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# 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 numpy as np
import torch
from nnunet.training.loss_functions.crossentropy import RobustCrossEntropyLoss
class TopKLoss(RobustCrossEntropyLoss):
"""
Network has to have NO LINEARITY!
"""
def __init__(self, weight=None, ignore_index=-100, k=10):
self.k = k
super(TopKLoss, self).__init__(weight, False, ignore_index, reduce=False)
def forward(self, inp, target):
target = target[:, 0].long()
res = super(TopKLoss, self).forward(inp, target)
num_voxels = np.prod(res.shape, dtype=np.int64)
res, _ = torch.topk(res.view((-1, )), int(num_voxels * self.k / 100), sorted=False)
return res.mean()
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