forked from mrq/DL-Art-School
61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
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import torch
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import torch.nn as nn
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from utils.util import sequential_checkpoint
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from models.archs.arch_util import ConvGnSilu, make_layer
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class TecoResblock(nn.Module):
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def __init__(self, nf):
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self.nf = nf
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self.conv1 = ConvGnSilu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False, weight_init_factor=.1)
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self.conv2 = ConvGnSilu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False, weight_init_factor=.1)
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def forward(self, x):
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identity = x
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x = self.conv1(x)
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x = self.conv2(x)
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return identity + x
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class TecoUpconv(nn.Module):
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def __init__(self, nf, scale):
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self.nf = nf
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self.scale = scale
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self.conv1 = ConvGnSilu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.conv2 = ConvGnSilu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.conv3 = ConvGnSilu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.final_conv = ConvGnSilu(nf, 3, kernel_size=1, norm=False, activation=False, bias=False)
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def forward(self, x):
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identity = x
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x = self.conv1(x)
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x = self.conv2(x)
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x = nn.functional.interpolate(x, scale_factor=self.scale, mode="nearest")
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x = self.conv3(x)
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return identity + self.final_conv(x)
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# Extremely simple resnet based generator that is very similar to the one used in the tecogan paper.
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# Main differences:
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# - Uses SiLU instead of ReLU
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# - Reference input is in HR space (just makes more sense)
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# - Doesn't use transposed convolutions - just uses interpolation instead.
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# - Upsample block is slightly more complicated.
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class TecoGen(nn.Module):
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def __init__(self, nf, scale):
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self.nf = nf
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self.scale = scale
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fea_conv = ConvGnSilu(6, nf, kernel_size=7, stride=self.scale, bias=True, norm=False, activation=True)
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res_layers = [TecoResblock(nf) for i in range(15)]
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upsample = TecoUpconv(nf)
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everything = [fea_conv] + res_layers + upsample
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self.core = nn.Sequential(*everything)
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def forward(self, x, ref=None):
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x = nn.functional.interpolate(x, scale_factor=self.scale, mode="bicubic")
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if ref is None:
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ref = torch.zeros_like(x)
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join = torch.cat([x, ref], dim=1)
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return sequential_checkpoint(self.core, 6, join)
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