def get_sampled_proposal(self, conv_fpn_feat, gt_bbox, im_info):
p = self.p
batch_image = p.batch_image
proposal_wo_gt = p.subsample_proposal.proposal_wo_gt
image_roi = p.subsample_proposal.image_roi
fg_fraction = p.subsample_proposal.fg_fraction
fg_thr = p.subsample_proposal.fg_thr
bg_thr_hi = p.subsample_proposal.bg_thr_hi
bg_thr_lo = p.subsample_proposal.bg_thr_lo
post_nms_top_n = p.proposal.post_nms_top_n
num_reg_class = p.bbox_target.num_reg_class
class_agnostic = p.bbox_target.class_agnostic
bbox_target_weight = p.bbox_target.weight
bbox_target_mean = p.bbox_target.mean
bbox_target_std = p.bbox_target.std
(proposal,
proposal_score) = self.get_all_proposal(conv_fpn_feat, im_info)
(bbox, label, bbox_target,
bbox_weight) = X.proposal_target(rois=proposal,
gt_boxes=gt_bbox,
num_classes=num_reg_class,
class_agnostic=class_agnostic,
batch_images=batch_image,
proposal_without_gt=proposal_wo_gt,
image_rois=image_roi,
fg_fraction=fg_fraction,
fg_thresh=fg_thr,
bg_thresh_hi=bg_thr_hi,
bg_thresh_lo=bg_thr_lo,
bbox_weight=bbox_target_weight,
bbox_mean=bbox_target_mean,
bbox_std=bbox_target_std,
name="subsample_proposal")
label = X.reshape(label, (-3, -2))
bbox_target = X.reshape(bbox_target, (-3, -2))
bbox_weight = X.reshape(bbox_weight, (-3, -2))
return bbox, label, bbox_target, bbox_weight
def get_sampled_proposal(self, rois, bbox_pred, gt_bbox, im_info):
p = self.p
stage = self.stage
batch_image = p.batch_image
proposal_wo_gt = p.subsample_proposal.proposal_wo_gt
image_roi = -1 # do not subsample rois
fg_fraction = p.subsample_proposal.fg_fraction
fg_thr = p.subsample_proposal.fg_thr
bg_thr_hi = p.subsample_proposal.bg_thr_hi
bg_thr_lo = p.subsample_proposal.bg_thr_lo
num_reg_class = p.bbox_target.num_reg_class
class_agnostic = p.bbox_target.class_agnostic
bbox_target_weight = p.bbox_target.weight
bbox_target_mean = p.bbox_target.mean
bbox_target_std = p.bbox_target.std
proposal = self.get_all_proposal(rois, bbox_pred, im_info)
(bbox, label, bbox_target,
bbox_weight) = X.proposal_target(rois=proposal,
gt_boxes=gt_bbox,
num_classes=num_reg_class,
class_agnostic=class_agnostic,
batch_images=batch_image,
proposal_without_gt=proposal_wo_gt,
image_rois=image_roi,
fg_fraction=fg_fraction,
fg_thresh=fg_thr,
bg_thresh_hi=bg_thr_hi,
bg_thresh_lo=bg_thr_lo,
bbox_weight=bbox_target_weight,
bbox_mean=bbox_target_mean,
bbox_std=bbox_target_std,
name="subsample_proposal_" + stage)
label = X.reshape(label, (-3, -2))
bbox_target = X.reshape(bbox_target, (-3, -2))
bbox_weight = X.reshape(bbox_weight, (-3, -2))
return bbox, label, bbox_target, bbox_weight
