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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 torch import nn
class InitWeights_He(object):
def __init__(self, neg_slope=1e-2):
self.neg_slope = neg_slope
def __call__(self, module):
if isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d):
module.weight = nn.init.kaiming_normal_(module.weight, a=self.neg_slope)
if module.bias is not None:
module.bias = nn.init.constant_(module.bias, 0)
class InitWeights_XavierUniform(object):
def __init__(self, gain=1):
self.gain = gain
def __call__(self, module):
if isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d):
module.weight = nn.init.xavier_uniform_(module.weight, self.gain)
if module.bias is not None:
module.bias = nn.init.constant_(module.bias, 0)
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