Cleanup name and comments
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@ -38,8 +38,8 @@ def lag2eul(
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Implementation follows pmesh/cic.py by Yu Feng.
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Implementation follows pmesh/cic.py by Yu Feng.
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"""
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"""
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# NOTE the following factor assumes normalized displacements
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# NOTE the following factor assumes the displacements have been normalized
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# and thus undoes it
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# by data.norms.cosmology.dis, and thus undoes it
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dis_norm = dis_std * D(z) * meshsize / boxsize # to mesh unit
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dis_norm = dis_std * D(z) * meshsize / boxsize # to mesh unit
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dis_norm *= eul_scale_factor
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dis_norm *= eul_scale_factor
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@ -2,7 +2,7 @@ from math import log2
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import torch
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import torch
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import torch.nn as nn
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import torch.nn as nn
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from .narrow import narrow_by, narrow_like
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from .narrow import narrow_by
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from .resample import Resampler
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from .resample import Resampler
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@ -32,21 +32,21 @@ class G(nn.Module):
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for b in range(num_blocks):
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for b in range(num_blocks):
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prev_chan, next_chan = chan(b), chan(b+1)
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prev_chan, next_chan = chan(b), chan(b+1)
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self.blocks.append(
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self.blocks.append(
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SkipBlock(prev_chan, next_chan, out_chan, cat_noise))
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HBlock(prev_chan, next_chan, out_chan, cat_noise))
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def forward(self, x):
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def forward(self, x):
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y = x
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y = x # direct upsampling from the input
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x = self.block0(x)
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x = self.block0(x)
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#y = None
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#y = None # no direct upsampling from the input
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for block in self.blocks:
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for block in self.blocks:
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x, y = block(x, y)
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x, y = block(x, y)
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return y
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return y
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class SkipBlock(nn.Module):
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class HBlock(nn.Module):
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"""The "I" block of the StyleGAN2 generator.
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"""The "H" block of the StyleGAN2 generator.
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x_p y_p
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x_p y_p
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