def __init__(self, opt, model, testnames):
self.opt = opt
self.model = model
self.testnames = testnames
self.loss_fn1 = m.Dice_Loss()
self.loss_fn2 = torch.nn.BCELoss(size_average=True)
self.loss_fn3 = torch.nn.MSELoss(size_average=True)
def __init__(self, opt, model, testdata, setsize, thresholds):
self.opt = opt
self.model = model
self.testdata = testdata
self.setsize = setsize
self.loss_fn1 = m.Dice_Loss()
self.loss_fn2 = torch.nn.BCELoss(size_average=True)
self.loss_fn3 = torch.nn.MSELoss(size_average=True)
self.thresholds = thresholds
def __init__(self, opt, model, testnames, dataset):
self.opt = opt
self.model = model
self.testnames = testnames
self.dataset = dataset
self.loss_fn1 = m.Dice_Loss()
self.loss_fn2 = torch.nn.BCELoss(size_average=True)
self.loss_fn3 = torch.nn.MSELoss(size_average=True)
self.deployloader = DataLoader(self.dataset,
batch_size=1,
shuffle=False)
def __init__(self, opt, model, traindata, valdata, trainsetsize, valsetsize, checkpoint):
self.opt = opt
self.model = model
self.traindata = traindata
self.valdata = valdata
self.trainsetsize = trainsetsize
self.valsetsize = valsetsize
self.loss_fn1 = m.Dice_Loss()
#self.loss_fn2 = torch.nn.BCELoss(size_average=True)
#self.loss_fn3 = torch.nn.MSELoss(size_average=True)
self.loss_fn2 = torch.nn.BCELoss(size_average=False)
self.loss_fn3 = torch.nn.MSELoss(size_average=False)
self.checkpoint = checkpoint
self.sched = j.combine_scheds([0.3, 0.7], [j.sched_cos(0.0001, 0.6), j.sched_cos(0.6, 2e-06)])