File size: 4,484 Bytes
a04730e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | from __future__ import annotations
import torch
from torch import nn
import torch.nn.functional as F
from src.models.autoencoder.blocks import (
ResBlock,
Upsample,
MidBlock,
normalization,
)
class Decoder(nn.Module):
"""
VAE decoder.
Converts latent tensor back into image space.
Shape path:
[B, 4, 32, 32]
-> conv_in
[B, 512, 32, 32]
-> mid block
[B, 512, 32, 32]
-> upsample
[B, 512, 64, 64]
-> upsample
[B, 256, 128, 128]
-> upsample
[B, 128, 256, 256]
-> conv_out
[B, 3, 256, 256]
Output is in [-1, 1] because training images are normalized to [-1, 1].
"""
def __init__(
self,
out_channels: int = 3,
latent_channels: int = 4,
base_channels: int = 128,
channel_multipliers: list[int] | tuple[int, ...] = (1, 2, 4, 4),
num_res_blocks: int = 2,
dropout: float = 0.0,
use_attention: bool = True,
attention_heads: int = 1
):
super().__init__()
if len(channel_multipliers) < 2:
raise ValueError("channel_multipliers must contain at least 2 levels.")
self.out_channels = out_channels
self.latent_channels = latent_channels
self.base_channels = base_channels
self.channel_multipliers = list(channel_multipliers)
self.num_res_blocks = num_res_blocks
# Number of spatial upsampling operations
# Example:
# [1, 2, 4, 4] has 4 levels, so decoder upsamples 3 times:
# 32 -> 64 -> 128 -> 256
self.num_upsamples = len(self.channel_multipliers) - 1
# Start from the deepest encoder channel count
current_channels = base_channels * self.channel_multipliers[-1]
self.conv_in = nn.Conv2d(
latent_channels,
current_channels,
kernel_size=3,
stride=1,
padding=1,
)
# Bottleneck block at the lowest spatial resolution.
self.mid = MidBlock(
channels=current_channels,
dropout=dropout,
use_attention=use_attention,
num_heads=attention_heads,
)
self.up_blocks = nn.ModuleList()
reversed_multipliers = list(reversed(self.channel_multipliers))
for level, multiplier in enumerate(reversed_multipliers):
out_stage_channels = base_channels * multiplier
resblocks = nn.ModuleList()
# one extra ResBlock per level
for _ in range(num_res_blocks + 1):
resblocks.append(
ResBlock(
in_channels=current_channels,
out_channels=out_stage_channels,
dropout=dropout,
)
)
current_channels = out_stage_channels
# Upsample after every stage except the full-resolution
if level < len(reversed_multipliers) - 1:
upsample = Upsample(
channels=current_channels
)
else:
upsample = nn.Identity()
self.up_blocks.append(
nn.ModuleDict(
{
"resblocks": resblocks,
"upsample": upsample,
}
)
)
self.norm_out = normalization(current_channels)
self.conv_out = nn.Conv2d(
current_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
)
def forward(self, z: torch.Tensor) -> torch.Tensor:
"""
Args:
z:
Latent tensor with shape [B, latent_channels, H/8, W/8].
For 256x256 images and downsample factor 8:
[B, latent_channels, 32, 32]
Returns:
x_recon:
Reconstructed image tensor with shape [B, 3, H, W].
Values are in [-1, 1].
"""
h = self.conv_in(z)
h = self.mid(h)
for stage in self.up_blocks:
for block in stage["resblocks"]:
h = block(h)
h = stage["upsample"](h)
h = self.norm_out(h)
h = F.silu(h)
h = self.conv_out(h)
x_recon = torch.tanh(h)
return x_recon |