def __init__(self, target_name='sigmoid', endpoint_name="sigmoid"): super().__init__() self.cross_entropy = nn.BCEWithLogitsLoss(reduce=False) self.tensorboard_logger = get_tensorboard_logger() self.logger = get_logger() self.endpoint_name = endpoint_name self.target_name = target_name
def __init__(self, endpoint_name, target_name): super().__init__() self.endpoint_name = endpoint_name self.target_name = target_name self.logger = get_logger() self.tensorboard_logger = get_tensorboard_logger() self.l1 = nn.L1Loss(reduction='none')
def __init__(self, alpha, gamma, loss_module, name): super().__init__(loss_module) self.alpha = alpha self.gamma = gamma self.k = None self.tensorboard_logger = logger.get_tensorboard_logger() self.name = name
def __init__(self, target_name, endpoint_name): super().__init__() self.cross_entropy = nn.CrossEntropyLoss(reduce=False) self.tensorboard_logger = get_tensorboard_logger() self.logger = get_logger() self.target_name = target_name self.endpoint_name = endpoint_name
def __init__(self, m, cdist_fn=calc_cdist, endpoint_name='triplet'): super().__init__() self.name = "BatchHard(m={})".format(m) self.m = m self.cdist_fn = cdist_fn self.tensorboard_logger = get_tensorboard_logger() self.logger = get_logger() self.endpoint_name = endpoint_name
def __init__(self, target_name, endpoint_name, reduction='mean'): super(CrossEntropyLoss, self).__init__() self.cross_entropy = nn.CrossEntropyLoss(ignore_index=255, reduction=reduction) self.tensorboard_logger = get_tensorboard_logger() #self.logger = get_logger() self.target_name = target_name self.endpoint_name = endpoint_name
def __init__(self, loss_module, name, init): """ losses (dic): A name-> loss dictionary. init (float): 0 -> factor is 1 """ super().__init__(loss_module) #log(o^2) tensor = torch.tensor(init, requires_grad=True) self.log_var = torch.nn.Parameter(tensor, requires_grad=True) self.name = name self.logger = logger.get_tensorboard_logger()
def __init__(self, m, cdist_fn=calc_cdist, T=1.0, endpoint_name="triplet"): """ Args: m: margin T: Softmax temperature """ super(BatchSoft, self).__init__() self.name = "BatchSoft(m={}, T={})".format(m, T) self.m = m self.T = T self.cdist_fn = cdist_fn self.tensorboard_logger = get_tensorboard_logger() self.logger = get_logger()
def __init__(self, delta, tr_loss, id_loss): """ Args: tr_loss tuple(name, DynamicFocalLoss): batch hard loss id_loss tuple(name, DynamicFocalLoss): softmax loss """ # TODO super().__init__({}) # name is name of dataset self.tr_name, self.tr_loss = tr_loss self.id_name, self.id_loss = id_loss self.delta = delta self.tensorboard_logger = logger.get_tensorboard_logger()
def __init__(self, schedule_fn, optimizer, last_epoch=-1): """ Args: last_epoch: counting from zero """ self.schedule_fn = schedule_fn # Manage underlying pytorch behaviour, # -1 is special value if last_epoch == 0: # from scratch last_epoch = -1 else: # restored last_epoch -= 1 self.last_epoch = last_epoch self.tensorboard_logger = get_tensorboard_logger() super().__init__(optimizer, last_epoch)
def __init__(self, target_name, endpoint_name, top_k_percent_pixels=1.0, hard_mining_step=0): """ Args: hard_mining_step: Training step in which the hard mining kicks off """ super().__init__() self.cross_entropy = nn.CrossEntropyLoss(ignore_index=255, reduction='none') self.tensorboard_logger = get_tensorboard_logger() #self.logger = get_logger() self.target_name = target_name self.endpoint_name = endpoint_name if top_k_percent_pixels == 1.0: # just default cross entropy loss self.forward = super().forward self.top_k_percent_pixels = top_k_percent_pixels self.hard_mining_step = hard_mining_step # TODO global step self.step = 0
def __init__(self, attributes): super().__init__() self.attributes = attributes self.cross_entropy = nn.CrossEntropyLoss(reduce=False) self.M = len(attributes) self.tensorboard_logger = get_tensorboard_logger()
def __init__(self, endpoint_name, target_name): super().__init__() self.endpoint_name = endpoint_name self.target_name = target_name self.logger = get_logger() self.tensorboard_logger = get_tensorboard_logger()
def __init__(self, losses): super().__init__() self.losses = torch.nn.ModuleDict(losses) self.logger = logger.get_tensorboard_logger()
def __init__(self, tasks, model, scale_only_backbone=False): super().__init__() self.model = model self.tasks = tasks self.tb = logger.get_tensorboard_logger() self.scale_only_backbone = scale_only_backbone