DL-Art-School/codes/models/archs/teco_resgen.py

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2020-10-28 02:59:55 +00:00
import torch
import torch.nn as nn
from utils.util import sequential_checkpoint
from models.archs.arch_util import ConvGnSilu, make_layer
class TecoResblock(nn.Module):
def __init__(self, nf):
self.nf = nf
self.conv1 = ConvGnSilu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False, weight_init_factor=.1)
self.conv2 = ConvGnSilu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False, weight_init_factor=.1)
def forward(self, x):
identity = x
x = self.conv1(x)
x = self.conv2(x)
return identity + x
class TecoUpconv(nn.Module):
def __init__(self, nf, scale):
self.nf = nf
self.scale = scale
self.conv1 = ConvGnSilu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.conv2 = ConvGnSilu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.conv3 = ConvGnSilu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.final_conv = ConvGnSilu(nf, 3, kernel_size=1, norm=False, activation=False, bias=False)
def forward(self, x):
identity = x
x = self.conv1(x)
x = self.conv2(x)
x = nn.functional.interpolate(x, scale_factor=self.scale, mode="nearest")
x = self.conv3(x)
return identity + self.final_conv(x)
# Extremely simple resnet based generator that is very similar to the one used in the tecogan paper.
# Main differences:
# - Uses SiLU instead of ReLU
# - Reference input is in HR space (just makes more sense)
# - Doesn't use transposed convolutions - just uses interpolation instead.
# - Upsample block is slightly more complicated.
class TecoGen(nn.Module):
def __init__(self, nf, scale):
self.nf = nf
self.scale = scale
fea_conv = ConvGnSilu(6, nf, kernel_size=7, stride=self.scale, bias=True, norm=False, activation=True)
res_layers = [TecoResblock(nf) for i in range(15)]
upsample = TecoUpconv(nf)
everything = [fea_conv] + res_layers + upsample
self.core = nn.Sequential(*everything)
def forward(self, x, ref=None):
x = nn.functional.interpolate(x, scale_factor=self.scale, mode="bicubic")
if ref is None:
ref = torch.zeros_like(x)
join = torch.cat([x, ref], dim=1)
return sequential_checkpoint(self.core, 6, join)