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| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| from helperVAE import VAE_ResidualBlock, VAE_AttentionBlock | |
| class VAE_Encoder(nn.Sequential): | |
| def __init__(self): | |
| super().__init__( | |
| # (Batch_Size, Channel, Height, Width) -> (Batch_Size, 128, Height, Width) | |
| nn.Conv2d(3, 128, kernel_size=3, padding=1), | |
| # (Batch_Size, 128, Height, Width) -> (Batch_Size, 128, Height, Width) | |
| VAE_ResidualBlock(128, 128), | |
| # (Batch_Size, 128, Height, Width) -> (Batch_Size, 128, Height, Width) | |
| VAE_ResidualBlock(128, 128), | |
| # (Batch_Size, 128, Height, Width) -> (Batch_Size, 128, Height / 2, Width / 2) | |
| nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=0), | |
| # (Batch_Size, 128, Height / 2, Width / 2) -> (Batch_Size, 256, Height / 2, Width / 2) | |
| VAE_ResidualBlock(128, 256), | |
| # (Batch_Size, 256, Height / 2, Width / 2) -> (Batch_Size, 256, Height / 2, Width / 2) | |
| VAE_ResidualBlock(256, 256), | |
| # (Batch_Size, 256, Height / 2, Width / 2) -> (Batch_Size, 256, Height / 4, Width / 4) | |
| nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=0), | |
| # (Batch_Size, 256, Height / 4, Width / 4) -> (Batch_Size, 512, Height / 4, Width / 4) | |
| VAE_ResidualBlock(256, 512), | |
| # (Batch_Size, 512, Height / 4, Width / 4) -> (Batch_Size, 512, Height / 4, Width / 4) | |
| VAE_ResidualBlock(512, 512), | |
| # (Batch_Size, 512, Height / 4, Width / 4) -> (Batch_Size, 512, Height / 8, Width / 8) | |
| nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=0), | |
| # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) | |
| VAE_ResidualBlock(512, 512), | |
| # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) | |
| VAE_ResidualBlock(512, 512), | |
| # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) | |
| VAE_ResidualBlock(512, 512), | |
| # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) | |
| VAE_AttentionBlock(512), | |
| # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) | |
| VAE_ResidualBlock(512, 512), | |
| # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) | |
| nn.GroupNorm(32, 512), | |
| # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 512, Height / 8, Width / 8) | |
| nn.SiLU(), | |
| # Because the padding=1, it means the width and height will increase by 2 | |
| # Out_Height = In_Height + Padding_Top + Padding_Bottom | |
| # Out_Width = In_Width + Padding_Left + Padding_Right | |
| # Since padding = 1 means Padding_Top = Padding_Bottom = Padding_Left = Padding_Right = 1, | |
| # Since the Out_Width = In_Width + 2 (same for Out_Height), it will compensate for the Kernel size of 3 | |
| # (Batch_Size, 512, Height / 8, Width / 8) -> (Batch_Size, 8, Height / 8, Width / 8). | |
| nn.Conv2d(512, 8, kernel_size=3, padding=1), | |
| # (Batch_Size, 8, Height / 8, Width / 8) -> (Batch_Size, 8, Height / 8, Width / 8) | |
| nn.Conv2d(8, 8, kernel_size=1, padding=0), | |
| ) | |
| def forward(self, x, noise): | |
| # x: (Batch_Size, Channel, Height, Width) | |
| # noise: (Batch_Size, 4, Height / 8, Width / 8) | |
| for module in self: | |
| if getattr(module, 'stride', None) == (2, 2): # Padding at downsampling should be asymmetric (see #8) | |
| # Pad: (Padding_Left, Padding_Right, Padding_Top, Padding_Bottom). | |
| # Pad with zeros on the right and bottom. | |
| # (Batch_Size, Channel, Height, Width) -> (Batch_Size, Channel, Height + Padding_Top + Padding_Bottom, Width + Padding_Left + Padding_Right) = (Batch_Size, Channel, Height + 1, Width + 1) | |
| x = F.pad(x, (0, 1, 0, 1)) | |
| x = module(x) | |
| # (Batch_Size, 8, Height / 8, Width / 8) -> two tensors of shape (Batch_Size, 4, Height / 8, Width / 8) | |
| mean, log_variance = torch.chunk(x, 2, dim=1) | |
| # Clamp the log variance between -30 and 20, so that the variance is between (circa) 1e-14 and 1e8. | |
| # (Batch_Size, 4, Height / 8, Width / 8) -> (Batch_Size, 4, Height / 8, Width / 8) | |
| log_variance = torch.clamp(log_variance, -30, 20) | |
| # (Batch_Size, 4, Height / 8, Width / 8) -> (Batch_Size, 4, Height / 8, Width / 8) | |
| variance = log_variance.exp() | |
| # (Batch_Size, 4, Height / 8, Width / 8) -> (Batch_Size, 4, Height / 8, Width / 8) | |
| stdev = variance.sqrt() | |
| # Transform N(0, 1) -> N(mean, stdev) | |
| # (Batch_Size, 4, Height / 8, Width / 8) -> (Batch_Size, 4, Height / 8, Width / 8) | |
| x = mean + stdev * noise | |
| # Scale by a constant | |
| x *= 0.18215 | |
| return x |