Spaces:
Runtime error
Runtime error
File size: 6,774 Bytes
2d5f249 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
# -*- 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: [email protected]
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]]
|