28 lines
963 B
Python
28 lines
963 B
Python
import torch.nn as nn
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class SISUGenerator(nn.Module):
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def __init__(self, upscale_scale=1): # No noise_dim parameter
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super(SISUGenerator, self).__init__()
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self.layers1 = nn.Sequential(
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nn.Conv1d(2, 128, kernel_size=3, padding=1),
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# nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(128, 256, kernel_size=3, padding=1),
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# nn.LeakyReLU(0.2, inplace=True),
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)
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self.layers2 = nn.Sequential(
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nn.Conv1d(256, 128, kernel_size=3, padding=1),
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# nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(128, 64, kernel_size=3, padding=1),
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# nn.LeakyReLU(0.2, inplace=True),
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nn.Conv1d(64, 2, kernel_size=3, padding=1),
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# nn.Tanh()
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)
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def forward(self, x, scale):
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x = self.layers1(x)
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upsample = nn.Upsample(scale_factor=scale, mode='nearest')
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x = upsample(x)
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x = self.layers2(x)
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return x
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