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from collections import OrderedDict
import torch
import torch.nn as nn
class UNet1d(nn.Module):
def __init__(self, in_channels=3, out_channels=1, init_features=128, multi=None):
super(UNet1d, self).__init__()
if multi is None:
multi = [1, 2, 2, 4]
features = init_features
self.encoder1 = UNet1d._block(in_channels, features * multi[0], name="enc1")
self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2)
self.encoder2 = UNet1d._block(features * multi[0], features * multi[1], name="enc2")
self.pool2 = nn.MaxPool1d(kernel_size=2, stride=2)
self.encoder3 = UNet1d._block(features * multi[1], features * multi[2], name="enc3")
self.pool3 = nn.MaxPool1d(kernel_size=2, stride=2)
self.encoder4 = UNet1d._block(features * multi[2], features * multi[3], name="enc4")
self.pool4 = nn.MaxPool1d(kernel_size=2, stride=2)
self.bottleneck = UNet1d._block(features * multi[3], features * multi[3], name="bottleneck")
self.upconv4 = nn.ConvTranspose1d(
features * multi[3], features * multi[3], kernel_size=2, stride=2
)
self.decoder4 = UNet1d._block((features * multi[3]) * 2, features * multi[3], name="dec4")
self.upconv3 = nn.ConvTranspose1d(
features * multi[3], features * multi[2], kernel_size=2, stride=2
)
self.decoder3 = UNet1d._block((features * multi[2]) * 2, features * multi[2], name="dec3")
self.upconv2 = nn.ConvTranspose1d(
features * multi[2], features * multi[1], kernel_size=2, stride=2
)
self.decoder2 = UNet1d._block((features * multi[1]) * 2, features * multi[1], name="dec2")
self.upconv1 = nn.ConvTranspose1d(
features * multi[1], features * multi[0], kernel_size=2, stride=2
)
self.decoder1 = UNet1d._block(features * multi[0] * 2, features * multi[0], name="dec1")
self.conv = nn.Conv1d(
in_channels=features * multi[0], out_channels=out_channels, kernel_size=1
)
def forward(self, x, nonpadding=None):
if nonpadding is None:
nonpadding = torch.ones_like(x)[:, :, :1]
enc1 = self.encoder1(x.transpose(1, 2)) * nonpadding.transpose(1, 2)
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).transpose(1, 2) * nonpadding
@staticmethod
def _block(in_channels, features, name):
return nn.Sequential(
OrderedDict(
[
(
name + "conv1",
nn.Conv1d(
in_channels=in_channels,
out_channels=features,
kernel_size=5,
padding=2,
bias=False,
),
),
(name + "norm1", nn.GroupNorm(4, features)),
(name + "tanh1", nn.Tanh()),
(
name + "conv2",
nn.Conv1d(
in_channels=features,
out_channels=features,
kernel_size=5,
padding=2,
bias=False,
),
),
(name + "norm2", nn.GroupNorm(4, features)),
(name + "tanh2", nn.Tanh()),
]
)
)
class UNet2d(nn.Module):
def __init__(self, in_channels=3, out_channels=1, init_features=32, multi=None):
super(UNet2d, self).__init__()
features = init_features
self.encoder1 = UNet2d._block(in_channels, features * multi[0], name="enc1")
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder2 = UNet2d._block(features * multi[0], features * multi[1], name="enc2")
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder3 = UNet2d._block(features * multi[1], features * multi[2], name="enc3")
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder4 = UNet2d._block(features * multi[2], features * multi[3], name="enc4")
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.bottleneck = UNet2d._block(features * multi[3], features * multi[3], name="bottleneck")
self.upconv4 = nn.ConvTranspose2d(
features * multi[3], features * multi[3], kernel_size=2, stride=2
)
self.decoder4 = UNet2d._block((features * multi[3]) * 2, features * multi[3], name="dec4")
self.upconv3 = nn.ConvTranspose2d(
features * multi[3], features * multi[2], kernel_size=2, stride=2
)
self.decoder3 = UNet2d._block((features * multi[2]) * 2, features * multi[2], name="dec3")
self.upconv2 = nn.ConvTranspose2d(
features * multi[2], features * multi[1], kernel_size=2, stride=2
)
self.decoder2 = UNet2d._block((features * multi[1]) * 2, features * multi[1], name="dec2")
self.upconv1 = nn.ConvTranspose2d(
features * multi[1], features * multi[0], kernel_size=2, stride=2
)
self.decoder1 = UNet2d._block(features * multi[0] * 2, features * multi[0], name="dec1")
self.conv = nn.Conv2d(
in_channels=features * multi[0], 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)
x = self.conv(dec1)
return x
@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.GroupNorm(4, 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.GroupNorm(4, features)),
(name + "tanh2", nn.Tanh()),
(name + "conv3", nn.Conv2d(
in_channels=features,
out_channels=features,
kernel_size=1,
padding=0,
bias=True,
)),
]
)
)
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