def subsample(self, proposals, targets):
positive_proposals = keep_only_positive_boxes(proposals)
positive_proposals = self.prepare_targets(positive_proposals, targets)
self.positive_proposals = positive_proposals
all_num_positive_proposals = 0
for positive_proposals_per_image in positive_proposals:
all_num_positive_proposals += len(positive_proposals_per_image)
if all_num_positive_proposals == 0:
positive_proposals = [proposals[0][:1]]
return positive_proposals
def resample(self, proposals, targets):
# get all positive proposals (for single image on per GPU)
positive_proposals = keep_only_positive_boxes(proposals)
# resample for getting targets or matching new IoU
positive_proposals = self.prepare_targets(positive_proposals, targets)
self.positive_proposals = positive_proposals
all_num_positive_proposals = 0
for positive_proposals_per_image in positive_proposals:
all_num_positive_proposals += len(positive_proposals_per_image)
if all_num_positive_proposals == 0:
positive_proposals = [proposals[0][:1]]
return positive_proposals
def resample(self, proposals, targets):
# get all positive proposals (for single image on per GPU)
positive_proposals = keep_only_positive_boxes(proposals)
# resample for getting targets or matching new IoU
positive_proposals = self.prepare_targets(positive_proposals, targets)
# apply across-sample strategy (for a batch of images on per GPU)
positive_proposals = across_sample(
positive_proposals, roi_size_per_img=self.roi_size_per_img, across_sample=self.across_sample
)
self.positive_proposals = positive_proposals
all_num_positive_proposals = 0
for positive_proposals_per_image in positive_proposals:
all_num_positive_proposals += len(positive_proposals_per_image)
if all_num_positive_proposals == 0:
positive_proposals = [proposals[0][:1]]
return positive_proposals
