Merge new-arch, because it has proven to give the best results #1
@ -20,12 +20,9 @@ class SISUDiscriminator(nn.Module):
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# Core Discriminator Blocks with varied kernels and dilations
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discriminator_block(layers, layers * 2, kernel_size=5, stride=2, dilation=1), # Downsample
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discriminator_block(layers * 2, layers * 2, kernel_size=3, dilation=2),
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discriminator_block(layers * 2, layers * 4, kernel_size=5, dilation=4),
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discriminator_block(layers * 4, layers * 4, kernel_size=3, dilation=8),
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discriminator_block(layers * 4, layers * 8, kernel_size=5, dilation=16),
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discriminator_block(layers * 8, layers * 8, kernel_size=3, dilation=8),
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discriminator_block(layers * 8, layers * 4, kernel_size=5, dilation=4),
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discriminator_block(layers * 8, layers * 4, kernel_size=3, dilation=8),
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discriminator_block(layers * 4, layers * 2, kernel_size=3, dilation=2),
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discriminator_block(layers * 2, layers, kernel_size=5, dilation=1),
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# Final Convolution
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@ -18,12 +18,9 @@ class SISUGenerator(nn.Module):
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)
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self.conv_blocks = nn.Sequential(
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conv_block(layer, layer, kernel_size=3, dilation=1), # Local details
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conv_block(layer, layer*2, kernel_size=5, dilation=2), # Local Context
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conv_block(layer*2, layer*2, kernel_size=3, dilation=4), # Wider context
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conv_block(layer*2, layer*4, kernel_size=7, dilation=8), # Longer range dependencies
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conv_block(layer, layer*4, kernel_size=5, dilation=2), # Local Context
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conv_block(layer*4, layer*4, kernel_size=3, dilation=16), # Longer range dependencies
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conv_block(layer*4, layer*2, kernel_size=5, dilation=8), # Wider context
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conv_block(layer*2, layer*2, kernel_size=3, dilation=4), # Wider context
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conv_block(layer*2, layer, kernel_size=5, dilation=2), # Local Context
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conv_block(layer, layer, kernel_size=3, dilation=1), # Local details
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)
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