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nnUNet_calvingfront_detection

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init

ecf08bc about 1 year ago

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1.67 kB

	# 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)