nnUNet_calvingfront_detection
/
nnunet
/training
/network_training
/nnUNet_variants
/optimizer_and_lr
/nnUNetTrainerV2_warmup.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. | |
from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 | |
class nnUNetTrainerV2_warmup(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) | |
self.max_num_epochs = 1050 | |
def maybe_update_lr(self, epoch=None): | |
if self.epoch < 50: | |
# epoch 49 is max | |
# we increase lr linearly from 0 to initial_lr | |
lr = (self.epoch + 1) / 50 * self.initial_lr | |
self.optimizer.param_groups[0]['lr'] = lr | |
self.print_to_log_file("epoch:", self.epoch, "lr:", lr) | |
else: | |
if epoch is not None: | |
ep = epoch - 49 | |
else: | |
ep = self.epoch - 49 | |
assert ep > 0, "epoch must be >0" | |
return super().maybe_update_lr(ep) | |