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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.
from nnunet.utilities.tensor_utilities import mean_tensor
from torch import nn
import torch
from torch.nn.parameter import Parameter
import torch.jit
class FRN3D(nn.Module):
def __init__(self, num_features: int, eps=1e-6, **kwargs):
super().__init__()
self.eps = eps
self.num_features = num_features
self.weight = Parameter(torch.ones(1, num_features, 1, 1, 1), True)
self.bias = Parameter(torch.zeros(1, num_features, 1, 1, 1), True)
self.tau = Parameter(torch.zeros(1, num_features, 1, 1, 1), True)
def forward(self, x: torch.Tensor):
x = x * torch.rsqrt(mean_tensor(x * x, [2, 3, 4], keepdim=True) + self.eps)
return torch.max(self.weight * x + self.bias, self.tau)
if __name__ == "__main__":
tmp = torch.rand((3, 32, 16, 16, 16))
frn = FRN3D(32)
out = frn(tmp)
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