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import torch
import torch.nn as nn
class ResBlock(nn.Module):
"""Block with residuals"""
def __init__(self, ch):
super().__init__()
self.join = nn.ReLU()
self.norm = nn.BatchNorm2d(ch)
self.long = nn.Sequential(
nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
nn.SiLU(),
nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
nn.SiLU(),
nn.Conv2d(ch, ch, kernel_size=3, stride=1, padding=1),
nn.Dropout(0.1)
)
def forward(self, x):
x = self.norm(x)
return self.join(self.long(x) + x)
class ExtractBlock(nn.Module):
"""Increase no. of channels by [out/in]"""
def __init__(self, ch_in, ch_out):
super().__init__()
self.join = nn.ReLU()
self.short = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1)
self.long = nn.Sequential(
nn.Conv2d( ch_in, ch_out, kernel_size=3, stride=1, padding=1),
nn.SiLU(),
nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
nn.SiLU(),
nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1),
nn.Dropout(0.1)
)
def forward(self, x):
return self.join(self.long(x) + self.short(x))
class InterposerModel(nn.Module):
"""Main neural network"""
def __init__(self, ch_in=4, ch_out=4, ch_mid=64, scale=1.0, blocks=12):
super().__init__()
self.ch_in = ch_in
self.ch_out = ch_out
self.ch_mid = ch_mid
self.blocks = blocks
self.scale = scale
self.head = ExtractBlock(self.ch_in, self.ch_mid)
self.core = nn.Sequential(
nn.Upsample(scale_factor=self.scale, mode="nearest"),
*[ResBlock(self.ch_mid) for _ in range(blocks)],
nn.BatchNorm2d(self.ch_mid),
nn.SiLU(),
)
self.tail = nn.Conv2d(self.ch_mid, self.ch_out, kernel_size=3, stride=1, padding=1)
def forward(self, x):
y = self.head(x)
z = self.core(y)
return self.tail(z)
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