96 lines
2.9 KiB
Python
96 lines
2.9 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: CNBlockConfig, kernel_size: int = 7):
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super(VariationalEncoder, self).__init__()
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self.encoder = ConvNeXt(block_setting=block_setting, kernel_size=kernel_size)
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self.mean_branch = nn.Sequential([CNBlock(block_setting[-1].out_channels, kernel_size=kernel_size) for _ in range(5)])
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self.var_branch = nn.Sequential([CNBlock(block_setting[-1].out_channels, kernel_size=kernel_size) for _ in range(5)])
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self.flatten_mean = nn.Flatten()
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self.flatten_var = nn.Flatten()
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def call(self, inputs, training):
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x = inputs
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x = self.encoder(x, training=training)
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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', device='cuda'):
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super(AEEncoder, self).__init__()
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self.encoder = ConvNeXt(block_setting=block_setting, kernel_size=kernel_size, device=device)
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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],512,256,1), device=mask.get_device()) * (1 - mask[:,:,:,None])
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x = torch.concat((background, x), axis=-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 |