def __init__(self,
label_loss,
density_map_loss,
count_loss,
output_folder=None):
super(CountingDiceLoss, self).__init__()
self.loss = SoftDiceLoss(
softmax_helper, **{
'batch_dice': False,
'smooth': 1e-5,
'do_bg': False
})
self.loss_density_map = torch.nn.MSELoss(
) # WeightedRobustCrossEntropyLoss([0.001, 0.999])
self.loss_n_ma = torch.nn.MSELoss()
self.n = 0
self.output_folder = output_folder
self.label_loss = label_loss
self.density_map_loss = density_map_loss
self.count_loss = count_loss
self.l_ = []
self.l_dm = []
self.l_n = []
self.l_total = []
self.sizes = []
def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
unpack_data=True, deterministic=True, fp16=False):
super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data,
deterministic, fp16, deep_supervision=False)
soft_dice_kwargs = {'batch_dice': self.batch_dice, 'smooth': 1e-5, 'do_bg': False}
self.loss = SoftDiceLoss(**soft_dice_kwargs)
self.max_num_epochs = 500
def __init__(self, alpha=.25, gamma=2):
super(SAWLoss, self).__init__()
self.dice = SoftDiceLoss(
softmax_helper, **{
'batch_dice': False,
'smooth': 1e-5,
'do_bg': False
})
self.l2 = MSELoss()
def __init__(self,
plans_file,
fold,
output_folder=None,
dataset_directory=None,
batch_dice=True,
stage=None,
unpack_data=True,
deterministic=True,
fp16=False):
super().__init__(plans_file, fold, output_folder, dataset_directory,
batch_dice, stage, unpack_data, deterministic, fp16)
self.apply_nonlin = softmax_helper
self.loss = SoftDiceLoss(apply_nonlin=self.apply_nonlin,
batch_dice=self.batch_dice,
smooth=1e-5,
do_bg=False)
def __init__(self,
plans_file,
fold,
output_folder=None,
dataset_directory=None,
batch_dice=True,
stage=None,
unpack_data=True,
deterministic=True,
fp16=False):
super().__init__(plans_file, fold, output_folder, dataset_directory,
batch_dice, stage, unpack_data, deterministic, fp16)
self.loss = SoftDiceLoss(apply_nonlin=torch.sigmoid,
**{
'batch_dice': False,
'do_bg': True,
'smooth': 0
})
def __init__(self,
plans_file,
fold,
output_folder=None,
dataset_directory=None,
batch_dice=True,
stage=None,
unpack_data=True,
deterministic=True,
fp16=False):
super().__init__(plans_file, fold, output_folder, dataset_directory,
batch_dice, stage, unpack_data, deterministic, fp16)
self.loss = SoftDiceLoss(
**{
'apply_nonlin': softmax_helper,
'batch_dice': self.batch_dice,
'smooth': 1e-5,
'do_bg': True
})
def initialize_optimizer_and_scheduler(self):
self.opt_loss.append((torch.optim.Adam(self.network.parameters(), self.initial_lrs[0]),
SoftDiceLoss(softmax_helper, **{'batch_dice': False, 'smooth': 1e-5, 'do_bg': False})))
self.opt_loss.append((torch.optim.Adam(self.network.parameters(), self.initial_lrs[1]),
torch.nn.MSELoss()))