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235e4f9 | 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 | import os
from abc import ABC, abstractmethod
import torch
import torch.nn as nn
from torchsummary import summary
from base_model import BaseModel
def passthrough(x, **kwargs):
return x
def ELUCons(elu, nchan):
if elu:
return nn.ELU(inplace=True)
else:
return nn.PReLU(nchan)
class LUConv(nn.Module):
def __init__(self, nchan, elu):
super(LUConv, self).__init__()
self.relu1 = ELUCons(elu, nchan)
self.conv1 = nn.Conv3d(nchan, nchan, kernel_size=5, padding=2)
self.bn1 = torch.nn.BatchNorm3d(nchan)
def forward(self, x):
out = self.relu1(self.bn1(self.conv1(x)))
return out
def _make_nConv(nchan, depth, elu):
layers = []
for _ in range(depth):
layers.append(LUConv(nchan, elu))
return nn.Sequential(*layers)
class InputTransition(nn.Module):
def __init__(self, in_channels, elu):
super(InputTransition, self).__init__()
self.num_features = 16
self.in_channels = in_channels
self.conv1 = nn.Conv3d(self.in_channels, self.num_features, kernel_size=5, padding=2)
self.bn1 = torch.nn.BatchNorm3d(self.num_features)
self.relu1 = ELUCons(elu, self.num_features)
def forward(self, x):
out = self.conv1(x)
repeat_rate = int(self.num_features / self.in_channels)
out = self.bn1(out)
x16 = x.repeat(1, repeat_rate, 1, 1, 1)
return self.relu1(torch.add(out, x16))
class DownTransition(nn.Module):
def __init__(self, inChans, nConvs, elu, dropout=False):
super(DownTransition, self).__init__()
outChans = 2 * inChans
self.down_conv = nn.Conv3d(inChans, outChans, kernel_size=2, stride=2)
self.bn1 = torch.nn.BatchNorm3d(outChans)
self.do1 = passthrough
self.relu1 = ELUCons(elu, outChans)
self.relu2 = ELUCons(elu, outChans)
if dropout:
self.do1 = nn.Dropout3d()
self.ops = _make_nConv(outChans, nConvs, elu)
def forward(self, x):
down = self.relu1(self.bn1(self.down_conv(x)))
out = self.do1(down)
out = self.ops(out)
out = self.relu2(torch.add(out, down))
return out
class UpTransition(nn.Module):
def __init__(self, inChans, outChans, nConvs, elu, dropout=False):
super(UpTransition, self).__init__()
self.up_conv = nn.ConvTranspose3d(inChans, outChans // 2, kernel_size=2, stride=2)
self.bn1 = torch.nn.BatchNorm3d(outChans // 2)
self.do1 = passthrough
self.do2 = nn.Dropout3d()
self.relu1 = ELUCons(elu, outChans // 2)
self.relu2 = ELUCons(elu, outChans)
if dropout:
self.do1 = nn.Dropout3d()
self.ops = _make_nConv(outChans, nConvs, elu)
def forward(self, x, skipx):
out = self.do1(x)
skipxdo = self.do2(skipx)
out = self.relu1(self.bn1(self.up_conv(out)))
xcat = torch.cat((out, skipxdo), 1)
out = self.ops(xcat)
out = self.relu2(torch.add(out, xcat))
return out
class OutputTransition(nn.Module):
def __init__(self, in_channels, classes, elu):
super(OutputTransition, self).__init__()
self.classes = classes
self.conv1 = nn.Conv3d(in_channels, classes, kernel_size=5, padding=2)
self.bn1 = torch.nn.BatchNorm3d(classes)
self.conv2 = nn.Conv3d(classes, classes, kernel_size=1)
self.relu1 = ELUCons(elu, classes)
def forward(self, x):
# convolve 32 down to channels as the desired classes
out = self.relu1(self.bn1(self.conv1(x)))
out = self.conv2(out)
return out |