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import torch.nn as nn
from collections import OrderedDict
class UNet1d(nn.Module):
def __init__(self, in_channels=3, out_channels=1, init_features=32):
super(UNet1d, self).__init__()
features = init_features
self.encoder1 = UNet1d._block(in_channels, features, name="enc1")
self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2)
self.encoder2 = UNet1d._block(features, features * 2, name="enc2")
self.pool2 = nn.MaxPool1d(kernel_size=2, stride=2)
self.encoder3 = UNet1d._block(features * 2, features * 4, name="enc3")
self.pool3 = nn.MaxPool1d(kernel_size=2, stride=2)
self.encoder4 = UNet1d._block(features * 4, features * 8, name="enc4")
self.pool4 = nn.MaxPool1d(kernel_size=2, stride=2)
self.bottleneck = UNet1d._block(features * 8, features * 16, name="bottleneck")
self.upconv4 = nn.ConvTranspose1d(features * 16, features * 8, kernel_size=2, stride=2)
self.decoder4 = UNet1d._block((features * 8) * 2, features * 8, name="dec4")
self.upconv3 = nn.ConvTranspose1d(features * 8, features * 4, kernel_size=2, stride=2)
self.decoder3 = UNet1d._block((features * 4) * 2, features * 4, name="dec3")
self.upconv2 = nn.ConvTranspose1d(features * 4, features * 2, kernel_size=2, stride=2)
self.decoder2 = UNet1d._block((features * 2) * 2, features * 2, name="dec2")
self.upconv1 = nn.ConvTranspose1d(features * 2, features, kernel_size=2, stride=2)
self.decoder1 = UNet1d._block(features * 2, features, name="dec1")
self.conv = nn.Conv1d(in_channels=features, out_channels=out_channels, kernel_size=1)
def forward(self, x):
enc1 = self.encoder1(x)
enc2 = self.encoder2(self.pool1(enc1))
enc3 = self.encoder3(self.pool2(enc2))
enc4 = self.encoder4(self.pool3(enc3))
bottleneck = self.bottleneck(self.pool4(enc4))
dec4 = self.upconv4(bottleneck)
dec4 = torch.cat((dec4, enc4), dim=1)
dec4 = self.decoder4(dec4)
dec3 = self.upconv3(dec4)
dec3 = torch.cat((dec3, enc3), dim=1)
dec3 = self.decoder3(dec3)
dec2 = self.upconv2(dec3)
dec2 = torch.cat((dec2, enc2), dim=1)
dec2 = self.decoder2(dec2)
dec1 = self.upconv1(dec2)
dec1 = torch.cat((dec1, enc1), dim=1)
dec1 = self.decoder1(dec1)
out = self.conv(dec1)
return out
@staticmethod
def _block(in_channels, features, name):
return nn.Sequential(
OrderedDict(
[
(
name + "conv1",
nn.Conv1d(
in_channels=in_channels,
out_channels=features,
kernel_size=3,
padding=1,
bias=False,
),
),
(name + "norm1", nn.BatchNorm1d(num_features=features)),
(name + "tanh1", nn.Tanh()),
(
name + "conv2",
nn.Conv1d(
in_channels=features,
out_channels=features,
kernel_size=3,
padding=1,
bias=False,
),
),
(name + "norm2", nn.BatchNorm1d(num_features=features)),
(name + "tanh2", nn.Tanh()),
]
)
)
class UNet2d(nn.Module):
def __init__(self, in_channels=3, out_channels=1, init_features=32):
super(UNet2d, self).__init__()
features = init_features
self.encoder1 = UNet2d._block(in_channels, features, name="enc1")
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder2 = UNet2d._block(features, features * 2, name="enc2")
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder3 = UNet2d._block(features * 2, features * 4, name="enc3")
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder4 = UNet2d._block(features * 4, features * 8, name="enc4")
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.bottleneck = UNet2d._block(features * 8, features * 16, name="bottleneck")
self.upconv4 = nn.ConvTranspose2d(features * 16, features * 8, kernel_size=2, stride=2)
self.decoder4 = UNet2d._block((features * 8) * 2, features * 8, name="dec4")
self.upconv3 = nn.ConvTranspose2d(features * 8, features * 4, kernel_size=2, stride=2)
self.decoder3 = UNet2d._block((features * 4) * 2, features * 4, name="dec3")
self.upconv2 = nn.ConvTranspose2d(features * 4, features * 2, kernel_size=2, stride=2)
self.decoder2 = UNet2d._block((features * 2) * 2, features * 2, name="dec2")
self.upconv1 = nn.ConvTranspose2d(features * 2, features, kernel_size=2, stride=2)
self.decoder1 = UNet2d._block(features * 2, features, name="dec1")
self.conv = nn.Conv2d(in_channels=features, out_channels=out_channels, kernel_size=1)
def forward(self, x):
enc1 = self.encoder1(x)
enc2 = self.encoder2(self.pool1(enc1))
enc3 = self.encoder3(self.pool2(enc2))
enc4 = self.encoder4(self.pool3(enc3))
bottleneck = self.bottleneck(self.pool4(enc4))
dec4 = self.upconv4(bottleneck)
dec4 = torch.cat((dec4, enc4), dim=1)
dec4 = self.decoder4(dec4)
dec3 = self.upconv3(dec4)
dec3 = torch.cat((dec3, enc3), dim=1)
dec3 = self.decoder3(dec3)
dec2 = self.upconv2(dec3)
dec2 = torch.cat((dec2, enc2), dim=1)
dec2 = self.decoder2(dec2)
dec1 = self.upconv1(dec2)
dec1 = torch.cat((dec1, enc1), dim=1)
dec1 = self.decoder1(dec1)
return self.conv(dec1)
@staticmethod
def _block(in_channels, features, name):
return nn.Sequential(
OrderedDict(
[
(
name + "conv1",
nn.Conv2d(
in_channels=in_channels,
out_channels=features,
kernel_size=3,
padding=1,
bias=False,
),
),
(name + "norm1", nn.BatchNorm2d(num_features=features)),
(name + "tanh1", nn.Tanh()),
(
name + "conv2",
nn.Conv2d(
in_channels=features,
out_channels=features,
kernel_size=3,
padding=1,
bias=False,
),
),
(name + "norm2", nn.BatchNorm2d(num_features=features)),
(name + "tanh2", nn.Tanh()),
]
)
)
class UNet3d(nn.Module):
def __init__(self, in_channels=3, out_channels=1, init_features=32):
super(UNet3d, self).__init__()
features = init_features
self.encoder1 = UNet3d._block(in_channels, features, name="enc1")
self.pool1 = nn.MaxPool3d(kernel_size=2, stride=2)
self.encoder2 = UNet3d._block(features, features * 2, name="enc2")
self.pool2 = nn.MaxPool3d(kernel_size=2, stride=2)
self.encoder3 = UNet3d._block(features * 2, features * 4, name="enc3")
self.pool3 = nn.MaxPool3d(kernel_size=2, stride=2)
self.encoder4 = UNet3d._block(features * 4, features * 8, name="enc4")
self.pool4 = nn.MaxPool3d(kernel_size=2, stride=2)
self.bottleneck = UNet3d._block(features * 8, features * 16, name="bottleneck")
self.upconv4 = nn.ConvTranspose3d(features * 16, features * 8, kernel_size=2, stride=2)
self.decoder4 = UNet3d._block((features * 8) * 2, features * 8, name="dec4")
self.upconv3 = nn.ConvTranspose3d(features * 8, features * 4, kernel_size=2, stride=2)
self.decoder3 = UNet3d._block((features * 4) * 2, features * 4, name="dec3")
self.upconv2 = nn.ConvTranspose3d(features * 4, features * 2, kernel_size=2, stride=2)
self.decoder2 = UNet3d._block((features * 2) * 2, features * 2, name="dec2")
self.upconv1 = nn.ConvTranspose3d(features * 2, features, kernel_size=2, stride=2)
self.decoder1 = UNet3d._block(features * 2, features, name="dec1")
self.conv = nn.Conv3d(in_channels=features, out_channels=out_channels, kernel_size=1)
def forward(self, x):
enc1 = self.encoder1(x)
enc2 = self.encoder2(self.pool1(enc1))
enc3 = self.encoder3(self.pool2(enc2))
enc4 = self.encoder4(self.pool3(enc3))
bottleneck = self.bottleneck(self.pool4(enc4))
dec4 = self.upconv4(bottleneck)
dec4 = torch.cat((dec4, enc4), dim=1)
dec4 = self.decoder4(dec4)
dec3 = self.upconv3(dec4)
dec3 = torch.cat((dec3, enc3), dim=1)
dec3 = self.decoder3(dec3)
dec2 = self.upconv2(dec3)
dec2 = torch.cat((dec2, enc2), dim=1)
dec2 = self.decoder2(dec2)
dec1 = self.upconv1(dec2)
dec1 = torch.cat((dec1, enc1), dim=1)
dec1 = self.decoder1(dec1)
return self.conv(dec1)
@staticmethod
def _block(in_channels, features, name):
return nn.Sequential(
OrderedDict(
[
(
name + "conv1",
nn.Conv3d(
in_channels=in_channels,
out_channels=features,
kernel_size=3,
padding=1,
bias=False,
),
),
(name + "norm1", nn.BatchNorm3d(num_features=features)),
(name + "tanh1", nn.Tanh()),
(
name + "conv2",
nn.Conv3d(
in_channels=features,
out_channels=features,
kernel_size=3,
padding=1,
bias=False,
),
),
(name + "norm2", nn.BatchNorm3d(num_features=features)),
(name + "tanh2", nn.Tanh()),
]
)
) |