79 lines
2.9 KiB
Python
79 lines
2.9 KiB
Python
import os
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import torch
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import torch.nn as nn
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import torchvision
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from trainer.networks import register_model
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from utils.util import sequential_checkpoint
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from models.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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super(TecoResblock, self).__init__()
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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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super(TecoUpconv, self).__init__()
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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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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 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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super(TecoGen, self).__init__()
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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, scale)
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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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join = sequential_checkpoint(self.core, 6, join)
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self.join = join.detach().clone() + .5
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return x + join
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def visual_dbg(self, step, path):
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torchvision.utils.save_image(self.join.cpu().float(), os.path.join(path, "%i_join.png" % (step,)))
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def get_debug_values(self, step, net_name):
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return {'branch_std': self.join.std()}
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@register_model
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def register_tecogen(opt_net, opt):
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return TecoGen(opt_net['nf'], opt_net['scale']) |