def restore_bbox(rois, deltas, unnormalize=True):
if unnormalize:
std_opr = torch.tensor(config.bbox_normalize_stds[None, :]).type_as(deltas)
mean_opr = torch.tensor(config.bbox_normalize_means[None, :]).type_as(deltas)
deltas = deltas * std_opr
deltas = deltas + mean_opr
pred_bbox = bbox_transform_inv_opr(rois, deltas)
return pred_bbox
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,
all_anchors_list, im_info, batches):
prev_nms_top_n = config.train_prev_nms_top_n \
if is_train else config.test_prev_nms_top_n
post_nms_top_n = config.train_post_nms_top_n \
if is_train else config.test_post_nms_top_n
batch_per_gpu = batches
nms_threshold = config.rpn_nms_threshold
box_min_size = config.rpn_min_box_size
bbox_normalize_targets = config.rpn_bbox_normalize_targets
bbox_normalize_means = config.bbox_normalize_means
bbox_normalize_stds = config.bbox_normalize_stds
list_size = len(rpn_bbox_offsets_list)
return_rois = []
return_inds = []
shapes = []
for bid in range(batch_per_gpu):
batch_proposals_list = []
batch_probs_list = []
for l in range(list_size):
# get proposals and probs
offsets = rpn_bbox_offsets_list[l][bid] \
.permute(1, 2, 0).reshape(-1, 4)
if bbox_normalize_targets:
std_opr = torch.tensor(
config.bbox_normalize_stds[None, :]).type_as(bbox_targets)
mean_opr = torch.tensor(
config.bbox_normalize_means[None, :]).type_as(bbox_targets)
pred_offsets = pred_offsets * std_opr
pred_offsets = pred_offsets + mean_opr
all_anchors = all_anchors_list[l]
proposals = bbox_transform_inv_opr(all_anchors, offsets)
if config.anchor_within_border:
proposals = clip_boxes_opr(proposals, im_info[bid, :])
probs = rpn_cls_prob_list[l][bid] \
.permute(1,2,0).reshape(-1, 2)
probs = torch.softmax(probs, dim=-1)[:, 1]
# gather the proposals and probs
batch_proposals_list.append(proposals)
batch_probs_list.append(probs)
batch_proposals = torch.cat(batch_proposals_list, dim=0)
batch_probs = torch.cat(batch_probs_list, dim=0)
# filter the zero boxes.
batch_keep_mask = filter_boxes_opr(batch_proposals,
box_min_size * im_info[bid, 2])
batch_proposals = batch_proposals[batch_keep_mask]
batch_probs = batch_probs[batch_keep_mask]
# prev_nms_top_n
num_proposals = min(prev_nms_top_n, batch_probs.shape[0])
batch_probs, idx = batch_probs.sort(descending=True)
batch_probs = batch_probs[:num_proposals]
topk_idx = idx[:num_proposals].flatten()
batch_proposals = batch_proposals[topk_idx]
# For each image, run a total-level NMS, and choose topk results.
keep = nms(batch_proposals, batch_probs, nms_threshold)
keep = keep[:post_nms_top_n]
batch_proposals = batch_proposals[keep]
#batch_probs = batch_probs[keep]
# cons the rois
batch_inds = torch.ones(batch_proposals.shape[0],
1).type_as(batch_proposals) * bid
batch_rois = torch.cat([batch_inds, batch_proposals], axis=1)
return_rois.append(batch_rois)
shapes.append(batch_rois.shape[0])
if batch_per_gpu == 1:
return batch_rois
else:
concated_rois = torch.cat(return_rois, axis=0)
return concated_rois, shapes
def find_top_rpn_proposals(is_train, rpn_bbox_offsets_list, rpn_cls_prob_list,
all_anchors_list, im_info):
prev_nms_top_n = config.train_prev_nms_top_n \
if is_train else config.test_prev_nms_top_n
post_nms_top_n = config.train_post_nms_top_n \
if is_train else config.test_post_nms_top_n
batch_per_gpu = config.train_batch_per_gpu if is_train else 1
nms_threshold = config.rpn_nms_threshold
box_min_size = config.rpn_min_box_size
bbox_normalize_targets = config.rpn_bbox_normalize_targets
bbox_normalize_means = config.bbox_normalize_means
bbox_normalize_stds = config.bbox_normalize_stds
list_size = len(rpn_bbox_offsets_list)
return_rois = []
return_inds = []
for bid in range(batch_per_gpu):
batch_proposals_list = []
batch_probs_list = []
for l in range(list_size):
# get proposals and probs
offsets = rpn_bbox_offsets_list[l][bid] \
.transpose((1, 2, 0)).reshape((-1, 4))
if bbox_normalize_targets:
std_opr = torch.to_tensor(
config.bbox_normalize_stds[None, :]).cast('float32')
mean_opr = torch.to_tensor(
config.bbox_normalize_means[None, :]).cast('float32')
pred_offsets = pred_offsets * std_opr
pred_offsets = pred_offsets + mean_opr
all_anchors = all_anchors_list[l]
proposals = bbox_transform_inv_opr(all_anchors, offsets)
if config.anchor_within_border:
proposals = clip_boxes_opr(proposals, im_info[bid, :])
probs = rpn_cls_prob_list[l][bid] \
.transpose((1,2,0)).reshape((-1, 2))
probs = F.softmax(probs, axis=-1)[:, 1]
# gather the proposals and probs
batch_proposals_list.append(proposals)
batch_probs_list.append(probs)
batch_proposals = cat(batch_proposals_list, axis=0)
batch_probs = cat(batch_probs_list, axis=0)
# filter the zero boxes.
batch_keep_mask = filter_boxes_opr(batch_proposals,
box_min_size * im_info[bid, 2])
batch_keep_mask = torch.nonzero(batch_keep_mask)
batch_proposals = torch.gather(batch_proposals, batch_keep_mask)
batch_probs = torch.gather(batch_probs, batch_keep_mask)
# prev_nms_top_n
num_proposals = min(prev_nms_top_n, batch_probs.shape[0])
batch_probs = batch_probs.sort(descending=True)
idx = batch_probs.argsort(descending=True)
batch_probs = batch_probs[:num_proposals]
topk_idx = idx[:num_proposals].flatten()
batch_proposals = torch.gather(batch_proposals, topk_idx)
#nmss(tt.tensor(batch_proposals.numpy()), tt.tensor(batch_probs.numpy()), nms_threshold).shape
# For each image, run a total-level NMS, and choose topk results.
keep = nms(bboxes=batch_proposals.unsqueeze(axis=0),
scores=batch_probs.unsqueeze(axis=0).unsqueeze(axis=0),
score_threshold=nms_threshold,
nms_top_k=post_nms_top_n,
keep_top_k=post_nms_top_n,
normalized=False)
#keep = keep[:post_nms_top_n]
batch_proposals = keep[:, 2:] #batch_proposals[keep]
#batch_probs = batch_probs[keep]
# cons the rois
batch_inds = torch.ones(
(batch_proposals.shape[0], 1)).cast('float32') * bid
batch_rois = cat([batch_inds, batch_proposals], axis=1)
return_rois.append(batch_rois)
if batch_per_gpu == 1:
return batch_rois
else:
concated_rois = cat(return_rois, axis=0)
return concated_rois
