def __init__(
self,
light_head_rcnn,
rpn_sigma=3.,
roi_sigma=1.,
n_ohem_sample=256,
anchor_target_creator=None,
proposal_target_creator=None,
):
super(LightHeadRCNNTrainChain, self).__init__()
with self.init_scope():
self.light_head_rcnn = light_head_rcnn
self.rpn_sigma = rpn_sigma
self.roi_sigma = roi_sigma
self.n_ohem_sample = n_ohem_sample
if anchor_target_creator is None:
self.anchor_target_creator = AnchorTargetCreator()
else:
self.anchor_target_creator = anchor_target_creator
if proposal_target_creator is None:
self.proposal_target_creator = ProposalTargetCreator(n_sample=None)
else:
self.proposal_target_creator = proposal_target_creator
self.loc_normalize_mean = light_head_rcnn.loc_normalize_mean
self.loc_normalize_std = light_head_rcnn.loc_normalize_std
def __init__(
self,
mask_rcnn,
rpn_sigma=3.,
roi_sigma=1.,
anchor_target_creator=AnchorTargetCreator(),
proposal_target_creator=ProposalTargetCreator(),
notrain=None,
pix_loss_scale=1.,
):
super(MaskRCNNPanopticTrainChain, self).__init__()
with self.init_scope():
self.mask_rcnn = mask_rcnn
self.rpn_sigma = rpn_sigma
self.roi_sigma = roi_sigma
self.anchor_target_creator = anchor_target_creator
self.proposal_target_creator = proposal_target_creator
self.loc_normalize_mean = mask_rcnn.loc_normalize_mean
self.loc_normalize_std = mask_rcnn.loc_normalize_std
assert notrain in [None, 'pix', 'ins']
self._notrain = notrain
assert isinstance(pix_loss_scale, (int, float))
assert pix_loss_scale > 0
self._pix_loss_scale = pix_loss_scale
def __init__(self,
fcis,
rpn_sigma=3.0,
roi_sigma=1.0,
n_sample=None,
loc_normalize_mean=(0., 0., 0., 0.),
loc_normalize_std=(0.2, 0.2, 0.5, 0.5),
fg_ratio=0.25,
fg_iou_thresh=0.5,
bg_iou_thresh_hi=0.5,
bg_iou_thresh_lo=0.0,
mask_size=21,
binary_thresh=0.4):
super(FCISTrainChain, self).__init__()
with self.init_scope():
self.fcis = fcis
self.rpn_sigma = rpn_sigma
self.roi_sigma = roi_sigma
self.loc_normalize_mean = fcis.loc_normalize_mean
self.loc_normalize_std = fcis.loc_normalize_std
self.anchor_target_creator = AnchorTargetCreator()
self.proposal_target_creator = ProposalTargetCreator(
n_sample=n_sample,
loc_normalize_mean=self.loc_normalize_mean,
loc_normalize_std=self.loc_normalize_std,
fg_ratio=fg_ratio,
fg_iou_thresh=fg_iou_thresh,
bg_iou_thresh_hi=bg_iou_thresh_hi,
bg_iou_thresh_lo=bg_iou_thresh_lo,
mask_size=mask_size,
binary_thresh=binary_thresh)
def __init__(self,
faster_rcnn,
rpn_sigma=3.,
roi_sigma=1.,
anchor_target_creator=AnchorTargetCreator(),
proposal_target_creator=ProposalTargetCreator()):
super(MaskRCNNTrainChain,
self).__init__(faster_rcnn,
proposal_target_creator=proposal_target_creator)
def __init__(self,
faster_rcnn,
mask_loss_fun,
binary_mask=True,
rpn_sigma=3.,
roi_sigma=1.,
anchor_target_creator=AnchorTargetCreator()):
# todo: clean up class dependencies
proposal_target_creator = ProposalTargetCreator(
faster_rcnn.extractor.anchor_sizes)
super().__init__(faster_rcnn,
proposal_target_creator=proposal_target_creator)
self.mask_loss_fun = mask_loss_fun
self.binary_mask = binary_mask
def __init__(self, faster_rcnn, rpn_sigma=3., roi_sigma=1.,
anchor_target_creator=AnchorTargetCreator(),
proposal_target_creator=ProposalTargetCreator()):
super(FasterRCNNTrainChain, self).__init__()
with self.init_scope():
self.faster_rcnn = faster_rcnn
self.rpn_sigma = rpn_sigma
self.roi_sigma = roi_sigma
self.anchor_target_creator = anchor_target_creator
self.proposal_target_creator = proposal_target_creator
self.loc_normalize_mean = faster_rcnn.loc_normalize_mean
self.loc_normalize_std = faster_rcnn.loc_normalize_std
def __init__(self, mask_rcnn, rpn_sigma=3., roi_sigma=1., gamma=1,
anchor_target_creator=AnchorTargetCreator(),
roi_size=7):
super(MaskRCNNTrainChain, self).__init__()
with self.init_scope():
self.mask_rcnn = mask_rcnn
self.rpn_sigma = rpn_sigma
self.roi_sigma = roi_sigma
self.anchor_target_creator = anchor_target_creator
self.proposal_target_creator = ProposalTargetCreator(roi_size=roi_size)
self.loc_normalize_mean = mask_rcnn.loc_normalize_mean
self.loc_normalize_std = mask_rcnn.loc_normalize_std
self.decayrate=0.99
self.avg_loss = None
self.gamma=gamma
def __init__(self,
fcis,
rpn_sigma=3.0,
roi_sigma=1.0,
anchor_target_creator=AnchorTargetCreator(),
proposal_target_creator=ProposalTargetCreator()):
super(FCISTrainChain, self).__init__()
with self.init_scope():
self.fcis = fcis
self.rpn_sigma = rpn_sigma
self.roi_sigma = roi_sigma
self.mask_size = self.fcis.head.roi_size
self.loc_normalize_mean = fcis.loc_normalize_mean
self.loc_normalize_std = fcis.loc_normalize_std
self.anchor_target_creator = anchor_target_creator
self.proposal_target_creator = proposal_target_creator