def make_rpn_loss_evaluator(cfg, box_coder):
matcher = Matcher(
cfg.MODEL.RPN.FG_IOU_THRESHOLD,
cfg.MODEL.RPN.BG_IOU_THRESHOLD,
allow_low_quality_matches=True,
)
fg_bg_sampler = BalancedPositiveNegativeSampler(
cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE, cfg.MODEL.RPN.POSITIVE_FRACTION)
if cfg.MODEL.RPN.USE_WING_LOSS:
wing_loss = WingLoss(
width=cfg.MODEL.RPN.WING_LOSS.WIDTH,
curvature=cfg.MODEL.RPN.WING_LOSS.SIGMA,
)
else:
wing_loss = None
if cfg.MODEL.RPN.USE_FOCAL_LOSS:
focal_loss = SigmoidFocalLoss(
cfg.MODEL.RPN.FOCAL_LOSS.GAMMA,
cfg.MODEL.RPN.FOCAL_LOSS.ALPHA,
)
else:
focal_loss = None
if cfg.MODEL.RPN.USE_SELF_ADJUST_SMOOTH_L1_LOSS:
adjust_smooth_l1_loss = AdjustSmoothL1Loss(
4, beta=cfg.MODEL.RPN.SELF_ADJUST_SMOOTH_L1_LOSS.BBOX_REG_BETA)
else:
adjust_smooth_l1_loss = None
if cfg.MODEL.RPN.USE_COMBINATION_LOSS:
combination_weight = cfg.MODEL.RPN.COMBINATION_LOSS.WEIGHT
else:
combination_weight = 0
if cfg.MODEL.RPN.USE_BALANCE_L1_LOSS:
balance_l1_loss = BalancedL1Loss(
alpha=cfg.MODEL.RPN.BALANCE_L1_LOSS.ALPHA,
beta=cfg.MODEL.RPN.BALANCE_L1_LOSS.BETA,
gamma=cfg.MODEL.RPN.BALANCE_L1_LOSS.GAMMA)
else:
balance_l1_loss = None
loss_evaluator = RPNLossComputation(
matcher,
fg_bg_sampler,
box_coder,
generate_rpn_labels,
wing_loss=wing_loss,
adjust_smooth_l1_loss=adjust_smooth_l1_loss,
balance_l1_loss=balance_l1_loss,
focal_loss=focal_loss,
combination_weight=combination_weight)
return loss_evaluator
def __init__(self, cfg, proposal_matcher, box_coder):
"""
Arguments:
proposal_matcher (Matcher)
box_coder (BoxCoder)
"""
# self.target_preparator = target_preparator
self.proposal_matcher = proposal_matcher
self.box_coder = box_coder
self.num_classes = cfg.RETINANET.NUM_CLASSES - 1
self.box_cls_loss_func = SigmoidFocalLoss(self.num_classes,
cfg.RETINANET.LOSS_GAMMA,
cfg.RETINANET.LOSS_ALPHA)
if cfg.RETINANET.SELFADJUST_SMOOTH_L1:
self.regression_loss = AdjustSmoothL1Loss(
4, beta=cfg.RETINANET.BBOX_REG_BETA)
else:
self.regression_loss = SmoothL1Loss(
beta=cfg.RETINANET.BBOX_REG_BETA)
def make_retinanet_loss_evaluator(cfg, box_coder):
matcher = Matcher(
cfg.MODEL.RETINANET.FG_IOU_THRESHOLD,
cfg.MODEL.RETINANET.BG_IOU_THRESHOLD,
allow_low_quality_matches=cfg.RETINANET.LOW_QUALITY_MATCHES,
low_quality_threshold=cfg.RETINANET.LOW_QUALITY_THRESHOLD)
sigmoid_focal_loss = SigmoidFocalLoss(cfg.MODEL.RETINANET.LOSS_GAMMA,
cfg.MODEL.RETINANET.LOSS_ALPHA)
if cfg.RETINANET.USE_SELF_ADJUST_SMOOTH_L1_LOSS:
adjust_smooth_l1_loss = AdjustSmoothL1Loss(
4, beta=cfg.RETINANET.BBOX_REG_BETA)
loss_evaluator = RetinaNetLossComputation(
matcher,
box_coder,
generate_retinanet_labels,
sigmoid_focal_loss=sigmoid_focal_loss,
adjust_smooth_l1_loss=adjust_smooth_l1_loss,
bbox_reg_beta=cfg.MODEL.RETINANET.BBOX_REG_BETA,
regress_norm=cfg.MODEL.RETINANET.BBOX_REG_WEIGHT,
)
return loss_evaluator
def make_roi_box_loss_evaluator(cfg):
matcher = Matcher(
cfg.MODEL.ROI_HEADS.FG_IOU_THRESHOLD,
cfg.MODEL.ROI_HEADS.BG_IOU_THRESHOLD,
allow_low_quality_matches=False,
)
bbox_reg_weights = cfg.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS
box_coder = BoxCoder(weights=bbox_reg_weights)
fg_bg_sampler = BalancedPositiveNegativeSampler(
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE,
cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION)
cls_agnostic_bbox_reg = cfg.MODEL.CLS_AGNOSTIC_BBOX_REG
if cfg.MODEL.ROI_BOX_HEAD.USE_FOCAL_LOSS:
focal_loss = SigmoidFocalLoss(cfg.MODEL.ROI_BOX_HEAD.FOCAL_LOSS.GAMMA,
cfg.MODEL.ROI_BOX_HEAD.FOCAL_LOSS.ALPHA)
# focal_loss = SoftmaxFocalLoss(
# class_num = cfg.MODEL.ROI_BOX_HEAD.NUM_CLASSES-1,
# gamma=cfg.MODEL.RPN.FOCAL_LOSS.GAMMA,
# alpha=cfg.MODEL.RPN.FOCAL_LOSS.ALPHA,
# )
else:
focal_loss = None
if cfg.MODEL.ROI_BOX_HEAD.USE_CLASS_BALANCE_LOSS and \
os.path.isfile(cfg.MODEL.ROI_BOX_HEAD.CLASS_BALANCE_LOSS.WEIGHT_FILE):
num_class_list = ClassBalanceLoss.load_class_samples(
filename=cfg.MODEL.ROI_BOX_HEAD.CLASS_BALANCE_LOSS.WEIGHT_FILE,
category_type='category')
class_balance_weight = ClassBalanceLoss(
device=torch.device(cfg.MODEL.DEVICE),
num_class_list=num_class_list,
alpha=cfg.MODEL.ROI_BOX_HEAD.CLASS_BALANCE_LOSS.ALPHA,
beta=cfg.MODEL.ROI_BOX_HEAD.CLASS_BALANCE_LOSS.BETA)
else:
class_balance_weight = None
if cfg.MODEL.ROI_BOX_HEAD.USE_WING_LOSS:
wing_loss = WingLoss(
width=cfg.MODEL.ROI_BOX_HEAD.WING_LOSS.WIDTH,
curvature=cfg.MODEL.ROI_BOX_HEAD.WING_LOSS.SIGMA,
)
else:
wing_loss = None
if cfg.MODEL.ROI_BOX_HEAD.USE_SELF_ADJUST_SMOOTH_L1_LOSS:
adjust_smooth_l1_loss = AdjustSmoothL1Loss(
4,
beta=cfg.MODEL.ROI_BOX_HEAD.SELF_ADJUST_SMOOTH_L1_LOSS.
BBOX_REG_BETA)
else:
adjust_smooth_l1_loss = None
if cfg.MODEL.ROI_BOX_HEAD.USE_BALANCE_L1_LOSS:
balance_l1_loss = BalancedL1Loss(
alpha=cfg.MODEL.ROI_BOX_HEAD.BALANCE_L1_LOSS.ALPHA,
beta=cfg.MODEL.ROI_BOX_HEAD.BALANCE_L1_LOSS.BETA,
gamma=cfg.MODEL.ROI_BOX_HEAD.BALANCE_L1_LOSS.GAMMA)
else:
balance_l1_loss = None
loss_evaluator = FastRCNNLossComputation(
matcher,
fg_bg_sampler,
box_coder,
cls_agnostic_bbox_reg,
focal_loss=focal_loss,
class_balance_weight=class_balance_weight,
wing_loss=wing_loss,
adjust_smooth_l1_loss=adjust_smooth_l1_loss,
balance_l1_loss=balance_l1_loss,
)
return loss_evaluator