nnUNet_calvingfront_detection
/
nnunet
/training
/network_training
/nnUNet_variants
/optimizer_and_lr
/nnUNetTrainerV2_momentum09in2D.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.nnUNetTrainerV2 import nnUNetTrainerV2 | |
class nnUNetTrainerV2_momentum09in2D(nnUNetTrainerV2): | |
def initialize_optimizer_and_scheduler(self): | |
if self.threeD: | |
momentum = 0.99 | |
else: | |
momentum = 0.9 | |
assert self.network is not None, "self.initialize_network must be called first" | |
self.optimizer = torch.optim.SGD(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay, | |
momentum=momentum, nesterov=True) | |
self.lr_scheduler = None | |