def __init__(self,
priors_cxcy,
config,
threshold=0.5,
neg_pos_ratio=3,
theta=0.01):
super(RefineDetBofTrafficLoss, self).__init__()
self.priors_cxcy = priors_cxcy
self.priors_xy = cxcy_to_xy(priors_cxcy)
self.threshold = threshold
self.neg_pos_ratio = neg_pos_ratio
self.alpha = config.reg_weights
self.device = config.device
self.n_classes = config.n_classes
self.config = config
self.theta = theta
self.arm_loss = IouLoss(pred_mode='Corner',
reduce='mean',
losstype='Diou')
self.odm_loss = IouLoss(pred_mode='Corner',
reduce='mean',
losstype='Diou')
self.arm_cross_entropy = nn.CrossEntropyLoss(reduce=False)
self.odm_cross_entropy = nn.CrossEntropyLoss(reduce=False)
def __init__(self, priors_cxcy, config, n_candidates=9):
super(RetinaATSSNetLoss, self).__init__()
self.priors_cxcy = priors_cxcy
self.priors_xy = [cxcy_to_xy(prior) for prior in self.priors_cxcy]
self.n_candidates = n_candidates
self.alpha = config.reg_weights
self.device = config.device
self.n_classes = config.n_classes - 1
fmap_dims = {
'c3': [64, 64],
'c4': [32, 32],
'c5': [16, 16],
'c6': [8, 8],
'c7': [4, 4]
}
self.prior_split_points = [0]
fmap_keys = ['c3', 'c4', 'c5', 'c6', 'c7']
for k in fmap_keys:
self.prior_split_points.append(self.prior_split_points[-1] +
fmap_dims[k][0] * fmap_dims[k][1])
self.regression_loss = IouLoss(pred_mode='Corner',
reduce='mean',
losstype='Ciou')
self.classification_loss = SigmoidFocalLoss(gamma=2.0,
alpha=0.25,
config=config)
def __init__(self, priors_cxcy, config, threshold=0.5, neg_pos_ratio=3):
super(RetinaFocalLoss, self).__init__()
self.priors_cxcy = priors_cxcy
self.priors_xy = cxcy_to_xy(priors_cxcy)
self.threshold = threshold
self.neg_pos_ratio = neg_pos_ratio
self.alpha = config.reg_weights
self.device = config.device
self.n_classes = config.n_classes
self.config = config
self.smooth_l1 = SmoothL1Loss(reduction='mean')
self.Diou_loss = IouLoss(pred_mode='Corner', reduce='mean', losstype='Diou')
self.cross_entropy = nn.CrossEntropyLoss(reduce=False)
# self.Focal_loss = FocalLoss()
self.Focal_loss = focal_loss
def __init__(self, priors_cxcy, config, threshold=0.5, theta=0.1):
super(VGGNETNetDetectorLoss, self).__init__()
self.priors_cxcy = priors_cxcy
self.priors_xy = cxcy_to_xy(priors_cxcy)
self.threshold = threshold
self.alpha = config.reg_weights
self.device = config.device
self.n_classes = config.n_classes - 1
self.theta = theta
self.regression_loss = IouLoss(pred_mode='Corner',
reduce='mean',
losstype='Ciou')
self.classification_loss = SigmoidFocalLoss(gamma=2.0,
alpha=0.25,
config=config)
def __init__(self, priors_cxcy, config, threshold=0.5):
super(RetinaFocalLoss, self).__init__()
self.priors_cxcy = priors_cxcy
self.priors_xy = cxcy_to_xy(priors_cxcy)
self.threshold = threshold
self.alpha = config.reg_weights
self.device = config.device
self.n_classes = config.n_classes - 1
self.smooth_l1 = SmoothL1Loss(reduction='mean')
self.Diou_loss = IouLoss(pred_mode='Corner',
reduce='mean',
losstype='Ciou')
self.Focal_loss = SigmoidFocalLoss(gamma=2.0,
alpha=0.25,
config=config)
def __init__(self, locations, config, threshold=0.5, center_sample=True):
super(FCOSLoss, self).__init__()
self.threshold = threshold
self.alpha = config.reg_weights
self.device = config.device
self.n_classes = config.n_classes
self.config = config
self.locations = locations
self.center_sample = center_sample
self.INF = 1e6
self.Diou_loss = IouLoss(pred_mode='Corner',
reduce='mean',
losstype='Diou')
self.Focal_loss = SigmoidFocalLoss(gamma=2.0,
alpha=0.25,
config=config)
self.center_loss = nn.BCEWithLogitsLoss()
self.fpn_strides = [
8 / 512., 16 / 512., 32 / 512., 64 / 512., 128 / 512.
]
self.sizes = [[0., 0.08], [0.08, 0.16], [0.16, 0.32], [0.32, 0.64],
[0.64, 1.]]
self.radius = 1.5