Upload layers.py
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layers.py
ADDED
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1 |
+
import torch
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2 |
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import torch.nn as nn
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import torch.nn.functional as F
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4 |
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from torch.nn.modules.batchnorm import BatchNorm2d
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+
from torch.nn.utils import spectral_norm
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+
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+
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+
class SpectralConv2d(nn.Module):
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+
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def __init__(self, *args, **kwargs):
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+
super().__init__()
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+
self._conv = spectral_norm(
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nn.Conv2d(*args, **kwargs)
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+
)
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+
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+
def forward(self, input: torch.Tensor) -> torch.Tensor:
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return self._conv(input)
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+
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+
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+
class SpectralConvTranspose2d(nn.Module):
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+
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def __init__(self, *args, **kwargs):
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super().__init__()
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self._conv = spectral_norm(
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nn.ConvTranspose2d(*args, **kwargs)
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+
)
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+
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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return self._conv(input)
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+
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+
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+
class Noise(nn.Module):
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+
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def __init__(self):
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super().__init__()
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+
self._weight = nn.Parameter(
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+
torch.zeros(1),
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+
requires_grad=True,
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+
)
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+
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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batch_size, _, height, width = input.shape
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noise = torch.randn(batch_size, 1, height, width, device=input.device)
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return self._weight * noise + input
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+
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+
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class InitLayer(nn.Module):
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+
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def __init__(self, in_channels: int,
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out_channels: int):
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super().__init__()
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+
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+
self._layers = nn.Sequential(
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54 |
+
SpectralConvTranspose2d(
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+
in_channels=in_channels,
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+
out_channels=out_channels * 2,
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kernel_size=4,
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stride=1,
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padding=0,
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bias=False,
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+
),
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nn.BatchNorm2d(num_features=out_channels * 2),
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nn.GLU(dim=1),
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+
)
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+
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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return self._layers(input)
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+
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+
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+
class SLEBlock(nn.Module):
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+
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def __init__(self, in_channels: int,
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out_channels: int):
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super().__init__()
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+
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76 |
+
self._layers = nn.Sequential(
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nn.AdaptiveAvgPool2d(output_size=4),
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78 |
+
SpectralConv2d(
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79 |
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=4,
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stride=1,
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padding=0,
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bias=False,
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+
),
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nn.SiLU(),
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+
SpectralConv2d(
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in_channels=out_channels,
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+
out_channels=out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=False,
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+
),
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+
nn.Sigmoid(),
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+
)
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+
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+
def forward(self, low_dim: torch.Tensor,
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+
high_dim: torch.Tensor) -> torch.Tensor:
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+
return high_dim * self._layers(low_dim)
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+
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+
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+
class UpsampleBlockT1(nn.Module):
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+
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def __init__(self, in_channels: int,
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106 |
+
out_channels: int):
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107 |
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super().__init__()
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+
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109 |
+
self._layers = nn.Sequential(
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+
nn.Upsample(scale_factor=2, mode='nearest'),
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+
SpectralConv2d(
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+
in_channels=in_channels,
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+
out_channels=out_channels * 2,
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kernel_size=3,
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stride=1,
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padding='same',
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bias=False,
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+
),
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nn.BatchNorm2d(num_features=out_channels * 2),
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+
nn.GLU(dim=1),
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)
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+
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+
def forward(self, input: torch.Tensor) -> torch.Tensor:
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return self._layers(input)
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+
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+
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+
class UpsampleBlockT2(nn.Module):
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+
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+
def __init__(self, in_channels: int,
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+
out_channels: int):
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super().__init__()
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+
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+
self._layers = nn.Sequential(
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+
nn.Upsample(scale_factor=2, mode='nearest'),
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+
SpectralConv2d(
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+
in_channels=in_channels,
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+
out_channels=out_channels * 2,
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+
kernel_size=3,
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+
stride=1,
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+
padding='same',
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+
bias=False,
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142 |
+
),
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+
Noise(),
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+
BatchNorm2d(num_features=out_channels * 2),
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+
nn.GLU(dim=1),
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146 |
+
SpectralConv2d(
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147 |
+
in_channels=out_channels,
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148 |
+
out_channels=out_channels * 2,
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+
kernel_size=3,
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+
stride=1,
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+
padding='same',
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152 |
+
bias=False,
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+
),
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154 |
+
Noise(),
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155 |
+
nn.BatchNorm2d(num_features=out_channels * 2),
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156 |
+
nn.GLU(dim=1),
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157 |
+
)
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158 |
+
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159 |
+
def forward(self, input: torch.Tensor) -> torch.Tensor:
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160 |
+
return self._layers(input)
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161 |
+
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162 |
+
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163 |
+
class DownsampleBlockT1(nn.Module):
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+
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165 |
+
def __init__(self, in_channels: int,
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166 |
+
out_channels: int):
|
167 |
+
super().__init__()
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168 |
+
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169 |
+
self._layers = nn.Sequential(
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170 |
+
SpectralConv2d(
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171 |
+
in_channels=in_channels,
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172 |
+
out_channels=out_channels,
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173 |
+
kernel_size=4,
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174 |
+
stride=2,
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175 |
+
padding=1,
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176 |
+
bias=False,
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177 |
+
),
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178 |
+
nn.BatchNorm2d(num_features=out_channels),
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179 |
+
nn.LeakyReLU(negative_slope=0.2),
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180 |
+
)
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181 |
+
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182 |
+
def forward(self, input: torch.Tensor) -> torch.Tensor:
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183 |
+
return self._layers(input)
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184 |
+
|
185 |
+
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186 |
+
class DownsampleBlockT2(nn.Module):
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187 |
+
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188 |
+
def __init__(self, in_channels: int,
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189 |
+
out_channels: int):
|
190 |
+
super().__init__()
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191 |
+
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192 |
+
self._layers_1 = nn.Sequential(
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193 |
+
SpectralConv2d(
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194 |
+
in_channels=in_channels,
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195 |
+
out_channels=out_channels,
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196 |
+
kernel_size=4,
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197 |
+
stride=2,
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198 |
+
padding=1,
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199 |
+
bias=False,
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200 |
+
),
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201 |
+
nn.BatchNorm2d(num_features=out_channels),
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202 |
+
nn.LeakyReLU(negative_slope=0.2),
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203 |
+
SpectralConv2d(
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204 |
+
in_channels=out_channels,
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205 |
+
out_channels=out_channels,
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206 |
+
kernel_size=3,
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207 |
+
stride=1,
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208 |
+
padding='same',
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209 |
+
bias=False,
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210 |
+
),
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211 |
+
nn.BatchNorm2d(num_features=out_channels),
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212 |
+
nn.LeakyReLU(negative_slope=0.2),
|
213 |
+
)
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214 |
+
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215 |
+
self._layers_2 = nn.Sequential(
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216 |
+
nn.AvgPool2d(
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217 |
+
kernel_size=2,
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218 |
+
stride=2,
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219 |
+
),
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220 |
+
SpectralConv2d(
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221 |
+
in_channels=in_channels,
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222 |
+
out_channels=out_channels,
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223 |
+
kernel_size=1,
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224 |
+
stride=1,
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225 |
+
padding=0,
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226 |
+
bias=False,
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227 |
+
),
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228 |
+
nn.BatchNorm2d(num_features=out_channels),
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229 |
+
nn.LeakyReLU(negative_slope=0.2),
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230 |
+
)
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231 |
+
|
232 |
+
def forward(self, input: torch.Tensor) -> torch.Tensor:
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233 |
+
t1 = self._layers_1(input)
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234 |
+
t2 = self._layers_2(input)
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235 |
+
return (t1 + t2) / 2
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236 |
+
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237 |
+
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238 |
+
class Decoder(nn.Module):
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239 |
+
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240 |
+
def __init__(self, in_channels: int,
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241 |
+
out_channels: int):
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242 |
+
super().__init__()
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243 |
+
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244 |
+
self._channels = {
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245 |
+
16: 128,
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246 |
+
32: 64,
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+
64: 64,
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248 |
+
128: 32,
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249 |
+
256: 16,
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250 |
+
512: 8,
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251 |
+
1024: 4,
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252 |
+
}
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253 |
+
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254 |
+
self._layers = nn.Sequential(
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255 |
+
nn.AdaptiveAvgPool2d(output_size=8),
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256 |
+
UpsampleBlockT1(in_channels=in_channels, out_channels=self._channels[16]),
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257 |
+
UpsampleBlockT1(in_channels=self._channels[16], out_channels=self._channels[32]),
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258 |
+
UpsampleBlockT1(in_channels=self._channels[32], out_channels=self._channels[64]),
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259 |
+
UpsampleBlockT1(in_channels=self._channels[64], out_channels=self._channels[128]),
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260 |
+
SpectralConv2d(
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261 |
+
in_channels=self._channels[128],
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262 |
+
out_channels=out_channels,
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263 |
+
kernel_size=3,
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264 |
+
stride=1,
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265 |
+
padding='same',
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266 |
+
bias=False,
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267 |
+
),
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268 |
+
nn.Tanh(),
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269 |
+
)
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270 |
+
|
271 |
+
def forward(self, input: torch.Tensor) -> torch.Tensor:
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272 |
+
return self._layers(input)
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