| ---
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| license: mit
|
| ---
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| Model weight for Fast Style Transfer
|
|
|
| ```
|
|
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| class TransformerNetwork(nn.Module):
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| """Feedforward Transformation Network without Tanh
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| reference: https://arxiv.org/abs/1603.08155
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| exact architecture: https://cs.stanford.edu/people/jcjohns/papers/fast-style/fast-style-supp.pdf
|
| """
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| def __init__(self, tanh_multiplier=None):
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| super(TransformerNetwork, self).__init__()
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| self.ConvBlock = nn.Sequential(
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| ConvLayer(3, 32, 9, 1),
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| nn.ReLU(),
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| ConvLayer(32, 64, 3, 2),
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| nn.ReLU(),
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| ConvLayer(64, 128, 3, 2),
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| nn.ReLU()
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| )
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| self.ResidualBlock = nn.Sequential(
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| ResidualLayer(128, 3),
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| ResidualLayer(128, 3),
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| ResidualLayer(128, 3),
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| ResidualLayer(128, 3),
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| ResidualLayer(128, 3)
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| )
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| self.DeconvBlock = nn.Sequential(
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| DeconvLayer(128, 64, 3, 2, 1),
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| nn.ReLU(),
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| DeconvLayer(64, 32, 3, 2, 1),
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| nn.ReLU(),
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| ConvLayer(32, 3, 9, 1, norm="None")
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| )
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| self.tanh_multiplier = tanh_multiplier
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|
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| def forward(self, x):
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| x = self.ConvBlock(x)
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| x = self.ResidualBlock(x)
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| x = self.DeconvBlock(x)
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| if isinstance(self.tanh_multiplier, int):
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| x = self.tanh_multiplier * F.tanh(x)
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| return x
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|
|
| class ConvLayer(nn.Module):
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| def __init__(self, in_channels, out_channels, kernel_size, stride, norm="instance"):
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| super(ConvLayer, self).__init__()
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| # Padding Layers
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| padding_size = kernel_size // 2
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| self.pad = nn.ReflectionPad2d(padding_size)
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|
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| # Convolution Layer
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| self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride)
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|
|
| # Normalization Layers
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| if norm == "instance":
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| self.norm = nn.InstanceNorm2d(out_channels, affine=True)
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| elif norm == "batch":
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| self.norm = nn.BatchNorm2d(out_channels, affine=True)
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| else:
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| self.norm = nn.Identity()
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|
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| def forward(self, x):
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| x = self.pad(x)
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| x = self.conv(x)
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| x = self.norm(x)
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| return x
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|
|
| class ResidualLayer(nn.Module):
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| """
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| Deep Residual Learning for Image Recognition
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| https://arxiv.org/abs/1512.03385
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| """
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| def __init__(self, channels=128, kernel_size=3):
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| super(ResidualLayer, self).__init__()
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| self.conv1 = ConvLayer(channels, channels, kernel_size, stride=1)
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| self.relu = nn.ReLU()
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| self.conv2 = ConvLayer(channels, channels, kernel_size, stride=1)
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|
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| def forward(self, x):
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| identity = x # preserve residual
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| out = self.relu(self.conv1(x)) # 1st conv layer + activation
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| out = self.conv2(out) # 2nd conv layer
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| out = out + identity # add residual
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| return out
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|
|
| class DeconvLayer(nn.Module):
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| def __init__(self, in_channels, out_channels, kernel_size, stride, output_padding, norm="instance"):
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| super(DeconvLayer, self).__init__()
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|
|
| # Transposed Convolution
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| padding_size = kernel_size // 2
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| self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding_size, output_padding)
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|
|
| # Normalization Layers
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| if norm == "instance":
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| self.norm = nn.InstanceNorm2d(out_channels, affine=True)
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| elif norm == "batch":
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| self.norm = nn.BatchNorm2d(out_channels, affine=True)
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| else:
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| self.norm = nn.Identity()
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|
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| def forward(self, x):
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| x = self.conv_transpose(x)
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| out = self.norm(x)
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| return out
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| ``` |
