98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
import torch
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from torch import nn
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from models.convnext_orig import ConvNeXt, CNBlock, CNBlockConfig
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from typing import Any, Callable, List, Optional, Sequence
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class VariationalEncoder(nn.Module):
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def __init__(self, block_setting: List[CNBlockConfig], kernel_size: int = 7, input_features: int = 16, device: str = 'cuda'):
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super(VariationalEncoder, self).__init__()
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self.encoder = ConvNeXt(block_setting=block_setting, kernel_size=kernel_size, input_features=input_features, device=device)
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layers = [CNBlock(block_setting[-1].input_channels, kernel_size=kernel_size) for _ in range(5)]
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self.mean_branch = nn.Sequential(*layers).to(device)
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layers = [CNBlock(block_setting[-1].input_channels, kernel_size=kernel_size) for _ in range(5)]
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self.var_branch = nn.Sequential(*layers).to(device)
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self.flatten_mean = nn.Flatten()
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self.flatten_var = nn.Flatten()
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def forward(self, inputs):
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x = inputs
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x = self.encoder(x)
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mean = self.mean_branch(x)
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log_var = self.var_branch(x)
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mean = self.flatten_mean(mean)
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log_var = self.flatten_var(log_var)
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return mean, log_var
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class AEEncoder(nn.Module):
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def __init__(self, block_setting: CNBlockConfig, kernel_size: int = 7, activation='tanh', is_label=False, nr_of_labels=None, device='cuda', input_features=3):
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super(AEEncoder, self).__init__()
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self.encoder = ConvNeXt(block_setting=block_setting, kernel_size=kernel_size, is_label=is_label, nr_of_labels=nr_of_labels, device=device, input_features=input_features)
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if activation == 'tanh':
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self.act = nn.Tanh()
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elif activation == 'sigmoid':
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self.act = nn.Sigmoid()
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else:
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raise ValueError('Activation function not supported')
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def forward(self, inputs):
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if len(inputs) == 2:
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x, _ = inputs
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else:
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x = inputs
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x = self.encoder(x)
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x = self.act(x)
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if len(inputs) == 2:
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return x, inputs[1]
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else:
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return x
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class Decoder(nn.Module):
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def __init__(self, block_setting: CNBlockConfig, kernel_size=7, is_label=False, nr_of_classes=None, device='cuda'):
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super(Decoder, self).__init__()
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self.is_label = is_label
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self.decoder = ConvNeXt(block_setting=block_setting, kernel_size=kernel_size, is_label=is_label, nr_of_labels=nr_of_classes, device=device, reversed=True)
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if self.is_label:
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self.softmax = nn.Softmax(dim=1)
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def forward(self, inputs):
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if len(inputs) == 2:
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x, mask = inputs
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else:
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x = inputs
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x = self.decoder(x)
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if self.is_label:
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x = x * mask[:,None,:,:]
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background = torch.ones((mask.shape[0],1,512,256), device=mask.get_device()) * (1 - mask[:,None,:,:])
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x = torch.concat((background, x), dim=1)
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x = self.softmax(x)
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if len(inputs) == 2:
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if len(mask.shape) == 2:
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x = x * mask[None,None,:,:]
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else:
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x = x * mask[:,None,:,:]
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return x |