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Diffstat (limited to 'models/auto_encoder.py')
| -rw-r--r-- | models/auto_encoder.py | 87 |
1 files changed, 87 insertions, 0 deletions
diff --git a/models/auto_encoder.py b/models/auto_encoder.py new file mode 100644 index 0000000..e35ed23 --- /dev/null +++ b/models/auto_encoder.py @@ -0,0 +1,87 @@ +import torch +from torch import nn as nn +from torch.nn import functional as F + + +class Encoder(nn.Module): + def __init__(self, in_channels: int, opt): + super(Encoder, self).__init__() + self.opt = opt + self.em_dim = opt.em_dim + nf = 64 + + # Cx[HxW] + # Conv1 3x64x32 -> 64x64x32 + self.conv1 = nn.Conv2d(in_channels, nf, kernel_size=3, padding=1) + self.batch_norm1 = nn.BatchNorm2d(nf) + # MaxPool1 64x64x32 -> 64x32x16 + self.max_pool1 = nn.AdaptiveMaxPool2d((32, 16)) + # Conv2 64x32x16 -> 256x32x16 + self.conv2 = nn.Conv2d(nf, nf * 4, kernel_size=3, padding=1) + self.batch_norm2 = nn.BatchNorm2d(nf * 4) + # MaxPool2 256x32x16 -> 256x16x8 + self.max_pool2 = nn.AdaptiveMaxPool2d((16, 8)) + # Conv3 256x16x8 -> 512x16x8 + self.conv3 = nn.Conv2d(nf * 4, nf * 8, kernel_size=3, padding=1) + self.batch_norm3 = nn.BatchNorm2d(nf * 8) + # Conv4 512x16x8 -> 512x16x8 (for large dataset) + self.conv4 = nn.Conv2d(nf * 8, nf * 8, kernel_size=3, padding=1) + self.batch_norm4 = nn.BatchNorm2d(nf * 8) + # MaxPool3 512x16x8 -> 512x4x2 + self.max_pool3 = nn.AdaptiveMaxPool2d((4, 2)) + # FC 512*4*2 -> 320 + self.fc = nn.Linear(nf * 8 * 2 * 4, self.em_dim) + self.batch_norm_fc = nn.BatchNorm1d(self.em_dim) + + def forward(self, x): + x = F.leaky_relu(self.batch_norm1(self.conv1(x)), 0.2) + x = self.max_pool1(x) + x = F.leaky_relu(self.batch_norm2(self.conv2(x)), 0.2) + x = self.max_pool2(x) + x = F.leaky_relu(self.batch_norm3(self.conv3(x)), 0.2) + x = F.leaky_relu(self.batch_norm4(self.conv4(x)), 0.2) + x = self.max_pool3(x) + x = x.view(-1, (64 * 8) * 2 * 4) + embedding = self.batch_norm_fc(self.fc(x)) + + fa, fgs, fgd = embedding.split( + (self.opt.fa_dim, self.opt.fg_dim / 2, self.opt.fg_dim / 2), dim=1 + ) + return fa, fgs, fgd + + +class Decoder(nn.Module): + def __init__(self, out_channels: int, opt): + super(Decoder, self).__init__() + self.em_dim = opt.em_dim + nf = 64 + + # Cx[HxW] + # FC 320 -> 512*4*2 + self.fc = nn.Linear(self.em_dim, nf * 8 * 2 * 4) + self.batch_norm_fc = nn.BatchNorm1d(nf * 8 * 2 * 4) + # TransConv1 512x4x2 -> 256x8x4 + self.trans_conv1 = nn.ConvTranspose2d(nf * 8, nf * 4, kernel_size=4, + stride=2, padding=1) + self.batch_norm1 = nn.BatchNorm2d(nf * 4) + # TransConv2 256x8x4 -> 128x16x8 + self.trans_conv2 = nn.ConvTranspose2d(nf * 4, nf * 2, kernel_size=4, + stride=2, padding=1) + self.batch_norm2 = nn.BatchNorm2d(nf * 2) + # TransConv3 128x16x8 -> 64x32x16 + self.trans_conv3 = nn.ConvTranspose2d(nf * 2, nf, kernel_size=4, + stride=2, padding=1) + self.batch_norm3 = nn.BatchNorm2d(nf) + # TransConv4 3x32x16 + self.trans_conv4 = nn.ConvTranspose2d(nf, out_channels, kernel_size=4, + stride=2, padding=1) + + def forward(self, fa, fgs, fgd): + x = torch.cat((fa, fgs, fgd), dim=1).view(-1, self.em_dim) + x = F.leaky_relu(self.batch_norm_fc(self.fc(x)), 0.2) + x = F.leaky_relu(self.batch_norm1(self.trans_conv1(x)), 0.2) + x = F.leaky_relu(self.batch_norm2(self.trans_conv2(x)), 0.2) + x = F.leaky_relu(self.batch_norm3(self.trans_conv3(x)), 0.2) + x = F.sigmoid(self.trans_conv4(x)) + + return x
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