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fd8b97e | 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 | """TransformNet from ebylmz/fast-neural-style-transfer (MIT)."""
import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int, relu: bool = True):
super().__init__()
layers = [
nn.Conv2d(
in_channels,
out_channels,
kernel_size,
stride,
padding=kernel_size // 2,
padding_mode="reflect",
),
nn.InstanceNorm2d(out_channels, affine=True),
]
if relu:
layers.append(nn.ReLU(inplace=True))
self.block = nn.Sequential(*layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.block(x)
class ResidualBlock(nn.Module):
def __init__(self, channels: int):
super().__init__()
self.block = nn.Sequential(
ConvLayer(channels, channels, kernel_size=3, stride=1, relu=True),
ConvLayer(channels, channels, kernel_size=3, stride=1, relu=False),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x + self.block(x)
class UpsampleConvLayer(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
upsample: int | None = None,
):
super().__init__()
layers: list[nn.Module] = []
if upsample:
layers.append(nn.Upsample(scale_factor=upsample, mode="nearest"))
layers.extend(
[
nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=kernel_size // 2),
nn.InstanceNorm2d(out_channels, affine=True),
nn.ReLU(inplace=True),
]
)
self.block = nn.Sequential(*layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.block(x)
class TransformNet(nn.Module):
def __init__(self):
super().__init__()
self.downsampling = nn.Sequential(
ConvLayer(3, 32, kernel_size=9, stride=1),
ConvLayer(32, 64, kernel_size=3, stride=2),
ConvLayer(64, 128, kernel_size=3, stride=2),
)
self.residuals = nn.Sequential(
ResidualBlock(128),
ResidualBlock(128),
ResidualBlock(128),
ResidualBlock(128),
ResidualBlock(128),
)
self.upsampling = nn.Sequential(
UpsampleConvLayer(128, 64, kernel_size=3, upsample=2),
UpsampleConvLayer(64, 32, kernel_size=3, upsample=2),
nn.Conv2d(32, 3, kernel_size=9, stride=1, padding=4, padding_mode="reflect"),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.downsampling(x)
x = self.residuals(x)
return self.upsampling(x)
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