nnUNet_calvingfront_detection
/
nnunet
/training
/network_training
/nnUNet_variants
/optimizer_and_lr
/nnUNetTrainerV2_SGD_ReduceOnPlateau.py
# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import torch | |
from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer | |
from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 | |
from torch.optim import lr_scheduler | |
class nnUNetTrainerV2_SGD_ReduceOnPlateau(nnUNetTrainerV2): | |
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) | |
def initialize_optimizer_and_scheduler(self): | |
self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, | |
momentum=0.99, nesterov=True) | |
self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='min', factor=0.2, | |
patience=self.lr_scheduler_patience, | |
verbose=True, threshold=self.lr_scheduler_eps, | |
threshold_mode="abs") | |
def maybe_update_lr(self, epoch=None): | |
# maybe update learning rate | |
if self.lr_scheduler is not None: | |
assert isinstance(self.lr_scheduler, (lr_scheduler.ReduceLROnPlateau, lr_scheduler._LRScheduler)) | |
if isinstance(self.lr_scheduler, lr_scheduler.ReduceLROnPlateau): | |
# lr scheduler is updated with moving average val loss. should be more robust | |
if self.epoch > 0: # otherwise self.train_loss_MA is None | |
self.lr_scheduler.step(self.train_loss_MA) | |
else: | |
self.lr_scheduler.step(self.epoch + 1) | |
self.print_to_log_file("lr is now (scheduler) %s" % str(self.optimizer.param_groups[0]['lr'])) | |
def on_epoch_end(self): | |
return nnUNetTrainer.on_epoch_end(self) | |