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# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de
import torch.nn as nn
import pytorch_lightning as pl
class BaseNetwork(pl.LightningModule):
def __init__(self):
super(BaseNetwork, self).__init__()
def init_weights(self, init_type='xavier', gain=0.02):
'''
initializes network's weights
init_type: normal | xavier | kaiming | orthogonal
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39
'''
def init_func(m):
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1
or classname.find('Linear') != -1):
if init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, gain)
elif init_type == 'xavier':
nn.init.xavier_normal_(m.weight.data, gain=gain)
elif init_type == 'kaiming':
nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
nn.init.orthogonal_(m.weight.data, gain=gain)
if hasattr(m, 'bias') and m.bias is not None:
nn.init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, gain)
nn.init.constant_(m.bias.data, 0.0)
self.apply(init_func)
class Residual3D(BaseNetwork):
def __init__(self, numIn, numOut):
super(Residual3D, self).__init__()
self.numIn = numIn
self.numOut = numOut
self.with_bias = True
# self.bn = nn.GroupNorm(4, self.numIn)
self.bn = nn.BatchNorm3d(self.numIn)
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv3d(self.numIn,
self.numOut,
bias=self.with_bias,
kernel_size=3,
stride=1,
padding=2,
dilation=2)
# self.bn1 = nn.GroupNorm(4, self.numOut)
self.bn1 = nn.BatchNorm3d(self.numOut)
self.conv2 = nn.Conv3d(self.numOut,
self.numOut,
bias=self.with_bias,
kernel_size=3,
stride=1,
padding=1)
# self.bn2 = nn.GroupNorm(4, self.numOut)
self.bn2 = nn.BatchNorm3d(self.numOut)
self.conv3 = nn.Conv3d(self.numOut,
self.numOut,
bias=self.with_bias,
kernel_size=3,
stride=1,
padding=1)
if self.numIn != self.numOut:
self.conv4 = nn.Conv3d(self.numIn,
self.numOut,
bias=self.with_bias,
kernel_size=1)
self.init_weights()
def forward(self, x):
residual = x
# out = self.bn(x)
# out = self.relu(out)
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
# out = self.conv3(out)
# out = self.relu(out)
if self.numIn != self.numOut:
residual = self.conv4(x)
return out + residual
class VolumeEncoder(BaseNetwork):
"""CycleGan Encoder"""
def __init__(self, num_in=3, num_out=32, num_stacks=2):
super(VolumeEncoder, self).__init__()
self.num_in = num_in
self.num_out = num_out
self.num_inter = 8
self.num_stacks = num_stacks
self.with_bias = True
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv3d(self.num_in,
self.num_inter,
bias=self.with_bias,
kernel_size=5,
stride=2,
padding=4,
dilation=2)
# self.bn1 = nn.GroupNorm(4, self.num_inter)
self.bn1 = nn.BatchNorm3d(self.num_inter)
self.conv2 = nn.Conv3d(self.num_inter,
self.num_out,
bias=self.with_bias,
kernel_size=5,
stride=2,
padding=4,
dilation=2)
# self.bn2 = nn.GroupNorm(4, self.num_out)
self.bn2 = nn.BatchNorm3d(self.num_out)
self.conv_out1 = nn.Conv3d(self.num_out,
self.num_out,
bias=self.with_bias,
kernel_size=3,
stride=1,
padding=1,
dilation=1)
self.conv_out2 = nn.Conv3d(self.num_out,
self.num_out,
bias=self.with_bias,
kernel_size=3,
stride=1,
padding=1,
dilation=1)
for idx in range(self.num_stacks):
self.add_module("res" + str(idx),
Residual3D(self.num_out, self.num_out))
self.init_weights()
def forward(self, x, intermediate_output=True):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out_lst = []
for idx in range(self.num_stacks):
out = self._modules["res" + str(idx)](out)
out_lst.append(out)
if intermediate_output:
return out_lst
else:
return [out_lst[-1]]
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