def get_target(self, sampling_results, gt_masks, rcnn_train_cfg):
pos_proposals = [res.pos_bboxes for res in sampling_results]
pos_assigned_gt_inds = [
res.pos_assigned_gt_inds for res in sampling_results
] # NOTE pos_assigned_gt_inds: gt start from index 0
mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds,
gt_masks, rcnn_train_cfg)
return mask_targets
def get_target(self, sampling_result, gt_masks, rcnn_train_cfg):
pos_proposals = [res.pos_bboxes for res in sampling_result]
pos_assigned_gt_inds = [
res.pos_assigned_gt_inds for res in sampling_result
]
mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds,
gt_masks, rcnn_train_cfg)
return mask_targets
def get_target(self, sampling_results, gt_masks, rcnn_train_cfg):
# first get pos_proposals from rpn outputs.
pos_proposals = [res.pos_bboxes for res in sampling_results]
# proposals are assigned to gt according to their IoU overlaps.
# here to get the gt id for each proposal
pos_assigned_gt_inds = [
res.pos_assigned_gt_inds for res in sampling_results
]
mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds,
gt_masks, rcnn_train_cfg)
return mask_targets
def get_mask_target(self, sampling_results, gt_masks, rcnn_train_cfg):
# pos_proposals = [res.pos_bboxes for res in sampling_results]
# pos_assigned_gt_inds = [
# res.pos_assigned_gt_inds for res in sampling_results
# ]
# mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds,
# gt_masks, rcnn_train_cfg)
proposals = [res.bboxes for res in sampling_results]
assigned_gt_inds = [res.inds for res in sampling_results]
mask_targets = mask_target(proposals, assigned_gt_inds, gt_masks,
rcnn_train_cfg)
mask_bg_targets = mask_bg_target(proposals, gt_masks, rcnn_train_cfg)
return mask_targets, mask_bg_targets
def get_ms_target(self, sampling_results, gt_masks, labels, rcnn_train_cfg,
mask_pred):
pos_proposals = [res.pos_bboxes for res in sampling_results]
pos_assigned_gt_inds = [
res.pos_assigned_gt_inds for res in sampling_results
]
mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds,
gt_masks, rcnn_train_cfg)
if self.class_agnostic:
iou_targets = self.mask_iou(mask_pred, mask_targets,
torch.zeros_like(labels))
else:
iou_targets = self.mask_iou(mask_pred, mask_targets, labels)
return mask_targets, iou_targets
def get_all_target(self, sampling_results, gt_mask, rcnn_train_cfg):
proposals = [
torch.cat(res.pos_bboxes, res.neg_bboxes[:res.pos_bboxes.size(0)])
for res in sampling_results
]
assigned_gt_inds = [
torch.cat([
res.pos_assigned_gt_inds,
torch.full(res.pos_bboxes.size(0), -1)
]) for res in sampling_results
]
mask_targets = mask_target(proposals, assigned_gt_inds, gt_mask,
rcnn_train_cfg)
return mask_targets
def get_target_mask(self, sampling_results, gt_masks, rcnn_train_cfg):
# mix all samples (across images) together.
pos_bboxes = torch.cat([res.pos_bboxes for res in sampling_results],
dim=0)
pos_gt_bboxes = torch.cat(
[res.pos_gt_bboxes for res in sampling_results], dim=0)
assert pos_bboxes.shape == pos_gt_bboxes.shape
pos_assigned_gt_inds = [
res.pos_assigned_gt_inds for res in sampling_results
]
instances = mask_target([res.pos_bboxes for res in sampling_results],
pos_assigned_gt_inds, gt_masks, rcnn_train_cfg)
masks = F.interpolate(instances.unsqueeze(0),
scale_factor=1 / 4,
mode='bilinear',
align_corners=False).squeeze(0)
masks = masks.gt(0.5).float()
num_rois = pos_bboxes.shape[0]
map_size = 7
targets = pos_bboxes.new_zeros((num_rois, 4, map_size, map_size),
dtype=torch.float)
points = pos_bboxes.new_zeros((num_rois, 4, map_size, map_size),
dtype=torch.float)
for j in range(map_size):
y = pos_bboxes[:, 1] + (pos_bboxes[:, 3] -
pos_bboxes[:, 1]) / map_size * (j + 0.5)
for i in range(map_size):
x = pos_bboxes[:, 0] + (
pos_bboxes[:, 2] - pos_bboxes[:, 0]) / map_size * (i + 0.5)
targets[:, 0, j, i] = x - pos_gt_bboxes[:, 0]
targets[:, 1, j, i] = pos_gt_bboxes[:, 2] - x
targets[:, 2, j, i] = y - pos_gt_bboxes[:, 1]
targets[:, 3, j, i] = pos_gt_bboxes[:, 3] - y
points[:, 0, j, i] = x
points[:, 1, j, i] = y
points[:, 2, j, i] = pos_bboxes[:, 2] - pos_bboxes[:, 0]
points[:, 3, j, i] = pos_bboxes[:, 3] - pos_bboxes[:, 1]
return points, targets, masks, instances
def get_mask_target(self, pos_proposals, pos_assigned_gt_inds, gt_masks,
rcnn_train_cfg):
mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds,
gt_masks, rcnn_train_cfg)
return mask_targets
def get_target(self, sampling_results, gt_masks, proto_cfg):
proposals = [res.bboxes for res in sampling_results]
assgined_gt_inds = [res.inds for res in sampling_results]
all_target = mask_target(proposals, assgined_gt_inds, gt_masks,
proto_cfg)
return all_target
def get_all_target(self, sampling_results, gt_mask, rcnn_train_cfg):
proposals = [res.bboxes for res in sampling_results]
assigned_gt_inds = [res.inds for res in sampling_results]
all_target = mask_target(proposals, assigned_gt_inds, gt_mask,
rcnn_train_cfg)
return all_target
