out_dict['scores_top'] = det_scores_top
out_dict['image'] = res['image']
with open('VRD-{}-{}.pkl'.format(im_i, prd_k),
'wb') as fout:
pickle.dump(out_dict, fout, pickle.HIGHEST_PROTOCOL)
topk_dets.append(
dict(image=res['image'],
det_boxes_s_top=det_boxes_s_top,
det_boxes_o_top=det_boxes_o_top,
det_labels_s_top=det_labels_s_top,
det_labels_p_top=det_labels_p_top,
det_labels_o_top=det_labels_o_top,
det_scores_top=det_scores_top))
if do_val:
gt_boxes_sbj = res['gt_sbj_boxes'] # (#num_gt, 4)
gt_boxes_obj = res['gt_obj_boxes'] # (#num_gt, 4)
gt_labels_sbj = res['gt_sbj_labels'] # (#num_gt,)
gt_labels_obj = res['gt_obj_labels'] # (#num_gt,)
gt_labels_prd = res['gt_prd_labels'] # (#num_gt,)
gt_boxes_so = np.hstack((gt_boxes_sbj, gt_boxes_obj))
gt_labels_spo = np.vstack((gt_labels_sbj, gt_labels_prd,
gt_labels_obj)).transpose()
# Compute recall. It's most efficient to match once and then do recall after
# det_boxes_so_top is (#num_rel, 8)
# det_labels_spo_top is (#num_rel, 3)
if phrdet:
det_boxes_r_top = boxes_union(det_boxes_s_top,
det_boxes_o_top)
gt_boxes_r = boxes_union(gt_boxes_sbj, gt_boxes_obj)
pred_to_gt = _compute_pred_matches(gt_labels_spo,
det_labels_spo_top,
gt_boxes_r,
det_boxes_r_top,
phrdet=phrdet)
else:
pred_to_gt = _compute_pred_matches(gt_labels_spo,
det_labels_spo_top,
gt_boxes_so,
det_boxes_so_top,
phrdet=phrdet)
all_gt_cnt += gt_labels_spo.shape[0]
for k in recalls:
if len(pred_to_gt):
match = reduce(np.union1d, pred_to_gt[:k])
else:
match = []
recalls[k] += len(match)
topk_dets[-1].update(
dict(gt_boxes_sbj=gt_boxes_sbj,
gt_boxes_obj=gt_boxes_obj,
gt_labels_sbj=gt_labels_sbj,
gt_labels_obj=gt_labels_obj,
gt_labels_prd=gt_labels_prd))
if do_val:
for k in recalls:
recalls[k] = float(
recalls[k]) / (float(all_gt_cnt) + 1e-12)
print_stats(recalls)
def prepare_mAP_dets(topk_dets, cls_num):
cls_image_ids = [[] for _ in range(cls_num)]
cls_dets = [{
'confidence': np.empty(0),
'BB_s': np.empty((0, 4)),
'BB_o': np.empty((0, 4)),
'BB_r': np.empty((0, 4)),
'LBL_s': np.empty(0),
'LBL_o': np.empty(0)
} for _ in range(cls_num)]
cls_gts = [{} for _ in range(cls_num)]
npos = [0 for _ in range(cls_num)]
for dets in tqdm(topk_dets):
image_id = dets['image'].split('/')[-1].split('.')[0]
sbj_boxes = dets['det_boxes_s_top']
obj_boxes = dets['det_boxes_o_top']
rel_boxes = boxes_union(sbj_boxes, obj_boxes)
sbj_labels = dets['det_labels_s_top']
obj_labels = dets['det_labels_o_top']
prd_labels = dets['det_labels_p_top']
det_scores = dets['det_scores_top']
gt_boxes_sbj = dets['gt_boxes_sbj']
gt_boxes_obj = dets['gt_boxes_obj']
gt_boxes_rel = boxes_union(gt_boxes_sbj, gt_boxes_obj)
gt_labels_sbj = dets['gt_labels_sbj']
gt_labels_prd = dets['gt_labels_prd']
gt_labels_obj = dets['gt_labels_obj']
for c in range(cls_num):
cls_inds = np.where(prd_labels == c)[0]
if len(cls_inds):
cls_sbj_boxes = sbj_boxes[cls_inds]
cls_obj_boxes = obj_boxes[cls_inds]
cls_rel_boxes = rel_boxes[cls_inds]
cls_sbj_labels = sbj_labels[cls_inds]
cls_obj_labels = obj_labels[cls_inds]
cls_det_scores = det_scores[cls_inds]
cls_dets[c]['confidence'] = np.concatenate(
(cls_dets[c]['confidence'], cls_det_scores))
cls_dets[c]['BB_s'] = np.concatenate(
(cls_dets[c]['BB_s'], cls_sbj_boxes), 0)
cls_dets[c]['BB_o'] = np.concatenate(
(cls_dets[c]['BB_o'], cls_obj_boxes), 0)
cls_dets[c]['BB_r'] = np.concatenate(
(cls_dets[c]['BB_r'], cls_rel_boxes), 0)
cls_dets[c]['LBL_s'] = np.concatenate(
(cls_dets[c]['LBL_s'], cls_sbj_labels))
cls_dets[c]['LBL_o'] = np.concatenate(
(cls_dets[c]['LBL_o'], cls_obj_labels))
cls_image_ids[c] += [image_id] * len(cls_inds)
cls_gt_inds = np.where(gt_labels_prd == c)[0]
cls_gt_boxes_sbj = gt_boxes_sbj[cls_gt_inds]
cls_gt_boxes_obj = gt_boxes_obj[cls_gt_inds]
cls_gt_boxes_rel = gt_boxes_rel[cls_gt_inds]
cls_gt_labels_sbj = gt_labels_sbj[cls_gt_inds]
cls_gt_labels_obj = gt_labels_obj[cls_gt_inds]
cls_gt_num = len(cls_gt_inds)
det = [False] * cls_gt_num
npos[c] = npos[c] + cls_gt_num
cls_gts[c][image_id] = {
'gt_boxes_sbj': cls_gt_boxes_sbj,
'gt_boxes_obj': cls_gt_boxes_obj,
'gt_boxes_rel': cls_gt_boxes_rel,
'gt_labels_sbj': cls_gt_labels_sbj,
'gt_labels_obj': cls_gt_labels_obj,
'gt_num': cls_gt_num,
'det': det
}
return cls_image_ids, cls_dets, cls_gts, npos