def run_training(self):
self.maybe_update_lr(
self.epoch
) # if we dont overwrite epoch then self.epoch+1 is used which is not what we
# want at the start of the training
ds = self.network.decoder.deep_supervision
self.network.decoder.deep_supervision = True
ret = nnUNetTrainer.run_training(self)
self.network.decoder.deep_supervision = ds
return ret
def run_training(self):
self.maybe_update_lr(self.epoch)
# amp must be initialized before DP
ds = self.network.do_ds
self.network.do_ds = True
self.network = DataParallel(self.network, tuple(range(self.num_gpus)), )
ret = nnUNetTrainer.run_training(self)
self.network = self.network.module
self.network.do_ds = ds
return ret
def run_training(self):
"""
if we run with -c then we need to set the correct lr for the first epoch, otherwise it will run the first
continued epoch with self.initial_lr
we also need to make sure deep supervision in the network is enabled for training, thus the wrapper
:return:
"""
self.maybe_update_lr(self.epoch) # if we dont overwrite epoch then self.epoch+1 is used which is not what we
# want at the start of the training
if isinstance(self.network, DDP):
net = self.network.module
else:
net = self.network
ds = net.do_ds
net.do_ds = True
ret = nnUNetTrainer.run_training(self)
net.do_ds = ds
return ret
