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generator.py
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generator.py
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import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.PReLU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64)
)
def forward(self, x):
return x + self.layers(x)
class ShuffleBlock(nn.Module):
def __init__(self, in_planes: int = 64, out_planes: int = 256):
super().__init__()
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1, padding=1)
self.shuffle = nn.PixelShuffle(upscale_factor=2)
self.prelu = nn.PReLU()
def forward(self, x):
out = self.conv(x)
out = self.shuffle(out)
return self.prelu(out)
class Generator(nn.Module):
def __init__(self, n_blocks: int = 5):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=9, stride=1, padding=4)
self.prelu = nn.PReLU()
self.blocks = nn.Sequential(*[ResidualBlock() for _ in range(n_blocks)])
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
self.bn = nn.BatchNorm2d(64)
self.shuffle = nn.Sequential(ShuffleBlock(64, 256), ShuffleBlock(64, 256))
self.out = nn.Conv2d(64, 3, kernel_size=9, stride=1, padding=4)
def forward(self, x):
features = self.prelu(self.conv1(x))
out = self.blocks(features)
out = out + features
out = self.shuffle(out)
return self.out(out)
if __name__ == '__main__':
print(Generator()(torch.randn(1, 3, 224, 224)).shape)