127 lines
4.9 KiB
Python
127 lines
4.9 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 UNetEncLayer(nn.Module):
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def __init__(self, in_channels, out_channels, style_dim=None):
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super(UNetEncLayer, self).__init__()
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self.block = UNetBlock(in_channels, out_channels, style_dim)
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self.pool = nn.MaxPool3d(2)
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def forward(self, x, style=None):
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x = self.block(x, style)
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return x, self.pool(x)
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class UNetDecLayer(nn.Module):
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def __init__(self, in_channels, out_channels, skip_connection_channels, style_dim=None):
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super(UNetDecLayer, self).__init__()
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self.up = nn.ConvTranspose3d(in_channels, out_channels, kernel_size=2, stride=2)
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self.block = UNetBlock(out_channels + skip_connection_channels, out_channels, style_dim)
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def forward(self, x, skip_connection, style=None):
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x = self.up(x)
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x = torch.cat([x, skip_connection], dim=1)
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return self.block(x, style)
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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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first_layer_channel_exponent: int = 3,
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):
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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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import numpy as np
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self.depth = np.floor(np.log2(N)).astype(int) - 1 # Depth of the U-Net based on input size N
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self.first_layer_channel_exponent = first_layer_channel_exponent
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self.enc=[]
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for i in range(self.depth):
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in_ch = in_channels if i == 0 else 2**(self.first_layer_channel_exponent + i - 1)
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out_ch = 2**(self.first_layer_channel_exponent + i)
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self.enc.append(UNetEncLayer(in_ch, out_ch, style_dim))
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self.enc = nn.ModuleList(self.enc)
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self.bottleneck = UNetBlock(2**(self.first_layer_channel_exponent + self.depth - 1),
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2**(self.first_layer_channel_exponent + self.depth), style_dim)
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self.dec=[]
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for i in range(self.depth - 1, -1, -1):
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in_ch = 2**(self.first_layer_channel_exponent + i + 1)
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out_ch = 2**(self.first_layer_channel_exponent + i)
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skip_conn_ch = out_ch
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self.dec.append(UNetDecLayer(in_ch, out_ch, skip_conn_ch, style_dim))
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self.dec = nn.ModuleList(self.dec)
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self.final = nn.Conv3d(2**(self.first_layer_channel_exponent), out_channels, kernel_size=1)
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def forward(self, x, style):
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out = x
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outlist = []
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for i in range(self.depth):
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skip, out = self.enc[i](out, style)
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outlist.append(skip)
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out = self.bottleneck(out, style)
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for i in range(self.depth):
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out = self.dec[i](out, outlist[self.depth - 1 - i], style)
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return self.final(out)
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