| import math
|
|
|
| import torch
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| import torch.nn.functional as F
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| import torch.utils.model_zoo as model_zoo
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| from torch import nn
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| from torch.nn import Parameter
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| import pdb
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| import numpy as np
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|
|
|
|
| class ResidualBlock(nn.Module):
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| def __init__(self, inchannel, outchannel, stride=1):
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| super(ResidualBlock, self).__init__()
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| self.left = nn.Sequential(
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| nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False),
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| nn.BatchNorm2d(outchannel),
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| nn.ReLU(inplace=True),
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| nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),
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| nn.BatchNorm2d(outchannel)
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| )
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| self.shortcut = nn.Sequential()
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| if stride != 1 or inchannel != outchannel:
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| self.shortcut = nn.Sequential(
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| nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
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| nn.BatchNorm2d(outchannel)
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| )
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|
|
| def forward(self, x):
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| out = self.left(x)
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| out += self.shortcut(x)
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| out = F.relu(out)
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| return out
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|
|
|
|
| class ResNet(nn.Module):
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| def __init__(self, ResidualBlock = ResidualBlock):
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| super(ResNet, self).__init__()
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| self.inchannel = 64
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| self.conv1 = nn.Sequential(
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| nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
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| nn.BatchNorm2d(64),
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| nn.ReLU(),
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| )
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| self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride=1)
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| self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2)
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| self.layer3 = self.make_layer(ResidualBlock, 196, 2, stride=2)
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| self.layer4 = self.make_layer(ResidualBlock, 256, 2, stride=2)
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| self.lastconv1 = nn.Sequential(
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| nn.Conv2d(128 + 196 + 256, 128, kernel_size=3, stride=1, padding=1, bias=False),
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| nn.BatchNorm2d(128),
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| nn.ReLU(),
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| )
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| self.lastconv2 = nn.Sequential(
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| nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1, bias=False),
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| nn.BatchNorm2d(64),
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| nn.ReLU(),
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| )
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| self.lastconv3 = nn.Sequential(
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| nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1, bias=False),
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| nn.ReLU(),
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| )
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| self.downsample32x32 = nn.Upsample(size=(32, 32), mode='bilinear')
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|
|
| self.mu_head = nn.Sequential(
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| nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1, bias=False),
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|
|
| )
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| self.logvar_head = nn.Sequential(
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| nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1, bias=False),
|
|
|
| )
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|
|
| def make_layer(self, block, channels, num_blocks, stride):
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| strides = [stride] + [1] * (num_blocks - 1)
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| layers = []
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| for stride in strides:
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| layers.append(block(self.inchannel, channels, stride))
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| self.inchannel = channels
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| return nn.Sequential(*layers)
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|
|
| def _reparameterize(self, mu, logvar):
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| std = torch.exp(logvar).sqrt()
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| epsilon = torch.randn_like(std)
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| return mu + epsilon * std
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|
|
| def forward(self, x):
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| x_input = x
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| x = self.conv1(x)
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| x_block1 = self.layer1(x)
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| x_block2 = self.layer2(x_block1)
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| x_block2_32 = self.downsample32x32(x_block2)
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| x_block3 = self.layer3(x_block2)
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| x_block3_32 = self.downsample32x32(x_block3)
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| x_block4 = self.layer4(x_block3)
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| x_block4_32 = self.downsample32x32(x_block4)
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|
|
| x_concat = torch.cat((x_block2_32, x_block3_32, x_block4_32), dim=1)
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|
|
| x = self.lastconv1(x_concat)
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| x = self.lastconv2(x)
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| mu = self.mu_head(x)
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| mu = mu.squeeze(1)
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| logvar = self.logvar_head(x)
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| logvar = logvar.squeeze(1)
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| embedding = self._reparameterize(mu, logvar)
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|
|
| return mu, logvar, embedding, x_concat, x_block2, x_block3, x_block4, x_input
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|
|
|
|
| def ResNet18_u():
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|
|
| return ResNet(ResidualBlock)
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|
|
|
|
