76 lines
3.2 KiB
Python
76 lines
3.2 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .base_class_models import BaseModel
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class FiLM(nn.Module):
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def __init__(self, num_features, style_dim):
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super(FiLM, self).__init__()
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self.gamma = nn.Linear(style_dim, num_features)
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self.beta = nn.Linear(style_dim, num_features)
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def forward(self, x, style):
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gamma = self.gamma(style).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
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beta = self.beta(style).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
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return gamma * x + beta
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class UNetBlock(nn.Module):
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def __init__(self, in_channels, out_channels, style_dim=None):
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super(UNetBlock, self).__init__()
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self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_size=3, padding=1)
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self.conv2 = nn.Conv3d(out_channels, out_channels, kernel_size=3, padding=1)
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self.norm = nn.BatchNorm3d(out_channels)
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self.relu = nn.ReLU(inplace=True)
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self.film = FiLM(out_channels, style_dim) if style_dim else None
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def forward(self, x, style=None):
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x = self.relu(self.norm(self.conv1(x)))
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x = self.relu(self.norm(self.conv2(x)))
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if self.film:
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x = self.film(x, style)
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return x
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class UNet3D(BaseModel):
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def __init__(self, N: int = 128,
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in_channels: int = 2,
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out_channels: int = 1,
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style_dim: int = 2,
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device: torch.device = torch.device('cpu')):
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"""
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3D U-Net model with optional FiLM layers for style conditioning.
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Parameters:
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- N: Size of the input data: data will have the shape (B, C, N, N, N) with B the batch size, C the number of channels.
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- in_channels: Number of input channels (default is 2).
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- out_channels: Number of output channels (default is 1).
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- style_dim: Dimension of the style vector (default is 2).
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- device: Device to load the model onto (default is CPU).
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This model implements a 3D U-Net architecture with downsampling and upsampling blocks.
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The model uses convolutional layers with ReLU activations and batch normalization.
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The FiLM layers are used to condition the feature maps on style parameters.
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"""
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super().__init__(N=N,
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in_channels=in_channels,
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out_channels=out_channels,
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style_parameters=style_dim,
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device=device)
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self.enc1 = UNetBlock(in_channels, 32, style_dim)
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self.pool1 = nn.MaxPool3d(2)
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self.enc2 = UNetBlock(32, 64, style_dim)
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self.pool2 = nn.MaxPool3d(2)
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self.bottleneck = UNetBlock(64, 128, style_dim)
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self.up2 = nn.ConvTranspose3d(128, 64, kernel_size=2, stride=2)
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self.dec2 = UNetBlock(128, 64)
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self.up1 = nn.ConvTranspose3d(64, 32, kernel_size=2, stride=2)
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self.dec1 = UNetBlock(64, 32)
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self.final = nn.Conv3d(32, out_channels, kernel_size=1)
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def forward(self, x, style):
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e1 = self.enc1(x, style)
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e2 = self.enc2(self.pool1(e1), style)
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b = self.bottleneck(self.pool2(e2), style)
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d2 = self.dec2(torch.cat([self.up2(b), e2], dim=1))
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d1 = self.dec1(torch.cat([self.up1(d2), e1], dim=1))
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return self.final(d1)
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