def recall_torch(proposals, gt_boxes, ot):
if proposals.nelement() == 0:
return 0.0
overlap = bbox_overlaps(proposals, gt_boxes)
vals, inds = overlap.max(dim=1)
i = vals >= ot
covered = my_unique(inds[i])
recall = float(covered) / float(gt_boxes.size(0))
return recall
def postprocessing(features, loader, args, model):
score_nms_overlap = args.score_nms_overlap #For wordness scores
score_threshold = args.score_threshold
overlap_thresholds = args.overlap_thresholds
num_queries = args.num_queries
all_gt_boxes = []
joint_boxes = []
log = []
qbs_queries, qbs_qtargets = loader.dataset.get_queries(tensorize=True)
qbe_queries, gt_targets = [], []
for li, data in enumerate(loader):
qbe_queries.append(features[li][4].numpy())
gt_targets.append(torch.squeeze(data[5]).numpy())
qbe_queries, qbe_qtargets, _ = loader.dataset.dataset_query_filter(
qbe_queries, gt_targets, gt_targets, tensorize=True)
gt_targets = torch.from_numpy(np.concatenate(gt_targets, axis=0))
if num_queries < 1:
num_queries = len(qbe_queries) + len(qbs_queries) + 1
qbe_queries = qbe_queries[:num_queries]
qbs_queries = qbs_queries[:num_queries]
qbe_qtargets = qbe_qtargets[:num_queries]
qbs_qtargets = qbs_qtargets[:num_queries]
max_overlaps, amax_overlaps = [], []
overlaps = []
all_gt_boxes = []
db_targets = []
db = []
joint_boxes = []
log = []
offset = [0, 0]
n_gt = 0
for li, data in enumerate(loader):
roi_scores, eproposal_scores, proposals, embeddings, gt_embed, eproposal_embed = features[
li]
(img, oshape, gt_boxes, external_proposals, gt_embeddings,
gt_labels) = data
#boxes are xcycwh from dataloader, convert to x1y1x2y2
external_proposals = box_utils.xcycwh_to_x1y1x2y2(
external_proposals[0].float())
gt_boxes = box_utils.xcycwh_to_x1y1x2y2(gt_boxes[0].float())
img = torch.squeeze(img)
gt_boxes = torch.squeeze(gt_boxes)
all_gt_boxes.append(gt_boxes)
gt_labels = torch.squeeze(gt_labels)
gt_embeddings = torch.squeeze(gt_embeddings)
gt_boxes = gt_boxes.cuda()
gt_embeddings = gt_embeddings.cuda()
gt_labels = gt_labels.cuda()
roi_scores = roi_scores.cuda()
eproposal_scores = eproposal_scores.cuda()
eproposal_embed = eproposal_embed.cuda()
proposals = proposals.cuda()
embeddings = embeddings.cuda()
gt_embed = gt_embed.cuda()
external_proposals = external_proposals.cuda()
#convert to probabilities with sigmoid
scores = 1 / (1 + torch.exp(-roi_scores))
if args.use_external_proposals:
eproposal_scores = 1 / (1 + torch.exp(-eproposal_scores))
scores = torch.cat((scores, eproposal_scores), 0)
proposals = torch.cat((proposals, external_proposals), 0)
embeddings = torch.cat((embeddings, eproposal_embed), 0)
#calculate the different recalls before NMS
entry = {}
recalls(proposals, gt_boxes, overlap_thresholds, entry, '1_total')
#Since slicing empty array doesn't work in torch, we need to do this explicitly
if args.use_external_proposals:
nrpn = len(roi_scores)
rpn_proposals = proposals[:nrpn]
dtp_proposals = proposals[nrpn:]
recalls(dtp_proposals, gt_boxes, overlap_thresholds, entry,
'1_dtp')
recalls(rpn_proposals, gt_boxes, overlap_thresholds, entry,
'1_rpn')
threshold_pick = torch.squeeze(scores > score_threshold)
scores = scores[threshold_pick]
tmp = threshold_pick.view(-1, 1).expand(threshold_pick.size(0), 4)
proposals = proposals[tmp].view(-1, 4)
embeddings = embeddings[threshold_pick.view(-1, 1).expand(
threshold_pick.size(0),
embeddings.size(1))].view(-1, embeddings.size(1))
recalls(proposals, gt_boxes, overlap_thresholds, entry, '2_total')
if args.use_external_proposals:
rpn_proposals = rpn_proposals[tmp[:nrpn]].view(-1, 4)
dtp_proposals = dtp_proposals[tmp[nrpn:]].view(-1, 4)
recalls(dtp_proposals, gt_boxes, overlap_thresholds, entry,
'2_dtp')
recalls(rpn_proposals, gt_boxes, overlap_thresholds, entry,
'2_rpn')
# print proposals.shape, scores.shape
dets = torch.cat([proposals.float(), scores.unsqueeze(1)], 1)
if dets.size(0) <= 1:
continue
pick = box_utils.nms(dets, score_nms_overlap, args.nms_max_boxes)
tt = torch.zeros(len(dets)).byte().cuda()
tt[pick] = 1
proposals = proposals[pick]
embeddings = embeddings[pick]
scores = scores[pick]
recalls(proposals, gt_boxes, overlap_thresholds, entry, '3_total')
if args.use_external_proposals:
nrpn = rpn_proposals.size(0)
tmp = tt.view(-1, 1).expand(tt.size(0), 4)
rpn_proposals = rpn_proposals[tmp[:nrpn]].view(-1, 4)
dtp_proposals = dtp_proposals[tmp[nrpn:]].view(-1, 4)
recalls(dtp_proposals, gt_boxes, overlap_thresholds, entry,
'3_dtp')
recalls(rpn_proposals, gt_boxes, overlap_thresholds, entry,
'3_rpn')
overlap = bbox_overlaps(proposals, gt_boxes)
overlaps.append(overlap)
max_gt_overlap, amax_gt_overlap = overlap.max(dim=1)
proposal_labels = torch.Tensor([gt_labels[i] for i in amax_gt_overlap])
proposal_labels = proposal_labels.cuda()
mask = overlap.sum(dim=1) == 0
proposal_labels[mask] = loader.dataset.get_vocab_size() + 1
max_overlaps.append(max_gt_overlap)
amax_overlaps.append(amax_gt_overlap + n_gt)
n_gt += len(gt_boxes)
# Artificially make a huge image containing all the boxes to be able to
# perform nms on distance to query
proposals[:, 0] += offset[1]
proposals[:, 1] += offset[0]
proposals[:, 2] += offset[1]
proposals[:, 3] += offset[0]
joint_boxes.append(proposals)
offset[0] += img.shape[0]
offset[1] += img.shape[1]
db_targets.append(proposal_labels)
db.append(embeddings)
log.append(entry)
db = torch.cat(db, dim=0)
db_targets = torch.cat(db_targets, dim=0)
joint_boxes = torch.cat(joint_boxes, dim=0)
max_overlaps = torch.cat(max_overlaps, dim=0)
amax_overlaps = torch.cat(amax_overlaps, dim=0)
all_gt_boxes = torch.cat(all_gt_boxes, dim=0)
assert qbe_queries.shape[0] == qbe_qtargets.shape[0]
assert qbs_queries.shape[0] == qbs_qtargets.shape[0]
assert db.shape[0] == db_targets.shape[0]
qbe_queries = qbe_queries.cuda()
qbs_queries = qbs_queries.cuda()
qbe_dists = pairwise_cosine_distances(qbe_queries, db)
qbs_dists = pairwise_cosine_distances(qbs_queries, db)
qbe_dists = qbe_dists.cpu()
qbs_dists = qbs_dists.cpu()
db_targets = db_targets.cpu()
joint_boxes = joint_boxes.cpu()
max_overlaps = max_overlaps.cpu()
amax_overlaps = amax_overlaps.cpu()
gt_targets = gt_targets.numpy()
qbs_qtargets = qbs_qtargets.numpy()
qbe_qtargets = qbe_qtargets.numpy()
qbe_dists = qbe_dists.numpy()
qbs_dists = qbs_dists.numpy()
db_targets = db_targets.numpy()
joint_boxes = joint_boxes.numpy()
max_overlaps = max_overlaps.numpy()
amax_overlaps = amax_overlaps.numpy()
#A hack for some printing compatability
if not args.use_external_proposals:
keys = []
for i in range(1, 4):
for ot in args.overlap_thresholds:
keys += [
'%d_dtp_recall_%d' % (i, ot * 100),
'%d_rpn_recall_%d' % (i, ot * 100)
]
for entry in log:
for key in keys:
if not entry.has_key(key):
entry[key] = entry['1_total_recall_50']
return (qbe_dists, qbe_qtargets, qbs_dists, qbs_qtargets, db_targets,
gt_targets, joint_boxes, max_overlaps, amax_overlaps, log)
def postprocessing_dtp(features, loader, args, model):
score_nms_overlap = args.score_nms_overlap #For wordness scores
score_threshold = args.score_threshold
overlap_thresholds = args.overlap_thresholds
num_queries = args.num_queries
all_gt_boxes = []
joint_boxes = []
qbs_queries, qbs_qtargets = loader.dataset.get_queries(tensorize=True)
qbe_queries, gt_targets = [], []
for li, data in enumerate(loader):
qbe_queries.append(features[li][1].numpy())
gt_targets.append(torch.squeeze(data[5]).numpy())
qbe_queries, qbe_qtargets, _ = loader.dataset.dataset_query_filter(
qbe_queries, gt_targets, gt_targets, tensorize=True)
gt_targets = torch.from_numpy(np.concatenate(gt_targets, axis=0))
if num_queries < 1:
num_queries = len(qbe_queries) + len(qbs_queries) + 1
qbe_queries = qbe_queries[:num_queries]
qbs_queries = qbs_queries[:num_queries]
qbe_qtargets = qbe_qtargets[:num_queries]
qbs_qtargets = qbs_qtargets[:num_queries]
max_overlaps, amax_overlaps = [], []
prop2img, recalls = [], []
all_proposals = []
overlaps = []
all_gt_boxes = []
db_targets = []
db = []
joint_boxes = []
offset = [0, 0]
n_gt = 0
for li, data in enumerate(loader):
scores, gt_embed, embeddings = features[li]
(img, oshape, gt_boxes, dtp_proposals, gt_embeddings, gt_labels) = data
#boxes are xcycwh from dataloader, convert to x1y1x2y2
dtp_proposals = box_utils.xcycwh_to_x1y1x2y2(
dtp_proposals[0].float()) #.round()#.int()
gt_boxes = box_utils.xcycwh_to_x1y1x2y2(
gt_boxes[0].float()) #.round()#.int()
img = torch.squeeze(img)
gt_boxes = torch.squeeze(gt_boxes)
all_gt_boxes.append(gt_boxes)
gt_labels = torch.squeeze(gt_labels)
gt_embeddings = torch.squeeze(gt_embeddings)
gt_boxes = gt_boxes.cuda()
gt_embeddings = gt_embeddings.cuda()
gt_labels = gt_labels.cuda()
scores = scores.cuda()
embeddings = embeddings.cuda()
gt_embed = gt_embed.cuda()
dtp_proposals = dtp_proposals.cuda()
#convert to probabilities with sigmoid
scores = 1 / (1 + torch.exp(-scores))
#Scale boxes back to original size
sh, sw = img.shape
img_shape = np.array([oshape[0], oshape[1]])
scale = float(max(img_shape)) / max(sh, sw)
scale *= 2
dtp_proposals = torch.round((dtp_proposals - 1) * scale + 1).int()
gt_boxes = torch.round((gt_boxes - 1) * scale + 1).int()
threshold_pick = torch.squeeze(scores > score_threshold)
scores = scores[threshold_pick]
tmp = threshold_pick.view(-1, 1).expand(threshold_pick.size(0), 4)
dtp_proposals = dtp_proposals[tmp].view(-1, 4)
embeddings = embeddings[threshold_pick.view(-1, 1).expand(
threshold_pick.size(0),
embeddings.size(1))].view(-1, embeddings.size(1))
dets = torch.cat([dtp_proposals.float(), scores], 1)
pick = box_utils.nms(dets, score_nms_overlap, args.nms_max_boxes)
tt = torch.zeros(len(dets)).byte().cuda()
tt[pick] = 1
dtp_proposals = dtp_proposals[pick]
embeddings = embeddings[pick]
scores = scores[pick]
r1 = recall_torch(dtp_proposals.float(), gt_boxes.float(), 0.25)
r2 = recall_torch(dtp_proposals.float(), gt_boxes.float(), 0.5)
recalls.append((r1, r2))
for kk in range(len(dtp_proposals)):
prop2img.append(li)
all_proposals.append(dtp_proposals.cpu())
overlap = bbox_overlaps(dtp_proposals, gt_boxes)
overlaps.append(overlap)
max_gt_overlap, amax_gt_overlap = overlap.max(dim=1)
proposal_labels = torch.Tensor([gt_labels[i] for i in amax_gt_overlap])
proposal_labels = proposal_labels.cuda()
mask = overlap.sum(dim=1) == 0
proposal_labels[mask] = loader.dataset.get_vocab_size() + 1
max_overlaps.append(max_gt_overlap)
amax_overlaps.append(amax_gt_overlap + n_gt)
n_gt += len(gt_boxes)
# Artificially make a huge image containing all the boxes to be able to
# perform nms on distance to query
dtp_proposals[:, 0] += offset[1]
dtp_proposals[:, 1] += offset[0]
dtp_proposals[:, 2] += offset[1]
dtp_proposals[:, 3] += offset[0]
joint_boxes.append(dtp_proposals)
offset[0] += img.shape[0]
offset[1] += img.shape[1]
db_targets.append(proposal_labels)
db.append(embeddings)
db = torch.cat(db, dim=0)
db_targets = torch.cat(db_targets, dim=0)
joint_boxes = torch.cat(joint_boxes, dim=0)
max_overlaps = torch.cat(max_overlaps, dim=0)
amax_overlaps = torch.cat(amax_overlaps, dim=0)
all_gt_boxes = torch.cat(all_gt_boxes, dim=0)
assert qbe_queries.shape[0] == qbe_qtargets.shape[0]
assert qbs_queries.shape[0] == qbs_qtargets.shape[0]
assert db.shape[0] == db_targets.shape[0]
qbe_queries = qbe_queries.cuda()
qbs_queries = qbs_queries.cuda()
# print qbe_queries.shape, db.shape, qbs_queries.shape
qbe_dists = pairwise_cosine_distances(qbe_queries, db)
qbs_dists = pairwise_cosine_distances(qbs_queries, db)
if args.mAP_gpu:
gt_targets = gt_targets.cuda()
qbs_qtargets = qbs_qtargets.cuda()
qbe_qtargets = qbe_qtargets.cuda()
else:
qbe_dists = qbe_dists.cpu()
qbs_dists = qbs_dists.cpu()
db_targets = db_targets.cpu()
joint_boxes = joint_boxes.cpu()
max_overlaps = max_overlaps.cpu()
amax_overlaps = amax_overlaps.cpu()
if args.mAP_numpy:
gt_targets = gt_targets.numpy()
qbs_qtargets = qbs_qtargets.numpy()
qbe_qtargets = qbe_qtargets.numpy()
qbe_dists = qbe_dists.numpy()
qbs_dists = qbs_dists.numpy()
db_targets = db_targets.numpy()
joint_boxes = joint_boxes.numpy()
max_overlaps = max_overlaps.numpy()
amax_overlaps = amax_overlaps.numpy()
prop2img = np.array(prop2img)
recalls = np.array(recalls)
return (qbe_dists, qbe_qtargets, qbs_dists, qbs_qtargets, db_targets,
gt_targets, prop2img, joint_boxes, all_proposals, recalls,
max_overlaps, amax_overlaps)
def postprocessing(features, loader, args, model):
score_nms_overlap = args.score_nms_overlap #For wordness scores
score_threshold = args.score_threshold
overlap_thresholds = args.overlap_thresholds
num_queries = args.num_queries
all_gt_boxes = []
joint_boxes = []
log = []
qbs_queries, qbs_qtargets = loader.dataset.get_queries(tensorize=True)
qbe_queries, gt_targets = [], []
for li, data in enumerate(loader):
qbe_queries.append(features[li][1].numpy())
gt_targets.append(torch.squeeze(data[5]).numpy())
qbe_queries, qbe_qtargets = loader.dataset.dataset_query_filter(qbe_queries, gt_targets, tensorize=True)
gt_targets = torch.from_numpy(np.concatenate(gt_targets, axis=0))
if num_queries < 1:
num_queries = len(qbe_queries) + len(qbs_queries) + 1
qbe_queries = qbe_queries[:num_queries]
qbs_queries = qbs_queries[:num_queries]
qbe_qtargets = qbe_qtargets[:num_queries]
qbs_qtargets = qbs_qtargets[:num_queries]
max_overlaps, amax_overlaps = [], []
overlaps = []
all_gt_boxes = []
db_targets = []
db = []
joint_boxes = []
log = []
offset = [0, 0]
n_gt = 0
for li, data in enumerate(loader):
scores, gt_embed, embeddings = features[li]
(img, oshape, gt_boxes, dtp_proposals, gt_embeddings, gt_labels) = data
#boxes are xcycwh from dataloader, convert to x1y1x2y2
dtp_proposals = box_utils.xcycwh_to_x1y1x2y2(dtp_proposals[0].float())#.round()#.int()
gt_boxes = box_utils.xcycwh_to_x1y1x2y2(gt_boxes[0].float())#.round()#.int()
img = torch.squeeze(img)
gt_boxes = torch.squeeze(gt_boxes)
all_gt_boxes.append(gt_boxes)
gt_labels = torch.squeeze(gt_labels)
gt_embeddings = torch.squeeze(gt_embeddings)
gt_boxes = gt_boxes.cuda()
gt_embeddings = gt_embeddings.cuda()
gt_labels = gt_labels.cuda()
scores = scores.cuda()
embeddings = embeddings.cuda()
gt_embed = gt_embed.cuda()
dtp_proposals = dtp_proposals.cuda()
#convert to probabilities with sigmoid
scores = 1 / (1 + torch.exp(-scores))
#calculate the different recalls before NMS
entry = {}
recalls(dtp_proposals, gt_boxes, overlap_thresholds, entry, '1_total')
threshold_pick = torch.squeeze(scores > score_threshold)
scores = scores[threshold_pick]
tmp = threshold_pick.view(-1, 1).expand(threshold_pick.size(0), 4)
dtp_proposals = dtp_proposals[tmp].view(-1, 4)
embeddings = embeddings[threshold_pick.view(-1, 1).expand(threshold_pick.size(0), embeddings.size(1))].view(-1, embeddings.size(1))
recalls(dtp_proposals, gt_boxes, overlap_thresholds, entry, '2_total')
dets = torch.cat([dtp_proposals.float(), scores], 1)
pick = box_utils.nms(dets, score_nms_overlap)
tt = torch.zeros(len(dets)).byte().cuda()
tt[pick] = 1
dtp_proposals = dtp_proposals[pick]
embeddings = embeddings[pick]
scores = scores[pick]
recalls(dtp_proposals, gt_boxes, overlap_thresholds, entry, '3_total')
overlap = bbox_overlaps(dtp_proposals, gt_boxes)
overlaps.append(overlap)
max_gt_overlap, amax_gt_overlap = overlap.max(dim=1)
proposal_labels = torch.Tensor([gt_labels[i] for i in amax_gt_overlap])
proposal_labels = proposal_labels.cuda()
mask = overlap.sum(dim=1) == 0
proposal_labels[mask] = loader.dataset.get_vocab_size() + 1
max_overlaps.append(max_gt_overlap)
amax_overlaps.append(amax_gt_overlap + n_gt)
n_gt += len(gt_boxes)
# Artificially make a huge image containing all the boxes to be able to
# perform nms on distance to query
dtp_proposals[:, 0] += offset[1]
dtp_proposals[:, 1] += offset[0]
dtp_proposals[:, 2] += offset[1]
dtp_proposals[:, 3] += offset[0]
joint_boxes.append(dtp_proposals)
offset[0] += img.shape[0]
offset[1] += img.shape[1]
db_targets.append(proposal_labels)
db.append(embeddings)
log.append(entry)
db = torch.cat(db, dim=0)
db_targets = torch.cat(db_targets, dim=0)
joint_boxes = torch.cat(joint_boxes, dim=0)
max_overlaps = torch.cat(max_overlaps, dim=0)
amax_overlaps = torch.cat(amax_overlaps, dim=0)
all_gt_boxes = torch.cat(all_gt_boxes, dim=0)
assert qbe_queries.shape[0] == qbe_qtargets.shape[0]
assert qbs_queries.shape[0] == qbs_qtargets.shape[0]
assert db.shape[0] == db_targets.shape[0]
qbe_queries = qbe_queries.cuda()
qbs_queries = qbs_queries.cuda()
qbe_dists = pairwise_cosine_distances(qbe_queries, db)
qbs_dists = pairwise_cosine_distances(qbs_queries, db)
qbe_dists = qbe_dists.cpu()
qbs_dists = qbs_dists.cpu()
db_targets = db_targets.cpu()
joint_boxes = joint_boxes.cpu()
max_overlaps = max_overlaps.cpu()
amax_overlaps = amax_overlaps.cpu()
gt_targets = gt_targets.numpy()
qbs_qtargets = qbs_qtargets.numpy()
qbe_qtargets = qbe_qtargets.numpy()
qbe_dists = qbe_dists.numpy()
qbs_dists = qbs_dists.numpy()
db_targets = db_targets.numpy()
joint_boxes = joint_boxes.numpy()
max_overlaps = max_overlaps.numpy()
amax_overlaps = amax_overlaps.numpy()
return (qbe_dists, qbe_qtargets, qbs_dists, qbs_qtargets, db_targets, gt_targets,
joint_boxes, max_overlaps, amax_overlaps, log)
def postprocessing(features, loader, args, model):
score_nms_overlap = args.score_nms_overlap #For wordness scores
score_threshold = args.score_threshold
qbs_queries, qbs_qtargets = loader.dataset.get_queries(tensorize=True)
qbe_queries, gt_targets = [], []
for li, data in enumerate(loader):
qbe_queries.append(features[li][4].numpy())
gt_targets.append(torch.squeeze(data[5]).numpy())
qbe_queries, qbe_qtargets, _ = loader.dataset.dataset_query_filter(
qbe_queries, gt_targets, gt_targets, tensorize=True)
gt_targets = torch.from_numpy(np.concatenate(gt_targets, axis=0))
all_proposals, log, joint_boxes, db, all_gt_boxes = [], [], [], [], []
prop2img, recalls = [], []
db_targets, max_overlaps, amax_overlaps = [], [], []
n_gt = 0
overlaps = []
offset = [0, 0]
li = 0
for data in loader:
roi_scores, eproposal_scores, proposals, embeddings, gt_embed, eproposal_embed = features[
li]
(img, oshape, gt_boxes, external_proposals, gt_embeddings,
gt_labels) = data
#boxes are xcycwh from dataloader, convert to x1y1x2y2
external_proposals = box_utils.xcycwh_to_x1y1x2y2(
external_proposals[0].float()) #.round()#.int()
gt_boxes = box_utils.xcycwh_to_x1y1x2y2(
gt_boxes[0].float()) #.round()#.int()
img = torch.squeeze(img)
gt_boxes = torch.squeeze(gt_boxes)
all_gt_boxes.append(gt_boxes)
gt_labels = torch.squeeze(gt_labels)
gt_embeddings = torch.squeeze(gt_embeddings)
gt_boxes = gt_boxes.cuda()
gt_embeddings = gt_embeddings.cuda()
gt_labels = gt_labels.cuda()
roi_scores = roi_scores.cuda()
eproposal_scores = eproposal_scores.cuda()
eproposal_embed = eproposal_embed.cuda()
proposals = proposals.cuda()
embeddings = embeddings.cuda()
gt_embed = gt_embed.cuda()
external_proposals = external_proposals.cuda()
#convert to probabilities with sigmoid
scores = 1 / (1 + torch.exp(-roi_scores))
if args.use_external_proposals:
eproposal_scores = 1 / (1 + torch.exp(-eproposal_scores))
scores = torch.cat((scores, eproposal_scores), 0)
proposals = torch.cat((proposals, external_proposals), 0)
embeddings = torch.cat((embeddings, eproposal_embed), 0)
#Scale boxes back to original size
sh, sw = img.shape
img_shape = np.array([oshape[0], oshape[1]])
scale = float(max(img_shape)) / max(sh, sw)
scale *= 2
proposals = torch.round((proposals - 1) * scale + 1).int()
gt_boxes = torch.round((gt_boxes - 1) * scale + 1).int()
#calculate the different recalls before NMS
entry = {}
#Since slicing empty array doesn't work in torch, we need to do this explicitly
threshold_pick = torch.squeeze(scores > score_threshold)
scores = scores[threshold_pick]
tmp = threshold_pick.view(-1, 1).expand(threshold_pick.size(0), 4)
proposals = proposals[tmp].view(-1, 4)
embeddings = embeddings[threshold_pick.view(-1, 1).expand(
threshold_pick.size(0),
embeddings.size(1))].view(-1, embeddings.size(1))
dets = torch.cat([proposals.float(), scores.view(-1, 1)], 1)
if dets.size(0) <= 1:
continue
pick = box_utils.nms(dets, score_nms_overlap)
tt = torch.zeros(len(dets)).byte().cuda()
tt[pick] = 1
proposals = proposals[pick]
embeddings = embeddings[pick]
scores = scores[pick]
r1 = recall_torch(proposals.float(), gt_boxes.float(), 0.25)
r2 = recall_torch(proposals.float(), gt_boxes.float(), 0.5)
recalls.append((r1, r2))
for kk in range(len(proposals)):
prop2img.append(li)
all_proposals.append(proposals.cpu())
overlap = bbox_overlaps(proposals, gt_boxes)
overlaps.append(overlap)
max_gt_overlap, amax_gt_overlap = overlap.max(dim=1)
proposal_labels = torch.Tensor([gt_labels[i] for i in amax_gt_overlap])
proposal_labels = proposal_labels.cuda()
mask = overlap.sum(dim=1) == 0
proposal_labels[mask] = loader.dataset.get_vocab_size() + 1
max_overlaps.append(max_gt_overlap)
amax_overlaps.append(amax_gt_overlap + n_gt)
n_gt += len(gt_boxes)
# Artificially make a huge image containing all the boxes to be able to
# perform nms on distance to query
proposals[:, 0] += offset[1]
proposals[:, 1] += offset[0]
proposals[:, 2] += offset[1]
proposals[:, 3] += offset[0]
joint_boxes.append(proposals)
offset[0] += img.shape[0]
offset[1] += img.shape[1]
db_targets.append(proposal_labels)
db.append(embeddings)
log.append(entry)
li += 1
db = torch.cat(db, dim=0)
joint_boxes = torch.cat(joint_boxes, dim=0)
# joint_boxes = torch.FloatTensor(joint_boxes).cuda()
db_targets = torch.cat(db_targets, dim=0)
all_gt_boxes = torch.cat(all_gt_boxes, dim=0)
max_overlaps = torch.cat(max_overlaps, dim=0)
amax_overlaps = torch.cat(amax_overlaps, dim=0)
assert qbe_queries.shape[0] == qbe_qtargets.shape[0]
assert qbs_queries.shape[0] == qbs_qtargets.shape[0]
qbe_queries = qbe_queries.cuda()
qbs_queries = qbs_queries.cuda()
qbe_dists = pairwise_cosine_distances(qbe_queries, db)
qbs_dists = pairwise_cosine_distances(qbs_queries, db)
if args.mAP_gpu:
gt_targets = gt_targets.cuda()
qbs_qtargets = qbs_qtargets.cuda()
qbe_qtargets = qbe_qtargets.cuda()
else:
qbe_dists = qbe_dists.cpu()
qbs_dists = qbs_dists.cpu()
joint_boxes = joint_boxes.cpu()
max_overlaps = max_overlaps.cpu()
amax_overlaps = amax_overlaps.cpu()
db_targets = db_targets.cpu()
if args.mAP_numpy:
gt_targets = gt_targets.numpy()
qbs_qtargets = qbs_qtargets.numpy()
qbe_qtargets = qbe_qtargets.numpy()
qbe_dists = qbe_dists.numpy()
qbs_dists = qbs_dists.numpy()
joint_boxes = joint_boxes.numpy()
max_overlaps = max_overlaps.numpy()
amax_overlaps = amax_overlaps.numpy()
db_targets = db_targets.numpy()
prop2img = np.array(prop2img)
recalls = np.array(recalls)
#A hack for some printing compatability
if not args.use_external_proposals:
keys = []
for i in range(1, 4):
for ot in args.overlap_thresholds:
keys += [
'%d_dtp_recall_%d' % (i, ot * 100),
'%d_rpn_recall_%d' % (i, ot * 100)
]
for entry in log:
for key in keys:
if not entry.has_key(key):
entry[key] = entry['1_total_recall_50']
return (qbe_dists, qbe_qtargets, qbs_dists, qbs_qtargets, db_targets,
gt_targets, prop2img, joint_boxes, all_proposals, recalls,
max_overlaps, amax_overlaps)