def __init__(self, opt):
super(CtdetLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.crit_angle = RegL1Loss()
self.opt = opt
def __init__(self, opt):
super(CtdetLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.opt = opt
def __init__(self, opt):
super(MotLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = (
RegL1Loss()
if opt.reg_loss == "l1"
else RegLoss()
if opt.reg_loss == "sl1"
else None
)
self.crit_wh = (
torch.nn.L1Loss(reduction="sum")
if opt.dense_wh
else NormRegL1Loss()
if opt.norm_wh
else RegWeightedL1Loss()
if opt.cat_spec_wh
else self.crit_reg
)
self.opt = opt
self.emb_dim = opt.reid_dim
self.nID = opt.nID
self.classifier = nn.Linear(self.emb_dim, self.nID)
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
# self.TriLoss = TripletLoss()
self.emb_scale = math.sqrt(2) * math.log(self.nID - 1)
self.s_det = nn.Parameter(-1.85 * torch.ones(1))
self.s_id = nn.Parameter(-1.05 * torch.ones(1))
def __init__(self, opt):
super(MotLoss, self).__init__()
self.crit = paddle.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = paddle.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.opt = opt
self.emb_dim = opt.reid_dim
self.nID = opt.nID
# param_attr = paddle.ParamAttr(initializer=KaimingUniform())
# bound = 1 / math.sqrt(self.emb_dim)
# bias_attr = paddle.ParamAttr(initializer=Uniform(-bound, bound))
# self.classifier = nn.Linear(self.emb_dim, self.nID, weight_attr=param_attr, bias_attr=bias_attr)
self.classifier = nn.Linear(self.emb_dim, self.nID, bias_attr=True)
if opt.id_loss == 'focal': # 一般用不到
# torch.nn.init.normal_(self.classifier.weight, std=0.01)
prior_prob = 0.01
bias_value = -math.log((1 - prior_prob) / prior_prob)
# torch.nn.init.constant_(self.classifier.bias, bias_value)
weight_attr = paddle.framework.ParamAttr(initializer=nn.initializer.Normal(std=0.01))
bias_attr = paddle.framework.ParamAttr(initializer=nn.initializer.Constant(bias_value))
self.classifier = nn.Linear(self.emb_dim, self.nID, weight_attr=weight_attr, bias_attr=bias_attr)
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
self.emb_scale = math.sqrt(2) * math.log(self.nID - 1)
# self.s_det = nn.Parameter(-1.85 * torch.ones(1))
# self.s_id = nn.Parameter(-1.05 * torch.ones(1))
self.s_det = paddle.create_parameter([1], dtype='float32', default_initializer = nn.initializer.Constant(value=-1.85))
self.s_id = paddle.create_parameter([1], dtype='float32', default_initializer = nn.initializer.Constant(value=-1.05))
def __init__(self, opt):
super(PoseLoss, self).__init__()
self.crit_hm_hp = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_kp = RegWeightedL1Loss() if not opt.dense_hp else \
torch.nn.L1Loss(reduction='sum')
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.opt = opt
def __init__(self, opt):
super(CenterLandmarkLoss, self).__init__()
self.crit = FocalLoss()
self.crit_hm_hp = nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_kp = RegWeightedL1Loss() if not opt.dense_hp else nn.L1Loss(
reduction='sum') #####Why sum????
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else RegLoss(
) if opt.reg_loss == 'sl1' else None
self.opt = opt
def __init__(self, cfg, local_rank):
super(MultiPoseLoss, self).__init__()
self.crit = FocalLoss()
self.crit_hm_hp = torch.nn.MSELoss() if cfg.LOSS.MSE_LOSS else FocalLoss()
self.crit_kp = RegWeightedL1Loss() if not cfg.LOSS.DENSE_HP else \
torch.nn.L1Loss(reduction='sum')
self.crit_reg = RegL1Loss() if cfg.LOSS.REG_LOSS == 'l1' else \
RegLoss() if cfg.LOSS.REG_LOSS == 'sl1' else None
self.cfg = cfg
self.local_rank = local_rank
def __init__(self, opt):
super(DetLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.opt = opt
#self.TriLoss = TripletLoss()
self.s_det = nn.Parameter(-1.85 * torch.ones(1))
def __init__(self, opt):
super(MSPCtdetLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_centerness = FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.crit_scale = torch.nn.SmoothL1Loss(size_average=False)
self.opt = opt
def __init__(self, opt):
super(CtdetLoss_doublehm, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.crit_reconstruct_img = torch.nn.L1Loss()
self.opt = opt
self.test_reconstruct_conflict_with_class = True
def __init__(self, opt):
super(FvdetLoss, self).__init__()
self.crit = FocalLoss()
#self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
#RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_reg = RegL1Loss()
#self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
#NormRegL1Loss() if opt.norm_wh else \
#RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.crit_dep_iou = RegL1LossDepIOU()
self.opt = opt
def __init__(self, opt):
super(CtdetLossSpotNetVid, self).__init__()
self.crit_seg = torch.nn.BCEWithLogitsLoss(
) # torch.nn.MSELoss() # hughes
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.opt = opt
def __init__(self, opt):
super(CtdetLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = self.crit_reg
if opt.direct_loss == 'cls':
self.crit_direct = CrossEntropyLoss()
else:
self.crit_direct = RegL1Loss()
self.opt = opt
if opt.loss_hm_magnitude:
if opt.loss_hm_magnitude_pos_only:
self.crit_grad_magnitude = FocalLossMagnitudePosOnly()
elif opt.loss_hm_magnitude_neg_only:
self.crit_grad_magnitude = FocalLossMagnitudeNegOnly()
else:
self.crit_grad_magnitude = FocalLossMagnitude()
def __init__(self, opt):
super(CtsegLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
##########
self.crit_mask = FastDiceLoss(opt.seg_feat_channel)
self.opt = opt
def __init__(self, opt):
super(CircleLoss, self).__init__()
# if opt.mask_focal_loss:
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
# else:
# self.crit = FocalLoss_mask()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.opt = opt
def __init__(self, opt):
super(MultiPoseLoss, self).__init__()
self.crit = FocalLoss()
self.crit_hm_hp = FocalLoss()
if opt.mdn:
self.crit_kp = th_mdn_loss_dense if opt.dense_hp else \
th_mdn_loss_ind
else:
self.crit_kp = torch.nn.L1Loss(reduction='sum') if opt.dense_hp else \
RegWeightedL1Loss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.opt = opt
def __init__(self, opt):
super(MultiPoseLoss, self).__init__()
self.crit = FocalLoss()
self.crit_hm_hp = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_kp = RegWeightedL1Loss() if not opt.dense_hp else \
torch.nn.L1Loss(reduction='sum')
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
##changed
self.crit_view_side = CrossEntropyLossWMask()
self.crit_view_front_rear = CrossEntropyLossWMask()
self.opt = opt
def __init__(self, opt):
super(MultiKPSLoss, self).__init__()
self.crit = FocalLoss() #中心点hp
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None #中心点回归
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg #宽高回归
self.crit_hm_hp = torch.nn.MSELoss() if opt.mse_loss else FocalLoss(
) #关键点hp
self.crit_kp = RegWeightedL1Loss() if not opt.dense_hp else \
torch.nn.L1Loss(reduction='sum') #关键点回归
self.opt = opt
def __init__(self, opt):
super(HoidetLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
if opt.hard_negative:
self.crit_h = FocalLossHardNeg(7)
if opt.hm_rel_dcn25_i_casc_match or opt.hm_rel_dcn25_i_match:
self.crit_reg_offset = RegL1LossOffset()
self.bce = torch.nn.BCELoss()
self.opt = opt
def __init__(self, opt):
super(MultiPoseLoss, self).__init__()
self.crit = FocalLoss()
self.crit_hm_hp = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_kp = RegWeightedL1Loss() if not opt.dense_hp else \
torch.nn.L1Loss(reduction='sum')
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.opt = opt
self.loss_dict = {}
self.loss_dict['count'] = 0
self.loss_dict['pos_loss'] = 0
self.loss_dict['neg_loss'] = 0
self.loss_dict['neg_loss1'] = 0
self.loss_dict['num_pos'] = 0
def __init__(self, opt):
super(MultiPoseLoss, self).__init__()
self.crit = FocalLoss()
self.crit_hm_hp = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_kp = RegWeightedL1Loss() if not opt.dense_hp else \
torch.nn.L1Loss(reduction='sum')
# self.t_crit_kp = t_RegWeightedL1Loss()
#
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
# self.t_crit_reg = t_RegL1Loss() if opt.reg_loss == 'l1' else \
# t_RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_teacher = torch.nn.MSELoss(size_average=True)
self.opt = opt
def __init__(self, opt):
super(CtdetLoss, self).__init__()
# 对于2D目标检测，分类损失使用的是Focal Loss. 这里opt.mse_loss默认为False
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
# 对于2D目标检测，回归损失使用的是L1 Loss. 这里opt.reg_loss默认为l1
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
# 对于2D目标检测，偏移损失使用的L1 Loss. 这里opt.dense_wh默认为False
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
# self.crit = torch.nn.MSELoss()
# self.crit_reg = RegL1Loss()
# self.crit_wh = torch.nn.L1Loss(reduction='sum')
self.opt = opt
def __init__(self, opt):
super(MultiPoseLoss, self).__init__()
self.crit = FocalLoss()
self.crit_hm_hp = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_kp = RegWeightedL1Loss() if not opt.dense_hp else \
torch.nn.L1Loss(reduction='sum')
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
# 3d compatibility
self.crit = CritAuto(self.crit)
self.crit_hm_hp = CritAuto(self.crit_hm_hp)
self.crit_kp = CritAuto(self.crit_kp)
self.crit_reg = CritAuto(self.crit_reg)
self.opt = opt
def __init__(self, opt):
super(MotLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss() # 分类loss 中心点x,y offset loss w,h回归loss
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.opt = opt
self.emb_dim = opt.reid_dim # reid特征长度
self.nID = opt.nID # 所有目标的ID数
self.classifier = nn.Linear(self.emb_dim, self.nID)
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1) # Re-ID loss
#self.TriLoss = TripletLoss()
self.emb_scale = math.sqrt(2) * math.log(self.nID - 1)
self.s_det = nn.Parameter(-1.85 * torch.ones(1))
self.s_id = nn.Parameter(-1.05 * torch.ones(1))
def __init__(self, opt):
super(CtdetLoss, self).__init__()
# define loss here
# hm: modified focal loss
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
# smoothl1
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' \
else RegLoss() if opt.reg_loss == 'sl1' \
else None
# l1
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh \
else NormRegL1Loss() if opt.norm_wh \
else RegWeightedL1Loss() if opt.cat_spec_wh \
else self.crit_reg
self.opt = opt
def __init__(self, opt):
super(CtdetLoss_NFS, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
# self.centerloss = CenterLoss_gt()
if opt.eq1:
print("eq1 is True")
self.centerloss = CenterLoss_gt_eq1_cuda()
else:
print("eq1 is False")
self.centerloss = CenterLoss_gt_cuda()
# self.crit = torch.nn.MSELoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.opt = opt
def __init__(self, opt):
super(MotLossWithEdgeRegression, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.crit_edge = torch.nn.BCEWithLogitsLoss()
self.opt = opt
self.emb_dim = opt.reid_dim
self.nID = opt.nID
self.classifier = nn.Linear(self.emb_dim, self.nID)
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
#self.TriLoss = TripletLoss()
self.emb_scale = math.sqrt(2) * math.log(self.nID - 1)
self.s_det = nn.Parameter(-1.85 * torch.ones(1))
self.s_id = nn.Parameter(-1.05 * torch.ones(1))
def __init__(self, opt):
super(MotLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.crit_density_focal = FocalLoss()
self.crit_density_ssim = NORMMSSSIM()
self.crit_count = torch.nn.MSELoss()
self.opt = opt
self.emb_dim = opt.reid_dim
self.nID = opt.nID
self.classifier = nn.Linear(self.emb_dim, self.nID)
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
self.emb_scale = math.sqrt(2) * math.log(self.nID -
1) if self.nID != 1 else 0
self.s_det = nn.Parameter(-1.85 * torch.ones(1))
self.s_id = nn.Parameter(-1.05 * torch.ones(1))
def __init__(self, opt):
super(MotLoss, self).__init__()
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
self.opt = opt
self.emb_dim = opt.reid_dim
self.nID = opt.nID
self.classifier = nn.Linear(self.emb_dim, self.nID)
if opt.id_loss == 'focal':
torch.nn.init.normal_(self.classifier.weight, std=0.01)
prior_prob = 0.01
bias_value = -math.log((1 - prior_prob) / prior_prob)
torch.nn.init.constant_(self.classifier.bias, bias_value)
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
self.emb_scale = math.sqrt(2) * math.log(self.nID - 1)
self.s_det = nn.Parameter(-1.85 * torch.ones(1))
self.s_id = nn.Parameter(-1.05 * torch.ones(1))
def __init__(self, opt, loss_states):
super(MotLoss, self).__init__()
self.opt = opt
self.loss_states = loss_states
self.emb_dim = opt.reid_dim
self.nID = opt.nID
# Loss for heatmap
self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()
# Loss for offsets
self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \
RegLoss() if opt.reg_loss == 'sl1' else None
# Loss for object sizes
self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \
NormRegL1Loss() if opt.norm_wh else \
RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg
# Supervised loss for object IDs
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
# FC layer for supervised object ID prediction
self.classifier = nn.Linear(self.emb_dim, self.nID)
# Self supervised loss for object embeddings
self.SelfSupLoss = NTXentLoss(opt.device, 0.5) if opt.unsup_loss == 'nt_xent' else \
TripletLoss(opt.device, 'batch_all', 0.5) if opt.unsup_loss == 'triplet_all' else \
TripletLoss(opt.device, 'batch_hard', 0.5) if opt.unsup_loss == 'triplet_hard' else None
if opt.unsup and self.SelfSupLoss is None:
raise ValueError('{} is not a supported self-supervised loss. '.format(opt.unsup_loss) + \
'Choose nt_xent, triplet_all, or triplet_hard')
self.emb_scale = math.sqrt(2) * math.log(self.nID - 1)
self.s_det = nn.Parameter(-1.85 * torch.ones(1))
self.s_id = nn.Parameter(-1.05 * torch.ones(1))
