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, )), ] ) )